{"database": "metadata", "table": "run_metadata", "is_view": false, "human_description_en": "where experiment.library_source = \"TRANSCRIPTOMIC SINGLE CELL\", technology = \"10x\" and tissue_curation = \"Brain\"", "rows": [[11813, "ERR11788863", "ERX11187331", "ERS16221143", "ERP149944", "PRJEB64780", "A single cell transcriptomic atlas reveals a new myeloid parenchymal population in the zebrafish brain", "E-MTAB-13223", "Transcriptome Analysis", "To understand the cellular basis of the immune compartment of the zebrafish brain we have established reliable protocols for dissociation and prospective isolation of brain leukocytes  using fluorescent transgenic lines. By combining this approach with single cell RNA sequencing  we have generated a gene expression atlas composed of the distinct immune cells present in the homeostatic brain. These analyses revealed the presence of subpopulations of mononuclear phagocytes and other leukocytes  including cell types that have not been  or have been poorly  characterized so far. Here  we present the characterization of a new mononuclear phagocyte population that represents an important fraction among all brain leukocytes.  Adult brain single cell suspensions were prepared from adult Tgp2ry12:GFP; cd45:DsRed fish n=3 and a total of 14 000 cd45:DsRed+ cells were processed for single cell profiling using the 10x Genomics platform.", "ENA FIRST PUBLIC:2023 12 25|ENA LAST UPDATE:2023 12 25", null, "Protocols: FACS sorting of cd45:Dsred cells. Brains from adult Tgp2ry12:GFP; cd45:DsRed fish n=3  were dissected in 0.9X Dulbecco\u2032s Phosphate Buffered Saline DPBS were triturated and treated with Liberase TM at 33\u00b0C for 30 45 minutes  fully dissociated using a syringe with a 26G needle and washed in 2% fetal bovine serum diluted in 0.9X DPBS. Cell suspensions were centrifuged at 290g 4\u00baC 10 min and filtered through a 40\u00b5m nylon mesh. Just before flow cytometry analysis  using calcein violet to exclude dead cells 1\u00b5M  Thermo Fisher. Flow cytometry acquisition and cell sorting was performed on a FACS ARIA II Becton Dickinson. 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By combining this approach with single cell RNA sequencing  we have generated a gene expression atlas composed of the distinct immune cells present in the homeostatic brain. These analyses revealed the presence of subpopulations of mononuclear phagocytes and other leukocytes  including cell types that have not been  or have been poorly  characterized so far. Here  we present the characterization of a new mononuclear phagocyte population that represents an important fraction among all brain leukocytes.  Adult brain single cell suspensions were prepared from adult Tgp2ry12:GFP; cd45:DsRed fish n=3 and a total of 14 000 cd45:DsRed+ cells were processed for single cell profiling using the 10x Genomics platform.", "ENA FIRST PUBLIC:2023 12 25|ENA LAST UPDATE:2023 12 25", null, "Protocols: FACS sorting of cd45:Dsred cells. Brains from adult Tgp2ry12:GFP; cd45:DsRed fish n=3  were dissected in 0.9X Dulbecco\u2032s Phosphate Buffered Saline DPBS were triturated and treated with Liberase TM at 33\u00b0C for 30 45 minutes  fully dissociated using a syringe with a 26G needle and washed in 2% fetal bovine serum diluted in 0.9X DPBS. Cell suspensions were centrifuged at 290g 4\u00baC 10 min and filtered through a 40\u00b5m nylon mesh. Just before flow cytometry analysis  using calcein violet to exclude dead cells 1\u00b5M  Thermo Fisher. Flow cytometry acquisition and cell sorting was performed on a FACS ARIA II Becton Dickinson. RNA Illumina", "Sample 1", "SAMEA114235870", "Universite Libre de Bruxelles", "ENA FIRST PUBLIC:2023 12 25T01:15:29Z|ENA LAST UPDATE:2023 12 25T01:15:29Z|External Id:SAMEA114235870|INSDC center name:Universite Libre de Bruxelles|INSDC first public:2023 12 25T01:15:29Z|INSDC last update:2023 12 25T01:15:29Z|INSDC status:public|Submitter Id:E MTAB 13223:Sample 1|age:5|broker name:ArrayExpress|cell type:leukocyte|collection date:not collected|common name:zebrafish|developmental stage:adult|genotype:wild type genotype|geographic location country and/or sea:not collected|individual:pool of 3 fish|isolate:not applicable|organism part:brain|sample name:E MTAB 13223:Sample 1|scientific name:Danio rerio|sex:male and female|strain:AB", null, null, null, null, null, null, null, null, "A single cell transcriptomic atlas reveals a new myeloid parenchymal population in the zebrafish brain", "E MTAB 13223:Sample 1 p", "Sample 1 p", "A single cell transcriptomic atlas reveals a new myeloid parenchymal population in the zebrafish brain", "FACS sorting of cd45:Dsred cells. Brains from adult Tgp2ry12:GFP; cd45:DsRed fish n=3  were dissected in 0.9X Dulbecco\u2032s Phosphate Buffered Saline DPBS were triturated and treated with Liberase TM at 33\u00b0C for 30 45 minutes  fully dissociated using a syringe with a 26G needle and washed in 2% fetal bovine serum diluted in 0.9X DPBS. Cell suspensions were centrifuged at 290g 4\u00baC 10 min and filtered through a 40\u00b5m nylon mesh. Just before flow cytometry analysis  using calcein violet to exclude dead cells 1\u00b5M  Thermo Fisher. Flow cytometry acquisition and cell sorting was performed on a FACS ARIA II Becton Dickinson.  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We generated a CRISPR Cas9 mediated abca7 knockout zebrafish to explore ABCA7's role in AD. Single cell transcriptomics in heterozygous abca7+/  knockout combined with A\u00df42 toxicity revealed that ABCA7 is crucial for neuropeptide Y NPY  brain derived neurotrophic factor BDNF  and nerve growth factor receptor NGFR expressions  which are crucial for synaptic integrity  astroglial proliferation  and microglial prevalence. Impaired NPY induction decreased BDNF and synaptic density  which are rescuable with ectopic NPY. In induced pluripotent stem cell derived human neurons exposed to A\u00df42  ABCA7 /  suppresses NPY. Clinical data showed reduced NPY in AD correlated with elevated Braak stages  genetic variants in NPY associated with AD  and epigenetic changes in NPY  NGFR  and BDNF promoters linked to ABCA7 variants. Therefore  ABCA7 dependent NPY signaling via BDNF NGFR maintains synaptic integrity  implicating its impairment in increased AD risk through reduced brain resilience. Overall design: WT and abca7+/  zebrafish injected with Amyloid beta 42 peptide as described Bhatatrai et al. 2016 and cells were dissociated  sorted by FACS as described Cosacak et al. 2019. The single cell encapsulation and cDNA synthesis done by following 10X Genomics' workflows. The reads were aligned to zebrafish genome GRChZ 11  v 105 was used for gene annotation and assigning reads to genes. Cellranger software version 6.1.2 was used to process the data", null, "pubmed:39216475", null, "telencephalon  abca7 k/o   Ab42 injected", "GSM7819018", null, "source name:telencephalon|tissue:telencephalon|genotype:abca7 +/ |treatment:Ab42|geo loc name:missing|collection date:missing", "telencephalon  abca7 k/o   Ab42 injected", "The demultiplexing  barcoded processing  gene counting and aggregation were made using the 10x genomics' cellranger software version 6.1.2 https://support.10xgenomics.com/single cell gene expression/software/pipelines/latest/output/metrics The fastq files were aligned to zebrafish genome GRCz11 and ensemble transcripts from Ensembl Release 105 by using STAR. The BAM files were as input for Cell Ranger 10X genomics to generate processed data files that include gene names row names and cell names column names and counts. Further analysis done by using Seurat package in R. Assembly: GRCz11 Supplementary files format and content: filtered bc matrix outputs from Cell Ranger", "telencephalon", "WT AB and abca7 +/  knock out lines were injected with Amyloid beta 42 peptide  and PBS as control as descibed Bhatatrai et al. 2016.", "Cells from zebrafish telencephalon were dissociated. Viability indicator dyes Sytox Blue Invitrogen  Cat No. S34857 and Dycle Ruby Invitrogen  Cat. No. V10309 were used to sort the cells by FACS. The library preparation was performed by 10X Genomics as per manufacture's protocol. 10X genomics", "Zebrafish were kept in the re circulating system on a 14/10 h light/dark cycle  pH7.5  at 28\u00b0C \u00b11\u00b0C in groups of 20 animals per 2.8L.", "tissue:telencephalon|genotype:abca7 +/ |treatment:Ab42", "GSM7819018", "GSM7819018: telencephalon  abca7 k/o   Ab42 injected; Danio rerio; RNA Seq", "GSM7819018 r1", "GSM7819018", "1", "Cells from zebrafish telencephalon were dissociated. Viability indicator dyes Sytox Blue Invitrogen  Cat No. S34857 and Dycle Ruby Invitrogen  Cat. No. V10309 were used to sort the cells by FACS. The library preparation was performed by 10X Genomics as per manufacture's protocol. 10X genomics", null, "RNA-Seq", "TRANSCRIPTOMIC SINGLE CELL", "cDNA", "PAIRED", "ILLUMINA", "NextSeq 500", null, "SRP464334", null, "loader:fastq load.py", "CP021_S12_L001_I1_001.fastq.gz CP021_S12_L001_I2_001.fastq.gz CP021_S12_L001_R1_001.fastq.gz CP021_S12_L001_R2_001.fastq.gz", "fastq fastq fastq fastq", 46637839920.0, 210080360.0, "GSM7819018 r1", "0:10 1:10 2:101 3:101", "A:12160340644;C:7050177030;G:7302497483;T:15922643298;N:574265", 10, 10, 101, 101, 12160340644, 7050177030, 7302497483, 15922643298, 574265, "SRX21975998", "SRS19051834", "SRA1725563", "Neurology & TAUB Institute, Columbia University", "Neurology & TAUB Institute, Columbia University", 2, 0.00333, 0.89749, 0.00051, 0.26345, 0.99821, 0.73539, 0.54736, 0.5015, 101, 101, "T", "B", "mate1 technical by mapping diff", "illumina", "nextseq", "unknown", "cdna_unspecified", "unknown", "sc", "single_cell_droplet", "10x", null, "United States", "2023-10-03", "Undetermined", "Undetermined", "Brain", "Nervous System"], [28483, "SRR26266493", "SRX21975998", "SRS19051834", "SRP464334", "PRJNA1023540", "ABCA7 dependent induction of neuropeptide Y is required for synaptic resilience in Alzheimer's disease through BDNF/NGFR signaling", "GSE244550", "Transcriptome Analysis", "Genetic variants in ABCA7  an Alzheimer's disease AD associated gene  elevate AD risk  yet its functional relevance to the etiology is unclear. We generated a CRISPR Cas9 mediated abca7 knockout zebrafish to explore ABCA7's role in AD. Single cell transcriptomics in heterozygous abca7+/  knockout combined with A\u00df42 toxicity revealed that ABCA7 is crucial for neuropeptide Y NPY  brain derived neurotrophic factor BDNF  and nerve growth factor receptor NGFR expressions  which are crucial for synaptic integrity  astroglial proliferation  and microglial prevalence. Impaired NPY induction decreased BDNF and synaptic density  which are rescuable with ectopic NPY. In induced pluripotent stem cell derived human neurons exposed to A\u00df42  ABCA7 /  suppresses NPY. Clinical data showed reduced NPY in AD correlated with elevated Braak stages  genetic variants in NPY associated with AD  and epigenetic changes in NPY  NGFR  and BDNF promoters linked to ABCA7 variants. Therefore  ABCA7 dependent NPY signaling via BDNF NGFR maintains synaptic integrity  implicating its impairment in increased AD risk through reduced brain resilience. Overall design: WT and abca7+/  zebrafish injected with Amyloid beta 42 peptide as described Bhatatrai et al. 2016 and cells were dissociated  sorted by FACS as described Cosacak et al. 2019. The single cell encapsulation and cDNA synthesis done by following 10X Genomics' workflows. The reads were aligned to zebrafish genome GRChZ 11  v 105 was used for gene annotation and assigning reads to genes. Cellranger software version 6.1.2 was used to process the data", null, "pubmed:39216475", null, "telencephalon  abca7 k/o   Ab42 injected", "GSM7819018", null, "source name:telencephalon|tissue:telencephalon|genotype:abca7 +/ |treatment:Ab42|geo loc name:missing|collection date:missing", "telencephalon  abca7 k/o   Ab42 injected", "The demultiplexing  barcoded processing  gene counting and aggregation were made using the 10x genomics' cellranger software version 6.1.2 https://support.10xgenomics.com/single cell gene expression/software/pipelines/latest/output/metrics The fastq files were aligned to zebrafish genome GRCz11 and ensemble transcripts from Ensembl Release 105 by using STAR. The BAM files were as input for Cell Ranger 10X genomics to generate processed data files that include gene names row names and cell names column names and counts. Further analysis done by using Seurat package in R. Assembly: GRCz11 Supplementary files format and content: filtered bc matrix outputs from Cell Ranger", "telencephalon", "WT AB and abca7 +/  knock out lines were injected with Amyloid beta 42 peptide  and PBS as control as descibed Bhatatrai et al. 2016.", "Cells from zebrafish telencephalon were dissociated. Viability indicator dyes Sytox Blue Invitrogen  Cat No. S34857 and Dycle Ruby Invitrogen  Cat. No. V10309 were used to sort the cells by FACS. The library preparation was performed by 10X Genomics as per manufacture's protocol. 10X genomics", "Zebrafish were kept in the re circulating system on a 14/10 h light/dark cycle  pH7.5  at 28\u00b0C \u00b11\u00b0C in groups of 20 animals per 2.8L.", "tissue:telencephalon|genotype:abca7 +/ |treatment:Ab42", "GSM7819018", "GSM7819018: telencephalon  abca7 k/o   Ab42 injected; Danio rerio; RNA Seq", "GSM7819018 r1", "GSM7819018", "1", "Cells from zebrafish telencephalon were dissociated. Viability indicator dyes Sytox Blue Invitrogen  Cat No. S34857 and Dycle Ruby Invitrogen  Cat. No. V10309 were used to sort the cells by FACS. The library preparation was performed by 10X Genomics as per manufacture's protocol. 10X genomics", null, "RNA-Seq", "TRANSCRIPTOMIC SINGLE CELL", "cDNA", "PAIRED", "ILLUMINA", "NextSeq 500", null, "SRP464334", null, "loader:fastq load.py", "CP021_S12_L002_I1_001.fastq.gz CP021_S12_L002_I2_001.fastq.gz CP021_S12_L002_R1_001.fastq.gz CP021_S12_L002_R2_001.fastq.gz", "fastq fastq fastq fastq", 47673315186.0, 214744663.0, "GSM7819018 r2", "0:10 1:10 2:101 3:101", "A:12499321052;C:7231866994;G:7443419880;T:16203206561;N:607439", 10, 10, 101, 101, 12499321052, 7231866994, 7443419880, 16203206561, 607439, "SRX21975998", "SRS19051834", "SRA1725563", "Neurology & TAUB Institute, Columbia University", "Neurology & TAUB Institute, Columbia University", 2, 0.00305, 0.89832, 0.00064, 0.26179, 0.99851, 0.73434, 0.53932, 0.50016, 101, 101, "T", "B", "mate1 technical by mapping diff", "illumina", "nextseq", "unknown", "cdna_unspecified", "unknown", "sc", "single_cell_droplet", "10x", null, "United States", "2023-10-03", "Undetermined", "Undetermined", "Brain", "Nervous System"], [28484, "SRR26266494", "SRX21975997", "SRS19051833", "SRP464334", "PRJNA1023540", "ABCA7 dependent induction of neuropeptide Y is required for synaptic resilience in Alzheimer's disease through BDNF/NGFR signaling", "GSE244550", "Transcriptome Analysis", "Genetic variants in ABCA7  an Alzheimer's disease AD associated gene  elevate AD risk  yet its functional relevance to the etiology is unclear. We generated a CRISPR Cas9 mediated abca7 knockout zebrafish to explore ABCA7's role in AD. Single cell transcriptomics in heterozygous abca7+/  knockout combined with A\u00df42 toxicity revealed that ABCA7 is crucial for neuropeptide Y NPY  brain derived neurotrophic factor BDNF  and nerve growth factor receptor NGFR expressions  which are crucial for synaptic integrity  astroglial proliferation  and microglial prevalence. Impaired NPY induction decreased BDNF and synaptic density  which are rescuable with ectopic NPY. In induced pluripotent stem cell derived human neurons exposed to A\u00df42  ABCA7 /  suppresses NPY. Clinical data showed reduced NPY in AD correlated with elevated Braak stages  genetic variants in NPY associated with AD  and epigenetic changes in NPY  NGFR  and BDNF promoters linked to ABCA7 variants. Therefore  ABCA7 dependent NPY signaling via BDNF NGFR maintains synaptic integrity  implicating its impairment in increased AD risk through reduced brain resilience. Overall design: WT and abca7+/  zebrafish injected with Amyloid beta 42 peptide as described Bhatatrai et al. 2016 and cells were dissociated  sorted by FACS as described Cosacak et al. 2019. The single cell encapsulation and cDNA synthesis done by following 10X Genomics' workflows. The reads were aligned to zebrafish genome GRChZ 11  v 105 was used for gene annotation and assigning reads to genes. Cellranger software version 6.1.2 was used to process the data", null, "pubmed:39216475", null, "telencephalon  abca7 k/o   PBS injected", "GSM7819017", null, "source name:telencephalon|tissue:telencephalon|genotype:abca7 +/ |treatment:PBS|geo loc name:missing|collection date:missing", "telencephalon  abca7 k/o   PBS injected", "The demultiplexing  barcoded processing  gene counting and aggregation were made using the 10x genomics' cellranger software version 6.1.2 https://support.10xgenomics.com/single cell gene expression/software/pipelines/latest/output/metrics The fastq files were aligned to zebrafish genome GRCz11 and ensemble transcripts from Ensembl Release 105 by using STAR. The BAM files were as input for Cell Ranger 10X genomics to generate processed data files that include gene names row names and cell names column names and counts. Further analysis done by using Seurat package in R. Assembly: GRCz11 Supplementary files format and content: filtered bc matrix outputs from Cell Ranger", "telencephalon", "WT AB and abca7 +/  knock out lines were injected with Amyloid beta 42 peptide  and PBS as control as descibed Bhatatrai et al. 2016.", "Cells from zebrafish telencephalon were dissociated. Viability indicator dyes Sytox Blue Invitrogen  Cat No. S34857 and Dycle Ruby Invitrogen  Cat. No. V10309 were used to sort the cells by FACS. The library preparation was performed by 10X Genomics as per manufacture's protocol. 10X genomics", "Zebrafish were kept in the re circulating system on a 14/10 h light/dark cycle  pH7.5  at 28\u00b0C \u00b11\u00b0C in groups of 20 animals per 2.8L.", "tissue:telencephalon|genotype:abca7 +/ |treatment:PBS", "GSM7819017", "GSM7819017: telencephalon  abca7 k/o   PBS injected; Danio rerio; RNA Seq", "GSM7819017 r1", "GSM7819017", "1", "Cells from zebrafish telencephalon were dissociated. Viability indicator dyes Sytox Blue Invitrogen  Cat No. S34857 and Dycle Ruby Invitrogen  Cat. No. V10309 were used to sort the cells by FACS. The library preparation was performed by 10X Genomics as per manufacture's protocol. 10X genomics", null, "RNA-Seq", "TRANSCRIPTOMIC SINGLE CELL", "cDNA", "PAIRED", "ILLUMINA", "NextSeq 500", null, "SRP464334", null, "loader:fastq load.py", "CP020_S11_L001_I1_001.fastq.gz CP020_S11_L001_I2_001.fastq.gz CP020_S11_L001_R1_001.fastq.gz CP020_S11_L001_R2_001.fastq.gz", "fastq fastq fastq fastq", 45135900918.0, 203314869.0, "GSM7819017 r1", "0:10 1:10 2:101 3:101", "A:11807801920;C:6876592307;G:7115059785;T:15269592915;N:556611", 10, 10, 101, 101, 11807801920, 6876592307, 7115059785, 15269592915, 556611, "SRX21975997", "SRS19051833", "SRA1725563", "Neurology & TAUB Institute, Columbia University", "Neurology & TAUB Institute, Columbia University", 2, 0.00293, 0.90111, 0.00093, 0.28713, 0.99882, 0.73687, 0.58461, 0.50013, 101, 101, "T", "B", "mate1 technical by mapping diff", "illumina", "nextseq", "unknown", "cdna_unspecified", "unknown", "sc", "single_cell_droplet", "10x", null, "United States", "2023-10-03", "Undetermined", "Undetermined", "Brain", "Nervous System"], [28485, "SRR26266495", "SRX21975997", "SRS19051833", "SRP464334", "PRJNA1023540", "ABCA7 dependent induction of neuropeptide Y is required for synaptic resilience in Alzheimer's disease through BDNF/NGFR signaling", "GSE244550", "Transcriptome Analysis", "Genetic variants in ABCA7  an Alzheimer's disease AD associated gene  elevate AD risk  yet its functional relevance to the etiology is unclear. We generated a CRISPR Cas9 mediated abca7 knockout zebrafish to explore ABCA7's role in AD. Single cell transcriptomics in heterozygous abca7+/  knockout combined with A\u00df42 toxicity revealed that ABCA7 is crucial for neuropeptide Y NPY  brain derived neurotrophic factor BDNF  and nerve growth factor receptor NGFR expressions  which are crucial for synaptic integrity  astroglial proliferation  and microglial prevalence. Impaired NPY induction decreased BDNF and synaptic density  which are rescuable with ectopic NPY. In induced pluripotent stem cell derived human neurons exposed to A\u00df42  ABCA7 /  suppresses NPY. Clinical data showed reduced NPY in AD correlated with elevated Braak stages  genetic variants in NPY associated with AD  and epigenetic changes in NPY  NGFR  and BDNF promoters linked to ABCA7 variants. Therefore  ABCA7 dependent NPY signaling via BDNF NGFR maintains synaptic integrity  implicating its impairment in increased AD risk through reduced brain resilience. Overall design: WT and abca7+/  zebrafish injected with Amyloid beta 42 peptide as described Bhatatrai et al. 2016 and cells were dissociated  sorted by FACS as described Cosacak et al. 2019. The single cell encapsulation and cDNA synthesis done by following 10X Genomics' workflows. The reads were aligned to zebrafish genome GRChZ 11  v 105 was used for gene annotation and assigning reads to genes. Cellranger software version 6.1.2 was used to process the data", null, "pubmed:39216475", null, "telencephalon  abca7 k/o   PBS injected", "GSM7819017", null, "source name:telencephalon|tissue:telencephalon|genotype:abca7 +/ |treatment:PBS|geo loc name:missing|collection date:missing", "telencephalon  abca7 k/o   PBS injected", "The demultiplexing  barcoded processing  gene counting and aggregation were made using the 10x genomics' cellranger software version 6.1.2 https://support.10xgenomics.com/single cell gene expression/software/pipelines/latest/output/metrics The fastq files were aligned to zebrafish genome GRCz11 and ensemble transcripts from Ensembl Release 105 by using STAR. The BAM files were as input for Cell Ranger 10X genomics to generate processed data files that include gene names row names and cell names column names and counts. Further analysis done by using Seurat package in R. Assembly: GRCz11 Supplementary files format and content: filtered bc matrix outputs from Cell Ranger", "telencephalon", "WT AB and abca7 +/  knock out lines were injected with Amyloid beta 42 peptide  and PBS as control as descibed Bhatatrai et al. 2016.", "Cells from zebrafish telencephalon were dissociated. Viability indicator dyes Sytox Blue Invitrogen  Cat No. S34857 and Dycle Ruby Invitrogen  Cat. No. V10309 were used to sort the cells by FACS. The library preparation was performed by 10X Genomics as per manufacture's protocol. 10X genomics", "Zebrafish were kept in the re circulating system on a 14/10 h light/dark cycle  pH7.5  at 28\u00b0C \u00b11\u00b0C in groups of 20 animals per 2.8L.", "tissue:telencephalon|genotype:abca7 +/ |treatment:PBS", "GSM7819017", "GSM7819017: telencephalon  abca7 k/o   PBS injected; Danio rerio; RNA Seq", "GSM7819017 r1", "GSM7819017", "1", "Cells from zebrafish telencephalon were dissociated. Viability indicator dyes Sytox Blue Invitrogen  Cat No. S34857 and Dycle Ruby Invitrogen  Cat. No. V10309 were used to sort the cells by FACS. The library preparation was performed by 10X Genomics as per manufacture's protocol. 10X genomics", null, "RNA-Seq", "TRANSCRIPTOMIC SINGLE CELL", "cDNA", "PAIRED", "ILLUMINA", "NextSeq 500", null, "SRP464334", null, "loader:fastq load.py", "CP020_S11_L002_I1_001.fastq.gz CP020_S11_L002_I2_001.fastq.gz CP020_S11_L002_R1_001.fastq.gz CP020_S11_L002_R2_001.fastq.gz", "fastq fastq fastq fastq", 46689428280.0, 210312740.0, "GSM7819017 r2", "0:10 1:10 2:101 3:101", "A:12281435026;C:7136662243;G:7338982062;T:15725501987;N:592162", 10, 10, 101, 101, 12281435026, 7136662243, 7338982062, 15725501987, 592162, "SRX21975997", "SRS19051833", "SRA1725563", "Neurology & TAUB Institute, Columbia University", "Neurology & TAUB Institute, Columbia University", 2, 0.00317, 0.90041, 0.00069, 0.28878, 0.99819, 0.73657, 0.60869, 0.49401, 101, 101, "T", "B", "mate1 technical by mapping diff", "illumina", "nextseq", "unknown", "cdna_unspecified", "unknown", "sc", "single_cell_droplet", "10x", null, "United States", "2023-10-03", "Undetermined", "Undetermined", "Brain", "Nervous System"], [28486, "SRR26266496", "SRX21975996", "SRS19051832", "SRP464334", "PRJNA1023540", "ABCA7 dependent induction of neuropeptide Y is required for synaptic resilience in Alzheimer's disease through BDNF/NGFR signaling", "GSE244550", "Transcriptome Analysis", "Genetic variants in ABCA7  an Alzheimer's disease AD associated gene  elevate AD risk  yet its functional relevance to the etiology is unclear. We generated a CRISPR Cas9 mediated abca7 knockout zebrafish to explore ABCA7's role in AD. Single cell transcriptomics in heterozygous abca7+/  knockout combined with A\u00df42 toxicity revealed that ABCA7 is crucial for neuropeptide Y NPY  brain derived neurotrophic factor BDNF  and nerve growth factor receptor NGFR expressions  which are crucial for synaptic integrity  astroglial proliferation  and microglial prevalence. Impaired NPY induction decreased BDNF and synaptic density  which are rescuable with ectopic NPY. In induced pluripotent stem cell derived human neurons exposed to A\u00df42  ABCA7 /  suppresses NPY. Clinical data showed reduced NPY in AD correlated with elevated Braak stages  genetic variants in NPY associated with AD  and epigenetic changes in NPY  NGFR  and BDNF promoters linked to ABCA7 variants. Therefore  ABCA7 dependent NPY signaling via BDNF NGFR maintains synaptic integrity  implicating its impairment in increased AD risk through reduced brain resilience. Overall design: WT and abca7+/  zebrafish injected with Amyloid beta 42 peptide as described Bhatatrai et al. 2016 and cells were dissociated  sorted by FACS as described Cosacak et al. 2019. The single cell encapsulation and cDNA synthesis done by following 10X Genomics' workflows. The reads were aligned to zebrafish genome GRChZ 11  v 105 was used for gene annotation and assigning reads to genes. Cellranger software version 6.1.2 was used to process the data", null, "pubmed:39216475", null, "telencephalon  WT Ab42 injected", "GSM7819016", null, "source name:telencephalon|tissue:telencephalon|genotype:WT AB|treatment:Ab42|geo loc name:missing|collection date:missing", "telencephalon  WT Ab42 injected", "The demultiplexing  barcoded processing  gene counting and aggregation were made using the 10x genomics' cellranger software version 6.1.2 https://support.10xgenomics.com/single cell gene expression/software/pipelines/latest/output/metrics The fastq files were aligned to zebrafish genome GRCz11 and ensemble transcripts from Ensembl Release 105 by using STAR. The BAM files were as input for Cell Ranger 10X genomics to generate processed data files that include gene names row names and cell names column names and counts. Further analysis done by using Seurat package in R. Assembly: GRCz11 Supplementary files format and content: filtered bc matrix outputs from Cell Ranger", "telencephalon", "WT AB and abca7 +/  knock out lines were injected with Amyloid beta 42 peptide  and PBS as control as descibed Bhatatrai et al. 2016.", "Cells from zebrafish telencephalon were dissociated. Viability indicator dyes Sytox Blue Invitrogen  Cat No. S34857 and Dycle Ruby Invitrogen  Cat. No. V10309 were used to sort the cells by FACS. The library preparation was performed by 10X Genomics as per manufacture's protocol. 10X genomics", "Zebrafish were kept in the re circulating system on a 14/10 h light/dark cycle  pH7.5  at 28\u00b0C \u00b11\u00b0C in groups of 20 animals per 2.8L.", "tissue:telencephalon|genotype:WT AB|treatment:Ab42", "GSM7819016", "GSM7819016: telencephalon  WT Ab42 injected; Danio rerio; RNA Seq", "GSM7819016 r1", "GSM7819016", "1", "Cells from zebrafish telencephalon were dissociated. Viability indicator dyes Sytox Blue Invitrogen  Cat No. S34857 and Dycle Ruby Invitrogen  Cat. No. V10309 were used to sort the cells by FACS. The library preparation was performed by 10X Genomics as per manufacture's protocol. 10X genomics", null, "RNA-Seq", "TRANSCRIPTOMIC SINGLE CELL", "cDNA", "PAIRED", "ILLUMINA", "NextSeq 500", null, "SRP464334", null, "loader:fastq load.py", "CP019_S10_L001_I1_001.fastq.gz CP019_S10_L001_I2_001.fastq.gz CP019_S10_L001_R1_001.fastq.gz CP019_S10_L001_R2_001.fastq.gz", "fastq fastq fastq fastq", 48132368784.0, 216812472.0, "GSM7819016 r1", "0:10 1:10 2:101 3:101", "A:12479944041;C:7377568938;G:7622029272;T:16315981376;N:595717", 10, 10, 101, 101, 12479944041, 7377568938, 7622029272, 16315981376, 595717, "SRX21975996", "SRS19051832", "SRA1725563", "Neurology & TAUB Institute, Columbia University", "Neurology & TAUB Institute, Columbia University", 2, 0.00237, 0.90638, 0.00049, 0.23035, 0.99876, 0.73612, 0.50793, 0.50292, 101, 101, "T", "B", "mate1 technical by mapping diff", "illumina", "nextseq", "unknown", "cdna_unspecified", "unknown", "sc", "single_cell_droplet", "10x", null, "United States", "2023-10-03", "Undetermined", "Undetermined", "Brain", "Nervous System"], [28487, "SRR26266497", "SRX21975996", "SRS19051832", "SRP464334", "PRJNA1023540", "ABCA7 dependent induction of neuropeptide Y is required for synaptic resilience in Alzheimer's disease through BDNF/NGFR signaling", "GSE244550", "Transcriptome Analysis", "Genetic variants in ABCA7  an Alzheimer's disease AD associated gene  elevate AD risk  yet its functional relevance to the etiology is unclear. We generated a CRISPR Cas9 mediated abca7 knockout zebrafish to explore ABCA7's role in AD. Single cell transcriptomics in heterozygous abca7+/  knockout combined with A\u00df42 toxicity revealed that ABCA7 is crucial for neuropeptide Y NPY  brain derived neurotrophic factor BDNF  and nerve growth factor receptor NGFR expressions  which are crucial for synaptic integrity  astroglial proliferation  and microglial prevalence. Impaired NPY induction decreased BDNF and synaptic density  which are rescuable with ectopic NPY. In induced pluripotent stem cell derived human neurons exposed to A\u00df42  ABCA7 /  suppresses NPY. Clinical data showed reduced NPY in AD correlated with elevated Braak stages  genetic variants in NPY associated with AD  and epigenetic changes in NPY  NGFR  and BDNF promoters linked to ABCA7 variants. Therefore  ABCA7 dependent NPY signaling via BDNF NGFR maintains synaptic integrity  implicating its impairment in increased AD risk through reduced brain resilience. Overall design: WT and abca7+/  zebrafish injected with Amyloid beta 42 peptide as described Bhatatrai et al. 2016 and cells were dissociated  sorted by FACS as described Cosacak et al. 2019. The single cell encapsulation and cDNA synthesis done by following 10X Genomics' workflows. The reads were aligned to zebrafish genome GRChZ 11  v 105 was used for gene annotation and assigning reads to genes. Cellranger software version 6.1.2 was used to process the data", null, "pubmed:39216475", null, "telencephalon  WT Ab42 injected", "GSM7819016", null, "source name:telencephalon|tissue:telencephalon|genotype:WT AB|treatment:Ab42|geo loc name:missing|collection date:missing", "telencephalon  WT Ab42 injected", "The demultiplexing  barcoded processing  gene counting and aggregation were made using the 10x genomics' cellranger software version 6.1.2 https://support.10xgenomics.com/single cell gene expression/software/pipelines/latest/output/metrics The fastq files were aligned to zebrafish genome GRCz11 and ensemble transcripts from Ensembl Release 105 by using STAR. The BAM files were as input for Cell Ranger 10X genomics to generate processed data files that include gene names row names and cell names column names and counts. Further analysis done by using Seurat package in R. Assembly: GRCz11 Supplementary files format and content: filtered bc matrix outputs from Cell Ranger", "telencephalon", "WT AB and abca7 +/  knock out lines were injected with Amyloid beta 42 peptide  and PBS as control as descibed Bhatatrai et al. 2016.", "Cells from zebrafish telencephalon were dissociated. Viability indicator dyes Sytox Blue Invitrogen  Cat No. S34857 and Dycle Ruby Invitrogen  Cat. No. V10309 were used to sort the cells by FACS. The library preparation was performed by 10X Genomics as per manufacture's protocol. 10X genomics", "Zebrafish were kept in the re circulating system on a 14/10 h light/dark cycle  pH7.5  at 28\u00b0C \u00b11\u00b0C in groups of 20 animals per 2.8L.", "tissue:telencephalon|genotype:WT AB|treatment:Ab42", "GSM7819016", "GSM7819016: telencephalon  WT Ab42 injected; Danio rerio; RNA Seq", "GSM7819016 r1", "GSM7819016", "1", "Cells from zebrafish telencephalon were dissociated. Viability indicator dyes Sytox Blue Invitrogen  Cat No. S34857 and Dycle Ruby Invitrogen  Cat. No. V10309 were used to sort the cells by FACS. The library preparation was performed by 10X Genomics as per manufacture's protocol. 10X genomics", null, "RNA-Seq", "TRANSCRIPTOMIC SINGLE CELL", "cDNA", "PAIRED", "ILLUMINA", "NextSeq 500", null, "SRP464334", null, "loader:fastq load.py", "CP019_S10_L002_I1_001.fastq.gz CP019_S10_L002_I2_001.fastq.gz CP019_S10_L002_R1_001.fastq.gz CP019_S10_L002_R2_001.fastq.gz", "fastq fastq fastq fastq", 49675882836.0, 223765238.0, "GSM7819016 r2", "0:10 1:10 2:101 3:101", "A:12950211284;C:7640502677;G:7844712651;T:16764526831;N:624633", 10, 10, 101, 101, 12950211284, 7640502677, 7844712651, 16764526831, 624633, "SRX21975996", "SRS19051832", "SRA1725563", "Neurology & TAUB Institute, Columbia University", "Neurology & TAUB Institute, Columbia University", 2, 0.0034, 0.9058, 0.00062, 0.22634, 0.99825, 0.73423, 0.45714, 0.49934, 101, 101, "T", "B", "mate1 technical by mapping diff", "illumina", "nextseq", "unknown", "cdna_unspecified", "unknown", "sc", "single_cell_droplet", "10x", null, "United States", "2023-10-03", "Undetermined", "Undetermined", "Brain", "Nervous System"], [28488, "SRR26266498", "SRX21975995", "SRS19051831", "SRP464334", "PRJNA1023540", "ABCA7 dependent induction of neuropeptide Y is required for synaptic resilience in Alzheimer's disease through BDNF/NGFR signaling", "GSE244550", "Transcriptome Analysis", "Genetic variants in ABCA7  an Alzheimer's disease AD associated gene  elevate AD risk  yet its functional relevance to the etiology is unclear. We generated a CRISPR Cas9 mediated abca7 knockout zebrafish to explore ABCA7's role in AD. Single cell transcriptomics in heterozygous abca7+/  knockout combined with A\u00df42 toxicity revealed that ABCA7 is crucial for neuropeptide Y NPY  brain derived neurotrophic factor BDNF  and nerve growth factor receptor NGFR expressions  which are crucial for synaptic integrity  astroglial proliferation  and microglial prevalence. Impaired NPY induction decreased BDNF and synaptic density  which are rescuable with ectopic NPY. In induced pluripotent stem cell derived human neurons exposed to A\u00df42  ABCA7 /  suppresses NPY. Clinical data showed reduced NPY in AD correlated with elevated Braak stages  genetic variants in NPY associated with AD  and epigenetic changes in NPY  NGFR  and BDNF promoters linked to ABCA7 variants. Therefore  ABCA7 dependent NPY signaling via BDNF NGFR maintains synaptic integrity  implicating its impairment in increased AD risk through reduced brain resilience. Overall design: WT and abca7+/  zebrafish injected with Amyloid beta 42 peptide as described Bhatatrai et al. 2016 and cells were dissociated  sorted by FACS as described Cosacak et al. 2019. The single cell encapsulation and cDNA synthesis done by following 10X Genomics' workflows. The reads were aligned to zebrafish genome GRChZ 11  v 105 was used for gene annotation and assigning reads to genes. Cellranger software version 6.1.2 was used to process the data", null, "pubmed:39216475", null, "telencephalon  WT  PBS injected", "GSM7819015", null, "source name:telencephalon|tissue:telencephalon|genotype:WT AB|treatment:PBS|geo loc name:missing|collection date:missing", "telencephalon  WT  PBS injected", "The demultiplexing  barcoded processing  gene counting and aggregation were made using the 10x genomics' cellranger software version 6.1.2 https://support.10xgenomics.com/single cell gene expression/software/pipelines/latest/output/metrics The fastq files were aligned to zebrafish genome GRCz11 and ensemble transcripts from Ensembl Release 105 by using STAR. The BAM files were as input for Cell Ranger 10X genomics to generate processed data files that include gene names row names and cell names column names and counts. Further analysis done by using Seurat package in R. Assembly: GRCz11 Supplementary files format and content: filtered bc matrix outputs from Cell Ranger", "telencephalon", "WT AB and abca7 +/  knock out lines were injected with Amyloid beta 42 peptide  and PBS as control as descibed Bhatatrai et al. 2016.", "Cells from zebrafish telencephalon were dissociated. Viability indicator dyes Sytox Blue Invitrogen  Cat No. S34857 and Dycle Ruby Invitrogen  Cat. No. V10309 were used to sort the cells by FACS. The library preparation was performed by 10X Genomics as per manufacture's protocol. 10X genomics", "Zebrafish were kept in the re circulating system on a 14/10 h light/dark cycle  pH7.5  at 28\u00b0C \u00b11\u00b0C in groups of 20 animals per 2.8L.", "tissue:telencephalon|genotype:WT AB|treatment:PBS", "GSM7819015", "GSM7819015: telencephalon  WT  PBS injected; Danio rerio; RNA Seq", "GSM7819015 r1", "GSM7819015", "1", "Cells from zebrafish telencephalon were dissociated. Viability indicator dyes Sytox Blue Invitrogen  Cat No. S34857 and Dycle Ruby Invitrogen  Cat. No. V10309 were used to sort the cells by FACS. The library preparation was performed by 10X Genomics as per manufacture's protocol. 10X genomics", null, "RNA-Seq", "TRANSCRIPTOMIC SINGLE CELL", "cDNA", "PAIRED", "ILLUMINA", "NextSeq 500", null, "SRP464334", null, "loader:fastq load.py", "CP018_S9_L001_I1_001.fastq.gz CP018_S9_L001_I2_001.fastq.gz CP018_S9_L001_R1_001.fastq.gz CP018_S9_L001_R2_001.fastq.gz", "fastq fastq fastq fastq", 45948585420.0, 206975610.0, "GSM7819015 r1", "0:10 1:10 2:101 3:101", "A:12050390597;C:6986598890;G:7200535422;T:15570981408;N:566903", 10, 10, 101, 101, 12050390597, 6986598890, 7200535422, 15570981408, 566903, "SRX21975995", "SRS19051831", "SRA1725563", "Neurology & TAUB Institute, Columbia University", "Neurology & TAUB Institute, Columbia University", 2, 0.00353, 0.89238, 0.00096, 0.30364, 0.99841, 0.73456, 0.59523, 0.49758, 101, 101, "T", "B", "mate1 technical by mapping diff", "illumina", "nextseq", "unknown", "cdna_unspecified", "unknown", "sc", "single_cell_droplet", "10x", null, "United States", "2023-10-03", "Undetermined", "Undetermined", "Brain", "Nervous System"], [28489, "SRR26266499", "SRX21975995", "SRS19051831", "SRP464334", "PRJNA1023540", "ABCA7 dependent induction of neuropeptide Y is required for synaptic resilience in Alzheimer's disease through BDNF/NGFR signaling", "GSE244550", "Transcriptome Analysis", "Genetic variants in ABCA7  an Alzheimer's disease AD associated gene  elevate AD risk  yet its functional relevance to the etiology is unclear. We generated a CRISPR Cas9 mediated abca7 knockout zebrafish to explore ABCA7's role in AD. Single cell transcriptomics in heterozygous abca7+/  knockout combined with A\u00df42 toxicity revealed that ABCA7 is crucial for neuropeptide Y NPY  brain derived neurotrophic factor BDNF  and nerve growth factor receptor NGFR expressions  which are crucial for synaptic integrity  astroglial proliferation  and microglial prevalence. Impaired NPY induction decreased BDNF and synaptic density  which are rescuable with ectopic NPY. In induced pluripotent stem cell derived human neurons exposed to A\u00df42  ABCA7 /  suppresses NPY. Clinical data showed reduced NPY in AD correlated with elevated Braak stages  genetic variants in NPY associated with AD  and epigenetic changes in NPY  NGFR  and BDNF promoters linked to ABCA7 variants. Therefore  ABCA7 dependent NPY signaling via BDNF NGFR maintains synaptic integrity  implicating its impairment in increased AD risk through reduced brain resilience. Overall design: WT and abca7+/  zebrafish injected with Amyloid beta 42 peptide as described Bhatatrai et al. 2016 and cells were dissociated  sorted by FACS as described Cosacak et al. 2019. The single cell encapsulation and cDNA synthesis done by following 10X Genomics' workflows. The reads were aligned to zebrafish genome GRChZ 11  v 105 was used for gene annotation and assigning reads to genes. Cellranger software version 6.1.2 was used to process the data", null, "pubmed:39216475", null, "telencephalon  WT  PBS injected", "GSM7819015", null, "source name:telencephalon|tissue:telencephalon|genotype:WT AB|treatment:PBS|geo loc name:missing|collection date:missing", "telencephalon  WT  PBS injected", "The demultiplexing  barcoded processing  gene counting and aggregation were made using the 10x genomics' cellranger software version 6.1.2 https://support.10xgenomics.com/single cell gene expression/software/pipelines/latest/output/metrics The fastq files were aligned to zebrafish genome GRCz11 and ensemble transcripts from Ensembl Release 105 by using STAR. The BAM files were as input for Cell Ranger 10X genomics to generate processed data files that include gene names row names and cell names column names and counts. Further analysis done by using Seurat package in R. Assembly: GRCz11 Supplementary files format and content: filtered bc matrix outputs from Cell Ranger", "telencephalon", "WT AB and abca7 +/  knock out lines were injected with Amyloid beta 42 peptide  and PBS as control as descibed Bhatatrai et al. 2016.", "Cells from zebrafish telencephalon were dissociated. Viability indicator dyes Sytox Blue Invitrogen  Cat No. S34857 and Dycle Ruby Invitrogen  Cat. No. V10309 were used to sort the cells by FACS. The library preparation was performed by 10X Genomics as per manufacture's protocol. 10X genomics", "Zebrafish were kept in the re circulating system on a 14/10 h light/dark cycle  pH7.5  at 28\u00b0C \u00b11\u00b0C in groups of 20 animals per 2.8L.", "tissue:telencephalon|genotype:WT AB|treatment:PBS", "GSM7819015", "GSM7819015: telencephalon  WT  PBS injected; Danio rerio; RNA Seq", "GSM7819015 r1", "GSM7819015", "1", "Cells from zebrafish telencephalon were dissociated. Viability indicator dyes Sytox Blue Invitrogen  Cat No. S34857 and Dycle Ruby Invitrogen  Cat. No. V10309 were used to sort the cells by FACS. The library preparation was performed by 10X Genomics as per manufacture's protocol. 10X genomics", null, "RNA-Seq", "TRANSCRIPTOMIC SINGLE CELL", "cDNA", "PAIRED", "ILLUMINA", "NextSeq 500", null, "SRP464334", null, "loader:fastq load.py", "CP018_S9_L002_I1_001.fastq.gz CP018_S9_L002_I2_001.fastq.gz CP018_S9_L002_R1_001.fastq.gz CP018_S9_L002_R2_001.fastq.gz", "fastq fastq fastq fastq", 47434063566.0, 213666953.0, "GSM7819015 r2", "0:10 1:10 2:101 3:101", "A:12506355665;C:7237420299;G:7414157832;T:16002190524;N:600186", 10, 10, 101, 101, 12506355665, 7237420299, 7414157832, 16002190524, 600186, "SRX21975995", "SRS19051831", "SRA1725563", "Neurology & TAUB Institute, Columbia University", "Neurology & TAUB Institute, Columbia University", 2, 0.00327, 0.8932, 0.00092, 0.30198, 0.99831, 0.73401, 0.53488, 0.50008, 101, 101, "T", "B", "mate1 technical by mapping diff", "illumina", "nextseq", "unknown", "cdna_unspecified", "unknown", "sc", "single_cell_droplet", "10x", null, "United States", "2023-10-03", "Undetermined", "Undetermined", "Brain", "Nervous System"], [28731, "SRR26623268", "SRX22323915", "SRS19374444", "SRP469552", "PRJNA1034159", "Dissecting the spatiotemporal diversity of adult neural stem cells", "GSE246714", "Other", "Adult stem cells are important for tissue turnover and regeneration. However  in most adult systems it remains elusive how stem cells assume different functional states and support spatially patterned tissue architecture. Here  we dissected the diversity of neural stem cells in the adult zebrafish brain  an organ that is characterized by pronounced zonation and high regenerative capacity. We combined single cell transcriptomics of dissected brain regions with massively parallel lineage tracing and in vivo RNA metabolic labeling to analyze regulation of neural stem cells in space and time. We detected a large diversity of neural stem cells  with some subtypes being restricted to a single brain region  while others were found globally across the brain. Global stem cell states are linked to neurogenic differentiation  with different states being involved in proliferative and non proliferative differentiation. Our work reveals principles of adult stem cell organization and establishes a resource for functional manipulation of neural stem cell subtypes. Overall design: Single cell RNA seq of adult zebrafish brain performed using 10x Genomics Gene Expression protocols to explore transcriptomic diversity of adult cell types. The samples were prepared either from whole brain  FACS sorted cells GFP positive cells from gfap:GFP line or a dissected single brain region  as indicated in the sample title. CRISPR based lineage tracing was performed for a subset of samples to dissect developmental lineage relationships between cells. ScSLAM seq was performed in combination with Notch pathway inhibition to assess response of stem cells to perturbation.", null, "pubmed:38365956", null, "Brain 20   sorted  scRNAseq", "GSM7875184", null, "source name:adult brain|tissue:adult brain|tissue region:sorted|cell type:mixed tissue dissociation|genotype:Tg[gfap:GFP]|geo loc name:missing|collection date:missing", "Brain 20   sorted  scRNAseq", "The transcriptome libraries were demultiplexed and mapped with CellRanger 6.1.1. In order to reduce batch effect  the gene expression matrices were processed with the SoupX tool Young & Behjati  2020 for removing ambient RNA contamination. A subset of libraries was also mapped with Velocyto v0.17.17 La Manno et al  2018 to create count matrices for unspliced and spliced molecules. The targeted lineage tracing libraries were aligned using bwa mem3 v.0.7.12 to individual references of endogenous genes used in the lineage tracing experiment actb1  actb2  cfl1  cirbpb  rpl39 and ube2e1. The scars were filtered with a custom pipeline to eliminate sequences that originate from PCR or sequencing errors. For custom code and further downstream analysis  see: https://github.com/nimitic/radial glia. Assembly: GRCz11 Supplementary files format and content: Tab separated barcode files and matrix files along with the tab separated gene file supplementary file are the output of CellRanger mapping  further processed by the SoupX package for ambient RNA removal. Supplementary files format and content: Loom files are the output of velocyto mapping distinguishing spliced and unspliced mRNAs. Supplementary files format and content: filtered scars.csv files contain the output of a custom scar filtering pipeline.", "adult brain", null, "Adult zebrafish brain tissue was extracted  optionally dissected to select a specific region of interest telencephalon  diencephalon  mesencephalon or rhombencephalon and then dissociated with a trypsin protocol. Briefly  the samples were incubated in 750 \u00b5l of HBSS glucose solution and dissocated with 15 \u00b5l of 0.05% trypsin EDTA Gibco for 30 minutes at 37\u00b0C with intermittent mixing. The disociation was stopped by addition of 750 \u00b5l of a BSA EBSS HEPES solution. Further sample handling steps were carried out on ice and for centrifugation at 4\u00b0C. The sample was filtered through a 100 \u00b5m filter  washed with cold HBSS and resuspended in 100 200 \u00b5l HBSS with 0.05% BSA  then filtered through a 35 \u00b5m filter and loaded on the 10x Chromium Controller. For the scSLAM seq samples  a papain dissociation protocol Worthington kit  according to manufecturer's instructions was used followed by an adapted scSLAM seq protocol for labeling nascent transcripts see sample specific entry. The standard transcriptome libraries were constructed according to the manufacturer's protocol 10X Genomics. For targeted libraries of scars used for lineage tracing  the cDNA obtained during the 10x protocol was used to set up individual PCR reactions for each target  specifically amplifying the sgRNA target site. The libraries for targeted PCR were constructed with primers compatible with the 10X Genomics kit  so they could be sequenced on the same platform.", null, "tissue:adult brain|tissue region:sorted|cell type:mixed tissue dissociation|genotype:Tg[gfap:GFP]", "GSM7875184", "GSM7875184: Brain 20   sorted  scRNAseq; Danio rerio; RNA Seq", "GSM7875184 r1", "GSM7875184", "1", "Adult zebrafish brain tissue was extracted  optionally dissected to select a specific region of interest telencephalon  diencephalon  mesencephalon or rhombencephalon and then dissociated with a trypsin protocol. Briefly  the samples were incubated in 750 \u00b5l of HBSS glucose solution and dissocated with 15 \u00b5l of 0.05% trypsin EDTA Gibco for 30 minutes at 37\u00b0C with intermittent mixing. The disociation was stopped by addition of 750 \u00b5l of a BSA EBSS HEPES solution. Further sample handling steps were carried out on ice and for centrifugation at 4\u00b0C. The sample was filtered through a 100 \u00b5m filter  washed with cold HBSS and resuspended in 100 200 \u00b5l HBSS with 0.05% BSA  then filtered through a 35 \u00b5m filter and loaded on the 10x Chromium Controller. For the scSLAM seq samples  a papain dissociation protocol Worthington kit  according to manufecturer's instructions was used followed by an adapted scSLAM seq protocol for labeling nascent transcripts see sample specific entry. The standard transcriptome libraries were constructed according to the manufacturer's protocol 10X Genomics. For targeted libraries of scars used for lineage tracing  the cDNA obtained during the 10x protocol was used to set up individual PCR reactions for each target  specifically amplifying the sgRNA target site. The libraries for targeted PCR were constructed with primers compatible with the 10X Genomics kit  so they could be sequenced on the same platform.", null, "RNA-Seq", "TRANSCRIPTOMIC SINGLE CELL", "cDNA", "PAIRED", "ILLUMINA", "NextSeq 500", null, "SRP469552", null, "loader:fastq load.py", "b20_gfap_R1.fastq.gz b20_gfap_R2.fastq.gz", "fastq fastq", 7798133320.0, 52690090.0, "GSM7875184 r1", "0:28 1:120", "A:2399592536;C:1516740530;G:1752554415;T:2127715674;N:1530165", 28, 120, null, null, 2399592536, 1516740530, 1752554415, 2127715674, 1530165, "SRX22323915", "SRS19374444", "SRA1743007", "Junker Lab, Berlin Institute for Medical Systems Biology, Max-Delbr\u00fcck-Center for Molecular Medicine", "Junker Lab, Berlin Institute for Medical Systems Biology, Max-Delbr\u00fcck-Center for Molecular Medicine", 2, 0.0094, 0.86649, 0.00431, 0.28296, 0.9881, 0.77607, 0.40289, 0.50506, 28, 120, "T", "B", "sc-like readlen", "illumina", "nextseq", "unknown", "cdna_unspecified", "unknown", "sc", "single_cell_droplet", "10x", null, "Germany", "2023-10-31", "Adult", "Adult", "Brain", "Nervous System"], [28732, "SRR26623269", "SRX22323914", "SRS19374443", "SRP469552", "PRJNA1034159", "Dissecting the spatiotemporal diversity of adult neural stem cells", "GSE246714", "Other", "Adult stem cells are important for tissue turnover and regeneration. However  in most adult systems it remains elusive how stem cells assume different functional states and support spatially patterned tissue architecture. Here  we dissected the diversity of neural stem cells in the adult zebrafish brain  an organ that is characterized by pronounced zonation and high regenerative capacity. We combined single cell transcriptomics of dissected brain regions with massively parallel lineage tracing and in vivo RNA metabolic labeling to analyze regulation of neural stem cells in space and time. We detected a large diversity of neural stem cells  with some subtypes being restricted to a single brain region  while others were found globally across the brain. Global stem cell states are linked to neurogenic differentiation  with different states being involved in proliferative and non proliferative differentiation. Our work reveals principles of adult stem cell organization and establishes a resource for functional manipulation of neural stem cell subtypes. Overall design: Single cell RNA seq of adult zebrafish brain performed using 10x Genomics Gene Expression protocols to explore transcriptomic diversity of adult cell types. The samples were prepared either from whole brain  FACS sorted cells GFP positive cells from gfap:GFP line or a dissected single brain region  as indicated in the sample title. CRISPR based lineage tracing was performed for a subset of samples to dissect developmental lineage relationships between cells. ScSLAM seq was performed in combination with Notch pathway inhibition to assess response of stem cells to perturbation.", null, "pubmed:38365956", null, "Brain 19   whole  scRNAseq", "GSM7875183", null, "source name:adult brain|tissue:adult brain|tissue region:whole|cell type:mixed tissue dissociation|genotype:Tg[ubi:zebrabow M]|geo loc name:missing|collection date:missing", "Brain 19   whole  scRNAseq", "The transcriptome libraries were demultiplexed and mapped with CellRanger 6.1.1. In order to reduce batch effect  the gene expression matrices were processed with the SoupX tool Young & Behjati  2020 for removing ambient RNA contamination. A subset of libraries was also mapped with Velocyto v0.17.17 La Manno et al  2018 to create count matrices for unspliced and spliced molecules. The targeted lineage tracing libraries were aligned using bwa mem3 v.0.7.12 to individual references of endogenous genes used in the lineage tracing experiment actb1  actb2  cfl1  cirbpb  rpl39 and ube2e1. The scars were filtered with a custom pipeline to eliminate sequences that originate from PCR or sequencing errors. For custom code and further downstream analysis  see: https://github.com/nimitic/radial glia. Assembly: GRCz11 Supplementary files format and content: Tab separated barcode files and matrix files along with the tab separated gene file supplementary file are the output of CellRanger mapping  further processed by the SoupX package for ambient RNA removal. Supplementary files format and content: Loom files are the output of velocyto mapping distinguishing spliced and unspliced mRNAs. Supplementary files format and content: filtered scars.csv files contain the output of a custom scar filtering pipeline.", "adult brain", null, "Adult zebrafish brain tissue was extracted  optionally dissected to select a specific region of interest telencephalon  diencephalon  mesencephalon or rhombencephalon and then dissociated with a trypsin protocol. Briefly  the samples were incubated in 750 \u00b5l of HBSS glucose solution and dissocated with 15 \u00b5l of 0.05% trypsin EDTA Gibco for 30 minutes at 37\u00b0C with intermittent mixing. The disociation was stopped by addition of 750 \u00b5l of a BSA EBSS HEPES solution. Further sample handling steps were carried out on ice and for centrifugation at 4\u00b0C. The sample was filtered through a 100 \u00b5m filter  washed with cold HBSS and resuspended in 100 200 \u00b5l HBSS with 0.05% BSA  then filtered through a 35 \u00b5m filter and loaded on the 10x Chromium Controller. For the scSLAM seq samples  a papain dissociation protocol Worthington kit  according to manufecturer's instructions was used followed by an adapted scSLAM seq protocol for labeling nascent transcripts see sample specific entry. The standard transcriptome libraries were constructed according to the manufacturer's protocol 10X Genomics. For targeted libraries of scars used for lineage tracing  the cDNA obtained during the 10x protocol was used to set up individual PCR reactions for each target  specifically amplifying the sgRNA target site. The libraries for targeted PCR were constructed with primers compatible with the 10X Genomics kit  so they could be sequenced on the same platform.", null, "tissue:adult brain|tissue region:whole|cell type:mixed tissue dissociation|genotype:Tg[ubi:zebrabow M]", "GSM7875183", "GSM7875183: Brain 19   whole  scRNAseq; Danio rerio; RNA Seq", "GSM7875183 r1", "GSM7875183", "1", "Adult zebrafish brain tissue was extracted  optionally dissected to select a specific region of interest telencephalon  diencephalon  mesencephalon or rhombencephalon and then dissociated with a trypsin protocol. Briefly  the samples were incubated in 750 \u00b5l of HBSS glucose solution and dissocated with 15 \u00b5l of 0.05% trypsin EDTA Gibco for 30 minutes at 37\u00b0C with intermittent mixing. The disociation was stopped by addition of 750 \u00b5l of a BSA EBSS HEPES solution. Further sample handling steps were carried out on ice and for centrifugation at 4\u00b0C. The sample was filtered through a 100 \u00b5m filter  washed with cold HBSS and resuspended in 100 200 \u00b5l HBSS with 0.05% BSA  then filtered through a 35 \u00b5m filter and loaded on the 10x Chromium Controller. For the scSLAM seq samples  a papain dissociation protocol Worthington kit  according to manufecturer's instructions was used followed by an adapted scSLAM seq protocol for labeling nascent transcripts see sample specific entry. The standard transcriptome libraries were constructed according to the manufacturer's protocol 10X Genomics. For targeted libraries of scars used for lineage tracing  the cDNA obtained during the 10x protocol was used to set up individual PCR reactions for each target  specifically amplifying the sgRNA target site. The libraries for targeted PCR were constructed with primers compatible with the 10X Genomics kit  so they could be sequenced on the same platform.", null, "RNA-Seq", "TRANSCRIPTOMIC SINGLE CELL", "cDNA", "PAIRED", "ILLUMINA", "NextSeq 500", null, "SRP469552", null, "loader:fastq load.py", "b19_tdmr_seqrun1_R1.fastq.gz b19_tdmr_seqrun1_R2.fastq.gz", "fastq fastq", 17234149570.0, 96821065.0, "GSM7875183 r1", "0:28 1:150", "A:5205716192;C:3547964017;G:3688127103;T:4785052541;N:7289717", 28, 150, null, null, 5205716192, 3547964017, 3688127103, 4785052541, 7289717, "SRX22323914", "SRS19374443", "SRA1743007", "Junker Lab, Berlin Institute for Medical Systems Biology, Max-Delbr\u00fcck-Center for Molecular Medicine", "Junker Lab, Berlin Institute for Medical Systems Biology, Max-Delbr\u00fcck-Center for Molecular Medicine", 2, 0.0063, 0.8972, 0.00236, 0.22454, 0.99109, 0.79123, 0.52086, 0.54601, 28, 150, "T", "B", "sc-like readlen", "illumina", "nextseq", "unknown", "random_priming", "unknown", "sc", "single_cell_droplet", "10x", null, "Germany", "2023-10-31", "Adult", "Adult", "Brain", "Nervous System"], [28733, "SRR26623270", "SRX22323914", "SRS19374443", "SRP469552", "PRJNA1034159", "Dissecting the spatiotemporal diversity of adult neural stem cells", "GSE246714", "Other", "Adult stem cells are important for tissue turnover and regeneration. However  in most adult systems it remains elusive how stem cells assume different functional states and support spatially patterned tissue architecture. Here  we dissected the diversity of neural stem cells in the adult zebrafish brain  an organ that is characterized by pronounced zonation and high regenerative capacity. We combined single cell transcriptomics of dissected brain regions with massively parallel lineage tracing and in vivo RNA metabolic labeling to analyze regulation of neural stem cells in space and time. We detected a large diversity of neural stem cells  with some subtypes being restricted to a single brain region  while others were found globally across the brain. Global stem cell states are linked to neurogenic differentiation  with different states being involved in proliferative and non proliferative differentiation. Our work reveals principles of adult stem cell organization and establishes a resource for functional manipulation of neural stem cell subtypes. Overall design: Single cell RNA seq of adult zebrafish brain performed using 10x Genomics Gene Expression protocols to explore transcriptomic diversity of adult cell types. The samples were prepared either from whole brain  FACS sorted cells GFP positive cells from gfap:GFP line or a dissected single brain region  as indicated in the sample title. CRISPR based lineage tracing was performed for a subset of samples to dissect developmental lineage relationships between cells. ScSLAM seq was performed in combination with Notch pathway inhibition to assess response of stem cells to perturbation.", null, "pubmed:38365956", null, "Brain 19   whole  scRNAseq", "GSM7875183", null, "source name:adult brain|tissue:adult brain|tissue region:whole|cell type:mixed tissue dissociation|genotype:Tg[ubi:zebrabow M]|geo loc name:missing|collection date:missing", "Brain 19   whole  scRNAseq", "The transcriptome libraries were demultiplexed and mapped with CellRanger 6.1.1. In order to reduce batch effect  the gene expression matrices were processed with the SoupX tool Young & Behjati  2020 for removing ambient RNA contamination. A subset of libraries was also mapped with Velocyto v0.17.17 La Manno et al  2018 to create count matrices for unspliced and spliced molecules. The targeted lineage tracing libraries were aligned using bwa mem3 v.0.7.12 to individual references of endogenous genes used in the lineage tracing experiment actb1  actb2  cfl1  cirbpb  rpl39 and ube2e1. The scars were filtered with a custom pipeline to eliminate sequences that originate from PCR or sequencing errors. For custom code and further downstream analysis  see: https://github.com/nimitic/radial glia. Assembly: GRCz11 Supplementary files format and content: Tab separated barcode files and matrix files along with the tab separated gene file supplementary file are the output of CellRanger mapping  further processed by the SoupX package for ambient RNA removal. Supplementary files format and content: Loom files are the output of velocyto mapping distinguishing spliced and unspliced mRNAs. Supplementary files format and content: filtered scars.csv files contain the output of a custom scar filtering pipeline.", "adult brain", null, "Adult zebrafish brain tissue was extracted  optionally dissected to select a specific region of interest telencephalon  diencephalon  mesencephalon or rhombencephalon and then dissociated with a trypsin protocol. Briefly  the samples were incubated in 750 \u00b5l of HBSS glucose solution and dissocated with 15 \u00b5l of 0.05% trypsin EDTA Gibco for 30 minutes at 37\u00b0C with intermittent mixing. The disociation was stopped by addition of 750 \u00b5l of a BSA EBSS HEPES solution. Further sample handling steps were carried out on ice and for centrifugation at 4\u00b0C. The sample was filtered through a 100 \u00b5m filter  washed with cold HBSS and resuspended in 100 200 \u00b5l HBSS with 0.05% BSA  then filtered through a 35 \u00b5m filter and loaded on the 10x Chromium Controller. For the scSLAM seq samples  a papain dissociation protocol Worthington kit  according to manufecturer's instructions was used followed by an adapted scSLAM seq protocol for labeling nascent transcripts see sample specific entry. The standard transcriptome libraries were constructed according to the manufacturer's protocol 10X Genomics. For targeted libraries of scars used for lineage tracing  the cDNA obtained during the 10x protocol was used to set up individual PCR reactions for each target  specifically amplifying the sgRNA target site. The libraries for targeted PCR were constructed with primers compatible with the 10X Genomics kit  so they could be sequenced on the same platform.", null, "tissue:adult brain|tissue region:whole|cell type:mixed tissue dissociation|genotype:Tg[ubi:zebrabow M]", "GSM7875183", "GSM7875183: Brain 19   whole  scRNAseq; Danio rerio; RNA Seq", "GSM7875183 r1", "GSM7875183", "1", "Adult zebrafish brain tissue was extracted  optionally dissected to select a specific region of interest telencephalon  diencephalon  mesencephalon or rhombencephalon and then dissociated with a trypsin protocol. Briefly  the samples were incubated in 750 \u00b5l of HBSS glucose solution and dissocated with 15 \u00b5l of 0.05% trypsin EDTA Gibco for 30 minutes at 37\u00b0C with intermittent mixing. The disociation was stopped by addition of 750 \u00b5l of a BSA EBSS HEPES solution. Further sample handling steps were carried out on ice and for centrifugation at 4\u00b0C. The sample was filtered through a 100 \u00b5m filter  washed with cold HBSS and resuspended in 100 200 \u00b5l HBSS with 0.05% BSA  then filtered through a 35 \u00b5m filter and loaded on the 10x Chromium Controller. For the scSLAM seq samples  a papain dissociation protocol Worthington kit  according to manufecturer's instructions was used followed by an adapted scSLAM seq protocol for labeling nascent transcripts see sample specific entry. The standard transcriptome libraries were constructed according to the manufacturer's protocol 10X Genomics. For targeted libraries of scars used for lineage tracing  the cDNA obtained during the 10x protocol was used to set up individual PCR reactions for each target  specifically amplifying the sgRNA target site. The libraries for targeted PCR were constructed with primers compatible with the 10X Genomics kit  so they could be sequenced on the same platform.", null, "RNA-Seq", "TRANSCRIPTOMIC SINGLE CELL", "cDNA", "PAIRED", "ILLUMINA", "NextSeq 500", null, "SRP469552", null, "loader:fastq load.py", "b19_tdmr_seqrun2_R1.fastq.gz b19_tdmr_seqrun2_R2.fastq.gz", "fastq fastq", 8080308788.0, 54596681.0, "GSM7875183 r2", "0:28 1:120", "A:2402417612;C:1698661627;G:1736701450;T:2240964763;N:1563336", 28, 120, null, null, 2402417612, 1698661627, 1736701450, 2240964763, 1563336, "SRX22323914", "SRS19374443", "SRA1743007", "Junker Lab, Berlin Institute for Medical Systems Biology, Max-Delbr\u00fcck-Center for Molecular Medicine", "Junker Lab, Berlin Institute for Medical Systems Biology, Max-Delbr\u00fcck-Center for Molecular Medicine", 2, 0.00651, 0.88833, 0.00255, 0.2204, 0.99121, 0.7865, 0.532, 0.57621, 28, 120, "T", "B", "sc-like readlen", "illumina", "nextseq", "unknown", "random_priming", "unknown", "sc", "single_cell_droplet", "10x", null, "Germany", "2023-10-31", "Adult", "Adult", "Brain", "Nervous System"], [28734, "SRR26623271", "SRX22323913", "SRS19374442", "SRP469552", "PRJNA1034159", "Dissecting the spatiotemporal diversity of adult neural stem cells", "GSE246714", "Other", "Adult stem cells are important for tissue turnover and regeneration. However  in most adult systems it remains elusive how stem cells assume different functional states and support spatially patterned tissue architecture. Here  we dissected the diversity of neural stem cells in the adult zebrafish brain  an organ that is characterized by pronounced zonation and high regenerative capacity. We combined single cell transcriptomics of dissected brain regions with massively parallel lineage tracing and in vivo RNA metabolic labeling to analyze regulation of neural stem cells in space and time. We detected a large diversity of neural stem cells  with some subtypes being restricted to a single brain region  while others were found globally across the brain. Global stem cell states are linked to neurogenic differentiation  with different states being involved in proliferative and non proliferative differentiation. Our work reveals principles of adult stem cell organization and establishes a resource for functional manipulation of neural stem cell subtypes. Overall design: Single cell RNA seq of adult zebrafish brain performed using 10x Genomics Gene Expression protocols to explore transcriptomic diversity of adult cell types. The samples were prepared either from whole brain  FACS sorted cells GFP positive cells from gfap:GFP line or a dissected single brain region  as indicated in the sample title. CRISPR based lineage tracing was performed for a subset of samples to dissect developmental lineage relationships between cells. ScSLAM seq was performed in combination with Notch pathway inhibition to assess response of stem cells to perturbation.", null, "pubmed:38365956", null, "Brain 18   rhombencephalon  scRNAseq", "GSM7875182", null, "source name:adult brain|tissue:adult brain|tissue region:rhombencephalon|cell type:mixed tissue dissociation|genotype:wildtype  Tg[ubi:zebrabow M]|geo loc name:missing|collection date:missing", "Brain 18   rhombencephalon  scRNAseq", "The transcriptome libraries were demultiplexed and mapped with CellRanger 6.1.1. In order to reduce batch effect  the gene expression matrices were processed with the SoupX tool Young & Behjati  2020 for removing ambient RNA contamination. A subset of libraries was also mapped with Velocyto v0.17.17 La Manno et al  2018 to create count matrices for unspliced and spliced molecules. The targeted lineage tracing libraries were aligned using bwa mem3 v.0.7.12 to individual references of endogenous genes used in the lineage tracing experiment actb1  actb2  cfl1  cirbpb  rpl39 and ube2e1. The scars were filtered with a custom pipeline to eliminate sequences that originate from PCR or sequencing errors. For custom code and further downstream analysis  see: https://github.com/nimitic/radial glia. Assembly: GRCz11 Supplementary files format and content: Tab separated barcode files and matrix files along with the tab separated gene file supplementary file are the output of CellRanger mapping  further processed by the SoupX package for ambient RNA removal. Supplementary files format and content: Loom files are the output of velocyto mapping distinguishing spliced and unspliced mRNAs. Supplementary files format and content: filtered scars.csv files contain the output of a custom scar filtering pipeline.", "adult brain", null, "Adult zebrafish brain tissue was extracted  optionally dissected to select a specific region of interest telencephalon  diencephalon  mesencephalon or rhombencephalon and then dissociated with a trypsin protocol. Briefly  the samples were incubated in 750 \u00b5l of HBSS glucose solution and dissocated with 15 \u00b5l of 0.05% trypsin EDTA Gibco for 30 minutes at 37\u00b0C with intermittent mixing. The disociation was stopped by addition of 750 \u00b5l of a BSA EBSS HEPES solution. Further sample handling steps were carried out on ice and for centrifugation at 4\u00b0C. The sample was filtered through a 100 \u00b5m filter  washed with cold HBSS and resuspended in 100 200 \u00b5l HBSS with 0.05% BSA  then filtered through a 35 \u00b5m filter and loaded on the 10x Chromium Controller. For the scSLAM seq samples  a papain dissociation protocol Worthington kit  according to manufecturer's instructions was used followed by an adapted scSLAM seq protocol for labeling nascent transcripts see sample specific entry. The standard transcriptome libraries were constructed according to the manufacturer's protocol 10X Genomics. For targeted libraries of scars used for lineage tracing  the cDNA obtained during the 10x protocol was used to set up individual PCR reactions for each target  specifically amplifying the sgRNA target site. The libraries for targeted PCR were constructed with primers compatible with the 10X Genomics kit  so they could be sequenced on the same platform.", null, "tissue:adult brain|tissue region:rhombencephalon|cell type:mixed tissue dissociation|genotype:wildtype  Tg[ubi:zebrabow M]", "GSM7875182", "GSM7875182: Brain 18   rhombencephalon  scRNAseq; Danio rerio; RNA Seq", "GSM7875182 r1", "GSM7875182", "1", "Adult zebrafish brain tissue was extracted  optionally dissected to select a specific region of interest telencephalon  diencephalon  mesencephalon or rhombencephalon and then dissociated with a trypsin protocol. Briefly  the samples were incubated in 750 \u00b5l of HBSS glucose solution and dissocated with 15 \u00b5l of 0.05% trypsin EDTA Gibco for 30 minutes at 37\u00b0C with intermittent mixing. The disociation was stopped by addition of 750 \u00b5l of a BSA EBSS HEPES solution. Further sample handling steps were carried out on ice and for centrifugation at 4\u00b0C. The sample was filtered through a 100 \u00b5m filter  washed with cold HBSS and resuspended in 100 200 \u00b5l HBSS with 0.05% BSA  then filtered through a 35 \u00b5m filter and loaded on the 10x Chromium Controller. For the scSLAM seq samples  a papain dissociation protocol Worthington kit  according to manufecturer's instructions was used followed by an adapted scSLAM seq protocol for labeling nascent transcripts see sample specific entry. The standard transcriptome libraries were constructed according to the manufacturer's protocol 10X Genomics. For targeted libraries of scars used for lineage tracing  the cDNA obtained during the 10x protocol was used to set up individual PCR reactions for each target  specifically amplifying the sgRNA target site. The libraries for targeted PCR were constructed with primers compatible with the 10X Genomics kit  so they could be sequenced on the same platform.", null, "RNA-Seq", "TRANSCRIPTOMIC SINGLE CELL", "cDNA", "PAIRED", "ILLUMINA", "NextSeq 500", null, "SRP469552", null, "loader:fastq load.py", "b18_rhom_R1.fastq.gz b18_rhom_R2.fastq.gz", "fastq fastq", 18926303344.0, 127880428.0, "GSM7875182 r1", "0:28 1:120", "A:5713892455;C:3847687741;G:3928556839;T:5421348321;N:14817988", 28, 120, null, null, 5713892455, 3847687741, 3928556839, 5421348321, 14817988, "SRX22323913", "SRS19374442", "SRA1743007", "Junker Lab, Berlin Institute for Medical Systems Biology, Max-Delbr\u00fcck-Center for Molecular Medicine", "Junker Lab, Berlin Institute for Medical Systems Biology, Max-Delbr\u00fcck-Center for Molecular Medicine", 2, 0.00496, 0.88436, 0.00243, 0.29801, 0.99285, 0.78433, 0.50833, 0.63318, 28, 120, "T", "B", "sc-like readlen", "illumina", "nextseq", "unknown", "cdna_unspecified", "unknown", "sc", "single_cell_droplet", "10x", null, "Germany", "2023-10-31", "Adult", "Adult", "Brain", "Nervous System"], [28735, "SRR26623272", "SRX22323912", "SRS19374441", "SRP469552", "PRJNA1034159", "Dissecting the spatiotemporal diversity of adult neural stem cells", "GSE246714", "Other", "Adult stem cells are important for tissue turnover and regeneration. However  in most adult systems it remains elusive how stem cells assume different functional states and support spatially patterned tissue architecture. Here  we dissected the diversity of neural stem cells in the adult zebrafish brain  an organ that is characterized by pronounced zonation and high regenerative capacity. We combined single cell transcriptomics of dissected brain regions with massively parallel lineage tracing and in vivo RNA metabolic labeling to analyze regulation of neural stem cells in space and time. We detected a large diversity of neural stem cells  with some subtypes being restricted to a single brain region  while others were found globally across the brain. Global stem cell states are linked to neurogenic differentiation  with different states being involved in proliferative and non proliferative differentiation. Our work reveals principles of adult stem cell organization and establishes a resource for functional manipulation of neural stem cell subtypes. Overall design: Single cell RNA seq of adult zebrafish brain performed using 10x Genomics Gene Expression protocols to explore transcriptomic diversity of adult cell types. The samples were prepared either from whole brain  FACS sorted cells GFP positive cells from gfap:GFP line or a dissected single brain region  as indicated in the sample title. CRISPR based lineage tracing was performed for a subset of samples to dissect developmental lineage relationships between cells. ScSLAM seq was performed in combination with Notch pathway inhibition to assess response of stem cells to perturbation.", null, "pubmed:38365956", null, "Brain 18   mesencephalon  scRNAseq", "GSM7875181", null, "source name:adult brain|tissue:adult brain|tissue region:mesencephalon|cell type:mixed tissue dissociation|genotype:wildtype  Tg[ubi:zebrabow M]|geo loc name:missing|collection date:missing", "Brain 18   mesencephalon  scRNAseq", "The transcriptome libraries were demultiplexed and mapped with CellRanger 6.1.1. In order to reduce batch effect  the gene expression matrices were processed with the SoupX tool Young & Behjati  2020 for removing ambient RNA contamination. A subset of libraries was also mapped with Velocyto v0.17.17 La Manno et al  2018 to create count matrices for unspliced and spliced molecules. The targeted lineage tracing libraries were aligned using bwa mem3 v.0.7.12 to individual references of endogenous genes used in the lineage tracing experiment actb1  actb2  cfl1  cirbpb  rpl39 and ube2e1. The scars were filtered with a custom pipeline to eliminate sequences that originate from PCR or sequencing errors. For custom code and further downstream analysis  see: https://github.com/nimitic/radial glia. Assembly: GRCz11 Supplementary files format and content: Tab separated barcode files and matrix files along with the tab separated gene file supplementary file are the output of CellRanger mapping  further processed by the SoupX package for ambient RNA removal. Supplementary files format and content: Loom files are the output of velocyto mapping distinguishing spliced and unspliced mRNAs. Supplementary files format and content: filtered scars.csv files contain the output of a custom scar filtering pipeline.", "adult brain", null, "Adult zebrafish brain tissue was extracted  optionally dissected to select a specific region of interest telencephalon  diencephalon  mesencephalon or rhombencephalon and then dissociated with a trypsin protocol. Briefly  the samples were incubated in 750 \u00b5l of HBSS glucose solution and dissocated with 15 \u00b5l of 0.05% trypsin EDTA Gibco for 30 minutes at 37\u00b0C with intermittent mixing. The disociation was stopped by addition of 750 \u00b5l of a BSA EBSS HEPES solution. Further sample handling steps were carried out on ice and for centrifugation at 4\u00b0C. The sample was filtered through a 100 \u00b5m filter  washed with cold HBSS and resuspended in 100 200 \u00b5l HBSS with 0.05% BSA  then filtered through a 35 \u00b5m filter and loaded on the 10x Chromium Controller. For the scSLAM seq samples  a papain dissociation protocol Worthington kit  according to manufecturer's instructions was used followed by an adapted scSLAM seq protocol for labeling nascent transcripts see sample specific entry. The standard transcriptome libraries were constructed according to the manufacturer's protocol 10X Genomics. For targeted libraries of scars used for lineage tracing  the cDNA obtained during the 10x protocol was used to set up individual PCR reactions for each target  specifically amplifying the sgRNA target site. The libraries for targeted PCR were constructed with primers compatible with the 10X Genomics kit  so they could be sequenced on the same platform.", null, "tissue:adult brain|tissue region:mesencephalon|cell type:mixed tissue dissociation|genotype:wildtype  Tg[ubi:zebrabow M]", "GSM7875181", "GSM7875181: Brain 18   mesencephalon  scRNAseq; Danio rerio; RNA Seq", "GSM7875181 r1", "GSM7875181", "1", "Adult zebrafish brain tissue was extracted  optionally dissected to select a specific region of interest telencephalon  diencephalon  mesencephalon or rhombencephalon and then dissociated with a trypsin protocol. Briefly  the samples were incubated in 750 \u00b5l of HBSS glucose solution and dissocated with 15 \u00b5l of 0.05% trypsin EDTA Gibco for 30 minutes at 37\u00b0C with intermittent mixing. The disociation was stopped by addition of 750 \u00b5l of a BSA EBSS HEPES solution. Further sample handling steps were carried out on ice and for centrifugation at 4\u00b0C. The sample was filtered through a 100 \u00b5m filter  washed with cold HBSS and resuspended in 100 200 \u00b5l HBSS with 0.05% BSA  then filtered through a 35 \u00b5m filter and loaded on the 10x Chromium Controller. For the scSLAM seq samples  a papain dissociation protocol Worthington kit  according to manufecturer's instructions was used followed by an adapted scSLAM seq protocol for labeling nascent transcripts see sample specific entry. The standard transcriptome libraries were constructed according to the manufacturer's protocol 10X Genomics. For targeted libraries of scars used for lineage tracing  the cDNA obtained during the 10x protocol was used to set up individual PCR reactions for each target  specifically amplifying the sgRNA target site. The libraries for targeted PCR were constructed with primers compatible with the 10X Genomics kit  so they could be sequenced on the same platform.", null, "RNA-Seq", "TRANSCRIPTOMIC SINGLE CELL", "cDNA", "PAIRED", "ILLUMINA", "NextSeq 500", null, "SRP469552", null, "loader:fastq load.py", "b18_mes_R1.fastq.gz b18_mes_R2.fastq.gz", "fastq fastq", 18106862272.0, 122343664.0, "GSM7875181 r1", "0:28 1:120", "A:5459512927;C:3636958859;G:3755613924;T:5240531488;N:14245074", 28, 120, null, null, 5459512927, 3636958859, 3755613924, 5240531488, 14245074, "SRX22323912", "SRS19374441", "SRA1743007", "Junker Lab, Berlin Institute for Medical Systems Biology, Max-Delbr\u00fcck-Center for Molecular Medicine", "Junker Lab, Berlin Institute for Medical Systems Biology, Max-Delbr\u00fcck-Center for Molecular Medicine", 2, 0.00645, 0.88033, 0.00346, 0.34147, 0.99127, 0.76934, 0.48681, 0.60464, 28, 120, "T", "B", "sc-like readlen", "illumina", "nextseq", "unknown", "cdna_unspecified", "unknown", "sc", "single_cell_droplet", "10x", null, "Germany", "2023-10-31", "Adult", "Adult", "Brain", "Nervous System"], [28736, "SRR26623273", "SRX22323911", "SRS19374440", "SRP469552", "PRJNA1034159", "Dissecting the spatiotemporal diversity of adult neural stem cells", "GSE246714", "Other", "Adult stem cells are important for tissue turnover and regeneration. However  in most adult systems it remains elusive how stem cells assume different functional states and support spatially patterned tissue architecture. Here  we dissected the diversity of neural stem cells in the adult zebrafish brain  an organ that is characterized by pronounced zonation and high regenerative capacity. We combined single cell transcriptomics of dissected brain regions with massively parallel lineage tracing and in vivo RNA metabolic labeling to analyze regulation of neural stem cells in space and time. We detected a large diversity of neural stem cells  with some subtypes being restricted to a single brain region  while others were found globally across the brain. Global stem cell states are linked to neurogenic differentiation  with different states being involved in proliferative and non proliferative differentiation. Our work reveals principles of adult stem cell organization and establishes a resource for functional manipulation of neural stem cell subtypes. Overall design: Single cell RNA seq of adult zebrafish brain performed using 10x Genomics Gene Expression protocols to explore transcriptomic diversity of adult cell types. The samples were prepared either from whole brain  FACS sorted cells GFP positive cells from gfap:GFP line or a dissected single brain region  as indicated in the sample title. CRISPR based lineage tracing was performed for a subset of samples to dissect developmental lineage relationships between cells. ScSLAM seq was performed in combination with Notch pathway inhibition to assess response of stem cells to perturbation.", null, "pubmed:38365956", null, "Brain 18   diencephalon  scRNAseq", "GSM7875180", null, "source name:adult brain|tissue:adult brain|tissue region:diencephalon|cell type:mixed tissue dissociation|genotype:wildtype  Tg[ubi:zebrabow M]|geo loc name:missing|collection date:missing", "Brain 18   diencephalon  scRNAseq", "The transcriptome libraries were demultiplexed and mapped with CellRanger 6.1.1. In order to reduce batch effect  the gene expression matrices were processed with the SoupX tool Young & Behjati  2020 for removing ambient RNA contamination. A subset of libraries was also mapped with Velocyto v0.17.17 La Manno et al  2018 to create count matrices for unspliced and spliced molecules. The targeted lineage tracing libraries were aligned using bwa mem3 v.0.7.12 to individual references of endogenous genes used in the lineage tracing experiment actb1  actb2  cfl1  cirbpb  rpl39 and ube2e1. The scars were filtered with a custom pipeline to eliminate sequences that originate from PCR or sequencing errors. For custom code and further downstream analysis  see: https://github.com/nimitic/radial glia. Assembly: GRCz11 Supplementary files format and content: Tab separated barcode files and matrix files along with the tab separated gene file supplementary file are the output of CellRanger mapping  further processed by the SoupX package for ambient RNA removal. Supplementary files format and content: Loom files are the output of velocyto mapping distinguishing spliced and unspliced mRNAs. Supplementary files format and content: filtered scars.csv files contain the output of a custom scar filtering pipeline.", "adult brain", null, "Adult zebrafish brain tissue was extracted  optionally dissected to select a specific region of interest telencephalon  diencephalon  mesencephalon or rhombencephalon and then dissociated with a trypsin protocol. Briefly  the samples were incubated in 750 \u00b5l of HBSS glucose solution and dissocated with 15 \u00b5l of 0.05% trypsin EDTA Gibco for 30 minutes at 37\u00b0C with intermittent mixing. The disociation was stopped by addition of 750 \u00b5l of a BSA EBSS HEPES solution. Further sample handling steps were carried out on ice and for centrifugation at 4\u00b0C. The sample was filtered through a 100 \u00b5m filter  washed with cold HBSS and resuspended in 100 200 \u00b5l HBSS with 0.05% BSA  then filtered through a 35 \u00b5m filter and loaded on the 10x Chromium Controller. For the scSLAM seq samples  a papain dissociation protocol Worthington kit  according to manufecturer's instructions was used followed by an adapted scSLAM seq protocol for labeling nascent transcripts see sample specific entry. The standard transcriptome libraries were constructed according to the manufacturer's protocol 10X Genomics. For targeted libraries of scars used for lineage tracing  the cDNA obtained during the 10x protocol was used to set up individual PCR reactions for each target  specifically amplifying the sgRNA target site. The libraries for targeted PCR were constructed with primers compatible with the 10X Genomics kit  so they could be sequenced on the same platform.", null, "tissue:adult brain|tissue region:diencephalon|cell type:mixed tissue dissociation|genotype:wildtype  Tg[ubi:zebrabow M]", "GSM7875180", "GSM7875180: Brain 18   diencephalon  scRNAseq; Danio rerio; RNA Seq", "GSM7875180 r1", "GSM7875180", "1", "Adult zebrafish brain tissue was extracted  optionally dissected to select a specific region of interest telencephalon  diencephalon  mesencephalon or rhombencephalon and then dissociated with a trypsin protocol. Briefly  the samples were incubated in 750 \u00b5l of HBSS glucose solution and dissocated with 15 \u00b5l of 0.05% trypsin EDTA Gibco for 30 minutes at 37\u00b0C with intermittent mixing. The disociation was stopped by addition of 750 \u00b5l of a BSA EBSS HEPES solution. Further sample handling steps were carried out on ice and for centrifugation at 4\u00b0C. The sample was filtered through a 100 \u00b5m filter  washed with cold HBSS and resuspended in 100 200 \u00b5l HBSS with 0.05% BSA  then filtered through a 35 \u00b5m filter and loaded on the 10x Chromium Controller. For the scSLAM seq samples  a papain dissociation protocol Worthington kit  according to manufecturer's instructions was used followed by an adapted scSLAM seq protocol for labeling nascent transcripts see sample specific entry. The standard transcriptome libraries were constructed according to the manufacturer's protocol 10X Genomics. For targeted libraries of scars used for lineage tracing  the cDNA obtained during the 10x protocol was used to set up individual PCR reactions for each target  specifically amplifying the sgRNA target site. The libraries for targeted PCR were constructed with primers compatible with the 10X Genomics kit  so they could be sequenced on the same platform.", null, "RNA-Seq", "TRANSCRIPTOMIC SINGLE CELL", "cDNA", "PAIRED", "ILLUMINA", "NextSeq 500", null, "SRP469552", null, "loader:fastq load.py", "b18_dien_R1.fastq.gz b18_dien_R2.fastq.gz", "fastq fastq", 18653419612.0, 126036619.0, "GSM7875180 r1", "0:28 1:120", "A:5614496340;C:3825425595;G:3908771126;T:5290064867;N:14661684", 28, 120, null, null, 5614496340, 3825425595, 3908771126, 5290064867, 14661684, "SRX22323911", "SRS19374440", "SRA1743007", "Junker Lab, Berlin Institute for Medical Systems Biology, Max-Delbr\u00fcck-Center for Molecular Medicine", "Junker Lab, Berlin Institute for Medical Systems Biology, Max-Delbr\u00fcck-Center for Molecular Medicine", 2, 0.00521, 0.89782, 0.00244, 0.25692, 0.99302, 0.77291, 0.47528, 0.62168, 28, 120, "T", "B", "sc-like readlen", "illumina", "nextseq", "unknown", "cdna_unspecified", "unknown", "sc", "single_cell_droplet", "10x", null, "Germany", "2023-10-31", "Adult", "Adult", "Brain", "Nervous System"], [28737, "SRR26623274", "SRX22323910", "SRS19374439", "SRP469552", "PRJNA1034159", "Dissecting the spatiotemporal diversity of adult neural stem cells", "GSE246714", "Other", "Adult stem cells are important for tissue turnover and regeneration. However  in most adult systems it remains elusive how stem cells assume different functional states and support spatially patterned tissue architecture. Here  we dissected the diversity of neural stem cells in the adult zebrafish brain  an organ that is characterized by pronounced zonation and high regenerative capacity. We combined single cell transcriptomics of dissected brain regions with massively parallel lineage tracing and in vivo RNA metabolic labeling to analyze regulation of neural stem cells in space and time. We detected a large diversity of neural stem cells  with some subtypes being restricted to a single brain region  while others were found globally across the brain. Global stem cell states are linked to neurogenic differentiation  with different states being involved in proliferative and non proliferative differentiation. Our work reveals principles of adult stem cell organization and establishes a resource for functional manipulation of neural stem cell subtypes. Overall design: Single cell RNA seq of adult zebrafish brain performed using 10x Genomics Gene Expression protocols to explore transcriptomic diversity of adult cell types. The samples were prepared either from whole brain  FACS sorted cells GFP positive cells from gfap:GFP line or a dissected single brain region  as indicated in the sample title. CRISPR based lineage tracing was performed for a subset of samples to dissect developmental lineage relationships between cells. ScSLAM seq was performed in combination with Notch pathway inhibition to assess response of stem cells to perturbation.", null, "pubmed:38365956", null, "Brain 17   whole  scRNAseq", "GSM7875179", null, "source name:adult brain|tissue:adult brain|tissue region:whole|cell type:mixed tissue dissociation|genotype:wildtype|geo loc name:missing|collection date:missing", "Brain 17   whole  scRNAseq", "The transcriptome libraries were demultiplexed and mapped with CellRanger 6.1.1. In order to reduce batch effect  the gene expression matrices were processed with the SoupX tool Young & Behjati  2020 for removing ambient RNA contamination. A subset of libraries was also mapped with Velocyto v0.17.17 La Manno et al  2018 to create count matrices for unspliced and spliced molecules. The targeted lineage tracing libraries were aligned using bwa mem3 v.0.7.12 to individual references of endogenous genes used in the lineage tracing experiment actb1  actb2  cfl1  cirbpb  rpl39 and ube2e1. The scars were filtered with a custom pipeline to eliminate sequences that originate from PCR or sequencing errors. For custom code and further downstream analysis  see: https://github.com/nimitic/radial glia. Assembly: GRCz11 Supplementary files format and content: Tab separated barcode files and matrix files along with the tab separated gene file supplementary file are the output of CellRanger mapping  further processed by the SoupX package for ambient RNA removal. Supplementary files format and content: Loom files are the output of velocyto mapping distinguishing spliced and unspliced mRNAs. Supplementary files format and content: filtered scars.csv files contain the output of a custom scar filtering pipeline.", "adult brain", null, "Adult zebrafish brain tissue was extracted  optionally dissected to select a specific region of interest telencephalon  diencephalon  mesencephalon or rhombencephalon and then dissociated with a trypsin protocol. Briefly  the samples were incubated in 750 \u00b5l of HBSS glucose solution and dissocated with 15 \u00b5l of 0.05% trypsin EDTA Gibco for 30 minutes at 37\u00b0C with intermittent mixing. The disociation was stopped by addition of 750 \u00b5l of a BSA EBSS HEPES solution. Further sample handling steps were carried out on ice and for centrifugation at 4\u00b0C. The sample was filtered through a 100 \u00b5m filter  washed with cold HBSS and resuspended in 100 200 \u00b5l HBSS with 0.05% BSA  then filtered through a 35 \u00b5m filter and loaded on the 10x Chromium Controller. For the scSLAM seq samples  a papain dissociation protocol Worthington kit  according to manufecturer's instructions was used followed by an adapted scSLAM seq protocol for labeling nascent transcripts see sample specific entry. The standard transcriptome libraries were constructed according to the manufacturer's protocol 10X Genomics. For targeted libraries of scars used for lineage tracing  the cDNA obtained during the 10x protocol was used to set up individual PCR reactions for each target  specifically amplifying the sgRNA target site. The libraries for targeted PCR were constructed with primers compatible with the 10X Genomics kit  so they could be sequenced on the same platform.", null, "tissue:adult brain|tissue region:whole|cell type:mixed tissue dissociation|genotype:wildtype", "GSM7875179", "GSM7875179: Brain 17   whole  scRNAseq; Danio rerio; RNA Seq", "GSM7875179 r1", "GSM7875179", "1", "Adult zebrafish brain tissue was extracted  optionally dissected to select a specific region of interest telencephalon  diencephalon  mesencephalon or rhombencephalon and then dissociated with a trypsin protocol. Briefly  the samples were incubated in 750 \u00b5l of HBSS glucose solution and dissocated with 15 \u00b5l of 0.05% trypsin EDTA Gibco for 30 minutes at 37\u00b0C with intermittent mixing. The disociation was stopped by addition of 750 \u00b5l of a BSA EBSS HEPES solution. Further sample handling steps were carried out on ice and for centrifugation at 4\u00b0C. The sample was filtered through a 100 \u00b5m filter  washed with cold HBSS and resuspended in 100 200 \u00b5l HBSS with 0.05% BSA  then filtered through a 35 \u00b5m filter and loaded on the 10x Chromium Controller. For the scSLAM seq samples  a papain dissociation protocol Worthington kit  according to manufecturer's instructions was used followed by an adapted scSLAM seq protocol for labeling nascent transcripts see sample specific entry. The standard transcriptome libraries were constructed according to the manufacturer's protocol 10X Genomics. For targeted libraries of scars used for lineage tracing  the cDNA obtained during the 10x protocol was used to set up individual PCR reactions for each target  specifically amplifying the sgRNA target site. The libraries for targeted PCR were constructed with primers compatible with the 10X Genomics kit  so they could be sequenced on the same platform.", null, "RNA-Seq", "TRANSCRIPTOMIC SINGLE CELL", "cDNA", "PAIRED", "ILLUMINA", "NextSeq 500", null, "SRP469552", null, "loader:fastq load.py", "b17_tdmr_R1.fastq.gz b17_tdmr_R2.fastq.gz", "fastq fastq", 39604379236.0, 267597157.0, "GSM7875179 r1", "0:28 1:120", "A:12125317775;C:7792772449;G:8188608274;T:11493334367;N:4346371", 28, 120, null, null, 12125317775, 7792772449, 8188608274, 11493334367, 4346371, "SRX22323910", "SRS19374439", "SRA1743007", "Junker Lab, Berlin Institute for Medical Systems Biology, Max-Delbr\u00fcck-Center for Molecular Medicine", "Junker Lab, Berlin Institute for Medical Systems Biology, Max-Delbr\u00fcck-Center for Molecular Medicine", 2, 0.00523, 0.88138, 0.0024, 0.31504, 0.99162, 0.75895, 0.40373, 0.49629, 28, 120, "T", "B", "sc-like readlen", "illumina", "nextseq", "unknown", "random_priming", "unknown", "sc", "single_cell_droplet", "10x", null, "Germany", "2023-10-31", "Adult", "Adult", "Brain", "Nervous System"], [28738, "SRR26623275", "SRX22323909", "SRS19374438", "SRP469552", "PRJNA1034159", "Dissecting the spatiotemporal diversity of adult neural stem cells", "GSE246714", "Other", "Adult stem cells are important for tissue turnover and regeneration. However  in most adult systems it remains elusive how stem cells assume different functional states and support spatially patterned tissue architecture. Here  we dissected the diversity of neural stem cells in the adult zebrafish brain  an organ that is characterized by pronounced zonation and high regenerative capacity. We combined single cell transcriptomics of dissected brain regions with massively parallel lineage tracing and in vivo RNA metabolic labeling to analyze regulation of neural stem cells in space and time. We detected a large diversity of neural stem cells  with some subtypes being restricted to a single brain region  while others were found globally across the brain. Global stem cell states are linked to neurogenic differentiation  with different states being involved in proliferative and non proliferative differentiation. Our work reveals principles of adult stem cell organization and establishes a resource for functional manipulation of neural stem cell subtypes. Overall design: Single cell RNA seq of adult zebrafish brain performed using 10x Genomics Gene Expression protocols to explore transcriptomic diversity of adult cell types. The samples were prepared either from whole brain  FACS sorted cells GFP positive cells from gfap:GFP line or a dissected single brain region  as indicated in the sample title. CRISPR based lineage tracing was performed for a subset of samples to dissect developmental lineage relationships between cells. ScSLAM seq was performed in combination with Notch pathway inhibition to assess response of stem cells to perturbation.", null, "pubmed:38365956", null, "Brain 16   sorted  scRNAseq", "GSM7875178", null, "source name:adult brain|tissue:adult brain|tissue region:sorted|cell type:mixed tissue dissociation|genotype:Tg[gfap:GFP]|geo loc name:missing|collection date:missing", "Brain 16   sorted  scRNAseq", "The transcriptome libraries were demultiplexed and mapped with CellRanger 6.1.1. In order to reduce batch effect  the gene expression matrices were processed with the SoupX tool Young & Behjati  2020 for removing ambient RNA contamination. A subset of libraries was also mapped with Velocyto v0.17.17 La Manno et al  2018 to create count matrices for unspliced and spliced molecules. The targeted lineage tracing libraries were aligned using bwa mem3 v.0.7.12 to individual references of endogenous genes used in the lineage tracing experiment actb1  actb2  cfl1  cirbpb  rpl39 and ube2e1. The scars were filtered with a custom pipeline to eliminate sequences that originate from PCR or sequencing errors. For custom code and further downstream analysis  see: https://github.com/nimitic/radial glia. Assembly: GRCz11 Supplementary files format and content: Tab separated barcode files and matrix files along with the tab separated gene file supplementary file are the output of CellRanger mapping  further processed by the SoupX package for ambient RNA removal. Supplementary files format and content: Loom files are the output of velocyto mapping distinguishing spliced and unspliced mRNAs. Supplementary files format and content: filtered scars.csv files contain the output of a custom scar filtering pipeline.", "adult brain", null, "Adult zebrafish brain tissue was extracted  optionally dissected to select a specific region of interest telencephalon  diencephalon  mesencephalon or rhombencephalon and then dissociated with a trypsin protocol. Briefly  the samples were incubated in 750 \u00b5l of HBSS glucose solution and dissocated with 15 \u00b5l of 0.05% trypsin EDTA Gibco for 30 minutes at 37\u00b0C with intermittent mixing. The disociation was stopped by addition of 750 \u00b5l of a BSA EBSS HEPES solution. Further sample handling steps were carried out on ice and for centrifugation at 4\u00b0C. The sample was filtered through a 100 \u00b5m filter  washed with cold HBSS and resuspended in 100 200 \u00b5l HBSS with 0.05% BSA  then filtered through a 35 \u00b5m filter and loaded on the 10x Chromium Controller. For the scSLAM seq samples  a papain dissociation protocol Worthington kit  according to manufecturer's instructions was used followed by an adapted scSLAM seq protocol for labeling nascent transcripts see sample specific entry. The standard transcriptome libraries were constructed according to the manufacturer's protocol 10X Genomics. For targeted libraries of scars used for lineage tracing  the cDNA obtained during the 10x protocol was used to set up individual PCR reactions for each target  specifically amplifying the sgRNA target site. The libraries for targeted PCR were constructed with primers compatible with the 10X Genomics kit  so they could be sequenced on the same platform.", null, "tissue:adult brain|tissue region:sorted|cell type:mixed tissue dissociation|genotype:Tg[gfap:GFP]", "GSM7875178", "GSM7875178: Brain 16   sorted  scRNAseq; Danio rerio; RNA Seq", "GSM7875178 r1", "GSM7875178", "1", "Adult zebrafish brain tissue was extracted  optionally dissected to select a specific region of interest telencephalon  diencephalon  mesencephalon or rhombencephalon and then dissociated with a trypsin protocol. Briefly  the samples were incubated in 750 \u00b5l of HBSS glucose solution and dissocated with 15 \u00b5l of 0.05% trypsin EDTA Gibco for 30 minutes at 37\u00b0C with intermittent mixing. The disociation was stopped by addition of 750 \u00b5l of a BSA EBSS HEPES solution. Further sample handling steps were carried out on ice and for centrifugation at 4\u00b0C. The sample was filtered through a 100 \u00b5m filter  washed with cold HBSS and resuspended in 100 200 \u00b5l HBSS with 0.05% BSA  then filtered through a 35 \u00b5m filter and loaded on the 10x Chromium Controller. For the scSLAM seq samples  a papain dissociation protocol Worthington kit  according to manufecturer's instructions was used followed by an adapted scSLAM seq protocol for labeling nascent transcripts see sample specific entry. The standard transcriptome libraries were constructed according to the manufacturer's protocol 10X Genomics. For targeted libraries of scars used for lineage tracing  the cDNA obtained during the 10x protocol was used to set up individual PCR reactions for each target  specifically amplifying the sgRNA target site. The libraries for targeted PCR were constructed with primers compatible with the 10X Genomics kit  so they could be sequenced on the same platform.", null, "RNA-Seq", "TRANSCRIPTOMIC SINGLE CELL", "cDNA", "PAIRED", "ILLUMINA", "NextSeq 500", null, "SRP469552", null, "loader:fastq load.py", "b16_gfap_R2.fastq.gz b16_gfap_R1.fastq.gz", "fastq fastq", 5443014500.0, 36777125.0, "GSM7875178 r1", null, null, null, null, null, null, null, null, null, null, null, "SRX22323909", "SRS19374438", "SRA1743007", "Junker Lab, Berlin Institute for Medical Systems Biology, Max-Delbr\u00fcck-Center for Molecular Medicine", "Junker Lab, Berlin Institute for Medical Systems Biology, Max-Delbr\u00fcck-Center for Molecular Medicine", 2, 0.00627, 0.90735, 0.00227, 0.18428, 0.99115, 0.77242, 0.4456, 0.61483, 28, 120, "T", "B", "sc-like readlen", "illumina", "nextseq", "unknown", "cdna_unspecified", "unknown", "sc", "single_cell_droplet", "10x", null, "Germany", "2023-10-31", "Adult", "Adult", "Brain", "Nervous System"], [28739, "SRR26623276", "SRX22323908", "SRS19374437", "SRP469552", "PRJNA1034159", "Dissecting the spatiotemporal diversity of adult neural stem cells", "GSE246714", "Other", "Adult stem cells are important for tissue turnover and regeneration. However  in most adult systems it remains elusive how stem cells assume different functional states and support spatially patterned tissue architecture. Here  we dissected the diversity of neural stem cells in the adult zebrafish brain  an organ that is characterized by pronounced zonation and high regenerative capacity. We combined single cell transcriptomics of dissected brain regions with massively parallel lineage tracing and in vivo RNA metabolic labeling to analyze regulation of neural stem cells in space and time. We detected a large diversity of neural stem cells  with some subtypes being restricted to a single brain region  while others were found globally across the brain. Global stem cell states are linked to neurogenic differentiation  with different states being involved in proliferative and non proliferative differentiation. Our work reveals principles of adult stem cell organization and establishes a resource for functional manipulation of neural stem cell subtypes. Overall design: Single cell RNA seq of adult zebrafish brain performed using 10x Genomics Gene Expression protocols to explore transcriptomic diversity of adult cell types. The samples were prepared either from whole brain  FACS sorted cells GFP positive cells from gfap:GFP line or a dissected single brain region  as indicated in the sample title. CRISPR based lineage tracing was performed for a subset of samples to dissect developmental lineage relationships between cells. ScSLAM seq was performed in combination with Notch pathway inhibition to assess response of stem cells to perturbation.", null, "pubmed:38365956", null, "Brain 15   whole  scRNAseq", "GSM7875177", null, "source name:adult brain|tissue:adult brain|tissue region:whole|cell type:mixed tissue dissociation|genotype:wildtype|geo loc name:missing|collection date:missing", "Brain 15   whole  scRNAseq", "The transcriptome libraries were demultiplexed and mapped with CellRanger 6.1.1. In order to reduce batch effect  the gene expression matrices were processed with the SoupX tool Young & Behjati  2020 for removing ambient RNA contamination. A subset of libraries was also mapped with Velocyto v0.17.17 La Manno et al  2018 to create count matrices for unspliced and spliced molecules. The targeted lineage tracing libraries were aligned using bwa mem3 v.0.7.12 to individual references of endogenous genes used in the lineage tracing experiment actb1  actb2  cfl1  cirbpb  rpl39 and ube2e1. The scars were filtered with a custom pipeline to eliminate sequences that originate from PCR or sequencing errors. For custom code and further downstream analysis  see: https://github.com/nimitic/radial glia. Assembly: GRCz11 Supplementary files format and content: Tab separated barcode files and matrix files along with the tab separated gene file supplementary file are the output of CellRanger mapping  further processed by the SoupX package for ambient RNA removal. Supplementary files format and content: Loom files are the output of velocyto mapping distinguishing spliced and unspliced mRNAs. Supplementary files format and content: filtered scars.csv files contain the output of a custom scar filtering pipeline.", "adult brain", null, "Adult zebrafish brain tissue was extracted  optionally dissected to select a specific region of interest telencephalon  diencephalon  mesencephalon or rhombencephalon and then dissociated with a trypsin protocol. Briefly  the samples were incubated in 750 \u00b5l of HBSS glucose solution and dissocated with 15 \u00b5l of 0.05% trypsin EDTA Gibco for 30 minutes at 37\u00b0C with intermittent mixing. The disociation was stopped by addition of 750 \u00b5l of a BSA EBSS HEPES solution. Further sample handling steps were carried out on ice and for centrifugation at 4\u00b0C. The sample was filtered through a 100 \u00b5m filter  washed with cold HBSS and resuspended in 100 200 \u00b5l HBSS with 0.05% BSA  then filtered through a 35 \u00b5m filter and loaded on the 10x Chromium Controller. For the scSLAM seq samples  a papain dissociation protocol Worthington kit  according to manufecturer's instructions was used followed by an adapted scSLAM seq protocol for labeling nascent transcripts see sample specific entry. The standard transcriptome libraries were constructed according to the manufacturer's protocol 10X Genomics. For targeted libraries of scars used for lineage tracing  the cDNA obtained during the 10x protocol was used to set up individual PCR reactions for each target  specifically amplifying the sgRNA target site. The libraries for targeted PCR were constructed with primers compatible with the 10X Genomics kit  so they could be sequenced on the same platform.", null, "tissue:adult brain|tissue region:whole|cell type:mixed tissue dissociation|genotype:wildtype", "GSM7875177", "GSM7875177: Brain 15   whole  scRNAseq; Danio rerio; RNA Seq", "GSM7875177 r1", "GSM7875177", "1", "Adult zebrafish brain tissue was extracted  optionally dissected to select a specific region of interest telencephalon  diencephalon  mesencephalon or rhombencephalon and then dissociated with a trypsin protocol. Briefly  the samples were incubated in 750 \u00b5l of HBSS glucose solution and dissocated with 15 \u00b5l of 0.05% trypsin EDTA Gibco for 30 minutes at 37\u00b0C with intermittent mixing. The disociation was stopped by addition of 750 \u00b5l of a BSA EBSS HEPES solution. Further sample handling steps were carried out on ice and for centrifugation at 4\u00b0C. The sample was filtered through a 100 \u00b5m filter  washed with cold HBSS and resuspended in 100 200 \u00b5l HBSS with 0.05% BSA  then filtered through a 35 \u00b5m filter and loaded on the 10x Chromium Controller. For the scSLAM seq samples  a papain dissociation protocol Worthington kit  according to manufecturer's instructions was used followed by an adapted scSLAM seq protocol for labeling nascent transcripts see sample specific entry. The standard transcriptome libraries were constructed according to the manufacturer's protocol 10X Genomics. For targeted libraries of scars used for lineage tracing  the cDNA obtained during the 10x protocol was used to set up individual PCR reactions for each target  specifically amplifying the sgRNA target site. The libraries for targeted PCR were constructed with primers compatible with the 10X Genomics kit  so they could be sequenced on the same platform.", null, "RNA-Seq", "TRANSCRIPTOMIC SINGLE CELL", "cDNA", "PAIRED", "ILLUMINA", "NextSeq 500", null, "SRP469552", null, "loader:fastq load.py", "b15_mult_R2.fastq.gz b15_mult_R1.fastq.gz", "fastq fastq", 11845962180.0, 80040285.0, "GSM7875177 r1", "0:28 1:120", "A:2976260508;C:2811030160;G:2912422943;T:3143400974;N:2847595", 28, 120, null, null, 2976260508, 2811030160, 2912422943, 3143400974, 2847595, "SRX22323908", "SRS19374437", "SRA1743007", "Junker Lab, Berlin Institute for Medical Systems Biology, Max-Delbr\u00fcck-Center for Molecular Medicine", "Junker Lab, Berlin Institute for Medical Systems Biology, Max-Delbr\u00fcck-Center for Molecular Medicine", 2, 0.08396, 0.93662, 0.02595, 0.16615, 0.93744, 0.71364, 0.29067, 0.56326, 28, 120, "T", "B", "sc-like readlen", "illumina", "nextseq", "unknown", "random_priming", "unknown", "sc", "single_cell_droplet", "10x", null, "Germany", "2023-10-31", "Adult", "Adult", "Brain", "Nervous System"], [28740, "SRR26623277", "SRX22323907", "SRS19374436", "SRP469552", "PRJNA1034159", "Dissecting the spatiotemporal diversity of adult neural stem cells", "GSE246714", "Other", "Adult stem cells are important for tissue turnover and regeneration. However  in most adult systems it remains elusive how stem cells assume different functional states and support spatially patterned tissue architecture. Here  we dissected the diversity of neural stem cells in the adult zebrafish brain  an organ that is characterized by pronounced zonation and high regenerative capacity. We combined single cell transcriptomics of dissected brain regions with massively parallel lineage tracing and in vivo RNA metabolic labeling to analyze regulation of neural stem cells in space and time. We detected a large diversity of neural stem cells  with some subtypes being restricted to a single brain region  while others were found globally across the brain. Global stem cell states are linked to neurogenic differentiation  with different states being involved in proliferative and non proliferative differentiation. Our work reveals principles of adult stem cell organization and establishes a resource for functional manipulation of neural stem cell subtypes. Overall design: Single cell RNA seq of adult zebrafish brain performed using 10x Genomics Gene Expression protocols to explore transcriptomic diversity of adult cell types. The samples were prepared either from whole brain  FACS sorted cells GFP positive cells from gfap:GFP line or a dissected single brain region  as indicated in the sample title. CRISPR based lineage tracing was performed for a subset of samples to dissect developmental lineage relationships between cells. ScSLAM seq was performed in combination with Notch pathway inhibition to assess response of stem cells to perturbation.", null, "pubmed:38365956", null, "Brain 14   sorted  scRNAseq", "GSM7875176", null, "source name:adult brain|tissue:adult brain|tissue region:sorted|cell type:mixed tissue dissociation|genotype:Tg[gfap:GFP]|geo loc name:missing|collection date:missing", "Brain 14   sorted  scRNAseq", "The transcriptome libraries were demultiplexed and mapped with CellRanger 6.1.1. In order to reduce batch effect  the gene expression matrices were processed with the SoupX tool Young & Behjati  2020 for removing ambient RNA contamination. A subset of libraries was also mapped with Velocyto v0.17.17 La Manno et al  2018 to create count matrices for unspliced and spliced molecules. The targeted lineage tracing libraries were aligned using bwa mem3 v.0.7.12 to individual references of endogenous genes used in the lineage tracing experiment actb1  actb2  cfl1  cirbpb  rpl39 and ube2e1. The scars were filtered with a custom pipeline to eliminate sequences that originate from PCR or sequencing errors. For custom code and further downstream analysis  see: https://github.com/nimitic/radial glia. Assembly: GRCz11 Supplementary files format and content: Tab separated barcode files and matrix files along with the tab separated gene file supplementary file are the output of CellRanger mapping  further processed by the SoupX package for ambient RNA removal. Supplementary files format and content: Loom files are the output of velocyto mapping distinguishing spliced and unspliced mRNAs. Supplementary files format and content: filtered scars.csv files contain the output of a custom scar filtering pipeline.", "adult brain", null, "Adult zebrafish brain tissue was extracted  optionally dissected to select a specific region of interest telencephalon  diencephalon  mesencephalon or rhombencephalon and then dissociated with a trypsin protocol. Briefly  the samples were incubated in 750 \u00b5l of HBSS glucose solution and dissocated with 15 \u00b5l of 0.05% trypsin EDTA Gibco for 30 minutes at 37\u00b0C with intermittent mixing. The disociation was stopped by addition of 750 \u00b5l of a BSA EBSS HEPES solution. Further sample handling steps were carried out on ice and for centrifugation at 4\u00b0C. The sample was filtered through a 100 \u00b5m filter  washed with cold HBSS and resuspended in 100 200 \u00b5l HBSS with 0.05% BSA  then filtered through a 35 \u00b5m filter and loaded on the 10x Chromium Controller. For the scSLAM seq samples  a papain dissociation protocol Worthington kit  according to manufecturer's instructions was used followed by an adapted scSLAM seq protocol for labeling nascent transcripts see sample specific entry. The standard transcriptome libraries were constructed according to the manufacturer's protocol 10X Genomics. For targeted libraries of scars used for lineage tracing  the cDNA obtained during the 10x protocol was used to set up individual PCR reactions for each target  specifically amplifying the sgRNA target site. The libraries for targeted PCR were constructed with primers compatible with the 10X Genomics kit  so they could be sequenced on the same platform.", null, "tissue:adult brain|tissue region:sorted|cell type:mixed tissue dissociation|genotype:Tg[gfap:GFP]", "GSM7875176", "GSM7875176: Brain 14   sorted  scRNAseq; Danio rerio; RNA Seq", "GSM7875176 r1", "GSM7875176", "1", "Adult zebrafish brain tissue was extracted  optionally dissected to select a specific region of interest telencephalon  diencephalon  mesencephalon or rhombencephalon and then dissociated with a trypsin protocol. Briefly  the samples were incubated in 750 \u00b5l of HBSS glucose solution and dissocated with 15 \u00b5l of 0.05% trypsin EDTA Gibco for 30 minutes at 37\u00b0C with intermittent mixing. The disociation was stopped by addition of 750 \u00b5l of a BSA EBSS HEPES solution. Further sample handling steps were carried out on ice and for centrifugation at 4\u00b0C. The sample was filtered through a 100 \u00b5m filter  washed with cold HBSS and resuspended in 100 200 \u00b5l HBSS with 0.05% BSA  then filtered through a 35 \u00b5m filter and loaded on the 10x Chromium Controller. For the scSLAM seq samples  a papain dissociation protocol Worthington kit  according to manufecturer's instructions was used followed by an adapted scSLAM seq protocol for labeling nascent transcripts see sample specific entry. The standard transcriptome libraries were constructed according to the manufacturer's protocol 10X Genomics. For targeted libraries of scars used for lineage tracing  the cDNA obtained during the 10x protocol was used to set up individual PCR reactions for each target  specifically amplifying the sgRNA target site. The libraries for targeted PCR were constructed with primers compatible with the 10X Genomics kit  so they could be sequenced on the same platform.", null, "RNA-Seq", "TRANSCRIPTOMIC SINGLE CELL", "cDNA", "PAIRED", "ILLUMINA", "NextSeq 500", null, "SRP469552", null, "loader:fastq load.py", "b14_gfap_R2.fastq.gz b14_gfap_R1.fastq.gz", "fastq fastq", 17138451800.0, 115800350.0, "GSM7875176 r1", "0:28 1:120", "A:4079957203;C:3974923538;G:4289276957;T:4791971757;N:2322345", 28, 120, null, null, 4079957203, 3974923538, 4289276957, 4791971757, 2322345, "SRX22323907", "SRS19374436", "SRA1743007", "Junker Lab, Berlin Institute for Medical Systems Biology, Max-Delbr\u00fcck-Center for Molecular Medicine", "Junker Lab, Berlin Institute for Medical Systems Biology, Max-Delbr\u00fcck-Center for Molecular Medicine", 2, 0.10093, 0.95317, 0.0314, 0.10551, 0.97104, 0.82487, 0.39909, 0.4491, 28, 120, "T", "B", "sc-like readlen", "illumina", "nextseq", "unknown", "cdna_unspecified", "unknown", "sc", "single_cell_droplet", "10x", null, "Germany", "2023-10-31", "Adult", "Adult", "Brain", "Nervous System"], [28741, "SRR26623278", "SRX22323906", "SRS19374435", "SRP469552", "PRJNA1034159", "Dissecting the spatiotemporal diversity of adult neural stem cells", "GSE246714", "Other", "Adult stem cells are important for tissue turnover and regeneration. However  in most adult systems it remains elusive how stem cells assume different functional states and support spatially patterned tissue architecture. Here  we dissected the diversity of neural stem cells in the adult zebrafish brain  an organ that is characterized by pronounced zonation and high regenerative capacity. We combined single cell transcriptomics of dissected brain regions with massively parallel lineage tracing and in vivo RNA metabolic labeling to analyze regulation of neural stem cells in space and time. We detected a large diversity of neural stem cells  with some subtypes being restricted to a single brain region  while others were found globally across the brain. Global stem cell states are linked to neurogenic differentiation  with different states being involved in proliferative and non proliferative differentiation. Our work reveals principles of adult stem cell organization and establishes a resource for functional manipulation of neural stem cell subtypes. Overall design: Single cell RNA seq of adult zebrafish brain performed using 10x Genomics Gene Expression protocols to explore transcriptomic diversity of adult cell types. The samples were prepared either from whole brain  FACS sorted cells GFP positive cells from gfap:GFP line or a dissected single brain region  as indicated in the sample title. CRISPR based lineage tracing was performed for a subset of samples to dissect developmental lineage relationships between cells. ScSLAM seq was performed in combination with Notch pathway inhibition to assess response of stem cells to perturbation.", null, "pubmed:38365956", null, "Brain 13   whole  scRNAseq", "GSM7875175", null, "source name:adult brain|tissue:adult brain|tissue region:whole|cell type:mixed tissue dissociation|genotype:wildtype|geo loc name:missing|collection date:missing", "Brain 13   whole  scRNAseq", "The transcriptome libraries were demultiplexed and mapped with CellRanger 6.1.1. In order to reduce batch effect  the gene expression matrices were processed with the SoupX tool Young & Behjati  2020 for removing ambient RNA contamination. A subset of libraries was also mapped with Velocyto v0.17.17 La Manno et al  2018 to create count matrices for unspliced and spliced molecules. The targeted lineage tracing libraries were aligned using bwa mem3 v.0.7.12 to individual references of endogenous genes used in the lineage tracing experiment actb1  actb2  cfl1  cirbpb  rpl39 and ube2e1. The scars were filtered with a custom pipeline to eliminate sequences that originate from PCR or sequencing errors. For custom code and further downstream analysis  see: https://github.com/nimitic/radial glia. Assembly: GRCz11 Supplementary files format and content: Tab separated barcode files and matrix files along with the tab separated gene file supplementary file are the output of CellRanger mapping  further processed by the SoupX package for ambient RNA removal. Supplementary files format and content: Loom files are the output of velocyto mapping distinguishing spliced and unspliced mRNAs. Supplementary files format and content: filtered scars.csv files contain the output of a custom scar filtering pipeline.", "adult brain", null, "Adult zebrafish brain tissue was extracted  optionally dissected to select a specific region of interest telencephalon  diencephalon  mesencephalon or rhombencephalon and then dissociated with a trypsin protocol. Briefly  the samples were incubated in 750 \u00b5l of HBSS glucose solution and dissocated with 15 \u00b5l of 0.05% trypsin EDTA Gibco for 30 minutes at 37\u00b0C with intermittent mixing. The disociation was stopped by addition of 750 \u00b5l of a BSA EBSS HEPES solution. Further sample handling steps were carried out on ice and for centrifugation at 4\u00b0C. The sample was filtered through a 100 \u00b5m filter  washed with cold HBSS and resuspended in 100 200 \u00b5l HBSS with 0.05% BSA  then filtered through a 35 \u00b5m filter and loaded on the 10x Chromium Controller. For the scSLAM seq samples  a papain dissociation protocol Worthington kit  according to manufecturer's instructions was used followed by an adapted scSLAM seq protocol for labeling nascent transcripts see sample specific entry. The standard transcriptome libraries were constructed according to the manufacturer's protocol 10X Genomics. For targeted libraries of scars used for lineage tracing  the cDNA obtained during the 10x protocol was used to set up individual PCR reactions for each target  specifically amplifying the sgRNA target site. The libraries for targeted PCR were constructed with primers compatible with the 10X Genomics kit  so they could be sequenced on the same platform.", null, "tissue:adult brain|tissue region:whole|cell type:mixed tissue dissociation|genotype:wildtype", "GSM7875175", "GSM7875175: Brain 13   whole  scRNAseq; Danio rerio; RNA Seq", "GSM7875175 r1", "GSM7875175", "1", "Adult zebrafish brain tissue was extracted  optionally dissected to select a specific region of interest telencephalon  diencephalon  mesencephalon or rhombencephalon and then dissociated with a trypsin protocol. Briefly  the samples were incubated in 750 \u00b5l of HBSS glucose solution and dissocated with 15 \u00b5l of 0.05% trypsin EDTA Gibco for 30 minutes at 37\u00b0C with intermittent mixing. The disociation was stopped by addition of 750 \u00b5l of a BSA EBSS HEPES solution. Further sample handling steps were carried out on ice and for centrifugation at 4\u00b0C. The sample was filtered through a 100 \u00b5m filter  washed with cold HBSS and resuspended in 100 200 \u00b5l HBSS with 0.05% BSA  then filtered through a 35 \u00b5m filter and loaded on the 10x Chromium Controller. For the scSLAM seq samples  a papain dissociation protocol Worthington kit  according to manufecturer's instructions was used followed by an adapted scSLAM seq protocol for labeling nascent transcripts see sample specific entry. The standard transcriptome libraries were constructed according to the manufacturer's protocol 10X Genomics. For targeted libraries of scars used for lineage tracing  the cDNA obtained during the 10x protocol was used to set up individual PCR reactions for each target  specifically amplifying the sgRNA target site. The libraries for targeted PCR were constructed with primers compatible with the 10X Genomics kit  so they could be sequenced on the same platform.", null, "RNA-Seq", "TRANSCRIPTOMIC SINGLE CELL", "cDNA", "PAIRED", "ILLUMINA", "NextSeq 500", null, "SRP469552", null, "loader:fastq load.py", "b13_mult_R2.fastq.gz b13_mult_R1.fastq.gz", "fastq fastq", 14716484640.0, 94336440.0, "GSM7875175 r1", "0:28 1:128", "A:3656679229;C:3244551762;G:3483547888;T:4325660472;N:6045289", 28, 128, null, null, 3656679229, 3244551762, 3483547888, 4325660472, 6045289, "SRX22323906", "SRS19374435", "SRA1743007", "Junker Lab, Berlin Institute for Medical Systems Biology, Max-Delbr\u00fcck-Center for Molecular Medicine", "Junker Lab, Berlin Institute for Medical Systems Biology, Max-Delbr\u00fcck-Center for Molecular Medicine", 2, 0.0334, 0.93779, 0.01344, 0.20291, 0.97654, 0.72991, 0.35896, 0.56645, 28, 128, "T", "B", "sc-like readlen", "illumina", "nextseq", "unknown", "random_priming", "unknown", "sc", "single_cell_droplet", "10x", null, "Germany", "2023-10-31", "Adult", "Adult", "Brain", "Nervous System"], [28742, "SRR26623279", "SRX22323905", "SRS19374434", "SRP469552", "PRJNA1034159", "Dissecting the spatiotemporal diversity of adult neural stem cells", "GSE246714", "Other", "Adult stem cells are important for tissue turnover and regeneration. However  in most adult systems it remains elusive how stem cells assume different functional states and support spatially patterned tissue architecture. Here  we dissected the diversity of neural stem cells in the adult zebrafish brain  an organ that is characterized by pronounced zonation and high regenerative capacity. We combined single cell transcriptomics of dissected brain regions with massively parallel lineage tracing and in vivo RNA metabolic labeling to analyze regulation of neural stem cells in space and time. We detected a large diversity of neural stem cells  with some subtypes being restricted to a single brain region  while others were found globally across the brain. Global stem cell states are linked to neurogenic differentiation  with different states being involved in proliferative and non proliferative differentiation. Our work reveals principles of adult stem cell organization and establishes a resource for functional manipulation of neural stem cell subtypes. Overall design: Single cell RNA seq of adult zebrafish brain performed using 10x Genomics Gene Expression protocols to explore transcriptomic diversity of adult cell types. The samples were prepared either from whole brain  FACS sorted cells GFP positive cells from gfap:GFP line or a dissected single brain region  as indicated in the sample title. CRISPR based lineage tracing was performed for a subset of samples to dissect developmental lineage relationships between cells. ScSLAM seq was performed in combination with Notch pathway inhibition to assess response of stem cells to perturbation.", null, "pubmed:38365956", null, "Brain 12   whole  scRNAseq", "GSM7875174", null, "source name:adult brain|tissue:adult brain|tissue region:whole|cell type:mixed tissue dissociation|genotype:wildtype|geo loc name:missing|collection date:missing", "Brain 12   whole  scRNAseq", "The transcriptome libraries were demultiplexed and mapped with CellRanger 6.1.1. In order to reduce batch effect  the gene expression matrices were processed with the SoupX tool Young & Behjati  2020 for removing ambient RNA contamination. A subset of libraries was also mapped with Velocyto v0.17.17 La Manno et al  2018 to create count matrices for unspliced and spliced molecules. The targeted lineage tracing libraries were aligned using bwa mem3 v.0.7.12 to individual references of endogenous genes used in the lineage tracing experiment actb1  actb2  cfl1  cirbpb  rpl39 and ube2e1. The scars were filtered with a custom pipeline to eliminate sequences that originate from PCR or sequencing errors. For custom code and further downstream analysis  see: https://github.com/nimitic/radial glia. Assembly: GRCz11 Supplementary files format and content: Tab separated barcode files and matrix files along with the tab separated gene file supplementary file are the output of CellRanger mapping  further processed by the SoupX package for ambient RNA removal. Supplementary files format and content: Loom files are the output of velocyto mapping distinguishing spliced and unspliced mRNAs. Supplementary files format and content: filtered scars.csv files contain the output of a custom scar filtering pipeline.", "adult brain", null, "Adult zebrafish brain tissue was extracted  optionally dissected to select a specific region of interest telencephalon  diencephalon  mesencephalon or rhombencephalon and then dissociated with a trypsin protocol. Briefly  the samples were incubated in 750 \u00b5l of HBSS glucose solution and dissocated with 15 \u00b5l of 0.05% trypsin EDTA Gibco for 30 minutes at 37\u00b0C with intermittent mixing. The disociation was stopped by addition of 750 \u00b5l of a BSA EBSS HEPES solution. Further sample handling steps were carried out on ice and for centrifugation at 4\u00b0C. The sample was filtered through a 100 \u00b5m filter  washed with cold HBSS and resuspended in 100 200 \u00b5l HBSS with 0.05% BSA  then filtered through a 35 \u00b5m filter and loaded on the 10x Chromium Controller. For the scSLAM seq samples  a papain dissociation protocol Worthington kit  according to manufecturer's instructions was used followed by an adapted scSLAM seq protocol for labeling nascent transcripts see sample specific entry. The standard transcriptome libraries were constructed according to the manufacturer's protocol 10X Genomics. For targeted libraries of scars used for lineage tracing  the cDNA obtained during the 10x protocol was used to set up individual PCR reactions for each target  specifically amplifying the sgRNA target site. The libraries for targeted PCR were constructed with primers compatible with the 10X Genomics kit  so they could be sequenced on the same platform.", null, "tissue:adult brain|tissue region:whole|cell type:mixed tissue dissociation|genotype:wildtype", "GSM7875174", "GSM7875174: Brain 12   whole  scRNAseq; Danio rerio; RNA Seq", "GSM7875174 r1", "GSM7875174", "1", "Adult zebrafish brain tissue was extracted  optionally dissected to select a specific region of interest telencephalon  diencephalon  mesencephalon or rhombencephalon and then dissociated with a trypsin protocol. Briefly  the samples were incubated in 750 \u00b5l of HBSS glucose solution and dissocated with 15 \u00b5l of 0.05% trypsin EDTA Gibco for 30 minutes at 37\u00b0C with intermittent mixing. The disociation was stopped by addition of 750 \u00b5l of a BSA EBSS HEPES solution. Further sample handling steps were carried out on ice and for centrifugation at 4\u00b0C. The sample was filtered through a 100 \u00b5m filter  washed with cold HBSS and resuspended in 100 200 \u00b5l HBSS with 0.05% BSA  then filtered through a 35 \u00b5m filter and loaded on the 10x Chromium Controller. For the scSLAM seq samples  a papain dissociation protocol Worthington kit  according to manufecturer's instructions was used followed by an adapted scSLAM seq protocol for labeling nascent transcripts see sample specific entry. The standard transcriptome libraries were constructed according to the manufacturer's protocol 10X Genomics. For targeted libraries of scars used for lineage tracing  the cDNA obtained during the 10x protocol was used to set up individual PCR reactions for each target  specifically amplifying the sgRNA target site. The libraries for targeted PCR were constructed with primers compatible with the 10X Genomics kit  so they could be sequenced on the same platform.", null, "RNA-Seq", "TRANSCRIPTOMIC SINGLE CELL", "cDNA", "PAIRED", "ILLUMINA", "NextSeq 500", null, "SRP469552", null, "loader:fastq load.py", "b12_mult_R2.fastq.gz b12_mult_R1.fastq.gz", "fastq fastq", 12967118112.0, 83122552.0, "GSM7875174 r1", "0:28 1:128", "A:3152750696;C:2922178338;G:3166672580;T:3720207530;N:5308968", 28, 128, null, null, 3152750696, 2922178338, 3166672580, 3720207530, 5308968, "SRX22323905", "SRS19374434", "SRA1743007", "Junker Lab, Berlin Institute for Medical Systems Biology, Max-Delbr\u00fcck-Center for Molecular Medicine", "Junker Lab, Berlin Institute for Medical Systems Biology, Max-Delbr\u00fcck-Center for Molecular Medicine", 2, 0.06196, 0.94967, 0.02426, 0.19522, 0.97108, 0.73886, 0.33644, 0.62119, 28, 128, "T", "B", "sc-like readlen", "illumina", "nextseq", "unknown", "random_priming", "unknown", "sc", "single_cell_droplet", "10x", null, "Germany", "2023-10-31", "Adult", "Adult", "Brain", "Nervous System"], [28743, "SRR26623280", "SRX22323904", "SRS19374433", "SRP469552", "PRJNA1034159", "Dissecting the spatiotemporal diversity of adult neural stem cells", "GSE246714", "Other", "Adult stem cells are important for tissue turnover and regeneration. However  in most adult systems it remains elusive how stem cells assume different functional states and support spatially patterned tissue architecture. Here  we dissected the diversity of neural stem cells in the adult zebrafish brain  an organ that is characterized by pronounced zonation and high regenerative capacity. We combined single cell transcriptomics of dissected brain regions with massively parallel lineage tracing and in vivo RNA metabolic labeling to analyze regulation of neural stem cells in space and time. We detected a large diversity of neural stem cells  with some subtypes being restricted to a single brain region  while others were found globally across the brain. Global stem cell states are linked to neurogenic differentiation  with different states being involved in proliferative and non proliferative differentiation. Our work reveals principles of adult stem cell organization and establishes a resource for functional manipulation of neural stem cell subtypes. Overall design: Single cell RNA seq of adult zebrafish brain performed using 10x Genomics Gene Expression protocols to explore transcriptomic diversity of adult cell types. The samples were prepared either from whole brain  FACS sorted cells GFP positive cells from gfap:GFP line or a dissected single brain region  as indicated in the sample title. CRISPR based lineage tracing was performed for a subset of samples to dissect developmental lineage relationships between cells. ScSLAM seq was performed in combination with Notch pathway inhibition to assess response of stem cells to perturbation.", null, "pubmed:38365956", null, "Brain 11   whole  scRNAseq", "GSM7875173", null, "source name:adult brain|tissue:adult brain|tissue region:whole|cell type:mixed tissue dissociation|genotype:wildtype|geo loc name:missing|collection date:missing", "Brain 11   whole  scRNAseq", "The transcriptome libraries were demultiplexed and mapped with CellRanger 6.1.1. In order to reduce batch effect  the gene expression matrices were processed with the SoupX tool Young & Behjati  2020 for removing ambient RNA contamination. A subset of libraries was also mapped with Velocyto v0.17.17 La Manno et al  2018 to create count matrices for unspliced and spliced molecules. The targeted lineage tracing libraries were aligned using bwa mem3 v.0.7.12 to individual references of endogenous genes used in the lineage tracing experiment actb1  actb2  cfl1  cirbpb  rpl39 and ube2e1. The scars were filtered with a custom pipeline to eliminate sequences that originate from PCR or sequencing errors. For custom code and further downstream analysis  see: https://github.com/nimitic/radial glia. Assembly: GRCz11 Supplementary files format and content: Tab separated barcode files and matrix files along with the tab separated gene file supplementary file are the output of CellRanger mapping  further processed by the SoupX package for ambient RNA removal. Supplementary files format and content: Loom files are the output of velocyto mapping distinguishing spliced and unspliced mRNAs. Supplementary files format and content: filtered scars.csv files contain the output of a custom scar filtering pipeline.", "adult brain", null, "Adult zebrafish brain tissue was extracted  optionally dissected to select a specific region of interest telencephalon  diencephalon  mesencephalon or rhombencephalon and then dissociated with a trypsin protocol. Briefly  the samples were incubated in 750 \u00b5l of HBSS glucose solution and dissocated with 15 \u00b5l of 0.05% trypsin EDTA Gibco for 30 minutes at 37\u00b0C with intermittent mixing. The disociation was stopped by addition of 750 \u00b5l of a BSA EBSS HEPES solution. Further sample handling steps were carried out on ice and for centrifugation at 4\u00b0C. The sample was filtered through a 100 \u00b5m filter  washed with cold HBSS and resuspended in 100 200 \u00b5l HBSS with 0.05% BSA  then filtered through a 35 \u00b5m filter and loaded on the 10x Chromium Controller. For the scSLAM seq samples  a papain dissociation protocol Worthington kit  according to manufecturer's instructions was used followed by an adapted scSLAM seq protocol for labeling nascent transcripts see sample specific entry. The standard transcriptome libraries were constructed according to the manufacturer's protocol 10X Genomics. For targeted libraries of scars used for lineage tracing  the cDNA obtained during the 10x protocol was used to set up individual PCR reactions for each target  specifically amplifying the sgRNA target site. The libraries for targeted PCR were constructed with primers compatible with the 10X Genomics kit  so they could be sequenced on the same platform.", null, "tissue:adult brain|tissue region:whole|cell type:mixed tissue dissociation|genotype:wildtype", "GSM7875173", "GSM7875173: Brain 11   whole  scRNAseq; Danio rerio; RNA Seq", "GSM7875173 r1", "GSM7875173", "1", "Adult zebrafish brain tissue was extracted  optionally dissected to select a specific region of interest telencephalon  diencephalon  mesencephalon or rhombencephalon and then dissociated with a trypsin protocol. Briefly  the samples were incubated in 750 \u00b5l of HBSS glucose solution and dissocated with 15 \u00b5l of 0.05% trypsin EDTA Gibco for 30 minutes at 37\u00b0C with intermittent mixing. The disociation was stopped by addition of 750 \u00b5l of a BSA EBSS HEPES solution. Further sample handling steps were carried out on ice and for centrifugation at 4\u00b0C. The sample was filtered through a 100 \u00b5m filter  washed with cold HBSS and resuspended in 100 200 \u00b5l HBSS with 0.05% BSA  then filtered through a 35 \u00b5m filter and loaded on the 10x Chromium Controller. For the scSLAM seq samples  a papain dissociation protocol Worthington kit  according to manufecturer's instructions was used followed by an adapted scSLAM seq protocol for labeling nascent transcripts see sample specific entry. The standard transcriptome libraries were constructed according to the manufacturer's protocol 10X Genomics. For targeted libraries of scars used for lineage tracing  the cDNA obtained during the 10x protocol was used to set up individual PCR reactions for each target  specifically amplifying the sgRNA target site. The libraries for targeted PCR were constructed with primers compatible with the 10X Genomics kit  so they could be sequenced on the same platform.", null, "RNA-Seq", "TRANSCRIPTOMIC SINGLE CELL", "cDNA", "PAIRED", "ILLUMINA", "NextSeq 500", null, "SRP469552", null, "loader:fastq load.py", "b11_mult_R1.fastq.gz b11_mult_R2.fastq.gz", "fastq fastq", 14974963824.0, 101182188.0, "GSM7875173 r1", "0:28 1:120", "A:3753936293;C:3687961330;G:3650849763;T:3879258880;N:2957558", 28, 120, null, null, 3753936293, 3687961330, 3650849763, 3879258880, 2957558, "SRX22323904", "SRS19374433", "SRA1743007", "Junker Lab, Berlin Institute for Medical Systems Biology, Max-Delbr\u00fcck-Center for Molecular Medicine", "Junker Lab, Berlin Institute for Medical Systems Biology, Max-Delbr\u00fcck-Center for Molecular Medicine", 2, 0.16926, 0.9633, 0.06452, 0.20524, 0.93545, 0.76274, 0.26732, 0.59389, 28, 120, "T", "B", "sc-like readlen", "illumina", "nextseq", "unknown", "random_priming", "unknown", "sc", "single_cell_droplet", "10x", null, "Germany", "2023-10-31", "Adult", "Adult", "Brain", "Nervous System"], [28744, "SRR26623281", "SRX22323903", "SRS19374431", "SRP469552", "PRJNA1034159", "Dissecting the spatiotemporal diversity of adult neural stem cells", "GSE246714", "Other", "Adult stem cells are important for tissue turnover and regeneration. However  in most adult systems it remains elusive how stem cells assume different functional states and support spatially patterned tissue architecture. Here  we dissected the diversity of neural stem cells in the adult zebrafish brain  an organ that is characterized by pronounced zonation and high regenerative capacity. We combined single cell transcriptomics of dissected brain regions with massively parallel lineage tracing and in vivo RNA metabolic labeling to analyze regulation of neural stem cells in space and time. We detected a large diversity of neural stem cells  with some subtypes being restricted to a single brain region  while others were found globally across the brain. Global stem cell states are linked to neurogenic differentiation  with different states being involved in proliferative and non proliferative differentiation. Our work reveals principles of adult stem cell organization and establishes a resource for functional manipulation of neural stem cell subtypes. Overall design: Single cell RNA seq of adult zebrafish brain performed using 10x Genomics Gene Expression protocols to explore transcriptomic diversity of adult cell types. The samples were prepared either from whole brain  FACS sorted cells GFP positive cells from gfap:GFP line or a dissected single brain region  as indicated in the sample title. CRISPR based lineage tracing was performed for a subset of samples to dissect developmental lineage relationships between cells. ScSLAM seq was performed in combination with Notch pathway inhibition to assess response of stem cells to perturbation.", null, "pubmed:38365956", null, "Brain 10   telencephalon  scRNAseq", "GSM7875172", null, "source name:adult brain|tissue:adult brain|tissue region:telencephalon|cell type:mixed tissue dissociation|genotype:wildtype|geo loc name:missing|collection date:missing", "Brain 10   telencephalon  scRNAseq", "The transcriptome libraries were demultiplexed and mapped with CellRanger 6.1.1. In order to reduce batch effect  the gene expression matrices were processed with the SoupX tool Young & Behjati  2020 for removing ambient RNA contamination. A subset of libraries was also mapped with Velocyto v0.17.17 La Manno et al  2018 to create count matrices for unspliced and spliced molecules. The targeted lineage tracing libraries were aligned using bwa mem3 v.0.7.12 to individual references of endogenous genes used in the lineage tracing experiment actb1  actb2  cfl1  cirbpb  rpl39 and ube2e1. The scars were filtered with a custom pipeline to eliminate sequences that originate from PCR or sequencing errors. For custom code and further downstream analysis  see: https://github.com/nimitic/radial glia. Assembly: GRCz11 Supplementary files format and content: Tab separated barcode files and matrix files along with the tab separated gene file supplementary file are the output of CellRanger mapping  further processed by the SoupX package for ambient RNA removal. Supplementary files format and content: Loom files are the output of velocyto mapping distinguishing spliced and unspliced mRNAs. Supplementary files format and content: filtered scars.csv files contain the output of a custom scar filtering pipeline.", "adult brain", null, "Adult zebrafish brain tissue was extracted  optionally dissected to select a specific region of interest telencephalon  diencephalon  mesencephalon or rhombencephalon and then dissociated with a trypsin protocol. Briefly  the samples were incubated in 750 \u00b5l of HBSS glucose solution and dissocated with 15 \u00b5l of 0.05% trypsin EDTA Gibco for 30 minutes at 37\u00b0C with intermittent mixing. The disociation was stopped by addition of 750 \u00b5l of a BSA EBSS HEPES solution. Further sample handling steps were carried out on ice and for centrifugation at 4\u00b0C. The sample was filtered through a 100 \u00b5m filter  washed with cold HBSS and resuspended in 100 200 \u00b5l HBSS with 0.05% BSA  then filtered through a 35 \u00b5m filter and loaded on the 10x Chromium Controller. For the scSLAM seq samples  a papain dissociation protocol Worthington kit  according to manufecturer's instructions was used followed by an adapted scSLAM seq protocol for labeling nascent transcripts see sample specific entry. The standard transcriptome libraries were constructed according to the manufacturer's protocol 10X Genomics. For targeted libraries of scars used for lineage tracing  the cDNA obtained during the 10x protocol was used to set up individual PCR reactions for each target  specifically amplifying the sgRNA target site. The libraries for targeted PCR were constructed with primers compatible with the 10X Genomics kit  so they could be sequenced on the same platform.", null, "tissue:adult brain|tissue region:telencephalon|cell type:mixed tissue dissociation|genotype:wildtype", "GSM7875172", "GSM7875172: Brain 10   telencephalon  scRNAseq; Danio rerio; RNA Seq", "GSM7875172 r1", "GSM7875172", "1", "Adult zebrafish brain tissue was extracted  optionally dissected to select a specific region of interest telencephalon  diencephalon  mesencephalon or rhombencephalon and then dissociated with a trypsin protocol. Briefly  the samples were incubated in 750 \u00b5l of HBSS glucose solution and dissocated with 15 \u00b5l of 0.05% trypsin EDTA Gibco for 30 minutes at 37\u00b0C with intermittent mixing. The disociation was stopped by addition of 750 \u00b5l of a BSA EBSS HEPES solution. Further sample handling steps were carried out on ice and for centrifugation at 4\u00b0C. The sample was filtered through a 100 \u00b5m filter  washed with cold HBSS and resuspended in 100 200 \u00b5l HBSS with 0.05% BSA  then filtered through a 35 \u00b5m filter and loaded on the 10x Chromium Controller. For the scSLAM seq samples  a papain dissociation protocol Worthington kit  according to manufecturer's instructions was used followed by an adapted scSLAM seq protocol for labeling nascent transcripts see sample specific entry. The standard transcriptome libraries were constructed according to the manufacturer's protocol 10X Genomics. For targeted libraries of scars used for lineage tracing  the cDNA obtained during the 10x protocol was used to set up individual PCR reactions for each target  specifically amplifying the sgRNA target site. The libraries for targeted PCR were constructed with primers compatible with the 10X Genomics kit  so they could be sequenced on the same platform.", null, "RNA-Seq", "TRANSCRIPTOMIC SINGLE CELL", "cDNA", "PAIRED", "ILLUMINA", "NextSeq 500", null, "SRP469552", null, "loader:fastq load.py", "b10_tel_seqrun1_R2.fastq.gz b10_tel_seqrun1_R1.fastq.gz", "fastq fastq", 3731199140.0, 25210805.0, "GSM7875172 r1", "0:28 1:120", "A:1133663247;C:801786454;G:811411935;T:982912952;N:1424552", 28, 120, null, null, 1133663247, 801786454, 811411935, 982912952, 1424552, "SRX22323903", "SRS19374431", "SRA1743007", "Junker Lab, Berlin Institute for Medical Systems Biology, Max-Delbr\u00fcck-Center for Molecular Medicine", "Junker Lab, Berlin Institute for Medical Systems Biology, Max-Delbr\u00fcck-Center for Molecular Medicine", 2, 0.01267, 0.92045, 0.00689, 0.20316, 0.99101, 0.80876, 0.4565, 0.44433, 28, 120, "T", "B", "sc-like readlen", "illumina", "nextseq", "unknown", "cdna_unspecified", "unknown", "sc", "single_cell_droplet", "10x", null, "Germany", "2023-10-31", "Adult", "Adult", "Brain", "Nervous System"], [28745, "SRR26623282", "SRX22323903", "SRS19374431", "SRP469552", "PRJNA1034159", "Dissecting the spatiotemporal diversity of adult neural stem cells", "GSE246714", "Other", "Adult stem cells are important for tissue turnover and regeneration. However  in most adult systems it remains elusive how stem cells assume different functional states and support spatially patterned tissue architecture. Here  we dissected the diversity of neural stem cells in the adult zebrafish brain  an organ that is characterized by pronounced zonation and high regenerative capacity. We combined single cell transcriptomics of dissected brain regions with massively parallel lineage tracing and in vivo RNA metabolic labeling to analyze regulation of neural stem cells in space and time. We detected a large diversity of neural stem cells  with some subtypes being restricted to a single brain region  while others were found globally across the brain. Global stem cell states are linked to neurogenic differentiation  with different states being involved in proliferative and non proliferative differentiation. Our work reveals principles of adult stem cell organization and establishes a resource for functional manipulation of neural stem cell subtypes. Overall design: Single cell RNA seq of adult zebrafish brain performed using 10x Genomics Gene Expression protocols to explore transcriptomic diversity of adult cell types. The samples were prepared either from whole brain  FACS sorted cells GFP positive cells from gfap:GFP line or a dissected single brain region  as indicated in the sample title. CRISPR based lineage tracing was performed for a subset of samples to dissect developmental lineage relationships between cells. ScSLAM seq was performed in combination with Notch pathway inhibition to assess response of stem cells to perturbation.", null, "pubmed:38365956", null, "Brain 10   telencephalon  scRNAseq", "GSM7875172", null, "source name:adult brain|tissue:adult brain|tissue region:telencephalon|cell type:mixed tissue dissociation|genotype:wildtype|geo loc name:missing|collection date:missing", "Brain 10   telencephalon  scRNAseq", "The transcriptome libraries were demultiplexed and mapped with CellRanger 6.1.1. In order to reduce batch effect  the gene expression matrices were processed with the SoupX tool Young & Behjati  2020 for removing ambient RNA contamination. A subset of libraries was also mapped with Velocyto v0.17.17 La Manno et al  2018 to create count matrices for unspliced and spliced molecules. The targeted lineage tracing libraries were aligned using bwa mem3 v.0.7.12 to individual references of endogenous genes used in the lineage tracing experiment actb1  actb2  cfl1  cirbpb  rpl39 and ube2e1. The scars were filtered with a custom pipeline to eliminate sequences that originate from PCR or sequencing errors. For custom code and further downstream analysis  see: https://github.com/nimitic/radial glia. Assembly: GRCz11 Supplementary files format and content: Tab separated barcode files and matrix files along with the tab separated gene file supplementary file are the output of CellRanger mapping  further processed by the SoupX package for ambient RNA removal. Supplementary files format and content: Loom files are the output of velocyto mapping distinguishing spliced and unspliced mRNAs. Supplementary files format and content: filtered scars.csv files contain the output of a custom scar filtering pipeline.", "adult brain", null, "Adult zebrafish brain tissue was extracted  optionally dissected to select a specific region of interest telencephalon  diencephalon  mesencephalon or rhombencephalon and then dissociated with a trypsin protocol. Briefly  the samples were incubated in 750 \u00b5l of HBSS glucose solution and dissocated with 15 \u00b5l of 0.05% trypsin EDTA Gibco for 30 minutes at 37\u00b0C with intermittent mixing. The disociation was stopped by addition of 750 \u00b5l of a BSA EBSS HEPES solution. Further sample handling steps were carried out on ice and for centrifugation at 4\u00b0C. The sample was filtered through a 100 \u00b5m filter  washed with cold HBSS and resuspended in 100 200 \u00b5l HBSS with 0.05% BSA  then filtered through a 35 \u00b5m filter and loaded on the 10x Chromium Controller. For the scSLAM seq samples  a papain dissociation protocol Worthington kit  according to manufecturer's instructions was used followed by an adapted scSLAM seq protocol for labeling nascent transcripts see sample specific entry. The standard transcriptome libraries were constructed according to the manufacturer's protocol 10X Genomics. For targeted libraries of scars used for lineage tracing  the cDNA obtained during the 10x protocol was used to set up individual PCR reactions for each target  specifically amplifying the sgRNA target site. The libraries for targeted PCR were constructed with primers compatible with the 10X Genomics kit  so they could be sequenced on the same platform.", null, "tissue:adult brain|tissue region:telencephalon|cell type:mixed tissue dissociation|genotype:wildtype", "GSM7875172", "GSM7875172: Brain 10   telencephalon  scRNAseq; Danio rerio; RNA Seq", "GSM7875172 r1", "GSM7875172", "1", "Adult zebrafish brain tissue was extracted  optionally dissected to select a specific region of interest telencephalon  diencephalon  mesencephalon or rhombencephalon and then dissociated with a trypsin protocol. Briefly  the samples were incubated in 750 \u00b5l of HBSS glucose solution and dissocated with 15 \u00b5l of 0.05% trypsin EDTA Gibco for 30 minutes at 37\u00b0C with intermittent mixing. The disociation was stopped by addition of 750 \u00b5l of a BSA EBSS HEPES solution. Further sample handling steps were carried out on ice and for centrifugation at 4\u00b0C. The sample was filtered through a 100 \u00b5m filter  washed with cold HBSS and resuspended in 100 200 \u00b5l HBSS with 0.05% BSA  then filtered through a 35 \u00b5m filter and loaded on the 10x Chromium Controller. For the scSLAM seq samples  a papain dissociation protocol Worthington kit  according to manufecturer's instructions was used followed by an adapted scSLAM seq protocol for labeling nascent transcripts see sample specific entry. The standard transcriptome libraries were constructed according to the manufacturer's protocol 10X Genomics. For targeted libraries of scars used for lineage tracing  the cDNA obtained during the 10x protocol was used to set up individual PCR reactions for each target  specifically amplifying the sgRNA target site. The libraries for targeted PCR were constructed with primers compatible with the 10X Genomics kit  so they could be sequenced on the same platform.", null, "RNA-Seq", "TRANSCRIPTOMIC SINGLE CELL", "cDNA", "PAIRED", "ILLUMINA", "NextSeq 500", null, "SRP469552", null, "loader:fastq load.py", "b10_tel_seqrun2_R2.fastq.gz b10_tel_seqrun2_R1.fastq.gz", "fastq fastq", 506663940.0, 3423405.0, "GSM7875172 r2", null, null, null, null, null, null, null, null, null, null, null, "SRX22323903", "SRS19374431", "SRA1743007", "Junker Lab, Berlin Institute for Medical Systems Biology, Max-Delbr\u00fcck-Center for Molecular Medicine", "Junker Lab, Berlin Institute for Medical Systems Biology, Max-Delbr\u00fcck-Center for Molecular Medicine", 2, 0.01234, 0.92119, 0.00661, 0.20424, 0.99176, 0.81156, 0.50821, 0.7283, 28, 120, "T", "B", "sc-like readlen", "illumina", "nextseq", "unknown", "cdna_unspecified", "unknown", "sc", "single_cell_droplet", "10x", null, "Germany", "2023-10-31", "Adult", "Adult", "Brain", "Nervous System"], [28746, "SRR26623283", "SRX22323902", "SRS19374432", "SRP469552", "PRJNA1034159", "Dissecting the spatiotemporal diversity of adult neural stem cells", "GSE246714", "Other", "Adult stem cells are important for tissue turnover and regeneration. However  in most adult systems it remains elusive how stem cells assume different functional states and support spatially patterned tissue architecture. Here  we dissected the diversity of neural stem cells in the adult zebrafish brain  an organ that is characterized by pronounced zonation and high regenerative capacity. We combined single cell transcriptomics of dissected brain regions with massively parallel lineage tracing and in vivo RNA metabolic labeling to analyze regulation of neural stem cells in space and time. We detected a large diversity of neural stem cells  with some subtypes being restricted to a single brain region  while others were found globally across the brain. Global stem cell states are linked to neurogenic differentiation  with different states being involved in proliferative and non proliferative differentiation. Our work reveals principles of adult stem cell organization and establishes a resource for functional manipulation of neural stem cell subtypes. Overall design: Single cell RNA seq of adult zebrafish brain performed using 10x Genomics Gene Expression protocols to explore transcriptomic diversity of adult cell types. The samples were prepared either from whole brain  FACS sorted cells GFP positive cells from gfap:GFP line or a dissected single brain region  as indicated in the sample title. CRISPR based lineage tracing was performed for a subset of samples to dissect developmental lineage relationships between cells. ScSLAM seq was performed in combination with Notch pathway inhibition to assess response of stem cells to perturbation.", null, "pubmed:38365956", null, "Brain 10   mesencephalon  scRNAseq", "GSM7875171", null, "source name:adult brain|tissue:adult brain|tissue region:mesencephalon|cell type:mixed tissue dissociation|genotype:wildtype|geo loc name:missing|collection date:missing", "Brain 10   mesencephalon  scRNAseq", "The transcriptome libraries were demultiplexed and mapped with CellRanger 6.1.1. In order to reduce batch effect  the gene expression matrices were processed with the SoupX tool Young & Behjati  2020 for removing ambient RNA contamination. A subset of libraries was also mapped with Velocyto v0.17.17 La Manno et al  2018 to create count matrices for unspliced and spliced molecules. The targeted lineage tracing libraries were aligned using bwa mem3 v.0.7.12 to individual references of endogenous genes used in the lineage tracing experiment actb1  actb2  cfl1  cirbpb  rpl39 and ube2e1. The scars were filtered with a custom pipeline to eliminate sequences that originate from PCR or sequencing errors. For custom code and further downstream analysis  see: https://github.com/nimitic/radial glia. Assembly: GRCz11 Supplementary files format and content: Tab separated barcode files and matrix files along with the tab separated gene file supplementary file are the output of CellRanger mapping  further processed by the SoupX package for ambient RNA removal. Supplementary files format and content: Loom files are the output of velocyto mapping distinguishing spliced and unspliced mRNAs. Supplementary files format and content: filtered scars.csv files contain the output of a custom scar filtering pipeline.", "adult brain", null, "Adult zebrafish brain tissue was extracted  optionally dissected to select a specific region of interest telencephalon  diencephalon  mesencephalon or rhombencephalon and then dissociated with a trypsin protocol. Briefly  the samples were incubated in 750 \u00b5l of HBSS glucose solution and dissocated with 15 \u00b5l of 0.05% trypsin EDTA Gibco for 30 minutes at 37\u00b0C with intermittent mixing. The disociation was stopped by addition of 750 \u00b5l of a BSA EBSS HEPES solution. Further sample handling steps were carried out on ice and for centrifugation at 4\u00b0C. The sample was filtered through a 100 \u00b5m filter  washed with cold HBSS and resuspended in 100 200 \u00b5l HBSS with 0.05% BSA  then filtered through a 35 \u00b5m filter and loaded on the 10x Chromium Controller. For the scSLAM seq samples  a papain dissociation protocol Worthington kit  according to manufecturer's instructions was used followed by an adapted scSLAM seq protocol for labeling nascent transcripts see sample specific entry. The standard transcriptome libraries were constructed according to the manufacturer's protocol 10X Genomics. For targeted libraries of scars used for lineage tracing  the cDNA obtained during the 10x protocol was used to set up individual PCR reactions for each target  specifically amplifying the sgRNA target site. The libraries for targeted PCR were constructed with primers compatible with the 10X Genomics kit  so they could be sequenced on the same platform.", null, "tissue:adult brain|tissue region:mesencephalon|cell type:mixed tissue dissociation|genotype:wildtype", "GSM7875171", "GSM7875171: Brain 10   mesencephalon  scRNAseq; Danio rerio; RNA Seq", "GSM7875171 r1", "GSM7875171", "1", "Adult zebrafish brain tissue was extracted  optionally dissected to select a specific region of interest telencephalon  diencephalon  mesencephalon or rhombencephalon and then dissociated with a trypsin protocol. Briefly  the samples were incubated in 750 \u00b5l of HBSS glucose solution and dissocated with 15 \u00b5l of 0.05% trypsin EDTA Gibco for 30 minutes at 37\u00b0C with intermittent mixing. The disociation was stopped by addition of 750 \u00b5l of a BSA EBSS HEPES solution. Further sample handling steps were carried out on ice and for centrifugation at 4\u00b0C. The sample was filtered through a 100 \u00b5m filter  washed with cold HBSS and resuspended in 100 200 \u00b5l HBSS with 0.05% BSA  then filtered through a 35 \u00b5m filter and loaded on the 10x Chromium Controller. For the scSLAM seq samples  a papain dissociation protocol Worthington kit  according to manufecturer's instructions was used followed by an adapted scSLAM seq protocol for labeling nascent transcripts see sample specific entry. The standard transcriptome libraries were constructed according to the manufacturer's protocol 10X Genomics. For targeted libraries of scars used for lineage tracing  the cDNA obtained during the 10x protocol was used to set up individual PCR reactions for each target  specifically amplifying the sgRNA target site. The libraries for targeted PCR were constructed with primers compatible with the 10X Genomics kit  so they could be sequenced on the same platform.", null, "RNA-Seq", "TRANSCRIPTOMIC SINGLE CELL", "cDNA", "PAIRED", "ILLUMINA", "NextSeq 500", null, "SRP469552", null, "loader:fastq load.py", "b10_ot_seqrun1_R1.fastq.gz b10_ot_seqrun1_R2.fastq.gz", "fastq fastq", 13285400412.0, 89766219.0, "GSM7875171 r1", "0:28 1:120", "A:4046617952;C:2711660606;G:2795963083;T:3726083915;N:5074856", 28, 120, null, null, 4046617952, 2711660606, 2795963083, 3726083915, 5074856, "SRX22323902", "SRS19374432", "SRA1743007", "Junker Lab, Berlin Institute for Medical Systems Biology, Max-Delbr\u00fcck-Center for Molecular Medicine", "Junker Lab, Berlin Institute for Medical Systems Biology, Max-Delbr\u00fcck-Center for Molecular Medicine", 2, 0.01072, 0.91211, 0.00629, 0.2341, 0.99111, 0.78985, 0.46996, 0.63777, 28, 120, "T", "B", "sc-like readlen", "illumina", "nextseq", "unknown", "cdna_unspecified", "unknown", "sc", "single_cell_droplet", "10x", null, "Germany", "2023-10-31", "Adult", "Adult", "Brain", "Nervous System"], [28747, "SRR26623284", "SRX22323902", "SRS19374432", "SRP469552", "PRJNA1034159", "Dissecting the spatiotemporal diversity of adult neural stem cells", "GSE246714", "Other", "Adult stem cells are important for tissue turnover and regeneration. However  in most adult systems it remains elusive how stem cells assume different functional states and support spatially patterned tissue architecture. Here  we dissected the diversity of neural stem cells in the adult zebrafish brain  an organ that is characterized by pronounced zonation and high regenerative capacity. We combined single cell transcriptomics of dissected brain regions with massively parallel lineage tracing and in vivo RNA metabolic labeling to analyze regulation of neural stem cells in space and time. We detected a large diversity of neural stem cells  with some subtypes being restricted to a single brain region  while others were found globally across the brain. Global stem cell states are linked to neurogenic differentiation  with different states being involved in proliferative and non proliferative differentiation. Our work reveals principles of adult stem cell organization and establishes a resource for functional manipulation of neural stem cell subtypes. Overall design: Single cell RNA seq of adult zebrafish brain performed using 10x Genomics Gene Expression protocols to explore transcriptomic diversity of adult cell types. The samples were prepared either from whole brain  FACS sorted cells GFP positive cells from gfap:GFP line or a dissected single brain region  as indicated in the sample title. CRISPR based lineage tracing was performed for a subset of samples to dissect developmental lineage relationships between cells. ScSLAM seq was performed in combination with Notch pathway inhibition to assess response of stem cells to perturbation.", null, "pubmed:38365956", null, "Brain 10   mesencephalon  scRNAseq", "GSM7875171", null, "source name:adult brain|tissue:adult brain|tissue region:mesencephalon|cell type:mixed tissue dissociation|genotype:wildtype|geo loc name:missing|collection date:missing", "Brain 10   mesencephalon  scRNAseq", "The transcriptome libraries were demultiplexed and mapped with CellRanger 6.1.1. In order to reduce batch effect  the gene expression matrices were processed with the SoupX tool Young & Behjati  2020 for removing ambient RNA contamination. A subset of libraries was also mapped with Velocyto v0.17.17 La Manno et al  2018 to create count matrices for unspliced and spliced molecules. The targeted lineage tracing libraries were aligned using bwa mem3 v.0.7.12 to individual references of endogenous genes used in the lineage tracing experiment actb1  actb2  cfl1  cirbpb  rpl39 and ube2e1. The scars were filtered with a custom pipeline to eliminate sequences that originate from PCR or sequencing errors. For custom code and further downstream analysis  see: https://github.com/nimitic/radial glia. Assembly: GRCz11 Supplementary files format and content: Tab separated barcode files and matrix files along with the tab separated gene file supplementary file are the output of CellRanger mapping  further processed by the SoupX package for ambient RNA removal. Supplementary files format and content: Loom files are the output of velocyto mapping distinguishing spliced and unspliced mRNAs. Supplementary files format and content: filtered scars.csv files contain the output of a custom scar filtering pipeline.", "adult brain", null, "Adult zebrafish brain tissue was extracted  optionally dissected to select a specific region of interest telencephalon  diencephalon  mesencephalon or rhombencephalon and then dissociated with a trypsin protocol. Briefly  the samples were incubated in 750 \u00b5l of HBSS glucose solution and dissocated with 15 \u00b5l of 0.05% trypsin EDTA Gibco for 30 minutes at 37\u00b0C with intermittent mixing. The disociation was stopped by addition of 750 \u00b5l of a BSA EBSS HEPES solution. Further sample handling steps were carried out on ice and for centrifugation at 4\u00b0C. The sample was filtered through a 100 \u00b5m filter  washed with cold HBSS and resuspended in 100 200 \u00b5l HBSS with 0.05% BSA  then filtered through a 35 \u00b5m filter and loaded on the 10x Chromium Controller. For the scSLAM seq samples  a papain dissociation protocol Worthington kit  according to manufecturer's instructions was used followed by an adapted scSLAM seq protocol for labeling nascent transcripts see sample specific entry. The standard transcriptome libraries were constructed according to the manufacturer's protocol 10X Genomics. For targeted libraries of scars used for lineage tracing  the cDNA obtained during the 10x protocol was used to set up individual PCR reactions for each target  specifically amplifying the sgRNA target site. The libraries for targeted PCR were constructed with primers compatible with the 10X Genomics kit  so they could be sequenced on the same platform.", null, "tissue:adult brain|tissue region:mesencephalon|cell type:mixed tissue dissociation|genotype:wildtype", "GSM7875171", "GSM7875171: Brain 10   mesencephalon  scRNAseq; Danio rerio; RNA Seq", "GSM7875171 r1", "GSM7875171", "1", "Adult zebrafish brain tissue was extracted  optionally dissected to select a specific region of interest telencephalon  diencephalon  mesencephalon or rhombencephalon and then dissociated with a trypsin protocol. Briefly  the samples were incubated in 750 \u00b5l of HBSS glucose solution and dissocated with 15 \u00b5l of 0.05% trypsin EDTA Gibco for 30 minutes at 37\u00b0C with intermittent mixing. The disociation was stopped by addition of 750 \u00b5l of a BSA EBSS HEPES solution. Further sample handling steps were carried out on ice and for centrifugation at 4\u00b0C. The sample was filtered through a 100 \u00b5m filter  washed with cold HBSS and resuspended in 100 200 \u00b5l HBSS with 0.05% BSA  then filtered through a 35 \u00b5m filter and loaded on the 10x Chromium Controller. For the scSLAM seq samples  a papain dissociation protocol Worthington kit  according to manufecturer's instructions was used followed by an adapted scSLAM seq protocol for labeling nascent transcripts see sample specific entry. The standard transcriptome libraries were constructed according to the manufacturer's protocol 10X Genomics. For targeted libraries of scars used for lineage tracing  the cDNA obtained during the 10x protocol was used to set up individual PCR reactions for each target  specifically amplifying the sgRNA target site. The libraries for targeted PCR were constructed with primers compatible with the 10X Genomics kit  so they could be sequenced on the same platform.", null, "RNA-Seq", "TRANSCRIPTOMIC SINGLE CELL", "cDNA", "PAIRED", "ILLUMINA", "NextSeq 500", null, "SRP469552", null, "loader:fastq load.py", "b10_ot_seqrun2_R1.fastq.gz b10_ot_seqrun2_R2.fastq.gz", "fastq fastq", 1804356800.0, 12191600.0, "GSM7875171 r2", "0:28 1:120", "A:547173169;C:367866083;G:382757407;T:506285738;N:274403", 28, 120, null, null, 547173169, 367866083, 382757407, 506285738, 274403, "SRX22323902", "SRS19374432", "SRA1743007", "Junker Lab, Berlin Institute for Medical Systems Biology, Max-Delbr\u00fcck-Center for Molecular Medicine", "Junker Lab, Berlin Institute for Medical Systems Biology, Max-Delbr\u00fcck-Center for Molecular Medicine", 2, 0.0107, 0.91103, 0.00617, 0.23408, 0.99058, 0.79178, 0.4579, 0.63098, 28, 120, "T", "B", "sc-like readlen", "illumina", "nextseq", "unknown", "cdna_unspecified", "unknown", "sc", "single_cell_droplet", "10x", null, "Germany", "2023-10-31", "Adult", "Adult", "Brain", "Nervous System"], [28748, "SRR26623285", "SRX22323901", "SRS19374430", "SRP469552", "PRJNA1034159", "Dissecting the spatiotemporal diversity of adult neural stem cells", "GSE246714", "Other", "Adult stem cells are important for tissue turnover and regeneration. However  in most adult systems it remains elusive how stem cells assume different functional states and support spatially patterned tissue architecture. Here  we dissected the diversity of neural stem cells in the adult zebrafish brain  an organ that is characterized by pronounced zonation and high regenerative capacity. We combined single cell transcriptomics of dissected brain regions with massively parallel lineage tracing and in vivo RNA metabolic labeling to analyze regulation of neural stem cells in space and time. We detected a large diversity of neural stem cells  with some subtypes being restricted to a single brain region  while others were found globally across the brain. Global stem cell states are linked to neurogenic differentiation  with different states being involved in proliferative and non proliferative differentiation. Our work reveals principles of adult stem cell organization and establishes a resource for functional manipulation of neural stem cell subtypes. Overall design: Single cell RNA seq of adult zebrafish brain performed using 10x Genomics Gene Expression protocols to explore transcriptomic diversity of adult cell types. The samples were prepared either from whole brain  FACS sorted cells GFP positive cells from gfap:GFP line or a dissected single brain region  as indicated in the sample title. CRISPR based lineage tracing was performed for a subset of samples to dissect developmental lineage relationships between cells. ScSLAM seq was performed in combination with Notch pathway inhibition to assess response of stem cells to perturbation.", null, "pubmed:38365956", null, "Brain 10   diencephalon  scRNAseq", "GSM7875170", null, "source name:adult brain|tissue:adult brain|tissue region:diencephalon|cell type:mixed tissue dissociation|genotype:wildtype|geo loc name:missing|collection date:missing", "Brain 10   diencephalon  scRNAseq", "The transcriptome libraries were demultiplexed and mapped with CellRanger 6.1.1. In order to reduce batch effect  the gene expression matrices were processed with the SoupX tool Young & Behjati  2020 for removing ambient RNA contamination. A subset of libraries was also mapped with Velocyto v0.17.17 La Manno et al  2018 to create count matrices for unspliced and spliced molecules. The targeted lineage tracing libraries were aligned using bwa mem3 v.0.7.12 to individual references of endogenous genes used in the lineage tracing experiment actb1  actb2  cfl1  cirbpb  rpl39 and ube2e1. The scars were filtered with a custom pipeline to eliminate sequences that originate from PCR or sequencing errors. For custom code and further downstream analysis  see: https://github.com/nimitic/radial glia. Assembly: GRCz11 Supplementary files format and content: Tab separated barcode files and matrix files along with the tab separated gene file supplementary file are the output of CellRanger mapping  further processed by the SoupX package for ambient RNA removal. Supplementary files format and content: Loom files are the output of velocyto mapping distinguishing spliced and unspliced mRNAs. Supplementary files format and content: filtered scars.csv files contain the output of a custom scar filtering pipeline.", "adult brain", null, "Adult zebrafish brain tissue was extracted  optionally dissected to select a specific region of interest telencephalon  diencephalon  mesencephalon or rhombencephalon and then dissociated with a trypsin protocol. Briefly  the samples were incubated in 750 \u00b5l of HBSS glucose solution and dissocated with 15 \u00b5l of 0.05% trypsin EDTA Gibco for 30 minutes at 37\u00b0C with intermittent mixing. The disociation was stopped by addition of 750 \u00b5l of a BSA EBSS HEPES solution. Further sample handling steps were carried out on ice and for centrifugation at 4\u00b0C. The sample was filtered through a 100 \u00b5m filter  washed with cold HBSS and resuspended in 100 200 \u00b5l HBSS with 0.05% BSA  then filtered through a 35 \u00b5m filter and loaded on the 10x Chromium Controller. For the scSLAM seq samples  a papain dissociation protocol Worthington kit  according to manufecturer's instructions was used followed by an adapted scSLAM seq protocol for labeling nascent transcripts see sample specific entry. The standard transcriptome libraries were constructed according to the manufacturer's protocol 10X Genomics. For targeted libraries of scars used for lineage tracing  the cDNA obtained during the 10x protocol was used to set up individual PCR reactions for each target  specifically amplifying the sgRNA target site. The libraries for targeted PCR were constructed with primers compatible with the 10X Genomics kit  so they could be sequenced on the same platform.", null, "tissue:adult brain|tissue region:diencephalon|cell type:mixed tissue dissociation|genotype:wildtype", "GSM7875170", "GSM7875170: Brain 10   diencephalon  scRNAseq; Danio rerio; RNA Seq", "GSM7875170 r1", "GSM7875170", "1", "Adult zebrafish brain tissue was extracted  optionally dissected to select a specific region of interest telencephalon  diencephalon  mesencephalon or rhombencephalon and then dissociated with a trypsin protocol. Briefly  the samples were incubated in 750 \u00b5l of HBSS glucose solution and dissocated with 15 \u00b5l of 0.05% trypsin EDTA Gibco for 30 minutes at 37\u00b0C with intermittent mixing. The disociation was stopped by addition of 750 \u00b5l of a BSA EBSS HEPES solution. Further sample handling steps were carried out on ice and for centrifugation at 4\u00b0C. The sample was filtered through a 100 \u00b5m filter  washed with cold HBSS and resuspended in 100 200 \u00b5l HBSS with 0.05% BSA  then filtered through a 35 \u00b5m filter and loaded on the 10x Chromium Controller. For the scSLAM seq samples  a papain dissociation protocol Worthington kit  according to manufecturer's instructions was used followed by an adapted scSLAM seq protocol for labeling nascent transcripts see sample specific entry. The standard transcriptome libraries were constructed according to the manufacturer's protocol 10X Genomics. For targeted libraries of scars used for lineage tracing  the cDNA obtained during the 10x protocol was used to set up individual PCR reactions for each target  specifically amplifying the sgRNA target site. The libraries for targeted PCR were constructed with primers compatible with the 10X Genomics kit  so they could be sequenced on the same platform.", null, "RNA-Seq", "TRANSCRIPTOMIC SINGLE CELL", "cDNA", "PAIRED", "ILLUMINA", "NextSeq 500", null, "SRP469552", null, "loader:fastq load.py", "b10_dien_seqrun1_R1.fastq.gz b10_dien_seqrun1_R2.fastq.gz", "fastq fastq", 16775453532.0, 113347659.0, "GSM7875170 r1", "0:28 1:120", "A:5122230488;C:3380526476;G:3500039522;T:4766209405;N:6447641", 28, 120, null, null, 5122230488, 3380526476, 3500039522, 4766209405, 6447641, "SRX22323901", "SRS19374430", "SRA1743007", "Junker Lab, Berlin Institute for Medical Systems Biology, Max-Delbr\u00fcck-Center for Molecular Medicine", "Junker Lab, Berlin Institute for Medical Systems Biology, Max-Delbr\u00fcck-Center for Molecular Medicine", 2, 0.01031, 0.90264, 0.0062, 0.26938, 0.99107, 0.78283, 0.45373, 0.61666, 28, 120, "T", "B", "sc-like readlen", "illumina", "nextseq", "unknown", "cdna_unspecified", "unknown", "sc", "single_cell_droplet", "10x", null, "Germany", "2023-10-31", "Adult", "Adult", "Brain", "Nervous System"], [28749, "SRR26623286", "SRX22323901", "SRS19374430", "SRP469552", "PRJNA1034159", "Dissecting the spatiotemporal diversity of adult neural stem cells", "GSE246714", "Other", "Adult stem cells are important for tissue turnover and regeneration. However  in most adult systems it remains elusive how stem cells assume different functional states and support spatially patterned tissue architecture. Here  we dissected the diversity of neural stem cells in the adult zebrafish brain  an organ that is characterized by pronounced zonation and high regenerative capacity. We combined single cell transcriptomics of dissected brain regions with massively parallel lineage tracing and in vivo RNA metabolic labeling to analyze regulation of neural stem cells in space and time. We detected a large diversity of neural stem cells  with some subtypes being restricted to a single brain region  while others were found globally across the brain. Global stem cell states are linked to neurogenic differentiation  with different states being involved in proliferative and non proliferative differentiation. Our work reveals principles of adult stem cell organization and establishes a resource for functional manipulation of neural stem cell subtypes. Overall design: Single cell RNA seq of adult zebrafish brain performed using 10x Genomics Gene Expression protocols to explore transcriptomic diversity of adult cell types. The samples were prepared either from whole brain  FACS sorted cells GFP positive cells from gfap:GFP line or a dissected single brain region  as indicated in the sample title. CRISPR based lineage tracing was performed for a subset of samples to dissect developmental lineage relationships between cells. ScSLAM seq was performed in combination with Notch pathway inhibition to assess response of stem cells to perturbation.", null, "pubmed:38365956", null, "Brain 10   diencephalon  scRNAseq", "GSM7875170", null, "source name:adult brain|tissue:adult brain|tissue region:diencephalon|cell type:mixed tissue dissociation|genotype:wildtype|geo loc name:missing|collection date:missing", "Brain 10   diencephalon  scRNAseq", "The transcriptome libraries were demultiplexed and mapped with CellRanger 6.1.1. In order to reduce batch effect  the gene expression matrices were processed with the SoupX tool Young & Behjati  2020 for removing ambient RNA contamination. A subset of libraries was also mapped with Velocyto v0.17.17 La Manno et al  2018 to create count matrices for unspliced and spliced molecules. The targeted lineage tracing libraries were aligned using bwa mem3 v.0.7.12 to individual references of endogenous genes used in the lineage tracing experiment actb1  actb2  cfl1  cirbpb  rpl39 and ube2e1. The scars were filtered with a custom pipeline to eliminate sequences that originate from PCR or sequencing errors. For custom code and further downstream analysis  see: https://github.com/nimitic/radial glia. Assembly: GRCz11 Supplementary files format and content: Tab separated barcode files and matrix files along with the tab separated gene file supplementary file are the output of CellRanger mapping  further processed by the SoupX package for ambient RNA removal. Supplementary files format and content: Loom files are the output of velocyto mapping distinguishing spliced and unspliced mRNAs. Supplementary files format and content: filtered scars.csv files contain the output of a custom scar filtering pipeline.", "adult brain", null, "Adult zebrafish brain tissue was extracted  optionally dissected to select a specific region of interest telencephalon  diencephalon  mesencephalon or rhombencephalon and then dissociated with a trypsin protocol. Briefly  the samples were incubated in 750 \u00b5l of HBSS glucose solution and dissocated with 15 \u00b5l of 0.05% trypsin EDTA Gibco for 30 minutes at 37\u00b0C with intermittent mixing. The disociation was stopped by addition of 750 \u00b5l of a BSA EBSS HEPES solution. Further sample handling steps were carried out on ice and for centrifugation at 4\u00b0C. The sample was filtered through a 100 \u00b5m filter  washed with cold HBSS and resuspended in 100 200 \u00b5l HBSS with 0.05% BSA  then filtered through a 35 \u00b5m filter and loaded on the 10x Chromium Controller. For the scSLAM seq samples  a papain dissociation protocol Worthington kit  according to manufecturer's instructions was used followed by an adapted scSLAM seq protocol for labeling nascent transcripts see sample specific entry. The standard transcriptome libraries were constructed according to the manufacturer's protocol 10X Genomics. For targeted libraries of scars used for lineage tracing  the cDNA obtained during the 10x protocol was used to set up individual PCR reactions for each target  specifically amplifying the sgRNA target site. The libraries for targeted PCR were constructed with primers compatible with the 10X Genomics kit  so they could be sequenced on the same platform.", null, "tissue:adult brain|tissue region:diencephalon|cell type:mixed tissue dissociation|genotype:wildtype", "GSM7875170", "GSM7875170: Brain 10   diencephalon  scRNAseq; Danio rerio; RNA Seq", "GSM7875170 r1", "GSM7875170", "1", "Adult zebrafish brain tissue was extracted  optionally dissected to select a specific region of interest telencephalon  diencephalon  mesencephalon or rhombencephalon and then dissociated with a trypsin protocol. Briefly  the samples were incubated in 750 \u00b5l of HBSS glucose solution and dissocated with 15 \u00b5l of 0.05% trypsin EDTA Gibco for 30 minutes at 37\u00b0C with intermittent mixing. The disociation was stopped by addition of 750 \u00b5l of a BSA EBSS HEPES solution. Further sample handling steps were carried out on ice and for centrifugation at 4\u00b0C. The sample was filtered through a 100 \u00b5m filter  washed with cold HBSS and resuspended in 100 200 \u00b5l HBSS with 0.05% BSA  then filtered through a 35 \u00b5m filter and loaded on the 10x Chromium Controller. For the scSLAM seq samples  a papain dissociation protocol Worthington kit  according to manufecturer's instructions was used followed by an adapted scSLAM seq protocol for labeling nascent transcripts see sample specific entry. The standard transcriptome libraries were constructed according to the manufacturer's protocol 10X Genomics. For targeted libraries of scars used for lineage tracing  the cDNA obtained during the 10x protocol was used to set up individual PCR reactions for each target  specifically amplifying the sgRNA target site. The libraries for targeted PCR were constructed with primers compatible with the 10X Genomics kit  so they could be sequenced on the same platform.", null, "RNA-Seq", "TRANSCRIPTOMIC SINGLE CELL", "cDNA", "PAIRED", "ILLUMINA", "NextSeq 500", null, "SRP469552", null, "loader:fastq load.py", "b10_dien_seqrun2_R1.fastq.gz b10_dien_seqrun2_R2.fastq.gz", "fastq fastq", 2278985844.0, 15398553.0, "GSM7875170 r2", "0:28 1:120", "A:693086062;C:458642995;G:479031763;T:647875888;N:349136", 28, 120, null, null, 693086062, 458642995, 479031763, 647875888, 349136, "SRX22323901", "SRS19374430", "SRA1743007", "Junker Lab, Berlin Institute for Medical Systems Biology, Max-Delbr\u00fcck-Center for Molecular Medicine", "Junker Lab, Berlin Institute for Medical Systems Biology, Max-Delbr\u00fcck-Center for Molecular Medicine", 2, 0.01062, 0.90395, 0.0066, 0.27264, 0.99131, 0.78524, 0.45789, 0.61679, 28, 120, "T", "B", "sc-like readlen", "illumina", "nextseq", "unknown", "cdna_unspecified", "unknown", "sc", "single_cell_droplet", "10x", null, "Germany", "2023-10-31", "Adult", "Adult", "Brain", "Nervous System"], [28750, "SRR26623287", "SRX22323900", "SRS19374428", "SRP469552", "PRJNA1034159", "Dissecting the spatiotemporal diversity of adult neural stem cells", "GSE246714", "Other", "Adult stem cells are important for tissue turnover and regeneration. However  in most adult systems it remains elusive how stem cells assume different functional states and support spatially patterned tissue architecture. Here  we dissected the diversity of neural stem cells in the adult zebrafish brain  an organ that is characterized by pronounced zonation and high regenerative capacity. We combined single cell transcriptomics of dissected brain regions with massively parallel lineage tracing and in vivo RNA metabolic labeling to analyze regulation of neural stem cells in space and time. We detected a large diversity of neural stem cells  with some subtypes being restricted to a single brain region  while others were found globally across the brain. Global stem cell states are linked to neurogenic differentiation  with different states being involved in proliferative and non proliferative differentiation. Our work reveals principles of adult stem cell organization and establishes a resource for functional manipulation of neural stem cell subtypes. Overall design: Single cell RNA seq of adult zebrafish brain performed using 10x Genomics Gene Expression protocols to explore transcriptomic diversity of adult cell types. The samples were prepared either from whole brain  FACS sorted cells GFP positive cells from gfap:GFP line or a dissected single brain region  as indicated in the sample title. CRISPR based lineage tracing was performed for a subset of samples to dissect developmental lineage relationships between cells. ScSLAM seq was performed in combination with Notch pathway inhibition to assess response of stem cells to perturbation.", null, "pubmed:38365956", null, "Brain 10   rhombencephalon  scRNAseq", "GSM7875169", null, "source name:adult brain|tissue:adult brain|tissue region:rhombencephalon|cell type:mixed tissue dissociation|genotype:wildtype|geo loc name:missing|collection date:missing", "Brain 10   rhombencephalon  scRNAseq", "The transcriptome libraries were demultiplexed and mapped with CellRanger 6.1.1. In order to reduce batch effect  the gene expression matrices were processed with the SoupX tool Young & Behjati  2020 for removing ambient RNA contamination. A subset of libraries was also mapped with Velocyto v0.17.17 La Manno et al  2018 to create count matrices for unspliced and spliced molecules. The targeted lineage tracing libraries were aligned using bwa mem3 v.0.7.12 to individual references of endogenous genes used in the lineage tracing experiment actb1  actb2  cfl1  cirbpb  rpl39 and ube2e1. The scars were filtered with a custom pipeline to eliminate sequences that originate from PCR or sequencing errors. For custom code and further downstream analysis  see: https://github.com/nimitic/radial glia. Assembly: GRCz11 Supplementary files format and content: Tab separated barcode files and matrix files along with the tab separated gene file supplementary file are the output of CellRanger mapping  further processed by the SoupX package for ambient RNA removal. Supplementary files format and content: Loom files are the output of velocyto mapping distinguishing spliced and unspliced mRNAs. Supplementary files format and content: filtered scars.csv files contain the output of a custom scar filtering pipeline.", "adult brain", null, "Adult zebrafish brain tissue was extracted  optionally dissected to select a specific region of interest telencephalon  diencephalon  mesencephalon or rhombencephalon and then dissociated with a trypsin protocol. Briefly  the samples were incubated in 750 \u00b5l of HBSS glucose solution and dissocated with 15 \u00b5l of 0.05% trypsin EDTA Gibco for 30 minutes at 37\u00b0C with intermittent mixing. The disociation was stopped by addition of 750 \u00b5l of a BSA EBSS HEPES solution. Further sample handling steps were carried out on ice and for centrifugation at 4\u00b0C. The sample was filtered through a 100 \u00b5m filter  washed with cold HBSS and resuspended in 100 200 \u00b5l HBSS with 0.05% BSA  then filtered through a 35 \u00b5m filter and loaded on the 10x Chromium Controller. For the scSLAM seq samples  a papain dissociation protocol Worthington kit  according to manufecturer's instructions was used followed by an adapted scSLAM seq protocol for labeling nascent transcripts see sample specific entry. The standard transcriptome libraries were constructed according to the manufacturer's protocol 10X Genomics. For targeted libraries of scars used for lineage tracing  the cDNA obtained during the 10x protocol was used to set up individual PCR reactions for each target  specifically amplifying the sgRNA target site. The libraries for targeted PCR were constructed with primers compatible with the 10X Genomics kit  so they could be sequenced on the same platform.", null, "tissue:adult brain|tissue region:rhombencephalon|cell type:mixed tissue dissociation|genotype:wildtype", "GSM7875169", "GSM7875169: Brain 10   rhombencephalon  scRNAseq; Danio rerio; RNA Seq", "GSM7875169 r1", "GSM7875169", "1", "Adult zebrafish brain tissue was extracted  optionally dissected to select a specific region of interest telencephalon  diencephalon  mesencephalon or rhombencephalon and then dissociated with a trypsin protocol. Briefly  the samples were incubated in 750 \u00b5l of HBSS glucose solution and dissocated with 15 \u00b5l of 0.05% trypsin EDTA Gibco for 30 minutes at 37\u00b0C with intermittent mixing. The disociation was stopped by addition of 750 \u00b5l of a BSA EBSS HEPES solution. Further sample handling steps were carried out on ice and for centrifugation at 4\u00b0C. The sample was filtered through a 100 \u00b5m filter  washed with cold HBSS and resuspended in 100 200 \u00b5l HBSS with 0.05% BSA  then filtered through a 35 \u00b5m filter and loaded on the 10x Chromium Controller. For the scSLAM seq samples  a papain dissociation protocol Worthington kit  according to manufecturer's instructions was used followed by an adapted scSLAM seq protocol for labeling nascent transcripts see sample specific entry. The standard transcriptome libraries were constructed according to the manufacturer's protocol 10X Genomics. For targeted libraries of scars used for lineage tracing  the cDNA obtained during the 10x protocol was used to set up individual PCR reactions for each target  specifically amplifying the sgRNA target site. The libraries for targeted PCR were constructed with primers compatible with the 10X Genomics kit  so they could be sequenced on the same platform.", null, "RNA-Seq", "TRANSCRIPTOMIC SINGLE CELL", "cDNA", "PAIRED", "ILLUMINA", "NextSeq 500", null, "SRP469552", null, "loader:fastq load.py", "b10_cer_seqrun1_R1.fastq.gz b10_cer_seqrun1_R2.fastq.gz", "fastq fastq", 16240869244.0, 109735603.0, "GSM7875169 r1", "0:28 1:120", "A:4994572796;C:3251585988;G:3395902822;T:4592558542;N:6249096", 28, 120, null, null, 4994572796, 3251585988, 3395902822, 4592558542, 6249096, "SRX22323900", "SRS19374428", "SRA1743007", "Junker Lab, Berlin Institute for Medical Systems Biology, Max-Delbr\u00fcck-Center for Molecular Medicine", "Junker Lab, Berlin Institute for Medical Systems Biology, Max-Delbr\u00fcck-Center for Molecular Medicine", 2, 0.0128, 0.89159, 0.00813, 0.3597, 0.98991, 0.82278, 0.44977, 0.60211, 28, 120, "T", "B", "sc-like readlen", "illumina", "nextseq", "unknown", "cdna_unspecified", "unknown", "sc", "single_cell_droplet", "10x", null, "Germany", "2023-10-31", "Adult", "Adult", "Brain", "Nervous System"], [28751, "SRR26623288", "SRX22323900", "SRS19374428", "SRP469552", "PRJNA1034159", "Dissecting the spatiotemporal diversity of adult neural stem cells", "GSE246714", "Other", "Adult stem cells are important for tissue turnover and regeneration. However  in most adult systems it remains elusive how stem cells assume different functional states and support spatially patterned tissue architecture. Here  we dissected the diversity of neural stem cells in the adult zebrafish brain  an organ that is characterized by pronounced zonation and high regenerative capacity. We combined single cell transcriptomics of dissected brain regions with massively parallel lineage tracing and in vivo RNA metabolic labeling to analyze regulation of neural stem cells in space and time. We detected a large diversity of neural stem cells  with some subtypes being restricted to a single brain region  while others were found globally across the brain. Global stem cell states are linked to neurogenic differentiation  with different states being involved in proliferative and non proliferative differentiation. Our work reveals principles of adult stem cell organization and establishes a resource for functional manipulation of neural stem cell subtypes. Overall design: Single cell RNA seq of adult zebrafish brain performed using 10x Genomics Gene Expression protocols to explore transcriptomic diversity of adult cell types. The samples were prepared either from whole brain  FACS sorted cells GFP positive cells from gfap:GFP line or a dissected single brain region  as indicated in the sample title. CRISPR based lineage tracing was performed for a subset of samples to dissect developmental lineage relationships between cells. ScSLAM seq was performed in combination with Notch pathway inhibition to assess response of stem cells to perturbation.", null, "pubmed:38365956", null, "Brain 10   rhombencephalon  scRNAseq", "GSM7875169", null, "source name:adult brain|tissue:adult brain|tissue region:rhombencephalon|cell type:mixed tissue dissociation|genotype:wildtype|geo loc name:missing|collection date:missing", "Brain 10   rhombencephalon  scRNAseq", "The transcriptome libraries were demultiplexed and mapped with CellRanger 6.1.1. In order to reduce batch effect  the gene expression matrices were processed with the SoupX tool Young & Behjati  2020 for removing ambient RNA contamination. A subset of libraries was also mapped with Velocyto v0.17.17 La Manno et al  2018 to create count matrices for unspliced and spliced molecules. The targeted lineage tracing libraries were aligned using bwa mem3 v.0.7.12 to individual references of endogenous genes used in the lineage tracing experiment actb1  actb2  cfl1  cirbpb  rpl39 and ube2e1. The scars were filtered with a custom pipeline to eliminate sequences that originate from PCR or sequencing errors. For custom code and further downstream analysis  see: https://github.com/nimitic/radial glia. Assembly: GRCz11 Supplementary files format and content: Tab separated barcode files and matrix files along with the tab separated gene file supplementary file are the output of CellRanger mapping  further processed by the SoupX package for ambient RNA removal. Supplementary files format and content: Loom files are the output of velocyto mapping distinguishing spliced and unspliced mRNAs. Supplementary files format and content: filtered scars.csv files contain the output of a custom scar filtering pipeline.", "adult brain", null, "Adult zebrafish brain tissue was extracted  optionally dissected to select a specific region of interest telencephalon  diencephalon  mesencephalon or rhombencephalon and then dissociated with a trypsin protocol. Briefly  the samples were incubated in 750 \u00b5l of HBSS glucose solution and dissocated with 15 \u00b5l of 0.05% trypsin EDTA Gibco for 30 minutes at 37\u00b0C with intermittent mixing. The disociation was stopped by addition of 750 \u00b5l of a BSA EBSS HEPES solution. Further sample handling steps were carried out on ice and for centrifugation at 4\u00b0C. The sample was filtered through a 100 \u00b5m filter  washed with cold HBSS and resuspended in 100 200 \u00b5l HBSS with 0.05% BSA  then filtered through a 35 \u00b5m filter and loaded on the 10x Chromium Controller. For the scSLAM seq samples  a papain dissociation protocol Worthington kit  according to manufecturer's instructions was used followed by an adapted scSLAM seq protocol for labeling nascent transcripts see sample specific entry. The standard transcriptome libraries were constructed according to the manufacturer's protocol 10X Genomics. For targeted libraries of scars used for lineage tracing  the cDNA obtained during the 10x protocol was used to set up individual PCR reactions for each target  specifically amplifying the sgRNA target site. The libraries for targeted PCR were constructed with primers compatible with the 10X Genomics kit  so they could be sequenced on the same platform.", null, "tissue:adult brain|tissue region:rhombencephalon|cell type:mixed tissue dissociation|genotype:wildtype", "GSM7875169", "GSM7875169: Brain 10   rhombencephalon  scRNAseq; Danio rerio; RNA Seq", "GSM7875169 r1", "GSM7875169", "1", "Adult zebrafish brain tissue was extracted  optionally dissected to select a specific region of interest telencephalon  diencephalon  mesencephalon or rhombencephalon and then dissociated with a trypsin protocol. Briefly  the samples were incubated in 750 \u00b5l of HBSS glucose solution and dissocated with 15 \u00b5l of 0.05% trypsin EDTA Gibco for 30 minutes at 37\u00b0C with intermittent mixing. The disociation was stopped by addition of 750 \u00b5l of a BSA EBSS HEPES solution. Further sample handling steps were carried out on ice and for centrifugation at 4\u00b0C. The sample was filtered through a 100 \u00b5m filter  washed with cold HBSS and resuspended in 100 200 \u00b5l HBSS with 0.05% BSA  then filtered through a 35 \u00b5m filter and loaded on the 10x Chromium Controller. For the scSLAM seq samples  a papain dissociation protocol Worthington kit  according to manufecturer's instructions was used followed by an adapted scSLAM seq protocol for labeling nascent transcripts see sample specific entry. The standard transcriptome libraries were constructed according to the manufacturer's protocol 10X Genomics. For targeted libraries of scars used for lineage tracing  the cDNA obtained during the 10x protocol was used to set up individual PCR reactions for each target  specifically amplifying the sgRNA target site. The libraries for targeted PCR were constructed with primers compatible with the 10X Genomics kit  so they could be sequenced on the same platform.", null, "RNA-Seq", "TRANSCRIPTOMIC SINGLE CELL", "cDNA", "PAIRED", "ILLUMINA", "NextSeq 500", null, "SRP469552", null, "loader:fastq load.py", "b10_cer_seqrun2_R1.fastq.gz b10_cer_seqrun2_R2.fastq.gz", "fastq fastq", 2208762212.0, 14924069.0, "GSM7875169 r2", "0:28 1:120", "A:677215802;C:441481145;G:464737322;T:624989898;N:338045", 28, 120, null, null, 677215802, 441481145, 464737322, 624989898, 338045, "SRX22323900", "SRS19374428", "SRA1743007", "Junker Lab, Berlin Institute for Medical Systems Biology, Max-Delbr\u00fcck-Center for Molecular Medicine", "Junker Lab, Berlin Institute for Medical Systems Biology, Max-Delbr\u00fcck-Center for Molecular Medicine", 2, 0.0127, 0.89091, 0.00771, 0.35998, 0.9893, 0.82432, 0.41448, 0.59902, 28, 120, "T", "B", "sc-like readlen", "illumina", "nextseq", "unknown", "cdna_unspecified", "unknown", "sc", "single_cell_droplet", "10x", null, "Germany", "2023-10-31", "Adult", "Adult", "Brain", "Nervous System"], [28752, "SRR26623289", "SRX22323899", "SRS19374429", "SRP469552", "PRJNA1034159", "Dissecting the spatiotemporal diversity of adult neural stem cells", "GSE246714", "Other", "Adult stem cells are important for tissue turnover and regeneration. However  in most adult systems it remains elusive how stem cells assume different functional states and support spatially patterned tissue architecture. Here  we dissected the diversity of neural stem cells in the adult zebrafish brain  an organ that is characterized by pronounced zonation and high regenerative capacity. We combined single cell transcriptomics of dissected brain regions with massively parallel lineage tracing and in vivo RNA metabolic labeling to analyze regulation of neural stem cells in space and time. We detected a large diversity of neural stem cells  with some subtypes being restricted to a single brain region  while others were found globally across the brain. Global stem cell states are linked to neurogenic differentiation  with different states being involved in proliferative and non proliferative differentiation. Our work reveals principles of adult stem cell organization and establishes a resource for functional manipulation of neural stem cell subtypes. Overall design: Single cell RNA seq of adult zebrafish brain performed using 10x Genomics Gene Expression protocols to explore transcriptomic diversity of adult cell types. The samples were prepared either from whole brain  FACS sorted cells GFP positive cells from gfap:GFP line or a dissected single brain region  as indicated in the sample title. CRISPR based lineage tracing was performed for a subset of samples to dissect developmental lineage relationships between cells. ScSLAM seq was performed in combination with Notch pathway inhibition to assess response of stem cells to perturbation.", null, "pubmed:38365956", null, "Brain 9   telencephalon  scRNAseq", "GSM7875168", null, "source name:adult brain|tissue:adult brain|tissue region:telencephalon|cell type:mixed tissue dissociation|genotype:Tg[ubi:zebrabow M]|geo loc name:missing|collection date:missing", "Brain 9   telencephalon  scRNAseq", "The transcriptome libraries were demultiplexed and mapped with CellRanger 6.1.1. In order to reduce batch effect  the gene expression matrices were processed with the SoupX tool Young & Behjati  2020 for removing ambient RNA contamination. A subset of libraries was also mapped with Velocyto v0.17.17 La Manno et al  2018 to create count matrices for unspliced and spliced molecules. The targeted lineage tracing libraries were aligned using bwa mem3 v.0.7.12 to individual references of endogenous genes used in the lineage tracing experiment actb1  actb2  cfl1  cirbpb  rpl39 and ube2e1. The scars were filtered with a custom pipeline to eliminate sequences that originate from PCR or sequencing errors. For custom code and further downstream analysis  see: https://github.com/nimitic/radial glia. Assembly: GRCz11 Supplementary files format and content: Tab separated barcode files and matrix files along with the tab separated gene file supplementary file are the output of CellRanger mapping  further processed by the SoupX package for ambient RNA removal. Supplementary files format and content: Loom files are the output of velocyto mapping distinguishing spliced and unspliced mRNAs. Supplementary files format and content: filtered scars.csv files contain the output of a custom scar filtering pipeline.", "adult brain", null, "Adult zebrafish brain tissue was extracted  optionally dissected to select a specific region of interest telencephalon  diencephalon  mesencephalon or rhombencephalon and then dissociated with a trypsin protocol. Briefly  the samples were incubated in 750 \u00b5l of HBSS glucose solution and dissocated with 15 \u00b5l of 0.05% trypsin EDTA Gibco for 30 minutes at 37\u00b0C with intermittent mixing. The disociation was stopped by addition of 750 \u00b5l of a BSA EBSS HEPES solution. Further sample handling steps were carried out on ice and for centrifugation at 4\u00b0C. The sample was filtered through a 100 \u00b5m filter  washed with cold HBSS and resuspended in 100 200 \u00b5l HBSS with 0.05% BSA  then filtered through a 35 \u00b5m filter and loaded on the 10x Chromium Controller. For the scSLAM seq samples  a papain dissociation protocol Worthington kit  according to manufecturer's instructions was used followed by an adapted scSLAM seq protocol for labeling nascent transcripts see sample specific entry. The standard transcriptome libraries were constructed according to the manufacturer's protocol 10X Genomics. For targeted libraries of scars used for lineage tracing  the cDNA obtained during the 10x protocol was used to set up individual PCR reactions for each target  specifically amplifying the sgRNA target site. The libraries for targeted PCR were constructed with primers compatible with the 10X Genomics kit  so they could be sequenced on the same platform.", null, "tissue:adult brain|tissue region:telencephalon|cell type:mixed tissue dissociation|genotype:Tg[ubi:zebrabow M]", "GSM7875168", "GSM7875168: Brain 9   telencephalon  scRNAseq; Danio rerio; RNA Seq", "GSM7875168 r1", "GSM7875168", "1", "Adult zebrafish brain tissue was extracted  optionally dissected to select a specific region of interest telencephalon  diencephalon  mesencephalon or rhombencephalon and then dissociated with a trypsin protocol. Briefly  the samples were incubated in 750 \u00b5l of HBSS glucose solution and dissocated with 15 \u00b5l of 0.05% trypsin EDTA Gibco for 30 minutes at 37\u00b0C with intermittent mixing. The disociation was stopped by addition of 750 \u00b5l of a BSA EBSS HEPES solution. Further sample handling steps were carried out on ice and for centrifugation at 4\u00b0C. The sample was filtered through a 100 \u00b5m filter  washed with cold HBSS and resuspended in 100 200 \u00b5l HBSS with 0.05% BSA  then filtered through a 35 \u00b5m filter and loaded on the 10x Chromium Controller. For the scSLAM seq samples  a papain dissociation protocol Worthington kit  according to manufecturer's instructions was used followed by an adapted scSLAM seq protocol for labeling nascent transcripts see sample specific entry. The standard transcriptome libraries were constructed according to the manufacturer's protocol 10X Genomics. For targeted libraries of scars used for lineage tracing  the cDNA obtained during the 10x protocol was used to set up individual PCR reactions for each target  specifically amplifying the sgRNA target site. The libraries for targeted PCR were constructed with primers compatible with the 10X Genomics kit  so they could be sequenced on the same platform.", null, "RNA-Seq", "TRANSCRIPTOMIC SINGLE CELL", "cDNA", "PAIRED", "ILLUMINA", "Illumina HiSeq 4000", null, "SRP469552", null, "loader:fastq load.py", "b9_tel_R1.fastq.gz b9_tel_R2.fastq.gz", "fastq fastq", 15193441000.0, 122527750.0, "GSM7875168 r1", "0:26 1:98", "A:4477414579;C:3090884741;G:3307200604;T:4310709839;N:7231237", 26, 98, null, null, 4477414579, 3090884741, 3307200604, 4310709839, 7231237, "SRX22323899", "SRS19374429", "SRA1743007", "Junker Lab, Berlin Institute for Medical Systems Biology, Max-Delbr\u00fcck-Center for Molecular Medicine", "Junker Lab, Berlin Institute for Medical Systems Biology, Max-Delbr\u00fcck-Center for Molecular Medicine", 2, 0.00277, 0.88718, 0.00117, 0.25579, 0.99586, 0.79657, 0.30868, 0.52408, 26, 98, "T", "B", "sc-like readlen", "illumina", "hiseq_era", "unknown", "cdna_unspecified", "unknown", "sc", "single_cell_droplet", "10x", null, "Germany", "2023-10-31", "Adult", "Adult", "Brain", "Nervous System"], [28753, "SRR26623290", "SRX22323898", "SRS19374427", "SRP469552", "PRJNA1034159", "Dissecting the spatiotemporal diversity of adult neural stem cells", "GSE246714", "Other", "Adult stem cells are important for tissue turnover and regeneration. However  in most adult systems it remains elusive how stem cells assume different functional states and support spatially patterned tissue architecture. Here  we dissected the diversity of neural stem cells in the adult zebrafish brain  an organ that is characterized by pronounced zonation and high regenerative capacity. We combined single cell transcriptomics of dissected brain regions with massively parallel lineage tracing and in vivo RNA metabolic labeling to analyze regulation of neural stem cells in space and time. We detected a large diversity of neural stem cells  with some subtypes being restricted to a single brain region  while others were found globally across the brain. Global stem cell states are linked to neurogenic differentiation  with different states being involved in proliferative and non proliferative differentiation. Our work reveals principles of adult stem cell organization and establishes a resource for functional manipulation of neural stem cell subtypes. Overall design: Single cell RNA seq of adult zebrafish brain performed using 10x Genomics Gene Expression protocols to explore transcriptomic diversity of adult cell types. The samples were prepared either from whole brain  FACS sorted cells GFP positive cells from gfap:GFP line or a dissected single brain region  as indicated in the sample title. CRISPR based lineage tracing was performed for a subset of samples to dissect developmental lineage relationships between cells. ScSLAM seq was performed in combination with Notch pathway inhibition to assess response of stem cells to perturbation.", null, "pubmed:38365956", null, "Brain 8   whole  scRNAseq", "GSM7875167", null, "source name:adult brain|tissue:adult brain|tissue region:whole|cell type:mixed tissue dissociation|genotype:Tg[ubi:zebrabow M]|geo loc name:missing|collection date:missing", "Brain 8   whole  scRNAseq", "The transcriptome libraries were demultiplexed and mapped with CellRanger 6.1.1. In order to reduce batch effect  the gene expression matrices were processed with the SoupX tool Young & Behjati  2020 for removing ambient RNA contamination. A subset of libraries was also mapped with Velocyto v0.17.17 La Manno et al  2018 to create count matrices for unspliced and spliced molecules. The targeted lineage tracing libraries were aligned using bwa mem3 v.0.7.12 to individual references of endogenous genes used in the lineage tracing experiment actb1  actb2  cfl1  cirbpb  rpl39 and ube2e1. The scars were filtered with a custom pipeline to eliminate sequences that originate from PCR or sequencing errors. For custom code and further downstream analysis  see: https://github.com/nimitic/radial glia. Assembly: GRCz11 Supplementary files format and content: Tab separated barcode files and matrix files along with the tab separated gene file supplementary file are the output of CellRanger mapping  further processed by the SoupX package for ambient RNA removal. Supplementary files format and content: Loom files are the output of velocyto mapping distinguishing spliced and unspliced mRNAs. Supplementary files format and content: filtered scars.csv files contain the output of a custom scar filtering pipeline.", "adult brain", null, "Adult zebrafish brain tissue was extracted  optionally dissected to select a specific region of interest telencephalon  diencephalon  mesencephalon or rhombencephalon and then dissociated with a trypsin protocol. Briefly  the samples were incubated in 750 \u00b5l of HBSS glucose solution and dissocated with 15 \u00b5l of 0.05% trypsin EDTA Gibco for 30 minutes at 37\u00b0C with intermittent mixing. The disociation was stopped by addition of 750 \u00b5l of a BSA EBSS HEPES solution. Further sample handling steps were carried out on ice and for centrifugation at 4\u00b0C. The sample was filtered through a 100 \u00b5m filter  washed with cold HBSS and resuspended in 100 200 \u00b5l HBSS with 0.05% BSA  then filtered through a 35 \u00b5m filter and loaded on the 10x Chromium Controller. For the scSLAM seq samples  a papain dissociation protocol Worthington kit  according to manufecturer's instructions was used followed by an adapted scSLAM seq protocol for labeling nascent transcripts see sample specific entry. The standard transcriptome libraries were constructed according to the manufacturer's protocol 10X Genomics. For targeted libraries of scars used for lineage tracing  the cDNA obtained during the 10x protocol was used to set up individual PCR reactions for each target  specifically amplifying the sgRNA target site. The libraries for targeted PCR were constructed with primers compatible with the 10X Genomics kit  so they could be sequenced on the same platform.", null, "tissue:adult brain|tissue region:whole|cell type:mixed tissue dissociation|genotype:Tg[ubi:zebrabow M]", "GSM7875167", "GSM7875167: Brain 8   whole  scRNAseq; Danio rerio; RNA Seq", "GSM7875167 r1", "GSM7875167", "1", "Adult zebrafish brain tissue was extracted  optionally dissected to select a specific region of interest telencephalon  diencephalon  mesencephalon or rhombencephalon and then dissociated with a trypsin protocol. Briefly  the samples were incubated in 750 \u00b5l of HBSS glucose solution and dissocated with 15 \u00b5l of 0.05% trypsin EDTA Gibco for 30 minutes at 37\u00b0C with intermittent mixing. The disociation was stopped by addition of 750 \u00b5l of a BSA EBSS HEPES solution. Further sample handling steps were carried out on ice and for centrifugation at 4\u00b0C. The sample was filtered through a 100 \u00b5m filter  washed with cold HBSS and resuspended in 100 200 \u00b5l HBSS with 0.05% BSA  then filtered through a 35 \u00b5m filter and loaded on the 10x Chromium Controller. For the scSLAM seq samples  a papain dissociation protocol Worthington kit  according to manufecturer's instructions was used followed by an adapted scSLAM seq protocol for labeling nascent transcripts see sample specific entry. The standard transcriptome libraries were constructed according to the manufacturer's protocol 10X Genomics. For targeted libraries of scars used for lineage tracing  the cDNA obtained during the 10x protocol was used to set up individual PCR reactions for each target  specifically amplifying the sgRNA target site. The libraries for targeted PCR were constructed with primers compatible with the 10X Genomics kit  so they could be sequenced on the same platform.", null, "RNA-Seq", "TRANSCRIPTOMIC SINGLE CELL", "cDNA", "PAIRED", "ILLUMINA", "NextSeq 500", null, "SRP469552", null, "loader:fastq load.py", "b8_mult_R1.fastq.gz b8_mult_R2.fastq.gz", "fastq fastq", 16275377550.0, 108502517.0, "GSM7875167 r1", "0:26 1:124", "A:5027003801;C:3172670099;G:3435240300;T:4638692244;N:1771106", 26, 124, null, null, 5027003801, 3172670099, 3435240300, 4638692244, 1771106, "SRX22323898", "SRS19374427", "SRA1743007", "Junker Lab, Berlin Institute for Medical Systems Biology, Max-Delbr\u00fcck-Center for Molecular Medicine", "Junker Lab, Berlin Institute for Medical Systems Biology, Max-Delbr\u00fcck-Center for Molecular Medicine", 2, 0.02156, 0.88716, 0.00827, 0.36741, 0.97224, 0.83228, 0.41427, 0.52965, 26, 124, "T", "B", "sc-like readlen", "illumina", "nextseq", "unknown", "random_priming", "unknown", "sc", "single_cell_droplet", "10x", null, "Germany", "2023-10-31", "Adult", "Adult", "Brain", "Nervous System"], [28760, "SRR26623297", "SRX22323891", "SRS19374420", "SRP469552", "PRJNA1034159", "Dissecting the spatiotemporal diversity of adult neural stem cells", "GSE246714", "Other", "Adult stem cells are important for tissue turnover and regeneration. However  in most adult systems it remains elusive how stem cells assume different functional states and support spatially patterned tissue architecture. Here  we dissected the diversity of neural stem cells in the adult zebrafish brain  an organ that is characterized by pronounced zonation and high regenerative capacity. We combined single cell transcriptomics of dissected brain regions with massively parallel lineage tracing and in vivo RNA metabolic labeling to analyze regulation of neural stem cells in space and time. We detected a large diversity of neural stem cells  with some subtypes being restricted to a single brain region  while others were found globally across the brain. Global stem cell states are linked to neurogenic differentiation  with different states being involved in proliferative and non proliferative differentiation. Our work reveals principles of adult stem cell organization and establishes a resource for functional manipulation of neural stem cell subtypes. Overall design: Single cell RNA seq of adult zebrafish brain performed using 10x Genomics Gene Expression protocols to explore transcriptomic diversity of adult cell types. The samples were prepared either from whole brain  FACS sorted cells GFP positive cells from gfap:GFP line or a dissected single brain region  as indicated in the sample title. CRISPR based lineage tracing was performed for a subset of samples to dissect developmental lineage relationships between cells. ScSLAM seq was performed in combination with Notch pathway inhibition to assess response of stem cells to perturbation.", null, "pubmed:38365956", null, "Brain 7   telencephalon  scRNAseq", "GSM7875166", null, "source name:adult brain|tissue:adult brain|tissue region:telencephalon|cell type:mixed tissue dissociation|genotype:Tg[ubi:zebrabow M]|geo loc name:missing|collection date:missing", "Brain 7   telencephalon  scRNAseq", "The transcriptome libraries were demultiplexed and mapped with CellRanger 6.1.1. In order to reduce batch effect  the gene expression matrices were processed with the SoupX tool Young & Behjati  2020 for removing ambient RNA contamination. A subset of libraries was also mapped with Velocyto v0.17.17 La Manno et al  2018 to create count matrices for unspliced and spliced molecules. The targeted lineage tracing libraries were aligned using bwa mem3 v.0.7.12 to individual references of endogenous genes used in the lineage tracing experiment actb1  actb2  cfl1  cirbpb  rpl39 and ube2e1. The scars were filtered with a custom pipeline to eliminate sequences that originate from PCR or sequencing errors. For custom code and further downstream analysis  see: https://github.com/nimitic/radial glia. Assembly: GRCz11 Supplementary files format and content: Tab separated barcode files and matrix files along with the tab separated gene file supplementary file are the output of CellRanger mapping  further processed by the SoupX package for ambient RNA removal. Supplementary files format and content: Loom files are the output of velocyto mapping distinguishing spliced and unspliced mRNAs. Supplementary files format and content: filtered scars.csv files contain the output of a custom scar filtering pipeline.", "adult brain", null, "Adult zebrafish brain tissue was extracted  optionally dissected to select a specific region of interest telencephalon  diencephalon  mesencephalon or rhombencephalon and then dissociated with a trypsin protocol. Briefly  the samples were incubated in 750 \u00b5l of HBSS glucose solution and dissocated with 15 \u00b5l of 0.05% trypsin EDTA Gibco for 30 minutes at 37\u00b0C with intermittent mixing. The disociation was stopped by addition of 750 \u00b5l of a BSA EBSS HEPES solution. Further sample handling steps were carried out on ice and for centrifugation at 4\u00b0C. The sample was filtered through a 100 \u00b5m filter  washed with cold HBSS and resuspended in 100 200 \u00b5l HBSS with 0.05% BSA  then filtered through a 35 \u00b5m filter and loaded on the 10x Chromium Controller. For the scSLAM seq samples  a papain dissociation protocol Worthington kit  according to manufecturer's instructions was used followed by an adapted scSLAM seq protocol for labeling nascent transcripts see sample specific entry. The standard transcriptome libraries were constructed according to the manufacturer's protocol 10X Genomics. For targeted libraries of scars used for lineage tracing  the cDNA obtained during the 10x protocol was used to set up individual PCR reactions for each target  specifically amplifying the sgRNA target site. The libraries for targeted PCR were constructed with primers compatible with the 10X Genomics kit  so they could be sequenced on the same platform.", null, "tissue:adult brain|tissue region:telencephalon|cell type:mixed tissue dissociation|genotype:Tg[ubi:zebrabow M]", "GSM7875166", "GSM7875166: Brain 7   telencephalon  scRNAseq; Danio rerio; RNA Seq", "GSM7875166 r1", "GSM7875166", "1", "Adult zebrafish brain tissue was extracted  optionally dissected to select a specific region of interest telencephalon  diencephalon  mesencephalon or rhombencephalon and then dissociated with a trypsin protocol. Briefly  the samples were incubated in 750 \u00b5l of HBSS glucose solution and dissocated with 15 \u00b5l of 0.05% trypsin EDTA Gibco for 30 minutes at 37\u00b0C with intermittent mixing. The disociation was stopped by addition of 750 \u00b5l of a BSA EBSS HEPES solution. Further sample handling steps were carried out on ice and for centrifugation at 4\u00b0C. The sample was filtered through a 100 \u00b5m filter  washed with cold HBSS and resuspended in 100 200 \u00b5l HBSS with 0.05% BSA  then filtered through a 35 \u00b5m filter and loaded on the 10x Chromium Controller. For the scSLAM seq samples  a papain dissociation protocol Worthington kit  according to manufecturer's instructions was used followed by an adapted scSLAM seq protocol for labeling nascent transcripts see sample specific entry. The standard transcriptome libraries were constructed according to the manufacturer's protocol 10X Genomics. For targeted libraries of scars used for lineage tracing  the cDNA obtained during the 10x protocol was used to set up individual PCR reactions for each target  specifically amplifying the sgRNA target site. The libraries for targeted PCR were constructed with primers compatible with the 10X Genomics kit  so they could be sequenced on the same platform.", null, "RNA-Seq", "TRANSCRIPTOMIC SINGLE CELL", "cDNA", "PAIRED", "ILLUMINA", "Illumina HiSeq 4000", null, "SRP469552", null, "loader:fastq load.py", "b7_tel_R1.fastq.gz b7_tel_R2.fastq.gz", "fastq fastq", 7764380900.0, 62615975.0, "GSM7875166 r1", "0:26 1:98", "A:2261256925;C:1693767854;G:1688226491;T:2120472828;N:656802", 26, 98, null, null, 2261256925, 1693767854, 1688226491, 2120472828, 656802, "SRX22323891", "SRS19374420", "SRA1743007", "Junker Lab, Berlin Institute for Medical Systems Biology, Max-Delbr\u00fcck-Center for Molecular Medicine", "Junker Lab, Berlin Institute for Medical Systems Biology, Max-Delbr\u00fcck-Center for Molecular Medicine", 2, 0.01116, 0.89516, 0.0091, 0.16599, 0.99356, 0.80499, 0.47969, 0.71564, 26, 98, "T", "B", "sc-like readlen", "illumina", "hiseq_era", "unknown", "cdna_unspecified", "unknown", "sc", "single_cell_droplet", "10x", null, "Germany", "2023-10-31", "Adult", "Adult", "Brain", "Nervous System"], [28761, "SRR26623298", "SRX22323890", "SRS19374418", "SRP469552", "PRJNA1034159", "Dissecting the spatiotemporal diversity of adult neural stem cells", "GSE246714", "Other", "Adult stem cells are important for tissue turnover and regeneration. However  in most adult systems it remains elusive how stem cells assume different functional states and support spatially patterned tissue architecture. Here  we dissected the diversity of neural stem cells in the adult zebrafish brain  an organ that is characterized by pronounced zonation and high regenerative capacity. We combined single cell transcriptomics of dissected brain regions with massively parallel lineage tracing and in vivo RNA metabolic labeling to analyze regulation of neural stem cells in space and time. We detected a large diversity of neural stem cells  with some subtypes being restricted to a single brain region  while others were found globally across the brain. Global stem cell states are linked to neurogenic differentiation  with different states being involved in proliferative and non proliferative differentiation. Our work reveals principles of adult stem cell organization and establishes a resource for functional manipulation of neural stem cell subtypes. Overall design: Single cell RNA seq of adult zebrafish brain performed using 10x Genomics Gene Expression protocols to explore transcriptomic diversity of adult cell types. The samples were prepared either from whole brain  FACS sorted cells GFP positive cells from gfap:GFP line or a dissected single brain region  as indicated in the sample title. CRISPR based lineage tracing was performed for a subset of samples to dissect developmental lineage relationships between cells. ScSLAM seq was performed in combination with Notch pathway inhibition to assess response of stem cells to perturbation.", null, "pubmed:38365956", null, "Brain 7   diencephalon  scRNAseq", "GSM7875165", null, "source name:adult brain|tissue:adult brain|tissue region:diencephalon|cell type:mixed tissue dissociation|genotype:Tg[ubi:zebrabow M]|geo loc name:missing|collection date:missing", "Brain 7   diencephalon  scRNAseq", "The transcriptome libraries were demultiplexed and mapped with CellRanger 6.1.1. In order to reduce batch effect  the gene expression matrices were processed with the SoupX tool Young & Behjati  2020 for removing ambient RNA contamination. A subset of libraries was also mapped with Velocyto v0.17.17 La Manno et al  2018 to create count matrices for unspliced and spliced molecules. The targeted lineage tracing libraries were aligned using bwa mem3 v.0.7.12 to individual references of endogenous genes used in the lineage tracing experiment actb1  actb2  cfl1  cirbpb  rpl39 and ube2e1. The scars were filtered with a custom pipeline to eliminate sequences that originate from PCR or sequencing errors. For custom code and further downstream analysis  see: https://github.com/nimitic/radial glia. Assembly: GRCz11 Supplementary files format and content: Tab separated barcode files and matrix files along with the tab separated gene file supplementary file are the output of CellRanger mapping  further processed by the SoupX package for ambient RNA removal. Supplementary files format and content: Loom files are the output of velocyto mapping distinguishing spliced and unspliced mRNAs. Supplementary files format and content: filtered scars.csv files contain the output of a custom scar filtering pipeline.", "adult brain", null, "Adult zebrafish brain tissue was extracted  optionally dissected to select a specific region of interest telencephalon  diencephalon  mesencephalon or rhombencephalon and then dissociated with a trypsin protocol. Briefly  the samples were incubated in 750 \u00b5l of HBSS glucose solution and dissocated with 15 \u00b5l of 0.05% trypsin EDTA Gibco for 30 minutes at 37\u00b0C with intermittent mixing. The disociation was stopped by addition of 750 \u00b5l of a BSA EBSS HEPES solution. Further sample handling steps were carried out on ice and for centrifugation at 4\u00b0C. The sample was filtered through a 100 \u00b5m filter  washed with cold HBSS and resuspended in 100 200 \u00b5l HBSS with 0.05% BSA  then filtered through a 35 \u00b5m filter and loaded on the 10x Chromium Controller. For the scSLAM seq samples  a papain dissociation protocol Worthington kit  according to manufecturer's instructions was used followed by an adapted scSLAM seq protocol for labeling nascent transcripts see sample specific entry. The standard transcriptome libraries were constructed according to the manufacturer's protocol 10X Genomics. For targeted libraries of scars used for lineage tracing  the cDNA obtained during the 10x protocol was used to set up individual PCR reactions for each target  specifically amplifying the sgRNA target site. The libraries for targeted PCR were constructed with primers compatible with the 10X Genomics kit  so they could be sequenced on the same platform.", null, "tissue:adult brain|tissue region:diencephalon|cell type:mixed tissue dissociation|genotype:Tg[ubi:zebrabow M]", "GSM7875165", "GSM7875165: Brain 7   diencephalon  scRNAseq; Danio rerio; RNA Seq", "GSM7875165 r1", "GSM7875165", "1", "Adult zebrafish brain tissue was extracted  optionally dissected to select a specific region of interest telencephalon  diencephalon  mesencephalon or rhombencephalon and then dissociated with a trypsin protocol. Briefly  the samples were incubated in 750 \u00b5l of HBSS glucose solution and dissocated with 15 \u00b5l of 0.05% trypsin EDTA Gibco for 30 minutes at 37\u00b0C with intermittent mixing. The disociation was stopped by addition of 750 \u00b5l of a BSA EBSS HEPES solution. Further sample handling steps were carried out on ice and for centrifugation at 4\u00b0C. The sample was filtered through a 100 \u00b5m filter  washed with cold HBSS and resuspended in 100 200 \u00b5l HBSS with 0.05% BSA  then filtered through a 35 \u00b5m filter and loaded on the 10x Chromium Controller. For the scSLAM seq samples  a papain dissociation protocol Worthington kit  according to manufecturer's instructions was used followed by an adapted scSLAM seq protocol for labeling nascent transcripts see sample specific entry. The standard transcriptome libraries were constructed according to the manufacturer's protocol 10X Genomics. For targeted libraries of scars used for lineage tracing  the cDNA obtained during the 10x protocol was used to set up individual PCR reactions for each target  specifically amplifying the sgRNA target site. The libraries for targeted PCR were constructed with primers compatible with the 10X Genomics kit  so they could be sequenced on the same platform.", null, "RNA-Seq", "TRANSCRIPTOMIC SINGLE CELL", "cDNA", "PAIRED", "ILLUMINA", "Illumina HiSeq 4000", null, "SRP469552", null, "loader:fastq load.py", "b7_dien_R1.fastq.gz b7_dien_R2.fastq.gz", "fastq fastq", 7513293052.0, 60591073.0, "GSM7875165 r1", "0:26 1:98", "A:2197597087;C:1625633976;G:1631364985;T:2058064795;N:632209", 26, 98, null, null, 2197597087, 1625633976, 1631364985, 2058064795, 632209, "SRX22323890", "SRS19374418", "SRA1743007", "Junker Lab, Berlin Institute for Medical Systems Biology, Max-Delbr\u00fcck-Center for Molecular Medicine", "Junker Lab, Berlin Institute for Medical Systems Biology, Max-Delbr\u00fcck-Center for Molecular Medicine", 2, 0.00898, 0.89596, 0.00758, 0.17001, 0.99588, 0.81235, 0.32967, 0.59095, 26, 98, "T", "B", "sc-like readlen", "illumina", "hiseq_era", "unknown", "cdna_unspecified", "unknown", "sc", "single_cell_droplet", "10x", null, "Germany", "2023-10-31", "Adult", "Adult", "Brain", "Nervous System"], [28762, "SRR26623299", "SRX22323889", "SRS19374417", "SRP469552", "PRJNA1034159", "Dissecting the spatiotemporal diversity of adult neural stem cells", "GSE246714", "Other", "Adult stem cells are important for tissue turnover and regeneration. However  in most adult systems it remains elusive how stem cells assume different functional states and support spatially patterned tissue architecture. Here  we dissected the diversity of neural stem cells in the adult zebrafish brain  an organ that is characterized by pronounced zonation and high regenerative capacity. We combined single cell transcriptomics of dissected brain regions with massively parallel lineage tracing and in vivo RNA metabolic labeling to analyze regulation of neural stem cells in space and time. We detected a large diversity of neural stem cells  with some subtypes being restricted to a single brain region  while others were found globally across the brain. Global stem cell states are linked to neurogenic differentiation  with different states being involved in proliferative and non proliferative differentiation. Our work reveals principles of adult stem cell organization and establishes a resource for functional manipulation of neural stem cell subtypes. Overall design: Single cell RNA seq of adult zebrafish brain performed using 10x Genomics Gene Expression protocols to explore transcriptomic diversity of adult cell types. The samples were prepared either from whole brain  FACS sorted cells GFP positive cells from gfap:GFP line or a dissected single brain region  as indicated in the sample title. CRISPR based lineage tracing was performed for a subset of samples to dissect developmental lineage relationships between cells. ScSLAM seq was performed in combination with Notch pathway inhibition to assess response of stem cells to perturbation.", null, "pubmed:38365956", null, "Brain 7   rhombencephalon  scRNAseq", "GSM7875164", null, "source name:adult brain|tissue:adult brain|tissue region:rhombencephalon|cell type:mixed tissue dissociation|genotype:Tg[ubi:zebrabow M]|geo loc name:missing|collection date:missing", "Brain 7   rhombencephalon  scRNAseq", "The transcriptome libraries were demultiplexed and mapped with CellRanger 6.1.1. In order to reduce batch effect  the gene expression matrices were processed with the SoupX tool Young & Behjati  2020 for removing ambient RNA contamination. A subset of libraries was also mapped with Velocyto v0.17.17 La Manno et al  2018 to create count matrices for unspliced and spliced molecules. The targeted lineage tracing libraries were aligned using bwa mem3 v.0.7.12 to individual references of endogenous genes used in the lineage tracing experiment actb1  actb2  cfl1  cirbpb  rpl39 and ube2e1. The scars were filtered with a custom pipeline to eliminate sequences that originate from PCR or sequencing errors. For custom code and further downstream analysis  see: https://github.com/nimitic/radial glia. Assembly: GRCz11 Supplementary files format and content: Tab separated barcode files and matrix files along with the tab separated gene file supplementary file are the output of CellRanger mapping  further processed by the SoupX package for ambient RNA removal. Supplementary files format and content: Loom files are the output of velocyto mapping distinguishing spliced and unspliced mRNAs. Supplementary files format and content: filtered scars.csv files contain the output of a custom scar filtering pipeline.", "adult brain", null, "Adult zebrafish brain tissue was extracted  optionally dissected to select a specific region of interest telencephalon  diencephalon  mesencephalon or rhombencephalon and then dissociated with a trypsin protocol. Briefly  the samples were incubated in 750 \u00b5l of HBSS glucose solution and dissocated with 15 \u00b5l of 0.05% trypsin EDTA Gibco for 30 minutes at 37\u00b0C with intermittent mixing. The disociation was stopped by addition of 750 \u00b5l of a BSA EBSS HEPES solution. Further sample handling steps were carried out on ice and for centrifugation at 4\u00b0C. The sample was filtered through a 100 \u00b5m filter  washed with cold HBSS and resuspended in 100 200 \u00b5l HBSS with 0.05% BSA  then filtered through a 35 \u00b5m filter and loaded on the 10x Chromium Controller. For the scSLAM seq samples  a papain dissociation protocol Worthington kit  according to manufecturer's instructions was used followed by an adapted scSLAM seq protocol for labeling nascent transcripts see sample specific entry. The standard transcriptome libraries were constructed according to the manufacturer's protocol 10X Genomics. For targeted libraries of scars used for lineage tracing  the cDNA obtained during the 10x protocol was used to set up individual PCR reactions for each target  specifically amplifying the sgRNA target site. The libraries for targeted PCR were constructed with primers compatible with the 10X Genomics kit  so they could be sequenced on the same platform.", null, "tissue:adult brain|tissue region:rhombencephalon|cell type:mixed tissue dissociation|genotype:Tg[ubi:zebrabow M]", "GSM7875164", "GSM7875164: Brain 7   rhombencephalon  scRNAseq; Danio rerio; RNA Seq", "GSM7875164 r1", "GSM7875164", "1", "Adult zebrafish brain tissue was extracted  optionally dissected to select a specific region of interest telencephalon  diencephalon  mesencephalon or rhombencephalon and then dissociated with a trypsin protocol. Briefly  the samples were incubated in 750 \u00b5l of HBSS glucose solution and dissocated with 15 \u00b5l of 0.05% trypsin EDTA Gibco for 30 minutes at 37\u00b0C with intermittent mixing. The disociation was stopped by addition of 750 \u00b5l of a BSA EBSS HEPES solution. Further sample handling steps were carried out on ice and for centrifugation at 4\u00b0C. The sample was filtered through a 100 \u00b5m filter  washed with cold HBSS and resuspended in 100 200 \u00b5l HBSS with 0.05% BSA  then filtered through a 35 \u00b5m filter and loaded on the 10x Chromium Controller. For the scSLAM seq samples  a papain dissociation protocol Worthington kit  according to manufecturer's instructions was used followed by an adapted scSLAM seq protocol for labeling nascent transcripts see sample specific entry. The standard transcriptome libraries were constructed according to the manufacturer's protocol 10X Genomics. For targeted libraries of scars used for lineage tracing  the cDNA obtained during the 10x protocol was used to set up individual PCR reactions for each target  specifically amplifying the sgRNA target site. The libraries for targeted PCR were constructed with primers compatible with the 10X Genomics kit  so they could be sequenced on the same platform.", null, "RNA-Seq", "TRANSCRIPTOMIC SINGLE CELL", "cDNA", "PAIRED", "ILLUMINA", "Illumina HiSeq 4000", null, "SRP469552", null, "loader:fastq load.py", "b7_cer_R1.fastq.gz b7_cer_R2.fastq.gz", "fastq fastq", 4549622496.0, 36690504.0, "GSM7875164 r1", "0:26 1:98", "A:1326792866;C:971674770;G:1022429173;T:1227318552;N:1407135", 26, 98, null, null, 1326792866, 971674770, 1022429173, 1227318552, 1407135, "SRX22323889", "SRS19374417", "SRA1743007", "Junker Lab, Berlin Institute for Medical Systems Biology, Max-Delbr\u00fcck-Center for Molecular Medicine", "Junker Lab, Berlin Institute for Medical Systems Biology, Max-Delbr\u00fcck-Center for Molecular Medicine", 2, 0.02024, 0.89648, 0.01794, 0.19303, 0.99283, 0.81542, 0.39821, 0.6673, 26, 98, "T", "B", "sc-like readlen", "illumina", "hiseq_era", "unknown", "cdna_unspecified", "unknown", "sc", "single_cell_droplet", "10x", null, "Germany", "2023-10-31", "Adult", "Adult", "Brain", "Nervous System"], [28763, "SRR26623300", "SRX22323888", "SRS19374416", "SRP469552", "PRJNA1034159", "Dissecting the spatiotemporal diversity of adult neural stem cells", "GSE246714", "Other", "Adult stem cells are important for tissue turnover and regeneration. However  in most adult systems it remains elusive how stem cells assume different functional states and support spatially patterned tissue architecture. Here  we dissected the diversity of neural stem cells in the adult zebrafish brain  an organ that is characterized by pronounced zonation and high regenerative capacity. We combined single cell transcriptomics of dissected brain regions with massively parallel lineage tracing and in vivo RNA metabolic labeling to analyze regulation of neural stem cells in space and time. We detected a large diversity of neural stem cells  with some subtypes being restricted to a single brain region  while others were found globally across the brain. Global stem cell states are linked to neurogenic differentiation  with different states being involved in proliferative and non proliferative differentiation. Our work reveals principles of adult stem cell organization and establishes a resource for functional manipulation of neural stem cell subtypes. Overall design: Single cell RNA seq of adult zebrafish brain performed using 10x Genomics Gene Expression protocols to explore transcriptomic diversity of adult cell types. The samples were prepared either from whole brain  FACS sorted cells GFP positive cells from gfap:GFP line or a dissected single brain region  as indicated in the sample title. CRISPR based lineage tracing was performed for a subset of samples to dissect developmental lineage relationships between cells. ScSLAM seq was performed in combination with Notch pathway inhibition to assess response of stem cells to perturbation.", null, "pubmed:38365956", null, "Brain 6   telencephalon  scRNAseq", "GSM7875163", null, "source name:adult brain|tissue:adult brain|tissue region:telencephalon|cell type:mixed tissue dissociation|genotype:Tg[ubi:zebrabow M]|geo loc name:missing|collection date:missing", "Brain 6   telencephalon  scRNAseq", "The transcriptome libraries were demultiplexed and mapped with CellRanger 6.1.1. In order to reduce batch effect  the gene expression matrices were processed with the SoupX tool Young & Behjati  2020 for removing ambient RNA contamination. A subset of libraries was also mapped with Velocyto v0.17.17 La Manno et al  2018 to create count matrices for unspliced and spliced molecules. The targeted lineage tracing libraries were aligned using bwa mem3 v.0.7.12 to individual references of endogenous genes used in the lineage tracing experiment actb1  actb2  cfl1  cirbpb  rpl39 and ube2e1. The scars were filtered with a custom pipeline to eliminate sequences that originate from PCR or sequencing errors. For custom code and further downstream analysis  see: https://github.com/nimitic/radial glia. Assembly: GRCz11 Supplementary files format and content: Tab separated barcode files and matrix files along with the tab separated gene file supplementary file are the output of CellRanger mapping  further processed by the SoupX package for ambient RNA removal. Supplementary files format and content: Loom files are the output of velocyto mapping distinguishing spliced and unspliced mRNAs. Supplementary files format and content: filtered scars.csv files contain the output of a custom scar filtering pipeline.", "adult brain", null, "Adult zebrafish brain tissue was extracted  optionally dissected to select a specific region of interest telencephalon  diencephalon  mesencephalon or rhombencephalon and then dissociated with a trypsin protocol. Briefly  the samples were incubated in 750 \u00b5l of HBSS glucose solution and dissocated with 15 \u00b5l of 0.05% trypsin EDTA Gibco for 30 minutes at 37\u00b0C with intermittent mixing. The disociation was stopped by addition of 750 \u00b5l of a BSA EBSS HEPES solution. Further sample handling steps were carried out on ice and for centrifugation at 4\u00b0C. The sample was filtered through a 100 \u00b5m filter  washed with cold HBSS and resuspended in 100 200 \u00b5l HBSS with 0.05% BSA  then filtered through a 35 \u00b5m filter and loaded on the 10x Chromium Controller. For the scSLAM seq samples  a papain dissociation protocol Worthington kit  according to manufecturer's instructions was used followed by an adapted scSLAM seq protocol for labeling nascent transcripts see sample specific entry. The standard transcriptome libraries were constructed according to the manufacturer's protocol 10X Genomics. For targeted libraries of scars used for lineage tracing  the cDNA obtained during the 10x protocol was used to set up individual PCR reactions for each target  specifically amplifying the sgRNA target site. The libraries for targeted PCR were constructed with primers compatible with the 10X Genomics kit  so they could be sequenced on the same platform.", null, "tissue:adult brain|tissue region:telencephalon|cell type:mixed tissue dissociation|genotype:Tg[ubi:zebrabow M]", "GSM7875163", "GSM7875163: Brain 6   telencephalon  scRNAseq; Danio rerio; RNA Seq", "GSM7875163 r1", "GSM7875163", "1", "Adult zebrafish brain tissue was extracted  optionally dissected to select a specific region of interest telencephalon  diencephalon  mesencephalon or rhombencephalon and then dissociated with a trypsin protocol. Briefly  the samples were incubated in 750 \u00b5l of HBSS glucose solution and dissocated with 15 \u00b5l of 0.05% trypsin EDTA Gibco for 30 minutes at 37\u00b0C with intermittent mixing. The disociation was stopped by addition of 750 \u00b5l of a BSA EBSS HEPES solution. Further sample handling steps were carried out on ice and for centrifugation at 4\u00b0C. The sample was filtered through a 100 \u00b5m filter  washed with cold HBSS and resuspended in 100 200 \u00b5l HBSS with 0.05% BSA  then filtered through a 35 \u00b5m filter and loaded on the 10x Chromium Controller. For the scSLAM seq samples  a papain dissociation protocol Worthington kit  according to manufecturer's instructions was used followed by an adapted scSLAM seq protocol for labeling nascent transcripts see sample specific entry. The standard transcriptome libraries were constructed according to the manufacturer's protocol 10X Genomics. For targeted libraries of scars used for lineage tracing  the cDNA obtained during the 10x protocol was used to set up individual PCR reactions for each target  specifically amplifying the sgRNA target site. The libraries for targeted PCR were constructed with primers compatible with the 10X Genomics kit  so they could be sequenced on the same platform.", null, "RNA-Seq", "TRANSCRIPTOMIC SINGLE CELL", "cDNA", "PAIRED", "ILLUMINA", "Illumina HiSeq 4000", null, "SRP469552", null, "loader:fastq load.py", "b6_tel_R2.fastq.gz b6_tel_R1.fastq.gz", "fastq fastq", 35200219264.0, 283872736.0, "GSM7875163 r1", "0:26 1:98", "A:10338159874;C:7412176847;G:7655217994;T:9778922063;N:15742486", 26, 98, null, null, 10338159874, 7412176847, 7655217994, 9778922063, 15742486, "SRX22323888", "SRS19374416", "SRA1743007", "Junker Lab, Berlin Institute for Medical Systems Biology, Max-Delbr\u00fcck-Center for Molecular Medicine", "Junker Lab, Berlin Institute for Medical Systems Biology, Max-Delbr\u00fcck-Center for Molecular Medicine", 2, 0.00528, 0.8891, 0.00408, 0.20496, 0.99577, 0.78567, 0.41949, 0.62904, 26, 98, "T", "B", "sc-like readlen", "illumina", "hiseq_era", "unknown", "cdna_unspecified", "unknown", "sc", "single_cell_droplet", "10x", null, "Germany", "2023-10-31", "Adult", "Adult", "Brain", "Nervous System"], [28764, "SRR26623301", "SRX22323887", "SRS19374415", "SRP469552", "PRJNA1034159", "Dissecting the spatiotemporal diversity of adult neural stem cells", "GSE246714", "Other", "Adult stem cells are important for tissue turnover and regeneration. However  in most adult systems it remains elusive how stem cells assume different functional states and support spatially patterned tissue architecture. Here  we dissected the diversity of neural stem cells in the adult zebrafish brain  an organ that is characterized by pronounced zonation and high regenerative capacity. We combined single cell transcriptomics of dissected brain regions with massively parallel lineage tracing and in vivo RNA metabolic labeling to analyze regulation of neural stem cells in space and time. We detected a large diversity of neural stem cells  with some subtypes being restricted to a single brain region  while others were found globally across the brain. Global stem cell states are linked to neurogenic differentiation  with different states being involved in proliferative and non proliferative differentiation. Our work reveals principles of adult stem cell organization and establishes a resource for functional manipulation of neural stem cell subtypes. Overall design: Single cell RNA seq of adult zebrafish brain performed using 10x Genomics Gene Expression protocols to explore transcriptomic diversity of adult cell types. The samples were prepared either from whole brain  FACS sorted cells GFP positive cells from gfap:GFP line or a dissected single brain region  as indicated in the sample title. CRISPR based lineage tracing was performed for a subset of samples to dissect developmental lineage relationships between cells. ScSLAM seq was performed in combination with Notch pathway inhibition to assess response of stem cells to perturbation.", null, "pubmed:38365956", null, "Brain 5   telencephalon  scRNAseq", "GSM7875162", null, "source name:adult brain|tissue:adult brain|tissue region:telencephalon|cell type:mixed tissue dissociation|genotype:Tg[ubi:zebrabow M]|geo loc name:missing|collection date:missing", "Brain 5   telencephalon  scRNAseq", "The transcriptome libraries were demultiplexed and mapped with CellRanger 6.1.1. In order to reduce batch effect  the gene expression matrices were processed with the SoupX tool Young & Behjati  2020 for removing ambient RNA contamination. A subset of libraries was also mapped with Velocyto v0.17.17 La Manno et al  2018 to create count matrices for unspliced and spliced molecules. The targeted lineage tracing libraries were aligned using bwa mem3 v.0.7.12 to individual references of endogenous genes used in the lineage tracing experiment actb1  actb2  cfl1  cirbpb  rpl39 and ube2e1. The scars were filtered with a custom pipeline to eliminate sequences that originate from PCR or sequencing errors. For custom code and further downstream analysis  see: https://github.com/nimitic/radial glia. Assembly: GRCz11 Supplementary files format and content: Tab separated barcode files and matrix files along with the tab separated gene file supplementary file are the output of CellRanger mapping  further processed by the SoupX package for ambient RNA removal. Supplementary files format and content: Loom files are the output of velocyto mapping distinguishing spliced and unspliced mRNAs. Supplementary files format and content: filtered scars.csv files contain the output of a custom scar filtering pipeline.", "adult brain", null, "Adult zebrafish brain tissue was extracted  optionally dissected to select a specific region of interest telencephalon  diencephalon  mesencephalon or rhombencephalon and then dissociated with a trypsin protocol. Briefly  the samples were incubated in 750 \u00b5l of HBSS glucose solution and dissocated with 15 \u00b5l of 0.05% trypsin EDTA Gibco for 30 minutes at 37\u00b0C with intermittent mixing. The disociation was stopped by addition of 750 \u00b5l of a BSA EBSS HEPES solution. Further sample handling steps were carried out on ice and for centrifugation at 4\u00b0C. The sample was filtered through a 100 \u00b5m filter  washed with cold HBSS and resuspended in 100 200 \u00b5l HBSS with 0.05% BSA  then filtered through a 35 \u00b5m filter and loaded on the 10x Chromium Controller. For the scSLAM seq samples  a papain dissociation protocol Worthington kit  according to manufecturer's instructions was used followed by an adapted scSLAM seq protocol for labeling nascent transcripts see sample specific entry. The standard transcriptome libraries were constructed according to the manufacturer's protocol 10X Genomics. For targeted libraries of scars used for lineage tracing  the cDNA obtained during the 10x protocol was used to set up individual PCR reactions for each target  specifically amplifying the sgRNA target site. The libraries for targeted PCR were constructed with primers compatible with the 10X Genomics kit  so they could be sequenced on the same platform.", null, "tissue:adult brain|tissue region:telencephalon|cell type:mixed tissue dissociation|genotype:Tg[ubi:zebrabow M]", "GSM7875162", "GSM7875162: Brain 5   telencephalon  scRNAseq; Danio rerio; RNA Seq", "GSM7875162 r1", "GSM7875162", "1", "Adult zebrafish brain tissue was extracted  optionally dissected to select a specific region of interest telencephalon  diencephalon  mesencephalon or rhombencephalon and then dissociated with a trypsin protocol. Briefly  the samples were incubated in 750 \u00b5l of HBSS glucose solution and dissocated with 15 \u00b5l of 0.05% trypsin EDTA Gibco for 30 minutes at 37\u00b0C with intermittent mixing. The disociation was stopped by addition of 750 \u00b5l of a BSA EBSS HEPES solution. Further sample handling steps were carried out on ice and for centrifugation at 4\u00b0C. The sample was filtered through a 100 \u00b5m filter  washed with cold HBSS and resuspended in 100 200 \u00b5l HBSS with 0.05% BSA  then filtered through a 35 \u00b5m filter and loaded on the 10x Chromium Controller. For the scSLAM seq samples  a papain dissociation protocol Worthington kit  according to manufecturer's instructions was used followed by an adapted scSLAM seq protocol for labeling nascent transcripts see sample specific entry. The standard transcriptome libraries were constructed according to the manufacturer's protocol 10X Genomics. For targeted libraries of scars used for lineage tracing  the cDNA obtained during the 10x protocol was used to set up individual PCR reactions for each target  specifically amplifying the sgRNA target site. The libraries for targeted PCR were constructed with primers compatible with the 10X Genomics kit  so they could be sequenced on the same platform.", null, "RNA-Seq", "TRANSCRIPTOMIC SINGLE CELL", "cDNA", "PAIRED", "ILLUMINA", "NextSeq 500", null, "SRP469552", null, "loader:fastq load.py", "b5_tel_R1.fastq.gz b5_tel_R2.fastq.gz", "fastq fastq", 15272326692.0, 110669034.0, "GSM7875162 r1", "0:28 1:110", "A:4657813514;C:3027716754;G:3480263590;T:4105516942;N:1015892", 28, 110, null, null, 4657813514, 3027716754, 3480263590, 4105516942, 1015892, "SRX22323887", "SRS19374415", "SRA1743007", "Junker Lab, Berlin Institute for Medical Systems Biology, Max-Delbr\u00fcck-Center for Molecular Medicine", "Junker Lab, Berlin Institute for Medical Systems Biology, Max-Delbr\u00fcck-Center for Molecular Medicine", 2, 0.00158, 0.89454, 0.00083, 0.25984, 0.99752, 0.80413, 0.57553, 0.62731, 28, 110, "T", "B", "sc-like readlen", "illumina", "nextseq", "unknown", "cdna_unspecified", "unknown", "sc", "single_cell_droplet", "10x", null, "Germany", "2023-10-31", "Adult", "Adult", "Brain", "Nervous System"], [28765, "SRR26623302", "SRX22323886", "SRS19374419", "SRP469552", "PRJNA1034159", "Dissecting the spatiotemporal diversity of adult neural stem cells", "GSE246714", "Other", "Adult stem cells are important for tissue turnover and regeneration. However  in most adult systems it remains elusive how stem cells assume different functional states and support spatially patterned tissue architecture. Here  we dissected the diversity of neural stem cells in the adult zebrafish brain  an organ that is characterized by pronounced zonation and high regenerative capacity. We combined single cell transcriptomics of dissected brain regions with massively parallel lineage tracing and in vivo RNA metabolic labeling to analyze regulation of neural stem cells in space and time. We detected a large diversity of neural stem cells  with some subtypes being restricted to a single brain region  while others were found globally across the brain. Global stem cell states are linked to neurogenic differentiation  with different states being involved in proliferative and non proliferative differentiation. Our work reveals principles of adult stem cell organization and establishes a resource for functional manipulation of neural stem cell subtypes. Overall design: Single cell RNA seq of adult zebrafish brain performed using 10x Genomics Gene Expression protocols to explore transcriptomic diversity of adult cell types. The samples were prepared either from whole brain  FACS sorted cells GFP positive cells from gfap:GFP line or a dissected single brain region  as indicated in the sample title. CRISPR based lineage tracing was performed for a subset of samples to dissect developmental lineage relationships between cells. ScSLAM seq was performed in combination with Notch pathway inhibition to assess response of stem cells to perturbation.", null, "pubmed:38365956", null, "Brain 4   telencephalon  scRNAseq", "GSM7875161", null, "source name:adult brain|tissue:adult brain|tissue region:telencephalon|cell type:mixed tissue dissociation|genotype:Tg[ubi:zebrabow M]|geo loc name:missing|collection date:missing", "Brain 4   telencephalon  scRNAseq", "The transcriptome libraries were demultiplexed and mapped with CellRanger 6.1.1. In order to reduce batch effect  the gene expression matrices were processed with the SoupX tool Young & Behjati  2020 for removing ambient RNA contamination. A subset of libraries was also mapped with Velocyto v0.17.17 La Manno et al  2018 to create count matrices for unspliced and spliced molecules. The targeted lineage tracing libraries were aligned using bwa mem3 v.0.7.12 to individual references of endogenous genes used in the lineage tracing experiment actb1  actb2  cfl1  cirbpb  rpl39 and ube2e1. The scars were filtered with a custom pipeline to eliminate sequences that originate from PCR or sequencing errors. For custom code and further downstream analysis  see: https://github.com/nimitic/radial glia. Assembly: GRCz11 Supplementary files format and content: Tab separated barcode files and matrix files along with the tab separated gene file supplementary file are the output of CellRanger mapping  further processed by the SoupX package for ambient RNA removal. Supplementary files format and content: Loom files are the output of velocyto mapping distinguishing spliced and unspliced mRNAs. Supplementary files format and content: filtered scars.csv files contain the output of a custom scar filtering pipeline.", "adult brain", null, "Adult zebrafish brain tissue was extracted  optionally dissected to select a specific region of interest telencephalon  diencephalon  mesencephalon or rhombencephalon and then dissociated with a trypsin protocol. Briefly  the samples were incubated in 750 \u00b5l of HBSS glucose solution and dissocated with 15 \u00b5l of 0.05% trypsin EDTA Gibco for 30 minutes at 37\u00b0C with intermittent mixing. The disociation was stopped by addition of 750 \u00b5l of a BSA EBSS HEPES solution. Further sample handling steps were carried out on ice and for centrifugation at 4\u00b0C. The sample was filtered through a 100 \u00b5m filter  washed with cold HBSS and resuspended in 100 200 \u00b5l HBSS with 0.05% BSA  then filtered through a 35 \u00b5m filter and loaded on the 10x Chromium Controller. For the scSLAM seq samples  a papain dissociation protocol Worthington kit  according to manufecturer's instructions was used followed by an adapted scSLAM seq protocol for labeling nascent transcripts see sample specific entry. The standard transcriptome libraries were constructed according to the manufacturer's protocol 10X Genomics. For targeted libraries of scars used for lineage tracing  the cDNA obtained during the 10x protocol was used to set up individual PCR reactions for each target  specifically amplifying the sgRNA target site. The libraries for targeted PCR were constructed with primers compatible with the 10X Genomics kit  so they could be sequenced on the same platform.", null, "tissue:adult brain|tissue region:telencephalon|cell type:mixed tissue dissociation|genotype:Tg[ubi:zebrabow M]", "GSM7875161", "GSM7875161: Brain 4   telencephalon  scRNAseq; Danio rerio; RNA Seq", "GSM7875161 r1", "GSM7875161", "1", "Adult zebrafish brain tissue was extracted  optionally dissected to select a specific region of interest telencephalon  diencephalon  mesencephalon or rhombencephalon and then dissociated with a trypsin protocol. Briefly  the samples were incubated in 750 \u00b5l of HBSS glucose solution and dissocated with 15 \u00b5l of 0.05% trypsin EDTA Gibco for 30 minutes at 37\u00b0C with intermittent mixing. The disociation was stopped by addition of 750 \u00b5l of a BSA EBSS HEPES solution. Further sample handling steps were carried out on ice and for centrifugation at 4\u00b0C. The sample was filtered through a 100 \u00b5m filter  washed with cold HBSS and resuspended in 100 200 \u00b5l HBSS with 0.05% BSA  then filtered through a 35 \u00b5m filter and loaded on the 10x Chromium Controller. For the scSLAM seq samples  a papain dissociation protocol Worthington kit  according to manufecturer's instructions was used followed by an adapted scSLAM seq protocol for labeling nascent transcripts see sample specific entry. The standard transcriptome libraries were constructed according to the manufacturer's protocol 10X Genomics. For targeted libraries of scars used for lineage tracing  the cDNA obtained during the 10x protocol was used to set up individual PCR reactions for each target  specifically amplifying the sgRNA target site. The libraries for targeted PCR were constructed with primers compatible with the 10X Genomics kit  so they could be sequenced on the same platform.", null, "RNA-Seq", "TRANSCRIPTOMIC SINGLE CELL", "cDNA", "PAIRED", "ILLUMINA", "Illumina MiniSeq", null, "SRP469552", null, "loader:fastq load.py", "b4_tel_R1.fastq.gz b4_tel_R2.fastq.gz", "fastq fastq", 4538898000.0, 36023000.0, "GSM7875161 r1", "0:26 1:100", "A:1403578459;C:926366830;G:979609921;T:1228675807;N:666983", 26, 100, null, null, 1403578459, 926366830, 979609921, 1228675807, 666983, "SRX22323886", "SRS19374419", "SRA1743007", "Junker Lab, Berlin Institute for Medical Systems Biology, Max-Delbr\u00fcck-Center for Molecular Medicine", "Junker Lab, Berlin Institute for Medical Systems Biology, Max-Delbr\u00fcck-Center for Molecular Medicine", 2, 0.0031, 0.90947, 0.0017, 0.32332, 0.99592, 0.82881, 0.4664, 0.6686, 26, 100, "T", "B", "sc-like readlen", "illumina", "miseq", "unknown", "cdna_unspecified", "unknown", "sc", "single_cell_droplet", "10x", null, "Germany", "2023-10-31", "Adult", "Adult", "Brain", "Nervous System"], [28766, "SRR26623303", "SRX22323885", "SRS19374414", "SRP469552", "PRJNA1034159", "Dissecting the spatiotemporal diversity of adult neural stem cells", "GSE246714", "Other", "Adult stem cells are important for tissue turnover and regeneration. However  in most adult systems it remains elusive how stem cells assume different functional states and support spatially patterned tissue architecture. Here  we dissected the diversity of neural stem cells in the adult zebrafish brain  an organ that is characterized by pronounced zonation and high regenerative capacity. We combined single cell transcriptomics of dissected brain regions with massively parallel lineage tracing and in vivo RNA metabolic labeling to analyze regulation of neural stem cells in space and time. We detected a large diversity of neural stem cells  with some subtypes being restricted to a single brain region  while others were found globally across the brain. Global stem cell states are linked to neurogenic differentiation  with different states being involved in proliferative and non proliferative differentiation. Our work reveals principles of adult stem cell organization and establishes a resource for functional manipulation of neural stem cell subtypes. Overall design: Single cell RNA seq of adult zebrafish brain performed using 10x Genomics Gene Expression protocols to explore transcriptomic diversity of adult cell types. The samples were prepared either from whole brain  FACS sorted cells GFP positive cells from gfap:GFP line or a dissected single brain region  as indicated in the sample title. CRISPR based lineage tracing was performed for a subset of samples to dissect developmental lineage relationships between cells. ScSLAM seq was performed in combination with Notch pathway inhibition to assess response of stem cells to perturbation.", null, "pubmed:38365956", null, "Brain 3   telencephalon  scRNAseq", "GSM7875160", null, "source name:adult brain|tissue:adult brain|tissue region:telencephalon|cell type:mixed tissue dissociation|genotype:Tg[ubi:zebrabow M]|geo loc name:missing|collection date:missing", "Brain 3   telencephalon  scRNAseq", "The transcriptome libraries were demultiplexed and mapped with CellRanger 6.1.1. In order to reduce batch effect  the gene expression matrices were processed with the SoupX tool Young & Behjati  2020 for removing ambient RNA contamination. A subset of libraries was also mapped with Velocyto v0.17.17 La Manno et al  2018 to create count matrices for unspliced and spliced molecules. The targeted lineage tracing libraries were aligned using bwa mem3 v.0.7.12 to individual references of endogenous genes used in the lineage tracing experiment actb1  actb2  cfl1  cirbpb  rpl39 and ube2e1. The scars were filtered with a custom pipeline to eliminate sequences that originate from PCR or sequencing errors. For custom code and further downstream analysis  see: https://github.com/nimitic/radial glia. Assembly: GRCz11 Supplementary files format and content: Tab separated barcode files and matrix files along with the tab separated gene file supplementary file are the output of CellRanger mapping  further processed by the SoupX package for ambient RNA removal. Supplementary files format and content: Loom files are the output of velocyto mapping distinguishing spliced and unspliced mRNAs. Supplementary files format and content: filtered scars.csv files contain the output of a custom scar filtering pipeline.", "adult brain", null, "Adult zebrafish brain tissue was extracted  optionally dissected to select a specific region of interest telencephalon  diencephalon  mesencephalon or rhombencephalon and then dissociated with a trypsin protocol. Briefly  the samples were incubated in 750 \u00b5l of HBSS glucose solution and dissocated with 15 \u00b5l of 0.05% trypsin EDTA Gibco for 30 minutes at 37\u00b0C with intermittent mixing. The disociation was stopped by addition of 750 \u00b5l of a BSA EBSS HEPES solution. Further sample handling steps were carried out on ice and for centrifugation at 4\u00b0C. The sample was filtered through a 100 \u00b5m filter  washed with cold HBSS and resuspended in 100 200 \u00b5l HBSS with 0.05% BSA  then filtered through a 35 \u00b5m filter and loaded on the 10x Chromium Controller. For the scSLAM seq samples  a papain dissociation protocol Worthington kit  according to manufecturer's instructions was used followed by an adapted scSLAM seq protocol for labeling nascent transcripts see sample specific entry. The standard transcriptome libraries were constructed according to the manufacturer's protocol 10X Genomics. For targeted libraries of scars used for lineage tracing  the cDNA obtained during the 10x protocol was used to set up individual PCR reactions for each target  specifically amplifying the sgRNA target site. The libraries for targeted PCR were constructed with primers compatible with the 10X Genomics kit  so they could be sequenced on the same platform.", null, "tissue:adult brain|tissue region:telencephalon|cell type:mixed tissue dissociation|genotype:Tg[ubi:zebrabow M]", "GSM7875160", "GSM7875160: Brain 3   telencephalon  scRNAseq; Danio rerio; RNA Seq", "GSM7875160 r1", "GSM7875160", "1", "Adult zebrafish brain tissue was extracted  optionally dissected to select a specific region of interest telencephalon  diencephalon  mesencephalon or rhombencephalon and then dissociated with a trypsin protocol. Briefly  the samples were incubated in 750 \u00b5l of HBSS glucose solution and dissocated with 15 \u00b5l of 0.05% trypsin EDTA Gibco for 30 minutes at 37\u00b0C with intermittent mixing. The disociation was stopped by addition of 750 \u00b5l of a BSA EBSS HEPES solution. Further sample handling steps were carried out on ice and for centrifugation at 4\u00b0C. The sample was filtered through a 100 \u00b5m filter  washed with cold HBSS and resuspended in 100 200 \u00b5l HBSS with 0.05% BSA  then filtered through a 35 \u00b5m filter and loaded on the 10x Chromium Controller. For the scSLAM seq samples  a papain dissociation protocol Worthington kit  according to manufecturer's instructions was used followed by an adapted scSLAM seq protocol for labeling nascent transcripts see sample specific entry. The standard transcriptome libraries were constructed according to the manufacturer's protocol 10X Genomics. For targeted libraries of scars used for lineage tracing  the cDNA obtained during the 10x protocol was used to set up individual PCR reactions for each target  specifically amplifying the sgRNA target site. The libraries for targeted PCR were constructed with primers compatible with the 10X Genomics kit  so they could be sequenced on the same platform.", null, "RNA-Seq", "TRANSCRIPTOMIC SINGLE CELL", "cDNA", "PAIRED", "ILLUMINA", "NextSeq 500", null, "SRP469552", null, "loader:fastq load.py", "b3_tel_R2.fastq.gz b3_tel_R1.fastq.gz", "fastq fastq", 14555700180.0, 173282145.0, "GSM7875160 r1", "0:26 1:58", "A:4218664834;C:3011617868;G:3387306043;T:3936116224;N:1995211", 26, 58, null, null, 4218664834, 3011617868, 3387306043, 3936116224, 1995211, "SRX22323885", "SRS19374414", "SRA1743007", "Junker Lab, Berlin Institute for Medical Systems Biology, Max-Delbr\u00fcck-Center for Molecular Medicine", "Junker Lab, Berlin Institute for Medical Systems Biology, Max-Delbr\u00fcck-Center for Molecular Medicine", 2, 0.00416, 0.90236, 0.00175, 0.29743, 0.99247, 0.79539, 0.34888, 0.56327, 26, 58, "T", "B", "sc-like readlen", "illumina", "nextseq", "unknown", "cdna_unspecified", "unknown", "sc", "single_cell_droplet", "10x", null, "Germany", "2023-10-31", "Adult", "Adult", "Brain", "Nervous System"], [28767, "SRR26623304", "SRX22323884", "SRS19374412", "SRP469552", "PRJNA1034159", "Dissecting the spatiotemporal diversity of adult neural stem cells", "GSE246714", "Other", "Adult stem cells are important for tissue turnover and regeneration. However  in most adult systems it remains elusive how stem cells assume different functional states and support spatially patterned tissue architecture. Here  we dissected the diversity of neural stem cells in the adult zebrafish brain  an organ that is characterized by pronounced zonation and high regenerative capacity. We combined single cell transcriptomics of dissected brain regions with massively parallel lineage tracing and in vivo RNA metabolic labeling to analyze regulation of neural stem cells in space and time. We detected a large diversity of neural stem cells  with some subtypes being restricted to a single brain region  while others were found globally across the brain. Global stem cell states are linked to neurogenic differentiation  with different states being involved in proliferative and non proliferative differentiation. Our work reveals principles of adult stem cell organization and establishes a resource for functional manipulation of neural stem cell subtypes. Overall design: Single cell RNA seq of adult zebrafish brain performed using 10x Genomics Gene Expression protocols to explore transcriptomic diversity of adult cell types. The samples were prepared either from whole brain  FACS sorted cells GFP positive cells from gfap:GFP line or a dissected single brain region  as indicated in the sample title. CRISPR based lineage tracing was performed for a subset of samples to dissect developmental lineage relationships between cells. ScSLAM seq was performed in combination with Notch pathway inhibition to assess response of stem cells to perturbation.", null, "pubmed:38365956", null, "Brain 2   telencephalon  scRNAseq", "GSM7875159", null, "source name:adult brain|tissue:adult brain|tissue region:telencephalon|cell type:mixed tissue dissociation|genotype:Tg[ubi:zebrabow M]|geo loc name:missing|collection date:missing", "Brain 2   telencephalon  scRNAseq", "The transcriptome libraries were demultiplexed and mapped with CellRanger 6.1.1. In order to reduce batch effect  the gene expression matrices were processed with the SoupX tool Young & Behjati  2020 for removing ambient RNA contamination. A subset of libraries was also mapped with Velocyto v0.17.17 La Manno et al  2018 to create count matrices for unspliced and spliced molecules. The targeted lineage tracing libraries were aligned using bwa mem3 v.0.7.12 to individual references of endogenous genes used in the lineage tracing experiment actb1  actb2  cfl1  cirbpb  rpl39 and ube2e1. The scars were filtered with a custom pipeline to eliminate sequences that originate from PCR or sequencing errors. For custom code and further downstream analysis  see: https://github.com/nimitic/radial glia. Assembly: GRCz11 Supplementary files format and content: Tab separated barcode files and matrix files along with the tab separated gene file supplementary file are the output of CellRanger mapping  further processed by the SoupX package for ambient RNA removal. Supplementary files format and content: Loom files are the output of velocyto mapping distinguishing spliced and unspliced mRNAs. Supplementary files format and content: filtered scars.csv files contain the output of a custom scar filtering pipeline.", "adult brain", null, "Adult zebrafish brain tissue was extracted  optionally dissected to select a specific region of interest telencephalon  diencephalon  mesencephalon or rhombencephalon and then dissociated with a trypsin protocol. Briefly  the samples were incubated in 750 \u00b5l of HBSS glucose solution and dissocated with 15 \u00b5l of 0.05% trypsin EDTA Gibco for 30 minutes at 37\u00b0C with intermittent mixing. The disociation was stopped by addition of 750 \u00b5l of a BSA EBSS HEPES solution. Further sample handling steps were carried out on ice and for centrifugation at 4\u00b0C. The sample was filtered through a 100 \u00b5m filter  washed with cold HBSS and resuspended in 100 200 \u00b5l HBSS with 0.05% BSA  then filtered through a 35 \u00b5m filter and loaded on the 10x Chromium Controller. For the scSLAM seq samples  a papain dissociation protocol Worthington kit  according to manufecturer's instructions was used followed by an adapted scSLAM seq protocol for labeling nascent transcripts see sample specific entry. The standard transcriptome libraries were constructed according to the manufacturer's protocol 10X Genomics. For targeted libraries of scars used for lineage tracing  the cDNA obtained during the 10x protocol was used to set up individual PCR reactions for each target  specifically amplifying the sgRNA target site. The libraries for targeted PCR were constructed with primers compatible with the 10X Genomics kit  so they could be sequenced on the same platform.", null, "tissue:adult brain|tissue region:telencephalon|cell type:mixed tissue dissociation|genotype:Tg[ubi:zebrabow M]", "GSM7875159", "GSM7875159: Brain 2   telencephalon  scRNAseq; Danio rerio; RNA Seq", "GSM7875159 r1", "GSM7875159", "1", "Adult zebrafish brain tissue was extracted  optionally dissected to select a specific region of interest telencephalon  diencephalon  mesencephalon or rhombencephalon and then dissociated with a trypsin protocol. Briefly  the samples were incubated in 750 \u00b5l of HBSS glucose solution and dissocated with 15 \u00b5l of 0.05% trypsin EDTA Gibco for 30 minutes at 37\u00b0C with intermittent mixing. The disociation was stopped by addition of 750 \u00b5l of a BSA EBSS HEPES solution. Further sample handling steps were carried out on ice and for centrifugation at 4\u00b0C. The sample was filtered through a 100 \u00b5m filter  washed with cold HBSS and resuspended in 100 200 \u00b5l HBSS with 0.05% BSA  then filtered through a 35 \u00b5m filter and loaded on the 10x Chromium Controller. For the scSLAM seq samples  a papain dissociation protocol Worthington kit  according to manufecturer's instructions was used followed by an adapted scSLAM seq protocol for labeling nascent transcripts see sample specific entry. The standard transcriptome libraries were constructed according to the manufacturer's protocol 10X Genomics. For targeted libraries of scars used for lineage tracing  the cDNA obtained during the 10x protocol was used to set up individual PCR reactions for each target  specifically amplifying the sgRNA target site. The libraries for targeted PCR were constructed with primers compatible with the 10X Genomics kit  so they could be sequenced on the same platform.", null, "RNA-Seq", "TRANSCRIPTOMIC SINGLE CELL", "cDNA", "PAIRED", "ILLUMINA", "NextSeq 500", null, "SRP469552", null, "loader:fastq load.py", "b2_tel_I1.fastq.gz b2_tel_R1.fastq.gz b2_tel_R2.fastq.gz", "fastq fastq fastq", 18703678137.0, 144989753.0, "GSM7875159 r1", "0:14 1:95 2:20", "A:4322399040;C:2535898192;G:2691972122;T:4223349713;N:407468", 14, 95, 20, null, 4322399040, 2535898192, 2691972122, 4223349713, 407468, "SRX22323884", "SRS19374412", "SRA1743007", "Junker Lab, Berlin Institute for Medical Systems Biology, Max-Delbr\u00fcck-Center for Molecular Medicine", "Junker Lab, Berlin Institute for Medical Systems Biology, Max-Delbr\u00fcck-Center for Molecular Medicine", 1, 0.91473, null, 0.25102, null, 0.79231, null, 0.54865, null, 95, null, "B", null, "usable mapping rate", "illumina", "nextseq", "unknown", "cdna_unspecified", "unknown", "sc", "single_cell_droplet", "10x", null, "Germany", "2023-10-31", "Adult", "Adult", "Brain", "Nervous System"], [28768, "SRR26623305", "SRX22323883", "SRS19374413", "SRP469552", "PRJNA1034159", "Dissecting the spatiotemporal diversity of adult neural stem cells", "GSE246714", "Other", "Adult stem cells are important for tissue turnover and regeneration. However  in most adult systems it remains elusive how stem cells assume different functional states and support spatially patterned tissue architecture. Here  we dissected the diversity of neural stem cells in the adult zebrafish brain  an organ that is characterized by pronounced zonation and high regenerative capacity. We combined single cell transcriptomics of dissected brain regions with massively parallel lineage tracing and in vivo RNA metabolic labeling to analyze regulation of neural stem cells in space and time. We detected a large diversity of neural stem cells  with some subtypes being restricted to a single brain region  while others were found globally across the brain. Global stem cell states are linked to neurogenic differentiation  with different states being involved in proliferative and non proliferative differentiation. Our work reveals principles of adult stem cell organization and establishes a resource for functional manipulation of neural stem cell subtypes. Overall design: Single cell RNA seq of adult zebrafish brain performed using 10x Genomics Gene Expression protocols to explore transcriptomic diversity of adult cell types. The samples were prepared either from whole brain  FACS sorted cells GFP positive cells from gfap:GFP line or a dissected single brain region  as indicated in the sample title. CRISPR based lineage tracing was performed for a subset of samples to dissect developmental lineage relationships between cells. ScSLAM seq was performed in combination with Notch pathway inhibition to assess response of stem cells to perturbation.", null, "pubmed:38365956", null, "Brain 1   telencephalon  scRNAseq", "GSM7875158", null, "source name:adult brain|tissue:adult brain|tissue region:telencephalon|cell type:mixed tissue dissociation|genotype:Tg[ubi:zebrabow M]|geo loc name:missing|collection date:missing", "Brain 1   telencephalon  scRNAseq", "The transcriptome libraries were demultiplexed and mapped with CellRanger 6.1.1. In order to reduce batch effect  the gene expression matrices were processed with the SoupX tool Young & Behjati  2020 for removing ambient RNA contamination. A subset of libraries was also mapped with Velocyto v0.17.17 La Manno et al  2018 to create count matrices for unspliced and spliced molecules. The targeted lineage tracing libraries were aligned using bwa mem3 v.0.7.12 to individual references of endogenous genes used in the lineage tracing experiment actb1  actb2  cfl1  cirbpb  rpl39 and ube2e1. The scars were filtered with a custom pipeline to eliminate sequences that originate from PCR or sequencing errors. For custom code and further downstream analysis  see: https://github.com/nimitic/radial glia. Assembly: GRCz11 Supplementary files format and content: Tab separated barcode files and matrix files along with the tab separated gene file supplementary file are the output of CellRanger mapping  further processed by the SoupX package for ambient RNA removal. Supplementary files format and content: Loom files are the output of velocyto mapping distinguishing spliced and unspliced mRNAs. Supplementary files format and content: filtered scars.csv files contain the output of a custom scar filtering pipeline.", "adult brain", null, "Adult zebrafish brain tissue was extracted  optionally dissected to select a specific region of interest telencephalon  diencephalon  mesencephalon or rhombencephalon and then dissociated with a trypsin protocol. Briefly  the samples were incubated in 750 \u00b5l of HBSS glucose solution and dissocated with 15 \u00b5l of 0.05% trypsin EDTA Gibco for 30 minutes at 37\u00b0C with intermittent mixing. The disociation was stopped by addition of 750 \u00b5l of a BSA EBSS HEPES solution. Further sample handling steps were carried out on ice and for centrifugation at 4\u00b0C. The sample was filtered through a 100 \u00b5m filter  washed with cold HBSS and resuspended in 100 200 \u00b5l HBSS with 0.05% BSA  then filtered through a 35 \u00b5m filter and loaded on the 10x Chromium Controller. For the scSLAM seq samples  a papain dissociation protocol Worthington kit  according to manufecturer's instructions was used followed by an adapted scSLAM seq protocol for labeling nascent transcripts see sample specific entry. The standard transcriptome libraries were constructed according to the manufacturer's protocol 10X Genomics. For targeted libraries of scars used for lineage tracing  the cDNA obtained during the 10x protocol was used to set up individual PCR reactions for each target  specifically amplifying the sgRNA target site. The libraries for targeted PCR were constructed with primers compatible with the 10X Genomics kit  so they could be sequenced on the same platform.", null, "tissue:adult brain|tissue region:telencephalon|cell type:mixed tissue dissociation|genotype:Tg[ubi:zebrabow M]", "GSM7875158", "GSM7875158: Brain 1   telencephalon  scRNAseq; Danio rerio; RNA Seq", "GSM7875158 r1", "GSM7875158", "1", "Adult zebrafish brain tissue was extracted  optionally dissected to select a specific region of interest telencephalon  diencephalon  mesencephalon or rhombencephalon and then dissociated with a trypsin protocol. Briefly  the samples were incubated in 750 \u00b5l of HBSS glucose solution and dissocated with 15 \u00b5l of 0.05% trypsin EDTA Gibco for 30 minutes at 37\u00b0C with intermittent mixing. The disociation was stopped by addition of 750 \u00b5l of a BSA EBSS HEPES solution. Further sample handling steps were carried out on ice and for centrifugation at 4\u00b0C. The sample was filtered through a 100 \u00b5m filter  washed with cold HBSS and resuspended in 100 200 \u00b5l HBSS with 0.05% BSA  then filtered through a 35 \u00b5m filter and loaded on the 10x Chromium Controller. For the scSLAM seq samples  a papain dissociation protocol Worthington kit  according to manufecturer's instructions was used followed by an adapted scSLAM seq protocol for labeling nascent transcripts see sample specific entry. The standard transcriptome libraries were constructed according to the manufacturer's protocol 10X Genomics. For targeted libraries of scars used for lineage tracing  the cDNA obtained during the 10x protocol was used to set up individual PCR reactions for each target  specifically amplifying the sgRNA target site. The libraries for targeted PCR were constructed with primers compatible with the 10X Genomics kit  so they could be sequenced on the same platform.", null, "RNA-Seq", "TRANSCRIPTOMIC SINGLE CELL", "cDNA", "PAIRED", "ILLUMINA", "NextSeq 500", null, "SRP469552", null, "loader:fastq load.py", "b1_tel_R2.fastq.gz b1_tel_R1.fastq.gz b1_tel_I1.fastq.gz", "fastq fastq fastq", 21073338957.0, 132536723.0, "GSM7875158 r1", "0:14 1:95 2:50", "A:4785729675;C:2770008572;G:5684239548;T:5977128662;N:718378", 14, 95, 50, null, 4785729675, 2770008572, 5684239548, 5977128662, 718378, "SRX22323883", "SRS19374413", "SRA1743007", "Junker Lab, Berlin Institute for Medical Systems Biology, Max-Delbr\u00fcck-Center for Molecular Medicine", "Junker Lab, Berlin Institute for Medical Systems Biology, Max-Delbr\u00fcck-Center for Molecular Medicine", 2, 0.90262, 0.10312, 0.25979, 0.09812, 0.81182, 0.99951, 0.5478, 0.24137, 95, 50, "B", "T", "mate2 technical by mapping diff", "illumina", "nextseq", "unknown", "cdna_unspecified", "unknown", "sc", "single_cell_droplet", "10x", null, "Germany", "2023-10-31", "Adult", "Adult", "Brain", "Nervous System"], [30256, "SRR27747506", "SRX23412819", "SRS20268140", "SRP486180", "PRJNA1069776", "Molecular characterization of hindbrain vestibular neurons in the larval zebrafish scRNA Seq", "GSE254346", "Transcriptome Analysis", "The molecular logic that specifies and assembles closely related subtypes of neurons into functional sensorimotor circuits remains unclear. The goal of this study was to characterize the molecular profiles of hindbrain vestibular neurons in the larval zebrafish to identify candidate molecular programs that specify their subtype fate  topography  and circuit assembly. We used single cell RNA sequencing to generate a comprehensive atlas of hindbrain vestibular neurons and fluorecent in situ hybridization to annotate profiled neurons. Our dataset serves as a reference for evaluating developmental changes in molecular profiles following perturbations and identifies new candidate molecular solutions that assemble closely related subtypes into functional circuits. Overall design: Hindbrain vestibular neurons  labeled by Tg 6.7Tru.Hcrtr2:GAL4 VP16;TgUAS:E1b Kaede  harvested from zebrafish embryos between 72 hpf 74 hpf. Fluorescent neurons were isolated by fluorescence activated cell sorting FACS according to Kaede fluorescence. Three samples contained all hindbrain vestibular neurons sorted. One sample had two conditions: neurons born before or post a previously identified \"midpoint\" in hindbrain vestibular neuron development 36 hpf  labeled using Kaede photoconversions. Neurons were sequenced with 10x Genomics.", null, null, null, "Late born vestibular neurons  replicate 2", "GSM8038041", null, "tissue:Hindbrain vestibular neurons|time:72 hpf 74 hpf type:Hindbrain vestibular neurons|genotype:Tg 6.7Tru.Hcrtr2:GAL4 VP16; TgUAS:E1b Kaede|treatment:Neurons born post 36 hpf green unconverted Kaede|geo loc name:missing|collection date:missing", "Late born vestibular neurons  replicate 2", "Cell Ranger v7.0.0 Assembly: GRCz11 Supplementary files format and content: Tab separated values files and matrix files", "Hindbrain vestibular neurons", "Three replicates were untreated. One replicate was photoconverted at 36 hpf to isolate early born red  converted Kaede from late born green  unconverted Kaede neurons.", "Harvested neurons were sorted to isolate single cells  resuspended in L15+2% FBS  and kept on ice until cDNA synthesis Performed using manufacturer's instructions; 10x Genomics  three prime V3.1", null, "time:72 hpf 74 hpf type:Hindbrain vestibular neurons|genotype:Tg 6.7Tru.Hcrtr2:GAL4 VP16; TgUAS:E1b Kaede|treatment:Neurons born post 36 hpf green unconverted Kaede", "GSM8038041", "GSM8038041: Late born vestibular neurons  replicate 2; Danio rerio; RNA Seq", "GSM8038041 r1", "GSM8038041", "1", "Harvested neurons were sorted to isolate single cells  resuspended in L15+2% FBS  and kept on ice until cDNA synthesis Performed using manufacturer's instructions; 10x Genomics  three prime V3.1", null, "RNA-Seq", "TRANSCRIPTOMIC SINGLE CELL", "cDNA", "PAIRED", "ILLUMINA", "Illumina NovaSeq 6000", null, "SRP486180", null, "loader:fastq load.py", "Green_S19_L001_I1_001.fastq.gz Green_S19_L001_I2_001.fastq.gz Green_S19_L001_R1_001.fastq.gz Green_S19_L001_R2_001.fastq.gz", "fastq fastq fastq fastq", 4923159660.0, 35675070.0, "GSM8038041 r1", "0:10 1:10 2:28 3:90", "A:1637293866;C:391317121;G:716609880;T:465465343;N:70090", 10, 10, 28, 90, 1637293866, 391317121, 716609880, 465465343, 70090, "SRX23412819", "SRS20268140", "SRA1792542", "Neuroscience Institute, New York University Grossman School of Medicine", "Neuroscience Institute, New York University Grossman School of Medicine", null, null, null, null, null, null, null, null, null, null, null, "B", null, "usable mapping rate", "illumina", "novaseq_era", "unknown", "cdna_unspecified", "unknown", "sc", "single_cell_droplet", "10x", null, "United States", "2024-01-26", "Multi-stage", "Multi-stage", "Brain", "Nervous System"], [30257, "SRR27747507", "SRX23412819", "SRS20268140", "SRP486180", "PRJNA1069776", "Molecular characterization of hindbrain vestibular neurons in the larval zebrafish scRNA Seq", "GSE254346", "Transcriptome Analysis", "The molecular logic that specifies and assembles closely related subtypes of neurons into functional sensorimotor circuits remains unclear. The goal of this study was to characterize the molecular profiles of hindbrain vestibular neurons in the larval zebrafish to identify candidate molecular programs that specify their subtype fate  topography  and circuit assembly. We used single cell RNA sequencing to generate a comprehensive atlas of hindbrain vestibular neurons and fluorecent in situ hybridization to annotate profiled neurons. Our dataset serves as a reference for evaluating developmental changes in molecular profiles following perturbations and identifies new candidate molecular solutions that assemble closely related subtypes into functional circuits. Overall design: Hindbrain vestibular neurons  labeled by Tg 6.7Tru.Hcrtr2:GAL4 VP16;TgUAS:E1b Kaede  harvested from zebrafish embryos between 72 hpf 74 hpf. Fluorescent neurons were isolated by fluorescence activated cell sorting FACS according to Kaede fluorescence. Three samples contained all hindbrain vestibular neurons sorted. One sample had two conditions: neurons born before or post a previously identified \"midpoint\" in hindbrain vestibular neuron development 36 hpf  labeled using Kaede photoconversions. Neurons were sequenced with 10x Genomics.", null, null, null, "Late born vestibular neurons  replicate 2", "GSM8038041", null, "tissue:Hindbrain vestibular neurons|time:72 hpf 74 hpf type:Hindbrain vestibular neurons|genotype:Tg 6.7Tru.Hcrtr2:GAL4 VP16; TgUAS:E1b Kaede|treatment:Neurons born post 36 hpf green unconverted Kaede|geo loc name:missing|collection date:missing", "Late born vestibular neurons  replicate 2", "Cell Ranger v7.0.0 Assembly: GRCz11 Supplementary files format and content: Tab separated values files and matrix files", "Hindbrain vestibular neurons", "Three replicates were untreated. One replicate was photoconverted at 36 hpf to isolate early born red  converted Kaede from late born green  unconverted Kaede neurons.", "Harvested neurons were sorted to isolate single cells  resuspended in L15+2% FBS  and kept on ice until cDNA synthesis Performed using manufacturer's instructions; 10x Genomics  three prime V3.1", null, "time:72 hpf 74 hpf type:Hindbrain vestibular neurons|genotype:Tg 6.7Tru.Hcrtr2:GAL4 VP16; TgUAS:E1b Kaede|treatment:Neurons born post 36 hpf green unconverted Kaede", "GSM8038041", "GSM8038041: Late born vestibular neurons  replicate 2; Danio rerio; RNA Seq", "GSM8038041 r1", "GSM8038041", "1", "Harvested neurons were sorted to isolate single cells  resuspended in L15+2% FBS  and kept on ice until cDNA synthesis Performed using manufacturer's instructions; 10x Genomics  three prime V3.1", null, "RNA-Seq", "TRANSCRIPTOMIC SINGLE CELL", "cDNA", "PAIRED", "ILLUMINA", "Illumina NovaSeq 6000", null, "SRP486180", null, "loader:fastq load.py", "Green_S19_L002_I1_001.fastq.gz Green_S19_L002_I2_001.fastq.gz Green_S19_L002_R1_001.fastq.gz Green_S19_L002_R2_001.fastq.gz", "fastq fastq fastq fastq", 4858633206.0, 35207487.0, "GSM8038041 r2", "0:10 1:10 2:28 3:90", "A:1614338875;C:386568724;G:706765488;T:460927318;N:73425", 10, 10, 28, 90, 1614338875, 386568724, 706765488, 460927318, 73425, "SRX23412819", "SRS20268140", "SRA1792542", "Neuroscience Institute, New York University Grossman School of Medicine", "Neuroscience Institute, New York University Grossman School of Medicine", null, null, null, null, null, null, null, null, null, null, null, "B", null, "usable mapping rate", "illumina", "novaseq_era", "unknown", "cdna_unspecified", "unknown", "sc", "single_cell_droplet", "10x", null, "United States", "2024-01-26", "Multi-stage", "Multi-stage", "Brain", "Nervous System"], [30258, "SRR27747508", "SRX23412819", "SRS20268140", "SRP486180", "PRJNA1069776", "Molecular characterization of hindbrain vestibular neurons in the larval zebrafish scRNA Seq", "GSE254346", "Transcriptome Analysis", "The molecular logic that specifies and assembles closely related subtypes of neurons into functional sensorimotor circuits remains unclear. The goal of this study was to characterize the molecular profiles of hindbrain vestibular neurons in the larval zebrafish to identify candidate molecular programs that specify their subtype fate  topography  and circuit assembly. We used single cell RNA sequencing to generate a comprehensive atlas of hindbrain vestibular neurons and fluorecent in situ hybridization to annotate profiled neurons. Our dataset serves as a reference for evaluating developmental changes in molecular profiles following perturbations and identifies new candidate molecular solutions that assemble closely related subtypes into functional circuits. Overall design: Hindbrain vestibular neurons  labeled by Tg 6.7Tru.Hcrtr2:GAL4 VP16;TgUAS:E1b Kaede  harvested from zebrafish embryos between 72 hpf 74 hpf. Fluorescent neurons were isolated by fluorescence activated cell sorting FACS according to Kaede fluorescence. Three samples contained all hindbrain vestibular neurons sorted. One sample had two conditions: neurons born before or post a previously identified \"midpoint\" in hindbrain vestibular neuron development 36 hpf  labeled using Kaede photoconversions. Neurons were sequenced with 10x Genomics.", null, null, null, "Late born vestibular neurons  replicate 2", "GSM8038041", null, "tissue:Hindbrain vestibular neurons|time:72 hpf 74 hpf type:Hindbrain vestibular neurons|genotype:Tg 6.7Tru.Hcrtr2:GAL4 VP16; TgUAS:E1b Kaede|treatment:Neurons born post 36 hpf green unconverted Kaede|geo loc name:missing|collection date:missing", "Late born vestibular neurons  replicate 2", "Cell Ranger v7.0.0 Assembly: GRCz11 Supplementary files format and content: Tab separated values files and matrix files", "Hindbrain vestibular neurons", "Three replicates were untreated. One replicate was photoconverted at 36 hpf to isolate early born red  converted Kaede from late born green  unconverted Kaede neurons.", "Harvested neurons were sorted to isolate single cells  resuspended in L15+2% FBS  and kept on ice until cDNA synthesis Performed using manufacturer's instructions; 10x Genomics  three prime V3.1", null, "time:72 hpf 74 hpf type:Hindbrain vestibular neurons|genotype:Tg 6.7Tru.Hcrtr2:GAL4 VP16; TgUAS:E1b Kaede|treatment:Neurons born post 36 hpf green unconverted Kaede", "GSM8038041", "GSM8038041: Late born vestibular neurons  replicate 2; Danio rerio; RNA Seq", "GSM8038041 r1", "GSM8038041", "1", "Harvested neurons were sorted to isolate single cells  resuspended in L15+2% FBS  and kept on ice until cDNA synthesis Performed using manufacturer's instructions; 10x Genomics  three prime V3.1", null, "RNA-Seq", "TRANSCRIPTOMIC SINGLE CELL", "cDNA", "PAIRED", "ILLUMINA", "Illumina NovaSeq 6000", null, "SRP486180", null, "loader:fastq load.py", "Green_S19_L003_I1_001.fastq.gz Green_S19_L003_I2_001.fastq.gz Green_S19_L003_R1_001.fastq.gz Green_S19_L003_R2_001.fastq.gz", "fastq fastq fastq fastq", 4458723696.0, 32309592.0, "GSM8038041 r3", "0:10 1:10 2:28 3:90", "A:1463412697;C:359363166;G:655814899;T:429192204;N:80314", 10, 10, 28, 90, 1463412697, 359363166, 655814899, 429192204, 80314, "SRX23412819", "SRS20268140", "SRA1792542", "Neuroscience Institute, New York University Grossman School of Medicine", "Neuroscience Institute, New York University Grossman School of Medicine", null, null, null, null, null, null, null, null, null, null, null, "B", null, "usable mapping rate", "illumina", "novaseq_era", "unknown", "cdna_unspecified", "unknown", "sc", "single_cell_droplet", "10x", null, "United States", "2024-01-26", "Multi-stage", "Multi-stage", "Brain", "Nervous System"], [30259, "SRR27747509", "SRX23412819", "SRS20268140", "SRP486180", "PRJNA1069776", "Molecular characterization of hindbrain vestibular neurons in the larval zebrafish scRNA Seq", "GSE254346", "Transcriptome Analysis", "The molecular logic that specifies and assembles closely related subtypes of neurons into functional sensorimotor circuits remains unclear. The goal of this study was to characterize the molecular profiles of hindbrain vestibular neurons in the larval zebrafish to identify candidate molecular programs that specify their subtype fate  topography  and circuit assembly. We used single cell RNA sequencing to generate a comprehensive atlas of hindbrain vestibular neurons and fluorecent in situ hybridization to annotate profiled neurons. Our dataset serves as a reference for evaluating developmental changes in molecular profiles following perturbations and identifies new candidate molecular solutions that assemble closely related subtypes into functional circuits. Overall design: Hindbrain vestibular neurons  labeled by Tg 6.7Tru.Hcrtr2:GAL4 VP16;TgUAS:E1b Kaede  harvested from zebrafish embryos between 72 hpf 74 hpf. Fluorescent neurons were isolated by fluorescence activated cell sorting FACS according to Kaede fluorescence. Three samples contained all hindbrain vestibular neurons sorted. One sample had two conditions: neurons born before or post a previously identified \"midpoint\" in hindbrain vestibular neuron development 36 hpf  labeled using Kaede photoconversions. Neurons were sequenced with 10x Genomics.", null, null, null, "Late born vestibular neurons  replicate 2", "GSM8038041", null, "tissue:Hindbrain vestibular neurons|time:72 hpf 74 hpf type:Hindbrain vestibular neurons|genotype:Tg 6.7Tru.Hcrtr2:GAL4 VP16; TgUAS:E1b Kaede|treatment:Neurons born post 36 hpf green unconverted Kaede|geo loc name:missing|collection date:missing", "Late born vestibular neurons  replicate 2", "Cell Ranger v7.0.0 Assembly: GRCz11 Supplementary files format and content: Tab separated values files and matrix files", "Hindbrain vestibular neurons", "Three replicates were untreated. One replicate was photoconverted at 36 hpf to isolate early born red  converted Kaede from late born green  unconverted Kaede neurons.", "Harvested neurons were sorted to isolate single cells  resuspended in L15+2% FBS  and kept on ice until cDNA synthesis Performed using manufacturer's instructions; 10x Genomics  three prime V3.1", null, "time:72 hpf 74 hpf type:Hindbrain vestibular neurons|genotype:Tg 6.7Tru.Hcrtr2:GAL4 VP16; TgUAS:E1b Kaede|treatment:Neurons born post 36 hpf green unconverted Kaede", "GSM8038041", "GSM8038041: Late born vestibular neurons  replicate 2; Danio rerio; RNA Seq", "GSM8038041 r1", "GSM8038041", "1", "Harvested neurons were sorted to isolate single cells  resuspended in L15+2% FBS  and kept on ice until cDNA synthesis Performed using manufacturer's instructions; 10x Genomics  three prime V3.1", null, "RNA-Seq", "TRANSCRIPTOMIC SINGLE CELL", "cDNA", "PAIRED", "ILLUMINA", "Illumina NovaSeq 6000", null, "SRP486180", null, "loader:fastq load.py", "Green_S19_L004_I1_001.fastq.gz Green_S19_L004_I2_001.fastq.gz Green_S19_L004_R1_001.fastq.gz Green_S19_L004_R2_001.fastq.gz", "fastq fastq fastq fastq", 4425208602.0, 32066729.0, "GSM8038041 r4", "0:10 1:10 2:28 3:90", "A:1452148554;C:355763806;G:651302168;T:426718847;N:72235", 10, 10, 28, 90, 1452148554, 355763806, 651302168, 426718847, 72235, "SRX23412819", "SRS20268140", "SRA1792542", "Neuroscience Institute, New York University Grossman School of Medicine", "Neuroscience Institute, New York University Grossman School of Medicine", null, null, null, null, null, null, null, null, null, null, null, "B", null, "usable mapping rate", "illumina", "novaseq_era", "unknown", "cdna_unspecified", "unknown", "sc", "single_cell_droplet", "10x", null, "United States", "2024-01-26", "Multi-stage", "Multi-stage", "Brain", "Nervous System"], [30260, "SRR27747510", "SRX23412818", "SRS20268139", "SRP486180", "PRJNA1069776", "Molecular characterization of hindbrain vestibular neurons in the larval zebrafish scRNA Seq", "GSE254346", "Transcriptome Analysis", "The molecular logic that specifies and assembles closely related subtypes of neurons into functional sensorimotor circuits remains unclear. The goal of this study was to characterize the molecular profiles of hindbrain vestibular neurons in the larval zebrafish to identify candidate molecular programs that specify their subtype fate  topography  and circuit assembly. We used single cell RNA sequencing to generate a comprehensive atlas of hindbrain vestibular neurons and fluorecent in situ hybridization to annotate profiled neurons. Our dataset serves as a reference for evaluating developmental changes in molecular profiles following perturbations and identifies new candidate molecular solutions that assemble closely related subtypes into functional circuits. Overall design: Hindbrain vestibular neurons  labeled by Tg 6.7Tru.Hcrtr2:GAL4 VP16;TgUAS:E1b Kaede  harvested from zebrafish embryos between 72 hpf 74 hpf. Fluorescent neurons were isolated by fluorescence activated cell sorting FACS according to Kaede fluorescence. Three samples contained all hindbrain vestibular neurons sorted. One sample had two conditions: neurons born before or post a previously identified \"midpoint\" in hindbrain vestibular neuron development 36 hpf  labeled using Kaede photoconversions. Neurons were sequenced with 10x Genomics.", null, null, null, "Early born vestibular neurons  replicate 1", "GSM8038040", null, "tissue:Hindbrain vestibular neurons|time:72 hpf 74 hpf type:Hindbrain vestibular neurons|genotype:Tg 6.7Tru.Hcrtr2:GAL4 VP16; TgUAS:E1b Kaede|treatment:Neurons born before 36 hpf red photoconverted Kaede|geo loc name:missing|collection date:missing", "Early born vestibular neurons  replicate 1", "Cell Ranger v7.0.0 Assembly: GRCz11 Supplementary files format and content: Tab separated values files and matrix files", "Hindbrain vestibular neurons", "Three replicates were untreated. One replicate was photoconverted at 36 hpf to isolate early born red  converted Kaede from late born green  unconverted Kaede neurons.", "Harvested neurons were sorted to isolate single cells  resuspended in L15+2% FBS  and kept on ice until cDNA synthesis Performed using manufacturer's instructions; 10x Genomics  three prime V3.1", null, "time:72 hpf 74 hpf type:Hindbrain vestibular neurons|genotype:Tg 6.7Tru.Hcrtr2:GAL4 VP16; TgUAS:E1b Kaede|treatment:Neurons born before 36 hpf red photoconverted Kaede", "GSM8038040", "GSM8038040: Early born vestibular neurons  replicate 1; Danio rerio; RNA Seq", "GSM8038040 r1", "GSM8038040", "1", "Harvested neurons were sorted to isolate single cells  resuspended in L15+2% FBS  and kept on ice until cDNA synthesis Performed using manufacturer's instructions; 10x Genomics  three prime V3.1", null, "RNA-Seq", "TRANSCRIPTOMIC SINGLE CELL", "cDNA", "PAIRED", "ILLUMINA", "Illumina NovaSeq 6000", null, "SRP486180", null, "loader:fastq load.py", "Red_S20_L001_I1_001.fastq.gz Red_S20_L001_I2_001.fastq.gz Red_S20_L001_R1_001.fastq.gz Red_S20_L001_R2_001.fastq.gz", "fastq fastq fastq fastq", 5032560816.0, 36467832.0, "GSM8038040 r1", "0:10 1:10 2:28 3:90", "A:1626033872;C:414606578;G:755475703;T:485911557;N:77170", 10, 10, 28, 90, 1626033872, 414606578, 755475703, 485911557, 77170, "SRX23412818", "SRS20268139", "SRA1792542", "Neuroscience Institute, New York University Grossman School of Medicine", "Neuroscience Institute, New York University Grossman School of Medicine", null, null, null, null, null, null, null, null, null, null, null, "B", null, "usable mapping rate", "illumina", "novaseq_era", "unknown", "cdna_unspecified", "unknown", "sc", "single_cell_droplet", "10x", null, "United States", "2024-01-26", "Multi-stage", "Multi-stage", "Brain", "Nervous System"], [30261, "SRR27747511", "SRX23412818", "SRS20268139", "SRP486180", "PRJNA1069776", "Molecular characterization of hindbrain vestibular neurons in the larval zebrafish scRNA Seq", "GSE254346", "Transcriptome Analysis", "The molecular logic that specifies and assembles closely related subtypes of neurons into functional sensorimotor circuits remains unclear. The goal of this study was to characterize the molecular profiles of hindbrain vestibular neurons in the larval zebrafish to identify candidate molecular programs that specify their subtype fate  topography  and circuit assembly. We used single cell RNA sequencing to generate a comprehensive atlas of hindbrain vestibular neurons and fluorecent in situ hybridization to annotate profiled neurons. Our dataset serves as a reference for evaluating developmental changes in molecular profiles following perturbations and identifies new candidate molecular solutions that assemble closely related subtypes into functional circuits. Overall design: Hindbrain vestibular neurons  labeled by Tg 6.7Tru.Hcrtr2:GAL4 VP16;TgUAS:E1b Kaede  harvested from zebrafish embryos between 72 hpf 74 hpf. Fluorescent neurons were isolated by fluorescence activated cell sorting FACS according to Kaede fluorescence. Three samples contained all hindbrain vestibular neurons sorted. One sample had two conditions: neurons born before or post a previously identified \"midpoint\" in hindbrain vestibular neuron development 36 hpf  labeled using Kaede photoconversions. Neurons were sequenced with 10x Genomics.", null, null, null, "Early born vestibular neurons  replicate 1", "GSM8038040", null, "tissue:Hindbrain vestibular neurons|time:72 hpf 74 hpf type:Hindbrain vestibular neurons|genotype:Tg 6.7Tru.Hcrtr2:GAL4 VP16; TgUAS:E1b Kaede|treatment:Neurons born before 36 hpf red photoconverted Kaede|geo loc name:missing|collection date:missing", "Early born vestibular neurons  replicate 1", "Cell Ranger v7.0.0 Assembly: GRCz11 Supplementary files format and content: Tab separated values files and matrix files", "Hindbrain vestibular neurons", "Three replicates were untreated. One replicate was photoconverted at 36 hpf to isolate early born red  converted Kaede from late born green  unconverted Kaede neurons.", "Harvested neurons were sorted to isolate single cells  resuspended in L15+2% FBS  and kept on ice until cDNA synthesis Performed using manufacturer's instructions; 10x Genomics  three prime V3.1", null, "time:72 hpf 74 hpf type:Hindbrain vestibular neurons|genotype:Tg 6.7Tru.Hcrtr2:GAL4 VP16; TgUAS:E1b Kaede|treatment:Neurons born before 36 hpf red photoconverted Kaede", "GSM8038040", "GSM8038040: Early born vestibular neurons  replicate 1; Danio rerio; RNA Seq", "GSM8038040 r1", "GSM8038040", "1", "Harvested neurons were sorted to isolate single cells  resuspended in L15+2% FBS  and kept on ice until cDNA synthesis Performed using manufacturer's instructions; 10x Genomics  three prime V3.1", null, "RNA-Seq", "TRANSCRIPTOMIC SINGLE CELL", "cDNA", "PAIRED", "ILLUMINA", "Illumina NovaSeq 6000", null, "SRP486180", null, "loader:fastq load.py", "Red_S20_L002_I1_001.fastq.gz Red_S20_L002_I2_001.fastq.gz Red_S20_L002_R1_001.fastq.gz Red_S20_L002_R2_001.fastq.gz", "fastq fastq fastq fastq", 4998616680.0, 36221860.0, "GSM8038040 r2", "0:10 1:10 2:28 3:90", "A:1613203224;C:412246760;G:750087642;T:484348061;N:81713", 10, 10, 28, 90, 1613203224, 412246760, 750087642, 484348061, 81713, "SRX23412818", "SRS20268139", "SRA1792542", "Neuroscience Institute, New York University Grossman School of Medicine", "Neuroscience Institute, New York University Grossman School of Medicine", null, null, null, null, null, null, null, null, null, null, null, "B", null, "usable mapping rate", "illumina", "novaseq_era", "unknown", "cdna_unspecified", "unknown", "sc", "single_cell_droplet", "10x", null, "United States", "2024-01-26", "Multi-stage", "Multi-stage", "Brain", "Nervous System"], [30262, "SRR27747512", "SRX23412818", "SRS20268139", "SRP486180", "PRJNA1069776", "Molecular characterization of hindbrain vestibular neurons in the larval zebrafish scRNA Seq", "GSE254346", "Transcriptome Analysis", "The molecular logic that specifies and assembles closely related subtypes of neurons into functional sensorimotor circuits remains unclear. The goal of this study was to characterize the molecular profiles of hindbrain vestibular neurons in the larval zebrafish to identify candidate molecular programs that specify their subtype fate  topography  and circuit assembly. We used single cell RNA sequencing to generate a comprehensive atlas of hindbrain vestibular neurons and fluorecent in situ hybridization to annotate profiled neurons. Our dataset serves as a reference for evaluating developmental changes in molecular profiles following perturbations and identifies new candidate molecular solutions that assemble closely related subtypes into functional circuits. Overall design: Hindbrain vestibular neurons  labeled by Tg 6.7Tru.Hcrtr2:GAL4 VP16;TgUAS:E1b Kaede  harvested from zebrafish embryos between 72 hpf 74 hpf. Fluorescent neurons were isolated by fluorescence activated cell sorting FACS according to Kaede fluorescence. Three samples contained all hindbrain vestibular neurons sorted. One sample had two conditions: neurons born before or post a previously identified \"midpoint\" in hindbrain vestibular neuron development 36 hpf  labeled using Kaede photoconversions. Neurons were sequenced with 10x Genomics.", null, null, null, "Early born vestibular neurons  replicate 1", "GSM8038040", null, "tissue:Hindbrain vestibular neurons|time:72 hpf 74 hpf type:Hindbrain vestibular neurons|genotype:Tg 6.7Tru.Hcrtr2:GAL4 VP16; TgUAS:E1b Kaede|treatment:Neurons born before 36 hpf red photoconverted Kaede|geo loc name:missing|collection date:missing", "Early born vestibular neurons  replicate 1", "Cell Ranger v7.0.0 Assembly: GRCz11 Supplementary files format and content: Tab separated values files and matrix files", "Hindbrain vestibular neurons", "Three replicates were untreated. One replicate was photoconverted at 36 hpf to isolate early born red  converted Kaede from late born green  unconverted Kaede neurons.", "Harvested neurons were sorted to isolate single cells  resuspended in L15+2% FBS  and kept on ice until cDNA synthesis Performed using manufacturer's instructions; 10x Genomics  three prime V3.1", null, "time:72 hpf 74 hpf type:Hindbrain vestibular neurons|genotype:Tg 6.7Tru.Hcrtr2:GAL4 VP16; TgUAS:E1b Kaede|treatment:Neurons born before 36 hpf red photoconverted Kaede", "GSM8038040", "GSM8038040: Early born vestibular neurons  replicate 1; Danio rerio; RNA Seq", "GSM8038040 r1", "GSM8038040", "1", "Harvested neurons were sorted to isolate single cells  resuspended in L15+2% FBS  and kept on ice until cDNA synthesis Performed using manufacturer's instructions; 10x Genomics  three prime V3.1", null, "RNA-Seq", "TRANSCRIPTOMIC SINGLE CELL", "cDNA", "PAIRED", "ILLUMINA", "Illumina NovaSeq 6000", null, "SRP486180", null, "loader:fastq load.py", "Red_S20_L003_I1_001.fastq.gz Red_S20_L003_I2_001.fastq.gz Red_S20_L003_R1_001.fastq.gz Red_S20_L003_R2_001.fastq.gz", "fastq fastq fastq fastq", 4628879490.0, 33542605.0, "GSM8038040 r3", "0:10 1:10 2:28 3:90", "A:1475662550;C:386397974;G:701772418;T:454914612;N:86896", 10, 10, 28, 90, 1475662550, 386397974, 701772418, 454914612, 86896, "SRX23412818", "SRS20268139", "SRA1792542", "Neuroscience Institute, New York University Grossman School of Medicine", "Neuroscience Institute, New York University Grossman School of Medicine", null, null, null, null, null, null, null, null, null, null, null, "B", null, "usable mapping rate", "illumina", "novaseq_era", "unknown", "cdna_unspecified", "unknown", "sc", "single_cell_droplet", "10x", null, "United States", "2024-01-26", "Multi-stage", "Multi-stage", "Brain", "Nervous System"], [30263, "SRR27747513", "SRX23412818", "SRS20268139", "SRP486180", "PRJNA1069776", "Molecular characterization of hindbrain vestibular neurons in the larval zebrafish scRNA Seq", "GSE254346", "Transcriptome Analysis", "The molecular logic that specifies and assembles closely related subtypes of neurons into functional sensorimotor circuits remains unclear. The goal of this study was to characterize the molecular profiles of hindbrain vestibular neurons in the larval zebrafish to identify candidate molecular programs that specify their subtype fate  topography  and circuit assembly. We used single cell RNA sequencing to generate a comprehensive atlas of hindbrain vestibular neurons and fluorecent in situ hybridization to annotate profiled neurons. Our dataset serves as a reference for evaluating developmental changes in molecular profiles following perturbations and identifies new candidate molecular solutions that assemble closely related subtypes into functional circuits. Overall design: Hindbrain vestibular neurons  labeled by Tg 6.7Tru.Hcrtr2:GAL4 VP16;TgUAS:E1b Kaede  harvested from zebrafish embryos between 72 hpf 74 hpf. Fluorescent neurons were isolated by fluorescence activated cell sorting FACS according to Kaede fluorescence. Three samples contained all hindbrain vestibular neurons sorted. One sample had two conditions: neurons born before or post a previously identified \"midpoint\" in hindbrain vestibular neuron development 36 hpf  labeled using Kaede photoconversions. Neurons were sequenced with 10x Genomics.", null, null, null, "Early born vestibular neurons  replicate 1", "GSM8038040", null, "tissue:Hindbrain vestibular neurons|time:72 hpf 74 hpf type:Hindbrain vestibular neurons|genotype:Tg 6.7Tru.Hcrtr2:GAL4 VP16; TgUAS:E1b Kaede|treatment:Neurons born before 36 hpf red photoconverted Kaede|geo loc name:missing|collection date:missing", "Early born vestibular neurons  replicate 1", "Cell Ranger v7.0.0 Assembly: GRCz11 Supplementary files format and content: Tab separated values files and matrix files", "Hindbrain vestibular neurons", "Three replicates were untreated. One replicate was photoconverted at 36 hpf to isolate early born red  converted Kaede from late born green  unconverted Kaede neurons.", "Harvested neurons were sorted to isolate single cells  resuspended in L15+2% FBS  and kept on ice until cDNA synthesis Performed using manufacturer's instructions; 10x Genomics  three prime V3.1", null, "time:72 hpf 74 hpf type:Hindbrain vestibular neurons|genotype:Tg 6.7Tru.Hcrtr2:GAL4 VP16; TgUAS:E1b Kaede|treatment:Neurons born before 36 hpf red photoconverted Kaede", "GSM8038040", "GSM8038040: Early born vestibular neurons  replicate 1; Danio rerio; RNA Seq", "GSM8038040 r1", "GSM8038040", "1", "Harvested neurons were sorted to isolate single cells  resuspended in L15+2% FBS  and kept on ice until cDNA synthesis Performed using manufacturer's instructions; 10x Genomics  three prime V3.1", null, "RNA-Seq", "TRANSCRIPTOMIC SINGLE CELL", "cDNA", "PAIRED", "ILLUMINA", "Illumina NovaSeq 6000", null, "SRP486180", null, "loader:fastq load.py", "Red_S20_L004_I1_001.fastq.gz Red_S20_L004_I2_001.fastq.gz Red_S20_L004_R1_001.fastq.gz Red_S20_L004_R2_001.fastq.gz", "fastq fastq fastq fastq", 4582808190.0, 33208755.0, "GSM8038040 r4", "0:10 1:10 2:28 3:90", "A:1460627048;C:381772307;G:695044521;T:451264941;N:79133", 10, 10, 28, 90, 1460627048, 381772307, 695044521, 451264941, 79133, "SRX23412818", "SRS20268139", "SRA1792542", "Neuroscience Institute, New York University Grossman School of Medicine", "Neuroscience Institute, New York University Grossman School of Medicine", null, null, null, null, null, null, null, null, null, null, null, "B", null, "usable mapping rate", "illumina", "novaseq_era", "unknown", "cdna_unspecified", "unknown", "sc", "single_cell_droplet", "10x", null, "United States", "2024-01-26", "Multi-stage", "Multi-stage", "Brain", "Nervous System"], [30264, "SRR27747514", "SRX23412817", "SRS20268138", "SRP486180", "PRJNA1069776", "Molecular characterization of hindbrain vestibular neurons in the larval zebrafish scRNA Seq", "GSE254346", "Transcriptome Analysis", "The molecular logic that specifies and assembles closely related subtypes of neurons into functional sensorimotor circuits remains unclear. The goal of this study was to characterize the molecular profiles of hindbrain vestibular neurons in the larval zebrafish to identify candidate molecular programs that specify their subtype fate  topography  and circuit assembly. We used single cell RNA sequencing to generate a comprehensive atlas of hindbrain vestibular neurons and fluorecent in situ hybridization to annotate profiled neurons. Our dataset serves as a reference for evaluating developmental changes in molecular profiles following perturbations and identifies new candidate molecular solutions that assemble closely related subtypes into functional circuits. Overall design: Hindbrain vestibular neurons  labeled by Tg 6.7Tru.Hcrtr2:GAL4 VP16;TgUAS:E1b Kaede  harvested from zebrafish embryos between 72 hpf 74 hpf. Fluorescent neurons were isolated by fluorescence activated cell sorting FACS according to Kaede fluorescence. Three samples contained all hindbrain vestibular neurons sorted. One sample had two conditions: neurons born before or post a previously identified \"midpoint\" in hindbrain vestibular neuron development 36 hpf  labeled using Kaede photoconversions. Neurons were sequenced with 10x Genomics.", null, null, null, "All vestibular neurons  replicate 3", "GSM8038039", null, "tissue:Hindbrain vestibular neurons|time:72 hpf 74 hpf type:Hindbrain vestibular neurons|genotype:Tg 6.7Tru.Hcrtr2:GAL4 VP16; TgUAS:E1b Kaede|treatment:n/a|geo loc name:missing|collection date:missing", "All vestibular neurons  replicate 3", "Cell Ranger v7.0.0 Assembly: GRCz11 Supplementary files format and content: Tab separated values files and matrix files", "Hindbrain vestibular neurons", "Three replicates were untreated. One replicate was photoconverted at 36 hpf to isolate early born red  converted Kaede from late born green  unconverted Kaede neurons.", "Harvested neurons were sorted to isolate single cells  resuspended in L15+2% FBS  and kept on ice until cDNA synthesis Performed using manufacturer's instructions; 10x Genomics  three prime V3.1", null, "time:72 hpf 74 hpf type:Hindbrain vestibular neurons|genotype:Tg 6.7Tru.Hcrtr2:GAL4 VP16; TgUAS:E1b Kaede|treatment:n/a", "GSM8038039", "GSM8038039: All vestibular neurons  replicate 3; Danio rerio; RNA Seq", "GSM8038039 r1", "GSM8038039", "1", "Harvested neurons were sorted to isolate single cells  resuspended in L15+2% FBS  and kept on ice until cDNA synthesis Performed using manufacturer's instructions; 10x Genomics  three prime V3.1", null, "RNA-Seq", "TRANSCRIPTOMIC SINGLE CELL", "cDNA", "PAIRED", "ILLUMINA", "Illumina NovaSeq 6000", null, "SRP486180", null, "loader:fastq load.py", "Zebrafish-Replicate3_S3_L001_I1_001.fastq.gz Zebrafish-Replicate3_S3_L001_I2_001.fastq.gz Zebrafish-Replicate3_S3_L001_R1_001.fastq.gz Zebrafish-Replicate3_S3_L001_R2_001.fastq.gz", "fastq fastq fastq fastq", 10560652880.0, 75975920.0, "GSM8038039 r1", "0:10 1:10 2:28 3:91", "A:2623816838;C:1175510072;G:1638990770;T:1474946754;N:544286", 10, 10, 28, 91, 2623816838, 1175510072, 1638990770, 1474946754, 544286, "SRX23412817", "SRS20268138", "SRA1792542", "Neuroscience Institute, New York University Grossman School of Medicine", "Neuroscience Institute, New York University Grossman School of Medicine", null, null, null, null, null, null, null, null, null, null, null, "B", null, "usable mapping rate", "illumina", "novaseq_era", "unknown", "cdna_unspecified", "unknown", "sc", "single_cell_droplet", "10x", null, "United States", "2024-01-26", "Multi-stage", "Multi-stage", "Brain", "Nervous System"], [30265, "SRR27747515", "SRX23412817", "SRS20268138", "SRP486180", "PRJNA1069776", "Molecular characterization of hindbrain vestibular neurons in the larval zebrafish scRNA Seq", "GSE254346", "Transcriptome Analysis", "The molecular logic that specifies and assembles closely related subtypes of neurons into functional sensorimotor circuits remains unclear. The goal of this study was to characterize the molecular profiles of hindbrain vestibular neurons in the larval zebrafish to identify candidate molecular programs that specify their subtype fate  topography  and circuit assembly. We used single cell RNA sequencing to generate a comprehensive atlas of hindbrain vestibular neurons and fluorecent in situ hybridization to annotate profiled neurons. Our dataset serves as a reference for evaluating developmental changes in molecular profiles following perturbations and identifies new candidate molecular solutions that assemble closely related subtypes into functional circuits. Overall design: Hindbrain vestibular neurons  labeled by Tg 6.7Tru.Hcrtr2:GAL4 VP16;TgUAS:E1b Kaede  harvested from zebrafish embryos between 72 hpf 74 hpf. Fluorescent neurons were isolated by fluorescence activated cell sorting FACS according to Kaede fluorescence. Three samples contained all hindbrain vestibular neurons sorted. One sample had two conditions: neurons born before or post a previously identified \"midpoint\" in hindbrain vestibular neuron development 36 hpf  labeled using Kaede photoconversions. Neurons were sequenced with 10x Genomics.", null, null, null, "All vestibular neurons  replicate 3", "GSM8038039", null, "tissue:Hindbrain vestibular neurons|time:72 hpf 74 hpf type:Hindbrain vestibular neurons|genotype:Tg 6.7Tru.Hcrtr2:GAL4 VP16; TgUAS:E1b Kaede|treatment:n/a|geo loc name:missing|collection date:missing", "All vestibular neurons  replicate 3", "Cell Ranger v7.0.0 Assembly: GRCz11 Supplementary files format and content: Tab separated values files and matrix files", "Hindbrain vestibular neurons", "Three replicates were untreated. One replicate was photoconverted at 36 hpf to isolate early born red  converted Kaede from late born green  unconverted Kaede neurons.", "Harvested neurons were sorted to isolate single cells  resuspended in L15+2% FBS  and kept on ice until cDNA synthesis Performed using manufacturer's instructions; 10x Genomics  three prime V3.1", null, "time:72 hpf 74 hpf type:Hindbrain vestibular neurons|genotype:Tg 6.7Tru.Hcrtr2:GAL4 VP16; TgUAS:E1b Kaede|treatment:n/a", "GSM8038039", "GSM8038039: All vestibular neurons  replicate 3; Danio rerio; RNA Seq", "GSM8038039 r1", "GSM8038039", "1", "Harvested neurons were sorted to isolate single cells  resuspended in L15+2% FBS  and kept on ice until cDNA synthesis Performed using manufacturer's instructions; 10x Genomics  three prime V3.1", null, "RNA-Seq", "TRANSCRIPTOMIC SINGLE CELL", "cDNA", "PAIRED", "ILLUMINA", "Illumina NovaSeq 6000", null, "SRP486180", null, "loader:fastq load.py", "Zebrafish-Replicate3_S3_L002_I1_001.fastq.gz Zebrafish-Replicate3_S3_L002_I2_001.fastq.gz Zebrafish-Replicate3_S3_L002_R1_001.fastq.gz Zebrafish-Replicate3_S3_L002_R2_001.fastq.gz", "fastq fastq fastq fastq", 10571317933.0, 76052647.0, "GSM8038039 r2", "0:10 1:10 2:28 3:91", "A:2631345994;C:1175630391;G:1641110007;T:1472183634;N:520851", 10, 10, 28, 91, 2631345994, 1175630391, 1641110007, 1472183634, 520851, "SRX23412817", "SRS20268138", "SRA1792542", "Neuroscience Institute, New York University Grossman School of Medicine", "Neuroscience Institute, New York University Grossman School of Medicine", null, null, null, null, null, null, null, null, null, null, null, "B", null, "usable mapping rate", "illumina", "novaseq_era", "unknown", "cdna_unspecified", "unknown", "sc", "single_cell_droplet", "10x", null, "United States", "2024-01-26", "Multi-stage", "Multi-stage", "Brain", "Nervous System"], [30266, "SRR27747516", "SRX23412817", "SRS20268138", "SRP486180", "PRJNA1069776", "Molecular characterization of hindbrain vestibular neurons in the larval zebrafish scRNA Seq", "GSE254346", "Transcriptome Analysis", "The molecular logic that specifies and assembles closely related subtypes of neurons into functional sensorimotor circuits remains unclear. The goal of this study was to characterize the molecular profiles of hindbrain vestibular neurons in the larval zebrafish to identify candidate molecular programs that specify their subtype fate  topography  and circuit assembly. We used single cell RNA sequencing to generate a comprehensive atlas of hindbrain vestibular neurons and fluorecent in situ hybridization to annotate profiled neurons. Our dataset serves as a reference for evaluating developmental changes in molecular profiles following perturbations and identifies new candidate molecular solutions that assemble closely related subtypes into functional circuits. Overall design: Hindbrain vestibular neurons  labeled by Tg 6.7Tru.Hcrtr2:GAL4 VP16;TgUAS:E1b Kaede  harvested from zebrafish embryos between 72 hpf 74 hpf. Fluorescent neurons were isolated by fluorescence activated cell sorting FACS according to Kaede fluorescence. Three samples contained all hindbrain vestibular neurons sorted. One sample had two conditions: neurons born before or post a previously identified \"midpoint\" in hindbrain vestibular neuron development 36 hpf  labeled using Kaede photoconversions. Neurons were sequenced with 10x Genomics.", null, null, null, "All vestibular neurons  replicate 3", "GSM8038039", null, "tissue:Hindbrain vestibular neurons|time:72 hpf 74 hpf type:Hindbrain vestibular neurons|genotype:Tg 6.7Tru.Hcrtr2:GAL4 VP16; TgUAS:E1b Kaede|treatment:n/a|geo loc name:missing|collection date:missing", "All vestibular neurons  replicate 3", "Cell Ranger v7.0.0 Assembly: GRCz11 Supplementary files format and content: Tab separated values files and matrix files", "Hindbrain vestibular neurons", "Three replicates were untreated. One replicate was photoconverted at 36 hpf to isolate early born red  converted Kaede from late born green  unconverted Kaede neurons.", "Harvested neurons were sorted to isolate single cells  resuspended in L15+2% FBS  and kept on ice until cDNA synthesis Performed using manufacturer's instructions; 10x Genomics  three prime V3.1", null, "time:72 hpf 74 hpf type:Hindbrain vestibular neurons|genotype:Tg 6.7Tru.Hcrtr2:GAL4 VP16; TgUAS:E1b Kaede|treatment:n/a", "GSM8038039", "GSM8038039: All vestibular neurons  replicate 3; Danio rerio; RNA Seq", "GSM8038039 r1", "GSM8038039", "1", "Harvested neurons were sorted to isolate single cells  resuspended in L15+2% FBS  and kept on ice until cDNA synthesis Performed using manufacturer's instructions; 10x Genomics  three prime V3.1", null, "RNA-Seq", "TRANSCRIPTOMIC SINGLE CELL", "cDNA", "PAIRED", "ILLUMINA", "Illumina NovaSeq 6000", null, "SRP486180", null, "loader:fastq load.py", "Zebrafish-Replicate3_S3_L003_I1_001.fastq.gz Zebrafish-Replicate3_S3_L003_I2_001.fastq.gz Zebrafish-Replicate3_S3_L003_R1_001.fastq.gz Zebrafish-Replicate3_S3_L003_R2_001.fastq.gz", "fastq fastq fastq fastq", 10457106081.0, 75230979.0, "GSM8038039 r3", "0:10 1:10 2:28 3:91", "A:2588687752;C:1168371662;G:1623033341;T:1465362298;N:564036", 10, 10, 28, 91, 2588687752, 1168371662, 1623033341, 1465362298, 564036, "SRX23412817", "SRS20268138", "SRA1792542", "Neuroscience Institute, New York University Grossman School of Medicine", "Neuroscience Institute, New York University Grossman School of Medicine", null, null, null, null, null, null, null, null, null, null, null, "B", null, "usable mapping rate", "illumina", "novaseq_era", "unknown", "cdna_unspecified", "unknown", "sc", "single_cell_droplet", "10x", null, "United States", "2024-01-26", "Multi-stage", "Multi-stage", "Brain", "Nervous System"], [30267, "SRR27747517", "SRX23412817", "SRS20268138", "SRP486180", "PRJNA1069776", "Molecular characterization of hindbrain vestibular neurons in the larval zebrafish scRNA Seq", "GSE254346", "Transcriptome Analysis", "The molecular logic that specifies and assembles closely related subtypes of neurons into functional sensorimotor circuits remains unclear. The goal of this study was to characterize the molecular profiles of hindbrain vestibular neurons in the larval zebrafish to identify candidate molecular programs that specify their subtype fate  topography  and circuit assembly. We used single cell RNA sequencing to generate a comprehensive atlas of hindbrain vestibular neurons and fluorecent in situ hybridization to annotate profiled neurons. Our dataset serves as a reference for evaluating developmental changes in molecular profiles following perturbations and identifies new candidate molecular solutions that assemble closely related subtypes into functional circuits. Overall design: Hindbrain vestibular neurons  labeled by Tg 6.7Tru.Hcrtr2:GAL4 VP16;TgUAS:E1b Kaede  harvested from zebrafish embryos between 72 hpf 74 hpf. Fluorescent neurons were isolated by fluorescence activated cell sorting FACS according to Kaede fluorescence. Three samples contained all hindbrain vestibular neurons sorted. One sample had two conditions: neurons born before or post a previously identified \"midpoint\" in hindbrain vestibular neuron development 36 hpf  labeled using Kaede photoconversions. Neurons were sequenced with 10x Genomics.", null, null, null, "All vestibular neurons  replicate 3", "GSM8038039", null, "tissue:Hindbrain vestibular neurons|time:72 hpf 74 hpf type:Hindbrain vestibular neurons|genotype:Tg 6.7Tru.Hcrtr2:GAL4 VP16; TgUAS:E1b Kaede|treatment:n/a|geo loc name:missing|collection date:missing", "All vestibular neurons  replicate 3", "Cell Ranger v7.0.0 Assembly: GRCz11 Supplementary files format and content: Tab separated values files and matrix files", "Hindbrain vestibular neurons", "Three replicates were untreated. One replicate was photoconverted at 36 hpf to isolate early born red  converted Kaede from late born green  unconverted Kaede neurons.", "Harvested neurons were sorted to isolate single cells  resuspended in L15+2% FBS  and kept on ice until cDNA synthesis Performed using manufacturer's instructions; 10x Genomics  three prime V3.1", null, "time:72 hpf 74 hpf type:Hindbrain vestibular neurons|genotype:Tg 6.7Tru.Hcrtr2:GAL4 VP16; TgUAS:E1b Kaede|treatment:n/a", "GSM8038039", "GSM8038039: All vestibular neurons  replicate 3; Danio rerio; RNA Seq", "GSM8038039 r1", "GSM8038039", "1", "Harvested neurons were sorted to isolate single cells  resuspended in L15+2% FBS  and kept on ice until cDNA synthesis Performed using manufacturer's instructions; 10x Genomics  three prime V3.1", null, "RNA-Seq", "TRANSCRIPTOMIC SINGLE CELL", "cDNA", "PAIRED", "ILLUMINA", "Illumina NovaSeq 6000", null, "SRP486180", null, "loader:fastq load.py", "Zebrafish-Replicate3_S3_L004_I1_001.fastq.gz Zebrafish-Replicate3_S3_L004_I2_001.fastq.gz Zebrafish-Replicate3_S3_L004_R1_001.fastq.gz Zebrafish-Replicate3_S3_L004_R2_001.fastq.gz", "fastq fastq fastq fastq", 10455949462.0, 75222658.0, "GSM8038039 r4", "0:10 1:10 2:28 3:91", "A:2581792541;C:1170458560;G:1624194611;T:1468248274;N:567892", 10, 10, 28, 91, 2581792541, 1170458560, 1624194611, 1468248274, 567892, "SRX23412817", "SRS20268138", "SRA1792542", "Neuroscience Institute, New York University Grossman School of Medicine", "Neuroscience Institute, New York University Grossman School of Medicine", null, null, null, null, null, null, null, null, null, null, null, "B", null, "usable mapping rate", "illumina", "novaseq_era", "unknown", "cdna_unspecified", "unknown", "sc", "single_cell_droplet", "10x", null, "United States", "2024-01-26", "Multi-stage", "Multi-stage", "Brain", "Nervous System"], [30268, "SRR27747518", "SRX23412816", "SRS20268137", "SRP486180", "PRJNA1069776", "Molecular characterization of hindbrain vestibular neurons in the larval zebrafish scRNA Seq", "GSE254346", "Transcriptome Analysis", "The molecular logic that specifies and assembles closely related subtypes of neurons into functional sensorimotor circuits remains unclear. The goal of this study was to characterize the molecular profiles of hindbrain vestibular neurons in the larval zebrafish to identify candidate molecular programs that specify their subtype fate  topography  and circuit assembly. We used single cell RNA sequencing to generate a comprehensive atlas of hindbrain vestibular neurons and fluorecent in situ hybridization to annotate profiled neurons. Our dataset serves as a reference for evaluating developmental changes in molecular profiles following perturbations and identifies new candidate molecular solutions that assemble closely related subtypes into functional circuits. Overall design: Hindbrain vestibular neurons  labeled by Tg 6.7Tru.Hcrtr2:GAL4 VP16;TgUAS:E1b Kaede  harvested from zebrafish embryos between 72 hpf 74 hpf. Fluorescent neurons were isolated by fluorescence activated cell sorting FACS according to Kaede fluorescence. Three samples contained all hindbrain vestibular neurons sorted. One sample had two conditions: neurons born before or post a previously identified \"midpoint\" in hindbrain vestibular neuron development 36 hpf  labeled using Kaede photoconversions. Neurons were sequenced with 10x Genomics.", null, null, null, "All vestibular neurons  replicate 2", "GSM8038038", null, "tissue:Hindbrain vestibular neurons|time:72 hpf 74 hpf type:Hindbrain vestibular neurons|genotype:Tg 6.7Tru.Hcrtr2:GAL4 VP16; TgUAS:E1b Kaede|treatment:n/a|geo loc name:missing|collection date:missing", "All vestibular neurons  replicate 2", "Cell Ranger v7.0.0 Assembly: GRCz11 Supplementary files format and content: Tab separated values files and matrix files", "Hindbrain vestibular neurons", "Three replicates were untreated. One replicate was photoconverted at 36 hpf to isolate early born red  converted Kaede from late born green  unconverted Kaede neurons.", "Harvested neurons were sorted to isolate single cells  resuspended in L15+2% FBS  and kept on ice until cDNA synthesis Performed using manufacturer's instructions; 10x Genomics  three prime V3.1", null, "time:72 hpf 74 hpf type:Hindbrain vestibular neurons|genotype:Tg 6.7Tru.Hcrtr2:GAL4 VP16; TgUAS:E1b Kaede|treatment:n/a", "GSM8038038", "GSM8038038: All vestibular neurons  replicate 2; Danio rerio; RNA Seq", "GSM8038038 r1", "GSM8038038", "1", "Harvested neurons were sorted to isolate single cells  resuspended in L15+2% FBS  and kept on ice until cDNA synthesis Performed using manufacturer's instructions; 10x Genomics  three prime V3.1", null, "RNA-Seq", "TRANSCRIPTOMIC SINGLE CELL", "cDNA", "PAIRED", "ILLUMINA", "Illumina NovaSeq 6000", null, "SRP486180", null, "loader:fastq load.py", "Zebrafish-Replicate2_S2_L001_I1_001.fastq.gz Zebrafish-Replicate2_S2_L001_I2_001.fastq.gz Zebrafish-Replicate2_S2_L001_R1_001.fastq.gz Zebrafish-Replicate2_S2_L001_R2_001.fastq.gz", "fastq fastq fastq fastq", 9414131535.0, 67727565.0, "GSM8038038 r1", "0:10 1:10 2:28 3:91", "A:2659651693;C:894536960;G:1598721159;T:1009819179;N:479424", 10, 10, 28, 91, 2659651693, 894536960, 1598721159, 1009819179, 479424, "SRX23412816", "SRS20268137", "SRA1792542", "Neuroscience Institute, New York University Grossman School of Medicine", "Neuroscience Institute, New York University Grossman School of Medicine", null, null, null, null, null, null, null, null, null, null, null, "B", null, "usable mapping rate", "illumina", "novaseq_era", "unknown", "cdna_unspecified", "unknown", "sc", "single_cell_droplet", "10x", null, "United States", "2024-01-26", "Multi-stage", "Multi-stage", "Brain", "Nervous System"], [30269, "SRR27747519", "SRX23412816", "SRS20268137", "SRP486180", "PRJNA1069776", "Molecular characterization of hindbrain vestibular neurons in the larval zebrafish scRNA Seq", "GSE254346", "Transcriptome Analysis", "The molecular logic that specifies and assembles closely related subtypes of neurons into functional sensorimotor circuits remains unclear. The goal of this study was to characterize the molecular profiles of hindbrain vestibular neurons in the larval zebrafish to identify candidate molecular programs that specify their subtype fate  topography  and circuit assembly. We used single cell RNA sequencing to generate a comprehensive atlas of hindbrain vestibular neurons and fluorecent in situ hybridization to annotate profiled neurons. Our dataset serves as a reference for evaluating developmental changes in molecular profiles following perturbations and identifies new candidate molecular solutions that assemble closely related subtypes into functional circuits. Overall design: Hindbrain vestibular neurons  labeled by Tg 6.7Tru.Hcrtr2:GAL4 VP16;TgUAS:E1b Kaede  harvested from zebrafish embryos between 72 hpf 74 hpf. Fluorescent neurons were isolated by fluorescence activated cell sorting FACS according to Kaede fluorescence. Three samples contained all hindbrain vestibular neurons sorted. One sample had two conditions: neurons born before or post a previously identified \"midpoint\" in hindbrain vestibular neuron development 36 hpf  labeled using Kaede photoconversions. Neurons were sequenced with 10x Genomics.", null, null, null, "All vestibular neurons  replicate 2", "GSM8038038", null, "tissue:Hindbrain vestibular neurons|time:72 hpf 74 hpf type:Hindbrain vestibular neurons|genotype:Tg 6.7Tru.Hcrtr2:GAL4 VP16; TgUAS:E1b Kaede|treatment:n/a|geo loc name:missing|collection date:missing", "All vestibular neurons  replicate 2", "Cell Ranger v7.0.0 Assembly: GRCz11 Supplementary files format and content: Tab separated values files and matrix files", "Hindbrain vestibular neurons", "Three replicates were untreated. One replicate was photoconverted at 36 hpf to isolate early born red  converted Kaede from late born green  unconverted Kaede neurons.", "Harvested neurons were sorted to isolate single cells  resuspended in L15+2% FBS  and kept on ice until cDNA synthesis Performed using manufacturer's instructions; 10x Genomics  three prime V3.1", null, "time:72 hpf 74 hpf type:Hindbrain vestibular neurons|genotype:Tg 6.7Tru.Hcrtr2:GAL4 VP16; TgUAS:E1b Kaede|treatment:n/a", "GSM8038038", "GSM8038038: All vestibular neurons  replicate 2; Danio rerio; RNA Seq", "GSM8038038 r1", "GSM8038038", "1", "Harvested neurons were sorted to isolate single cells  resuspended in L15+2% FBS  and kept on ice until cDNA synthesis Performed using manufacturer's instructions; 10x Genomics  three prime V3.1", null, "RNA-Seq", "TRANSCRIPTOMIC SINGLE CELL", "cDNA", "PAIRED", "ILLUMINA", "Illumina NovaSeq 6000", null, "SRP486180", null, "loader:fastq load.py", "Zebrafish-Replicate2_S2_L002_I1_001.fastq.gz Zebrafish-Replicate2_S2_L002_I2_001.fastq.gz Zebrafish-Replicate2_S2_L002_R1_001.fastq.gz Zebrafish-Replicate2_S2_L002_R2_001.fastq.gz", "fastq fastq fastq fastq", 9495760675.0, 68314825.0, "GSM8038038 r2", "0:10 1:10 2:28 3:91", "A:2685940540;C:901627244;G:1612080998;T:1016532106;N:468187", 10, 10, 28, 91, 2685940540, 901627244, 1612080998, 1016532106, 468187, "SRX23412816", "SRS20268137", "SRA1792542", "Neuroscience Institute, New York University Grossman School of Medicine", "Neuroscience Institute, New York University Grossman School of Medicine", null, null, null, null, null, null, null, null, null, null, null, "B", null, "usable mapping rate", "illumina", "novaseq_era", "unknown", "cdna_unspecified", "unknown", "sc", "single_cell_droplet", "10x", null, "United States", "2024-01-26", "Multi-stage", "Multi-stage", "Brain", "Nervous System"], [30270, "SRR27747520", "SRX23412816", "SRS20268137", "SRP486180", "PRJNA1069776", "Molecular characterization of hindbrain vestibular neurons in the larval zebrafish scRNA Seq", "GSE254346", "Transcriptome Analysis", "The molecular logic that specifies and assembles closely related subtypes of neurons into functional sensorimotor circuits remains unclear. The goal of this study was to characterize the molecular profiles of hindbrain vestibular neurons in the larval zebrafish to identify candidate molecular programs that specify their subtype fate  topography  and circuit assembly. We used single cell RNA sequencing to generate a comprehensive atlas of hindbrain vestibular neurons and fluorecent in situ hybridization to annotate profiled neurons. Our dataset serves as a reference for evaluating developmental changes in molecular profiles following perturbations and identifies new candidate molecular solutions that assemble closely related subtypes into functional circuits. Overall design: Hindbrain vestibular neurons  labeled by Tg 6.7Tru.Hcrtr2:GAL4 VP16;TgUAS:E1b Kaede  harvested from zebrafish embryos between 72 hpf 74 hpf. Fluorescent neurons were isolated by fluorescence activated cell sorting FACS according to Kaede fluorescence. Three samples contained all hindbrain vestibular neurons sorted. One sample had two conditions: neurons born before or post a previously identified \"midpoint\" in hindbrain vestibular neuron development 36 hpf  labeled using Kaede photoconversions. Neurons were sequenced with 10x Genomics.", null, null, null, "All vestibular neurons  replicate 2", "GSM8038038", null, "tissue:Hindbrain vestibular neurons|time:72 hpf 74 hpf type:Hindbrain vestibular neurons|genotype:Tg 6.7Tru.Hcrtr2:GAL4 VP16; TgUAS:E1b Kaede|treatment:n/a|geo loc name:missing|collection date:missing", "All vestibular neurons  replicate 2", "Cell Ranger v7.0.0 Assembly: GRCz11 Supplementary files format and content: Tab separated values files and matrix files", "Hindbrain vestibular neurons", "Three replicates were untreated. One replicate was photoconverted at 36 hpf to isolate early born red  converted Kaede from late born green  unconverted Kaede neurons.", "Harvested neurons were sorted to isolate single cells  resuspended in L15+2% FBS  and kept on ice until cDNA synthesis Performed using manufacturer's instructions; 10x Genomics  three prime V3.1", null, "time:72 hpf 74 hpf type:Hindbrain vestibular neurons|genotype:Tg 6.7Tru.Hcrtr2:GAL4 VP16; TgUAS:E1b Kaede|treatment:n/a", "GSM8038038", "GSM8038038: All vestibular neurons  replicate 2; Danio rerio; RNA Seq", "GSM8038038 r1", "GSM8038038", "1", "Harvested neurons were sorted to isolate single cells  resuspended in L15+2% FBS  and kept on ice until cDNA synthesis Performed using manufacturer's instructions; 10x Genomics  three prime V3.1", null, "RNA-Seq", "TRANSCRIPTOMIC SINGLE CELL", "cDNA", "PAIRED", "ILLUMINA", "Illumina NovaSeq 6000", null, "SRP486180", null, "loader:fastq load.py", "Zebrafish-Replicate2_S2_L003_I1_001.fastq.gz Zebrafish-Replicate2_S2_L003_I2_001.fastq.gz Zebrafish-Replicate2_S2_L003_R1_001.fastq.gz Zebrafish-Replicate2_S2_L003_R2_001.fastq.gz", "fastq fastq fastq fastq", 9299053574.0, 66899666.0, "GSM8038038 r3", "0:10 1:10 2:28 3:91", "A:2619668178;C:887003175;G:1580306677;T:1000391033;N:500543", 10, 10, 28, 91, 2619668178, 887003175, 1580306677, 1000391033, 500543, "SRX23412816", "SRS20268137", "SRA1792542", "Neuroscience Institute, New York University Grossman School of Medicine", "Neuroscience Institute, New York University Grossman School of Medicine", null, null, null, null, null, null, null, null, null, null, null, "B", null, "usable mapping rate", "illumina", "novaseq_era", "unknown", "cdna_unspecified", "unknown", "sc", "single_cell_droplet", "10x", null, "United States", "2024-01-26", "Multi-stage", "Multi-stage", "Brain", "Nervous System"], [30271, "SRR27747521", "SRX23412816", "SRS20268137", "SRP486180", "PRJNA1069776", "Molecular characterization of hindbrain vestibular neurons in the larval zebrafish scRNA Seq", "GSE254346", "Transcriptome Analysis", "The molecular logic that specifies and assembles closely related subtypes of neurons into functional sensorimotor circuits remains unclear. The goal of this study was to characterize the molecular profiles of hindbrain vestibular neurons in the larval zebrafish to identify candidate molecular programs that specify their subtype fate  topography  and circuit assembly. We used single cell RNA sequencing to generate a comprehensive atlas of hindbrain vestibular neurons and fluorecent in situ hybridization to annotate profiled neurons. Our dataset serves as a reference for evaluating developmental changes in molecular profiles following perturbations and identifies new candidate molecular solutions that assemble closely related subtypes into functional circuits. Overall design: Hindbrain vestibular neurons  labeled by Tg 6.7Tru.Hcrtr2:GAL4 VP16;TgUAS:E1b Kaede  harvested from zebrafish embryos between 72 hpf 74 hpf. Fluorescent neurons were isolated by fluorescence activated cell sorting FACS according to Kaede fluorescence. Three samples contained all hindbrain vestibular neurons sorted. One sample had two conditions: neurons born before or post a previously identified \"midpoint\" in hindbrain vestibular neuron development 36 hpf  labeled using Kaede photoconversions. Neurons were sequenced with 10x Genomics.", null, null, null, "All vestibular neurons  replicate 2", "GSM8038038", null, "tissue:Hindbrain vestibular neurons|time:72 hpf 74 hpf type:Hindbrain vestibular neurons|genotype:Tg 6.7Tru.Hcrtr2:GAL4 VP16; TgUAS:E1b Kaede|treatment:n/a|geo loc name:missing|collection date:missing", "All vestibular neurons  replicate 2", "Cell Ranger v7.0.0 Assembly: GRCz11 Supplementary files format and content: Tab separated values files and matrix files", "Hindbrain vestibular neurons", "Three replicates were untreated. One replicate was photoconverted at 36 hpf to isolate early born red  converted Kaede from late born green  unconverted Kaede neurons.", "Harvested neurons were sorted to isolate single cells  resuspended in L15+2% FBS  and kept on ice until cDNA synthesis Performed using manufacturer's instructions; 10x Genomics  three prime V3.1", null, "time:72 hpf 74 hpf type:Hindbrain vestibular neurons|genotype:Tg 6.7Tru.Hcrtr2:GAL4 VP16; TgUAS:E1b Kaede|treatment:n/a", "GSM8038038", "GSM8038038: All vestibular neurons  replicate 2; Danio rerio; RNA Seq", "GSM8038038 r1", "GSM8038038", "1", "Harvested neurons were sorted to isolate single cells  resuspended in L15+2% FBS  and kept on ice until cDNA synthesis Performed using manufacturer's instructions; 10x Genomics  three prime V3.1", null, "RNA-Seq", "TRANSCRIPTOMIC SINGLE CELL", "cDNA", "PAIRED", "ILLUMINA", "Illumina NovaSeq 6000", null, "SRP486180", null, "loader:fastq load.py", "Zebrafish-Replicate2_S2_L004_I1_001.fastq.gz Zebrafish-Replicate2_S2_L004_I2_001.fastq.gz Zebrafish-Replicate2_S2_L004_R1_001.fastq.gz Zebrafish-Replicate2_S2_L004_R2_001.fastq.gz", "fastq fastq fastq fastq", 9269439763.0, 66686617.0, "GSM8038038 r4", "0:10 1:10 2:28 3:91", "A:2605497678;C:885933483;G:1577360287;T:999190994;N:499705", 10, 10, 28, 91, 2605497678, 885933483, 1577360287, 999190994, 499705, "SRX23412816", "SRS20268137", "SRA1792542", "Neuroscience Institute, New York University Grossman School of Medicine", "Neuroscience Institute, New York University Grossman School of Medicine", null, null, null, null, null, null, null, null, null, null, null, "B", null, "usable mapping rate", "illumina", "novaseq_era", "unknown", "cdna_unspecified", "unknown", "sc", "single_cell_droplet", "10x", null, "United States", "2024-01-26", "Multi-stage", "Multi-stage", "Brain", "Nervous System"], [30272, "SRR27747522", "SRX23412815", "SRS20268136", "SRP486180", "PRJNA1069776", "Molecular characterization of hindbrain vestibular neurons in the larval zebrafish scRNA Seq", "GSE254346", "Transcriptome Analysis", "The molecular logic that specifies and assembles closely related subtypes of neurons into functional sensorimotor circuits remains unclear. The goal of this study was to characterize the molecular profiles of hindbrain vestibular neurons in the larval zebrafish to identify candidate molecular programs that specify their subtype fate  topography  and circuit assembly. We used single cell RNA sequencing to generate a comprehensive atlas of hindbrain vestibular neurons and fluorecent in situ hybridization to annotate profiled neurons. Our dataset serves as a reference for evaluating developmental changes in molecular profiles following perturbations and identifies new candidate molecular solutions that assemble closely related subtypes into functional circuits. Overall design: Hindbrain vestibular neurons  labeled by Tg 6.7Tru.Hcrtr2:GAL4 VP16;TgUAS:E1b Kaede  harvested from zebrafish embryos between 72 hpf 74 hpf. Fluorescent neurons were isolated by fluorescence activated cell sorting FACS according to Kaede fluorescence. Three samples contained all hindbrain vestibular neurons sorted. One sample had two conditions: neurons born before or post a previously identified \"midpoint\" in hindbrain vestibular neuron development 36 hpf  labeled using Kaede photoconversions. Neurons were sequenced with 10x Genomics.", null, null, null, "All vestibular neurons  replicate 1", "GSM8038037", null, "tissue:Hindbrain vestibular neurons|time:72 hpf 74 hpf type:Hindbrain vestibular neurons|genotype:Tg 6.7Tru.Hcrtr2:GAL4 VP16; TgUAS:E1b Kaede|treatment:n/a|geo loc name:missing|collection date:missing", "All vestibular neurons  replicate 1", "Cell Ranger v7.0.0 Assembly: GRCz11 Supplementary files format and content: Tab separated values files and matrix files", "Hindbrain vestibular neurons", "Three replicates were untreated. One replicate was photoconverted at 36 hpf to isolate early born red  converted Kaede from late born green  unconverted Kaede neurons.", "Harvested neurons were sorted to isolate single cells  resuspended in L15+2% FBS  and kept on ice until cDNA synthesis Performed using manufacturer's instructions; 10x Genomics  three prime V3.1", null, "time:72 hpf 74 hpf type:Hindbrain vestibular neurons|genotype:Tg 6.7Tru.Hcrtr2:GAL4 VP16; TgUAS:E1b Kaede|treatment:n/a", "GSM8038037", "GSM8038037: All vestibular neurons  replicate 1; Danio rerio; RNA Seq", "GSM8038037 r1", "GSM8038037", "1", "Harvested neurons were sorted to isolate single cells  resuspended in L15+2% FBS  and kept on ice until cDNA synthesis Performed using manufacturer's instructions; 10x Genomics  three prime V3.1", null, "RNA-Seq", "TRANSCRIPTOMIC SINGLE CELL", "cDNA", "PAIRED", "ILLUMINA", "Illumina NovaSeq 6000", null, "SRP486180", null, "loader:fastq load.py", "Zebrafish-Replicate1_S1_L001_I1_001.fastq.gz Zebrafish-Replicate1_S1_L001_I2_001.fastq.gz Zebrafish-Replicate1_S1_L001_R1_001.fastq.gz Zebrafish-Replicate1_S1_L001_R2_001.fastq.gz", "fastq fastq fastq fastq", 11844241708.0, 85210372.0, "GSM8038037 r1", "0:10 1:10 2:28 3:91", "A:3114181870;C:1253864519;G:2080987238;T:1304504811;N:605414", 10, 10, 28, 91, 3114181870, 1253864519, 2080987238, 1304504811, 605414, "SRX23412815", "SRS20268136", "SRA1792542", "Neuroscience Institute, New York University Grossman School of Medicine", "Neuroscience Institute, New York University Grossman School of Medicine", null, null, null, null, null, null, null, null, null, null, null, "B", null, "usable mapping rate", "illumina", "novaseq_era", "unknown", "cdna_unspecified", "unknown", "sc", "single_cell_droplet", "10x", null, "United States", "2024-01-26", "Multi-stage", "Multi-stage", "Brain", "Nervous System"], [30273, "SRR27747523", "SRX23412815", "SRS20268136", "SRP486180", "PRJNA1069776", "Molecular characterization of hindbrain vestibular neurons in the larval zebrafish scRNA Seq", "GSE254346", "Transcriptome Analysis", "The molecular logic that specifies and assembles closely related subtypes of neurons into functional sensorimotor circuits remains unclear. The goal of this study was to characterize the molecular profiles of hindbrain vestibular neurons in the larval zebrafish to identify candidate molecular programs that specify their subtype fate  topography  and circuit assembly. We used single cell RNA sequencing to generate a comprehensive atlas of hindbrain vestibular neurons and fluorecent in situ hybridization to annotate profiled neurons. Our dataset serves as a reference for evaluating developmental changes in molecular profiles following perturbations and identifies new candidate molecular solutions that assemble closely related subtypes into functional circuits. Overall design: Hindbrain vestibular neurons  labeled by Tg 6.7Tru.Hcrtr2:GAL4 VP16;TgUAS:E1b Kaede  harvested from zebrafish embryos between 72 hpf 74 hpf. Fluorescent neurons were isolated by fluorescence activated cell sorting FACS according to Kaede fluorescence. Three samples contained all hindbrain vestibular neurons sorted. One sample had two conditions: neurons born before or post a previously identified \"midpoint\" in hindbrain vestibular neuron development 36 hpf  labeled using Kaede photoconversions. Neurons were sequenced with 10x Genomics.", null, null, null, "All vestibular neurons  replicate 1", "GSM8038037", null, "tissue:Hindbrain vestibular neurons|time:72 hpf 74 hpf type:Hindbrain vestibular neurons|genotype:Tg 6.7Tru.Hcrtr2:GAL4 VP16; TgUAS:E1b Kaede|treatment:n/a|geo loc name:missing|collection date:missing", "All vestibular neurons  replicate 1", "Cell Ranger v7.0.0 Assembly: GRCz11 Supplementary files format and content: Tab separated values files and matrix files", "Hindbrain vestibular neurons", "Three replicates were untreated. One replicate was photoconverted at 36 hpf to isolate early born red  converted Kaede from late born green  unconverted Kaede neurons.", "Harvested neurons were sorted to isolate single cells  resuspended in L15+2% FBS  and kept on ice until cDNA synthesis Performed using manufacturer's instructions; 10x Genomics  three prime V3.1", null, "time:72 hpf 74 hpf type:Hindbrain vestibular neurons|genotype:Tg 6.7Tru.Hcrtr2:GAL4 VP16; TgUAS:E1b Kaede|treatment:n/a", "GSM8038037", "GSM8038037: All vestibular neurons  replicate 1; Danio rerio; RNA Seq", "GSM8038037 r1", "GSM8038037", "1", "Harvested neurons were sorted to isolate single cells  resuspended in L15+2% FBS  and kept on ice until cDNA synthesis Performed using manufacturer's instructions; 10x Genomics  three prime V3.1", null, "RNA-Seq", "TRANSCRIPTOMIC SINGLE CELL", "cDNA", "PAIRED", "ILLUMINA", "Illumina NovaSeq 6000", null, "SRP486180", null, "loader:fastq load.py", "Zebrafish-Replicate1_S1_L002_I1_001.fastq.gz Zebrafish-Replicate1_S1_L002_I2_001.fastq.gz Zebrafish-Replicate1_S1_L002_R1_001.fastq.gz Zebrafish-Replicate1_S1_L002_R2_001.fastq.gz", "fastq fastq fastq fastq", 11897506230.0, 85593570.0, "GSM8038037 r2", "0:10 1:10 2:28 3:91", "A:3132719148;C:1257962906;G:2089627351;T:1308117927;N:587538", 10, 10, 28, 91, 3132719148, 1257962906, 2089627351, 1308117927, 587538, "SRX23412815", "SRS20268136", "SRA1792542", "Neuroscience Institute, New York University Grossman School of Medicine", "Neuroscience Institute, New York University Grossman School of Medicine", null, null, null, null, null, null, null, null, null, null, null, "B", null, "usable mapping rate", "illumina", "novaseq_era", "unknown", "cdna_unspecified", "unknown", "sc", "single_cell_droplet", "10x", null, "United States", "2024-01-26", "Multi-stage", "Multi-stage", "Brain", "Nervous System"], [30274, "SRR27747524", "SRX23412815", "SRS20268136", "SRP486180", "PRJNA1069776", "Molecular characterization of hindbrain vestibular neurons in the larval zebrafish scRNA Seq", "GSE254346", "Transcriptome Analysis", "The molecular logic that specifies and assembles closely related subtypes of neurons into functional sensorimotor circuits remains unclear. The goal of this study was to characterize the molecular profiles of hindbrain vestibular neurons in the larval zebrafish to identify candidate molecular programs that specify their subtype fate  topography  and circuit assembly. We used single cell RNA sequencing to generate a comprehensive atlas of hindbrain vestibular neurons and fluorecent in situ hybridization to annotate profiled neurons. Our dataset serves as a reference for evaluating developmental changes in molecular profiles following perturbations and identifies new candidate molecular solutions that assemble closely related subtypes into functional circuits. Overall design: Hindbrain vestibular neurons  labeled by Tg 6.7Tru.Hcrtr2:GAL4 VP16;TgUAS:E1b Kaede  harvested from zebrafish embryos between 72 hpf 74 hpf. Fluorescent neurons were isolated by fluorescence activated cell sorting FACS according to Kaede fluorescence. Three samples contained all hindbrain vestibular neurons sorted. One sample had two conditions: neurons born before or post a previously identified \"midpoint\" in hindbrain vestibular neuron development 36 hpf  labeled using Kaede photoconversions. Neurons were sequenced with 10x Genomics.", null, null, null, "All vestibular neurons  replicate 1", "GSM8038037", null, "tissue:Hindbrain vestibular neurons|time:72 hpf 74 hpf type:Hindbrain vestibular neurons|genotype:Tg 6.7Tru.Hcrtr2:GAL4 VP16; TgUAS:E1b Kaede|treatment:n/a|geo loc name:missing|collection date:missing", "All vestibular neurons  replicate 1", "Cell Ranger v7.0.0 Assembly: GRCz11 Supplementary files format and content: Tab separated values files and matrix files", "Hindbrain vestibular neurons", "Three replicates were untreated. One replicate was photoconverted at 36 hpf to isolate early born red  converted Kaede from late born green  unconverted Kaede neurons.", "Harvested neurons were sorted to isolate single cells  resuspended in L15+2% FBS  and kept on ice until cDNA synthesis Performed using manufacturer's instructions; 10x Genomics  three prime V3.1", null, "time:72 hpf 74 hpf type:Hindbrain vestibular neurons|genotype:Tg 6.7Tru.Hcrtr2:GAL4 VP16; TgUAS:E1b Kaede|treatment:n/a", "GSM8038037", "GSM8038037: All vestibular neurons  replicate 1; Danio rerio; RNA Seq", "GSM8038037 r1", "GSM8038037", "1", "Harvested neurons were sorted to isolate single cells  resuspended in L15+2% FBS  and kept on ice until cDNA synthesis Performed using manufacturer's instructions; 10x Genomics  three prime V3.1", null, "RNA-Seq", "TRANSCRIPTOMIC SINGLE CELL", "cDNA", "PAIRED", "ILLUMINA", "Illumina NovaSeq 6000", null, "SRP486180", null, "loader:fastq load.py", "Zebrafish-Replicate1_S1_L003_I1_001.fastq.gz Zebrafish-Replicate1_S1_L003_I2_001.fastq.gz Zebrafish-Replicate1_S1_L003_R1_001.fastq.gz Zebrafish-Replicate1_S1_L003_R2_001.fastq.gz", "fastq fastq fastq fastq", 11710536913.0, 84248467.0, "GSM8038037 r3", "0:10 1:10 2:28 3:91", "A:3071431897;C:1243257065;G:2058561915;T:1292732765;N:626855", 10, 10, 28, 91, 3071431897, 1243257065, 2058561915, 1292732765, 626855, "SRX23412815", "SRS20268136", "SRA1792542", "Neuroscience Institute, New York University Grossman School of Medicine", "Neuroscience Institute, New York University Grossman School of Medicine", null, null, null, null, null, null, null, null, null, null, null, "B", null, "usable mapping rate", "illumina", "novaseq_era", "unknown", "cdna_unspecified", "unknown", "sc", "single_cell_droplet", "10x", null, "United States", "2024-01-26", "Multi-stage", "Multi-stage", "Brain", "Nervous System"], [30275, "SRR27747525", "SRX23412815", "SRS20268136", "SRP486180", "PRJNA1069776", "Molecular characterization of hindbrain vestibular neurons in the larval zebrafish scRNA Seq", "GSE254346", "Transcriptome Analysis", "The molecular logic that specifies and assembles closely related subtypes of neurons into functional sensorimotor circuits remains unclear. The goal of this study was to characterize the molecular profiles of hindbrain vestibular neurons in the larval zebrafish to identify candidate molecular programs that specify their subtype fate  topography  and circuit assembly. We used single cell RNA sequencing to generate a comprehensive atlas of hindbrain vestibular neurons and fluorecent in situ hybridization to annotate profiled neurons. Our dataset serves as a reference for evaluating developmental changes in molecular profiles following perturbations and identifies new candidate molecular solutions that assemble closely related subtypes into functional circuits. Overall design: Hindbrain vestibular neurons  labeled by Tg 6.7Tru.Hcrtr2:GAL4 VP16;TgUAS:E1b Kaede  harvested from zebrafish embryos between 72 hpf 74 hpf. Fluorescent neurons were isolated by fluorescence activated cell sorting FACS according to Kaede fluorescence. Three samples contained all hindbrain vestibular neurons sorted. One sample had two conditions: neurons born before or post a previously identified \"midpoint\" in hindbrain vestibular neuron development 36 hpf  labeled using Kaede photoconversions. Neurons were sequenced with 10x Genomics.", null, null, null, "All vestibular neurons  replicate 1", "GSM8038037", null, "tissue:Hindbrain vestibular neurons|time:72 hpf 74 hpf type:Hindbrain vestibular neurons|genotype:Tg 6.7Tru.Hcrtr2:GAL4 VP16; TgUAS:E1b Kaede|treatment:n/a|geo loc name:missing|collection date:missing", "All vestibular neurons  replicate 1", "Cell Ranger v7.0.0 Assembly: GRCz11 Supplementary files format and content: Tab separated values files and matrix files", "Hindbrain vestibular neurons", "Three replicates were untreated. One replicate was photoconverted at 36 hpf to isolate early born red  converted Kaede from late born green  unconverted Kaede neurons.", "Harvested neurons were sorted to isolate single cells  resuspended in L15+2% FBS  and kept on ice until cDNA synthesis Performed using manufacturer's instructions; 10x Genomics  three prime V3.1", null, "time:72 hpf 74 hpf type:Hindbrain vestibular neurons|genotype:Tg 6.7Tru.Hcrtr2:GAL4 VP16; TgUAS:E1b Kaede|treatment:n/a", "GSM8038037", "GSM8038037: All vestibular neurons  replicate 1; Danio rerio; RNA Seq", "GSM8038037 r1", "GSM8038037", "1", "Harvested neurons were sorted to isolate single cells  resuspended in L15+2% FBS  and kept on ice until cDNA synthesis Performed using manufacturer's instructions; 10x Genomics  three prime V3.1", null, "RNA-Seq", "TRANSCRIPTOMIC SINGLE CELL", "cDNA", "PAIRED", "ILLUMINA", "Illumina NovaSeq 6000", null, "SRP486180", null, "loader:fastq load.py", "Zebrafish-Replicate1_S1_L004_I1_001.fastq.gz Zebrafish-Replicate1_S1_L004_I2_001.fastq.gz Zebrafish-Replicate1_S1_L004_R1_001.fastq.gz Zebrafish-Replicate1_S1_L004_R2_001.fastq.gz", "fastq fastq fastq fastq", 11691727294.0, 84113146.0, "GSM8038037 r4", "0:10 1:10 2:28 3:91", "A:3060624267;C:1243160034;G:2057238308;T:1292649886;N:623791", 10, 10, 28, 91, 3060624267, 1243160034, 2057238308, 1292649886, 623791, "SRX23412815", "SRS20268136", "SRA1792542", "Neuroscience Institute, New York University Grossman School of Medicine", "Neuroscience Institute, New York University Grossman School of Medicine", null, null, null, null, null, null, null, null, null, null, null, "B", null, "usable mapping rate", "illumina", "novaseq_era", "unknown", "cdna_unspecified", "unknown", "sc", "single_cell_droplet", "10x", null, "United States", "2024-01-26", "Multi-stage", "Multi-stage", "Brain", "Nervous System"], [30586, "SRR27848608", "SRX23511536", "SRS20362004", "SRP487839", "PRJNA1072736", "Transgenic tools targeting striatal and pallidal subpopulations reveal both evolutionary conservation and specialization of cortico basal ganglia circuits in zebrafish", "GSE254980", "Other", "The cortico basal ganglia circuit mediates decision making. Here  we generated transgenic tools for adult zebrafish targeting specific subpopulations of the components of this circuit and utilized them to identify evolutionary homologs of the mammalian direct  and indirect pathway striatal neurons which respectively project to the homologs of the internal and external segment of the globus pallidus dEN and Vl as in mammals. Unlike in mammals  the Vl mainly projected to the dEN directly  not by way of the subthalamic nucleus. Further single cell RNA sequencing analysis revealed two pallidal output pathways: a major shortcut pathway directly connecting the dEN with the pallium and the evolutionarily conserved closed loop by way of the thalamus. Our resources and circuit map provide the common basis for the functional study of the basal ganglia in a small and optically tractable zebrafish brain for the comprehensive mechanistic understanding of the cortico basal ganglia circuit. Overall design: To identify genetic markers of the npy negative thalamus projecting neurons in the dEN  we conducted single cell RNA sequencing analysis. We dissected the dEN and its surrounding regions from six adult individuals of TgBACnpy:GAL4VP16;TgUAS:GFP. We then dissociated the dissected tissue and used the droplet based three primeend scRNAseq system Chromium 10x Genomics. We obtained transcriptomic data from 3 381 cells and performed unbiased clustering by Seurat.", null, "pubmed:38484735", null, "zebrafish dEN dissected tissue", "GSM8061694", null, "source name:dorsal entopeduncular nucleus and its surrounding brain tissue|tissue:dorsal entopeduncular nucleus and its surrounding brain tissue|cell type:Neurons/glia/blood cells from telencephalon|genotype:TgBACnpy:GAL4VP16;TgUAS:GFP|treatment:regular laboratory cultivation condition|geo loc name:missing|collection date:missing", "zebrafish dEN dissected tissue", "The obtained scRNAseq data of 3 381 cells were analyzed by Seurat.47 As a quality control  cells with more than 6% mitochondrial genes  less than 200 unique genes  and more than 17500 UMIs were removed. Barcodes with less than 500 UMIs had been already removed by cellranger count pipeline. The remaining 3 043 cells were then data normalized by LogNormalize method with scale.factor = 10000. For further calculation of UMAP and clustering  variable genes were determined with FindVariableFeatures function with selection.method = \u201cvst\u201d  and the top 2000 most highly variable genes were used for further clustering. The expression of each gene was shifted so that the mean expression across cells is 0  and scaled so that the variance across cells is 1 by ScaleData function. PCA was run on the scaled data and then UMAP and clustering were performed with the FindNeighbors function with the top 35 PCs  the FindClusters function with resolution = 0.5  and the RunUMAP function with the top 35 PCs. Dot plots and violin plots were generated by Seurat and cell types were determined by the expression of marker genes that define specific cell types. Assembly: GRCz11 Supplementary files format and content: Tab separated values files and matrix files Supplementary files format and content: HTML output file obtained from Seurat analysis using R markdown", "dorsal entopeduncular nucleus and its surrounding brain tissue", null, "Dissection was performed on 6 individuals of TgBACnpy:GAL4VP16;TgUAS:GFP fish and 12 dissected tissue pieces from the left and right hemispheres were obtained in total. The dEN and its surrounding regions were carefully dissected with micro scissors and fine forceps in ice cold and oxygenized Neurobasal medium ThermoFisher Scientific 21103049 supplemented with 1x B 27 ThermoFisher Scientific 17504044 under a fluorescent dissection microscope  as shown in the Figure S5A. The dissected tissue was dissociated with the Papain Dissociation Kit Worthington; LK003150 with 0.1% 2 mercaptoethanol for 15 minutes with gentle shaking at 28.5 degrees Celsius. Then  the cells were dissociated by gentle trituration 15 times with a glass Pasteur pipet coated with 2% BSA in PBS and spun at 300xg for 5 minutes. The cells were resuspended in papain inhibitor solution Worthington and incubated for 10 minutes with gentle shaking at 28.5 degrees Celsius. Then  the cells were further dissociated by gentle trituration 20 times with a glass Pasteur pipet attached with a regular 200 \u00b5l tip coated with 2% BSA in PBS. The dissociated cell suspension was then filtered with pluriStrainer Mini 40 \u00b5m pluriSelect coated with 2% BSA in PBS and spun at 300xg for 5 minutes. The resulting cell suspension was resuspended in 2% BSA in PBS  and then cell debris and dead cells were removed by FACS FACSAria SORP  BD Biosciences using Hoechst to sort out cells from cell debris and Propidium Iodide to sort out living cells from dead cells. post FACS sorting  a small fraction of the cell suspension was used to estimate the total number of the cells and their viability using a dead cell stain Trypan Blue elabscience. The obtained suspension contained 16 000 cells with 85.0% viability. The resulting single cell suspension was loaded on the Chromium Next GEM Single Cell 3\u2019 Reagent Kits v3.1 10x Genomics  PN 1000269  and the cDNA library was prepared according to the manufacturer\u2019s instructions. The obtained cDNA library underwent Next generation sequencing by illumina Hiseq X GENEWIZ with 400 429 716 total reads and 85.9% of sequencing saturation. The obtained sequence was then analyzed by \u201cCell Ranger count\u201d pipeline provided by 10x Genomics with default options. The reads were aligned to zebrafish reference transcriptome ENSEMBL Zv11  release 99 and EGFP CDS  which were built by \u201cCell Ranger mkref\u201d command based on zebrafish reference genome GRCz11 and annotation Ensembl 99. This resulted in 3 381 estimated number of cells with 4890 median unique molecular identifier UMI counts per cell.", "regular laboratory cultivation condition", "tissue:dorsal entopeduncular nucleus and its surrounding brain tissue|cell type:Neurons/glia/blood cells from telencephalon|genotype:TgBACnpy:GAL4VP16;TgUAS:GFP|treatment:regular laboratory cultivation condition", "GSM8061694", "GSM8061694: zebrafish dEN dissected tissue; Danio rerio; RNA Seq", "GSM8061694 r1", "GSM8061694", "1", "Dissection was performed on 6 individuals of TgBACnpy:GAL4VP16;TgUAS:GFP fish and 12 dissected tissue pieces from the left and right hemispheres were obtained in total. The dEN and its surrounding regions were carefully dissected with micro scissors and fine forceps in ice cold and oxygenized Neurobasal medium ThermoFisher Scientific 21103049 supplemented with 1x B 27 ThermoFisher Scientific 17504044 under a fluorescent dissection microscope  as shown in the Figure S5A. The dissected tissue was dissociated with the Papain Dissociation Kit Worthington; LK003150 with 0.1% 2 mercaptoethanol for 15 minutes with gentle shaking at 28.5 degrees Celsius. Then  the cells were dissociated by gentle trituration 15 times with a glass Pasteur pipet coated with 2% BSA in PBS and spun at 300xg for 5 minutes. The cells were resuspended in papain inhibitor solution Worthington and incubated for 10 minutes with gentle shaking at 28.5 degrees Celsius. Then  the cells were further dissociated by gentle trituration 20 times with a glass Pasteur pipet attached with a regular 200 \u00b5l tip coated with 2% BSA in PBS. The dissociated cell suspension was then filtered with pluriStrainer Mini 40 \u00b5m pluriSelect coated with 2% BSA in PBS and spun at 300xg for 5 minutes. The resulting cell suspension was resuspended in 2% BSA in PBS  and then cell debris and dead cells were removed by FACS FACSAria SORP  BD Biosciences using Hoechst to sort out cells from cell debris and Propidium Iodide to sort out living cells from dead cells. post FACS sorting  a small fraction of the cell suspension was used to estimate the total number of the cells and their viability using a dead cell stain Trypan Blue elabscience. The obtained suspension contained 16 000 cells with 85.0% viability. The resulting single cell suspension was loaded on the Chromium Next GEM Single Cell three prime Reagent Kits v3.1 10x Genomics  PN 1000269  and the cDNA library was prepared according to the manufacturer's instructions. The obtained cDNA library underwent Next generation sequencing by illumina Hiseq X GENEWIZ with 400 429 716 total reads and 85.9% of sequencing saturation. The obtained sequence was then analyzed by \u201cCell Ranger count\u201d pipeline provided by 10x Genomics with default options. The reads were aligned to zebrafish reference transcriptome ENSEMBL Zv11  release 99 and EGFP CDS  which were built by \u201cCell Ranger mkref\u201d command based on zebrafish reference genome GRCz11 and annotation Ensembl 99. This resulted in 3 381 estimated number of cells with 4890 median unique molecular identifier UMI counts per cell.", null, "RNA-Seq", "TRANSCRIPTOMIC SINGLE CELL", "cDNA", "PAIRED", "ILLUMINA", "HiSeq X Ten", null, "SRP487839", null, "loader:fastq load.py", "ENcDNALibrary20210701_S1_L002_I1_001.fastq.gz ENcDNALibrary20210701_S1_L002_I2_001.fastq.gz ENcDNALibrary20210701_S1_L002_R1_001.fastq.gz ENcDNALibrary20210701_S1_L002_R2_001.fastq.gz", "fastq fastq fastq fastq", 53657581944.0, 400429716.0, "GSM8061694 r1", "0:8 1:8 2:28 3:90", "A:10793191703;C:7078696830;G:7719702279;T:10444591093;N:2492535", 8, 8, 28, 90, 10793191703, 7078696830, 7719702279, 10444591093, 2492535, "SRX23511536", "SRS20362004", "SRA1796958", "CBS, RIKEN", "CBS, RIKEN", null, null, null, null, null, null, null, null, null, null, null, "B", null, "usable mapping rate", "illumina", "hiseq_era", "3prime", "cdna_unspecified", "unknown", "sc", "single_cell_droplet", "10x", null, "Japan", "2024-02-02", "Undetermined", "Adult", "Brain", "Nervous System"], [31871, "SRR33650039", "SRX28877239", "SRS25103945", "SRP502657", "PRJNA1101966", "Pioneer neurons are molecularly distinct  and their axon targeting is regulated by retinoic acid signaling", "GSE264323", "Transcriptome Analysis", "During nervous system development  pioneer neurons are the first to extend their axons into target tissues  creating a scaffold for follower neurons. Despite years of study  whether pioneer neurons are a molecularly distinct population is unknown. Analysis of zebrafish posterior lateral line pLL sensory neurons during axon growth using single cell RNA sequencing scRNA seq revealed that pioneer and follower neurons are transcriptionally distinct. Expression profiling of differentiating pLL progenitors defined follower as the ground state  whereas \u201cpioneer\u201d is a later developmental state. The scRNA seq data revealed active retinoic acid RA signaling in followers  but not in pioneers. Modulation of RA signaling within single pLL neurons showed that its downregulation in pioneers is necessary for expression of neurotrophic factor receptor ret  which is required for correct targeting of pioneer axons. Our study provided insights into the molecular landscape of pioneer neurons and revealed the regulatory role of RA signaling in their development. Overall design: Fourteen hpf  eighteen hpf  twenty two hpf  forty eight hpf TgBACneurod1:EGFPnl1 zebrafish embryos were collected and euthanized in 1.7 ml microcentrifuge tubes. Embryos were deyolked using a calcium free Ringer's solution 116 mM NaCl  2.6 mM KCl  5 mM HEPES pH 7.0  by gently pipetting up and down with a P200 pipet. Embryos were incubated for 5 minutes in Ringer's solution. Embryos were transferred to pre warmed protease solutions 0.25% trypsin  1 mM EDTA  pH 8.0  PBS and collagenase P/HBSS 100 mg/mL was added. Embryos were incubated at 28\u00b0 C for 15 minutes and were homogenized every 5 minutes using a P1000 pipet. The Stop solution 6X  30% calf serum  6 mM CaCl2  PBS was added and samples were centrifuged 350xg  4\u00b0 C for 5 minutes. Supernatant was removed and 1 mL of chilled suspension solution was added 1% FBS  0.8 mM CaCl2  50 U/mL penicillin  0.05 mg/mL streptomycin  DMEM. Samples were centrifuged again 350g  4\u00b0 C for 5 minutes and supernatant was removed. 700 \u00b5l of chilled suspension solution was added and cells were resuspended by pipetting. Cells were passed through a 40 \u00b5m cell strainer into a FACs tube and kept on ice. GFP and RFP+ cells were FAC sorted on a BD Symphony cell sorter into sorting buffer 50 \u00b5l PBS/ 2% BSA in a siliconized 1.5mL tube.", null, null, null, "WT zebrafish neurons", "GSM8997253", null, "source name:neuron|tissue:neuron|genotype:mixed|geo loc name:missing|collection date:missing", "WT zebrafish neurons", "using Cell Ranger version 3.1.0; 10X Genomics  Pleasanton  CA. USA Assembly: ZebraFishGRCz11 Supplementary files format and content: Tab separated values files and matrix files", "neuron", null, "Twenty two old TgBACneurod1:EGFPnl1 zebrafish embryos were collected and euthanized in 1.7 ml microcentrifuge tubes. Embryos were deyolked using a calcium free Ringer\u2019s solution 116 mM NaCl  2.6 mM KCl  5 mM HEPES pH 7.0  by gently pipetting up and down with a P200 pipet. Embryos were incubated for 5 minutes in Ringer\u2019s solution. Embryos were transferred to pre warmed protease solutions 0.25% trypsin  1 mM EDTA  pH 8.0  PBS and collagenase P/HBSS 100 mg/mL was added. Embryos were incubated at 28\u00b0 C for 15 minutes and were homogenized every 5 minutes using a P1000 pipet. The Stop solution 6X  30% calf serum  6 mM CaCl2  PBS was added and samples were centrifuged 350xg  4\u00b0 C for 5 minutes. Supernatant was removed and 1 mL of chilled suspension solution was added 1% FBS  0.8 mM CaCl2  50 U/mL penicillin  0.05 mg/mL streptomycin  DMEM. Samples were centrifuged again 350g  4\u00b0 C for 5 minutes and supernatant was removed. 700 \u03bcl of chilled suspension solution was added and cells were resuspended by pipetting. Cells were passed through a 40 \u03bcm cell strainer into a FACs tube and kept on ice. GFP and RFP+ cells were FAC sorted on a BD Symphony cell sorter into sorting buffer 50 \u03bcl PBS/ 2% BSA in a siliconized 1.5mL tube. Library was performed according to the manufacter\u2019s instructions single cell 3\u2019 v3 protocol  10x Genomics.", null, "tissue:neuron|genotype:mixed", "GSM8997253", "GSM8997253: WT zebrafish neurons; Danio rerio; RNA Seq", "GSM8997253 r1", "GSM8997253", "1", "Twenty two old TgBACneurod1:EGFPnl1 zebrafish embryos were collected and euthanized in 1.7 ml microcentrifuge tubes. Embryos were deyolked using a calcium free Ringer's solution 116 mM NaCl  2.6 mM KCl  5 mM HEPES pH 7.0  by gently pipetting up and down with a P200 pipet. Embryos were incubated for 5 minutes in Ringer's solution. Embryos were transferred to pre warmed protease solutions 0.25% trypsin  1 mM EDTA  pH 8.0  PBS and collagenase P/HBSS 100 mg/mL was added. Embryos were incubated at 28\u00b0 C for 15 minutes and were homogenized every 5 minutes using a P1000 pipet. The Stop solution 6X  30% calf serum  6 mM CaCl2  PBS was added and samples were centrifuged 350xg  4\u00b0 C for 5 minutes. Supernatant was removed and 1 mL of chilled suspension solution was added 1% FBS  0.8 mM CaCl2  50 U/mL penicillin  0.05 mg/mL streptomycin  DMEM. Samples were centrifuged again 350g  4\u00b0 C for 5 minutes and supernatant was removed. 700 \u03bcl of chilled suspension solution was added and cells were resuspended by pipetting. Cells were passed through a 40 \u03bcm cell strainer into a FACs tube and kept on ice. GFP and RFP+ cells were FAC sorted on a BD Symphony cell sorter into sorting buffer 50 \u03bcl PBS/ 2% BSA in a siliconized 1.5mL tube. Library was performed according to the manufacter's instructions single cell three prime v3 protocol  10x Genomics.", null, "RNA-Seq", "TRANSCRIPTOMIC SINGLE CELL", "cDNA", "PAIRED", "ILLUMINA", "Illumina NovaSeq 6000", null, "SRP502657", null, null, "CEL221031AN_WT_18s_S3_L001_I1_001.fastq.gz CEL221031AN_WT_18s_S3_L001_I2_001.fastq.gz CEL221031AN_WT_18s_S3_L001_R1_001.fastq.gz CEL221031AN_WT_18s_S3_L001_R2_001.fastq.gz", "fastq fastq fastq fastq", 76460696208.0, 554063016.0, "GSM8997253 r1", "0:10 1:10 2:28 3:90", "A:15627919564;C:9793027889;G:11041478751;T:13400880335;N:2364901", 10, 10, 28, 90, 15627919564, 9793027889, 11041478751, 13400880335, 2364901, "SRX28877239", "SRS25103945", "SRA2133742", "Oregon Health and Science Univ", "Oregon Health and Science Univ", null, null, null, null, null, null, null, null, null, null, null, "B", null, "usable mapping rate", "illumina", "novaseq_era", "unknown", "cdna_unspecified", "unknown", "sc", "single_cell_droplet", "10x", null, "United States", "2025-05-20", "Undetermined", "Embryo", "Brain", "Nervous System"], [32373, "SRR29181693", "SRX24701872", "SRS21429401", "SRP509892", "PRJNA1116548", "Barcoding Notch signaling in the developing brain", "GSE268356", "Other", "Developmental signaling inputs are fundamental for shaping cell fates and behavior. However  traditional fluorescent based signaling reporters have limitations in scalability and molecular resolution of cell types. We present SABER seq  a CRISPR Cas molecular recorder that stores transient developmental signaling cues as permanent mutations in cellular genomes for deconstruction at later stages via single cell transcriptomics. We applied SABER seq to record Notch signaling in developing zebrafish brains. SABER seq has two components: a signaling sensor and a barcode recorder. The sensor activates Cas9 in a Notch dependent manner with inducible control while the recorder obtains mutations in ancestral cells where Notch is active. We combine SABER seq with an expanded juvenile brain atlas to identify cell types derived from Notch active founders. Our data reveals rare examples where differential Notch activity in ancestral progenitors is detected in terminally differentiated neuronal subtypes. SABER seq is a novel platform for rapid  scalable and high resolution mapping of signaling activity during development. Overall design: 10x Genomics libraries of single cell transcriptomes and SABER seq data  and genomic DNA SABER data", null, "pubmed:39575683", null, "SABERb4 linB2.2 14.124.2  brain4  SABER Seq", "GSM8290063", null, "source name:zebrafish whole brain|tissue:zebrafish whole brain|developmental stage:21 dpf 23 dpf  Juvenile|genotype:WT/TLAB|geo loc name:missing|collection date:missing", "SABERb4 linB2.2 14.124.2  brain4  SABER Seq", "Raw FASTQ reads were aligned against zebrafish genome  built from GRCz.109.gtf and the GRCz11.fa files  using the Cell Ranger count pipeline to generate gene by cell count matrices filtered barcode matrix. The count matrices were processed using the standard workflow of the Seurat v5.0.2 R package with modifications. Briefly  all count matrices were separately loaded into R. Seurat objects were then created with modified parameters  requiring features to be detected in at least 3 cells and cells to contain at least 300 genes. Subsequently  the Seurat objects were merged to create a single Seurat object. Cells with RNA counts between 300 and 4000 and a mitochondrial gene percentage of less than 9% were selected for downstream analysis. Gene expression values were log normalized with the default parameters and scaled for the top 3000 variable genes. The scaled data was then used to compute the 50 principal components. Cells were iteratively clustered using the Louvain algorithm post identifying neighboring cells. Differentially expressed genes for each cluster were identified using genes that are detected in at least 10% of cells in either of the two clusters and genes showing a minimum difference of 10% between the two clusters. Cell types were annotated using known marker genes.  The SABER seq FASTQ reads were processed using the Cell Ranger count pipeline  similar to the transcriptomic reads. A bash script was then used to generate a text file containing all FASTQ headers and error corrected cell barcodes for SABER seq data. post loading filtered barcode matrices into R  error corrected cell barcodes from transcriptomic reads were isolated and matched against those in the text file to identify common cell barcode that are shared between the transcriptomic and SABER seq datasets. The resulting text file contained redundant error corrected cell barcodes associated with different UMIs for the same cell. Thus  unique error corrected cell barcodes were isolated to create a text file containing unique FASTQ headers and error corrected barcodes. Subsequently  FASTQ reads matching the FASTQ headers in this file were extracted from the SABER seq raw reads using a Python script. SABER seq barcodes were analyzed using the scGESTALT pipeline available on Github as described previously7 35. To analyze differences in barcode detection and editing frequencies between cell types a binomial test was used. scRNA seq data was analyzed using the following protocol. Raw FASTQ reads were aligned against zebrafish genome  built from GRCz.109.gtf and the GRCz11.fa files  using the Cell Ranger count pipeline to generate gene by cell count matrices filtered barcode matrix. The count matrices were processed using the standard workflow of the Seurat v5.0.2 R package with modifications. Briefly  all count matrices were separately loaded into R. Seurat objects were then created with modified parameters  requiring features to be detected in at least 3 cells and cells to contain at least 300 genes. Subsequently  the Seurat objects were merged to create a single Seurat object. Cells with RNA counts between 300 and 4000 and a mitochondrial gene percentage of less than 9% were selected for downstream analysis. Gene expression values were log normalized with the default parameters and scaled for the top 3000 variable genes. The scaled data was then used to compute the 50 principal components. Cells were iteratively clustered using the Louvain algorithm post identifying neighboring cells. Differentially expressed genes for each cluster were identified using genes that are detected in at least 10% of cells in either of the two clusters and genes showing a minimum difference of 10% between the two clusters. Cell types were annotated using known marker genes. Finally  the data was visualized using UMAP. SABER seq barcode analysis was performed using the following protocol. The SABER seq FASTQ reads were processed using the Cell Ranger count pipeline.A bash script was then used to generate a text file containing all FASTQ headers and error corrected cell barcodes for SABER seq data. Error corrected cell barcodes from transcriptomic reads were isolated and matched against those in the text file to identify common cell barcode that are shared between the transcriptomic and SABER seq datasets. The resulting text file contained redundant error corrected cell barcodes associated with different UMIs for the same cell. Thus  unique error corrected cell barcodes were isolated to create a text file containing unique FASTQ headers and error corrected barcodes. Subsequently  FASTQ reads matching the FASTQ headers in this file were extracted from the SABER seq raw reads using a Python script. SABER seq barcodes were analyzed using the scGESTALT pipeline available on Github as described previously. To analyze differences in barcode detection and editing frequencies between cell types a binomial test was used. Assembly: GRCz11 Supplementary files format and content: *.h5 files contain fileted features/genes and barcode data generated from cell ranger pipeline. Supplementary files format and content: LinC1 Filtered Fastq Headers CB.txt LinC2 Filtered Fastq Headers CB.txt LinD1 Filtered Fastq Headers CB.txt LinD2 Filtered Fastq Headers CB.txt LinD3 Filtered Fastq Headers CB.txt files contain filtered and unique fastq headers and cell barcodes post matching the trasncriptomic cell barcodes. Supplementary files format and content: SABERb1 linC1 S4 BrainC1 S6.CB.xlsx SABERb1 linC2 S5 BrainC2 S7.CB.xlsx SABERb2 linD1 S1 BrainD1 S8.CB.xlsx SABERb2 linD2 S2 BrainD2 S9.CB.xlsx SABERb2 linD3 S3 BrainD3 S10.CB.xlsx SABERb4 linB2.1 13.123.2.With.Primer.stats.xlsx SABERb4 linB2.2 14.124.2.With.Primer.stats.xlsx files contain the SABER Seq output post matching reads to the reference sequence.", "zebrafish whole brain", null, "Wild type and SABER x her4.3:switchCas9 zebrafish brains were harvested and dissociated at 21 dpf 23 dpf as described previously with the following modifications. All microcentrifuge tubes were treated with 1% BSA/DPBS overnight to prevent cell loss due to adhesion. Brains were dissociated with 1 mL of 20 units/ml papain in EBSS media and incubated at 37 \u00b0C for 20 min. Cells were resuspended with 150 \u03bcl ice cold 1%BSA/DPBS solution. Transcriptome libraries were processed according to the manufacturer\u2019s instructions Single Cell 3\u2019 v3 kit. Transcriptome libraries were sequenced using HiSeq S4 300 cycle kits Novogene and Novaseq SP 100 and 200 cycle kits MedGenome. To generate SABER seq libraries  samples post cDNA amplification and prior to fragmentation were split into two parts. One part was processed for transcriptome libraries as instructed by the manufacturer. The other part was processed for SABER seq libraries as follows. To enrich for SABER barcodes  5 \u00b5l of the whole transcriptome cDNA was PCR amplified with 10X p140v1UP  GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCT CGTCGTAGATCTTCATCGTGATG and 10XPCR1F CTACACGACGCTCTTCCGATCT primers and Q5 polymerase. The reaction conditions were: 98 \u00b0C  30 s; [98 \u00b0C  10 s; 68 \u00b0C  25 s; 72 \u00b0C  15 s] x 14 cycles; 72 \u00b0C  2 min. The reaction was cleaned up with 0.8X SPRISelect beads and eluted in 20 \u00b5l elution buffer EB buffer. A second PCR was carried out using 3 \u00b5l of PCR1 product and 10XPCR1F and 10X p140v2Sa GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCT GAAACTACATGGGGTcaGCTC primers. Reaction conditions were same as PCR1 except only 13 cycles were completed. At this point  2 different clean up conditions were used: iteration 1 SABER seq v0 and iteration 2 SABER seq v1. For iteration 1  the reaction was cleaned up with 0.8X SPRISelect beads and eluted in 20 \u00b5l elution buffer EB buffer similar to PCR1.  For iteration 2  an optimization was trialed  and the reaction was cleaned up with right side size selection using SPRIselect beads 0.6X beads used initially and supernatant was rebound with 1.5X beads. The product was eluted in 20 \u00b5l EB. Finally  adapters and sample indices were incorporated in a third PCR reaction using 5 7 \u00b5l of a 1:10 dilution of PCR2 product. Reactions conditions were same as PCR1 except 9 10 cycles were completed. Primers used: 10XP5Dual Hx AATGATACGGCGACCACCGAGATCTACAC xxxxxxx xxx ACACTCTTTCCCTACACGACGCTCTTCCGATCT  10XP7Dual H5 AATGATACGGCGAC CACCGAGATCTACAC xxxxxxxxxx ACACTCTTTCCCTACACGACGCTCTTCCGATCT  where x represents index bases from the 10X Dual Index Kit TT  SetA H index entries. For iteration 1  the third PCR reaction was cleaned up with 0.8X SPRISelect beads and eluted in 20 \u00b5l elution buffer EB to generate final libraries. For iteration 2  the third PCR reaction was cleaned up with right side size selection using SPRIselect beads 0.65X beads used initially and supernatant was rebound with 1.5X beads. SABER seq libraries were sequenced using Novaseq SP 200 cycle kits MedGenome with 5 10% PhiX spike in. Sequencing parameters: Read1 28 cycles  Read2 151 cycles  Index1 10 cycles  Index2 10 cycles. Standard sequencing primers were used.", "This work was performed under protocol numbers 807110 and 807259  which were approved by the University of Pennsylvania\u2019s Office of Animal Welfare of Institutional Animal Care and Use Committee IACUC. All zebrafish work in this study follows the University of Pennsylvania Institutional Animal Care and Use Committee regulations.", "tissue:zebrafish whole brain|developmental stage:21 dpf 23 dpf  Juvenile|genotype:WT/TLAB", "GSM8290063", "GSM8290063: SABERb4 linB2.2 14.124.2  brain4  SABER Seq; Danio rerio; RNA Seq", "GSM8290063 r1", "GSM8290063", "1", "Wild type and SABER x her4.3:switchCas9 zebrafish brains were harvested and dissociated at 21 dpf 23 dpf as described previously with the following modifications. All microcentrifuge tubes were treated with 1% BSA/DPBS overnight to prevent cell loss due to adhesion. Brains were dissociated with 1 mL of 20 units/ml papain in EBSS media and incubated at 37 \u00b0C for 20 min. Cells were resuspended with 150 \u03bcl ice cold 1%BSA/DPBS solution. Transcriptome libraries were processed according to the manufacturer's instructions Single Cell three prime v3 kit. Transcriptome libraries were sequenced using HiSeq S4 300 cycle kits Novogene and Novaseq SP 100 and 200 cycle kits MedGenome. To generate SABER seq libraries  samples post cDNA amplification and prior to fragmentation were split into two parts. One part was processed for transcriptome libraries as instructed by the manufacturer. The other part was processed for SABER seq libraries as follows. To enrich for SABER barcodes  5 \u00b5l of the whole transcriptome cDNA was PCR amplified with 10X p140v1UP  GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCT CGTCGTAGATCTTCATCGTGATG and 10XPCR1F CTACACGACGCTCTTCCGATCT primers and Q5 polymerase. The reaction conditions were: 98 \u00b0C  30 s; [98 \u00b0C  10 s; 68 \u00b0C  25 s; 72 \u00b0C  15 s] x 14 cycles; 72 \u00b0C  2 min. The reaction was cleaned up with 0.8X SPRISelect beads and eluted in 20 \u00b5l elution buffer EB buffer. A second PCR was carried out using 3 \u00b5l of PCR1 product and 10XPCR1F and 10X p140v2Sa GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCT GAAACTACATGGGGTcaGCTC primers. Reaction conditions were same as PCR1 except only 13 cycles were completed. At this point  2 different clean up conditions were used: iteration 1 SABER seq v0 and iteration 2 SABER seq v1. For iteration 1  the reaction was cleaned up with 0.8X SPRISelect beads and eluted in 20 \u00b5l elution buffer EB buffer similar to PCR1.  For iteration 2  an optimization was trialed  and the reaction was cleaned up with right side size selection using SPRIselect beads 0.6X beads used initially and supernatant was rebound with 1.5X beads. The product was eluted in 20 \u00b5l EB. Finally  adapters and sample indices were incorporated in a third PCR reaction using 5 7 \u00b5l of a 1:10 dilution of PCR2 product. Reactions conditions were same as PCR1 except 9 10 cycles were completed. Primers used: 10XP5Dual Hx AATGATACGGCGACCACCGAGATCTACAC xxxxxxx xxx ACACTCTTTCCCTACACGACGCTCTTCCGATCT  10XP7Dual H5 AATGATACGGCGAC CACCGAGATCTACAC xxxxxxxxxx ACACTCTTTCCCTACACGACGCTCTTCCGATCT  where x represents index bases from the 10X Dual Index Kit TT  SetA H index entries. For iteration 1  the third PCR reaction was cleaned up with 0.8X SPRISelect beads and eluted in 20 \u00b5l elution buffer EB to generate final libraries. For iteration 2  the third PCR reaction was cleaned up with right side size selection using SPRIselect beads 0.65X beads used initially and supernatant was rebound with 1.5X beads. SABER seq libraries were sequenced using Novaseq SP 200 cycle kits MedGenome with 5 10% PhiX spike in. Sequencing parameters: Read1 28 cycles  Read2 151 cycles  Index1 10 cycles  Index2 10 cycles. Standard sequencing primers were used.", null, "RNA-Seq", "TRANSCRIPTOMIC SINGLE CELL", "cDNA", "PAIRED", "ILLUMINA", "Illumina NovaSeq 6000", null, "SRP509892", null, "loader:fastq load.py", "SABERb4_linB2.2-14.124.2_S2_L002_I1_001.fastq.gz SABERb4_linB2.2-14.124.2_S2_L002_I2_001.fastq.gz SABERb4_linB2.2-14.124.2_S2_L002_R1_001.fastq.gz SABERb4_linB2.2-14.124.2_S2_L002_R2_001.fastq.gz", "fastq fastq fastq fastq", 28009093188.0, 140749212.0, "GSM8290063 r1", "0:10 1:10 2:28 3:151", "A:5808319224;C:5111068156;G:5728880143;T:4604800178;N:63311", 10, 10, 28, 151, 5808319224, 5111068156, 5728880143, 4604800178, 63311, "SRX24701872", "SRS21429401", "SRA1878107", "University of Pennsylvania", "University of Pennsylvania", 1, 0.03458, null, 0.03418, null, 0.99959, null, 0.4035, null, 151, null, "B", null, "usable mapping rate", "illumina", "novaseq_era", "full_length", "random_priming", "unknown", "sc", "single_cell_droplet", "10x", null, "United States", "2024-05-25", "Larval", "Larval", "Brain", "Nervous System"], [32374, "SRR29181692", "SRX24701871", "SRS21429400", "SRP509892", "PRJNA1116548", "Barcoding Notch signaling in the developing brain", "GSE268356", "Other", "Developmental signaling inputs are fundamental for shaping cell fates and behavior. However  traditional fluorescent based signaling reporters have limitations in scalability and molecular resolution of cell types. We present SABER seq  a CRISPR Cas molecular recorder that stores transient developmental signaling cues as permanent mutations in cellular genomes for deconstruction at later stages via single cell transcriptomics. We applied SABER seq to record Notch signaling in developing zebrafish brains. SABER seq has two components: a signaling sensor and a barcode recorder. The sensor activates Cas9 in a Notch dependent manner with inducible control while the recorder obtains mutations in ancestral cells where Notch is active. We combine SABER seq with an expanded juvenile brain atlas to identify cell types derived from Notch active founders. Our data reveals rare examples where differential Notch activity in ancestral progenitors is detected in terminally differentiated neuronal subtypes. SABER seq is a novel platform for rapid  scalable and high resolution mapping of signaling activity during development. Overall design: 10x Genomics libraries of single cell transcriptomes and SABER seq data  and genomic DNA SABER data", null, "pubmed:39575683", null, "SABERb4 linB2.1 13.123.2  brain4  SABER Seq", "GSM8290062", null, "source name:zebrafish whole brain|tissue:zebrafish whole brain|developmental stage:21 dpf 23 dpf  Juvenile|genotype:WT/TLAB|geo loc name:missing|collection date:missing", "SABERb4 linB2.1 13.123.2  brain4  SABER Seq", "Raw FASTQ reads were aligned against zebrafish genome  built from GRCz.109.gtf and the GRCz11.fa files  using the Cell Ranger count pipeline to generate gene by cell count matrices filtered barcode matrix. The count matrices were processed using the standard workflow of the Seurat v5.0.2 R package with modifications. Briefly  all count matrices were separately loaded into R. Seurat objects were then created with modified parameters  requiring features to be detected in at least 3 cells and cells to contain at least 300 genes. Subsequently  the Seurat objects were merged to create a single Seurat object. Cells with RNA counts between 300 and 4000 and a mitochondrial gene percentage of less than 9% were selected for downstream analysis. Gene expression values were log normalized with the default parameters and scaled for the top 3000 variable genes. The scaled data was then used to compute the 50 principal components. Cells were iteratively clustered using the Louvain algorithm post identifying neighboring cells. Differentially expressed genes for each cluster were identified using genes that are detected in at least 10% of cells in either of the two clusters and genes showing a minimum difference of 10% between the two clusters. Cell types were annotated using known marker genes.  The SABER seq FASTQ reads were processed using the Cell Ranger count pipeline  similar to the transcriptomic reads. A bash script was then used to generate a text file containing all FASTQ headers and error corrected cell barcodes for SABER seq data. post loading filtered barcode matrices into R  error corrected cell barcodes from transcriptomic reads were isolated and matched against those in the text file to identify common cell barcode that are shared between the transcriptomic and SABER seq datasets. The resulting text file contained redundant error corrected cell barcodes associated with different UMIs for the same cell. Thus  unique error corrected cell barcodes were isolated to create a text file containing unique FASTQ headers and error corrected barcodes. Subsequently  FASTQ reads matching the FASTQ headers in this file were extracted from the SABER seq raw reads using a Python script. SABER seq barcodes were analyzed using the scGESTALT pipeline available on Github as described previously7 35. To analyze differences in barcode detection and editing frequencies between cell types a binomial test was used. scRNA seq data was analyzed using the following protocol. Raw FASTQ reads were aligned against zebrafish genome  built from GRCz.109.gtf and the GRCz11.fa files  using the Cell Ranger count pipeline to generate gene by cell count matrices filtered barcode matrix. The count matrices were processed using the standard workflow of the Seurat v5.0.2 R package with modifications. Briefly  all count matrices were separately loaded into R. Seurat objects were then created with modified parameters  requiring features to be detected in at least 3 cells and cells to contain at least 300 genes. Subsequently  the Seurat objects were merged to create a single Seurat object. Cells with RNA counts between 300 and 4000 and a mitochondrial gene percentage of less than 9% were selected for downstream analysis. Gene expression values were log normalized with the default parameters and scaled for the top 3000 variable genes. The scaled data was then used to compute the 50 principal components. Cells were iteratively clustered using the Louvain algorithm post identifying neighboring cells. Differentially expressed genes for each cluster were identified using genes that are detected in at least 10% of cells in either of the two clusters and genes showing a minimum difference of 10% between the two clusters. Cell types were annotated using known marker genes. Finally  the data was visualized using UMAP. SABER seq barcode analysis was performed using the following protocol. The SABER seq FASTQ reads were processed using the Cell Ranger count pipeline.A bash script was then used to generate a text file containing all FASTQ headers and error corrected cell barcodes for SABER seq data. Error corrected cell barcodes from transcriptomic reads were isolated and matched against those in the text file to identify common cell barcode that are shared between the transcriptomic and SABER seq datasets. The resulting text file contained redundant error corrected cell barcodes associated with different UMIs for the same cell. Thus  unique error corrected cell barcodes were isolated to create a text file containing unique FASTQ headers and error corrected barcodes. Subsequently  FASTQ reads matching the FASTQ headers in this file were extracted from the SABER seq raw reads using a Python script. SABER seq barcodes were analyzed using the scGESTALT pipeline available on Github as described previously. To analyze differences in barcode detection and editing frequencies between cell types a binomial test was used. Assembly: GRCz11 Supplementary files format and content: *.h5 files contain fileted features/genes and barcode data generated from cell ranger pipeline. Supplementary files format and content: LinC1 Filtered Fastq Headers CB.txt LinC2 Filtered Fastq Headers CB.txt LinD1 Filtered Fastq Headers CB.txt LinD2 Filtered Fastq Headers CB.txt LinD3 Filtered Fastq Headers CB.txt files contain filtered and unique fastq headers and cell barcodes post matching the trasncriptomic cell barcodes. Supplementary files format and content: SABERb1 linC1 S4 BrainC1 S6.CB.xlsx SABERb1 linC2 S5 BrainC2 S7.CB.xlsx SABERb2 linD1 S1 BrainD1 S8.CB.xlsx SABERb2 linD2 S2 BrainD2 S9.CB.xlsx SABERb2 linD3 S3 BrainD3 S10.CB.xlsx SABERb4 linB2.1 13.123.2.With.Primer.stats.xlsx SABERb4 linB2.2 14.124.2.With.Primer.stats.xlsx files contain the SABER Seq output post matching reads to the reference sequence.", "zebrafish whole brain", null, "Wild type and SABER x her4.3:switchCas9 zebrafish brains were harvested and dissociated at 21 dpf 23 dpf as described previously with the following modifications. All microcentrifuge tubes were treated with 1% BSA/DPBS overnight to prevent cell loss due to adhesion. Brains were dissociated with 1 mL of 20 units/ml papain in EBSS media and incubated at 37 \u00b0C for 20 min. Cells were resuspended with 150 \u03bcl ice cold 1%BSA/DPBS solution. Transcriptome libraries were processed according to the manufacturer\u2019s instructions Single Cell 3\u2019 v3 kit. Transcriptome libraries were sequenced using HiSeq S4 300 cycle kits Novogene and Novaseq SP 100 and 200 cycle kits MedGenome. To generate SABER seq libraries  samples post cDNA amplification and prior to fragmentation were split into two parts. One part was processed for transcriptome libraries as instructed by the manufacturer. The other part was processed for SABER seq libraries as follows. To enrich for SABER barcodes  5 \u00b5l of the whole transcriptome cDNA was PCR amplified with 10X p140v1UP  GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCT CGTCGTAGATCTTCATCGTGATG and 10XPCR1F CTACACGACGCTCTTCCGATCT primers and Q5 polymerase. The reaction conditions were: 98 \u00b0C  30 s; [98 \u00b0C  10 s; 68 \u00b0C  25 s; 72 \u00b0C  15 s] x 14 cycles; 72 \u00b0C  2 min. The reaction was cleaned up with 0.8X SPRISelect beads and eluted in 20 \u00b5l elution buffer EB buffer. A second PCR was carried out using 3 \u00b5l of PCR1 product and 10XPCR1F and 10X p140v2Sa GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCT GAAACTACATGGGGTcaGCTC primers. Reaction conditions were same as PCR1 except only 13 cycles were completed. At this point  2 different clean up conditions were used: iteration 1 SABER seq v0 and iteration 2 SABER seq v1. For iteration 1  the reaction was cleaned up with 0.8X SPRISelect beads and eluted in 20 \u00b5l elution buffer EB buffer similar to PCR1.  For iteration 2  an optimization was trialed  and the reaction was cleaned up with right side size selection using SPRIselect beads 0.6X beads used initially and supernatant was rebound with 1.5X beads. The product was eluted in 20 \u00b5l EB. Finally  adapters and sample indices were incorporated in a third PCR reaction using 5 7 \u00b5l of a 1:10 dilution of PCR2 product. Reactions conditions were same as PCR1 except 9 10 cycles were completed. Primers used: 10XP5Dual Hx AATGATACGGCGACCACCGAGATCTACAC xxxxxxx xxx ACACTCTTTCCCTACACGACGCTCTTCCGATCT  10XP7Dual H5 AATGATACGGCGAC CACCGAGATCTACAC xxxxxxxxxx ACACTCTTTCCCTACACGACGCTCTTCCGATCT  where x represents index bases from the 10X Dual Index Kit TT  SetA H index entries. For iteration 1  the third PCR reaction was cleaned up with 0.8X SPRISelect beads and eluted in 20 \u00b5l elution buffer EB to generate final libraries. For iteration 2  the third PCR reaction was cleaned up with right side size selection using SPRIselect beads 0.65X beads used initially and supernatant was rebound with 1.5X beads. SABER seq libraries were sequenced using Novaseq SP 200 cycle kits MedGenome with 5 10% PhiX spike in. Sequencing parameters: Read1 28 cycles  Read2 151 cycles  Index1 10 cycles  Index2 10 cycles. Standard sequencing primers were used.", "This work was performed under protocol numbers 807110 and 807259  which were approved by the University of Pennsylvania\u2019s Office of Animal Welfare of Institutional Animal Care and Use Committee IACUC. All zebrafish work in this study follows the University of Pennsylvania Institutional Animal Care and Use Committee regulations.", "tissue:zebrafish whole brain|developmental stage:21 dpf 23 dpf  Juvenile|genotype:WT/TLAB", "GSM8290062", "GSM8290062: SABERb4 linB2.1 13.123.2  brain4  SABER Seq; Danio rerio; RNA Seq", "GSM8290062 r1", "GSM8290062", "1", "Wild type and SABER x her4.3:switchCas9 zebrafish brains were harvested and dissociated at 21 dpf 23 dpf as described previously with the following modifications. All microcentrifuge tubes were treated with 1% BSA/DPBS overnight to prevent cell loss due to adhesion. Brains were dissociated with 1 mL of 20 units/ml papain in EBSS media and incubated at 37 \u00b0C for 20 min. Cells were resuspended with 150 \u03bcl ice cold 1%BSA/DPBS solution. Transcriptome libraries were processed according to the manufacturer's instructions Single Cell three prime v3 kit. Transcriptome libraries were sequenced using HiSeq S4 300 cycle kits Novogene and Novaseq SP 100 and 200 cycle kits MedGenome. To generate SABER seq libraries  samples post cDNA amplification and prior to fragmentation were split into two parts. One part was processed for transcriptome libraries as instructed by the manufacturer. The other part was processed for SABER seq libraries as follows. To enrich for SABER barcodes  5 \u00b5l of the whole transcriptome cDNA was PCR amplified with 10X p140v1UP  GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCT CGTCGTAGATCTTCATCGTGATG and 10XPCR1F CTACACGACGCTCTTCCGATCT primers and Q5 polymerase. The reaction conditions were: 98 \u00b0C  30 s; [98 \u00b0C  10 s; 68 \u00b0C  25 s; 72 \u00b0C  15 s] x 14 cycles; 72 \u00b0C  2 min. The reaction was cleaned up with 0.8X SPRISelect beads and eluted in 20 \u00b5l elution buffer EB buffer. A second PCR was carried out using 3 \u00b5l of PCR1 product and 10XPCR1F and 10X p140v2Sa GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCT GAAACTACATGGGGTcaGCTC primers. Reaction conditions were same as PCR1 except only 13 cycles were completed. At this point  2 different clean up conditions were used: iteration 1 SABER seq v0 and iteration 2 SABER seq v1. For iteration 1  the reaction was cleaned up with 0.8X SPRISelect beads and eluted in 20 \u00b5l elution buffer EB buffer similar to PCR1.  For iteration 2  an optimization was trialed  and the reaction was cleaned up with right side size selection using SPRIselect beads 0.6X beads used initially and supernatant was rebound with 1.5X beads. The product was eluted in 20 \u00b5l EB. Finally  adapters and sample indices were incorporated in a third PCR reaction using 5 7 \u00b5l of a 1:10 dilution of PCR2 product. Reactions conditions were same as PCR1 except 9 10 cycles were completed. Primers used: 10XP5Dual Hx AATGATACGGCGACCACCGAGATCTACAC xxxxxxx xxx ACACTCTTTCCCTACACGACGCTCTTCCGATCT  10XP7Dual H5 AATGATACGGCGAC CACCGAGATCTACAC xxxxxxxxxx ACACTCTTTCCCTACACGACGCTCTTCCGATCT  where x represents index bases from the 10X Dual Index Kit TT  SetA H index entries. For iteration 1  the third PCR reaction was cleaned up with 0.8X SPRISelect beads and eluted in 20 \u00b5l elution buffer EB to generate final libraries. For iteration 2  the third PCR reaction was cleaned up with right side size selection using SPRIselect beads 0.65X beads used initially and supernatant was rebound with 1.5X beads. SABER seq libraries were sequenced using Novaseq SP 200 cycle kits MedGenome with 5 10% PhiX spike in. Sequencing parameters: Read1 28 cycles  Read2 151 cycles  Index1 10 cycles  Index2 10 cycles. Standard sequencing primers were used.", null, "RNA-Seq", "TRANSCRIPTOMIC SINGLE CELL", "cDNA", "PAIRED", "ILLUMINA", "Illumina NovaSeq 6000", null, "SRP509892", null, "loader:fastq load.py", "SABERb4_linB2.1-13.123.2_S1_L002_R2_001.fastq.gz SABERb4_linB2.1-13.123.2_S1_L002_R1_001.fastq.gz SABERb4_linB2.1-13.123.2_S1_L002_I2_001.fastq.gz SABERb4_linB2.1-13.123.2_S1_L002_I1_001.fastq.gz", "fastq fastq fastq fastq", 22391839991.0, 112521809.0, "GSM8290062 r1", "0:10 1:10 2:28 3:151", "A:4658062266;C:4632110229;G:4512962055;T:3187608704;N:49905", 10, 10, 28, 151, 4658062266, 4632110229, 4512962055, 3187608704, 49905, "SRX24701871", "SRS21429400", "SRA1878107", "University of Pennsylvania", "University of Pennsylvania", 1, 0.00192, null, 0.00168, null, 0.99951, null, 0.67647, null, 151, null, "T", null, "under 1.2% mapping rate", "illumina", "novaseq_era", "full_length", "random_priming", "unknown", "sc", "single_cell_droplet", "10x", null, "United States", "2024-05-25", "Larval", "Larval", "Brain", "Nervous System"], [32375, "SRR29181679", "SRX24701870", "SRS21429399", "SRP509892", "PRJNA1116548", "Barcoding Notch signaling in the developing brain", "GSE268356", "Other", "Developmental signaling inputs are fundamental for shaping cell fates and behavior. However  traditional fluorescent based signaling reporters have limitations in scalability and molecular resolution of cell types. We present SABER seq  a CRISPR Cas molecular recorder that stores transient developmental signaling cues as permanent mutations in cellular genomes for deconstruction at later stages via single cell transcriptomics. We applied SABER seq to record Notch signaling in developing zebrafish brains. SABER seq has two components: a signaling sensor and a barcode recorder. The sensor activates Cas9 in a Notch dependent manner with inducible control while the recorder obtains mutations in ancestral cells where Notch is active. We combine SABER seq with an expanded juvenile brain atlas to identify cell types derived from Notch active founders. Our data reveals rare examples where differential Notch activity in ancestral progenitors is detected in terminally differentiated neuronal subtypes. SABER seq is a novel platform for rapid  scalable and high resolution mapping of signaling activity during development. Overall design: 10x Genomics libraries of single cell transcriptomes and SABER seq data  and genomic DNA SABER data", null, "pubmed:39575683", null, "SABERb4 brainB2 2  brain4  transcriptome", "GSM8290056", null, "source name:zebrafish whole brain|tissue:zebrafish whole brain|developmental stage:21 dpf 23 dpf  Juvenile|genotype:WT/TLAB|geo loc name:missing|collection date:missing", "SABERb4 brainB2 2  brain4  transcriptome", "Raw FASTQ reads were aligned against zebrafish genome  built from GRCz.109.gtf and the GRCz11.fa files  using the Cell Ranger count pipeline to generate gene by cell count matrices filtered barcode matrix. The count matrices were processed using the standard workflow of the Seurat v5.0.2 R package with modifications. Briefly  all count matrices were separately loaded into R. Seurat objects were then created with modified parameters  requiring features to be detected in at least 3 cells and cells to contain at least 300 genes. Subsequently  the Seurat objects were merged to create a single Seurat object. Cells with RNA counts between 300 and 4000 and a mitochondrial gene percentage of less than 9% were selected for downstream analysis. Gene expression values were log normalized with the default parameters and scaled for the top 3000 variable genes. The scaled data was then used to compute the 50 principal components. Cells were iteratively clustered using the Louvain algorithm post identifying neighboring cells. Differentially expressed genes for each cluster were identified using genes that are detected in at least 10% of cells in either of the two clusters and genes showing a minimum difference of 10% between the two clusters. Cell types were annotated using known marker genes.  The SABER seq FASTQ reads were processed using the Cell Ranger count pipeline  similar to the transcriptomic reads. A bash script was then used to generate a text file containing all FASTQ headers and error corrected cell barcodes for SABER seq data. post loading filtered barcode matrices into R  error corrected cell barcodes from transcriptomic reads were isolated and matched against those in the text file to identify common cell barcode that are shared between the transcriptomic and SABER seq datasets. The resulting text file contained redundant error corrected cell barcodes associated with different UMIs for the same cell. Thus  unique error corrected cell barcodes were isolated to create a text file containing unique FASTQ headers and error corrected barcodes. Subsequently  FASTQ reads matching the FASTQ headers in this file were extracted from the SABER seq raw reads using a Python script. SABER seq barcodes were analyzed using the scGESTALT pipeline available on Github as described previously7 35. To analyze differences in barcode detection and editing frequencies between cell types a binomial test was used. scRNA seq data was analyzed using the following protocol. Raw FASTQ reads were aligned against zebrafish genome  built from GRCz.109.gtf and the GRCz11.fa files  using the Cell Ranger count pipeline to generate gene by cell count matrices filtered barcode matrix. The count matrices were processed using the standard workflow of the Seurat v5.0.2 R package with modifications. Briefly  all count matrices were separately loaded into R. Seurat objects were then created with modified parameters  requiring features to be detected in at least 3 cells and cells to contain at least 300 genes. Subsequently  the Seurat objects were merged to create a single Seurat object. Cells with RNA counts between 300 and 4000 and a mitochondrial gene percentage of less than 9% were selected for downstream analysis. Gene expression values were log normalized with the default parameters and scaled for the top 3000 variable genes. The scaled data was then used to compute the 50 principal components. Cells were iteratively clustered using the Louvain algorithm post identifying neighboring cells. Differentially expressed genes for each cluster were identified using genes that are detected in at least 10% of cells in either of the two clusters and genes showing a minimum difference of 10% between the two clusters. Cell types were annotated using known marker genes. Finally  the data was visualized using UMAP. SABER seq barcode analysis was performed using the following protocol. The SABER seq FASTQ reads were processed using the Cell Ranger count pipeline.A bash script was then used to generate a text file containing all FASTQ headers and error corrected cell barcodes for SABER seq data. Error corrected cell barcodes from transcriptomic reads were isolated and matched against those in the text file to identify common cell barcode that are shared between the transcriptomic and SABER seq datasets. The resulting text file contained redundant error corrected cell barcodes associated with different UMIs for the same cell. Thus  unique error corrected cell barcodes were isolated to create a text file containing unique FASTQ headers and error corrected barcodes. Subsequently  FASTQ reads matching the FASTQ headers in this file were extracted from the SABER seq raw reads using a Python script. SABER seq barcodes were analyzed using the scGESTALT pipeline available on Github as described previously. To analyze differences in barcode detection and editing frequencies between cell types a binomial test was used. genome build/assembly: GRCz11 processed data files format and content: *.h5 files contain filtered features/genes and barcode data generated from cell ranger pipeline. processed data files format and content: LinC1 Filtered Fastq Headers CB.txt LinC2 Filtered Fastq Headers CB.txt LinD1 Filtered Fastq Headers CB.txt LinD2 Filtered Fastq Headers CB.txt LinD3 Filtered Fastq Headers CB.txt files contain filtered and unique fastq headers and cell barcodes post matching the trasncriptomic cell barcodes. processed data files format and content: SABERb1 linC1 S4 BrainC1 S6.CB.xlsx SABERb1 linC2 S5 BrainC2 S7.CB.xlsx SABERb2 linD1 S1 BrainD1 S8.CB.xlsx SABERb2 linD2 S2 BrainD2 S9.CB.xlsx SABERb2 linD3 S3 BrainD3 S10.CB.xlsx SABERb4 linB2.1 13.123.2.With.Primer.stats.xlsx SABERb4 linB2.2 14.124.2.With.Primer.stats.xlsx files contain the SABER Seq output post matching reads to the reference sequence.", "zebrafish whole brain", null, "Wild type and SABER x her4.3:switchCas9 zebrafish brains were harvested and dissociated at 21 dpf 23 dpf as described previously Raj et. al 2018  Nature Biotechnology  with the following modifications. All microcentrifuge tubes were treated with 1% BSA/DPBS overnight to prevent cell loss due to adhesion. Brains were dissociated with 1 mL of 20 units/ml papain in EBSS media and incubated at 37 \u00b0C for 20 min. Cells were resuspended with 150 \u03bcl ice cold 1%BSA/DPBS solution. Transcriptome libraries were processed according to the manufacturer\u2019s instructions Single Cell 3\u2019 v3 kit. Transcriptome libraries were sequenced using NovaSeq S4 300 cycle kits Novogene and Novaseq SP 100 and 200 cycle kits MedGenome. To generate SABER seq libraries  samples post cDNA amplification and prior to fragmentation were split into two parts. One part was processed for transcriptome libraries as instructed by the manufacturer. The other part was processed for SABER seq libraries as follows. To enrich for SABER barcodes  5 \u00b5l of the whole transcriptome cDNA was PCR amplified with 10X p140v1UP  GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCT CGTCGTAGATCTTCATCGTGATG and 10XPCR1F CTACACGACGCTCTTCCGATCT primers and Q5 polymerase. The reaction conditions were: 98 \u00b0C  30 s; [98 \u00b0C  10 s; 68 \u00b0C  25 s; 72 \u00b0C  15 s] x 14 cycles; 72 \u00b0C  2 min. The reaction was cleaned up with 0.8X SPRISelect beads and eluted in 20 \u00b5l elution buffer EB buffer. A second PCR was carried out using 3 \u00b5l of PCR1 product and 10XPCR1F and 10X p140v2Sa GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCT GAAACTACATGGGGTcaGCTC primers. Reaction conditions were same as PCR1 except only 13 cycles were completed. At this point  2 different clean up conditions were used: iteration 1 SABER seq v0 and iteration 2 SABER seq v1. For iteration 1  the reaction was cleaned up with 0.8X SPRISelect beads and eluted in 20 \u00b5l elution buffer EB buffer similar to PCR1.  For iteration 2  an optimization was trialed  and the reaction was cleaned up with right side size selection using SPRIselect beads 0.6X beads used initially and supernatant was rebound with 1.5X beads. The product was eluted in 20 \u00b5l EB. Finally  adapters and sample indices were incorporated in a third PCR reaction using 5 7 \u00b5l of a 1:10 dilution of PCR2 product. Reactions conditions were same as PCR1 except 9 10 cycles were completed. Primers used: 10XP5Dual Hx AATGATACGGCGACCACCGAGATCTACAC xxxxxxx xxx ACACTCTTTCCCTACACGACGCTCTTCCGATCT  10XP7Dual H5 AATGATACGGCGAC CACCGAGATCTACAC xxxxxxxxxx ACACTCTTTCCCTACACGACGCTCTTCCGATCT  where x represents index bases from the 10X Dual Index Kit TT  SetA H index entries. For iteration 1  the third PCR reaction was cleaned up with 0.8X SPRISelect beads and eluted in 20 \u00b5l elution buffer EB to generate final libraries. For iteration 2  the third PCR reaction was cleaned up with right side size selection using SPRIselect beads 0.65X beads used initially and supernatant was rebound with 1.5X beads. SABER seq libraries were sequenced using Novaseq SP 200 cycle kits MedGenome with 5 10% PhiX spike in. Sequencing parameters: Read1 28 cycles  Read2 151 cycles  Index1 10 cycles  Index2 10 cycles. Standard sequencing primers were used.", "This work was performed under protocol numbers 807110 and 807259  which were approved by the University of Pennsylvania\u2019s Office of Animal Welfare of Institutional Animal Care and Use Committee IACUC. All zebrafish work in this study follows the University of Pennsylvania Institutional Animal Care and Use Committee regulations.", "tissue:zebrafish whole brain|developmental stage:21 dpf 23 dpf  Juvenile|genotype:WT/TLAB", "GSM8290056", "GSM8290056: SABERb4 brainB2 2  brain4  transcriptome; Danio rerio; RNA Seq", "GSM8290056 r1", "GSM8290056", "1", "Wild type and SABER x her4.3:switchCas9 zebrafish brains were harvested and dissociated at 21 dpf 23 dpf as described previously Raj et. al 2018  Nature Biotechnology  with the following modifications. All microcentrifuge tubes were treated with 1% BSA/DPBS overnight to prevent cell loss due to adhesion. Brains were dissociated with 1 mL of 20 units/ml papain in EBSS media and incubated at 37 \u00b0C for 20 min. Cells were resuspended with 150 \u03bcl ice cold 1%BSA/DPBS solution. Transcriptome libraries were processed according to the manufacturer's instructions Single Cell three prime v3 kit. Transcriptome libraries were sequenced using NovaSeq S4 300 cycle kits Novogene and Novaseq SP 100 and 200 cycle kits MedGenome. To generate SABER seq libraries  samples post cDNA amplification and prior to fragmentation were split into two parts. One part was processed for transcriptome libraries as instructed by the manufacturer. The other part was processed for SABER seq libraries as follows. To enrich for SABER barcodes  5 \u00b5l of the whole transcriptome cDNA was PCR amplified with 10X p140v1UP  GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCT CGTCGTAGATCTTCATCGTGATG and 10XPCR1F CTACACGACGCTCTTCCGATCT primers and Q5 polymerase. The reaction conditions were: 98 \u00b0C  30 s; [98 \u00b0C  10 s; 68 \u00b0C  25 s; 72 \u00b0C  15 s] x 14 cycles; 72 \u00b0C  2 min. The reaction was cleaned up with 0.8X SPRISelect beads and eluted in 20 \u00b5l elution buffer EB buffer. A second PCR was carried out using 3 \u00b5l of PCR1 product and 10XPCR1F and 10X p140v2Sa GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCT GAAACTACATGGGGTcaGCTC primers. Reaction conditions were same as PCR1 except only 13 cycles were completed. At this point  2 different clean up conditions were used: iteration 1 SABER seq v0 and iteration 2 SABER seq v1. For iteration 1  the reaction was cleaned up with 0.8X SPRISelect beads and eluted in 20 \u00b5l elution buffer EB buffer similar to PCR1.  For iteration 2  an optimization was trialed  and the reaction was cleaned up with right side size selection using SPRIselect beads 0.6X beads used initially and supernatant was rebound with 1.5X beads. The product was eluted in 20 \u00b5l EB. Finally  adapters and sample indices were incorporated in a third PCR reaction using 5 7 \u00b5l of a 1:10 dilution of PCR2 product. Reactions conditions were same as PCR1 except 9 10 cycles were completed. Primers used: 10XP5Dual Hx AATGATACGGCGACCACCGAGATCTACAC xxxxxxx xxx ACACTCTTTCCCTACACGACGCTCTTCCGATCT  10XP7Dual H5 AATGATACGGCGAC CACCGAGATCTACAC xxxxxxxxxx ACACTCTTTCCCTACACGACGCTCTTCCGATCT  where x represents index bases from the 10X Dual Index Kit TT  SetA H index entries. For iteration 1  the third PCR reaction was cleaned up with 0.8X SPRISelect beads and eluted in 20 \u00b5l elution buffer EB to generate final libraries. For iteration 2  the third PCR reaction was cleaned up with right side size selection using SPRIselect beads 0.65X beads used initially and supernatant was rebound with 1.5X beads. SABER seq libraries were sequenced using Novaseq SP 200 cycle kits MedGenome with 5 10% PhiX spike in. Sequencing parameters: Read1 28 cycles  Read2 151 cycles  Index1 10 cycles  Index2 10 cycles. Standard sequencing primers were used.", null, "RNA-Seq", "TRANSCRIPTOMIC SINGLE CELL", "cDNA", "PAIRED", "ILLUMINA", "Illumina NovaSeq 6000", null, "SRP509892", null, "loader:fastq load.py", "SABERb4_brainB2_2_S4_L001_R2_001.fastq.gz SABERb4_brainB2_2_S4_L001_R1_001.fastq.gz SABERb4_brainB2_2_S4_L001_I2_001.fastq.gz SABERb4_brainB2_2_S4_L001_I1_001.fastq.gz", "fastq fastq fastq fastq", 22587540571.0, 113505229.0, "GSM8290056 r1", "0:10 1:10 2:28 3:151", "A:5299086616;C:3418923384;G:3717477937;T:4703769200;N:32442", 10, 10, 28, 151, 5299086616, 3418923384, 3717477937, 4703769200, 32442, "SRX24701870", "SRS21429399", "SRA1989588", "University of Pennsylvania", "University of Pennsylvania", 1, 0.88382, null, 0.22459, null, 0.76524, null, 0.54792, null, 151, null, "B", null, "usable mapping rate", "illumina", "novaseq_era", "full_length", "random_priming", "unknown", "sc", "single_cell_droplet", "10x", null, "United States", "2024-05-25", "Larval", "Larval", "Brain", "Nervous System"], [32376, "SRR29181684", "SRX24701869", "SRS21429398", "SRP509892", "PRJNA1116548", "Barcoding Notch signaling in the developing brain", "GSE268356", "Other", "Developmental signaling inputs are fundamental for shaping cell fates and behavior. However  traditional fluorescent based signaling reporters have limitations in scalability and molecular resolution of cell types. We present SABER seq  a CRISPR Cas molecular recorder that stores transient developmental signaling cues as permanent mutations in cellular genomes for deconstruction at later stages via single cell transcriptomics. We applied SABER seq to record Notch signaling in developing zebrafish brains. SABER seq has two components: a signaling sensor and a barcode recorder. The sensor activates Cas9 in a Notch dependent manner with inducible control while the recorder obtains mutations in ancestral cells where Notch is active. We combine SABER seq with an expanded juvenile brain atlas to identify cell types derived from Notch active founders. Our data reveals rare examples where differential Notch activity in ancestral progenitors is detected in terminally differentiated neuronal subtypes. SABER seq is a novel platform for rapid  scalable and high resolution mapping of signaling activity during development. Overall design: 10x Genomics libraries of single cell transcriptomes and SABER seq data  and genomic DNA SABER data", null, "pubmed:39575683", null, "SABERb4 brainB2 1  brain4  transcriptome", "GSM8290055", null, "source name:zebrafish whole brain|tissue:zebrafish whole brain|developmental stage:21 dpf 23 dpf  Juvenile|genotype:WT/TLAB|geo loc name:missing|collection date:missing", "SABERb4 brainB2 1  brain4  transcriptome", "Raw FASTQ reads were aligned against zebrafish genome  built from GRCz.109.gtf and the GRCz11.fa files  using the Cell Ranger count pipeline to generate gene by cell count matrices filtered barcode matrix. The count matrices were processed using the standard workflow of the Seurat v5.0.2 R package with modifications. Briefly  all count matrices were separately loaded into R. Seurat objects were then created with modified parameters  requiring features to be detected in at least 3 cells and cells to contain at least 300 genes. Subsequently  the Seurat objects were merged to create a single Seurat object. Cells with RNA counts between 300 and 4000 and a mitochondrial gene percentage of less than 9% were selected for downstream analysis. Gene expression values were log normalized with the default parameters and scaled for the top 3000 variable genes. The scaled data was then used to compute the 50 principal components. Cells were iteratively clustered using the Louvain algorithm post identifying neighboring cells. Differentially expressed genes for each cluster were identified using genes that are detected in at least 10% of cells in either of the two clusters and genes showing a minimum difference of 10% between the two clusters. Cell types were annotated using known marker genes.  The SABER seq FASTQ reads were processed using the Cell Ranger count pipeline  similar to the transcriptomic reads. A bash script was then used to generate a text file containing all FASTQ headers and error corrected cell barcodes for SABER seq data. post loading filtered barcode matrices into R  error corrected cell barcodes from transcriptomic reads were isolated and matched against those in the text file to identify common cell barcode that are shared between the transcriptomic and SABER seq datasets. The resulting text file contained redundant error corrected cell barcodes associated with different UMIs for the same cell. Thus  unique error corrected cell barcodes were isolated to create a text file containing unique FASTQ headers and error corrected barcodes. Subsequently  FASTQ reads matching the FASTQ headers in this file were extracted from the SABER seq raw reads using a Python script. SABER seq barcodes were analyzed using the scGESTALT pipeline available on Github as described previously7 35. To analyze differences in barcode detection and editing frequencies between cell types a binomial test was used. scRNA seq data was analyzed using the following protocol. Raw FASTQ reads were aligned against zebrafish genome  built from GRCz.109.gtf and the GRCz11.fa files  using the Cell Ranger count pipeline to generate gene by cell count matrices filtered barcode matrix. The count matrices were processed using the standard workflow of the Seurat v5.0.2 R package with modifications. Briefly  all count matrices were separately loaded into R. Seurat objects were then created with modified parameters  requiring features to be detected in at least 3 cells and cells to contain at least 300 genes. Subsequently  the Seurat objects were merged to create a single Seurat object. Cells with RNA counts between 300 and 4000 and a mitochondrial gene percentage of less than 9% were selected for downstream analysis. Gene expression values were log normalized with the default parameters and scaled for the top 3000 variable genes. The scaled data was then used to compute the 50 principal components. Cells were iteratively clustered using the Louvain algorithm post identifying neighboring cells. Differentially expressed genes for each cluster were identified using genes that are detected in at least 10% of cells in either of the two clusters and genes showing a minimum difference of 10% between the two clusters. Cell types were annotated using known marker genes. Finally  the data was visualized using UMAP. SABER seq barcode analysis was performed using the following protocol. The SABER seq FASTQ reads were processed using the Cell Ranger count pipeline.A bash script was then used to generate a text file containing all FASTQ headers and error corrected cell barcodes for SABER seq data. Error corrected cell barcodes from transcriptomic reads were isolated and matched against those in the text file to identify common cell barcode that are shared between the transcriptomic and SABER seq datasets. The resulting text file contained redundant error corrected cell barcodes associated with different UMIs for the same cell. Thus  unique error corrected cell barcodes were isolated to create a text file containing unique FASTQ headers and error corrected barcodes. Subsequently  FASTQ reads matching the FASTQ headers in this file were extracted from the SABER seq raw reads using a Python script. SABER seq barcodes were analyzed using the scGESTALT pipeline available on Github as described previously. To analyze differences in barcode detection and editing frequencies between cell types a binomial test was used. genome build/assembly: GRCz11 processed data files format and content: *.h5 files contain filtered features/genes and barcode data generated from cell ranger pipeline. processed data files format and content: LinC1 Filtered Fastq Headers CB.txt LinC2 Filtered Fastq Headers CB.txt LinD1 Filtered Fastq Headers CB.txt LinD2 Filtered Fastq Headers CB.txt LinD3 Filtered Fastq Headers CB.txt files contain filtered and unique fastq headers and cell barcodes post matching the trasncriptomic cell barcodes. processed data files format and content: SABERb1 linC1 S4 BrainC1 S6.CB.xlsx SABERb1 linC2 S5 BrainC2 S7.CB.xlsx SABERb2 linD1 S1 BrainD1 S8.CB.xlsx SABERb2 linD2 S2 BrainD2 S9.CB.xlsx SABERb2 linD3 S3 BrainD3 S10.CB.xlsx SABERb4 linB2.1 13.123.2.With.Primer.stats.xlsx SABERb4 linB2.2 14.124.2.With.Primer.stats.xlsx files contain the SABER Seq output post matching reads to the reference sequence.", "zebrafish whole brain", null, "Wild type and SABER x her4.3:switchCas9 zebrafish brains were harvested and dissociated at 21 dpf 23 dpf as described previously Raj et. al 2018  Nature Biotechnology  with the following modifications. All microcentrifuge tubes were treated with 1% BSA/DPBS overnight to prevent cell loss due to adhesion. Brains were dissociated with 1 mL of 20 units/ml papain in EBSS media and incubated at 37 \u00b0C for 20 min. Cells were resuspended with 150 \u03bcl ice cold 1%BSA/DPBS solution. Transcriptome libraries were processed according to the manufacturer\u2019s instructions Single Cell 3\u2019 v3 kit. Transcriptome libraries were sequenced using NovaSeq S4 300 cycle kits Novogene and Novaseq SP 100 and 200 cycle kits MedGenome. To generate SABER seq libraries  samples post cDNA amplification and prior to fragmentation were split into two parts. One part was processed for transcriptome libraries as instructed by the manufacturer. The other part was processed for SABER seq libraries as follows. To enrich for SABER barcodes  5 \u00b5l of the whole transcriptome cDNA was PCR amplified with 10X p140v1UP  GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCT CGTCGTAGATCTTCATCGTGATG and 10XPCR1F CTACACGACGCTCTTCCGATCT primers and Q5 polymerase. The reaction conditions were: 98 \u00b0C  30 s; [98 \u00b0C  10 s; 68 \u00b0C  25 s; 72 \u00b0C  15 s] x 14 cycles; 72 \u00b0C  2 min. The reaction was cleaned up with 0.8X SPRISelect beads and eluted in 20 \u00b5l elution buffer EB buffer. A second PCR was carried out using 3 \u00b5l of PCR1 product and 10XPCR1F and 10X p140v2Sa GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCT GAAACTACATGGGGTcaGCTC primers. Reaction conditions were same as PCR1 except only 13 cycles were completed. At this point  2 different clean up conditions were used: iteration 1 SABER seq v0 and iteration 2 SABER seq v1. For iteration 1  the reaction was cleaned up with 0.8X SPRISelect beads and eluted in 20 \u00b5l elution buffer EB buffer similar to PCR1.  For iteration 2  an optimization was trialed  and the reaction was cleaned up with right side size selection using SPRIselect beads 0.6X beads used initially and supernatant was rebound with 1.5X beads. The product was eluted in 20 \u00b5l EB. Finally  adapters and sample indices were incorporated in a third PCR reaction using 5 7 \u00b5l of a 1:10 dilution of PCR2 product. Reactions conditions were same as PCR1 except 9 10 cycles were completed. Primers used: 10XP5Dual Hx AATGATACGGCGACCACCGAGATCTACAC xxxxxxx xxx ACACTCTTTCCCTACACGACGCTCTTCCGATCT  10XP7Dual H5 AATGATACGGCGAC CACCGAGATCTACAC xxxxxxxxxx ACACTCTTTCCCTACACGACGCTCTTCCGATCT  where x represents index bases from the 10X Dual Index Kit TT  SetA H index entries. For iteration 1  the third PCR reaction was cleaned up with 0.8X SPRISelect beads and eluted in 20 \u00b5l elution buffer EB to generate final libraries. For iteration 2  the third PCR reaction was cleaned up with right side size selection using SPRIselect beads 0.65X beads used initially and supernatant was rebound with 1.5X beads. SABER seq libraries were sequenced using Novaseq SP 200 cycle kits MedGenome with 5 10% PhiX spike in. Sequencing parameters: Read1 28 cycles  Read2 151 cycles  Index1 10 cycles  Index2 10 cycles. Standard sequencing primers were used.", "This work was performed under protocol numbers 807110 and 807259  which were approved by the University of Pennsylvania\u2019s Office of Animal Welfare of Institutional Animal Care and Use Committee IACUC. All zebrafish work in this study follows the University of Pennsylvania Institutional Animal Care and Use Committee regulations.", "tissue:zebrafish whole brain|developmental stage:21 dpf 23 dpf  Juvenile|genotype:WT/TLAB", "GSM8290055", "GSM8290055: SABERb4 brainB2 1  brain4  transcriptome; Danio rerio; RNA Seq", "GSM8290055 r1", "GSM8290055", "1", "Wild type and SABER x her4.3:switchCas9 zebrafish brains were harvested and dissociated at 21 dpf 23 dpf as described previously Raj et. al 2018  Nature Biotechnology  with the following modifications. All microcentrifuge tubes were treated with 1% BSA/DPBS overnight to prevent cell loss due to adhesion. Brains were dissociated with 1 mL of 20 units/ml papain in EBSS media and incubated at 37 \u00b0C for 20 min. Cells were resuspended with 150 \u03bcl ice cold 1%BSA/DPBS solution. Transcriptome libraries were processed according to the manufacturer's instructions Single Cell three prime v3 kit. Transcriptome libraries were sequenced using NovaSeq S4 300 cycle kits Novogene and Novaseq SP 100 and 200 cycle kits MedGenome. To generate SABER seq libraries  samples post cDNA amplification and prior to fragmentation were split into two parts. One part was processed for transcriptome libraries as instructed by the manufacturer. The other part was processed for SABER seq libraries as follows. To enrich for SABER barcodes  5 \u00b5l of the whole transcriptome cDNA was PCR amplified with 10X p140v1UP  GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCT CGTCGTAGATCTTCATCGTGATG and 10XPCR1F CTACACGACGCTCTTCCGATCT primers and Q5 polymerase. The reaction conditions were: 98 \u00b0C  30 s; [98 \u00b0C  10 s; 68 \u00b0C  25 s; 72 \u00b0C  15 s] x 14 cycles; 72 \u00b0C  2 min. The reaction was cleaned up with 0.8X SPRISelect beads and eluted in 20 \u00b5l elution buffer EB buffer. A second PCR was carried out using 3 \u00b5l of PCR1 product and 10XPCR1F and 10X p140v2Sa GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCT GAAACTACATGGGGTcaGCTC primers. Reaction conditions were same as PCR1 except only 13 cycles were completed. At this point  2 different clean up conditions were used: iteration 1 SABER seq v0 and iteration 2 SABER seq v1. For iteration 1  the reaction was cleaned up with 0.8X SPRISelect beads and eluted in 20 \u00b5l elution buffer EB buffer similar to PCR1.  For iteration 2  an optimization was trialed  and the reaction was cleaned up with right side size selection using SPRIselect beads 0.6X beads used initially and supernatant was rebound with 1.5X beads. The product was eluted in 20 \u00b5l EB. Finally  adapters and sample indices were incorporated in a third PCR reaction using 5 7 \u00b5l of a 1:10 dilution of PCR2 product. Reactions conditions were same as PCR1 except 9 10 cycles were completed. Primers used: 10XP5Dual Hx AATGATACGGCGACCACCGAGATCTACAC xxxxxxx xxx ACACTCTTTCCCTACACGACGCTCTTCCGATCT  10XP7Dual H5 AATGATACGGCGAC CACCGAGATCTACAC xxxxxxxxxx ACACTCTTTCCCTACACGACGCTCTTCCGATCT  where x represents index bases from the 10X Dual Index Kit TT  SetA H index entries. For iteration 1  the third PCR reaction was cleaned up with 0.8X SPRISelect beads and eluted in 20 \u00b5l elution buffer EB to generate final libraries. For iteration 2  the third PCR reaction was cleaned up with right side size selection using SPRIselect beads 0.65X beads used initially and supernatant was rebound with 1.5X beads. SABER seq libraries were sequenced using Novaseq SP 200 cycle kits MedGenome with 5 10% PhiX spike in. Sequencing parameters: Read1 28 cycles  Read2 151 cycles  Index1 10 cycles  Index2 10 cycles. Standard sequencing primers were used.", null, "RNA-Seq", "TRANSCRIPTOMIC SINGLE CELL", "cDNA", "PAIRED", "ILLUMINA", "Illumina NovaSeq 6000", null, "SRP509892", null, "loader:fastq load.py", "SABERb4_brainB2_1_S3_L001_I1_001.fastq.gz SABERb4_brainB2_1_S3_L001_I2_001.fastq.gz SABERb4_brainB2_1_S3_L001_R1_001.fastq.gz SABERb4_brainB2_1_S3_L001_R2_001.fastq.gz", "fastq fastq fastq fastq", 20888284944.0, 104966256.0, "GSM8290055 r1", "0:10 1:10 2:28 3:151", "A:4874988462;C:3170609788;G:3451838907;T:4352436953;N:30546", 10, 10, 28, 151, 4874988462, 3170609788, 3451838907, 4352436953, 30546, "SRX24701869", "SRS21429398", "SRA1989588", "University of Pennsylvania", "University of Pennsylvania", 1, 0.88685, null, 0.21914, null, 0.76171, null, 0.54873, null, 151, null, "B", null, "usable mapping rate", "illumina", "novaseq_era", "full_length", "random_priming", "unknown", "sc", "single_cell_droplet", "10x", null, "United States", "2024-05-25", "Larval", "Larval", "Brain", "Nervous System"], [32377, "SRR29181683", "SRX24701868", "SRS21429397", "SRP509892", "PRJNA1116548", "Barcoding Notch signaling in the developing brain", "GSE268356", "Other", "Developmental signaling inputs are fundamental for shaping cell fates and behavior. However  traditional fluorescent based signaling reporters have limitations in scalability and molecular resolution of cell types. We present SABER seq  a CRISPR Cas molecular recorder that stores transient developmental signaling cues as permanent mutations in cellular genomes for deconstruction at later stages via single cell transcriptomics. We applied SABER seq to record Notch signaling in developing zebrafish brains. SABER seq has two components: a signaling sensor and a barcode recorder. The sensor activates Cas9 in a Notch dependent manner with inducible control while the recorder obtains mutations in ancestral cells where Notch is active. We combine SABER seq with an expanded juvenile brain atlas to identify cell types derived from Notch active founders. Our data reveals rare examples where differential Notch activity in ancestral progenitors is detected in terminally differentiated neuronal subtypes. SABER seq is a novel platform for rapid  scalable and high resolution mapping of signaling activity during development. Overall design: 10x Genomics libraries of single cell transcriptomes and SABER seq data  and genomic DNA SABER data", null, "pubmed:39575683", null, "SABERb3 brainB1 2  brain3  transcriptome", "GSM8290054", null, "source name:zebrafish whole brain|tissue:zebrafish whole brain|developmental stage:21 dpf 23 dpf  Juvenile|genotype:WT/TLAB|geo loc name:missing|collection date:missing", "SABERb3 brainB1 2  brain3  transcriptome", "Raw FASTQ reads were aligned against zebrafish genome  built from GRCz.109.gtf and the GRCz11.fa files  using the Cell Ranger count pipeline to generate gene by cell count matrices filtered barcode matrix. The count matrices were processed using the standard workflow of the Seurat v5.0.2 R package with modifications. Briefly  all count matrices were separately loaded into R. Seurat objects were then created with modified parameters  requiring features to be detected in at least 3 cells and cells to contain at least 300 genes. Subsequently  the Seurat objects were merged to create a single Seurat object. Cells with RNA counts between 300 and 4000 and a mitochondrial gene percentage of less than 9% were selected for downstream analysis. Gene expression values were log normalized with the default parameters and scaled for the top 3000 variable genes. The scaled data was then used to compute the 50 principal components. Cells were iteratively clustered using the Louvain algorithm post identifying neighboring cells. Differentially expressed genes for each cluster were identified using genes that are detected in at least 10% of cells in either of the two clusters and genes showing a minimum difference of 10% between the two clusters. Cell types were annotated using known marker genes.  The SABER seq FASTQ reads were processed using the Cell Ranger count pipeline  similar to the transcriptomic reads. A bash script was then used to generate a text file containing all FASTQ headers and error corrected cell barcodes for SABER seq data. post loading filtered barcode matrices into R  error corrected cell barcodes from transcriptomic reads were isolated and matched against those in the text file to identify common cell barcode that are shared between the transcriptomic and SABER seq datasets. The resulting text file contained redundant error corrected cell barcodes associated with different UMIs for the same cell. Thus  unique error corrected cell barcodes were isolated to create a text file containing unique FASTQ headers and error corrected barcodes. Subsequently  FASTQ reads matching the FASTQ headers in this file were extracted from the SABER seq raw reads using a Python script. SABER seq barcodes were analyzed using the scGESTALT pipeline available on Github as described previously7 35. To analyze differences in barcode detection and editing frequencies between cell types a binomial test was used. scRNA seq data was analyzed using the following protocol. Raw FASTQ reads were aligned against zebrafish genome  built from GRCz.109.gtf and the GRCz11.fa files  using the Cell Ranger count pipeline to generate gene by cell count matrices filtered barcode matrix. The count matrices were processed using the standard workflow of the Seurat v5.0.2 R package with modifications. Briefly  all count matrices were separately loaded into R. Seurat objects were then created with modified parameters  requiring features to be detected in at least 3 cells and cells to contain at least 300 genes. Subsequently  the Seurat objects were merged to create a single Seurat object. Cells with RNA counts between 300 and 4000 and a mitochondrial gene percentage of less than 9% were selected for downstream analysis. Gene expression values were log normalized with the default parameters and scaled for the top 3000 variable genes. The scaled data was then used to compute the 50 principal components. Cells were iteratively clustered using the Louvain algorithm post identifying neighboring cells. Differentially expressed genes for each cluster were identified using genes that are detected in at least 10% of cells in either of the two clusters and genes showing a minimum difference of 10% between the two clusters. Cell types were annotated using known marker genes. Finally  the data was visualized using UMAP. SABER seq barcode analysis was performed using the following protocol. The SABER seq FASTQ reads were processed using the Cell Ranger count pipeline.A bash script was then used to generate a text file containing all FASTQ headers and error corrected cell barcodes for SABER seq data. Error corrected cell barcodes from transcriptomic reads were isolated and matched against those in the text file to identify common cell barcode that are shared between the transcriptomic and SABER seq datasets. The resulting text file contained redundant error corrected cell barcodes associated with different UMIs for the same cell. Thus  unique error corrected cell barcodes were isolated to create a text file containing unique FASTQ headers and error corrected barcodes. Subsequently  FASTQ reads matching the FASTQ headers in this file were extracted from the SABER seq raw reads using a Python script. SABER seq barcodes were analyzed using the scGESTALT pipeline available on Github as described previously. To analyze differences in barcode detection and editing frequencies between cell types a binomial test was used. genome build/assembly: GRCz11 processed data files format and content: *.h5 files contain filtered features/genes and barcode data generated from cell ranger pipeline. processed data files format and content: LinC1 Filtered Fastq Headers CB.txt LinC2 Filtered Fastq Headers CB.txt LinD1 Filtered Fastq Headers CB.txt LinD2 Filtered Fastq Headers CB.txt LinD3 Filtered Fastq Headers CB.txt files contain filtered and unique fastq headers and cell barcodes post matching the trasncriptomic cell barcodes. processed data files format and content: SABERb1 linC1 S4 BrainC1 S6.CB.xlsx SABERb1 linC2 S5 BrainC2 S7.CB.xlsx SABERb2 linD1 S1 BrainD1 S8.CB.xlsx SABERb2 linD2 S2 BrainD2 S9.CB.xlsx SABERb2 linD3 S3 BrainD3 S10.CB.xlsx SABERb4 linB2.1 13.123.2.With.Primer.stats.xlsx SABERb4 linB2.2 14.124.2.With.Primer.stats.xlsx files contain the SABER Seq output post matching reads to the reference sequence.", "zebrafish whole brain", null, "Wild type and SABER x her4.3:switchCas9 zebrafish brains were harvested and dissociated at 21 dpf 23 dpf as described previously Raj et. al 2018  Nature Biotechnology  with the following modifications. All microcentrifuge tubes were treated with 1% BSA/DPBS overnight to prevent cell loss due to adhesion. Brains were dissociated with 1 mL of 20 units/ml papain in EBSS media and incubated at 37 \u00b0C for 20 min. Cells were resuspended with 150 \u03bcl ice cold 1%BSA/DPBS solution. Transcriptome libraries were processed according to the manufacturer\u2019s instructions Single Cell 3\u2019 v3 kit. Transcriptome libraries were sequenced using NovaSeq S4 300 cycle kits Novogene and Novaseq SP 100 and 200 cycle kits MedGenome. To generate SABER seq libraries  samples post cDNA amplification and prior to fragmentation were split into two parts. One part was processed for transcriptome libraries as instructed by the manufacturer. The other part was processed for SABER seq libraries as follows. To enrich for SABER barcodes  5 \u00b5l of the whole transcriptome cDNA was PCR amplified with 10X p140v1UP  GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCT CGTCGTAGATCTTCATCGTGATG and 10XPCR1F CTACACGACGCTCTTCCGATCT primers and Q5 polymerase. The reaction conditions were: 98 \u00b0C  30 s; [98 \u00b0C  10 s; 68 \u00b0C  25 s; 72 \u00b0C  15 s] x 14 cycles; 72 \u00b0C  2 min. The reaction was cleaned up with 0.8X SPRISelect beads and eluted in 20 \u00b5l elution buffer EB buffer. A second PCR was carried out using 3 \u00b5l of PCR1 product and 10XPCR1F and 10X p140v2Sa GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCT GAAACTACATGGGGTcaGCTC primers. Reaction conditions were same as PCR1 except only 13 cycles were completed. At this point  2 different clean up conditions were used: iteration 1 SABER seq v0 and iteration 2 SABER seq v1. For iteration 1  the reaction was cleaned up with 0.8X SPRISelect beads and eluted in 20 \u00b5l elution buffer EB buffer similar to PCR1.  For iteration 2  an optimization was trialed  and the reaction was cleaned up with right side size selection using SPRIselect beads 0.6X beads used initially and supernatant was rebound with 1.5X beads. The product was eluted in 20 \u00b5l EB. Finally  adapters and sample indices were incorporated in a third PCR reaction using 5 7 \u00b5l of a 1:10 dilution of PCR2 product. Reactions conditions were same as PCR1 except 9 10 cycles were completed. Primers used: 10XP5Dual Hx AATGATACGGCGACCACCGAGATCTACAC xxxxxxx xxx ACACTCTTTCCCTACACGACGCTCTTCCGATCT  10XP7Dual H5 AATGATACGGCGAC CACCGAGATCTACAC xxxxxxxxxx ACACTCTTTCCCTACACGACGCTCTTCCGATCT  where x represents index bases from the 10X Dual Index Kit TT  SetA H index entries. For iteration 1  the third PCR reaction was cleaned up with 0.8X SPRISelect beads and eluted in 20 \u00b5l elution buffer EB to generate final libraries. For iteration 2  the third PCR reaction was cleaned up with right side size selection using SPRIselect beads 0.65X beads used initially and supernatant was rebound with 1.5X beads. SABER seq libraries were sequenced using Novaseq SP 200 cycle kits MedGenome with 5 10% PhiX spike in. Sequencing parameters: Read1 28 cycles  Read2 151 cycles  Index1 10 cycles  Index2 10 cycles. Standard sequencing primers were used.", "This work was performed under protocol numbers 807110 and 807259  which were approved by the University of Pennsylvania\u2019s Office of Animal Welfare of Institutional Animal Care and Use Committee IACUC. All zebrafish work in this study follows the University of Pennsylvania Institutional Animal Care and Use Committee regulations.", "tissue:zebrafish whole brain|developmental stage:21 dpf 23 dpf  Juvenile|genotype:WT/TLAB", "GSM8290054", "GSM8290054: SABERb3 brainB1 2  brain3  transcriptome; Danio rerio; RNA Seq", "GSM8290054 r1", "GSM8290054", "1", "Wild type and SABER x her4.3:switchCas9 zebrafish brains were harvested and dissociated at 21 dpf 23 dpf as described previously Raj et. al 2018  Nature Biotechnology  with the following modifications. All microcentrifuge tubes were treated with 1% BSA/DPBS overnight to prevent cell loss due to adhesion. Brains were dissociated with 1 mL of 20 units/ml papain in EBSS media and incubated at 37 \u00b0C for 20 min. Cells were resuspended with 150 \u03bcl ice cold 1%BSA/DPBS solution. Transcriptome libraries were processed according to the manufacturer's instructions Single Cell three prime v3 kit. Transcriptome libraries were sequenced using NovaSeq S4 300 cycle kits Novogene and Novaseq SP 100 and 200 cycle kits MedGenome. To generate SABER seq libraries  samples post cDNA amplification and prior to fragmentation were split into two parts. One part was processed for transcriptome libraries as instructed by the manufacturer. The other part was processed for SABER seq libraries as follows. To enrich for SABER barcodes  5 \u00b5l of the whole transcriptome cDNA was PCR amplified with 10X p140v1UP  GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCT CGTCGTAGATCTTCATCGTGATG and 10XPCR1F CTACACGACGCTCTTCCGATCT primers and Q5 polymerase. The reaction conditions were: 98 \u00b0C  30 s; [98 \u00b0C  10 s; 68 \u00b0C  25 s; 72 \u00b0C  15 s] x 14 cycles; 72 \u00b0C  2 min. The reaction was cleaned up with 0.8X SPRISelect beads and eluted in 20 \u00b5l elution buffer EB buffer. A second PCR was carried out using 3 \u00b5l of PCR1 product and 10XPCR1F and 10X p140v2Sa GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCT GAAACTACATGGGGTcaGCTC primers. Reaction conditions were same as PCR1 except only 13 cycles were completed. At this point  2 different clean up conditions were used: iteration 1 SABER seq v0 and iteration 2 SABER seq v1. For iteration 1  the reaction was cleaned up with 0.8X SPRISelect beads and eluted in 20 \u00b5l elution buffer EB buffer similar to PCR1.  For iteration 2  an optimization was trialed  and the reaction was cleaned up with right side size selection using SPRIselect beads 0.6X beads used initially and supernatant was rebound with 1.5X beads. The product was eluted in 20 \u00b5l EB. Finally  adapters and sample indices were incorporated in a third PCR reaction using 5 7 \u00b5l of a 1:10 dilution of PCR2 product. Reactions conditions were same as PCR1 except 9 10 cycles were completed. Primers used: 10XP5Dual Hx AATGATACGGCGACCACCGAGATCTACAC xxxxxxx xxx ACACTCTTTCCCTACACGACGCTCTTCCGATCT  10XP7Dual H5 AATGATACGGCGAC CACCGAGATCTACAC xxxxxxxxxx ACACTCTTTCCCTACACGACGCTCTTCCGATCT  where x represents index bases from the 10X Dual Index Kit TT  SetA H index entries. For iteration 1  the third PCR reaction was cleaned up with 0.8X SPRISelect beads and eluted in 20 \u00b5l elution buffer EB to generate final libraries. For iteration 2  the third PCR reaction was cleaned up with right side size selection using SPRIselect beads 0.65X beads used initially and supernatant was rebound with 1.5X beads. SABER seq libraries were sequenced using Novaseq SP 200 cycle kits MedGenome with 5 10% PhiX spike in. Sequencing parameters: Read1 28 cycles  Read2 151 cycles  Index1 10 cycles  Index2 10 cycles. Standard sequencing primers were used.", null, "RNA-Seq", "TRANSCRIPTOMIC SINGLE CELL", "cDNA", "PAIRED", "ILLUMINA", "Illumina NovaSeq 6000", null, "SRP509892", null, "loader:fastq load.py", "SABERb3_brainB1_2_S2_L001_I1_001.fastq.gz SABERb3_brainB1_2_S2_L001_I2_001.fastq.gz SABERb3_brainB1_2_S2_L001_R1_001.fastq.gz SABERb3_brainB1_2_S2_L001_R2_001.fastq.gz", "fastq fastq fastq fastq", 20587460226.0, 103454574.0, "GSM8290054 r1", "0:10 1:10 2:28 3:151", "A:4824812347;C:3103279976;G:3383117839;T:4310400919;N:29593", 10, 10, 28, 151, 4824812347, 3103279976, 3383117839, 4310400919, 29593, "SRX24701868", "SRS21429397", "SRA1989588", "University of Pennsylvania", "University of Pennsylvania", 1, 0.87976, null, 0.21404, null, 0.77433, null, 0.52975, null, 151, null, "B", null, "usable mapping rate", "illumina", "novaseq_era", "full_length", "random_priming", "unknown", "sc", "single_cell_droplet", "10x", null, "United States", "2024-05-25", "Larval", "Larval", "Brain", "Nervous System"], [32378, "SRR29181682", "SRX24701867", "SRS21429396", "SRP509892", "PRJNA1116548", "Barcoding Notch signaling in the developing brain", "GSE268356", "Other", "Developmental signaling inputs are fundamental for shaping cell fates and behavior. However  traditional fluorescent based signaling reporters have limitations in scalability and molecular resolution of cell types. We present SABER seq  a CRISPR Cas molecular recorder that stores transient developmental signaling cues as permanent mutations in cellular genomes for deconstruction at later stages via single cell transcriptomics. We applied SABER seq to record Notch signaling in developing zebrafish brains. SABER seq has two components: a signaling sensor and a barcode recorder. The sensor activates Cas9 in a Notch dependent manner with inducible control while the recorder obtains mutations in ancestral cells where Notch is active. We combine SABER seq with an expanded juvenile brain atlas to identify cell types derived from Notch active founders. Our data reveals rare examples where differential Notch activity in ancestral progenitors is detected in terminally differentiated neuronal subtypes. SABER seq is a novel platform for rapid  scalable and high resolution mapping of signaling activity during development. Overall design: 10x Genomics libraries of single cell transcriptomes and SABER seq data  and genomic DNA SABER data", null, "pubmed:39575683", null, "SABERb3 brainB1 1  brain3  transcriptome", "GSM8290053", null, "source name:zebrafish whole brain|tissue:zebrafish whole brain|developmental stage:21 dpf 23 dpf  Juvenile|genotype:WT/TLAB|geo loc name:missing|collection date:missing", "SABERb3 brainB1 1  brain3  transcriptome", "Raw FASTQ reads were aligned against zebrafish genome  built from GRCz.109.gtf and the GRCz11.fa files  using the Cell Ranger count pipeline to generate gene by cell count matrices filtered barcode matrix. The count matrices were processed using the standard workflow of the Seurat v5.0.2 R package with modifications. Briefly  all count matrices were separately loaded into R. Seurat objects were then created with modified parameters  requiring features to be detected in at least 3 cells and cells to contain at least 300 genes. Subsequently  the Seurat objects were merged to create a single Seurat object. Cells with RNA counts between 300 and 4000 and a mitochondrial gene percentage of less than 9% were selected for downstream analysis. Gene expression values were log normalized with the default parameters and scaled for the top 3000 variable genes. The scaled data was then used to compute the 50 principal components. Cells were iteratively clustered using the Louvain algorithm post identifying neighboring cells. Differentially expressed genes for each cluster were identified using genes that are detected in at least 10% of cells in either of the two clusters and genes showing a minimum difference of 10% between the two clusters. Cell types were annotated using known marker genes.  The SABER seq FASTQ reads were processed using the Cell Ranger count pipeline  similar to the transcriptomic reads. A bash script was then used to generate a text file containing all FASTQ headers and error corrected cell barcodes for SABER seq data. post loading filtered barcode matrices into R  error corrected cell barcodes from transcriptomic reads were isolated and matched against those in the text file to identify common cell barcode that are shared between the transcriptomic and SABER seq datasets. The resulting text file contained redundant error corrected cell barcodes associated with different UMIs for the same cell. Thus  unique error corrected cell barcodes were isolated to create a text file containing unique FASTQ headers and error corrected barcodes. Subsequently  FASTQ reads matching the FASTQ headers in this file were extracted from the SABER seq raw reads using a Python script. SABER seq barcodes were analyzed using the scGESTALT pipeline available on Github as described previously7 35. To analyze differences in barcode detection and editing frequencies between cell types a binomial test was used. scRNA seq data was analyzed using the following protocol. Raw FASTQ reads were aligned against zebrafish genome  built from GRCz.109.gtf and the GRCz11.fa files  using the Cell Ranger count pipeline to generate gene by cell count matrices filtered barcode matrix. The count matrices were processed using the standard workflow of the Seurat v5.0.2 R package with modifications. Briefly  all count matrices were separately loaded into R. Seurat objects were then created with modified parameters  requiring features to be detected in at least 3 cells and cells to contain at least 300 genes. Subsequently  the Seurat objects were merged to create a single Seurat object. Cells with RNA counts between 300 and 4000 and a mitochondrial gene percentage of less than 9% were selected for downstream analysis. Gene expression values were log normalized with the default parameters and scaled for the top 3000 variable genes. The scaled data was then used to compute the 50 principal components. Cells were iteratively clustered using the Louvain algorithm post identifying neighboring cells. Differentially expressed genes for each cluster were identified using genes that are detected in at least 10% of cells in either of the two clusters and genes showing a minimum difference of 10% between the two clusters. Cell types were annotated using known marker genes. Finally  the data was visualized using UMAP. SABER seq barcode analysis was performed using the following protocol. The SABER seq FASTQ reads were processed using the Cell Ranger count pipeline.A bash script was then used to generate a text file containing all FASTQ headers and error corrected cell barcodes for SABER seq data. Error corrected cell barcodes from transcriptomic reads were isolated and matched against those in the text file to identify common cell barcode that are shared between the transcriptomic and SABER seq datasets. The resulting text file contained redundant error corrected cell barcodes associated with different UMIs for the same cell. Thus  unique error corrected cell barcodes were isolated to create a text file containing unique FASTQ headers and error corrected barcodes. Subsequently  FASTQ reads matching the FASTQ headers in this file were extracted from the SABER seq raw reads using a Python script. SABER seq barcodes were analyzed using the scGESTALT pipeline available on Github as described previously. To analyze differences in barcode detection and editing frequencies between cell types a binomial test was used. genome build/assembly: GRCz11 processed data files format and content: *.h5 files contain filtered features/genes and barcode data generated from cell ranger pipeline. processed data files format and content: LinC1 Filtered Fastq Headers CB.txt LinC2 Filtered Fastq Headers CB.txt LinD1 Filtered Fastq Headers CB.txt LinD2 Filtered Fastq Headers CB.txt LinD3 Filtered Fastq Headers CB.txt files contain filtered and unique fastq headers and cell barcodes post matching the trasncriptomic cell barcodes. processed data files format and content: SABERb1 linC1 S4 BrainC1 S6.CB.xlsx SABERb1 linC2 S5 BrainC2 S7.CB.xlsx SABERb2 linD1 S1 BrainD1 S8.CB.xlsx SABERb2 linD2 S2 BrainD2 S9.CB.xlsx SABERb2 linD3 S3 BrainD3 S10.CB.xlsx SABERb4 linB2.1 13.123.2.With.Primer.stats.xlsx SABERb4 linB2.2 14.124.2.With.Primer.stats.xlsx files contain the SABER Seq output post matching reads to the reference sequence.", "zebrafish whole brain", null, "Wild type and SABER x her4.3:switchCas9 zebrafish brains were harvested and dissociated at 21 dpf 23 dpf as described previously Raj et. al 2018  Nature Biotechnology  with the following modifications. All microcentrifuge tubes were treated with 1% BSA/DPBS overnight to prevent cell loss due to adhesion. Brains were dissociated with 1 mL of 20 units/ml papain in EBSS media and incubated at 37 \u00b0C for 20 min. Cells were resuspended with 150 \u03bcl ice cold 1%BSA/DPBS solution. Transcriptome libraries were processed according to the manufacturer\u2019s instructions Single Cell 3\u2019 v3 kit. Transcriptome libraries were sequenced using NovaSeq S4 300 cycle kits Novogene and Novaseq SP 100 and 200 cycle kits MedGenome. To generate SABER seq libraries  samples post cDNA amplification and prior to fragmentation were split into two parts. One part was processed for transcriptome libraries as instructed by the manufacturer. The other part was processed for SABER seq libraries as follows. To enrich for SABER barcodes  5 \u00b5l of the whole transcriptome cDNA was PCR amplified with 10X p140v1UP  GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCT CGTCGTAGATCTTCATCGTGATG and 10XPCR1F CTACACGACGCTCTTCCGATCT primers and Q5 polymerase. The reaction conditions were: 98 \u00b0C  30 s; [98 \u00b0C  10 s; 68 \u00b0C  25 s; 72 \u00b0C  15 s] x 14 cycles; 72 \u00b0C  2 min. The reaction was cleaned up with 0.8X SPRISelect beads and eluted in 20 \u00b5l elution buffer EB buffer. A second PCR was carried out using 3 \u00b5l of PCR1 product and 10XPCR1F and 10X p140v2Sa GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCT GAAACTACATGGGGTcaGCTC primers. Reaction conditions were same as PCR1 except only 13 cycles were completed. At this point  2 different clean up conditions were used: iteration 1 SABER seq v0 and iteration 2 SABER seq v1. For iteration 1  the reaction was cleaned up with 0.8X SPRISelect beads and eluted in 20 \u00b5l elution buffer EB buffer similar to PCR1.  For iteration 2  an optimization was trialed  and the reaction was cleaned up with right side size selection using SPRIselect beads 0.6X beads used initially and supernatant was rebound with 1.5X beads. The product was eluted in 20 \u00b5l EB. Finally  adapters and sample indices were incorporated in a third PCR reaction using 5 7 \u00b5l of a 1:10 dilution of PCR2 product. Reactions conditions were same as PCR1 except 9 10 cycles were completed. Primers used: 10XP5Dual Hx AATGATACGGCGACCACCGAGATCTACAC xxxxxxx xxx ACACTCTTTCCCTACACGACGCTCTTCCGATCT  10XP7Dual H5 AATGATACGGCGAC CACCGAGATCTACAC xxxxxxxxxx ACACTCTTTCCCTACACGACGCTCTTCCGATCT  where x represents index bases from the 10X Dual Index Kit TT  SetA H index entries. For iteration 1  the third PCR reaction was cleaned up with 0.8X SPRISelect beads and eluted in 20 \u00b5l elution buffer EB to generate final libraries. For iteration 2  the third PCR reaction was cleaned up with right side size selection using SPRIselect beads 0.65X beads used initially and supernatant was rebound with 1.5X beads. SABER seq libraries were sequenced using Novaseq SP 200 cycle kits MedGenome with 5 10% PhiX spike in. Sequencing parameters: Read1 28 cycles  Read2 151 cycles  Index1 10 cycles  Index2 10 cycles. Standard sequencing primers were used.", "This work was performed under protocol numbers 807110 and 807259  which were approved by the University of Pennsylvania\u2019s Office of Animal Welfare of Institutional Animal Care and Use Committee IACUC. All zebrafish work in this study follows the University of Pennsylvania Institutional Animal Care and Use Committee regulations.", "tissue:zebrafish whole brain|developmental stage:21 dpf 23 dpf  Juvenile|genotype:WT/TLAB", "GSM8290053", "GSM8290053: SABERb3 brainB1 1  brain3  transcriptome; Danio rerio; RNA Seq", "GSM8290053 r1", "GSM8290053", "1", "Wild type and SABER x her4.3:switchCas9 zebrafish brains were harvested and dissociated at 21 dpf 23 dpf as described previously Raj et. al 2018  Nature Biotechnology  with the following modifications. All microcentrifuge tubes were treated with 1% BSA/DPBS overnight to prevent cell loss due to adhesion. Brains were dissociated with 1 mL of 20 units/ml papain in EBSS media and incubated at 37 \u00b0C for 20 min. Cells were resuspended with 150 \u03bcl ice cold 1%BSA/DPBS solution. Transcriptome libraries were processed according to the manufacturer's instructions Single Cell three prime v3 kit. Transcriptome libraries were sequenced using NovaSeq S4 300 cycle kits Novogene and Novaseq SP 100 and 200 cycle kits MedGenome. To generate SABER seq libraries  samples post cDNA amplification and prior to fragmentation were split into two parts. One part was processed for transcriptome libraries as instructed by the manufacturer. The other part was processed for SABER seq libraries as follows. To enrich for SABER barcodes  5 \u00b5l of the whole transcriptome cDNA was PCR amplified with 10X p140v1UP  GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCT CGTCGTAGATCTTCATCGTGATG and 10XPCR1F CTACACGACGCTCTTCCGATCT primers and Q5 polymerase. The reaction conditions were: 98 \u00b0C  30 s; [98 \u00b0C  10 s; 68 \u00b0C  25 s; 72 \u00b0C  15 s] x 14 cycles; 72 \u00b0C  2 min. The reaction was cleaned up with 0.8X SPRISelect beads and eluted in 20 \u00b5l elution buffer EB buffer. A second PCR was carried out using 3 \u00b5l of PCR1 product and 10XPCR1F and 10X p140v2Sa GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCT GAAACTACATGGGGTcaGCTC primers. Reaction conditions were same as PCR1 except only 13 cycles were completed. At this point  2 different clean up conditions were used: iteration 1 SABER seq v0 and iteration 2 SABER seq v1. For iteration 1  the reaction was cleaned up with 0.8X SPRISelect beads and eluted in 20 \u00b5l elution buffer EB buffer similar to PCR1.  For iteration 2  an optimization was trialed  and the reaction was cleaned up with right side size selection using SPRIselect beads 0.6X beads used initially and supernatant was rebound with 1.5X beads. The product was eluted in 20 \u00b5l EB. Finally  adapters and sample indices were incorporated in a third PCR reaction using 5 7 \u00b5l of a 1:10 dilution of PCR2 product. Reactions conditions were same as PCR1 except 9 10 cycles were completed. Primers used: 10XP5Dual Hx AATGATACGGCGACCACCGAGATCTACAC xxxxxxx xxx ACACTCTTTCCCTACACGACGCTCTTCCGATCT  10XP7Dual H5 AATGATACGGCGAC CACCGAGATCTACAC xxxxxxxxxx ACACTCTTTCCCTACACGACGCTCTTCCGATCT  where x represents index bases from the 10X Dual Index Kit TT  SetA H index entries. For iteration 1  the third PCR reaction was cleaned up with 0.8X SPRISelect beads and eluted in 20 \u00b5l elution buffer EB to generate final libraries. For iteration 2  the third PCR reaction was cleaned up with right side size selection using SPRIselect beads 0.65X beads used initially and supernatant was rebound with 1.5X beads. SABER seq libraries were sequenced using Novaseq SP 200 cycle kits MedGenome with 5 10% PhiX spike in. Sequencing parameters: Read1 28 cycles  Read2 151 cycles  Index1 10 cycles  Index2 10 cycles. Standard sequencing primers were used.", null, "RNA-Seq", "TRANSCRIPTOMIC SINGLE CELL", "cDNA", "PAIRED", "ILLUMINA", "Illumina NovaSeq 6000", null, "SRP509892", null, "loader:fastq load.py", "SABERb3_brainB1_1_S1_L001_I1_001.fastq.gz SABERb3_brainB1_1_S1_L001_I2_001.fastq.gz SABERb3_brainB1_1_S1_L001_R1_001.fastq.gz SABERb3_brainB1_1_S1_L001_R2_001.fastq.gz", "fastq fastq fastq fastq", 23045577279.0, 115806921.0, "GSM8290053 r1", "0:10 1:10 2:28 3:151", "A:5424649891;C:3461670114;G:3767563304;T:4832928748;N:33014", 10, 10, 28, 151, 5424649891, 3461670114, 3767563304, 4832928748, 33014, "SRX24701867", "SRS21429396", "SRA1989588", "University of Pennsylvania", "University of Pennsylvania", 1, 0.87779, null, 0.21879, null, 0.76934, null, 0.52923, null, 151, null, "B", null, "usable mapping rate", "illumina", "novaseq_era", "full_length", "random_priming", "unknown", "sc", "single_cell_droplet", "10x", null, "United States", "2024-05-25", "Larval", "Larval", "Brain", "Nervous System"], [32379, "SRR29181681", "SRX24701866", "SRS21429395", "SRP509892", "PRJNA1116548", "Barcoding Notch signaling in the developing brain", "GSE268356", "Other", "Developmental signaling inputs are fundamental for shaping cell fates and behavior. However  traditional fluorescent based signaling reporters have limitations in scalability and molecular resolution of cell types. We present SABER seq  a CRISPR Cas molecular recorder that stores transient developmental signaling cues as permanent mutations in cellular genomes for deconstruction at later stages via single cell transcriptomics. We applied SABER seq to record Notch signaling in developing zebrafish brains. SABER seq has two components: a signaling sensor and a barcode recorder. The sensor activates Cas9 in a Notch dependent manner with inducible control while the recorder obtains mutations in ancestral cells where Notch is active. We combine SABER seq with an expanded juvenile brain atlas to identify cell types derived from Notch active founders. Our data reveals rare examples where differential Notch activity in ancestral progenitors is detected in terminally differentiated neuronal subtypes. SABER seq is a novel platform for rapid  scalable and high resolution mapping of signaling activity during development. Overall design: 10x Genomics libraries of single cell transcriptomes and SABER seq data  and genomic DNA SABER data", null, "pubmed:39575683", null, "SABERb2 brainD3091523wk3  Novogene Run1  brain2  transcriptome", "GSM8290052", null, "source name:zebrafish whole brain|tissue:zebrafish whole brain|developmental stage:21 dpf 23 dpf  Juvenile|genotype:WT/TLAB|geo loc name:missing|collection date:missing", "SABERb2 brainD3091523wk3  Novogene Run1  brain2  transcriptome", "Raw FASTQ reads were aligned against zebrafish genome  built from GRCz.109.gtf and the GRCz11.fa files  using the Cell Ranger count pipeline to generate gene by cell count matrices filtered barcode matrix. The count matrices were processed using the standard workflow of the Seurat v5.0.2 R package with modifications. Briefly  all count matrices were separately loaded into R. Seurat objects were then created with modified parameters  requiring features to be detected in at least 3 cells and cells to contain at least 300 genes. Subsequently  the Seurat objects were merged to create a single Seurat object. Cells with RNA counts between 300 and 4000 and a mitochondrial gene percentage of less than 9% were selected for downstream analysis. Gene expression values were log normalized with the default parameters and scaled for the top 3000 variable genes. The scaled data was then used to compute the 50 principal components. Cells were iteratively clustered using the Louvain algorithm post identifying neighboring cells. Differentially expressed genes for each cluster were identified using genes that are detected in at least 10% of cells in either of the two clusters and genes showing a minimum difference of 10% between the two clusters. Cell types were annotated using known marker genes.  The SABER seq FASTQ reads were processed using the Cell Ranger count pipeline  similar to the transcriptomic reads. A bash script was then used to generate a text file containing all FASTQ headers and error corrected cell barcodes for SABER seq data. post loading filtered barcode matrices into R  error corrected cell barcodes from transcriptomic reads were isolated and matched against those in the text file to identify common cell barcode that are shared between the transcriptomic and SABER seq datasets. The resulting text file contained redundant error corrected cell barcodes associated with different UMIs for the same cell. Thus  unique error corrected cell barcodes were isolated to create a text file containing unique FASTQ headers and error corrected barcodes. Subsequently  FASTQ reads matching the FASTQ headers in this file were extracted from the SABER seq raw reads using a Python script. SABER seq barcodes were analyzed using the scGESTALT pipeline available on Github as described previously7 35. To analyze differences in barcode detection and editing frequencies between cell types a binomial test was used. scRNA seq data was analyzed using the following protocol. Raw FASTQ reads were aligned against zebrafish genome  built from GRCz.109.gtf and the GRCz11.fa files  using the Cell Ranger count pipeline to generate gene by cell count matrices filtered barcode matrix. The count matrices were processed using the standard workflow of the Seurat v5.0.2 R package with modifications. Briefly  all count matrices were separately loaded into R. Seurat objects were then created with modified parameters  requiring features to be detected in at least 3 cells and cells to contain at least 300 genes. Subsequently  the Seurat objects were merged to create a single Seurat object. Cells with RNA counts between 300 and 4000 and a mitochondrial gene percentage of less than 9% were selected for downstream analysis. Gene expression values were log normalized with the default parameters and scaled for the top 3000 variable genes. The scaled data was then used to compute the 50 principal components. Cells were iteratively clustered using the Louvain algorithm post identifying neighboring cells. Differentially expressed genes for each cluster were identified using genes that are detected in at least 10% of cells in either of the two clusters and genes showing a minimum difference of 10% between the two clusters. Cell types were annotated using known marker genes. Finally  the data was visualized using UMAP. SABER seq barcode analysis was performed using the following protocol. The SABER seq FASTQ reads were processed using the Cell Ranger count pipeline.A bash script was then used to generate a text file containing all FASTQ headers and error corrected cell barcodes for SABER seq data. Error corrected cell barcodes from transcriptomic reads were isolated and matched against those in the text file to identify common cell barcode that are shared between the transcriptomic and SABER seq datasets. The resulting text file contained redundant error corrected cell barcodes associated with different UMIs for the same cell. Thus  unique error corrected cell barcodes were isolated to create a text file containing unique FASTQ headers and error corrected barcodes. Subsequently  FASTQ reads matching the FASTQ headers in this file were extracted from the SABER seq raw reads using a Python script. SABER seq barcodes were analyzed using the scGESTALT pipeline available on Github as described previously. To analyze differences in barcode detection and editing frequencies between cell types a binomial test was used. genome build/assembly: GRCz11 processed data files format and content: *.h5 files contain filtered features/genes and barcode data generated from cell ranger pipeline. processed data files format and content: LinC1 Filtered Fastq Headers CB.txt LinC2 Filtered Fastq Headers CB.txt LinD1 Filtered Fastq Headers CB.txt LinD2 Filtered Fastq Headers CB.txt LinD3 Filtered Fastq Headers CB.txt files contain filtered and unique fastq headers and cell barcodes post matching the trasncriptomic cell barcodes. processed data files format and content: SABERb1 linC1 S4 BrainC1 S6.CB.xlsx SABERb1 linC2 S5 BrainC2 S7.CB.xlsx SABERb2 linD1 S1 BrainD1 S8.CB.xlsx SABERb2 linD2 S2 BrainD2 S9.CB.xlsx SABERb2 linD3 S3 BrainD3 S10.CB.xlsx SABERb4 linB2.1 13.123.2.With.Primer.stats.xlsx SABERb4 linB2.2 14.124.2.With.Primer.stats.xlsx files contain the SABER Seq output post matching reads to the reference sequence.", "zebrafish whole brain", null, "Wild type and SABER x her4.3:switchCas9 zebrafish brains were harvested and dissociated at 21 dpf 23 dpf as described previously Raj et. al 2018  Nature Biotechnology  with the following modifications. All microcentrifuge tubes were treated with 1% BSA/DPBS overnight to prevent cell loss due to adhesion. Brains were dissociated with 1 mL of 20 units/ml papain in EBSS media and incubated at 37 \u00b0C for 20 min. Cells were resuspended with 150 \u03bcl ice cold 1%BSA/DPBS solution. Transcriptome libraries were processed according to the manufacturer\u2019s instructions Single Cell 3\u2019 v3 kit. Transcriptome libraries were sequenced using NovaSeq S4 300 cycle kits Novogene and Novaseq SP 100 and 200 cycle kits MedGenome. To generate SABER seq libraries  samples post cDNA amplification and prior to fragmentation were split into two parts. One part was processed for transcriptome libraries as instructed by the manufacturer. The other part was processed for SABER seq libraries as follows. To enrich for SABER barcodes  5 \u00b5l of the whole transcriptome cDNA was PCR amplified with 10X p140v1UP  GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCT CGTCGTAGATCTTCATCGTGATG and 10XPCR1F CTACACGACGCTCTTCCGATCT primers and Q5 polymerase. The reaction conditions were: 98 \u00b0C  30 s; [98 \u00b0C  10 s; 68 \u00b0C  25 s; 72 \u00b0C  15 s] x 14 cycles; 72 \u00b0C  2 min. The reaction was cleaned up with 0.8X SPRISelect beads and eluted in 20 \u00b5l elution buffer EB buffer. A second PCR was carried out using 3 \u00b5l of PCR1 product and 10XPCR1F and 10X p140v2Sa GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCT GAAACTACATGGGGTcaGCTC primers. Reaction conditions were same as PCR1 except only 13 cycles were completed. At this point  2 different clean up conditions were used: iteration 1 SABER seq v0 and iteration 2 SABER seq v1. For iteration 1  the reaction was cleaned up with 0.8X SPRISelect beads and eluted in 20 \u00b5l elution buffer EB buffer similar to PCR1.  For iteration 2  an optimization was trialed  and the reaction was cleaned up with right side size selection using SPRIselect beads 0.6X beads used initially and supernatant was rebound with 1.5X beads. The product was eluted in 20 \u00b5l EB. Finally  adapters and sample indices were incorporated in a third PCR reaction using 5 7 \u00b5l of a 1:10 dilution of PCR2 product. Reactions conditions were same as PCR1 except 9 10 cycles were completed. Primers used: 10XP5Dual Hx AATGATACGGCGACCACCGAGATCTACAC xxxxxxx xxx ACACTCTTTCCCTACACGACGCTCTTCCGATCT  10XP7Dual H5 AATGATACGGCGAC CACCGAGATCTACAC xxxxxxxxxx ACACTCTTTCCCTACACGACGCTCTTCCGATCT  where x represents index bases from the 10X Dual Index Kit TT  SetA H index entries. For iteration 1  the third PCR reaction was cleaned up with 0.8X SPRISelect beads and eluted in 20 \u00b5l elution buffer EB to generate final libraries. For iteration 2  the third PCR reaction was cleaned up with right side size selection using SPRIselect beads 0.65X beads used initially and supernatant was rebound with 1.5X beads. SABER seq libraries were sequenced using Novaseq SP 200 cycle kits MedGenome with 5 10% PhiX spike in. Sequencing parameters: Read1 28 cycles  Read2 151 cycles  Index1 10 cycles  Index2 10 cycles. Standard sequencing primers were used.", "This work was performed under protocol numbers 807110 and 807259  which were approved by the University of Pennsylvania\u2019s Office of Animal Welfare of Institutional Animal Care and Use Committee IACUC. All zebrafish work in this study follows the University of Pennsylvania Institutional Animal Care and Use Committee regulations.", "tissue:zebrafish whole brain|developmental stage:21 dpf 23 dpf  Juvenile|genotype:WT/TLAB", "GSM8290052", "GSM8290052: SABERb2 brainD3091523wk3  Novogene Run1  brain2  transcriptome; Danio rerio; RNA Seq", "GSM8290052 r1", "GSM8290052", "1", "Wild type and SABER x her4.3:switchCas9 zebrafish brains were harvested and dissociated at 21 dpf 23 dpf as described previously Raj et. al 2018  Nature Biotechnology  with the following modifications. All microcentrifuge tubes were treated with 1% BSA/DPBS overnight to prevent cell loss due to adhesion. Brains were dissociated with 1 mL of 20 units/ml papain in EBSS media and incubated at 37 \u00b0C for 20 min. Cells were resuspended with 150 \u03bcl ice cold 1%BSA/DPBS solution. Transcriptome libraries were processed according to the manufacturer's instructions Single Cell three prime v3 kit. Transcriptome libraries were sequenced using NovaSeq S4 300 cycle kits Novogene and Novaseq SP 100 and 200 cycle kits MedGenome. To generate SABER seq libraries  samples post cDNA amplification and prior to fragmentation were split into two parts. One part was processed for transcriptome libraries as instructed by the manufacturer. The other part was processed for SABER seq libraries as follows. To enrich for SABER barcodes  5 \u00b5l of the whole transcriptome cDNA was PCR amplified with 10X p140v1UP  GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCT CGTCGTAGATCTTCATCGTGATG and 10XPCR1F CTACACGACGCTCTTCCGATCT primers and Q5 polymerase. The reaction conditions were: 98 \u00b0C  30 s; [98 \u00b0C  10 s; 68 \u00b0C  25 s; 72 \u00b0C  15 s] x 14 cycles; 72 \u00b0C  2 min. The reaction was cleaned up with 0.8X SPRISelect beads and eluted in 20 \u00b5l elution buffer EB buffer. A second PCR was carried out using 3 \u00b5l of PCR1 product and 10XPCR1F and 10X p140v2Sa GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCT GAAACTACATGGGGTcaGCTC primers. Reaction conditions were same as PCR1 except only 13 cycles were completed. At this point  2 different clean up conditions were used: iteration 1 SABER seq v0 and iteration 2 SABER seq v1. For iteration 1  the reaction was cleaned up with 0.8X SPRISelect beads and eluted in 20 \u00b5l elution buffer EB buffer similar to PCR1.  For iteration 2  an optimization was trialed  and the reaction was cleaned up with right side size selection using SPRIselect beads 0.6X beads used initially and supernatant was rebound with 1.5X beads. The product was eluted in 20 \u00b5l EB. Finally  adapters and sample indices were incorporated in a third PCR reaction using 5 7 \u00b5l of a 1:10 dilution of PCR2 product. Reactions conditions were same as PCR1 except 9 10 cycles were completed. Primers used: 10XP5Dual Hx AATGATACGGCGACCACCGAGATCTACAC xxxxxxx xxx ACACTCTTTCCCTACACGACGCTCTTCCGATCT  10XP7Dual H5 AATGATACGGCGAC CACCGAGATCTACAC xxxxxxxxxx ACACTCTTTCCCTACACGACGCTCTTCCGATCT  where x represents index bases from the 10X Dual Index Kit TT  SetA H index entries. For iteration 1  the third PCR reaction was cleaned up with 0.8X SPRISelect beads and eluted in 20 \u00b5l elution buffer EB to generate final libraries. For iteration 2  the third PCR reaction was cleaned up with right side size selection using SPRIselect beads 0.65X beads used initially and supernatant was rebound with 1.5X beads. SABER seq libraries were sequenced using Novaseq SP 200 cycle kits MedGenome with 5 10% PhiX spike in. Sequencing parameters: Read1 28 cycles  Read2 151 cycles  Index1 10 cycles  Index2 10 cycles. Standard sequencing primers were used.", null, "RNA-Seq", "TRANSCRIPTOMIC SINGLE CELL", "cDNA", "PAIRED", "ILLUMINA", "Illumina NovaSeq 6000", null, "SRP509892", null, "loader:fastq load.py", "SABERb2_brainD3091523wk3_CKDL230038569-1A_HHVLTDSX7_S6_L002_I1_001.fastq.gz SABERb2_brainD3091523wk3_CKDL230038569-1A_HHVLTDSX7_S6_L002_I2_001.fastq.gz SABERb2_brainD3091523wk3_CKDL230038569-1A_HHVLTDSX7_S6_L002_R1_001.fastq.gz SABERb2_brainD3091523wk3_CKDL230038569-1A_HHVLTDSX7_S6_L002_R2_001.fastq.gz", "fastq fastq fastq fastq", 14986744960.0, 46833578.0, "GSM8290052 r1", "0:10 1:10 2:150 3:150", "A:4103966301;C:2123224314;G:2431703551;T:5390322981;N:856253", 10, 10, 150, 150, 4103966301, 2123224314, 2431703551, 5390322981, 856253, "SRX24701866", "SRS21429395", "SRA1989588", "University of Pennsylvania", "University of Pennsylvania", 2, 0.31404, 0.88682, 0.119, 0.21308, 0.96812, 0.76295, 0.52318, 0.54494, 150, 150, "T", "B", "mate1 technical by mapping diff", "illumina", "novaseq_era", "full_length", "random_priming", "unknown", "sc", "single_cell_droplet", "10x", null, "United States", "2024-05-25", "Larval", "Larval", "Brain", "Nervous System"], [32380, "SRR29181680", "SRX24701865", "SRS21429394", "SRP509892", "PRJNA1116548", "Barcoding Notch signaling in the developing brain", "GSE268356", "Other", "Developmental signaling inputs are fundamental for shaping cell fates and behavior. However  traditional fluorescent based signaling reporters have limitations in scalability and molecular resolution of cell types. We present SABER seq  a CRISPR Cas molecular recorder that stores transient developmental signaling cues as permanent mutations in cellular genomes for deconstruction at later stages via single cell transcriptomics. We applied SABER seq to record Notch signaling in developing zebrafish brains. SABER seq has two components: a signaling sensor and a barcode recorder. The sensor activates Cas9 in a Notch dependent manner with inducible control while the recorder obtains mutations in ancestral cells where Notch is active. We combine SABER seq with an expanded juvenile brain atlas to identify cell types derived from Notch active founders. Our data reveals rare examples where differential Notch activity in ancestral progenitors is detected in terminally differentiated neuronal subtypes. SABER seq is a novel platform for rapid  scalable and high resolution mapping of signaling activity during development. Overall design: 10x Genomics libraries of single cell transcriptomes and SABER seq data  and genomic DNA SABER data", null, "pubmed:39575683", null, "SABERb2 brainD2091523wk3  Novogene Run1  brain2  transcriptome", "GSM8290051", null, "source name:zebrafish whole brain|tissue:zebrafish whole brain|developmental stage:21 dpf 23 dpf  Juvenile|genotype:WT/TLAB|geo loc name:missing|collection date:missing", "SABERb2 brainD2091523wk3  Novogene Run1  brain2  transcriptome", "Raw FASTQ reads were aligned against zebrafish genome  built from GRCz.109.gtf and the GRCz11.fa files  using the Cell Ranger count pipeline to generate gene by cell count matrices filtered barcode matrix. The count matrices were processed using the standard workflow of the Seurat v5.0.2 R package with modifications. Briefly  all count matrices were separately loaded into R. Seurat objects were then created with modified parameters  requiring features to be detected in at least 3 cells and cells to contain at least 300 genes. Subsequently  the Seurat objects were merged to create a single Seurat object. Cells with RNA counts between 300 and 4000 and a mitochondrial gene percentage of less than 9% were selected for downstream analysis. Gene expression values were log normalized with the default parameters and scaled for the top 3000 variable genes. The scaled data was then used to compute the 50 principal components. Cells were iteratively clustered using the Louvain algorithm post identifying neighboring cells. Differentially expressed genes for each cluster were identified using genes that are detected in at least 10% of cells in either of the two clusters and genes showing a minimum difference of 10% between the two clusters. Cell types were annotated using known marker genes.  The SABER seq FASTQ reads were processed using the Cell Ranger count pipeline  similar to the transcriptomic reads. A bash script was then used to generate a text file containing all FASTQ headers and error corrected cell barcodes for SABER seq data. post loading filtered barcode matrices into R  error corrected cell barcodes from transcriptomic reads were isolated and matched against those in the text file to identify common cell barcode that are shared between the transcriptomic and SABER seq datasets. The resulting text file contained redundant error corrected cell barcodes associated with different UMIs for the same cell. Thus  unique error corrected cell barcodes were isolated to create a text file containing unique FASTQ headers and error corrected barcodes. Subsequently  FASTQ reads matching the FASTQ headers in this file were extracted from the SABER seq raw reads using a Python script. SABER seq barcodes were analyzed using the scGESTALT pipeline available on Github as described previously7 35. To analyze differences in barcode detection and editing frequencies between cell types a binomial test was used. scRNA seq data was analyzed using the following protocol. Raw FASTQ reads were aligned against zebrafish genome  built from GRCz.109.gtf and the GRCz11.fa files  using the Cell Ranger count pipeline to generate gene by cell count matrices filtered barcode matrix. The count matrices were processed using the standard workflow of the Seurat v5.0.2 R package with modifications. Briefly  all count matrices were separately loaded into R. Seurat objects were then created with modified parameters  requiring features to be detected in at least 3 cells and cells to contain at least 300 genes. Subsequently  the Seurat objects were merged to create a single Seurat object. Cells with RNA counts between 300 and 4000 and a mitochondrial gene percentage of less than 9% were selected for downstream analysis. Gene expression values were log normalized with the default parameters and scaled for the top 3000 variable genes. The scaled data was then used to compute the 50 principal components. Cells were iteratively clustered using the Louvain algorithm post identifying neighboring cells. Differentially expressed genes for each cluster were identified using genes that are detected in at least 10% of cells in either of the two clusters and genes showing a minimum difference of 10% between the two clusters. Cell types were annotated using known marker genes. Finally  the data was visualized using UMAP. SABER seq barcode analysis was performed using the following protocol. The SABER seq FASTQ reads were processed using the Cell Ranger count pipeline.A bash script was then used to generate a text file containing all FASTQ headers and error corrected cell barcodes for SABER seq data. Error corrected cell barcodes from transcriptomic reads were isolated and matched against those in the text file to identify common cell barcode that are shared between the transcriptomic and SABER seq datasets. The resulting text file contained redundant error corrected cell barcodes associated with different UMIs for the same cell. Thus  unique error corrected cell barcodes were isolated to create a text file containing unique FASTQ headers and error corrected barcodes. Subsequently  FASTQ reads matching the FASTQ headers in this file were extracted from the SABER seq raw reads using a Python script. SABER seq barcodes were analyzed using the scGESTALT pipeline available on Github as described previously. To analyze differences in barcode detection and editing frequencies between cell types a binomial test was used. genome build/assembly: GRCz11 processed data files format and content: *.h5 files contain filtered features/genes and barcode data generated from cell ranger pipeline. processed data files format and content: LinC1 Filtered Fastq Headers CB.txt LinC2 Filtered Fastq Headers CB.txt LinD1 Filtered Fastq Headers CB.txt LinD2 Filtered Fastq Headers CB.txt LinD3 Filtered Fastq Headers CB.txt files contain filtered and unique fastq headers and cell barcodes post matching the trasncriptomic cell barcodes. processed data files format and content: SABERb1 linC1 S4 BrainC1 S6.CB.xlsx SABERb1 linC2 S5 BrainC2 S7.CB.xlsx SABERb2 linD1 S1 BrainD1 S8.CB.xlsx SABERb2 linD2 S2 BrainD2 S9.CB.xlsx SABERb2 linD3 S3 BrainD3 S10.CB.xlsx SABERb4 linB2.1 13.123.2.With.Primer.stats.xlsx SABERb4 linB2.2 14.124.2.With.Primer.stats.xlsx files contain the SABER Seq output post matching reads to the reference sequence.", "zebrafish whole brain", null, "Wild type and SABER x her4.3:switchCas9 zebrafish brains were harvested and dissociated at 21 dpf 23 dpf as described previously Raj et. al 2018  Nature Biotechnology  with the following modifications. All microcentrifuge tubes were treated with 1% BSA/DPBS overnight to prevent cell loss due to adhesion. Brains were dissociated with 1 mL of 20 units/ml papain in EBSS media and incubated at 37 \u00b0C for 20 min. Cells were resuspended with 150 \u03bcl ice cold 1%BSA/DPBS solution. Transcriptome libraries were processed according to the manufacturer\u2019s instructions Single Cell 3\u2019 v3 kit. Transcriptome libraries were sequenced using NovaSeq S4 300 cycle kits Novogene and Novaseq SP 100 and 200 cycle kits MedGenome. To generate SABER seq libraries  samples post cDNA amplification and prior to fragmentation were split into two parts. One part was processed for transcriptome libraries as instructed by the manufacturer. The other part was processed for SABER seq libraries as follows. To enrich for SABER barcodes  5 \u00b5l of the whole transcriptome cDNA was PCR amplified with 10X p140v1UP  GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCT CGTCGTAGATCTTCATCGTGATG and 10XPCR1F CTACACGACGCTCTTCCGATCT primers and Q5 polymerase. The reaction conditions were: 98 \u00b0C  30 s; [98 \u00b0C  10 s; 68 \u00b0C  25 s; 72 \u00b0C  15 s] x 14 cycles; 72 \u00b0C  2 min. The reaction was cleaned up with 0.8X SPRISelect beads and eluted in 20 \u00b5l elution buffer EB buffer. A second PCR was carried out using 3 \u00b5l of PCR1 product and 10XPCR1F and 10X p140v2Sa GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCT GAAACTACATGGGGTcaGCTC primers. Reaction conditions were same as PCR1 except only 13 cycles were completed. At this point  2 different clean up conditions were used: iteration 1 SABER seq v0 and iteration 2 SABER seq v1. For iteration 1  the reaction was cleaned up with 0.8X SPRISelect beads and eluted in 20 \u00b5l elution buffer EB buffer similar to PCR1.  For iteration 2  an optimization was trialed  and the reaction was cleaned up with right side size selection using SPRIselect beads 0.6X beads used initially and supernatant was rebound with 1.5X beads. The product was eluted in 20 \u00b5l EB. Finally  adapters and sample indices were incorporated in a third PCR reaction using 5 7 \u00b5l of a 1:10 dilution of PCR2 product. Reactions conditions were same as PCR1 except 9 10 cycles were completed. Primers used: 10XP5Dual Hx AATGATACGGCGACCACCGAGATCTACAC xxxxxxx xxx ACACTCTTTCCCTACACGACGCTCTTCCGATCT  10XP7Dual H5 AATGATACGGCGAC CACCGAGATCTACAC xxxxxxxxxx ACACTCTTTCCCTACACGACGCTCTTCCGATCT  where x represents index bases from the 10X Dual Index Kit TT  SetA H index entries. For iteration 1  the third PCR reaction was cleaned up with 0.8X SPRISelect beads and eluted in 20 \u00b5l elution buffer EB to generate final libraries. For iteration 2  the third PCR reaction was cleaned up with right side size selection using SPRIselect beads 0.65X beads used initially and supernatant was rebound with 1.5X beads. SABER seq libraries were sequenced using Novaseq SP 200 cycle kits MedGenome with 5 10% PhiX spike in. Sequencing parameters: Read1 28 cycles  Read2 151 cycles  Index1 10 cycles  Index2 10 cycles. Standard sequencing primers were used.", "This work was performed under protocol numbers 807110 and 807259  which were approved by the University of Pennsylvania\u2019s Office of Animal Welfare of Institutional Animal Care and Use Committee IACUC. All zebrafish work in this study follows the University of Pennsylvania Institutional Animal Care and Use Committee regulations.", "tissue:zebrafish whole brain|developmental stage:21 dpf 23 dpf  Juvenile|genotype:WT/TLAB", "GSM8290051", "GSM8290051: SABERb2 brainD2091523wk3  Novogene Run1  brain2  transcriptome; Danio rerio; RNA Seq", "GSM8290051 r1", "GSM8290051", "1", "Wild type and SABER x her4.3:switchCas9 zebrafish brains were harvested and dissociated at 21 dpf 23 dpf as described previously Raj et. al 2018  Nature Biotechnology  with the following modifications. All microcentrifuge tubes were treated with 1% BSA/DPBS overnight to prevent cell loss due to adhesion. Brains were dissociated with 1 mL of 20 units/ml papain in EBSS media and incubated at 37 \u00b0C for 20 min. Cells were resuspended with 150 \u03bcl ice cold 1%BSA/DPBS solution. Transcriptome libraries were processed according to the manufacturer's instructions Single Cell three prime v3 kit. Transcriptome libraries were sequenced using NovaSeq S4 300 cycle kits Novogene and Novaseq SP 100 and 200 cycle kits MedGenome. To generate SABER seq libraries  samples post cDNA amplification and prior to fragmentation were split into two parts. One part was processed for transcriptome libraries as instructed by the manufacturer. The other part was processed for SABER seq libraries as follows. To enrich for SABER barcodes  5 \u00b5l of the whole transcriptome cDNA was PCR amplified with 10X p140v1UP  GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCT CGTCGTAGATCTTCATCGTGATG and 10XPCR1F CTACACGACGCTCTTCCGATCT primers and Q5 polymerase. The reaction conditions were: 98 \u00b0C  30 s; [98 \u00b0C  10 s; 68 \u00b0C  25 s; 72 \u00b0C  15 s] x 14 cycles; 72 \u00b0C  2 min. The reaction was cleaned up with 0.8X SPRISelect beads and eluted in 20 \u00b5l elution buffer EB buffer. A second PCR was carried out using 3 \u00b5l of PCR1 product and 10XPCR1F and 10X p140v2Sa GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCT GAAACTACATGGGGTcaGCTC primers. Reaction conditions were same as PCR1 except only 13 cycles were completed. At this point  2 different clean up conditions were used: iteration 1 SABER seq v0 and iteration 2 SABER seq v1. For iteration 1  the reaction was cleaned up with 0.8X SPRISelect beads and eluted in 20 \u00b5l elution buffer EB buffer similar to PCR1.  For iteration 2  an optimization was trialed  and the reaction was cleaned up with right side size selection using SPRIselect beads 0.6X beads used initially and supernatant was rebound with 1.5X beads. The product was eluted in 20 \u00b5l EB. Finally  adapters and sample indices were incorporated in a third PCR reaction using 5 7 \u00b5l of a 1:10 dilution of PCR2 product. Reactions conditions were same as PCR1 except 9 10 cycles were completed. Primers used: 10XP5Dual Hx AATGATACGGCGACCACCGAGATCTACAC xxxxxxx xxx ACACTCTTTCCCTACACGACGCTCTTCCGATCT  10XP7Dual H5 AATGATACGGCGAC CACCGAGATCTACAC xxxxxxxxxx ACACTCTTTCCCTACACGACGCTCTTCCGATCT  where x represents index bases from the 10X Dual Index Kit TT  SetA H index entries. For iteration 1  the third PCR reaction was cleaned up with 0.8X SPRISelect beads and eluted in 20 \u00b5l elution buffer EB to generate final libraries. For iteration 2  the third PCR reaction was cleaned up with right side size selection using SPRIselect beads 0.65X beads used initially and supernatant was rebound with 1.5X beads. SABER seq libraries were sequenced using Novaseq SP 200 cycle kits MedGenome with 5 10% PhiX spike in. Sequencing parameters: Read1 28 cycles  Read2 151 cycles  Index1 10 cycles  Index2 10 cycles. Standard sequencing primers were used.", null, "RNA-Seq", "TRANSCRIPTOMIC SINGLE CELL", "cDNA", "PAIRED", "ILLUMINA", "Illumina NovaSeq 6000", null, "SRP509892", null, "loader:fastq load.py", "SABERb2_brainD2091523wk3_CKDL230038569-1A_HHVLTDSX7_S1_L002_I1_001.fastq.gz SABERb2_brainD2091523wk3_CKDL230038569-1A_HHVLTDSX7_S1_L002_I2_001.fastq.gz SABERb2_brainD2091523wk3_CKDL230038569-1A_HHVLTDSX7_S1_L002_R1_001.fastq.gz SABERb2_brainD2091523wk3_CKDL230038569-1A_HHVLTDSX7_S1_L002_R2_001.fastq.gz", "fastq fastq fastq fastq", 13432883520.0, 41977761.0, "GSM8290051 r1", "0:10 1:10 2:150 3:150", "A:3699233635;C:1894101532;G:2175321862;T:4823892315;N:778956", 10, 10, 150, 150, 3699233635, 1894101532, 2175321862, 4823892315, 778956, "SRX24701865", "SRS21429394", "SRA1989588", "University of Pennsylvania", "University of Pennsylvania", 2, 0.32482, 0.8828, 0.12828, 0.21873, 0.96668, 0.76534, 0.51473, 0.5505, 150, 150, "T", "B", "mate1 technical by mapping diff", "illumina", "novaseq_era", "full_length", "random_priming", "unknown", "sc", "single_cell_droplet", "10x", null, "United States", "2024-05-25", "Larval", "Larval", "Brain", "Nervous System"], [32381, "SRR29181685", "SRX24701864", "SRS21429393", "SRP509892", "PRJNA1116548", "Barcoding Notch signaling in the developing brain", "GSE268356", "Other", "Developmental signaling inputs are fundamental for shaping cell fates and behavior. However  traditional fluorescent based signaling reporters have limitations in scalability and molecular resolution of cell types. We present SABER seq  a CRISPR Cas molecular recorder that stores transient developmental signaling cues as permanent mutations in cellular genomes for deconstruction at later stages via single cell transcriptomics. We applied SABER seq to record Notch signaling in developing zebrafish brains. SABER seq has two components: a signaling sensor and a barcode recorder. The sensor activates Cas9 in a Notch dependent manner with inducible control while the recorder obtains mutations in ancestral cells where Notch is active. We combine SABER seq with an expanded juvenile brain atlas to identify cell types derived from Notch active founders. Our data reveals rare examples where differential Notch activity in ancestral progenitors is detected in terminally differentiated neuronal subtypes. SABER seq is a novel platform for rapid  scalable and high resolution mapping of signaling activity during development. Overall design: 10x Genomics libraries of single cell transcriptomes and SABER seq data  and genomic DNA SABER data", null, "pubmed:39575683", null, "SABERb2 brainD1091523wk3  Novogene Run1  brain2  transcriptome", "GSM8290050", null, "source name:zebrafish whole brain|tissue:zebrafish whole brain|developmental stage:21 dpf 23 dpf  Juvenile|genotype:WT/TLAB|geo loc name:missing|collection date:missing", "SABERb2 brainD1091523wk3  Novogene Run1  brain2  transcriptome", "Raw FASTQ reads were aligned against zebrafish genome  built from GRCz.109.gtf and the GRCz11.fa files  using the Cell Ranger count pipeline to generate gene by cell count matrices filtered barcode matrix. The count matrices were processed using the standard workflow of the Seurat v5.0.2 R package with modifications. Briefly  all count matrices were separately loaded into R. Seurat objects were then created with modified parameters  requiring features to be detected in at least 3 cells and cells to contain at least 300 genes. Subsequently  the Seurat objects were merged to create a single Seurat object. Cells with RNA counts between 300 and 4000 and a mitochondrial gene percentage of less than 9% were selected for downstream analysis. Gene expression values were log normalized with the default parameters and scaled for the top 3000 variable genes. The scaled data was then used to compute the 50 principal components. Cells were iteratively clustered using the Louvain algorithm post identifying neighboring cells. Differentially expressed genes for each cluster were identified using genes that are detected in at least 10% of cells in either of the two clusters and genes showing a minimum difference of 10% between the two clusters. Cell types were annotated using known marker genes.  The SABER seq FASTQ reads were processed using the Cell Ranger count pipeline  similar to the transcriptomic reads. A bash script was then used to generate a text file containing all FASTQ headers and error corrected cell barcodes for SABER seq data. post loading filtered barcode matrices into R  error corrected cell barcodes from transcriptomic reads were isolated and matched against those in the text file to identify common cell barcode that are shared between the transcriptomic and SABER seq datasets. The resulting text file contained redundant error corrected cell barcodes associated with different UMIs for the same cell. Thus  unique error corrected cell barcodes were isolated to create a text file containing unique FASTQ headers and error corrected barcodes. Subsequently  FASTQ reads matching the FASTQ headers in this file were extracted from the SABER seq raw reads using a Python script. SABER seq barcodes were analyzed using the scGESTALT pipeline available on Github as described previously7 35. To analyze differences in barcode detection and editing frequencies between cell types a binomial test was used. scRNA seq data was analyzed using the following protocol. Raw FASTQ reads were aligned against zebrafish genome  built from GRCz.109.gtf and the GRCz11.fa files  using the Cell Ranger count pipeline to generate gene by cell count matrices filtered barcode matrix. The count matrices were processed using the standard workflow of the Seurat v5.0.2 R package with modifications. Briefly  all count matrices were separately loaded into R. Seurat objects were then created with modified parameters  requiring features to be detected in at least 3 cells and cells to contain at least 300 genes. Subsequently  the Seurat objects were merged to create a single Seurat object. Cells with RNA counts between 300 and 4000 and a mitochondrial gene percentage of less than 9% were selected for downstream analysis. Gene expression values were log normalized with the default parameters and scaled for the top 3000 variable genes. The scaled data was then used to compute the 50 principal components. Cells were iteratively clustered using the Louvain algorithm post identifying neighboring cells. Differentially expressed genes for each cluster were identified using genes that are detected in at least 10% of cells in either of the two clusters and genes showing a minimum difference of 10% between the two clusters. Cell types were annotated using known marker genes. Finally  the data was visualized using UMAP. SABER seq barcode analysis was performed using the following protocol. The SABER seq FASTQ reads were processed using the Cell Ranger count pipeline.A bash script was then used to generate a text file containing all FASTQ headers and error corrected cell barcodes for SABER seq data. Error corrected cell barcodes from transcriptomic reads were isolated and matched against those in the text file to identify common cell barcode that are shared between the transcriptomic and SABER seq datasets. The resulting text file contained redundant error corrected cell barcodes associated with different UMIs for the same cell. Thus  unique error corrected cell barcodes were isolated to create a text file containing unique FASTQ headers and error corrected barcodes. Subsequently  FASTQ reads matching the FASTQ headers in this file were extracted from the SABER seq raw reads using a Python script. SABER seq barcodes were analyzed using the scGESTALT pipeline available on Github as described previously. To analyze differences in barcode detection and editing frequencies between cell types a binomial test was used. genome build/assembly: GRCz11 processed data files format and content: *.h5 files contain filtered features/genes and barcode data generated from cell ranger pipeline. processed data files format and content: LinC1 Filtered Fastq Headers CB.txt LinC2 Filtered Fastq Headers CB.txt LinD1 Filtered Fastq Headers CB.txt LinD2 Filtered Fastq Headers CB.txt LinD3 Filtered Fastq Headers CB.txt files contain filtered and unique fastq headers and cell barcodes post matching the trasncriptomic cell barcodes. processed data files format and content: SABERb1 linC1 S4 BrainC1 S6.CB.xlsx SABERb1 linC2 S5 BrainC2 S7.CB.xlsx SABERb2 linD1 S1 BrainD1 S8.CB.xlsx SABERb2 linD2 S2 BrainD2 S9.CB.xlsx SABERb2 linD3 S3 BrainD3 S10.CB.xlsx SABERb4 linB2.1 13.123.2.With.Primer.stats.xlsx SABERb4 linB2.2 14.124.2.With.Primer.stats.xlsx files contain the SABER Seq output post matching reads to the reference sequence.", "zebrafish whole brain", null, "Wild type and SABER x her4.3:switchCas9 zebrafish brains were harvested and dissociated at 21 dpf 23 dpf as described previously Raj et. al 2018  Nature Biotechnology  with the following modifications. All microcentrifuge tubes were treated with 1% BSA/DPBS overnight to prevent cell loss due to adhesion. Brains were dissociated with 1 mL of 20 units/ml papain in EBSS media and incubated at 37 \u00b0C for 20 min. Cells were resuspended with 150 \u03bcl ice cold 1%BSA/DPBS solution. Transcriptome libraries were processed according to the manufacturer\u2019s instructions Single Cell 3\u2019 v3 kit. Transcriptome libraries were sequenced using NovaSeq S4 300 cycle kits Novogene and Novaseq SP 100 and 200 cycle kits MedGenome. To generate SABER seq libraries  samples post cDNA amplification and prior to fragmentation were split into two parts. One part was processed for transcriptome libraries as instructed by the manufacturer. The other part was processed for SABER seq libraries as follows. To enrich for SABER barcodes  5 \u00b5l of the whole transcriptome cDNA was PCR amplified with 10X p140v1UP  GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCT CGTCGTAGATCTTCATCGTGATG and 10XPCR1F CTACACGACGCTCTTCCGATCT primers and Q5 polymerase. The reaction conditions were: 98 \u00b0C  30 s; [98 \u00b0C  10 s; 68 \u00b0C  25 s; 72 \u00b0C  15 s] x 14 cycles; 72 \u00b0C  2 min. The reaction was cleaned up with 0.8X SPRISelect beads and eluted in 20 \u00b5l elution buffer EB buffer. A second PCR was carried out using 3 \u00b5l of PCR1 product and 10XPCR1F and 10X p140v2Sa GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCT GAAACTACATGGGGTcaGCTC primers. Reaction conditions were same as PCR1 except only 13 cycles were completed. At this point  2 different clean up conditions were used: iteration 1 SABER seq v0 and iteration 2 SABER seq v1. For iteration 1  the reaction was cleaned up with 0.8X SPRISelect beads and eluted in 20 \u00b5l elution buffer EB buffer similar to PCR1.  For iteration 2  an optimization was trialed  and the reaction was cleaned up with right side size selection using SPRIselect beads 0.6X beads used initially and supernatant was rebound with 1.5X beads. The product was eluted in 20 \u00b5l EB. Finally  adapters and sample indices were incorporated in a third PCR reaction using 5 7 \u00b5l of a 1:10 dilution of PCR2 product. Reactions conditions were same as PCR1 except 9 10 cycles were completed. Primers used: 10XP5Dual Hx AATGATACGGCGACCACCGAGATCTACAC xxxxxxx xxx ACACTCTTTCCCTACACGACGCTCTTCCGATCT  10XP7Dual H5 AATGATACGGCGAC CACCGAGATCTACAC xxxxxxxxxx ACACTCTTTCCCTACACGACGCTCTTCCGATCT  where x represents index bases from the 10X Dual Index Kit TT  SetA H index entries. For iteration 1  the third PCR reaction was cleaned up with 0.8X SPRISelect beads and eluted in 20 \u00b5l elution buffer EB to generate final libraries. For iteration 2  the third PCR reaction was cleaned up with right side size selection using SPRIselect beads 0.65X beads used initially and supernatant was rebound with 1.5X beads. SABER seq libraries were sequenced using Novaseq SP 200 cycle kits MedGenome with 5 10% PhiX spike in. Sequencing parameters: Read1 28 cycles  Read2 151 cycles  Index1 10 cycles  Index2 10 cycles. Standard sequencing primers were used.", "This work was performed under protocol numbers 807110 and 807259  which were approved by the University of Pennsylvania\u2019s Office of Animal Welfare of Institutional Animal Care and Use Committee IACUC. All zebrafish work in this study follows the University of Pennsylvania Institutional Animal Care and Use Committee regulations.", "tissue:zebrafish whole brain|developmental stage:21 dpf 23 dpf  Juvenile|genotype:WT/TLAB", "GSM8290050", "GSM8290050: SABERb2 brainD1091523wk3  Novogene Run1  brain2  transcriptome; Danio rerio; RNA Seq", "GSM8290050 r1", "GSM8290050", "1", "Wild type and SABER x her4.3:switchCas9 zebrafish brains were harvested and dissociated at 21 dpf 23 dpf as described previously Raj et. al 2018  Nature Biotechnology  with the following modifications. All microcentrifuge tubes were treated with 1% BSA/DPBS overnight to prevent cell loss due to adhesion. Brains were dissociated with 1 mL of 20 units/ml papain in EBSS media and incubated at 37 \u00b0C for 20 min. Cells were resuspended with 150 \u03bcl ice cold 1%BSA/DPBS solution. Transcriptome libraries were processed according to the manufacturer's instructions Single Cell three prime v3 kit. Transcriptome libraries were sequenced using NovaSeq S4 300 cycle kits Novogene and Novaseq SP 100 and 200 cycle kits MedGenome. To generate SABER seq libraries  samples post cDNA amplification and prior to fragmentation were split into two parts. One part was processed for transcriptome libraries as instructed by the manufacturer. The other part was processed for SABER seq libraries as follows. To enrich for SABER barcodes  5 \u00b5l of the whole transcriptome cDNA was PCR amplified with 10X p140v1UP  GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCT CGTCGTAGATCTTCATCGTGATG and 10XPCR1F CTACACGACGCTCTTCCGATCT primers and Q5 polymerase. The reaction conditions were: 98 \u00b0C  30 s; [98 \u00b0C  10 s; 68 \u00b0C  25 s; 72 \u00b0C  15 s] x 14 cycles; 72 \u00b0C  2 min. The reaction was cleaned up with 0.8X SPRISelect beads and eluted in 20 \u00b5l elution buffer EB buffer. A second PCR was carried out using 3 \u00b5l of PCR1 product and 10XPCR1F and 10X p140v2Sa GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCT GAAACTACATGGGGTcaGCTC primers. Reaction conditions were same as PCR1 except only 13 cycles were completed. At this point  2 different clean up conditions were used: iteration 1 SABER seq v0 and iteration 2 SABER seq v1. For iteration 1  the reaction was cleaned up with 0.8X SPRISelect beads and eluted in 20 \u00b5l elution buffer EB buffer similar to PCR1.  For iteration 2  an optimization was trialed  and the reaction was cleaned up with right side size selection using SPRIselect beads 0.6X beads used initially and supernatant was rebound with 1.5X beads. The product was eluted in 20 \u00b5l EB. Finally  adapters and sample indices were incorporated in a third PCR reaction using 5 7 \u00b5l of a 1:10 dilution of PCR2 product. Reactions conditions were same as PCR1 except 9 10 cycles were completed. Primers used: 10XP5Dual Hx AATGATACGGCGACCACCGAGATCTACAC xxxxxxx xxx ACACTCTTTCCCTACACGACGCTCTTCCGATCT  10XP7Dual H5 AATGATACGGCGAC CACCGAGATCTACAC xxxxxxxxxx ACACTCTTTCCCTACACGACGCTCTTCCGATCT  where x represents index bases from the 10X Dual Index Kit TT  SetA H index entries. For iteration 1  the third PCR reaction was cleaned up with 0.8X SPRISelect beads and eluted in 20 \u00b5l elution buffer EB to generate final libraries. For iteration 2  the third PCR reaction was cleaned up with right side size selection using SPRIselect beads 0.65X beads used initially and supernatant was rebound with 1.5X beads. SABER seq libraries were sequenced using Novaseq SP 200 cycle kits MedGenome with 5 10% PhiX spike in. Sequencing parameters: Read1 28 cycles  Read2 151 cycles  Index1 10 cycles  Index2 10 cycles. Standard sequencing primers were used.", null, "RNA-Seq", "TRANSCRIPTOMIC SINGLE CELL", "cDNA", "PAIRED", "ILLUMINA", "Illumina NovaSeq 6000", null, "SRP509892", null, "loader:fastq load.py", "SABERb2_brainD1091523wk3_CKDL230038569-1A_HHVLTDSX7_S2_L002_I1_001.fastq.gz SABERb2_brainD1091523wk3_CKDL230038569-1A_HHVLTDSX7_S2_L002_I2_001.fastq.gz SABERb2_brainD1091523wk3_CKDL230038569-1A_HHVLTDSX7_S2_L002_R1_001.fastq.gz SABERb2_brainD1091523wk3_CKDL230038569-1A_HHVLTDSX7_S2_L002_R2_001.fastq.gz", "fastq fastq fastq fastq", 17465671680.0, 54580224.0, "GSM8290050 r1", "0:10 1:10 2:150 3:150", "A:4799554780;C:2476125093;G:2831285237;T:6266103981;N:998109", 10, 10, 150, 150, 4799554780, 2476125093, 2831285237, 6266103981, 998109, "SRX24701864", "SRS21429393", "SRA1989588", "University of Pennsylvania", "University of Pennsylvania", 2, 0.32322, 0.88514, 0.12316, 0.2147, 0.96644, 0.76378, 0.54067, 0.54053, 150, 150, "T", "B", "mate1 technical by mapping diff", "illumina", "novaseq_era", "full_length", "random_priming", "unknown", "sc", "single_cell_droplet", "10x", null, "United States", "2024-05-25", "Larval", "Larval", "Brain", "Nervous System"], [32382, "SRR29181686", "SRX24701863", "SRS21429392", "SRP509892", "PRJNA1116548", "Barcoding Notch signaling in the developing brain", "GSE268356", "Other", "Developmental signaling inputs are fundamental for shaping cell fates and behavior. However  traditional fluorescent based signaling reporters have limitations in scalability and molecular resolution of cell types. We present SABER seq  a CRISPR Cas molecular recorder that stores transient developmental signaling cues as permanent mutations in cellular genomes for deconstruction at later stages via single cell transcriptomics. We applied SABER seq to record Notch signaling in developing zebrafish brains. SABER seq has two components: a signaling sensor and a barcode recorder. The sensor activates Cas9 in a Notch dependent manner with inducible control while the recorder obtains mutations in ancestral cells where Notch is active. We combine SABER seq with an expanded juvenile brain atlas to identify cell types derived from Notch active founders. Our data reveals rare examples where differential Notch activity in ancestral progenitors is detected in terminally differentiated neuronal subtypes. SABER seq is a novel platform for rapid  scalable and high resolution mapping of signaling activity during development. Overall design: 10x Genomics libraries of single cell transcriptomes and SABER seq data  and genomic DNA SABER data", null, "pubmed:39575683", null, "SABERb2 brainD3  Medgenome Run2  brain2  transcriptome", "GSM8290049", null, "source name:zebrafish whole brain|tissue:zebrafish whole brain|developmental stage:21 dpf 23 dpf  Juvenile|genotype:WT/TLAB|geo loc name:missing|collection date:missing", "SABERb2 brainD3  Medgenome Run2  brain2  transcriptome", "Raw FASTQ reads were aligned against zebrafish genome  built from GRCz.109.gtf and the GRCz11.fa files  using the Cell Ranger count pipeline to generate gene by cell count matrices filtered barcode matrix. The count matrices were processed using the standard workflow of the Seurat v5.0.2 R package with modifications. Briefly  all count matrices were separately loaded into R. Seurat objects were then created with modified parameters  requiring features to be detected in at least 3 cells and cells to contain at least 300 genes. Subsequently  the Seurat objects were merged to create a single Seurat object. Cells with RNA counts between 300 and 4000 and a mitochondrial gene percentage of less than 9% were selected for downstream analysis. Gene expression values were log normalized with the default parameters and scaled for the top 3000 variable genes. The scaled data was then used to compute the 50 principal components. Cells were iteratively clustered using the Louvain algorithm post identifying neighboring cells. Differentially expressed genes for each cluster were identified using genes that are detected in at least 10% of cells in either of the two clusters and genes showing a minimum difference of 10% between the two clusters. Cell types were annotated using known marker genes.  The SABER seq FASTQ reads were processed using the Cell Ranger count pipeline  similar to the transcriptomic reads. A bash script was then used to generate a text file containing all FASTQ headers and error corrected cell barcodes for SABER seq data. post loading filtered barcode matrices into R  error corrected cell barcodes from transcriptomic reads were isolated and matched against those in the text file to identify common cell barcode that are shared between the transcriptomic and SABER seq datasets. The resulting text file contained redundant error corrected cell barcodes associated with different UMIs for the same cell. Thus  unique error corrected cell barcodes were isolated to create a text file containing unique FASTQ headers and error corrected barcodes. Subsequently  FASTQ reads matching the FASTQ headers in this file were extracted from the SABER seq raw reads using a Python script. SABER seq barcodes were analyzed using the scGESTALT pipeline available on Github as described previously7 35. To analyze differences in barcode detection and editing frequencies between cell types a binomial test was used. scRNA seq data was analyzed using the following protocol. Raw FASTQ reads were aligned against zebrafish genome  built from GRCz.109.gtf and the GRCz11.fa files  using the Cell Ranger count pipeline to generate gene by cell count matrices filtered barcode matrix. The count matrices were processed using the standard workflow of the Seurat v5.0.2 R package with modifications. Briefly  all count matrices were separately loaded into R. Seurat objects were then created with modified parameters  requiring features to be detected in at least 3 cells and cells to contain at least 300 genes. Subsequently  the Seurat objects were merged to create a single Seurat object. Cells with RNA counts between 300 and 4000 and a mitochondrial gene percentage of less than 9% were selected for downstream analysis. Gene expression values were log normalized with the default parameters and scaled for the top 3000 variable genes. The scaled data was then used to compute the 50 principal components. Cells were iteratively clustered using the Louvain algorithm post identifying neighboring cells. Differentially expressed genes for each cluster were identified using genes that are detected in at least 10% of cells in either of the two clusters and genes showing a minimum difference of 10% between the two clusters. Cell types were annotated using known marker genes. Finally  the data was visualized using UMAP. SABER seq barcode analysis was performed using the following protocol. The SABER seq FASTQ reads were processed using the Cell Ranger count pipeline.A bash script was then used to generate a text file containing all FASTQ headers and error corrected cell barcodes for SABER seq data. Error corrected cell barcodes from transcriptomic reads were isolated and matched against those in the text file to identify common cell barcode that are shared between the transcriptomic and SABER seq datasets. The resulting text file contained redundant error corrected cell barcodes associated with different UMIs for the same cell. Thus  unique error corrected cell barcodes were isolated to create a text file containing unique FASTQ headers and error corrected barcodes. Subsequently  FASTQ reads matching the FASTQ headers in this file were extracted from the SABER seq raw reads using a Python script. SABER seq barcodes were analyzed using the scGESTALT pipeline available on Github as described previously. To analyze differences in barcode detection and editing frequencies between cell types a binomial test was used. genome build/assembly: GRCz11 processed data files format and content: *.h5 files contain filtered features/genes and barcode data generated from cell ranger pipeline. processed data files format and content: LinC1 Filtered Fastq Headers CB.txt LinC2 Filtered Fastq Headers CB.txt LinD1 Filtered Fastq Headers CB.txt LinD2 Filtered Fastq Headers CB.txt LinD3 Filtered Fastq Headers CB.txt files contain filtered and unique fastq headers and cell barcodes post matching the trasncriptomic cell barcodes. processed data files format and content: SABERb1 linC1 S4 BrainC1 S6.CB.xlsx SABERb1 linC2 S5 BrainC2 S7.CB.xlsx SABERb2 linD1 S1 BrainD1 S8.CB.xlsx SABERb2 linD2 S2 BrainD2 S9.CB.xlsx SABERb2 linD3 S3 BrainD3 S10.CB.xlsx SABERb4 linB2.1 13.123.2.With.Primer.stats.xlsx SABERb4 linB2.2 14.124.2.With.Primer.stats.xlsx files contain the SABER Seq output post matching reads to the reference sequence.", "zebrafish whole brain", null, "Wild type and SABER x her4.3:switchCas9 zebrafish brains were harvested and dissociated at 21 dpf 23 dpf as described previously Raj et. al 2018  Nature Biotechnology  with the following modifications. All microcentrifuge tubes were treated with 1% BSA/DPBS overnight to prevent cell loss due to adhesion. Brains were dissociated with 1 mL of 20 units/ml papain in EBSS media and incubated at 37 \u00b0C for 20 min. Cells were resuspended with 150 \u03bcl ice cold 1%BSA/DPBS solution. Transcriptome libraries were processed according to the manufacturer\u2019s instructions Single Cell 3\u2019 v3 kit. Transcriptome libraries were sequenced using NovaSeq S4 300 cycle kits Novogene and Novaseq SP 100 and 200 cycle kits MedGenome. To generate SABER seq libraries  samples post cDNA amplification and prior to fragmentation were split into two parts. One part was processed for transcriptome libraries as instructed by the manufacturer. The other part was processed for SABER seq libraries as follows. To enrich for SABER barcodes  5 \u00b5l of the whole transcriptome cDNA was PCR amplified with 10X p140v1UP  GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCT CGTCGTAGATCTTCATCGTGATG and 10XPCR1F CTACACGACGCTCTTCCGATCT primers and Q5 polymerase. The reaction conditions were: 98 \u00b0C  30 s; [98 \u00b0C  10 s; 68 \u00b0C  25 s; 72 \u00b0C  15 s] x 14 cycles; 72 \u00b0C  2 min. The reaction was cleaned up with 0.8X SPRISelect beads and eluted in 20 \u00b5l elution buffer EB buffer. A second PCR was carried out using 3 \u00b5l of PCR1 product and 10XPCR1F and 10X p140v2Sa GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCT GAAACTACATGGGGTcaGCTC primers. Reaction conditions were same as PCR1 except only 13 cycles were completed. At this point  2 different clean up conditions were used: iteration 1 SABER seq v0 and iteration 2 SABER seq v1. For iteration 1  the reaction was cleaned up with 0.8X SPRISelect beads and eluted in 20 \u00b5l elution buffer EB buffer similar to PCR1.  For iteration 2  an optimization was trialed  and the reaction was cleaned up with right side size selection using SPRIselect beads 0.6X beads used initially and supernatant was rebound with 1.5X beads. The product was eluted in 20 \u00b5l EB. Finally  adapters and sample indices were incorporated in a third PCR reaction using 5 7 \u00b5l of a 1:10 dilution of PCR2 product. Reactions conditions were same as PCR1 except 9 10 cycles were completed. Primers used: 10XP5Dual Hx AATGATACGGCGACCACCGAGATCTACAC xxxxxxx xxx ACACTCTTTCCCTACACGACGCTCTTCCGATCT  10XP7Dual H5 AATGATACGGCGAC CACCGAGATCTACAC xxxxxxxxxx ACACTCTTTCCCTACACGACGCTCTTCCGATCT  where x represents index bases from the 10X Dual Index Kit TT  SetA H index entries. For iteration 1  the third PCR reaction was cleaned up with 0.8X SPRISelect beads and eluted in 20 \u00b5l elution buffer EB to generate final libraries. For iteration 2  the third PCR reaction was cleaned up with right side size selection using SPRIselect beads 0.65X beads used initially and supernatant was rebound with 1.5X beads. SABER seq libraries were sequenced using Novaseq SP 200 cycle kits MedGenome with 5 10% PhiX spike in. Sequencing parameters: Read1 28 cycles  Read2 151 cycles  Index1 10 cycles  Index2 10 cycles. Standard sequencing primers were used.", "This work was performed under protocol numbers 807110 and 807259  which were approved by the University of Pennsylvania\u2019s Office of Animal Welfare of Institutional Animal Care and Use Committee IACUC. All zebrafish work in this study follows the University of Pennsylvania Institutional Animal Care and Use Committee regulations.", "tissue:zebrafish whole brain|developmental stage:21 dpf 23 dpf  Juvenile|genotype:WT/TLAB", "GSM8290049", "GSM8290049: SABERb2 brainD3  Medgenome Run2  brain2  transcriptome; Danio rerio; RNA Seq", "GSM8290049 r1", "GSM8290049", "1", "Wild type and SABER x her4.3:switchCas9 zebrafish brains were harvested and dissociated at 21 dpf 23 dpf as described previously Raj et. al 2018  Nature Biotechnology  with the following modifications. All microcentrifuge tubes were treated with 1% BSA/DPBS overnight to prevent cell loss due to adhesion. Brains were dissociated with 1 mL of 20 units/ml papain in EBSS media and incubated at 37 \u00b0C for 20 min. Cells were resuspended with 150 \u03bcl ice cold 1%BSA/DPBS solution. Transcriptome libraries were processed according to the manufacturer's instructions Single Cell three prime v3 kit. Transcriptome libraries were sequenced using NovaSeq S4 300 cycle kits Novogene and Novaseq SP 100 and 200 cycle kits MedGenome. To generate SABER seq libraries  samples post cDNA amplification and prior to fragmentation were split into two parts. One part was processed for transcriptome libraries as instructed by the manufacturer. The other part was processed for SABER seq libraries as follows. To enrich for SABER barcodes  5 \u00b5l of the whole transcriptome cDNA was PCR amplified with 10X p140v1UP  GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCT CGTCGTAGATCTTCATCGTGATG and 10XPCR1F CTACACGACGCTCTTCCGATCT primers and Q5 polymerase. The reaction conditions were: 98 \u00b0C  30 s; [98 \u00b0C  10 s; 68 \u00b0C  25 s; 72 \u00b0C  15 s] x 14 cycles; 72 \u00b0C  2 min. The reaction was cleaned up with 0.8X SPRISelect beads and eluted in 20 \u00b5l elution buffer EB buffer. A second PCR was carried out using 3 \u00b5l of PCR1 product and 10XPCR1F and 10X p140v2Sa GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCT GAAACTACATGGGGTcaGCTC primers. Reaction conditions were same as PCR1 except only 13 cycles were completed. At this point  2 different clean up conditions were used: iteration 1 SABER seq v0 and iteration 2 SABER seq v1. For iteration 1  the reaction was cleaned up with 0.8X SPRISelect beads and eluted in 20 \u00b5l elution buffer EB buffer similar to PCR1.  For iteration 2  an optimization was trialed  and the reaction was cleaned up with right side size selection using SPRIselect beads 0.6X beads used initially and supernatant was rebound with 1.5X beads. The product was eluted in 20 \u00b5l EB. Finally  adapters and sample indices were incorporated in a third PCR reaction using 5 7 \u00b5l of a 1:10 dilution of PCR2 product. Reactions conditions were same as PCR1 except 9 10 cycles were completed. Primers used: 10XP5Dual Hx AATGATACGGCGACCACCGAGATCTACAC xxxxxxx xxx ACACTCTTTCCCTACACGACGCTCTTCCGATCT  10XP7Dual H5 AATGATACGGCGAC CACCGAGATCTACAC xxxxxxxxxx ACACTCTTTCCCTACACGACGCTCTTCCGATCT  where x represents index bases from the 10X Dual Index Kit TT  SetA H index entries. For iteration 1  the third PCR reaction was cleaned up with 0.8X SPRISelect beads and eluted in 20 \u00b5l elution buffer EB to generate final libraries. For iteration 2  the third PCR reaction was cleaned up with right side size selection using SPRIselect beads 0.65X beads used initially and supernatant was rebound with 1.5X beads. SABER seq libraries were sequenced using Novaseq SP 200 cycle kits MedGenome with 5 10% PhiX spike in. Sequencing parameters: Read1 28 cycles  Read2 151 cycles  Index1 10 cycles  Index2 10 cycles. Standard sequencing primers were used.", null, "RNA-Seq", "TRANSCRIPTOMIC SINGLE CELL", "cDNA", "PAIRED", "ILLUMINA", "Illumina NovaSeq 6000", null, "SRP509892", null, "loader:fastq load.py", "SABERb2_brainD3_S5_L002_I1_001.fastq.gz SABERb2_brainD3_S5_L002_I2_001.fastq.gz SABERb2_brainD3_S5_L002_R1_001.fastq.gz SABERb2_brainD3_S5_L002_R2_001.fastq.gz", "fastq fastq fastq fastq", 3868768652.0, 27832868.0, "GSM8290049 r1", "0:10 1:10 2:28 3:91", "A:764050790;C:511319919;G:550304072;T:707108210;N:7997", 10, 10, 28, 91, 764050790, 511319919, 550304072, 707108210, 7997, "SRX24701863", "SRS21429392", "SRA1989588", "University of Pennsylvania", "University of Pennsylvania", 1, 0.91168, null, 0.2331, null, 0.75933, null, 0.55432, null, 91, null, "B", null, "usable mapping rate", "illumina", "novaseq_era", "full_length", "random_priming", "unknown", "sc", "single_cell_droplet", "10x", null, "United States", "2024-05-25", "Larval", "Larval", "Brain", "Nervous System"], [32383, "SRR29181687", "SRX24701863", "SRS21429392", "SRP509892", "PRJNA1116548", "Barcoding Notch signaling in the developing brain", "GSE268356", "Other", "Developmental signaling inputs are fundamental for shaping cell fates and behavior. However  traditional fluorescent based signaling reporters have limitations in scalability and molecular resolution of cell types. We present SABER seq  a CRISPR Cas molecular recorder that stores transient developmental signaling cues as permanent mutations in cellular genomes for deconstruction at later stages via single cell transcriptomics. We applied SABER seq to record Notch signaling in developing zebrafish brains. SABER seq has two components: a signaling sensor and a barcode recorder. The sensor activates Cas9 in a Notch dependent manner with inducible control while the recorder obtains mutations in ancestral cells where Notch is active. We combine SABER seq with an expanded juvenile brain atlas to identify cell types derived from Notch active founders. Our data reveals rare examples where differential Notch activity in ancestral progenitors is detected in terminally differentiated neuronal subtypes. SABER seq is a novel platform for rapid  scalable and high resolution mapping of signaling activity during development. Overall design: 10x Genomics libraries of single cell transcriptomes and SABER seq data  and genomic DNA SABER data", null, "pubmed:39575683", null, "SABERb2 brainD3  Medgenome Run2  brain2  transcriptome", "GSM8290049", null, "source name:zebrafish whole brain|tissue:zebrafish whole brain|developmental stage:21 dpf 23 dpf  Juvenile|genotype:WT/TLAB|geo loc name:missing|collection date:missing", "SABERb2 brainD3  Medgenome Run2  brain2  transcriptome", "Raw FASTQ reads were aligned against zebrafish genome  built from GRCz.109.gtf and the GRCz11.fa files  using the Cell Ranger count pipeline to generate gene by cell count matrices filtered barcode matrix. The count matrices were processed using the standard workflow of the Seurat v5.0.2 R package with modifications. Briefly  all count matrices were separately loaded into R. Seurat objects were then created with modified parameters  requiring features to be detected in at least 3 cells and cells to contain at least 300 genes. Subsequently  the Seurat objects were merged to create a single Seurat object. Cells with RNA counts between 300 and 4000 and a mitochondrial gene percentage of less than 9% were selected for downstream analysis. Gene expression values were log normalized with the default parameters and scaled for the top 3000 variable genes. The scaled data was then used to compute the 50 principal components. Cells were iteratively clustered using the Louvain algorithm post identifying neighboring cells. Differentially expressed genes for each cluster were identified using genes that are detected in at least 10% of cells in either of the two clusters and genes showing a minimum difference of 10% between the two clusters. Cell types were annotated using known marker genes.  The SABER seq FASTQ reads were processed using the Cell Ranger count pipeline  similar to the transcriptomic reads. A bash script was then used to generate a text file containing all FASTQ headers and error corrected cell barcodes for SABER seq data. post loading filtered barcode matrices into R  error corrected cell barcodes from transcriptomic reads were isolated and matched against those in the text file to identify common cell barcode that are shared between the transcriptomic and SABER seq datasets. The resulting text file contained redundant error corrected cell barcodes associated with different UMIs for the same cell. Thus  unique error corrected cell barcodes were isolated to create a text file containing unique FASTQ headers and error corrected barcodes. Subsequently  FASTQ reads matching the FASTQ headers in this file were extracted from the SABER seq raw reads using a Python script. SABER seq barcodes were analyzed using the scGESTALT pipeline available on Github as described previously7 35. To analyze differences in barcode detection and editing frequencies between cell types a binomial test was used. scRNA seq data was analyzed using the following protocol. Raw FASTQ reads were aligned against zebrafish genome  built from GRCz.109.gtf and the GRCz11.fa files  using the Cell Ranger count pipeline to generate gene by cell count matrices filtered barcode matrix. The count matrices were processed using the standard workflow of the Seurat v5.0.2 R package with modifications. Briefly  all count matrices were separately loaded into R. Seurat objects were then created with modified parameters  requiring features to be detected in at least 3 cells and cells to contain at least 300 genes. Subsequently  the Seurat objects were merged to create a single Seurat object. Cells with RNA counts between 300 and 4000 and a mitochondrial gene percentage of less than 9% were selected for downstream analysis. Gene expression values were log normalized with the default parameters and scaled for the top 3000 variable genes. The scaled data was then used to compute the 50 principal components. Cells were iteratively clustered using the Louvain algorithm post identifying neighboring cells. Differentially expressed genes for each cluster were identified using genes that are detected in at least 10% of cells in either of the two clusters and genes showing a minimum difference of 10% between the two clusters. Cell types were annotated using known marker genes. Finally  the data was visualized using UMAP. SABER seq barcode analysis was performed using the following protocol. The SABER seq FASTQ reads were processed using the Cell Ranger count pipeline.A bash script was then used to generate a text file containing all FASTQ headers and error corrected cell barcodes for SABER seq data. Error corrected cell barcodes from transcriptomic reads were isolated and matched against those in the text file to identify common cell barcode that are shared between the transcriptomic and SABER seq datasets. The resulting text file contained redundant error corrected cell barcodes associated with different UMIs for the same cell. Thus  unique error corrected cell barcodes were isolated to create a text file containing unique FASTQ headers and error corrected barcodes. Subsequently  FASTQ reads matching the FASTQ headers in this file were extracted from the SABER seq raw reads using a Python script. SABER seq barcodes were analyzed using the scGESTALT pipeline available on Github as described previously. To analyze differences in barcode detection and editing frequencies between cell types a binomial test was used. genome build/assembly: GRCz11 processed data files format and content: *.h5 files contain filtered features/genes and barcode data generated from cell ranger pipeline. processed data files format and content: LinC1 Filtered Fastq Headers CB.txt LinC2 Filtered Fastq Headers CB.txt LinD1 Filtered Fastq Headers CB.txt LinD2 Filtered Fastq Headers CB.txt LinD3 Filtered Fastq Headers CB.txt files contain filtered and unique fastq headers and cell barcodes post matching the trasncriptomic cell barcodes. processed data files format and content: SABERb1 linC1 S4 BrainC1 S6.CB.xlsx SABERb1 linC2 S5 BrainC2 S7.CB.xlsx SABERb2 linD1 S1 BrainD1 S8.CB.xlsx SABERb2 linD2 S2 BrainD2 S9.CB.xlsx SABERb2 linD3 S3 BrainD3 S10.CB.xlsx SABERb4 linB2.1 13.123.2.With.Primer.stats.xlsx SABERb4 linB2.2 14.124.2.With.Primer.stats.xlsx files contain the SABER Seq output post matching reads to the reference sequence.", "zebrafish whole brain", null, "Wild type and SABER x her4.3:switchCas9 zebrafish brains were harvested and dissociated at 21 dpf 23 dpf as described previously Raj et. al 2018  Nature Biotechnology  with the following modifications. All microcentrifuge tubes were treated with 1% BSA/DPBS overnight to prevent cell loss due to adhesion. Brains were dissociated with 1 mL of 20 units/ml papain in EBSS media and incubated at 37 \u00b0C for 20 min. Cells were resuspended with 150 \u03bcl ice cold 1%BSA/DPBS solution. Transcriptome libraries were processed according to the manufacturer\u2019s instructions Single Cell 3\u2019 v3 kit. Transcriptome libraries were sequenced using NovaSeq S4 300 cycle kits Novogene and Novaseq SP 100 and 200 cycle kits MedGenome. To generate SABER seq libraries  samples post cDNA amplification and prior to fragmentation were split into two parts. One part was processed for transcriptome libraries as instructed by the manufacturer. The other part was processed for SABER seq libraries as follows. To enrich for SABER barcodes  5 \u00b5l of the whole transcriptome cDNA was PCR amplified with 10X p140v1UP  GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCT CGTCGTAGATCTTCATCGTGATG and 10XPCR1F CTACACGACGCTCTTCCGATCT primers and Q5 polymerase. The reaction conditions were: 98 \u00b0C  30 s; [98 \u00b0C  10 s; 68 \u00b0C  25 s; 72 \u00b0C  15 s] x 14 cycles; 72 \u00b0C  2 min. The reaction was cleaned up with 0.8X SPRISelect beads and eluted in 20 \u00b5l elution buffer EB buffer. A second PCR was carried out using 3 \u00b5l of PCR1 product and 10XPCR1F and 10X p140v2Sa GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCT GAAACTACATGGGGTcaGCTC primers. Reaction conditions were same as PCR1 except only 13 cycles were completed. At this point  2 different clean up conditions were used: iteration 1 SABER seq v0 and iteration 2 SABER seq v1. For iteration 1  the reaction was cleaned up with 0.8X SPRISelect beads and eluted in 20 \u00b5l elution buffer EB buffer similar to PCR1.  For iteration 2  an optimization was trialed  and the reaction was cleaned up with right side size selection using SPRIselect beads 0.6X beads used initially and supernatant was rebound with 1.5X beads. The product was eluted in 20 \u00b5l EB. Finally  adapters and sample indices were incorporated in a third PCR reaction using 5 7 \u00b5l of a 1:10 dilution of PCR2 product. Reactions conditions were same as PCR1 except 9 10 cycles were completed. Primers used: 10XP5Dual Hx AATGATACGGCGACCACCGAGATCTACAC xxxxxxx xxx ACACTCTTTCCCTACACGACGCTCTTCCGATCT  10XP7Dual H5 AATGATACGGCGAC CACCGAGATCTACAC xxxxxxxxxx ACACTCTTTCCCTACACGACGCTCTTCCGATCT  where x represents index bases from the 10X Dual Index Kit TT  SetA H index entries. For iteration 1  the third PCR reaction was cleaned up with 0.8X SPRISelect beads and eluted in 20 \u00b5l elution buffer EB to generate final libraries. For iteration 2  the third PCR reaction was cleaned up with right side size selection using SPRIselect beads 0.65X beads used initially and supernatant was rebound with 1.5X beads. SABER seq libraries were sequenced using Novaseq SP 200 cycle kits MedGenome with 5 10% PhiX spike in. Sequencing parameters: Read1 28 cycles  Read2 151 cycles  Index1 10 cycles  Index2 10 cycles. Standard sequencing primers were used.", "This work was performed under protocol numbers 807110 and 807259  which were approved by the University of Pennsylvania\u2019s Office of Animal Welfare of Institutional Animal Care and Use Committee IACUC. All zebrafish work in this study follows the University of Pennsylvania Institutional Animal Care and Use Committee regulations.", "tissue:zebrafish whole brain|developmental stage:21 dpf 23 dpf  Juvenile|genotype:WT/TLAB", "GSM8290049", "GSM8290049: SABERb2 brainD3  Medgenome Run2  brain2  transcriptome; Danio rerio; RNA Seq", "GSM8290049 r1", "GSM8290049", "1", "Wild type and SABER x her4.3:switchCas9 zebrafish brains were harvested and dissociated at 21 dpf 23 dpf as described previously Raj et. al 2018  Nature Biotechnology  with the following modifications. All microcentrifuge tubes were treated with 1% BSA/DPBS overnight to prevent cell loss due to adhesion. Brains were dissociated with 1 mL of 20 units/ml papain in EBSS media and incubated at 37 \u00b0C for 20 min. Cells were resuspended with 150 \u03bcl ice cold 1%BSA/DPBS solution. Transcriptome libraries were processed according to the manufacturer's instructions Single Cell three prime v3 kit. Transcriptome libraries were sequenced using NovaSeq S4 300 cycle kits Novogene and Novaseq SP 100 and 200 cycle kits MedGenome. To generate SABER seq libraries  samples post cDNA amplification and prior to fragmentation were split into two parts. One part was processed for transcriptome libraries as instructed by the manufacturer. The other part was processed for SABER seq libraries as follows. To enrich for SABER barcodes  5 \u00b5l of the whole transcriptome cDNA was PCR amplified with 10X p140v1UP  GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCT CGTCGTAGATCTTCATCGTGATG and 10XPCR1F CTACACGACGCTCTTCCGATCT primers and Q5 polymerase. The reaction conditions were: 98 \u00b0C  30 s; [98 \u00b0C  10 s; 68 \u00b0C  25 s; 72 \u00b0C  15 s] x 14 cycles; 72 \u00b0C  2 min. The reaction was cleaned up with 0.8X SPRISelect beads and eluted in 20 \u00b5l elution buffer EB buffer. A second PCR was carried out using 3 \u00b5l of PCR1 product and 10XPCR1F and 10X p140v2Sa GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCT GAAACTACATGGGGTcaGCTC primers. Reaction conditions were same as PCR1 except only 13 cycles were completed. At this point  2 different clean up conditions were used: iteration 1 SABER seq v0 and iteration 2 SABER seq v1. For iteration 1  the reaction was cleaned up with 0.8X SPRISelect beads and eluted in 20 \u00b5l elution buffer EB buffer similar to PCR1.  For iteration 2  an optimization was trialed  and the reaction was cleaned up with right side size selection using SPRIselect beads 0.6X beads used initially and supernatant was rebound with 1.5X beads. The product was eluted in 20 \u00b5l EB. Finally  adapters and sample indices were incorporated in a third PCR reaction using 5 7 \u00b5l of a 1:10 dilution of PCR2 product. Reactions conditions were same as PCR1 except 9 10 cycles were completed. Primers used: 10XP5Dual Hx AATGATACGGCGACCACCGAGATCTACAC xxxxxxx xxx ACACTCTTTCCCTACACGACGCTCTTCCGATCT  10XP7Dual H5 AATGATACGGCGAC CACCGAGATCTACAC xxxxxxxxxx ACACTCTTTCCCTACACGACGCTCTTCCGATCT  where x represents index bases from the 10X Dual Index Kit TT  SetA H index entries. For iteration 1  the third PCR reaction was cleaned up with 0.8X SPRISelect beads and eluted in 20 \u00b5l elution buffer EB to generate final libraries. For iteration 2  the third PCR reaction was cleaned up with right side size selection using SPRIselect beads 0.65X beads used initially and supernatant was rebound with 1.5X beads. SABER seq libraries were sequenced using Novaseq SP 200 cycle kits MedGenome with 5 10% PhiX spike in. Sequencing parameters: Read1 28 cycles  Read2 151 cycles  Index1 10 cycles  Index2 10 cycles. Standard sequencing primers were used.", null, "RNA-Seq", "TRANSCRIPTOMIC SINGLE CELL", "cDNA", "PAIRED", "ILLUMINA", "Illumina NovaSeq 6000", null, "SRP509892", null, "loader:fastq load.py", "SABERb2_brainD3_S10_L002_I1_001.fastq.gz SABERb2_brainD3_S10_L002_I2_001.fastq.gz SABERb2_brainD3_S10_L002_R1_001.fastq.gz SABERb2_brainD3_S10_L002_R2_001.fastq.gz", "fastq fastq fastq fastq", 13116158150.0, 94360850.0, "GSM8290049 r2", "0:10 1:10 2:28 3:91", "A:2577084188;C:1740165795;G:1880702045;T:2388863291;N:22031", 10, 10, 28, 91, 2577084188, 1740165795, 1880702045, 2388863291, 22031, "SRX24701863", "SRS21429392", "SRA1989588", "University of Pennsylvania", "University of Pennsylvania", 1, 0.91186, null, 0.23328, null, 0.76088, null, 0.55129, null, 91, null, "B", null, "usable mapping rate", "illumina", "novaseq_era", "full_length", "random_priming", "unknown", "sc", "single_cell_droplet", "10x", null, "United States", "2024-05-25", "Larval", "Larval", "Brain", "Nervous System"], [32384, "SRR29181688", "SRX24701862", "SRS21429391", "SRP509892", "PRJNA1116548", "Barcoding Notch signaling in the developing brain", "GSE268356", "Other", "Developmental signaling inputs are fundamental for shaping cell fates and behavior. However  traditional fluorescent based signaling reporters have limitations in scalability and molecular resolution of cell types. We present SABER seq  a CRISPR Cas molecular recorder that stores transient developmental signaling cues as permanent mutations in cellular genomes for deconstruction at later stages via single cell transcriptomics. We applied SABER seq to record Notch signaling in developing zebrafish brains. SABER seq has two components: a signaling sensor and a barcode recorder. The sensor activates Cas9 in a Notch dependent manner with inducible control while the recorder obtains mutations in ancestral cells where Notch is active. We combine SABER seq with an expanded juvenile brain atlas to identify cell types derived from Notch active founders. Our data reveals rare examples where differential Notch activity in ancestral progenitors is detected in terminally differentiated neuronal subtypes. SABER seq is a novel platform for rapid  scalable and high resolution mapping of signaling activity during development. Overall design: 10x Genomics libraries of single cell transcriptomes and SABER seq data  and genomic DNA SABER data", null, "pubmed:39575683", null, "SABERb2 brainD2  Medgenome Run2  brain2  transcriptome", "GSM8290048", null, "source name:zebrafish whole brain|tissue:zebrafish whole brain|developmental stage:21 dpf 23 dpf  Juvenile|genotype:WT/TLAB|geo loc name:missing|collection date:missing", "SABERb2 brainD2  Medgenome Run2  brain2  transcriptome", "Raw FASTQ reads were aligned against zebrafish genome  built from GRCz.109.gtf and the GRCz11.fa files  using the Cell Ranger count pipeline to generate gene by cell count matrices filtered barcode matrix. The count matrices were processed using the standard workflow of the Seurat v5.0.2 R package with modifications. Briefly  all count matrices were separately loaded into R. Seurat objects were then created with modified parameters  requiring features to be detected in at least 3 cells and cells to contain at least 300 genes. Subsequently  the Seurat objects were merged to create a single Seurat object. Cells with RNA counts between 300 and 4000 and a mitochondrial gene percentage of less than 9% were selected for downstream analysis. Gene expression values were log normalized with the default parameters and scaled for the top 3000 variable genes. The scaled data was then used to compute the 50 principal components. Cells were iteratively clustered using the Louvain algorithm post identifying neighboring cells. Differentially expressed genes for each cluster were identified using genes that are detected in at least 10% of cells in either of the two clusters and genes showing a minimum difference of 10% between the two clusters. Cell types were annotated using known marker genes.  The SABER seq FASTQ reads were processed using the Cell Ranger count pipeline  similar to the transcriptomic reads. A bash script was then used to generate a text file containing all FASTQ headers and error corrected cell barcodes for SABER seq data. post loading filtered barcode matrices into R  error corrected cell barcodes from transcriptomic reads were isolated and matched against those in the text file to identify common cell barcode that are shared between the transcriptomic and SABER seq datasets. The resulting text file contained redundant error corrected cell barcodes associated with different UMIs for the same cell. Thus  unique error corrected cell barcodes were isolated to create a text file containing unique FASTQ headers and error corrected barcodes. Subsequently  FASTQ reads matching the FASTQ headers in this file were extracted from the SABER seq raw reads using a Python script. SABER seq barcodes were analyzed using the scGESTALT pipeline available on Github as described previously7 35. To analyze differences in barcode detection and editing frequencies between cell types a binomial test was used. scRNA seq data was analyzed using the following protocol. Raw FASTQ reads were aligned against zebrafish genome  built from GRCz.109.gtf and the GRCz11.fa files  using the Cell Ranger count pipeline to generate gene by cell count matrices filtered barcode matrix. The count matrices were processed using the standard workflow of the Seurat v5.0.2 R package with modifications. Briefly  all count matrices were separately loaded into R. Seurat objects were then created with modified parameters  requiring features to be detected in at least 3 cells and cells to contain at least 300 genes. Subsequently  the Seurat objects were merged to create a single Seurat object. Cells with RNA counts between 300 and 4000 and a mitochondrial gene percentage of less than 9% were selected for downstream analysis. Gene expression values were log normalized with the default parameters and scaled for the top 3000 variable genes. The scaled data was then used to compute the 50 principal components. Cells were iteratively clustered using the Louvain algorithm post identifying neighboring cells. Differentially expressed genes for each cluster were identified using genes that are detected in at least 10% of cells in either of the two clusters and genes showing a minimum difference of 10% between the two clusters. Cell types were annotated using known marker genes. Finally  the data was visualized using UMAP. SABER seq barcode analysis was performed using the following protocol. The SABER seq FASTQ reads were processed using the Cell Ranger count pipeline.A bash script was then used to generate a text file containing all FASTQ headers and error corrected cell barcodes for SABER seq data. Error corrected cell barcodes from transcriptomic reads were isolated and matched against those in the text file to identify common cell barcode that are shared between the transcriptomic and SABER seq datasets. The resulting text file contained redundant error corrected cell barcodes associated with different UMIs for the same cell. Thus  unique error corrected cell barcodes were isolated to create a text file containing unique FASTQ headers and error corrected barcodes. Subsequently  FASTQ reads matching the FASTQ headers in this file were extracted from the SABER seq raw reads using a Python script. SABER seq barcodes were analyzed using the scGESTALT pipeline available on Github as described previously. To analyze differences in barcode detection and editing frequencies between cell types a binomial test was used. genome build/assembly: GRCz11 processed data files format and content: *.h5 files contain filtered features/genes and barcode data generated from cell ranger pipeline. processed data files format and content: LinC1 Filtered Fastq Headers CB.txt LinC2 Filtered Fastq Headers CB.txt LinD1 Filtered Fastq Headers CB.txt LinD2 Filtered Fastq Headers CB.txt LinD3 Filtered Fastq Headers CB.txt files contain filtered and unique fastq headers and cell barcodes post matching the trasncriptomic cell barcodes. processed data files format and content: SABERb1 linC1 S4 BrainC1 S6.CB.xlsx SABERb1 linC2 S5 BrainC2 S7.CB.xlsx SABERb2 linD1 S1 BrainD1 S8.CB.xlsx SABERb2 linD2 S2 BrainD2 S9.CB.xlsx SABERb2 linD3 S3 BrainD3 S10.CB.xlsx SABERb4 linB2.1 13.123.2.With.Primer.stats.xlsx SABERb4 linB2.2 14.124.2.With.Primer.stats.xlsx files contain the SABER Seq output post matching reads to the reference sequence.", "zebrafish whole brain", null, "Wild type and SABER x her4.3:switchCas9 zebrafish brains were harvested and dissociated at 21 dpf 23 dpf as described previously Raj et. al 2018  Nature Biotechnology  with the following modifications. All microcentrifuge tubes were treated with 1% BSA/DPBS overnight to prevent cell loss due to adhesion. Brains were dissociated with 1 mL of 20 units/ml papain in EBSS media and incubated at 37 \u00b0C for 20 min. Cells were resuspended with 150 \u03bcl ice cold 1%BSA/DPBS solution. Transcriptome libraries were processed according to the manufacturer\u2019s instructions Single Cell 3\u2019 v3 kit. Transcriptome libraries were sequenced using NovaSeq S4 300 cycle kits Novogene and Novaseq SP 100 and 200 cycle kits MedGenome. To generate SABER seq libraries  samples post cDNA amplification and prior to fragmentation were split into two parts. One part was processed for transcriptome libraries as instructed by the manufacturer. The other part was processed for SABER seq libraries as follows. To enrich for SABER barcodes  5 \u00b5l of the whole transcriptome cDNA was PCR amplified with 10X p140v1UP  GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCT CGTCGTAGATCTTCATCGTGATG and 10XPCR1F CTACACGACGCTCTTCCGATCT primers and Q5 polymerase. The reaction conditions were: 98 \u00b0C  30 s; [98 \u00b0C  10 s; 68 \u00b0C  25 s; 72 \u00b0C  15 s] x 14 cycles; 72 \u00b0C  2 min. The reaction was cleaned up with 0.8X SPRISelect beads and eluted in 20 \u00b5l elution buffer EB buffer. A second PCR was carried out using 3 \u00b5l of PCR1 product and 10XPCR1F and 10X p140v2Sa GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCT GAAACTACATGGGGTcaGCTC primers. Reaction conditions were same as PCR1 except only 13 cycles were completed. At this point  2 different clean up conditions were used: iteration 1 SABER seq v0 and iteration 2 SABER seq v1. For iteration 1  the reaction was cleaned up with 0.8X SPRISelect beads and eluted in 20 \u00b5l elution buffer EB buffer similar to PCR1.  For iteration 2  an optimization was trialed  and the reaction was cleaned up with right side size selection using SPRIselect beads 0.6X beads used initially and supernatant was rebound with 1.5X beads. The product was eluted in 20 \u00b5l EB. Finally  adapters and sample indices were incorporated in a third PCR reaction using 5 7 \u00b5l of a 1:10 dilution of PCR2 product. Reactions conditions were same as PCR1 except 9 10 cycles were completed. Primers used: 10XP5Dual Hx AATGATACGGCGACCACCGAGATCTACAC xxxxxxx xxx ACACTCTTTCCCTACACGACGCTCTTCCGATCT  10XP7Dual H5 AATGATACGGCGAC CACCGAGATCTACAC xxxxxxxxxx ACACTCTTTCCCTACACGACGCTCTTCCGATCT  where x represents index bases from the 10X Dual Index Kit TT  SetA H index entries. For iteration 1  the third PCR reaction was cleaned up with 0.8X SPRISelect beads and eluted in 20 \u00b5l elution buffer EB to generate final libraries. For iteration 2  the third PCR reaction was cleaned up with right side size selection using SPRIselect beads 0.65X beads used initially and supernatant was rebound with 1.5X beads. SABER seq libraries were sequenced using Novaseq SP 200 cycle kits MedGenome with 5 10% PhiX spike in. Sequencing parameters: Read1 28 cycles  Read2 151 cycles  Index1 10 cycles  Index2 10 cycles. Standard sequencing primers were used.", "This work was performed under protocol numbers 807110 and 807259  which were approved by the University of Pennsylvania\u2019s Office of Animal Welfare of Institutional Animal Care and Use Committee IACUC. All zebrafish work in this study follows the University of Pennsylvania Institutional Animal Care and Use Committee regulations.", "tissue:zebrafish whole brain|developmental stage:21 dpf 23 dpf  Juvenile|genotype:WT/TLAB", "GSM8290048", "GSM8290048: SABERb2 brainD2  Medgenome Run2  brain2  transcriptome; Danio rerio; RNA Seq", "GSM8290048 r1", "GSM8290048", "1", "Wild type and SABER x her4.3:switchCas9 zebrafish brains were harvested and dissociated at 21 dpf 23 dpf as described previously Raj et. al 2018  Nature Biotechnology  with the following modifications. All microcentrifuge tubes were treated with 1% BSA/DPBS overnight to prevent cell loss due to adhesion. Brains were dissociated with 1 mL of 20 units/ml papain in EBSS media and incubated at 37 \u00b0C for 20 min. Cells were resuspended with 150 \u03bcl ice cold 1%BSA/DPBS solution. Transcriptome libraries were processed according to the manufacturer's instructions Single Cell three prime v3 kit. Transcriptome libraries were sequenced using NovaSeq S4 300 cycle kits Novogene and Novaseq SP 100 and 200 cycle kits MedGenome. To generate SABER seq libraries  samples post cDNA amplification and prior to fragmentation were split into two parts. One part was processed for transcriptome libraries as instructed by the manufacturer. The other part was processed for SABER seq libraries as follows. To enrich for SABER barcodes  5 \u00b5l of the whole transcriptome cDNA was PCR amplified with 10X p140v1UP  GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCT CGTCGTAGATCTTCATCGTGATG and 10XPCR1F CTACACGACGCTCTTCCGATCT primers and Q5 polymerase. The reaction conditions were: 98 \u00b0C  30 s; [98 \u00b0C  10 s; 68 \u00b0C  25 s; 72 \u00b0C  15 s] x 14 cycles; 72 \u00b0C  2 min. The reaction was cleaned up with 0.8X SPRISelect beads and eluted in 20 \u00b5l elution buffer EB buffer. A second PCR was carried out using 3 \u00b5l of PCR1 product and 10XPCR1F and 10X p140v2Sa GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCT GAAACTACATGGGGTcaGCTC primers. Reaction conditions were same as PCR1 except only 13 cycles were completed. At this point  2 different clean up conditions were used: iteration 1 SABER seq v0 and iteration 2 SABER seq v1. For iteration 1  the reaction was cleaned up with 0.8X SPRISelect beads and eluted in 20 \u00b5l elution buffer EB buffer similar to PCR1.  For iteration 2  an optimization was trialed  and the reaction was cleaned up with right side size selection using SPRIselect beads 0.6X beads used initially and supernatant was rebound with 1.5X beads. The product was eluted in 20 \u00b5l EB. Finally  adapters and sample indices were incorporated in a third PCR reaction using 5 7 \u00b5l of a 1:10 dilution of PCR2 product. Reactions conditions were same as PCR1 except 9 10 cycles were completed. Primers used: 10XP5Dual Hx AATGATACGGCGACCACCGAGATCTACAC xxxxxxx xxx ACACTCTTTCCCTACACGACGCTCTTCCGATCT  10XP7Dual H5 AATGATACGGCGAC CACCGAGATCTACAC xxxxxxxxxx ACACTCTTTCCCTACACGACGCTCTTCCGATCT  where x represents index bases from the 10X Dual Index Kit TT  SetA H index entries. For iteration 1  the third PCR reaction was cleaned up with 0.8X SPRISelect beads and eluted in 20 \u00b5l elution buffer EB to generate final libraries. For iteration 2  the third PCR reaction was cleaned up with right side size selection using SPRIselect beads 0.65X beads used initially and supernatant was rebound with 1.5X beads. SABER seq libraries were sequenced using Novaseq SP 200 cycle kits MedGenome with 5 10% PhiX spike in. Sequencing parameters: Read1 28 cycles  Read2 151 cycles  Index1 10 cycles  Index2 10 cycles. Standard sequencing primers were used.", null, "RNA-Seq", "TRANSCRIPTOMIC SINGLE CELL", "cDNA", "PAIRED", "ILLUMINA", "Illumina NovaSeq 6000", null, "SRP509892", null, "loader:fastq load.py", "SABERb2_brainD2_S9_L002_I1_001.fastq.gz SABERb2_brainD2_S9_L002_I2_001.fastq.gz SABERb2_brainD2_S9_L002_R1_001.fastq.gz SABERb2_brainD2_S9_L002_R2_001.fastq.gz", "fastq fastq fastq fastq", 11592953894.0, 83402546.0, "GSM8290048 r2", "0:10 1:10 2:28 3:91", "A:2297377264;C:1526045472;G:1656319764;T:2109869221;N:19965", 10, 10, 28, 91, 2297377264, 1526045472, 1656319764, 2109869221, 19965, "SRX24701862", "SRS21429391", "SRA1989588", "University of Pennsylvania", "University of Pennsylvania", 1, 0.90894, null, 0.24209, null, 0.76106, null, 0.55154, null, 91, null, "B", null, "usable mapping rate", "illumina", "novaseq_era", "full_length", "random_priming", "unknown", "sc", "single_cell_droplet", "10x", null, "United States", "2024-05-25", "Larval", "Larval", "Brain", "Nervous System"], [32385, "SRR29181700", "SRX24701862", "SRS21429391", "SRP509892", "PRJNA1116548", "Barcoding Notch signaling in the developing brain", "GSE268356", "Other", "Developmental signaling inputs are fundamental for shaping cell fates and behavior. However  traditional fluorescent based signaling reporters have limitations in scalability and molecular resolution of cell types. We present SABER seq  a CRISPR Cas molecular recorder that stores transient developmental signaling cues as permanent mutations in cellular genomes for deconstruction at later stages via single cell transcriptomics. We applied SABER seq to record Notch signaling in developing zebrafish brains. SABER seq has two components: a signaling sensor and a barcode recorder. The sensor activates Cas9 in a Notch dependent manner with inducible control while the recorder obtains mutations in ancestral cells where Notch is active. We combine SABER seq with an expanded juvenile brain atlas to identify cell types derived from Notch active founders. Our data reveals rare examples where differential Notch activity in ancestral progenitors is detected in terminally differentiated neuronal subtypes. SABER seq is a novel platform for rapid  scalable and high resolution mapping of signaling activity during development. Overall design: 10x Genomics libraries of single cell transcriptomes and SABER seq data  and genomic DNA SABER data", null, "pubmed:39575683", null, "SABERb2 brainD2  Medgenome Run2  brain2  transcriptome", "GSM8290048", null, "source name:zebrafish whole brain|tissue:zebrafish whole brain|developmental stage:21 dpf 23 dpf  Juvenile|genotype:WT/TLAB|geo loc name:missing|collection date:missing", "SABERb2 brainD2  Medgenome Run2  brain2  transcriptome", "Raw FASTQ reads were aligned against zebrafish genome  built from GRCz.109.gtf and the GRCz11.fa files  using the Cell Ranger count pipeline to generate gene by cell count matrices filtered barcode matrix. The count matrices were processed using the standard workflow of the Seurat v5.0.2 R package with modifications. Briefly  all count matrices were separately loaded into R. Seurat objects were then created with modified parameters  requiring features to be detected in at least 3 cells and cells to contain at least 300 genes. Subsequently  the Seurat objects were merged to create a single Seurat object. Cells with RNA counts between 300 and 4000 and a mitochondrial gene percentage of less than 9% were selected for downstream analysis. Gene expression values were log normalized with the default parameters and scaled for the top 3000 variable genes. The scaled data was then used to compute the 50 principal components. Cells were iteratively clustered using the Louvain algorithm post identifying neighboring cells. Differentially expressed genes for each cluster were identified using genes that are detected in at least 10% of cells in either of the two clusters and genes showing a minimum difference of 10% between the two clusters. Cell types were annotated using known marker genes.  The SABER seq FASTQ reads were processed using the Cell Ranger count pipeline  similar to the transcriptomic reads. A bash script was then used to generate a text file containing all FASTQ headers and error corrected cell barcodes for SABER seq data. post loading filtered barcode matrices into R  error corrected cell barcodes from transcriptomic reads were isolated and matched against those in the text file to identify common cell barcode that are shared between the transcriptomic and SABER seq datasets. The resulting text file contained redundant error corrected cell barcodes associated with different UMIs for the same cell. Thus  unique error corrected cell barcodes were isolated to create a text file containing unique FASTQ headers and error corrected barcodes. Subsequently  FASTQ reads matching the FASTQ headers in this file were extracted from the SABER seq raw reads using a Python script. SABER seq barcodes were analyzed using the scGESTALT pipeline available on Github as described previously7 35. To analyze differences in barcode detection and editing frequencies between cell types a binomial test was used. scRNA seq data was analyzed using the following protocol. Raw FASTQ reads were aligned against zebrafish genome  built from GRCz.109.gtf and the GRCz11.fa files  using the Cell Ranger count pipeline to generate gene by cell count matrices filtered barcode matrix. The count matrices were processed using the standard workflow of the Seurat v5.0.2 R package with modifications. Briefly  all count matrices were separately loaded into R. Seurat objects were then created with modified parameters  requiring features to be detected in at least 3 cells and cells to contain at least 300 genes. Subsequently  the Seurat objects were merged to create a single Seurat object. Cells with RNA counts between 300 and 4000 and a mitochondrial gene percentage of less than 9% were selected for downstream analysis. Gene expression values were log normalized with the default parameters and scaled for the top 3000 variable genes. The scaled data was then used to compute the 50 principal components. Cells were iteratively clustered using the Louvain algorithm post identifying neighboring cells. Differentially expressed genes for each cluster were identified using genes that are detected in at least 10% of cells in either of the two clusters and genes showing a minimum difference of 10% between the two clusters. Cell types were annotated using known marker genes. Finally  the data was visualized using UMAP. SABER seq barcode analysis was performed using the following protocol. The SABER seq FASTQ reads were processed using the Cell Ranger count pipeline.A bash script was then used to generate a text file containing all FASTQ headers and error corrected cell barcodes for SABER seq data. Error corrected cell barcodes from transcriptomic reads were isolated and matched against those in the text file to identify common cell barcode that are shared between the transcriptomic and SABER seq datasets. The resulting text file contained redundant error corrected cell barcodes associated with different UMIs for the same cell. Thus  unique error corrected cell barcodes were isolated to create a text file containing unique FASTQ headers and error corrected barcodes. Subsequently  FASTQ reads matching the FASTQ headers in this file were extracted from the SABER seq raw reads using a Python script. SABER seq barcodes were analyzed using the scGESTALT pipeline available on Github as described previously. To analyze differences in barcode detection and editing frequencies between cell types a binomial test was used. genome build/assembly: GRCz11 processed data files format and content: *.h5 files contain filtered features/genes and barcode data generated from cell ranger pipeline. processed data files format and content: LinC1 Filtered Fastq Headers CB.txt LinC2 Filtered Fastq Headers CB.txt LinD1 Filtered Fastq Headers CB.txt LinD2 Filtered Fastq Headers CB.txt LinD3 Filtered Fastq Headers CB.txt files contain filtered and unique fastq headers and cell barcodes post matching the trasncriptomic cell barcodes. processed data files format and content: SABERb1 linC1 S4 BrainC1 S6.CB.xlsx SABERb1 linC2 S5 BrainC2 S7.CB.xlsx SABERb2 linD1 S1 BrainD1 S8.CB.xlsx SABERb2 linD2 S2 BrainD2 S9.CB.xlsx SABERb2 linD3 S3 BrainD3 S10.CB.xlsx SABERb4 linB2.1 13.123.2.With.Primer.stats.xlsx SABERb4 linB2.2 14.124.2.With.Primer.stats.xlsx files contain the SABER Seq output post matching reads to the reference sequence.", "zebrafish whole brain", null, "Wild type and SABER x her4.3:switchCas9 zebrafish brains were harvested and dissociated at 21 dpf 23 dpf as described previously Raj et. al 2018  Nature Biotechnology  with the following modifications. All microcentrifuge tubes were treated with 1% BSA/DPBS overnight to prevent cell loss due to adhesion. Brains were dissociated with 1 mL of 20 units/ml papain in EBSS media and incubated at 37 \u00b0C for 20 min. Cells were resuspended with 150 \u03bcl ice cold 1%BSA/DPBS solution. Transcriptome libraries were processed according to the manufacturer\u2019s instructions Single Cell 3\u2019 v3 kit. Transcriptome libraries were sequenced using NovaSeq S4 300 cycle kits Novogene and Novaseq SP 100 and 200 cycle kits MedGenome. To generate SABER seq libraries  samples post cDNA amplification and prior to fragmentation were split into two parts. One part was processed for transcriptome libraries as instructed by the manufacturer. The other part was processed for SABER seq libraries as follows. To enrich for SABER barcodes  5 \u00b5l of the whole transcriptome cDNA was PCR amplified with 10X p140v1UP  GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCT CGTCGTAGATCTTCATCGTGATG and 10XPCR1F CTACACGACGCTCTTCCGATCT primers and Q5 polymerase. The reaction conditions were: 98 \u00b0C  30 s; [98 \u00b0C  10 s; 68 \u00b0C  25 s; 72 \u00b0C  15 s] x 14 cycles; 72 \u00b0C  2 min. The reaction was cleaned up with 0.8X SPRISelect beads and eluted in 20 \u00b5l elution buffer EB buffer. A second PCR was carried out using 3 \u00b5l of PCR1 product and 10XPCR1F and 10X p140v2Sa GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCT GAAACTACATGGGGTcaGCTC primers. Reaction conditions were same as PCR1 except only 13 cycles were completed. At this point  2 different clean up conditions were used: iteration 1 SABER seq v0 and iteration 2 SABER seq v1. For iteration 1  the reaction was cleaned up with 0.8X SPRISelect beads and eluted in 20 \u00b5l elution buffer EB buffer similar to PCR1.  For iteration 2  an optimization was trialed  and the reaction was cleaned up with right side size selection using SPRIselect beads 0.6X beads used initially and supernatant was rebound with 1.5X beads. The product was eluted in 20 \u00b5l EB. Finally  adapters and sample indices were incorporated in a third PCR reaction using 5 7 \u00b5l of a 1:10 dilution of PCR2 product. Reactions conditions were same as PCR1 except 9 10 cycles were completed. Primers used: 10XP5Dual Hx AATGATACGGCGACCACCGAGATCTACAC xxxxxxx xxx ACACTCTTTCCCTACACGACGCTCTTCCGATCT  10XP7Dual H5 AATGATACGGCGAC CACCGAGATCTACAC xxxxxxxxxx ACACTCTTTCCCTACACGACGCTCTTCCGATCT  where x represents index bases from the 10X Dual Index Kit TT  SetA H index entries. For iteration 1  the third PCR reaction was cleaned up with 0.8X SPRISelect beads and eluted in 20 \u00b5l elution buffer EB to generate final libraries. For iteration 2  the third PCR reaction was cleaned up with right side size selection using SPRIselect beads 0.65X beads used initially and supernatant was rebound with 1.5X beads. SABER seq libraries were sequenced using Novaseq SP 200 cycle kits MedGenome with 5 10% PhiX spike in. Sequencing parameters: Read1 28 cycles  Read2 151 cycles  Index1 10 cycles  Index2 10 cycles. Standard sequencing primers were used.", "This work was performed under protocol numbers 807110 and 807259  which were approved by the University of Pennsylvania\u2019s Office of Animal Welfare of Institutional Animal Care and Use Committee IACUC. All zebrafish work in this study follows the University of Pennsylvania Institutional Animal Care and Use Committee regulations.", "tissue:zebrafish whole brain|developmental stage:21 dpf 23 dpf  Juvenile|genotype:WT/TLAB", "GSM8290048", "GSM8290048: SABERb2 brainD2  Medgenome Run2  brain2  transcriptome; Danio rerio; RNA Seq", "GSM8290048 r1", "GSM8290048", "1", "Wild type and SABER x her4.3:switchCas9 zebrafish brains were harvested and dissociated at 21 dpf 23 dpf as described previously Raj et. al 2018  Nature Biotechnology  with the following modifications. All microcentrifuge tubes were treated with 1% BSA/DPBS overnight to prevent cell loss due to adhesion. Brains were dissociated with 1 mL of 20 units/ml papain in EBSS media and incubated at 37 \u00b0C for 20 min. Cells were resuspended with 150 \u03bcl ice cold 1%BSA/DPBS solution. Transcriptome libraries were processed according to the manufacturer's instructions Single Cell three prime v3 kit. Transcriptome libraries were sequenced using NovaSeq S4 300 cycle kits Novogene and Novaseq SP 100 and 200 cycle kits MedGenome. To generate SABER seq libraries  samples post cDNA amplification and prior to fragmentation were split into two parts. One part was processed for transcriptome libraries as instructed by the manufacturer. The other part was processed for SABER seq libraries as follows. To enrich for SABER barcodes  5 \u00b5l of the whole transcriptome cDNA was PCR amplified with 10X p140v1UP  GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCT CGTCGTAGATCTTCATCGTGATG and 10XPCR1F CTACACGACGCTCTTCCGATCT primers and Q5 polymerase. The reaction conditions were: 98 \u00b0C  30 s; [98 \u00b0C  10 s; 68 \u00b0C  25 s; 72 \u00b0C  15 s] x 14 cycles; 72 \u00b0C  2 min. The reaction was cleaned up with 0.8X SPRISelect beads and eluted in 20 \u00b5l elution buffer EB buffer. A second PCR was carried out using 3 \u00b5l of PCR1 product and 10XPCR1F and 10X p140v2Sa GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCT GAAACTACATGGGGTcaGCTC primers. Reaction conditions were same as PCR1 except only 13 cycles were completed. At this point  2 different clean up conditions were used: iteration 1 SABER seq v0 and iteration 2 SABER seq v1. For iteration 1  the reaction was cleaned up with 0.8X SPRISelect beads and eluted in 20 \u00b5l elution buffer EB buffer similar to PCR1.  For iteration 2  an optimization was trialed  and the reaction was cleaned up with right side size selection using SPRIselect beads 0.6X beads used initially and supernatant was rebound with 1.5X beads. The product was eluted in 20 \u00b5l EB. Finally  adapters and sample indices were incorporated in a third PCR reaction using 5 7 \u00b5l of a 1:10 dilution of PCR2 product. Reactions conditions were same as PCR1 except 9 10 cycles were completed. Primers used: 10XP5Dual Hx AATGATACGGCGACCACCGAGATCTACAC xxxxxxx xxx ACACTCTTTCCCTACACGACGCTCTTCCGATCT  10XP7Dual H5 AATGATACGGCGAC CACCGAGATCTACAC xxxxxxxxxx ACACTCTTTCCCTACACGACGCTCTTCCGATCT  where x represents index bases from the 10X Dual Index Kit TT  SetA H index entries. For iteration 1  the third PCR reaction was cleaned up with 0.8X SPRISelect beads and eluted in 20 \u00b5l elution buffer EB to generate final libraries. For iteration 2  the third PCR reaction was cleaned up with right side size selection using SPRIselect beads 0.65X beads used initially and supernatant was rebound with 1.5X beads. SABER seq libraries were sequenced using Novaseq SP 200 cycle kits MedGenome with 5 10% PhiX spike in. Sequencing parameters: Read1 28 cycles  Read2 151 cycles  Index1 10 cycles  Index2 10 cycles. Standard sequencing primers were used.", null, "RNA-Seq", "TRANSCRIPTOMIC SINGLE CELL", "cDNA", "PAIRED", "ILLUMINA", "Illumina NovaSeq 6000", null, "SRP509892", null, "loader:fastq load.py", "SABERb2_brainD2_S4_L002_R2_001.fastq.gz SABERb2_brainD2_S4_L002_R1_001.fastq.gz SABERb2_brainD2_S4_L002_I2_001.fastq.gz SABERb2_brainD2_S4_L002_I1_001.fastq.gz", "fastq fastq fastq fastq", 3703512386.0, 26643974.0, "GSM8290048 r1", "0:10 1:10 2:28 3:91", "A:738064510;C:484883852;G:525588346;T:676057082;N:7844", 10, 10, 28, 91, 738064510, 484883852, 525588346, 676057082, 7844, "SRX24701862", "SRS21429391", "SRA1989588", "University of Pennsylvania", "University of Pennsylvania", 1, 0.90684, null, 0.24166, null, 0.75982, null, 0.55283, null, 91, null, "B", null, "usable mapping rate", "illumina", "novaseq_era", "full_length", "random_priming", "unknown", "sc", "single_cell_droplet", "10x", null, "United States", "2024-05-25", "Larval", "Larval", "Brain", "Nervous System"], [32386, "SRR29181698", "SRX24701861", "SRS21429390", "SRP509892", "PRJNA1116548", "Barcoding Notch signaling in the developing brain", "GSE268356", "Other", "Developmental signaling inputs are fundamental for shaping cell fates and behavior. However  traditional fluorescent based signaling reporters have limitations in scalability and molecular resolution of cell types. We present SABER seq  a CRISPR Cas molecular recorder that stores transient developmental signaling cues as permanent mutations in cellular genomes for deconstruction at later stages via single cell transcriptomics. We applied SABER seq to record Notch signaling in developing zebrafish brains. SABER seq has two components: a signaling sensor and a barcode recorder. The sensor activates Cas9 in a Notch dependent manner with inducible control while the recorder obtains mutations in ancestral cells where Notch is active. We combine SABER seq with an expanded juvenile brain atlas to identify cell types derived from Notch active founders. Our data reveals rare examples where differential Notch activity in ancestral progenitors is detected in terminally differentiated neuronal subtypes. SABER seq is a novel platform for rapid  scalable and high resolution mapping of signaling activity during development. Overall design: 10x Genomics libraries of single cell transcriptomes and SABER seq data  and genomic DNA SABER data", null, "pubmed:39575683", null, "SABERb2 brainD1  Medgenome Run2  brain2  transcriptome", "GSM8290047", null, "source name:zebrafish whole brain|tissue:zebrafish whole brain|developmental stage:21 dpf 23 dpf  Juvenile|genotype:WT/TLAB|geo loc name:missing|collection date:missing", "SABERb2 brainD1  Medgenome Run2  brain2  transcriptome", "Raw FASTQ reads were aligned against zebrafish genome  built from GRCz.109.gtf and the GRCz11.fa files  using the Cell Ranger count pipeline to generate gene by cell count matrices filtered barcode matrix. The count matrices were processed using the standard workflow of the Seurat v5.0.2 R package with modifications. Briefly  all count matrices were separately loaded into R. Seurat objects were then created with modified parameters  requiring features to be detected in at least 3 cells and cells to contain at least 300 genes. Subsequently  the Seurat objects were merged to create a single Seurat object. Cells with RNA counts between 300 and 4000 and a mitochondrial gene percentage of less than 9% were selected for downstream analysis. Gene expression values were log normalized with the default parameters and scaled for the top 3000 variable genes. The scaled data was then used to compute the 50 principal components. Cells were iteratively clustered using the Louvain algorithm post identifying neighboring cells. Differentially expressed genes for each cluster were identified using genes that are detected in at least 10% of cells in either of the two clusters and genes showing a minimum difference of 10% between the two clusters. Cell types were annotated using known marker genes.  The SABER seq FASTQ reads were processed using the Cell Ranger count pipeline  similar to the transcriptomic reads. A bash script was then used to generate a text file containing all FASTQ headers and error corrected cell barcodes for SABER seq data. post loading filtered barcode matrices into R  error corrected cell barcodes from transcriptomic reads were isolated and matched against those in the text file to identify common cell barcode that are shared between the transcriptomic and SABER seq datasets. The resulting text file contained redundant error corrected cell barcodes associated with different UMIs for the same cell. Thus  unique error corrected cell barcodes were isolated to create a text file containing unique FASTQ headers and error corrected barcodes. Subsequently  FASTQ reads matching the FASTQ headers in this file were extracted from the SABER seq raw reads using a Python script. SABER seq barcodes were analyzed using the scGESTALT pipeline available on Github as described previously7 35. To analyze differences in barcode detection and editing frequencies between cell types a binomial test was used. scRNA seq data was analyzed using the following protocol. Raw FASTQ reads were aligned against zebrafish genome  built from GRCz.109.gtf and the GRCz11.fa files  using the Cell Ranger count pipeline to generate gene by cell count matrices filtered barcode matrix. The count matrices were processed using the standard workflow of the Seurat v5.0.2 R package with modifications. Briefly  all count matrices were separately loaded into R. Seurat objects were then created with modified parameters  requiring features to be detected in at least 3 cells and cells to contain at least 300 genes. Subsequently  the Seurat objects were merged to create a single Seurat object. Cells with RNA counts between 300 and 4000 and a mitochondrial gene percentage of less than 9% were selected for downstream analysis. Gene expression values were log normalized with the default parameters and scaled for the top 3000 variable genes. The scaled data was then used to compute the 50 principal components. Cells were iteratively clustered using the Louvain algorithm post identifying neighboring cells. Differentially expressed genes for each cluster were identified using genes that are detected in at least 10% of cells in either of the two clusters and genes showing a minimum difference of 10% between the two clusters. Cell types were annotated using known marker genes. Finally  the data was visualized using UMAP. SABER seq barcode analysis was performed using the following protocol. The SABER seq FASTQ reads were processed using the Cell Ranger count pipeline.A bash script was then used to generate a text file containing all FASTQ headers and error corrected cell barcodes for SABER seq data. Error corrected cell barcodes from transcriptomic reads were isolated and matched against those in the text file to identify common cell barcode that are shared between the transcriptomic and SABER seq datasets. The resulting text file contained redundant error corrected cell barcodes associated with different UMIs for the same cell. Thus  unique error corrected cell barcodes were isolated to create a text file containing unique FASTQ headers and error corrected barcodes. Subsequently  FASTQ reads matching the FASTQ headers in this file were extracted from the SABER seq raw reads using a Python script. SABER seq barcodes were analyzed using the scGESTALT pipeline available on Github as described previously. To analyze differences in barcode detection and editing frequencies between cell types a binomial test was used. genome build/assembly: GRCz11 processed data files format and content: *.h5 files contain filtered features/genes and barcode data generated from cell ranger pipeline. processed data files format and content: LinC1 Filtered Fastq Headers CB.txt LinC2 Filtered Fastq Headers CB.txt LinD1 Filtered Fastq Headers CB.txt LinD2 Filtered Fastq Headers CB.txt LinD3 Filtered Fastq Headers CB.txt files contain filtered and unique fastq headers and cell barcodes post matching the trasncriptomic cell barcodes. processed data files format and content: SABERb1 linC1 S4 BrainC1 S6.CB.xlsx SABERb1 linC2 S5 BrainC2 S7.CB.xlsx SABERb2 linD1 S1 BrainD1 S8.CB.xlsx SABERb2 linD2 S2 BrainD2 S9.CB.xlsx SABERb2 linD3 S3 BrainD3 S10.CB.xlsx SABERb4 linB2.1 13.123.2.With.Primer.stats.xlsx SABERb4 linB2.2 14.124.2.With.Primer.stats.xlsx files contain the SABER Seq output post matching reads to the reference sequence.", "zebrafish whole brain", null, "Wild type and SABER x her4.3:switchCas9 zebrafish brains were harvested and dissociated at 21 dpf 23 dpf as described previously Raj et. al 2018  Nature Biotechnology  with the following modifications. All microcentrifuge tubes were treated with 1% BSA/DPBS overnight to prevent cell loss due to adhesion. Brains were dissociated with 1 mL of 20 units/ml papain in EBSS media and incubated at 37 \u00b0C for 20 min. Cells were resuspended with 150 \u03bcl ice cold 1%BSA/DPBS solution. Transcriptome libraries were processed according to the manufacturer\u2019s instructions Single Cell 3\u2019 v3 kit. Transcriptome libraries were sequenced using NovaSeq S4 300 cycle kits Novogene and Novaseq SP 100 and 200 cycle kits MedGenome. To generate SABER seq libraries  samples post cDNA amplification and prior to fragmentation were split into two parts. One part was processed for transcriptome libraries as instructed by the manufacturer. The other part was processed for SABER seq libraries as follows. To enrich for SABER barcodes  5 \u00b5l of the whole transcriptome cDNA was PCR amplified with 10X p140v1UP  GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCT CGTCGTAGATCTTCATCGTGATG and 10XPCR1F CTACACGACGCTCTTCCGATCT primers and Q5 polymerase. The reaction conditions were: 98 \u00b0C  30 s; [98 \u00b0C  10 s; 68 \u00b0C  25 s; 72 \u00b0C  15 s] x 14 cycles; 72 \u00b0C  2 min. The reaction was cleaned up with 0.8X SPRISelect beads and eluted in 20 \u00b5l elution buffer EB buffer. A second PCR was carried out using 3 \u00b5l of PCR1 product and 10XPCR1F and 10X p140v2Sa GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCT GAAACTACATGGGGTcaGCTC primers. Reaction conditions were same as PCR1 except only 13 cycles were completed. At this point  2 different clean up conditions were used: iteration 1 SABER seq v0 and iteration 2 SABER seq v1. For iteration 1  the reaction was cleaned up with 0.8X SPRISelect beads and eluted in 20 \u00b5l elution buffer EB buffer similar to PCR1.  For iteration 2  an optimization was trialed  and the reaction was cleaned up with right side size selection using SPRIselect beads 0.6X beads used initially and supernatant was rebound with 1.5X beads. The product was eluted in 20 \u00b5l EB. Finally  adapters and sample indices were incorporated in a third PCR reaction using 5 7 \u00b5l of a 1:10 dilution of PCR2 product. Reactions conditions were same as PCR1 except 9 10 cycles were completed. Primers used: 10XP5Dual Hx AATGATACGGCGACCACCGAGATCTACAC xxxxxxx xxx ACACTCTTTCCCTACACGACGCTCTTCCGATCT  10XP7Dual H5 AATGATACGGCGAC CACCGAGATCTACAC xxxxxxxxxx ACACTCTTTCCCTACACGACGCTCTTCCGATCT  where x represents index bases from the 10X Dual Index Kit TT  SetA H index entries. For iteration 1  the third PCR reaction was cleaned up with 0.8X SPRISelect beads and eluted in 20 \u00b5l elution buffer EB to generate final libraries. For iteration 2  the third PCR reaction was cleaned up with right side size selection using SPRIselect beads 0.65X beads used initially and supernatant was rebound with 1.5X beads. SABER seq libraries were sequenced using Novaseq SP 200 cycle kits MedGenome with 5 10% PhiX spike in. Sequencing parameters: Read1 28 cycles  Read2 151 cycles  Index1 10 cycles  Index2 10 cycles. Standard sequencing primers were used.", "This work was performed under protocol numbers 807110 and 807259  which were approved by the University of Pennsylvania\u2019s Office of Animal Welfare of Institutional Animal Care and Use Committee IACUC. All zebrafish work in this study follows the University of Pennsylvania Institutional Animal Care and Use Committee regulations.", "tissue:zebrafish whole brain|developmental stage:21 dpf 23 dpf  Juvenile|genotype:WT/TLAB", "GSM8290047", "GSM8290047: SABERb2 brainD1  Medgenome Run2  brain2  transcriptome; Danio rerio; RNA Seq", "GSM8290047 r1", "GSM8290047", "1", "Wild type and SABER x her4.3:switchCas9 zebrafish brains were harvested and dissociated at 21 dpf 23 dpf as described previously Raj et. al 2018  Nature Biotechnology  with the following modifications. All microcentrifuge tubes were treated with 1% BSA/DPBS overnight to prevent cell loss due to adhesion. Brains were dissociated with 1 mL of 20 units/ml papain in EBSS media and incubated at 37 \u00b0C for 20 min. Cells were resuspended with 150 \u03bcl ice cold 1%BSA/DPBS solution. Transcriptome libraries were processed according to the manufacturer's instructions Single Cell three prime v3 kit. Transcriptome libraries were sequenced using NovaSeq S4 300 cycle kits Novogene and Novaseq SP 100 and 200 cycle kits MedGenome. To generate SABER seq libraries  samples post cDNA amplification and prior to fragmentation were split into two parts. One part was processed for transcriptome libraries as instructed by the manufacturer. The other part was processed for SABER seq libraries as follows. To enrich for SABER barcodes  5 \u00b5l of the whole transcriptome cDNA was PCR amplified with 10X p140v1UP  GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCT CGTCGTAGATCTTCATCGTGATG and 10XPCR1F CTACACGACGCTCTTCCGATCT primers and Q5 polymerase. The reaction conditions were: 98 \u00b0C  30 s; [98 \u00b0C  10 s; 68 \u00b0C  25 s; 72 \u00b0C  15 s] x 14 cycles; 72 \u00b0C  2 min. The reaction was cleaned up with 0.8X SPRISelect beads and eluted in 20 \u00b5l elution buffer EB buffer. A second PCR was carried out using 3 \u00b5l of PCR1 product and 10XPCR1F and 10X p140v2Sa GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCT GAAACTACATGGGGTcaGCTC primers. Reaction conditions were same as PCR1 except only 13 cycles were completed. At this point  2 different clean up conditions were used: iteration 1 SABER seq v0 and iteration 2 SABER seq v1. For iteration 1  the reaction was cleaned up with 0.8X SPRISelect beads and eluted in 20 \u00b5l elution buffer EB buffer similar to PCR1.  For iteration 2  an optimization was trialed  and the reaction was cleaned up with right side size selection using SPRIselect beads 0.6X beads used initially and supernatant was rebound with 1.5X beads. The product was eluted in 20 \u00b5l EB. Finally  adapters and sample indices were incorporated in a third PCR reaction using 5 7 \u00b5l of a 1:10 dilution of PCR2 product. Reactions conditions were same as PCR1 except 9 10 cycles were completed. Primers used: 10XP5Dual Hx AATGATACGGCGACCACCGAGATCTACAC xxxxxxx xxx ACACTCTTTCCCTACACGACGCTCTTCCGATCT  10XP7Dual H5 AATGATACGGCGAC CACCGAGATCTACAC xxxxxxxxxx ACACTCTTTCCCTACACGACGCTCTTCCGATCT  where x represents index bases from the 10X Dual Index Kit TT  SetA H index entries. For iteration 1  the third PCR reaction was cleaned up with 0.8X SPRISelect beads and eluted in 20 \u00b5l elution buffer EB to generate final libraries. For iteration 2  the third PCR reaction was cleaned up with right side size selection using SPRIselect beads 0.65X beads used initially and supernatant was rebound with 1.5X beads. SABER seq libraries were sequenced using Novaseq SP 200 cycle kits MedGenome with 5 10% PhiX spike in. Sequencing parameters: Read1 28 cycles  Read2 151 cycles  Index1 10 cycles  Index2 10 cycles. Standard sequencing primers were used.", null, "RNA-Seq", "TRANSCRIPTOMIC SINGLE CELL", "cDNA", "PAIRED", "ILLUMINA", "Illumina NovaSeq 6000", null, "SRP509892", null, "loader:fastq load.py", "SABERb2_brainD1_S3_L002_R2_001.fastq.gz SABERb2_brainD1_S3_L002_R1_001.fastq.gz SABERb2_brainD1_S3_L002_I2_001.fastq.gz SABERb2_brainD1_S3_L002_I1_001.fastq.gz", "fastq fastq fastq fastq", 4228010816.0, 30417344.0, "GSM8290047 r1", "0:10 1:10 2:28 3:91", "A:837646182;C:556752228;G:601753997;T:771817125;N:8772", 10, 10, 28, 91, 837646182, 556752228, 601753997, 771817125, 8772, "SRX24701861", "SRS21429390", "SRA1989588", "University of Pennsylvania", "University of Pennsylvania", 1, 0.90883, null, 0.23676, null, 0.75885, null, 0.54147, null, 91, null, "B", null, "usable mapping rate", "illumina", "novaseq_era", "full_length", "random_priming", "unknown", "sc", "single_cell_droplet", "10x", null, "United States", "2024-05-25", "Larval", "Larval", "Brain", "Nervous System"], [32387, "SRR29181699", "SRX24701861", "SRS21429390", "SRP509892", "PRJNA1116548", "Barcoding Notch signaling in the developing brain", "GSE268356", "Other", "Developmental signaling inputs are fundamental for shaping cell fates and behavior. However  traditional fluorescent based signaling reporters have limitations in scalability and molecular resolution of cell types. We present SABER seq  a CRISPR Cas molecular recorder that stores transient developmental signaling cues as permanent mutations in cellular genomes for deconstruction at later stages via single cell transcriptomics. We applied SABER seq to record Notch signaling in developing zebrafish brains. SABER seq has two components: a signaling sensor and a barcode recorder. The sensor activates Cas9 in a Notch dependent manner with inducible control while the recorder obtains mutations in ancestral cells where Notch is active. We combine SABER seq with an expanded juvenile brain atlas to identify cell types derived from Notch active founders. Our data reveals rare examples where differential Notch activity in ancestral progenitors is detected in terminally differentiated neuronal subtypes. SABER seq is a novel platform for rapid  scalable and high resolution mapping of signaling activity during development. Overall design: 10x Genomics libraries of single cell transcriptomes and SABER seq data  and genomic DNA SABER data", null, "pubmed:39575683", null, "SABERb2 brainD1  Medgenome Run2  brain2  transcriptome", "GSM8290047", null, "source name:zebrafish whole brain|tissue:zebrafish whole brain|developmental stage:21 dpf 23 dpf  Juvenile|genotype:WT/TLAB|geo loc name:missing|collection date:missing", "SABERb2 brainD1  Medgenome Run2  brain2  transcriptome", "Raw FASTQ reads were aligned against zebrafish genome  built from GRCz.109.gtf and the GRCz11.fa files  using the Cell Ranger count pipeline to generate gene by cell count matrices filtered barcode matrix. The count matrices were processed using the standard workflow of the Seurat v5.0.2 R package with modifications. Briefly  all count matrices were separately loaded into R. Seurat objects were then created with modified parameters  requiring features to be detected in at least 3 cells and cells to contain at least 300 genes. Subsequently  the Seurat objects were merged to create a single Seurat object. Cells with RNA counts between 300 and 4000 and a mitochondrial gene percentage of less than 9% were selected for downstream analysis. Gene expression values were log normalized with the default parameters and scaled for the top 3000 variable genes. The scaled data was then used to compute the 50 principal components. Cells were iteratively clustered using the Louvain algorithm post identifying neighboring cells. Differentially expressed genes for each cluster were identified using genes that are detected in at least 10% of cells in either of the two clusters and genes showing a minimum difference of 10% between the two clusters. Cell types were annotated using known marker genes.  The SABER seq FASTQ reads were processed using the Cell Ranger count pipeline  similar to the transcriptomic reads. A bash script was then used to generate a text file containing all FASTQ headers and error corrected cell barcodes for SABER seq data. post loading filtered barcode matrices into R  error corrected cell barcodes from transcriptomic reads were isolated and matched against those in the text file to identify common cell barcode that are shared between the transcriptomic and SABER seq datasets. The resulting text file contained redundant error corrected cell barcodes associated with different UMIs for the same cell. Thus  unique error corrected cell barcodes were isolated to create a text file containing unique FASTQ headers and error corrected barcodes. Subsequently  FASTQ reads matching the FASTQ headers in this file were extracted from the SABER seq raw reads using a Python script. SABER seq barcodes were analyzed using the scGESTALT pipeline available on Github as described previously7 35. To analyze differences in barcode detection and editing frequencies between cell types a binomial test was used. scRNA seq data was analyzed using the following protocol. Raw FASTQ reads were aligned against zebrafish genome  built from GRCz.109.gtf and the GRCz11.fa files  using the Cell Ranger count pipeline to generate gene by cell count matrices filtered barcode matrix. The count matrices were processed using the standard workflow of the Seurat v5.0.2 R package with modifications. Briefly  all count matrices were separately loaded into R. Seurat objects were then created with modified parameters  requiring features to be detected in at least 3 cells and cells to contain at least 300 genes. Subsequently  the Seurat objects were merged to create a single Seurat object. Cells with RNA counts between 300 and 4000 and a mitochondrial gene percentage of less than 9% were selected for downstream analysis. Gene expression values were log normalized with the default parameters and scaled for the top 3000 variable genes. The scaled data was then used to compute the 50 principal components. Cells were iteratively clustered using the Louvain algorithm post identifying neighboring cells. Differentially expressed genes for each cluster were identified using genes that are detected in at least 10% of cells in either of the two clusters and genes showing a minimum difference of 10% between the two clusters. Cell types were annotated using known marker genes. Finally  the data was visualized using UMAP. SABER seq barcode analysis was performed using the following protocol. The SABER seq FASTQ reads were processed using the Cell Ranger count pipeline.A bash script was then used to generate a text file containing all FASTQ headers and error corrected cell barcodes for SABER seq data. Error corrected cell barcodes from transcriptomic reads were isolated and matched against those in the text file to identify common cell barcode that are shared between the transcriptomic and SABER seq datasets. The resulting text file contained redundant error corrected cell barcodes associated with different UMIs for the same cell. Thus  unique error corrected cell barcodes were isolated to create a text file containing unique FASTQ headers and error corrected barcodes. Subsequently  FASTQ reads matching the FASTQ headers in this file were extracted from the SABER seq raw reads using a Python script. SABER seq barcodes were analyzed using the scGESTALT pipeline available on Github as described previously. To analyze differences in barcode detection and editing frequencies between cell types a binomial test was used. genome build/assembly: GRCz11 processed data files format and content: *.h5 files contain filtered features/genes and barcode data generated from cell ranger pipeline. processed data files format and content: LinC1 Filtered Fastq Headers CB.txt LinC2 Filtered Fastq Headers CB.txt LinD1 Filtered Fastq Headers CB.txt LinD2 Filtered Fastq Headers CB.txt LinD3 Filtered Fastq Headers CB.txt files contain filtered and unique fastq headers and cell barcodes post matching the trasncriptomic cell barcodes. processed data files format and content: SABERb1 linC1 S4 BrainC1 S6.CB.xlsx SABERb1 linC2 S5 BrainC2 S7.CB.xlsx SABERb2 linD1 S1 BrainD1 S8.CB.xlsx SABERb2 linD2 S2 BrainD2 S9.CB.xlsx SABERb2 linD3 S3 BrainD3 S10.CB.xlsx SABERb4 linB2.1 13.123.2.With.Primer.stats.xlsx SABERb4 linB2.2 14.124.2.With.Primer.stats.xlsx files contain the SABER Seq output post matching reads to the reference sequence.", "zebrafish whole brain", null, "Wild type and SABER x her4.3:switchCas9 zebrafish brains were harvested and dissociated at 21 dpf 23 dpf as described previously Raj et. al 2018  Nature Biotechnology  with the following modifications. All microcentrifuge tubes were treated with 1% BSA/DPBS overnight to prevent cell loss due to adhesion. Brains were dissociated with 1 mL of 20 units/ml papain in EBSS media and incubated at 37 \u00b0C for 20 min. Cells were resuspended with 150 \u03bcl ice cold 1%BSA/DPBS solution. Transcriptome libraries were processed according to the manufacturer\u2019s instructions Single Cell 3\u2019 v3 kit. Transcriptome libraries were sequenced using NovaSeq S4 300 cycle kits Novogene and Novaseq SP 100 and 200 cycle kits MedGenome. To generate SABER seq libraries  samples post cDNA amplification and prior to fragmentation were split into two parts. One part was processed for transcriptome libraries as instructed by the manufacturer. The other part was processed for SABER seq libraries as follows. To enrich for SABER barcodes  5 \u00b5l of the whole transcriptome cDNA was PCR amplified with 10X p140v1UP  GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCT CGTCGTAGATCTTCATCGTGATG and 10XPCR1F CTACACGACGCTCTTCCGATCT primers and Q5 polymerase. The reaction conditions were: 98 \u00b0C  30 s; [98 \u00b0C  10 s; 68 \u00b0C  25 s; 72 \u00b0C  15 s] x 14 cycles; 72 \u00b0C  2 min. The reaction was cleaned up with 0.8X SPRISelect beads and eluted in 20 \u00b5l elution buffer EB buffer. A second PCR was carried out using 3 \u00b5l of PCR1 product and 10XPCR1F and 10X p140v2Sa GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCT GAAACTACATGGGGTcaGCTC primers. Reaction conditions were same as PCR1 except only 13 cycles were completed. At this point  2 different clean up conditions were used: iteration 1 SABER seq v0 and iteration 2 SABER seq v1. For iteration 1  the reaction was cleaned up with 0.8X SPRISelect beads and eluted in 20 \u00b5l elution buffer EB buffer similar to PCR1.  For iteration 2  an optimization was trialed  and the reaction was cleaned up with right side size selection using SPRIselect beads 0.6X beads used initially and supernatant was rebound with 1.5X beads. The product was eluted in 20 \u00b5l EB. Finally  adapters and sample indices were incorporated in a third PCR reaction using 5 7 \u00b5l of a 1:10 dilution of PCR2 product. Reactions conditions were same as PCR1 except 9 10 cycles were completed. Primers used: 10XP5Dual Hx AATGATACGGCGACCACCGAGATCTACAC xxxxxxx xxx ACACTCTTTCCCTACACGACGCTCTTCCGATCT  10XP7Dual H5 AATGATACGGCGAC CACCGAGATCTACAC xxxxxxxxxx ACACTCTTTCCCTACACGACGCTCTTCCGATCT  where x represents index bases from the 10X Dual Index Kit TT  SetA H index entries. For iteration 1  the third PCR reaction was cleaned up with 0.8X SPRISelect beads and eluted in 20 \u00b5l elution buffer EB to generate final libraries. For iteration 2  the third PCR reaction was cleaned up with right side size selection using SPRIselect beads 0.65X beads used initially and supernatant was rebound with 1.5X beads. SABER seq libraries were sequenced using Novaseq SP 200 cycle kits MedGenome with 5 10% PhiX spike in. Sequencing parameters: Read1 28 cycles  Read2 151 cycles  Index1 10 cycles  Index2 10 cycles. Standard sequencing primers were used.", "This work was performed under protocol numbers 807110 and 807259  which were approved by the University of Pennsylvania\u2019s Office of Animal Welfare of Institutional Animal Care and Use Committee IACUC. All zebrafish work in this study follows the University of Pennsylvania Institutional Animal Care and Use Committee regulations.", "tissue:zebrafish whole brain|developmental stage:21 dpf 23 dpf  Juvenile|genotype:WT/TLAB", "GSM8290047", "GSM8290047: SABERb2 brainD1  Medgenome Run2  brain2  transcriptome; Danio rerio; RNA Seq", "GSM8290047 r1", "GSM8290047", "1", "Wild type and SABER x her4.3:switchCas9 zebrafish brains were harvested and dissociated at 21 dpf 23 dpf as described previously Raj et. al 2018  Nature Biotechnology  with the following modifications. All microcentrifuge tubes were treated with 1% BSA/DPBS overnight to prevent cell loss due to adhesion. Brains were dissociated with 1 mL of 20 units/ml papain in EBSS media and incubated at 37 \u00b0C for 20 min. Cells were resuspended with 150 \u03bcl ice cold 1%BSA/DPBS solution. Transcriptome libraries were processed according to the manufacturer's instructions Single Cell three prime v3 kit. Transcriptome libraries were sequenced using NovaSeq S4 300 cycle kits Novogene and Novaseq SP 100 and 200 cycle kits MedGenome. To generate SABER seq libraries  samples post cDNA amplification and prior to fragmentation were split into two parts. One part was processed for transcriptome libraries as instructed by the manufacturer. The other part was processed for SABER seq libraries as follows. To enrich for SABER barcodes  5 \u00b5l of the whole transcriptome cDNA was PCR amplified with 10X p140v1UP  GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCT CGTCGTAGATCTTCATCGTGATG and 10XPCR1F CTACACGACGCTCTTCCGATCT primers and Q5 polymerase. The reaction conditions were: 98 \u00b0C  30 s; [98 \u00b0C  10 s; 68 \u00b0C  25 s; 72 \u00b0C  15 s] x 14 cycles; 72 \u00b0C  2 min. The reaction was cleaned up with 0.8X SPRISelect beads and eluted in 20 \u00b5l elution buffer EB buffer. A second PCR was carried out using 3 \u00b5l of PCR1 product and 10XPCR1F and 10X p140v2Sa GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCT GAAACTACATGGGGTcaGCTC primers. Reaction conditions were same as PCR1 except only 13 cycles were completed. At this point  2 different clean up conditions were used: iteration 1 SABER seq v0 and iteration 2 SABER seq v1. For iteration 1  the reaction was cleaned up with 0.8X SPRISelect beads and eluted in 20 \u00b5l elution buffer EB buffer similar to PCR1.  For iteration 2  an optimization was trialed  and the reaction was cleaned up with right side size selection using SPRIselect beads 0.6X beads used initially and supernatant was rebound with 1.5X beads. The product was eluted in 20 \u00b5l EB. Finally  adapters and sample indices were incorporated in a third PCR reaction using 5 7 \u00b5l of a 1:10 dilution of PCR2 product. Reactions conditions were same as PCR1 except 9 10 cycles were completed. Primers used: 10XP5Dual Hx AATGATACGGCGACCACCGAGATCTACAC xxxxxxx xxx ACACTCTTTCCCTACACGACGCTCTTCCGATCT  10XP7Dual H5 AATGATACGGCGAC CACCGAGATCTACAC xxxxxxxxxx ACACTCTTTCCCTACACGACGCTCTTCCGATCT  where x represents index bases from the 10X Dual Index Kit TT  SetA H index entries. For iteration 1  the third PCR reaction was cleaned up with 0.8X SPRISelect beads and eluted in 20 \u00b5l elution buffer EB to generate final libraries. For iteration 2  the third PCR reaction was cleaned up with right side size selection using SPRIselect beads 0.65X beads used initially and supernatant was rebound with 1.5X beads. SABER seq libraries were sequenced using Novaseq SP 200 cycle kits MedGenome with 5 10% PhiX spike in. Sequencing parameters: Read1 28 cycles  Read2 151 cycles  Index1 10 cycles  Index2 10 cycles. Standard sequencing primers were used.", null, "RNA-Seq", "TRANSCRIPTOMIC SINGLE CELL", "cDNA", "PAIRED", "ILLUMINA", "Illumina NovaSeq 6000", null, "SRP509892", null, "loader:fastq load.py", "SABERb2_brainD1_S8_L002_R2_001.fastq.gz SABERb2_brainD1_S8_L002_R1_001.fastq.gz SABERb2_brainD1_S8_L002_I2_001.fastq.gz SABERb2_brainD1_S8_L002_I1_001.fastq.gz", "fastq fastq fastq fastq", 13317155486.0, 95806874.0, "GSM8290047 r2", "0:10 1:10 2:28 3:91", "A:2627361333;C:1762774581;G:1907012086;T:2421255352;N:22182", 10, 10, 28, 91, 2627361333, 1762774581, 1907012086, 2421255352, 22182, "SRX24701861", "SRS21429390", "SRA1989588", "University of Pennsylvania", "University of Pennsylvania", 1, 0.91093, null, 0.23629, null, 0.75913, null, 0.55525, null, 91, null, "B", null, "usable mapping rate", "illumina", "novaseq_era", "full_length", "random_priming", "unknown", "sc", "single_cell_droplet", "10x", null, "United States", "2024-05-25", "Larval", "Larval", "Brain", "Nervous System"], [32388, "SRR29181697", "SRX24701860", "SRS21429389", "SRP509892", "PRJNA1116548", "Barcoding Notch signaling in the developing brain", "GSE268356", "Other", "Developmental signaling inputs are fundamental for shaping cell fates and behavior. However  traditional fluorescent based signaling reporters have limitations in scalability and molecular resolution of cell types. We present SABER seq  a CRISPR Cas molecular recorder that stores transient developmental signaling cues as permanent mutations in cellular genomes for deconstruction at later stages via single cell transcriptomics. We applied SABER seq to record Notch signaling in developing zebrafish brains. SABER seq has two components: a signaling sensor and a barcode recorder. The sensor activates Cas9 in a Notch dependent manner with inducible control while the recorder obtains mutations in ancestral cells where Notch is active. We combine SABER seq with an expanded juvenile brain atlas to identify cell types derived from Notch active founders. Our data reveals rare examples where differential Notch activity in ancestral progenitors is detected in terminally differentiated neuronal subtypes. SABER seq is a novel platform for rapid  scalable and high resolution mapping of signaling activity during development. Overall design: 10x Genomics libraries of single cell transcriptomes and SABER seq data  and genomic DNA SABER data", null, "pubmed:39575683", null, "SABERb1 brainC2091523wk3  Novogene Run1  brain1  transcriptome", "GSM8290046", null, "source name:zebrafish whole brain|tissue:zebrafish whole brain|developmental stage:21 dpf 23 dpf  Juvenile|genotype:WT/TLAB|geo loc name:missing|collection date:missing", "SABERb1 brainC2091523wk3  Novogene Run1  brain1  transcriptome", "Raw FASTQ reads were aligned against zebrafish genome  built from GRCz.109.gtf and the GRCz11.fa files  using the Cell Ranger count pipeline to generate gene by cell count matrices filtered barcode matrix. The count matrices were processed using the standard workflow of the Seurat v5.0.2 R package with modifications. Briefly  all count matrices were separately loaded into R. Seurat objects were then created with modified parameters  requiring features to be detected in at least 3 cells and cells to contain at least 300 genes. Subsequently  the Seurat objects were merged to create a single Seurat object. Cells with RNA counts between 300 and 4000 and a mitochondrial gene percentage of less than 9% were selected for downstream analysis. Gene expression values were log normalized with the default parameters and scaled for the top 3000 variable genes. The scaled data was then used to compute the 50 principal components. Cells were iteratively clustered using the Louvain algorithm post identifying neighboring cells. Differentially expressed genes for each cluster were identified using genes that are detected in at least 10% of cells in either of the two clusters and genes showing a minimum difference of 10% between the two clusters. Cell types were annotated using known marker genes.  The SABER seq FASTQ reads were processed using the Cell Ranger count pipeline  similar to the transcriptomic reads. A bash script was then used to generate a text file containing all FASTQ headers and error corrected cell barcodes for SABER seq data. post loading filtered barcode matrices into R  error corrected cell barcodes from transcriptomic reads were isolated and matched against those in the text file to identify common cell barcode that are shared between the transcriptomic and SABER seq datasets. The resulting text file contained redundant error corrected cell barcodes associated with different UMIs for the same cell. Thus  unique error corrected cell barcodes were isolated to create a text file containing unique FASTQ headers and error corrected barcodes. Subsequently  FASTQ reads matching the FASTQ headers in this file were extracted from the SABER seq raw reads using a Python script. SABER seq barcodes were analyzed using the scGESTALT pipeline available on Github as described previously7 35. To analyze differences in barcode detection and editing frequencies between cell types a binomial test was used. scRNA seq data was analyzed using the following protocol. Raw FASTQ reads were aligned against zebrafish genome  built from GRCz.109.gtf and the GRCz11.fa files  using the Cell Ranger count pipeline to generate gene by cell count matrices filtered barcode matrix. The count matrices were processed using the standard workflow of the Seurat v5.0.2 R package with modifications. Briefly  all count matrices were separately loaded into R. Seurat objects were then created with modified parameters  requiring features to be detected in at least 3 cells and cells to contain at least 300 genes. Subsequently  the Seurat objects were merged to create a single Seurat object. Cells with RNA counts between 300 and 4000 and a mitochondrial gene percentage of less than 9% were selected for downstream analysis. Gene expression values were log normalized with the default parameters and scaled for the top 3000 variable genes. The scaled data was then used to compute the 50 principal components. Cells were iteratively clustered using the Louvain algorithm post identifying neighboring cells. Differentially expressed genes for each cluster were identified using genes that are detected in at least 10% of cells in either of the two clusters and genes showing a minimum difference of 10% between the two clusters. Cell types were annotated using known marker genes. Finally  the data was visualized using UMAP. SABER seq barcode analysis was performed using the following protocol. The SABER seq FASTQ reads were processed using the Cell Ranger count pipeline.A bash script was then used to generate a text file containing all FASTQ headers and error corrected cell barcodes for SABER seq data. Error corrected cell barcodes from transcriptomic reads were isolated and matched against those in the text file to identify common cell barcode that are shared between the transcriptomic and SABER seq datasets. The resulting text file contained redundant error corrected cell barcodes associated with different UMIs for the same cell. Thus  unique error corrected cell barcodes were isolated to create a text file containing unique FASTQ headers and error corrected barcodes. Subsequently  FASTQ reads matching the FASTQ headers in this file were extracted from the SABER seq raw reads using a Python script. SABER seq barcodes were analyzed using the scGESTALT pipeline available on Github as described previously. To analyze differences in barcode detection and editing frequencies between cell types a binomial test was used. genome build/assembly: GRCz11 processed data files format and content: *.h5 files contain filtered features/genes and barcode data generated from cell ranger pipeline. processed data files format and content: LinC1 Filtered Fastq Headers CB.txt LinC2 Filtered Fastq Headers CB.txt LinD1 Filtered Fastq Headers CB.txt LinD2 Filtered Fastq Headers CB.txt LinD3 Filtered Fastq Headers CB.txt files contain filtered and unique fastq headers and cell barcodes post matching the trasncriptomic cell barcodes. processed data files format and content: SABERb1 linC1 S4 BrainC1 S6.CB.xlsx SABERb1 linC2 S5 BrainC2 S7.CB.xlsx SABERb2 linD1 S1 BrainD1 S8.CB.xlsx SABERb2 linD2 S2 BrainD2 S9.CB.xlsx SABERb2 linD3 S3 BrainD3 S10.CB.xlsx SABERb4 linB2.1 13.123.2.With.Primer.stats.xlsx SABERb4 linB2.2 14.124.2.With.Primer.stats.xlsx files contain the SABER Seq output post matching reads to the reference sequence.", "zebrafish whole brain", null, "Wild type and SABER x her4.3:switchCas9 zebrafish brains were harvested and dissociated at 21 dpf 23 dpf as described previously Raj et. al 2018  Nature Biotechnology  with the following modifications. All microcentrifuge tubes were treated with 1% BSA/DPBS overnight to prevent cell loss due to adhesion. Brains were dissociated with 1 mL of 20 units/ml papain in EBSS media and incubated at 37 \u00b0C for 20 min. Cells were resuspended with 150 \u03bcl ice cold 1%BSA/DPBS solution. Transcriptome libraries were processed according to the manufacturer\u2019s instructions Single Cell 3\u2019 v3 kit. Transcriptome libraries were sequenced using NovaSeq S4 300 cycle kits Novogene and Novaseq SP 100 and 200 cycle kits MedGenome. To generate SABER seq libraries  samples post cDNA amplification and prior to fragmentation were split into two parts. One part was processed for transcriptome libraries as instructed by the manufacturer. The other part was processed for SABER seq libraries as follows. To enrich for SABER barcodes  5 \u00b5l of the whole transcriptome cDNA was PCR amplified with 10X p140v1UP  GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCT CGTCGTAGATCTTCATCGTGATG and 10XPCR1F CTACACGACGCTCTTCCGATCT primers and Q5 polymerase. The reaction conditions were: 98 \u00b0C  30 s; [98 \u00b0C  10 s; 68 \u00b0C  25 s; 72 \u00b0C  15 s] x 14 cycles; 72 \u00b0C  2 min. The reaction was cleaned up with 0.8X SPRISelect beads and eluted in 20 \u00b5l elution buffer EB buffer. A second PCR was carried out using 3 \u00b5l of PCR1 product and 10XPCR1F and 10X p140v2Sa GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCT GAAACTACATGGGGTcaGCTC primers. Reaction conditions were same as PCR1 except only 13 cycles were completed. At this point  2 different clean up conditions were used: iteration 1 SABER seq v0 and iteration 2 SABER seq v1. For iteration 1  the reaction was cleaned up with 0.8X SPRISelect beads and eluted in 20 \u00b5l elution buffer EB buffer similar to PCR1.  For iteration 2  an optimization was trialed  and the reaction was cleaned up with right side size selection using SPRIselect beads 0.6X beads used initially and supernatant was rebound with 1.5X beads. The product was eluted in 20 \u00b5l EB. Finally  adapters and sample indices were incorporated in a third PCR reaction using 5 7 \u00b5l of a 1:10 dilution of PCR2 product. Reactions conditions were same as PCR1 except 9 10 cycles were completed. Primers used: 10XP5Dual Hx AATGATACGGCGACCACCGAGATCTACAC xxxxxxx xxx ACACTCTTTCCCTACACGACGCTCTTCCGATCT  10XP7Dual H5 AATGATACGGCGAC CACCGAGATCTACAC xxxxxxxxxx ACACTCTTTCCCTACACGACGCTCTTCCGATCT  where x represents index bases from the 10X Dual Index Kit TT  SetA H index entries. For iteration 1  the third PCR reaction was cleaned up with 0.8X SPRISelect beads and eluted in 20 \u00b5l elution buffer EB to generate final libraries. For iteration 2  the third PCR reaction was cleaned up with right side size selection using SPRIselect beads 0.65X beads used initially and supernatant was rebound with 1.5X beads. SABER seq libraries were sequenced using Novaseq SP 200 cycle kits MedGenome with 5 10% PhiX spike in. Sequencing parameters: Read1 28 cycles  Read2 151 cycles  Index1 10 cycles  Index2 10 cycles. Standard sequencing primers were used.", "This work was performed under protocol numbers 807110 and 807259  which were approved by the University of Pennsylvania\u2019s Office of Animal Welfare of Institutional Animal Care and Use Committee IACUC. All zebrafish work in this study follows the University of Pennsylvania Institutional Animal Care and Use Committee regulations.", "tissue:zebrafish whole brain|developmental stage:21 dpf 23 dpf  Juvenile|genotype:WT/TLAB", "GSM8290046", "GSM8290046: SABERb1 brainC2091523wk3  Novogene Run1  brain1  transcriptome; Danio rerio; RNA Seq", "GSM8290046 r1", "GSM8290046", "1", "Wild type and SABER x her4.3:switchCas9 zebrafish brains were harvested and dissociated at 21 dpf 23 dpf as described previously Raj et. al 2018  Nature Biotechnology  with the following modifications. All microcentrifuge tubes were treated with 1% BSA/DPBS overnight to prevent cell loss due to adhesion. Brains were dissociated with 1 mL of 20 units/ml papain in EBSS media and incubated at 37 \u00b0C for 20 min. Cells were resuspended with 150 \u03bcl ice cold 1%BSA/DPBS solution. Transcriptome libraries were processed according to the manufacturer's instructions Single Cell three prime v3 kit. Transcriptome libraries were sequenced using NovaSeq S4 300 cycle kits Novogene and Novaseq SP 100 and 200 cycle kits MedGenome. To generate SABER seq libraries  samples post cDNA amplification and prior to fragmentation were split into two parts. One part was processed for transcriptome libraries as instructed by the manufacturer. The other part was processed for SABER seq libraries as follows. To enrich for SABER barcodes  5 \u00b5l of the whole transcriptome cDNA was PCR amplified with 10X p140v1UP  GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCT CGTCGTAGATCTTCATCGTGATG and 10XPCR1F CTACACGACGCTCTTCCGATCT primers and Q5 polymerase. The reaction conditions were: 98 \u00b0C  30 s; [98 \u00b0C  10 s; 68 \u00b0C  25 s; 72 \u00b0C  15 s] x 14 cycles; 72 \u00b0C  2 min. The reaction was cleaned up with 0.8X SPRISelect beads and eluted in 20 \u00b5l elution buffer EB buffer. A second PCR was carried out using 3 \u00b5l of PCR1 product and 10XPCR1F and 10X p140v2Sa GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCT GAAACTACATGGGGTcaGCTC primers. Reaction conditions were same as PCR1 except only 13 cycles were completed. At this point  2 different clean up conditions were used: iteration 1 SABER seq v0 and iteration 2 SABER seq v1. For iteration 1  the reaction was cleaned up with 0.8X SPRISelect beads and eluted in 20 \u00b5l elution buffer EB buffer similar to PCR1.  For iteration 2  an optimization was trialed  and the reaction was cleaned up with right side size selection using SPRIselect beads 0.6X beads used initially and supernatant was rebound with 1.5X beads. The product was eluted in 20 \u00b5l EB. Finally  adapters and sample indices were incorporated in a third PCR reaction using 5 7 \u00b5l of a 1:10 dilution of PCR2 product. Reactions conditions were same as PCR1 except 9 10 cycles were completed. Primers used: 10XP5Dual Hx AATGATACGGCGACCACCGAGATCTACAC xxxxxxx xxx ACACTCTTTCCCTACACGACGCTCTTCCGATCT  10XP7Dual H5 AATGATACGGCGAC CACCGAGATCTACAC xxxxxxxxxx ACACTCTTTCCCTACACGACGCTCTTCCGATCT  where x represents index bases from the 10X Dual Index Kit TT  SetA H index entries. For iteration 1  the third PCR reaction was cleaned up with 0.8X SPRISelect beads and eluted in 20 \u00b5l elution buffer EB to generate final libraries. For iteration 2  the third PCR reaction was cleaned up with right side size selection using SPRIselect beads 0.65X beads used initially and supernatant was rebound with 1.5X beads. SABER seq libraries were sequenced using Novaseq SP 200 cycle kits MedGenome with 5 10% PhiX spike in. Sequencing parameters: Read1 28 cycles  Read2 151 cycles  Index1 10 cycles  Index2 10 cycles. Standard sequencing primers were used.", null, "RNA-Seq", "TRANSCRIPTOMIC SINGLE CELL", "cDNA", "PAIRED", "ILLUMINA", "Illumina NovaSeq 6000", null, "SRP509892", null, "loader:fastq load.py", "SABERb1_brainC2091523wk3_CKDL230038568-1A_HHVLTDSX7_S1_L003_I1_001.fastq.gz SABERb1_brainC2091523wk3_CKDL230038568-1A_HHVLTDSX7_S1_L003_I2_001.fastq.gz SABERb1_brainC2091523wk3_CKDL230038568-1A_HHVLTDSX7_S1_L003_R1_001.fastq.gz SABERb1_brainC2091523wk3_CKDL230038568-1A_HHVLTDSX7_S1_L003_R2_001.fastq.gz", "fastq fastq fastq fastq", 11765562880.0, 36767384.0, "GSM8290046 r1", "0:10 1:10 2:150 3:150", "A:3147712431;C:1679368969;G:1942592895;T:4259959905;N:581000", 10, 10, 150, 150, 3147712431, 1679368969, 1942592895, 4259959905, 581000, "SRX24701860", "SRS21429389", "SRA1989588", "University of Pennsylvania", "University of Pennsylvania", 2, 0.30773, 0.88409, 0.11212, 0.21315, 0.968, 0.77344, 0.54045, 0.54846, 150, 150, "T", "B", "mate1 technical by mapping diff", "illumina", "novaseq_era", "full_length", "random_priming", "unknown", "sc", "single_cell_droplet", "10x", null, "United States", "2024-05-25", "Larval", "Larval", "Brain", "Nervous System"], [32389, "SRR29181691", "SRX24701859", "SRS21429388", "SRP509892", "PRJNA1116548", "Barcoding Notch signaling in the developing brain", "GSE268356", "Other", "Developmental signaling inputs are fundamental for shaping cell fates and behavior. However  traditional fluorescent based signaling reporters have limitations in scalability and molecular resolution of cell types. We present SABER seq  a CRISPR Cas molecular recorder that stores transient developmental signaling cues as permanent mutations in cellular genomes for deconstruction at later stages via single cell transcriptomics. We applied SABER seq to record Notch signaling in developing zebrafish brains. SABER seq has two components: a signaling sensor and a barcode recorder. The sensor activates Cas9 in a Notch dependent manner with inducible control while the recorder obtains mutations in ancestral cells where Notch is active. We combine SABER seq with an expanded juvenile brain atlas to identify cell types derived from Notch active founders. Our data reveals rare examples where differential Notch activity in ancestral progenitors is detected in terminally differentiated neuronal subtypes. SABER seq is a novel platform for rapid  scalable and high resolution mapping of signaling activity during development. Overall design: 10x Genomics libraries of single cell transcriptomes and SABER seq data  and genomic DNA SABER data", null, "pubmed:39575683", null, "SABERb2 linD3  brain2  SABER Seq", "GSM8290061", null, "source name:zebrafish whole brain|tissue:zebrafish whole brain|developmental stage:21 dpf 23 dpf  Juvenile|genotype:WT/TLAB|geo loc name:missing|collection date:missing", "SABERb2 linD3  brain2  SABER Seq", "Raw FASTQ reads were aligned against zebrafish genome  built from GRCz.109.gtf and the GRCz11.fa files  using the Cell Ranger count pipeline to generate gene by cell count matrices filtered barcode matrix. The count matrices were processed using the standard workflow of the Seurat v5.0.2 R package with modifications. Briefly  all count matrices were separately loaded into R. Seurat objects were then created with modified parameters  requiring features to be detected in at least 3 cells and cells to contain at least 300 genes. Subsequently  the Seurat objects were merged to create a single Seurat object. Cells with RNA counts between 300 and 4000 and a mitochondrial gene percentage of less than 9% were selected for downstream analysis. Gene expression values were log normalized with the default parameters and scaled for the top 3000 variable genes. The scaled data was then used to compute the 50 principal components. Cells were iteratively clustered using the Louvain algorithm post identifying neighboring cells. Differentially expressed genes for each cluster were identified using genes that are detected in at least 10% of cells in either of the two clusters and genes showing a minimum difference of 10% between the two clusters. Cell types were annotated using known marker genes.  The SABER seq FASTQ reads were processed using the Cell Ranger count pipeline  similar to the transcriptomic reads. A bash script was then used to generate a text file containing all FASTQ headers and error corrected cell barcodes for SABER seq data. post loading filtered barcode matrices into R  error corrected cell barcodes from transcriptomic reads were isolated and matched against those in the text file to identify common cell barcode that are shared between the transcriptomic and SABER seq datasets. The resulting text file contained redundant error corrected cell barcodes associated with different UMIs for the same cell. Thus  unique error corrected cell barcodes were isolated to create a text file containing unique FASTQ headers and error corrected barcodes. Subsequently  FASTQ reads matching the FASTQ headers in this file were extracted from the SABER seq raw reads using a Python script. SABER seq barcodes were analyzed using the scGESTALT pipeline available on Github as described previously7 35. To analyze differences in barcode detection and editing frequencies between cell types a binomial test was used. scRNA seq data was analyzed using the following protocol. Raw FASTQ reads were aligned against zebrafish genome  built from GRCz.109.gtf and the GRCz11.fa files  using the Cell Ranger count pipeline to generate gene by cell count matrices filtered barcode matrix. The count matrices were processed using the standard workflow of the Seurat v5.0.2 R package with modifications. Briefly  all count matrices were separately loaded into R. Seurat objects were then created with modified parameters  requiring features to be detected in at least 3 cells and cells to contain at least 300 genes. Subsequently  the Seurat objects were merged to create a single Seurat object. Cells with RNA counts between 300 and 4000 and a mitochondrial gene percentage of less than 9% were selected for downstream analysis. Gene expression values were log normalized with the default parameters and scaled for the top 3000 variable genes. The scaled data was then used to compute the 50 principal components. Cells were iteratively clustered using the Louvain algorithm post identifying neighboring cells. Differentially expressed genes for each cluster were identified using genes that are detected in at least 10% of cells in either of the two clusters and genes showing a minimum difference of 10% between the two clusters. Cell types were annotated using known marker genes. Finally  the data was visualized using UMAP. SABER seq barcode analysis was performed using the following protocol. The SABER seq FASTQ reads were processed using the Cell Ranger count pipeline.A bash script was then used to generate a text file containing all FASTQ headers and error corrected cell barcodes for SABER seq data. Error corrected cell barcodes from transcriptomic reads were isolated and matched against those in the text file to identify common cell barcode that are shared between the transcriptomic and SABER seq datasets. The resulting text file contained redundant error corrected cell barcodes associated with different UMIs for the same cell. Thus  unique error corrected cell barcodes were isolated to create a text file containing unique FASTQ headers and error corrected barcodes. Subsequently  FASTQ reads matching the FASTQ headers in this file were extracted from the SABER seq raw reads using a Python script. SABER seq barcodes were analyzed using the scGESTALT pipeline available on Github as described previously. To analyze differences in barcode detection and editing frequencies between cell types a binomial test was used. Assembly: GRCz11 Supplementary files format and content: *.h5 files contain fileted features/genes and barcode data generated from cell ranger pipeline. Supplementary files format and content: LinC1 Filtered Fastq Headers CB.txt LinC2 Filtered Fastq Headers CB.txt LinD1 Filtered Fastq Headers CB.txt LinD2 Filtered Fastq Headers CB.txt LinD3 Filtered Fastq Headers CB.txt files contain filtered and unique fastq headers and cell barcodes post matching the trasncriptomic cell barcodes. Supplementary files format and content: SABERb1 linC1 S4 BrainC1 S6.CB.xlsx SABERb1 linC2 S5 BrainC2 S7.CB.xlsx SABERb2 linD1 S1 BrainD1 S8.CB.xlsx SABERb2 linD2 S2 BrainD2 S9.CB.xlsx SABERb2 linD3 S3 BrainD3 S10.CB.xlsx SABERb4 linB2.1 13.123.2.With.Primer.stats.xlsx SABERb4 linB2.2 14.124.2.With.Primer.stats.xlsx files contain the SABER Seq output post matching reads to the reference sequence.", "zebrafish whole brain", null, "Wild type and SABER x her4.3:switchCas9 zebrafish brains were harvested and dissociated at 21 dpf 23 dpf as described previously with the following modifications. All microcentrifuge tubes were treated with 1% BSA/DPBS overnight to prevent cell loss due to adhesion. Brains were dissociated with 1 mL of 20 units/ml papain in EBSS media and incubated at 37 \u00b0C for 20 min. Cells were resuspended with 150 \u03bcl ice cold 1%BSA/DPBS solution. Transcriptome libraries were processed according to the manufacturer\u2019s instructions Single Cell 3\u2019 v3 kit. Transcriptome libraries were sequenced using HiSeq S4 300 cycle kits Novogene and Novaseq SP 100 and 200 cycle kits MedGenome. To generate SABER seq libraries  samples post cDNA amplification and prior to fragmentation were split into two parts. One part was processed for transcriptome libraries as instructed by the manufacturer. The other part was processed for SABER seq libraries as follows. To enrich for SABER barcodes  5 \u00b5l of the whole transcriptome cDNA was PCR amplified with 10X p140v1UP  GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCT CGTCGTAGATCTTCATCGTGATG and 10XPCR1F CTACACGACGCTCTTCCGATCT primers and Q5 polymerase. The reaction conditions were: 98 \u00b0C  30 s; [98 \u00b0C  10 s; 68 \u00b0C  25 s; 72 \u00b0C  15 s] x 14 cycles; 72 \u00b0C  2 min. The reaction was cleaned up with 0.8X SPRISelect beads and eluted in 20 \u00b5l elution buffer EB buffer. A second PCR was carried out using 3 \u00b5l of PCR1 product and 10XPCR1F and 10X p140v2Sa GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCT GAAACTACATGGGGTcaGCTC primers. Reaction conditions were same as PCR1 except only 13 cycles were completed. At this point  2 different clean up conditions were used: iteration 1 SABER seq v0 and iteration 2 SABER seq v1. For iteration 1  the reaction was cleaned up with 0.8X SPRISelect beads and eluted in 20 \u00b5l elution buffer EB buffer similar to PCR1.  For iteration 2  an optimization was trialed  and the reaction was cleaned up with right side size selection using SPRIselect beads 0.6X beads used initially and supernatant was rebound with 1.5X beads. The product was eluted in 20 \u00b5l EB. Finally  adapters and sample indices were incorporated in a third PCR reaction using 5 7 \u00b5l of a 1:10 dilution of PCR2 product. Reactions conditions were same as PCR1 except 9 10 cycles were completed. Primers used: 10XP5Dual Hx AATGATACGGCGACCACCGAGATCTACAC xxxxxxx xxx ACACTCTTTCCCTACACGACGCTCTTCCGATCT  10XP7Dual H5 AATGATACGGCGAC CACCGAGATCTACAC xxxxxxxxxx ACACTCTTTCCCTACACGACGCTCTTCCGATCT  where x represents index bases from the 10X Dual Index Kit TT  SetA H index entries. For iteration 1  the third PCR reaction was cleaned up with 0.8X SPRISelect beads and eluted in 20 \u00b5l elution buffer EB to generate final libraries. For iteration 2  the third PCR reaction was cleaned up with right side size selection using SPRIselect beads 0.65X beads used initially and supernatant was rebound with 1.5X beads. SABER seq libraries were sequenced using Novaseq SP 200 cycle kits MedGenome with 5 10% PhiX spike in. Sequencing parameters: Read1 28 cycles  Read2 151 cycles  Index1 10 cycles  Index2 10 cycles. Standard sequencing primers were used.", "This work was performed under protocol numbers 807110 and 807259  which were approved by the University of Pennsylvania\u2019s Office of Animal Welfare of Institutional Animal Care and Use Committee IACUC. All zebrafish work in this study follows the University of Pennsylvania Institutional Animal Care and Use Committee regulations.", "tissue:zebrafish whole brain|developmental stage:21 dpf 23 dpf  Juvenile|genotype:WT/TLAB", "GSM8290061", "GSM8290061: SABERb2 linD3  brain2  SABER Seq; Danio rerio; RNA Seq", "GSM8290061 r1", "GSM8290061", "1", "Wild type and SABER x her4.3:switchCas9 zebrafish brains were harvested and dissociated at 21 dpf 23 dpf as described previously with the following modifications. All microcentrifuge tubes were treated with 1% BSA/DPBS overnight to prevent cell loss due to adhesion. Brains were dissociated with 1 mL of 20 units/ml papain in EBSS media and incubated at 37 \u00b0C for 20 min. Cells were resuspended with 150 \u03bcl ice cold 1%BSA/DPBS solution. Transcriptome libraries were processed according to the manufacturer's instructions Single Cell three prime v3 kit. Transcriptome libraries were sequenced using HiSeq S4 300 cycle kits Novogene and Novaseq SP 100 and 200 cycle kits MedGenome. To generate SABER seq libraries  samples post cDNA amplification and prior to fragmentation were split into two parts. One part was processed for transcriptome libraries as instructed by the manufacturer. The other part was processed for SABER seq libraries as follows. To enrich for SABER barcodes  5 \u00b5l of the whole transcriptome cDNA was PCR amplified with 10X p140v1UP  GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCT CGTCGTAGATCTTCATCGTGATG and 10XPCR1F CTACACGACGCTCTTCCGATCT primers and Q5 polymerase. The reaction conditions were: 98 \u00b0C  30 s; [98 \u00b0C  10 s; 68 \u00b0C  25 s; 72 \u00b0C  15 s] x 14 cycles; 72 \u00b0C  2 min. The reaction was cleaned up with 0.8X SPRISelect beads and eluted in 20 \u00b5l elution buffer EB buffer. A second PCR was carried out using 3 \u00b5l of PCR1 product and 10XPCR1F and 10X p140v2Sa GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCT GAAACTACATGGGGTcaGCTC primers. Reaction conditions were same as PCR1 except only 13 cycles were completed. At this point  2 different clean up conditions were used: iteration 1 SABER seq v0 and iteration 2 SABER seq v1. For iteration 1  the reaction was cleaned up with 0.8X SPRISelect beads and eluted in 20 \u00b5l elution buffer EB buffer similar to PCR1.  For iteration 2  an optimization was trialed  and the reaction was cleaned up with right side size selection using SPRIselect beads 0.6X beads used initially and supernatant was rebound with 1.5X beads. The product was eluted in 20 \u00b5l EB. Finally  adapters and sample indices were incorporated in a third PCR reaction using 5 7 \u00b5l of a 1:10 dilution of PCR2 product. Reactions conditions were same as PCR1 except 9 10 cycles were completed. Primers used: 10XP5Dual Hx AATGATACGGCGACCACCGAGATCTACAC xxxxxxx xxx ACACTCTTTCCCTACACGACGCTCTTCCGATCT  10XP7Dual H5 AATGATACGGCGAC CACCGAGATCTACAC xxxxxxxxxx ACACTCTTTCCCTACACGACGCTCTTCCGATCT  where x represents index bases from the 10X Dual Index Kit TT  SetA H index entries. For iteration 1  the third PCR reaction was cleaned up with 0.8X SPRISelect beads and eluted in 20 \u00b5l elution buffer EB to generate final libraries. For iteration 2  the third PCR reaction was cleaned up with right side size selection using SPRIselect beads 0.65X beads used initially and supernatant was rebound with 1.5X beads. SABER seq libraries were sequenced using Novaseq SP 200 cycle kits MedGenome with 5 10% PhiX spike in. Sequencing parameters: Read1 28 cycles  Read2 151 cycles  Index1 10 cycles  Index2 10 cycles. Standard sequencing primers were used.", null, "RNA-Seq", "TRANSCRIPTOMIC SINGLE CELL", "cDNA", "PAIRED", "ILLUMINA", "Illumina NovaSeq 6000", null, "SRP509892", null, "loader:fastq load.py", "SABERb2_linD3_S3_L001_I1_001.fastq.gz SABERb2_linD3_S3_L001_I2_001.fastq.gz SABERb2_linD3_S3_L001_R1_001.fastq.gz SABERb2_linD3_S3_L001_R2_001.fastq.gz", "fastq fastq fastq fastq", 6191572570.0, 31113430.0, "GSM8290061 r1", "0:10 1:10 2:28 3:151", "A:1332572499;C:1250769086;G:1304188511;T:810567656;N:30178", 10, 10, 28, 151, 1332572499, 1250769086, 1304188511, 810567656, 30178, "SRX24701859", "SRS21429388", "SRA1878107", "University of Pennsylvania", "University of Pennsylvania", 1, 0.00992, null, 0.00222, null, 0.98762, null, 0.5329, null, 151, null, "T", null, "under 1.2% mapping rate", "illumina", "novaseq_era", "full_length", "random_priming", "unknown", "sc", "single_cell_droplet", "10x", null, "United States", "2024-05-25", "Larval", "Larval", "Brain", "Nervous System"], [32390, "SRR29181690", "SRX24701858", "SRS21429387", "SRP509892", "PRJNA1116548", "Barcoding Notch signaling in the developing brain", "GSE268356", "Other", "Developmental signaling inputs are fundamental for shaping cell fates and behavior. However  traditional fluorescent based signaling reporters have limitations in scalability and molecular resolution of cell types. We present SABER seq  a CRISPR Cas molecular recorder that stores transient developmental signaling cues as permanent mutations in cellular genomes for deconstruction at later stages via single cell transcriptomics. We applied SABER seq to record Notch signaling in developing zebrafish brains. SABER seq has two components: a signaling sensor and a barcode recorder. The sensor activates Cas9 in a Notch dependent manner with inducible control while the recorder obtains mutations in ancestral cells where Notch is active. We combine SABER seq with an expanded juvenile brain atlas to identify cell types derived from Notch active founders. Our data reveals rare examples where differential Notch activity in ancestral progenitors is detected in terminally differentiated neuronal subtypes. SABER seq is a novel platform for rapid  scalable and high resolution mapping of signaling activity during development. Overall design: 10x Genomics libraries of single cell transcriptomes and SABER seq data  and genomic DNA SABER data", null, "pubmed:39575683", null, "SABERb2 linD2  brain2  SABER Seq", "GSM8290060", null, "source name:zebrafish whole brain|tissue:zebrafish whole brain|developmental stage:21 dpf 23 dpf  Juvenile|genotype:WT/TLAB|geo loc name:missing|collection date:missing", "SABERb2 linD2  brain2  SABER Seq", "Raw FASTQ reads were aligned against zebrafish genome  built from GRCz.109.gtf and the GRCz11.fa files  using the Cell Ranger count pipeline to generate gene by cell count matrices filtered barcode matrix. The count matrices were processed using the standard workflow of the Seurat v5.0.2 R package with modifications. Briefly  all count matrices were separately loaded into R. Seurat objects were then created with modified parameters  requiring features to be detected in at least 3 cells and cells to contain at least 300 genes. Subsequently  the Seurat objects were merged to create a single Seurat object. Cells with RNA counts between 300 and 4000 and a mitochondrial gene percentage of less than 9% were selected for downstream analysis. Gene expression values were log normalized with the default parameters and scaled for the top 3000 variable genes. The scaled data was then used to compute the 50 principal components. Cells were iteratively clustered using the Louvain algorithm post identifying neighboring cells. Differentially expressed genes for each cluster were identified using genes that are detected in at least 10% of cells in either of the two clusters and genes showing a minimum difference of 10% between the two clusters. Cell types were annotated using known marker genes.  The SABER seq FASTQ reads were processed using the Cell Ranger count pipeline  similar to the transcriptomic reads. A bash script was then used to generate a text file containing all FASTQ headers and error corrected cell barcodes for SABER seq data. post loading filtered barcode matrices into R  error corrected cell barcodes from transcriptomic reads were isolated and matched against those in the text file to identify common cell barcode that are shared between the transcriptomic and SABER seq datasets. The resulting text file contained redundant error corrected cell barcodes associated with different UMIs for the same cell. Thus  unique error corrected cell barcodes were isolated to create a text file containing unique FASTQ headers and error corrected barcodes. Subsequently  FASTQ reads matching the FASTQ headers in this file were extracted from the SABER seq raw reads using a Python script. SABER seq barcodes were analyzed using the scGESTALT pipeline available on Github as described previously7 35. To analyze differences in barcode detection and editing frequencies between cell types a binomial test was used. scRNA seq data was analyzed using the following protocol. Raw FASTQ reads were aligned against zebrafish genome  built from GRCz.109.gtf and the GRCz11.fa files  using the Cell Ranger count pipeline to generate gene by cell count matrices filtered barcode matrix. The count matrices were processed using the standard workflow of the Seurat v5.0.2 R package with modifications. Briefly  all count matrices were separately loaded into R. Seurat objects were then created with modified parameters  requiring features to be detected in at least 3 cells and cells to contain at least 300 genes. Subsequently  the Seurat objects were merged to create a single Seurat object. Cells with RNA counts between 300 and 4000 and a mitochondrial gene percentage of less than 9% were selected for downstream analysis. Gene expression values were log normalized with the default parameters and scaled for the top 3000 variable genes. The scaled data was then used to compute the 50 principal components. Cells were iteratively clustered using the Louvain algorithm post identifying neighboring cells. Differentially expressed genes for each cluster were identified using genes that are detected in at least 10% of cells in either of the two clusters and genes showing a minimum difference of 10% between the two clusters. Cell types were annotated using known marker genes. Finally  the data was visualized using UMAP. SABER seq barcode analysis was performed using the following protocol. The SABER seq FASTQ reads were processed using the Cell Ranger count pipeline.A bash script was then used to generate a text file containing all FASTQ headers and error corrected cell barcodes for SABER seq data. Error corrected cell barcodes from transcriptomic reads were isolated and matched against those in the text file to identify common cell barcode that are shared between the transcriptomic and SABER seq datasets. The resulting text file contained redundant error corrected cell barcodes associated with different UMIs for the same cell. Thus  unique error corrected cell barcodes were isolated to create a text file containing unique FASTQ headers and error corrected barcodes. Subsequently  FASTQ reads matching the FASTQ headers in this file were extracted from the SABER seq raw reads using a Python script. SABER seq barcodes were analyzed using the scGESTALT pipeline available on Github as described previously. To analyze differences in barcode detection and editing frequencies between cell types a binomial test was used. Assembly: GRCz11 Supplementary files format and content: *.h5 files contain fileted features/genes and barcode data generated from cell ranger pipeline. Supplementary files format and content: LinC1 Filtered Fastq Headers CB.txt LinC2 Filtered Fastq Headers CB.txt LinD1 Filtered Fastq Headers CB.txt LinD2 Filtered Fastq Headers CB.txt LinD3 Filtered Fastq Headers CB.txt files contain filtered and unique fastq headers and cell barcodes post matching the trasncriptomic cell barcodes. Supplementary files format and content: SABERb1 linC1 S4 BrainC1 S6.CB.xlsx SABERb1 linC2 S5 BrainC2 S7.CB.xlsx SABERb2 linD1 S1 BrainD1 S8.CB.xlsx SABERb2 linD2 S2 BrainD2 S9.CB.xlsx SABERb2 linD3 S3 BrainD3 S10.CB.xlsx SABERb4 linB2.1 13.123.2.With.Primer.stats.xlsx SABERb4 linB2.2 14.124.2.With.Primer.stats.xlsx files contain the SABER Seq output post matching reads to the reference sequence.", "zebrafish whole brain", null, "Wild type and SABER x her4.3:switchCas9 zebrafish brains were harvested and dissociated at 21 dpf 23 dpf as described previously with the following modifications. All microcentrifuge tubes were treated with 1% BSA/DPBS overnight to prevent cell loss due to adhesion. Brains were dissociated with 1 mL of 20 units/ml papain in EBSS media and incubated at 37 \u00b0C for 20 min. Cells were resuspended with 150 \u03bcl ice cold 1%BSA/DPBS solution. Transcriptome libraries were processed according to the manufacturer\u2019s instructions Single Cell 3\u2019 v3 kit. Transcriptome libraries were sequenced using HiSeq S4 300 cycle kits Novogene and Novaseq SP 100 and 200 cycle kits MedGenome. To generate SABER seq libraries  samples post cDNA amplification and prior to fragmentation were split into two parts. One part was processed for transcriptome libraries as instructed by the manufacturer. The other part was processed for SABER seq libraries as follows. To enrich for SABER barcodes  5 \u00b5l of the whole transcriptome cDNA was PCR amplified with 10X p140v1UP  GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCT CGTCGTAGATCTTCATCGTGATG and 10XPCR1F CTACACGACGCTCTTCCGATCT primers and Q5 polymerase. The reaction conditions were: 98 \u00b0C  30 s; [98 \u00b0C  10 s; 68 \u00b0C  25 s; 72 \u00b0C  15 s] x 14 cycles; 72 \u00b0C  2 min. The reaction was cleaned up with 0.8X SPRISelect beads and eluted in 20 \u00b5l elution buffer EB buffer. A second PCR was carried out using 3 \u00b5l of PCR1 product and 10XPCR1F and 10X p140v2Sa GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCT GAAACTACATGGGGTcaGCTC primers. Reaction conditions were same as PCR1 except only 13 cycles were completed. At this point  2 different clean up conditions were used: iteration 1 SABER seq v0 and iteration 2 SABER seq v1. For iteration 1  the reaction was cleaned up with 0.8X SPRISelect beads and eluted in 20 \u00b5l elution buffer EB buffer similar to PCR1.  For iteration 2  an optimization was trialed  and the reaction was cleaned up with right side size selection using SPRIselect beads 0.6X beads used initially and supernatant was rebound with 1.5X beads. The product was eluted in 20 \u00b5l EB. Finally  adapters and sample indices were incorporated in a third PCR reaction using 5 7 \u00b5l of a 1:10 dilution of PCR2 product. Reactions conditions were same as PCR1 except 9 10 cycles were completed. Primers used: 10XP5Dual Hx AATGATACGGCGACCACCGAGATCTACAC xxxxxxx xxx ACACTCTTTCCCTACACGACGCTCTTCCGATCT  10XP7Dual H5 AATGATACGGCGAC CACCGAGATCTACAC xxxxxxxxxx ACACTCTTTCCCTACACGACGCTCTTCCGATCT  where x represents index bases from the 10X Dual Index Kit TT  SetA H index entries. For iteration 1  the third PCR reaction was cleaned up with 0.8X SPRISelect beads and eluted in 20 \u00b5l elution buffer EB to generate final libraries. For iteration 2  the third PCR reaction was cleaned up with right side size selection using SPRIselect beads 0.65X beads used initially and supernatant was rebound with 1.5X beads. SABER seq libraries were sequenced using Novaseq SP 200 cycle kits MedGenome with 5 10% PhiX spike in. Sequencing parameters: Read1 28 cycles  Read2 151 cycles  Index1 10 cycles  Index2 10 cycles. Standard sequencing primers were used.", "This work was performed under protocol numbers 807110 and 807259  which were approved by the University of Pennsylvania\u2019s Office of Animal Welfare of Institutional Animal Care and Use Committee IACUC. All zebrafish work in this study follows the University of Pennsylvania Institutional Animal Care and Use Committee regulations.", "tissue:zebrafish whole brain|developmental stage:21 dpf 23 dpf  Juvenile|genotype:WT/TLAB", "GSM8290060", "GSM8290060: SABERb2 linD2  brain2  SABER Seq; Danio rerio; RNA Seq", "GSM8290060 r1", "GSM8290060", "1", "Wild type and SABER x her4.3:switchCas9 zebrafish brains were harvested and dissociated at 21 dpf 23 dpf as described previously with the following modifications. All microcentrifuge tubes were treated with 1% BSA/DPBS overnight to prevent cell loss due to adhesion. Brains were dissociated with 1 mL of 20 units/ml papain in EBSS media and incubated at 37 \u00b0C for 20 min. Cells were resuspended with 150 \u03bcl ice cold 1%BSA/DPBS solution. Transcriptome libraries were processed according to the manufacturer's instructions Single Cell three prime v3 kit. Transcriptome libraries were sequenced using HiSeq S4 300 cycle kits Novogene and Novaseq SP 100 and 200 cycle kits MedGenome. To generate SABER seq libraries  samples post cDNA amplification and prior to fragmentation were split into two parts. One part was processed for transcriptome libraries as instructed by the manufacturer. The other part was processed for SABER seq libraries as follows. To enrich for SABER barcodes  5 \u00b5l of the whole transcriptome cDNA was PCR amplified with 10X p140v1UP  GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCT CGTCGTAGATCTTCATCGTGATG and 10XPCR1F CTACACGACGCTCTTCCGATCT primers and Q5 polymerase. The reaction conditions were: 98 \u00b0C  30 s; [98 \u00b0C  10 s; 68 \u00b0C  25 s; 72 \u00b0C  15 s] x 14 cycles; 72 \u00b0C  2 min. The reaction was cleaned up with 0.8X SPRISelect beads and eluted in 20 \u00b5l elution buffer EB buffer. A second PCR was carried out using 3 \u00b5l of PCR1 product and 10XPCR1F and 10X p140v2Sa GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCT GAAACTACATGGGGTcaGCTC primers. Reaction conditions were same as PCR1 except only 13 cycles were completed. At this point  2 different clean up conditions were used: iteration 1 SABER seq v0 and iteration 2 SABER seq v1. For iteration 1  the reaction was cleaned up with 0.8X SPRISelect beads and eluted in 20 \u00b5l elution buffer EB buffer similar to PCR1.  For iteration 2  an optimization was trialed  and the reaction was cleaned up with right side size selection using SPRIselect beads 0.6X beads used initially and supernatant was rebound with 1.5X beads. The product was eluted in 20 \u00b5l EB. Finally  adapters and sample indices were incorporated in a third PCR reaction using 5 7 \u00b5l of a 1:10 dilution of PCR2 product. Reactions conditions were same as PCR1 except 9 10 cycles were completed. Primers used: 10XP5Dual Hx AATGATACGGCGACCACCGAGATCTACAC xxxxxxx xxx ACACTCTTTCCCTACACGACGCTCTTCCGATCT  10XP7Dual H5 AATGATACGGCGAC CACCGAGATCTACAC xxxxxxxxxx ACACTCTTTCCCTACACGACGCTCTTCCGATCT  where x represents index bases from the 10X Dual Index Kit TT  SetA H index entries. For iteration 1  the third PCR reaction was cleaned up with 0.8X SPRISelect beads and eluted in 20 \u00b5l elution buffer EB to generate final libraries. For iteration 2  the third PCR reaction was cleaned up with right side size selection using SPRIselect beads 0.65X beads used initially and supernatant was rebound with 1.5X beads. SABER seq libraries were sequenced using Novaseq SP 200 cycle kits MedGenome with 5 10% PhiX spike in. Sequencing parameters: Read1 28 cycles  Read2 151 cycles  Index1 10 cycles  Index2 10 cycles. Standard sequencing primers were used.", null, "RNA-Seq", "TRANSCRIPTOMIC SINGLE CELL", "cDNA", "PAIRED", "ILLUMINA", "Illumina NovaSeq 6000", null, "SRP509892", null, "loader:fastq load.py", "SABERb2_linD2_S2_L001_I1_001.fastq.gz SABERb2_linD2_S2_L001_I2_001.fastq.gz SABERb2_linD2_S2_L001_R1_001.fastq.gz SABERb2_linD2_S2_L001_R2_001.fastq.gz", "fastq fastq fastq fastq", 4643470776.0, 23334024.0, "GSM8290060 r1", "0:10 1:10 2:28 3:151", "A:780662288;C:1082797960;G:929037112;T:730916888;N:23376", 10, 10, 28, 151, 780662288, 1082797960, 929037112, 730916888, 23376, "SRX24701858", "SRS21429387", "SRA1878107", "University of Pennsylvania", "University of Pennsylvania", 1, 0.01556, null, 0.00414, null, 0.98571, null, 0.48742, null, 151, null, "B", null, "usable mapping rate", "illumina", "novaseq_era", "full_length", "random_priming", "unknown", "sc", "single_cell_droplet", "10x", null, "United States", "2024-05-25", "Larval", "Larval", "Brain", "Nervous System"], [32391, "SRR29181689", "SRX24701857", "SRS21429386", "SRP509892", "PRJNA1116548", "Barcoding Notch signaling in the developing brain", "GSE268356", "Other", "Developmental signaling inputs are fundamental for shaping cell fates and behavior. However  traditional fluorescent based signaling reporters have limitations in scalability and molecular resolution of cell types. We present SABER seq  a CRISPR Cas molecular recorder that stores transient developmental signaling cues as permanent mutations in cellular genomes for deconstruction at later stages via single cell transcriptomics. We applied SABER seq to record Notch signaling in developing zebrafish brains. SABER seq has two components: a signaling sensor and a barcode recorder. The sensor activates Cas9 in a Notch dependent manner with inducible control while the recorder obtains mutations in ancestral cells where Notch is active. We combine SABER seq with an expanded juvenile brain atlas to identify cell types derived from Notch active founders. Our data reveals rare examples where differential Notch activity in ancestral progenitors is detected in terminally differentiated neuronal subtypes. SABER seq is a novel platform for rapid  scalable and high resolution mapping of signaling activity during development. Overall design: 10x Genomics libraries of single cell transcriptomes and SABER seq data  and genomic DNA SABER data", null, "pubmed:39575683", null, "SABERb2 linD1  brain2  SABER Seq", "GSM8290059", null, "source name:zebrafish whole brain|tissue:zebrafish whole brain|developmental stage:21 dpf 23 dpf  Juvenile|genotype:WT/TLAB|geo loc name:missing|collection date:missing", "SABERb2 linD1  brain2  SABER Seq", "Raw FASTQ reads were aligned against zebrafish genome  built from GRCz.109.gtf and the GRCz11.fa files  using the Cell Ranger count pipeline to generate gene by cell count matrices filtered barcode matrix. The count matrices were processed using the standard workflow of the Seurat v5.0.2 R package with modifications. Briefly  all count matrices were separately loaded into R. Seurat objects were then created with modified parameters  requiring features to be detected in at least 3 cells and cells to contain at least 300 genes. Subsequently  the Seurat objects were merged to create a single Seurat object. Cells with RNA counts between 300 and 4000 and a mitochondrial gene percentage of less than 9% were selected for downstream analysis. Gene expression values were log normalized with the default parameters and scaled for the top 3000 variable genes. The scaled data was then used to compute the 50 principal components. Cells were iteratively clustered using the Louvain algorithm post identifying neighboring cells. Differentially expressed genes for each cluster were identified using genes that are detected in at least 10% of cells in either of the two clusters and genes showing a minimum difference of 10% between the two clusters. Cell types were annotated using known marker genes.  The SABER seq FASTQ reads were processed using the Cell Ranger count pipeline  similar to the transcriptomic reads. A bash script was then used to generate a text file containing all FASTQ headers and error corrected cell barcodes for SABER seq data. post loading filtered barcode matrices into R  error corrected cell barcodes from transcriptomic reads were isolated and matched against those in the text file to identify common cell barcode that are shared between the transcriptomic and SABER seq datasets. The resulting text file contained redundant error corrected cell barcodes associated with different UMIs for the same cell. Thus  unique error corrected cell barcodes were isolated to create a text file containing unique FASTQ headers and error corrected barcodes. Subsequently  FASTQ reads matching the FASTQ headers in this file were extracted from the SABER seq raw reads using a Python script. SABER seq barcodes were analyzed using the scGESTALT pipeline available on Github as described previously7 35. To analyze differences in barcode detection and editing frequencies between cell types a binomial test was used. scRNA seq data was analyzed using the following protocol. Raw FASTQ reads were aligned against zebrafish genome  built from GRCz.109.gtf and the GRCz11.fa files  using the Cell Ranger count pipeline to generate gene by cell count matrices filtered barcode matrix. The count matrices were processed using the standard workflow of the Seurat v5.0.2 R package with modifications. Briefly  all count matrices were separately loaded into R. Seurat objects were then created with modified parameters  requiring features to be detected in at least 3 cells and cells to contain at least 300 genes. Subsequently  the Seurat objects were merged to create a single Seurat object. Cells with RNA counts between 300 and 4000 and a mitochondrial gene percentage of less than 9% were selected for downstream analysis. Gene expression values were log normalized with the default parameters and scaled for the top 3000 variable genes. The scaled data was then used to compute the 50 principal components. Cells were iteratively clustered using the Louvain algorithm post identifying neighboring cells. Differentially expressed genes for each cluster were identified using genes that are detected in at least 10% of cells in either of the two clusters and genes showing a minimum difference of 10% between the two clusters. Cell types were annotated using known marker genes. Finally  the data was visualized using UMAP. SABER seq barcode analysis was performed using the following protocol. The SABER seq FASTQ reads were processed using the Cell Ranger count pipeline.A bash script was then used to generate a text file containing all FASTQ headers and error corrected cell barcodes for SABER seq data. Error corrected cell barcodes from transcriptomic reads were isolated and matched against those in the text file to identify common cell barcode that are shared between the transcriptomic and SABER seq datasets. The resulting text file contained redundant error corrected cell barcodes associated with different UMIs for the same cell. Thus  unique error corrected cell barcodes were isolated to create a text file containing unique FASTQ headers and error corrected barcodes. Subsequently  FASTQ reads matching the FASTQ headers in this file were extracted from the SABER seq raw reads using a Python script. SABER seq barcodes were analyzed using the scGESTALT pipeline available on Github as described previously. To analyze differences in barcode detection and editing frequencies between cell types a binomial test was used. Assembly: GRCz11 Supplementary files format and content: *.h5 files contain fileted features/genes and barcode data generated from cell ranger pipeline. Supplementary files format and content: LinC1 Filtered Fastq Headers CB.txt LinC2 Filtered Fastq Headers CB.txt LinD1 Filtered Fastq Headers CB.txt LinD2 Filtered Fastq Headers CB.txt LinD3 Filtered Fastq Headers CB.txt files contain filtered and unique fastq headers and cell barcodes post matching the trasncriptomic cell barcodes. Supplementary files format and content: SABERb1 linC1 S4 BrainC1 S6.CB.xlsx SABERb1 linC2 S5 BrainC2 S7.CB.xlsx SABERb2 linD1 S1 BrainD1 S8.CB.xlsx SABERb2 linD2 S2 BrainD2 S9.CB.xlsx SABERb2 linD3 S3 BrainD3 S10.CB.xlsx SABERb4 linB2.1 13.123.2.With.Primer.stats.xlsx SABERb4 linB2.2 14.124.2.With.Primer.stats.xlsx files contain the SABER Seq output post matching reads to the reference sequence.", "zebrafish whole brain", null, "Wild type and SABER x her4.3:switchCas9 zebrafish brains were harvested and dissociated at 21 dpf 23 dpf as described previously with the following modifications. All microcentrifuge tubes were treated with 1% BSA/DPBS overnight to prevent cell loss due to adhesion. Brains were dissociated with 1 mL of 20 units/ml papain in EBSS media and incubated at 37 \u00b0C for 20 min. Cells were resuspended with 150 \u03bcl ice cold 1%BSA/DPBS solution. Transcriptome libraries were processed according to the manufacturer\u2019s instructions Single Cell 3\u2019 v3 kit. Transcriptome libraries were sequenced using HiSeq S4 300 cycle kits Novogene and Novaseq SP 100 and 200 cycle kits MedGenome. To generate SABER seq libraries  samples post cDNA amplification and prior to fragmentation were split into two parts. One part was processed for transcriptome libraries as instructed by the manufacturer. The other part was processed for SABER seq libraries as follows. To enrich for SABER barcodes  5 \u00b5l of the whole transcriptome cDNA was PCR amplified with 10X p140v1UP  GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCT CGTCGTAGATCTTCATCGTGATG and 10XPCR1F CTACACGACGCTCTTCCGATCT primers and Q5 polymerase. The reaction conditions were: 98 \u00b0C  30 s; [98 \u00b0C  10 s; 68 \u00b0C  25 s; 72 \u00b0C  15 s] x 14 cycles; 72 \u00b0C  2 min. The reaction was cleaned up with 0.8X SPRISelect beads and eluted in 20 \u00b5l elution buffer EB buffer. A second PCR was carried out using 3 \u00b5l of PCR1 product and 10XPCR1F and 10X p140v2Sa GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCT GAAACTACATGGGGTcaGCTC primers. Reaction conditions were same as PCR1 except only 13 cycles were completed. At this point  2 different clean up conditions were used: iteration 1 SABER seq v0 and iteration 2 SABER seq v1. For iteration 1  the reaction was cleaned up with 0.8X SPRISelect beads and eluted in 20 \u00b5l elution buffer EB buffer similar to PCR1.  For iteration 2  an optimization was trialed  and the reaction was cleaned up with right side size selection using SPRIselect beads 0.6X beads used initially and supernatant was rebound with 1.5X beads. The product was eluted in 20 \u00b5l EB. Finally  adapters and sample indices were incorporated in a third PCR reaction using 5 7 \u00b5l of a 1:10 dilution of PCR2 product. Reactions conditions were same as PCR1 except 9 10 cycles were completed. Primers used: 10XP5Dual Hx AATGATACGGCGACCACCGAGATCTACAC xxxxxxx xxx ACACTCTTTCCCTACACGACGCTCTTCCGATCT  10XP7Dual H5 AATGATACGGCGAC CACCGAGATCTACAC xxxxxxxxxx ACACTCTTTCCCTACACGACGCTCTTCCGATCT  where x represents index bases from the 10X Dual Index Kit TT  SetA H index entries. For iteration 1  the third PCR reaction was cleaned up with 0.8X SPRISelect beads and eluted in 20 \u00b5l elution buffer EB to generate final libraries. For iteration 2  the third PCR reaction was cleaned up with right side size selection using SPRIselect beads 0.65X beads used initially and supernatant was rebound with 1.5X beads. SABER seq libraries were sequenced using Novaseq SP 200 cycle kits MedGenome with 5 10% PhiX spike in. Sequencing parameters: Read1 28 cycles  Read2 151 cycles  Index1 10 cycles  Index2 10 cycles. Standard sequencing primers were used.", "This work was performed under protocol numbers 807110 and 807259  which were approved by the University of Pennsylvania\u2019s Office of Animal Welfare of Institutional Animal Care and Use Committee IACUC. All zebrafish work in this study follows the University of Pennsylvania Institutional Animal Care and Use Committee regulations.", "tissue:zebrafish whole brain|developmental stage:21 dpf 23 dpf  Juvenile|genotype:WT/TLAB", "GSM8290059", "GSM8290059: SABERb2 linD1  brain2  SABER Seq; Danio rerio; RNA Seq", "GSM8290059 r1", "GSM8290059", "1", "Wild type and SABER x her4.3:switchCas9 zebrafish brains were harvested and dissociated at 21 dpf 23 dpf as described previously with the following modifications. All microcentrifuge tubes were treated with 1% BSA/DPBS overnight to prevent cell loss due to adhesion. Brains were dissociated with 1 mL of 20 units/ml papain in EBSS media and incubated at 37 \u00b0C for 20 min. Cells were resuspended with 150 \u03bcl ice cold 1%BSA/DPBS solution. Transcriptome libraries were processed according to the manufacturer's instructions Single Cell three prime v3 kit. Transcriptome libraries were sequenced using HiSeq S4 300 cycle kits Novogene and Novaseq SP 100 and 200 cycle kits MedGenome. To generate SABER seq libraries  samples post cDNA amplification and prior to fragmentation were split into two parts. One part was processed for transcriptome libraries as instructed by the manufacturer. The other part was processed for SABER seq libraries as follows. To enrich for SABER barcodes  5 \u00b5l of the whole transcriptome cDNA was PCR amplified with 10X p140v1UP  GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCT CGTCGTAGATCTTCATCGTGATG and 10XPCR1F CTACACGACGCTCTTCCGATCT primers and Q5 polymerase. The reaction conditions were: 98 \u00b0C  30 s; [98 \u00b0C  10 s; 68 \u00b0C  25 s; 72 \u00b0C  15 s] x 14 cycles; 72 \u00b0C  2 min. The reaction was cleaned up with 0.8X SPRISelect beads and eluted in 20 \u00b5l elution buffer EB buffer. A second PCR was carried out using 3 \u00b5l of PCR1 product and 10XPCR1F and 10X p140v2Sa GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCT GAAACTACATGGGGTcaGCTC primers. Reaction conditions were same as PCR1 except only 13 cycles were completed. At this point  2 different clean up conditions were used: iteration 1 SABER seq v0 and iteration 2 SABER seq v1. For iteration 1  the reaction was cleaned up with 0.8X SPRISelect beads and eluted in 20 \u00b5l elution buffer EB buffer similar to PCR1.  For iteration 2  an optimization was trialed  and the reaction was cleaned up with right side size selection using SPRIselect beads 0.6X beads used initially and supernatant was rebound with 1.5X beads. The product was eluted in 20 \u00b5l EB. Finally  adapters and sample indices were incorporated in a third PCR reaction using 5 7 \u00b5l of a 1:10 dilution of PCR2 product. Reactions conditions were same as PCR1 except 9 10 cycles were completed. Primers used: 10XP5Dual Hx AATGATACGGCGACCACCGAGATCTACAC xxxxxxx xxx ACACTCTTTCCCTACACGACGCTCTTCCGATCT  10XP7Dual H5 AATGATACGGCGAC CACCGAGATCTACAC xxxxxxxxxx ACACTCTTTCCCTACACGACGCTCTTCCGATCT  where x represents index bases from the 10X Dual Index Kit TT  SetA H index entries. For iteration 1  the third PCR reaction was cleaned up with 0.8X SPRISelect beads and eluted in 20 \u00b5l elution buffer EB to generate final libraries. For iteration 2  the third PCR reaction was cleaned up with right side size selection using SPRIselect beads 0.65X beads used initially and supernatant was rebound with 1.5X beads. SABER seq libraries were sequenced using Novaseq SP 200 cycle kits MedGenome with 5 10% PhiX spike in. Sequencing parameters: Read1 28 cycles  Read2 151 cycles  Index1 10 cycles  Index2 10 cycles. Standard sequencing primers were used.", null, "RNA-Seq", "TRANSCRIPTOMIC SINGLE CELL", "cDNA", "PAIRED", "ILLUMINA", "Illumina NovaSeq 6000", null, "SRP509892", null, "loader:fastq load.py", "SABERb2_linD1_S1_L001_I1_001.fastq.gz SABERb2_linD1_S1_L001_I2_001.fastq.gz SABERb2_linD1_S1_L001_R1_001.fastq.gz SABERb2_linD1_S1_L001_R2_001.fastq.gz", "fastq fastq fastq fastq", 1567865877.0, 7878723.0, "GSM8290059 r1", "0:10 1:10 2:28 3:151", "A:285711189;C:311873916;G:350305565;T:241788400;N:8103", 10, 10, 28, 151, 285711189, 311873916, 350305565, 241788400, 8103, "SRX24701857", "SRS21429386", "SRA1878107", "University of Pennsylvania", "University of Pennsylvania", 1, 0.04511, null, 0.013, null, 0.96644, null, 0.48124, null, 151, null, "B", null, "usable mapping rate", "illumina", "novaseq_era", "full_length", "random_priming", "unknown", "sc", "single_cell_droplet", "10x", null, "United States", "2024-05-25", "Larval", "Larval", "Brain", "Nervous System"], [32392, "SRR29181694", "SRX24701856", "SRS21429385", "SRP509892", "PRJNA1116548", "Barcoding Notch signaling in the developing brain", "GSE268356", "Other", "Developmental signaling inputs are fundamental for shaping cell fates and behavior. However  traditional fluorescent based signaling reporters have limitations in scalability and molecular resolution of cell types. We present SABER seq  a CRISPR Cas molecular recorder that stores transient developmental signaling cues as permanent mutations in cellular genomes for deconstruction at later stages via single cell transcriptomics. We applied SABER seq to record Notch signaling in developing zebrafish brains. SABER seq has two components: a signaling sensor and a barcode recorder. The sensor activates Cas9 in a Notch dependent manner with inducible control while the recorder obtains mutations in ancestral cells where Notch is active. We combine SABER seq with an expanded juvenile brain atlas to identify cell types derived from Notch active founders. Our data reveals rare examples where differential Notch activity in ancestral progenitors is detected in terminally differentiated neuronal subtypes. SABER seq is a novel platform for rapid  scalable and high resolution mapping of signaling activity during development. Overall design: 10x Genomics libraries of single cell transcriptomes and SABER seq data  and genomic DNA SABER data", null, "pubmed:39575683", null, "SABERb1 linC2  brain1  SABER Seq", "GSM8290058", null, "source name:zebrafish whole brain|tissue:zebrafish whole brain|developmental stage:21 dpf 23 dpf  Juvenile|genotype:WT/TLAB|geo loc name:missing|collection date:missing", "SABERb1 linC2  brain1  SABER Seq", "Raw FASTQ reads were aligned against zebrafish genome  built from GRCz.109.gtf and the GRCz11.fa files  using the Cell Ranger count pipeline to generate gene by cell count matrices filtered barcode matrix. The count matrices were processed using the standard workflow of the Seurat v5.0.2 R package with modifications. Briefly  all count matrices were separately loaded into R. Seurat objects were then created with modified parameters  requiring features to be detected in at least 3 cells and cells to contain at least 300 genes. Subsequently  the Seurat objects were merged to create a single Seurat object. Cells with RNA counts between 300 and 4000 and a mitochondrial gene percentage of less than 9% were selected for downstream analysis. Gene expression values were log normalized with the default parameters and scaled for the top 3000 variable genes. The scaled data was then used to compute the 50 principal components. Cells were iteratively clustered using the Louvain algorithm post identifying neighboring cells. Differentially expressed genes for each cluster were identified using genes that are detected in at least 10% of cells in either of the two clusters and genes showing a minimum difference of 10% between the two clusters. Cell types were annotated using known marker genes.  The SABER seq FASTQ reads were processed using the Cell Ranger count pipeline  similar to the transcriptomic reads. A bash script was then used to generate a text file containing all FASTQ headers and error corrected cell barcodes for SABER seq data. post loading filtered barcode matrices into R  error corrected cell barcodes from transcriptomic reads were isolated and matched against those in the text file to identify common cell barcode that are shared between the transcriptomic and SABER seq datasets. The resulting text file contained redundant error corrected cell barcodes associated with different UMIs for the same cell. Thus  unique error corrected cell barcodes were isolated to create a text file containing unique FASTQ headers and error corrected barcodes. Subsequently  FASTQ reads matching the FASTQ headers in this file were extracted from the SABER seq raw reads using a Python script. SABER seq barcodes were analyzed using the scGESTALT pipeline available on Github as described previously7 35. To analyze differences in barcode detection and editing frequencies between cell types a binomial test was used. scRNA seq data was analyzed using the following protocol. Raw FASTQ reads were aligned against zebrafish genome  built from GRCz.109.gtf and the GRCz11.fa files  using the Cell Ranger count pipeline to generate gene by cell count matrices filtered barcode matrix. The count matrices were processed using the standard workflow of the Seurat v5.0.2 R package with modifications. Briefly  all count matrices were separately loaded into R. Seurat objects were then created with modified parameters  requiring features to be detected in at least 3 cells and cells to contain at least 300 genes. Subsequently  the Seurat objects were merged to create a single Seurat object. Cells with RNA counts between 300 and 4000 and a mitochondrial gene percentage of less than 9% were selected for downstream analysis. Gene expression values were log normalized with the default parameters and scaled for the top 3000 variable genes. The scaled data was then used to compute the 50 principal components. Cells were iteratively clustered using the Louvain algorithm post identifying neighboring cells. Differentially expressed genes for each cluster were identified using genes that are detected in at least 10% of cells in either of the two clusters and genes showing a minimum difference of 10% between the two clusters. Cell types were annotated using known marker genes. Finally  the data was visualized using UMAP. SABER seq barcode analysis was performed using the following protocol. The SABER seq FASTQ reads were processed using the Cell Ranger count pipeline.A bash script was then used to generate a text file containing all FASTQ headers and error corrected cell barcodes for SABER seq data. Error corrected cell barcodes from transcriptomic reads were isolated and matched against those in the text file to identify common cell barcode that are shared between the transcriptomic and SABER seq datasets. The resulting text file contained redundant error corrected cell barcodes associated with different UMIs for the same cell. Thus  unique error corrected cell barcodes were isolated to create a text file containing unique FASTQ headers and error corrected barcodes. Subsequently  FASTQ reads matching the FASTQ headers in this file were extracted from the SABER seq raw reads using a Python script. SABER seq barcodes were analyzed using the scGESTALT pipeline available on Github as described previously. To analyze differences in barcode detection and editing frequencies between cell types a binomial test was used. Assembly: GRCz11 Supplementary files format and content: *.h5 files contain fileted features/genes and barcode data generated from cell ranger pipeline. Supplementary files format and content: LinC1 Filtered Fastq Headers CB.txt LinC2 Filtered Fastq Headers CB.txt LinD1 Filtered Fastq Headers CB.txt LinD2 Filtered Fastq Headers CB.txt LinD3 Filtered Fastq Headers CB.txt files contain filtered and unique fastq headers and cell barcodes post matching the trasncriptomic cell barcodes. Supplementary files format and content: SABERb1 linC1 S4 BrainC1 S6.CB.xlsx SABERb1 linC2 S5 BrainC2 S7.CB.xlsx SABERb2 linD1 S1 BrainD1 S8.CB.xlsx SABERb2 linD2 S2 BrainD2 S9.CB.xlsx SABERb2 linD3 S3 BrainD3 S10.CB.xlsx SABERb4 linB2.1 13.123.2.With.Primer.stats.xlsx SABERb4 linB2.2 14.124.2.With.Primer.stats.xlsx files contain the SABER Seq output post matching reads to the reference sequence.", "zebrafish whole brain", null, "Wild type and SABER x her4.3:switchCas9 zebrafish brains were harvested and dissociated at 21 dpf 23 dpf as described previously with the following modifications. All microcentrifuge tubes were treated with 1% BSA/DPBS overnight to prevent cell loss due to adhesion. Brains were dissociated with 1 mL of 20 units/ml papain in EBSS media and incubated at 37 \u00b0C for 20 min. Cells were resuspended with 150 \u03bcl ice cold 1%BSA/DPBS solution. Transcriptome libraries were processed according to the manufacturer\u2019s instructions Single Cell 3\u2019 v3 kit. Transcriptome libraries were sequenced using HiSeq S4 300 cycle kits Novogene and Novaseq SP 100 and 200 cycle kits MedGenome. To generate SABER seq libraries  samples post cDNA amplification and prior to fragmentation were split into two parts. One part was processed for transcriptome libraries as instructed by the manufacturer. The other part was processed for SABER seq libraries as follows. To enrich for SABER barcodes  5 \u00b5l of the whole transcriptome cDNA was PCR amplified with 10X p140v1UP  GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCT CGTCGTAGATCTTCATCGTGATG and 10XPCR1F CTACACGACGCTCTTCCGATCT primers and Q5 polymerase. The reaction conditions were: 98 \u00b0C  30 s; [98 \u00b0C  10 s; 68 \u00b0C  25 s; 72 \u00b0C  15 s] x 14 cycles; 72 \u00b0C  2 min. The reaction was cleaned up with 0.8X SPRISelect beads and eluted in 20 \u00b5l elution buffer EB buffer. A second PCR was carried out using 3 \u00b5l of PCR1 product and 10XPCR1F and 10X p140v2Sa GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCT GAAACTACATGGGGTcaGCTC primers. Reaction conditions were same as PCR1 except only 13 cycles were completed. At this point  2 different clean up conditions were used: iteration 1 SABER seq v0 and iteration 2 SABER seq v1. For iteration 1  the reaction was cleaned up with 0.8X SPRISelect beads and eluted in 20 \u00b5l elution buffer EB buffer similar to PCR1.  For iteration 2  an optimization was trialed  and the reaction was cleaned up with right side size selection using SPRIselect beads 0.6X beads used initially and supernatant was rebound with 1.5X beads. The product was eluted in 20 \u00b5l EB. Finally  adapters and sample indices were incorporated in a third PCR reaction using 5 7 \u00b5l of a 1:10 dilution of PCR2 product. Reactions conditions were same as PCR1 except 9 10 cycles were completed. Primers used: 10XP5Dual Hx AATGATACGGCGACCACCGAGATCTACAC xxxxxxx xxx ACACTCTTTCCCTACACGACGCTCTTCCGATCT  10XP7Dual H5 AATGATACGGCGAC CACCGAGATCTACAC xxxxxxxxxx ACACTCTTTCCCTACACGACGCTCTTCCGATCT  where x represents index bases from the 10X Dual Index Kit TT  SetA H index entries. For iteration 1  the third PCR reaction was cleaned up with 0.8X SPRISelect beads and eluted in 20 \u00b5l elution buffer EB to generate final libraries. For iteration 2  the third PCR reaction was cleaned up with right side size selection using SPRIselect beads 0.65X beads used initially and supernatant was rebound with 1.5X beads. SABER seq libraries were sequenced using Novaseq SP 200 cycle kits MedGenome with 5 10% PhiX spike in. Sequencing parameters: Read1 28 cycles  Read2 151 cycles  Index1 10 cycles  Index2 10 cycles. Standard sequencing primers were used.", "This work was performed under protocol numbers 807110 and 807259  which were approved by the University of Pennsylvania\u2019s Office of Animal Welfare of Institutional Animal Care and Use Committee IACUC. All zebrafish work in this study follows the University of Pennsylvania Institutional Animal Care and Use Committee regulations.", "tissue:zebrafish whole brain|developmental stage:21 dpf 23 dpf  Juvenile|genotype:WT/TLAB", "GSM8290058", "GSM8290058: SABERb1 linC2  brain1  SABER Seq; Danio rerio; RNA Seq", "GSM8290058 r1", "GSM8290058", "1", "Wild type and SABER x her4.3:switchCas9 zebrafish brains were harvested and dissociated at 21 dpf 23 dpf as described previously with the following modifications. All microcentrifuge tubes were treated with 1% BSA/DPBS overnight to prevent cell loss due to adhesion. Brains were dissociated with 1 mL of 20 units/ml papain in EBSS media and incubated at 37 \u00b0C for 20 min. Cells were resuspended with 150 \u03bcl ice cold 1%BSA/DPBS solution. Transcriptome libraries were processed according to the manufacturer's instructions Single Cell three prime v3 kit. Transcriptome libraries were sequenced using HiSeq S4 300 cycle kits Novogene and Novaseq SP 100 and 200 cycle kits MedGenome. To generate SABER seq libraries  samples post cDNA amplification and prior to fragmentation were split into two parts. One part was processed for transcriptome libraries as instructed by the manufacturer. The other part was processed for SABER seq libraries as follows. To enrich for SABER barcodes  5 \u00b5l of the whole transcriptome cDNA was PCR amplified with 10X p140v1UP  GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCT CGTCGTAGATCTTCATCGTGATG and 10XPCR1F CTACACGACGCTCTTCCGATCT primers and Q5 polymerase. The reaction conditions were: 98 \u00b0C  30 s; [98 \u00b0C  10 s; 68 \u00b0C  25 s; 72 \u00b0C  15 s] x 14 cycles; 72 \u00b0C  2 min. The reaction was cleaned up with 0.8X SPRISelect beads and eluted in 20 \u00b5l elution buffer EB buffer. A second PCR was carried out using 3 \u00b5l of PCR1 product and 10XPCR1F and 10X p140v2Sa GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCT GAAACTACATGGGGTcaGCTC primers. Reaction conditions were same as PCR1 except only 13 cycles were completed. At this point  2 different clean up conditions were used: iteration 1 SABER seq v0 and iteration 2 SABER seq v1. For iteration 1  the reaction was cleaned up with 0.8X SPRISelect beads and eluted in 20 \u00b5l elution buffer EB buffer similar to PCR1.  For iteration 2  an optimization was trialed  and the reaction was cleaned up with right side size selection using SPRIselect beads 0.6X beads used initially and supernatant was rebound with 1.5X beads. The product was eluted in 20 \u00b5l EB. Finally  adapters and sample indices were incorporated in a third PCR reaction using 5 7 \u00b5l of a 1:10 dilution of PCR2 product. Reactions conditions were same as PCR1 except 9 10 cycles were completed. Primers used: 10XP5Dual Hx AATGATACGGCGACCACCGAGATCTACAC xxxxxxx xxx ACACTCTTTCCCTACACGACGCTCTTCCGATCT  10XP7Dual H5 AATGATACGGCGAC CACCGAGATCTACAC xxxxxxxxxx ACACTCTTTCCCTACACGACGCTCTTCCGATCT  where x represents index bases from the 10X Dual Index Kit TT  SetA H index entries. For iteration 1  the third PCR reaction was cleaned up with 0.8X SPRISelect beads and eluted in 20 \u00b5l elution buffer EB to generate final libraries. For iteration 2  the third PCR reaction was cleaned up with right side size selection using SPRIselect beads 0.65X beads used initially and supernatant was rebound with 1.5X beads. SABER seq libraries were sequenced using Novaseq SP 200 cycle kits MedGenome with 5 10% PhiX spike in. Sequencing parameters: Read1 28 cycles  Read2 151 cycles  Index1 10 cycles  Index2 10 cycles. Standard sequencing primers were used.", null, "RNA-Seq", "TRANSCRIPTOMIC SINGLE CELL", "cDNA", "PAIRED", "ILLUMINA", "Illumina NovaSeq 6000", null, "SRP509892", null, "loader:fastq load.py", "SABERb1_linC2_S5_L001_I1_001.fastq.gz SABERb1_linC2_S5_L001_I2_001.fastq.gz SABERb1_linC2_S5_L001_R1_001.fastq.gz SABERb1_linC2_S5_L001_R2_001.fastq.gz", "fastq fastq fastq fastq", 5550858638.0, 27893762.0, "GSM8290058 r1", "0:10 1:10 2:28 3:151", "A:1146922069;C:1156473192;G:1103601022;T:804934064;N:27715", 10, 10, 28, 151, 1146922069, 1156473192, 1103601022, 804934064, 27715, "SRX24701856", "SRS21429385", "SRA1878107", "University of Pennsylvania", "University of Pennsylvania", 1, 0.01311, null, 0.00298, null, 0.98762, null, 0.53959, null, 151, null, "B", null, "usable mapping rate", "illumina", "novaseq_era", "full_length", "random_priming", "unknown", "sc", "single_cell_droplet", "10x", null, "United States", "2024-05-25", "Larval", "Larval", "Brain", "Nervous System"], [32393, "SRR29181695", "SRX24701855", "SRS21429384", "SRP509892", "PRJNA1116548", "Barcoding Notch signaling in the developing brain", "GSE268356", "Other", "Developmental signaling inputs are fundamental for shaping cell fates and behavior. However  traditional fluorescent based signaling reporters have limitations in scalability and molecular resolution of cell types. We present SABER seq  a CRISPR Cas molecular recorder that stores transient developmental signaling cues as permanent mutations in cellular genomes for deconstruction at later stages via single cell transcriptomics. We applied SABER seq to record Notch signaling in developing zebrafish brains. SABER seq has two components: a signaling sensor and a barcode recorder. The sensor activates Cas9 in a Notch dependent manner with inducible control while the recorder obtains mutations in ancestral cells where Notch is active. We combine SABER seq with an expanded juvenile brain atlas to identify cell types derived from Notch active founders. Our data reveals rare examples where differential Notch activity in ancestral progenitors is detected in terminally differentiated neuronal subtypes. SABER seq is a novel platform for rapid  scalable and high resolution mapping of signaling activity during development. Overall design: 10x Genomics libraries of single cell transcriptomes and SABER seq data  and genomic DNA SABER data", null, "pubmed:39575683", null, "SABERb1 linC1  brain1  SABER Seq", "GSM8290057", null, "source name:zebrafish whole brain|tissue:zebrafish whole brain|developmental stage:21 dpf 23 dpf  Juvenile|genotype:WT/TLAB|geo loc name:missing|collection date:missing", "SABERb1 linC1  brain1  SABER Seq", "Raw FASTQ reads were aligned against zebrafish genome  built from GRCz.109.gtf and the GRCz11.fa files  using the Cell Ranger count pipeline to generate gene by cell count matrices filtered barcode matrix. The count matrices were processed using the standard workflow of the Seurat v5.0.2 R package with modifications. Briefly  all count matrices were separately loaded into R. Seurat objects were then created with modified parameters  requiring features to be detected in at least 3 cells and cells to contain at least 300 genes. Subsequently  the Seurat objects were merged to create a single Seurat object. Cells with RNA counts between 300 and 4000 and a mitochondrial gene percentage of less than 9% were selected for downstream analysis. Gene expression values were log normalized with the default parameters and scaled for the top 3000 variable genes. The scaled data was then used to compute the 50 principal components. Cells were iteratively clustered using the Louvain algorithm post identifying neighboring cells. Differentially expressed genes for each cluster were identified using genes that are detected in at least 10% of cells in either of the two clusters and genes showing a minimum difference of 10% between the two clusters. Cell types were annotated using known marker genes.  The SABER seq FASTQ reads were processed using the Cell Ranger count pipeline  similar to the transcriptomic reads. A bash script was then used to generate a text file containing all FASTQ headers and error corrected cell barcodes for SABER seq data. post loading filtered barcode matrices into R  error corrected cell barcodes from transcriptomic reads were isolated and matched against those in the text file to identify common cell barcode that are shared between the transcriptomic and SABER seq datasets. The resulting text file contained redundant error corrected cell barcodes associated with different UMIs for the same cell. Thus  unique error corrected cell barcodes were isolated to create a text file containing unique FASTQ headers and error corrected barcodes. Subsequently  FASTQ reads matching the FASTQ headers in this file were extracted from the SABER seq raw reads using a Python script. SABER seq barcodes were analyzed using the scGESTALT pipeline available on Github as described previously7 35. To analyze differences in barcode detection and editing frequencies between cell types a binomial test was used. scRNA seq data was analyzed using the following protocol. Raw FASTQ reads were aligned against zebrafish genome  built from GRCz.109.gtf and the GRCz11.fa files  using the Cell Ranger count pipeline to generate gene by cell count matrices filtered barcode matrix. The count matrices were processed using the standard workflow of the Seurat v5.0.2 R package with modifications. Briefly  all count matrices were separately loaded into R. Seurat objects were then created with modified parameters  requiring features to be detected in at least 3 cells and cells to contain at least 300 genes. Subsequently  the Seurat objects were merged to create a single Seurat object. Cells with RNA counts between 300 and 4000 and a mitochondrial gene percentage of less than 9% were selected for downstream analysis. Gene expression values were log normalized with the default parameters and scaled for the top 3000 variable genes. The scaled data was then used to compute the 50 principal components. Cells were iteratively clustered using the Louvain algorithm post identifying neighboring cells. Differentially expressed genes for each cluster were identified using genes that are detected in at least 10% of cells in either of the two clusters and genes showing a minimum difference of 10% between the two clusters. Cell types were annotated using known marker genes. Finally  the data was visualized using UMAP. SABER seq barcode analysis was performed using the following protocol. The SABER seq FASTQ reads were processed using the Cell Ranger count pipeline.A bash script was then used to generate a text file containing all FASTQ headers and error corrected cell barcodes for SABER seq data. Error corrected cell barcodes from transcriptomic reads were isolated and matched against those in the text file to identify common cell barcode that are shared between the transcriptomic and SABER seq datasets. The resulting text file contained redundant error corrected cell barcodes associated with different UMIs for the same cell. Thus  unique error corrected cell barcodes were isolated to create a text file containing unique FASTQ headers and error corrected barcodes. Subsequently  FASTQ reads matching the FASTQ headers in this file were extracted from the SABER seq raw reads using a Python script. SABER seq barcodes were analyzed using the scGESTALT pipeline available on Github as described previously. To analyze differences in barcode detection and editing frequencies between cell types a binomial test was used. Assembly: GRCz11 Supplementary files format and content: *.h5 files contain fileted features/genes and barcode data generated from cell ranger pipeline. Supplementary files format and content: LinC1 Filtered Fastq Headers CB.txt LinC2 Filtered Fastq Headers CB.txt LinD1 Filtered Fastq Headers CB.txt LinD2 Filtered Fastq Headers CB.txt LinD3 Filtered Fastq Headers CB.txt files contain filtered and unique fastq headers and cell barcodes post matching the trasncriptomic cell barcodes. Supplementary files format and content: SABERb1 linC1 S4 BrainC1 S6.CB.xlsx SABERb1 linC2 S5 BrainC2 S7.CB.xlsx SABERb2 linD1 S1 BrainD1 S8.CB.xlsx SABERb2 linD2 S2 BrainD2 S9.CB.xlsx SABERb2 linD3 S3 BrainD3 S10.CB.xlsx SABERb4 linB2.1 13.123.2.With.Primer.stats.xlsx SABERb4 linB2.2 14.124.2.With.Primer.stats.xlsx files contain the SABER Seq output post matching reads to the reference sequence.", "zebrafish whole brain", null, "Wild type and SABER x her4.3:switchCas9 zebrafish brains were harvested and dissociated at 21 dpf 23 dpf as described previously with the following modifications. All microcentrifuge tubes were treated with 1% BSA/DPBS overnight to prevent cell loss due to adhesion. Brains were dissociated with 1 mL of 20 units/ml papain in EBSS media and incubated at 37 \u00b0C for 20 min. Cells were resuspended with 150 \u03bcl ice cold 1%BSA/DPBS solution. Transcriptome libraries were processed according to the manufacturer\u2019s instructions Single Cell 3\u2019 v3 kit. Transcriptome libraries were sequenced using HiSeq S4 300 cycle kits Novogene and Novaseq SP 100 and 200 cycle kits MedGenome. To generate SABER seq libraries  samples post cDNA amplification and prior to fragmentation were split into two parts. One part was processed for transcriptome libraries as instructed by the manufacturer. The other part was processed for SABER seq libraries as follows. To enrich for SABER barcodes  5 \u00b5l of the whole transcriptome cDNA was PCR amplified with 10X p140v1UP  GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCT CGTCGTAGATCTTCATCGTGATG and 10XPCR1F CTACACGACGCTCTTCCGATCT primers and Q5 polymerase. The reaction conditions were: 98 \u00b0C  30 s; [98 \u00b0C  10 s; 68 \u00b0C  25 s; 72 \u00b0C  15 s] x 14 cycles; 72 \u00b0C  2 min. The reaction was cleaned up with 0.8X SPRISelect beads and eluted in 20 \u00b5l elution buffer EB buffer. A second PCR was carried out using 3 \u00b5l of PCR1 product and 10XPCR1F and 10X p140v2Sa GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCT GAAACTACATGGGGTcaGCTC primers. Reaction conditions were same as PCR1 except only 13 cycles were completed. At this point  2 different clean up conditions were used: iteration 1 SABER seq v0 and iteration 2 SABER seq v1. For iteration 1  the reaction was cleaned up with 0.8X SPRISelect beads and eluted in 20 \u00b5l elution buffer EB buffer similar to PCR1.  For iteration 2  an optimization was trialed  and the reaction was cleaned up with right side size selection using SPRIselect beads 0.6X beads used initially and supernatant was rebound with 1.5X beads. The product was eluted in 20 \u00b5l EB. Finally  adapters and sample indices were incorporated in a third PCR reaction using 5 7 \u00b5l of a 1:10 dilution of PCR2 product. Reactions conditions were same as PCR1 except 9 10 cycles were completed. Primers used: 10XP5Dual Hx AATGATACGGCGACCACCGAGATCTACAC xxxxxxx xxx ACACTCTTTCCCTACACGACGCTCTTCCGATCT  10XP7Dual H5 AATGATACGGCGAC CACCGAGATCTACAC xxxxxxxxxx ACACTCTTTCCCTACACGACGCTCTTCCGATCT  where x represents index bases from the 10X Dual Index Kit TT  SetA H index entries. For iteration 1  the third PCR reaction was cleaned up with 0.8X SPRISelect beads and eluted in 20 \u00b5l elution buffer EB to generate final libraries. For iteration 2  the third PCR reaction was cleaned up with right side size selection using SPRIselect beads 0.65X beads used initially and supernatant was rebound with 1.5X beads. SABER seq libraries were sequenced using Novaseq SP 200 cycle kits MedGenome with 5 10% PhiX spike in. Sequencing parameters: Read1 28 cycles  Read2 151 cycles  Index1 10 cycles  Index2 10 cycles. Standard sequencing primers were used.", "This work was performed under protocol numbers 807110 and 807259  which were approved by the University of Pennsylvania\u2019s Office of Animal Welfare of Institutional Animal Care and Use Committee IACUC. All zebrafish work in this study follows the University of Pennsylvania Institutional Animal Care and Use Committee regulations.", "tissue:zebrafish whole brain|developmental stage:21 dpf 23 dpf  Juvenile|genotype:WT/TLAB", "GSM8290057", "GSM8290057: SABERb1 linC1  brain1  SABER Seq; Danio rerio; RNA Seq", "GSM8290057 r1", "GSM8290057", "1", "Wild type and SABER x her4.3:switchCas9 zebrafish brains were harvested and dissociated at 21 dpf 23 dpf as described previously with the following modifications. All microcentrifuge tubes were treated with 1% BSA/DPBS overnight to prevent cell loss due to adhesion. Brains were dissociated with 1 mL of 20 units/ml papain in EBSS media and incubated at 37 \u00b0C for 20 min. Cells were resuspended with 150 \u03bcl ice cold 1%BSA/DPBS solution. Transcriptome libraries were processed according to the manufacturer's instructions Single Cell three prime v3 kit. Transcriptome libraries were sequenced using HiSeq S4 300 cycle kits Novogene and Novaseq SP 100 and 200 cycle kits MedGenome. To generate SABER seq libraries  samples post cDNA amplification and prior to fragmentation were split into two parts. One part was processed for transcriptome libraries as instructed by the manufacturer. The other part was processed for SABER seq libraries as follows. To enrich for SABER barcodes  5 \u00b5l of the whole transcriptome cDNA was PCR amplified with 10X p140v1UP  GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCT CGTCGTAGATCTTCATCGTGATG and 10XPCR1F CTACACGACGCTCTTCCGATCT primers and Q5 polymerase. The reaction conditions were: 98 \u00b0C  30 s; [98 \u00b0C  10 s; 68 \u00b0C  25 s; 72 \u00b0C  15 s] x 14 cycles; 72 \u00b0C  2 min. The reaction was cleaned up with 0.8X SPRISelect beads and eluted in 20 \u00b5l elution buffer EB buffer. A second PCR was carried out using 3 \u00b5l of PCR1 product and 10XPCR1F and 10X p140v2Sa GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCT GAAACTACATGGGGTcaGCTC primers. Reaction conditions were same as PCR1 except only 13 cycles were completed. At this point  2 different clean up conditions were used: iteration 1 SABER seq v0 and iteration 2 SABER seq v1. For iteration 1  the reaction was cleaned up with 0.8X SPRISelect beads and eluted in 20 \u00b5l elution buffer EB buffer similar to PCR1.  For iteration 2  an optimization was trialed  and the reaction was cleaned up with right side size selection using SPRIselect beads 0.6X beads used initially and supernatant was rebound with 1.5X beads. The product was eluted in 20 \u00b5l EB. Finally  adapters and sample indices were incorporated in a third PCR reaction using 5 7 \u00b5l of a 1:10 dilution of PCR2 product. Reactions conditions were same as PCR1 except 9 10 cycles were completed. Primers used: 10XP5Dual Hx AATGATACGGCGACCACCGAGATCTACAC xxxxxxx xxx ACACTCTTTCCCTACACGACGCTCTTCCGATCT  10XP7Dual H5 AATGATACGGCGAC CACCGAGATCTACAC xxxxxxxxxx ACACTCTTTCCCTACACGACGCTCTTCCGATCT  where x represents index bases from the 10X Dual Index Kit TT  SetA H index entries. For iteration 1  the third PCR reaction was cleaned up with 0.8X SPRISelect beads and eluted in 20 \u00b5l elution buffer EB to generate final libraries. For iteration 2  the third PCR reaction was cleaned up with right side size selection using SPRIselect beads 0.65X beads used initially and supernatant was rebound with 1.5X beads. SABER seq libraries were sequenced using Novaseq SP 200 cycle kits MedGenome with 5 10% PhiX spike in. Sequencing parameters: Read1 28 cycles  Read2 151 cycles  Index1 10 cycles  Index2 10 cycles. Standard sequencing primers were used.", null, "RNA-Seq", "TRANSCRIPTOMIC SINGLE CELL", "cDNA", "PAIRED", "ILLUMINA", "Illumina NovaSeq 6000", null, "SRP509892", null, "loader:fastq load.py", "SABERb1_linC1_S4_L001_I1_001.fastq.gz SABERb1_linC1_S4_L001_I2_001.fastq.gz SABERb1_linC1_S4_L001_R1_001.fastq.gz SABERb1_linC1_S4_L001_R2_001.fastq.gz", "fastq fastq fastq fastq", 6470197047.0, 32513553.0, "GSM8290057 r1", "0:10 1:10 2:28 3:151", "A:1299285410;C:1322983894;G:1431353449;T:855891198;N:32552", 10, 10, 28, 151, 1299285410, 1322983894, 1431353449, 855891198, 32552, "SRX24701855", "SRS21429384", "SRA1878107", "University of Pennsylvania", "University of Pennsylvania", 1, 0.01874, null, 0.00435, null, 0.98334, null, 0.48429, null, 151, null, "B", null, "usable mapping rate", "illumina", "novaseq_era", "full_length", "random_priming", "unknown", "sc", "single_cell_droplet", "10x", null, "United States", "2024-05-25", "Larval", "Larval", "Brain", "Nervous System"], [32394, "SRR29181696", "SRX24701854", "SRS21429383", "SRP509892", "PRJNA1116548", "Barcoding Notch signaling in the developing brain", "GSE268356", "Other", "Developmental signaling inputs are fundamental for shaping cell fates and behavior. However  traditional fluorescent based signaling reporters have limitations in scalability and molecular resolution of cell types. We present SABER seq  a CRISPR Cas molecular recorder that stores transient developmental signaling cues as permanent mutations in cellular genomes for deconstruction at later stages via single cell transcriptomics. We applied SABER seq to record Notch signaling in developing zebrafish brains. SABER seq has two components: a signaling sensor and a barcode recorder. The sensor activates Cas9 in a Notch dependent manner with inducible control while the recorder obtains mutations in ancestral cells where Notch is active. We combine SABER seq with an expanded juvenile brain atlas to identify cell types derived from Notch active founders. Our data reveals rare examples where differential Notch activity in ancestral progenitors is detected in terminally differentiated neuronal subtypes. SABER seq is a novel platform for rapid  scalable and high resolution mapping of signaling activity during development. Overall design: 10x Genomics libraries of single cell transcriptomes and SABER seq data  and genomic DNA SABER data", null, "pubmed:39575683", null, "SABERb1 brainC1091523wk3  Novogene Run1  brain1  transcriptome", "GSM8290045", null, "source name:zebrafish whole brain|tissue:zebrafish whole brain|developmental stage:21 dpf 23 dpf  Juvenile|genotype:WT/TLAB|geo loc name:missing|collection date:missing", "SABERb1 brainC1091523wk3  Novogene Run1  brain1  transcriptome", "Raw FASTQ reads were aligned against zebrafish genome  built from GRCz.109.gtf and the GRCz11.fa files  using the Cell Ranger count pipeline to generate gene by cell count matrices filtered barcode matrix. The count matrices were processed using the standard workflow of the Seurat v5.0.2 R package with modifications. Briefly  all count matrices were separately loaded into R. Seurat objects were then created with modified parameters  requiring features to be detected in at least 3 cells and cells to contain at least 300 genes. Subsequently  the Seurat objects were merged to create a single Seurat object. Cells with RNA counts between 300 and 4000 and a mitochondrial gene percentage of less than 9% were selected for downstream analysis. Gene expression values were log normalized with the default parameters and scaled for the top 3000 variable genes. The scaled data was then used to compute the 50 principal components. Cells were iteratively clustered using the Louvain algorithm post identifying neighboring cells. Differentially expressed genes for each cluster were identified using genes that are detected in at least 10% of cells in either of the two clusters and genes showing a minimum difference of 10% between the two clusters. Cell types were annotated using known marker genes.  The SABER seq FASTQ reads were processed using the Cell Ranger count pipeline  similar to the transcriptomic reads. A bash script was then used to generate a text file containing all FASTQ headers and error corrected cell barcodes for SABER seq data. post loading filtered barcode matrices into R  error corrected cell barcodes from transcriptomic reads were isolated and matched against those in the text file to identify common cell barcode that are shared between the transcriptomic and SABER seq datasets. The resulting text file contained redundant error corrected cell barcodes associated with different UMIs for the same cell. Thus  unique error corrected cell barcodes were isolated to create a text file containing unique FASTQ headers and error corrected barcodes. Subsequently  FASTQ reads matching the FASTQ headers in this file were extracted from the SABER seq raw reads using a Python script. SABER seq barcodes were analyzed using the scGESTALT pipeline available on Github as described previously7 35. To analyze differences in barcode detection and editing frequencies between cell types a binomial test was used. scRNA seq data was analyzed using the following protocol. Raw FASTQ reads were aligned against zebrafish genome  built from GRCz.109.gtf and the GRCz11.fa files  using the Cell Ranger count pipeline to generate gene by cell count matrices filtered barcode matrix. The count matrices were processed using the standard workflow of the Seurat v5.0.2 R package with modifications. Briefly  all count matrices were separately loaded into R. Seurat objects were then created with modified parameters  requiring features to be detected in at least 3 cells and cells to contain at least 300 genes. Subsequently  the Seurat objects were merged to create a single Seurat object. Cells with RNA counts between 300 and 4000 and a mitochondrial gene percentage of less than 9% were selected for downstream analysis. Gene expression values were log normalized with the default parameters and scaled for the top 3000 variable genes. The scaled data was then used to compute the 50 principal components. Cells were iteratively clustered using the Louvain algorithm post identifying neighboring cells. Differentially expressed genes for each cluster were identified using genes that are detected in at least 10% of cells in either of the two clusters and genes showing a minimum difference of 10% between the two clusters. Cell types were annotated using known marker genes. Finally  the data was visualized using UMAP. SABER seq barcode analysis was performed using the following protocol. The SABER seq FASTQ reads were processed using the Cell Ranger count pipeline.A bash script was then used to generate a text file containing all FASTQ headers and error corrected cell barcodes for SABER seq data. Error corrected cell barcodes from transcriptomic reads were isolated and matched against those in the text file to identify common cell barcode that are shared between the transcriptomic and SABER seq datasets. The resulting text file contained redundant error corrected cell barcodes associated with different UMIs for the same cell. Thus  unique error corrected cell barcodes were isolated to create a text file containing unique FASTQ headers and error corrected barcodes. Subsequently  FASTQ reads matching the FASTQ headers in this file were extracted from the SABER seq raw reads using a Python script. SABER seq barcodes were analyzed using the scGESTALT pipeline available on Github as described previously. To analyze differences in barcode detection and editing frequencies between cell types a binomial test was used. genome build/assembly: GRCz11 processed data files format and content: *.h5 files contain filtered features/genes and barcode data generated from cell ranger pipeline. processed data files format and content: LinC1 Filtered Fastq Headers CB.txt LinC2 Filtered Fastq Headers CB.txt LinD1 Filtered Fastq Headers CB.txt LinD2 Filtered Fastq Headers CB.txt LinD3 Filtered Fastq Headers CB.txt files contain filtered and unique fastq headers and cell barcodes post matching the trasncriptomic cell barcodes. processed data files format and content: SABERb1 linC1 S4 BrainC1 S6.CB.xlsx SABERb1 linC2 S5 BrainC2 S7.CB.xlsx SABERb2 linD1 S1 BrainD1 S8.CB.xlsx SABERb2 linD2 S2 BrainD2 S9.CB.xlsx SABERb2 linD3 S3 BrainD3 S10.CB.xlsx SABERb4 linB2.1 13.123.2.With.Primer.stats.xlsx SABERb4 linB2.2 14.124.2.With.Primer.stats.xlsx files contain the SABER Seq output post matching reads to the reference sequence.", "zebrafish whole brain", null, "Wild type and SABER x her4.3:switchCas9 zebrafish brains were harvested and dissociated at 21 dpf 23 dpf as described previously Raj et. al 2018  Nature Biotechnology  with the following modifications. All microcentrifuge tubes were treated with 1% BSA/DPBS overnight to prevent cell loss due to adhesion. Brains were dissociated with 1 mL of 20 units/ml papain in EBSS media and incubated at 37 \u00b0C for 20 min. Cells were resuspended with 150 \u03bcl ice cold 1%BSA/DPBS solution. Transcriptome libraries were processed according to the manufacturer\u2019s instructions Single Cell 3\u2019 v3 kit. Transcriptome libraries were sequenced using NovaSeq S4 300 cycle kits Novogene and Novaseq SP 100 and 200 cycle kits MedGenome. To generate SABER seq libraries  samples post cDNA amplification and prior to fragmentation were split into two parts. One part was processed for transcriptome libraries as instructed by the manufacturer. The other part was processed for SABER seq libraries as follows. To enrich for SABER barcodes  5 \u00b5l of the whole transcriptome cDNA was PCR amplified with 10X p140v1UP  GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCT CGTCGTAGATCTTCATCGTGATG and 10XPCR1F CTACACGACGCTCTTCCGATCT primers and Q5 polymerase. The reaction conditions were: 98 \u00b0C  30 s; [98 \u00b0C  10 s; 68 \u00b0C  25 s; 72 \u00b0C  15 s] x 14 cycles; 72 \u00b0C  2 min. The reaction was cleaned up with 0.8X SPRISelect beads and eluted in 20 \u00b5l elution buffer EB buffer. A second PCR was carried out using 3 \u00b5l of PCR1 product and 10XPCR1F and 10X p140v2Sa GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCT GAAACTACATGGGGTcaGCTC primers. Reaction conditions were same as PCR1 except only 13 cycles were completed. At this point  2 different clean up conditions were used: iteration 1 SABER seq v0 and iteration 2 SABER seq v1. For iteration 1  the reaction was cleaned up with 0.8X SPRISelect beads and eluted in 20 \u00b5l elution buffer EB buffer similar to PCR1.  For iteration 2  an optimization was trialed  and the reaction was cleaned up with right side size selection using SPRIselect beads 0.6X beads used initially and supernatant was rebound with 1.5X beads. The product was eluted in 20 \u00b5l EB. Finally  adapters and sample indices were incorporated in a third PCR reaction using 5 7 \u00b5l of a 1:10 dilution of PCR2 product. Reactions conditions were same as PCR1 except 9 10 cycles were completed. Primers used: 10XP5Dual Hx AATGATACGGCGACCACCGAGATCTACAC xxxxxxx xxx ACACTCTTTCCCTACACGACGCTCTTCCGATCT  10XP7Dual H5 AATGATACGGCGAC CACCGAGATCTACAC xxxxxxxxxx ACACTCTTTCCCTACACGACGCTCTTCCGATCT  where x represents index bases from the 10X Dual Index Kit TT  SetA H index entries. For iteration 1  the third PCR reaction was cleaned up with 0.8X SPRISelect beads and eluted in 20 \u00b5l elution buffer EB to generate final libraries. For iteration 2  the third PCR reaction was cleaned up with right side size selection using SPRIselect beads 0.65X beads used initially and supernatant was rebound with 1.5X beads. SABER seq libraries were sequenced using Novaseq SP 200 cycle kits MedGenome with 5 10% PhiX spike in. Sequencing parameters: Read1 28 cycles  Read2 151 cycles  Index1 10 cycles  Index2 10 cycles. Standard sequencing primers were used.", "This work was performed under protocol numbers 807110 and 807259  which were approved by the University of Pennsylvania\u2019s Office of Animal Welfare of Institutional Animal Care and Use Committee IACUC. All zebrafish work in this study follows the University of Pennsylvania Institutional Animal Care and Use Committee regulations.", "tissue:zebrafish whole brain|developmental stage:21 dpf 23 dpf  Juvenile|genotype:WT/TLAB", "GSM8290045", "GSM8290045: SABERb1 brainC1091523wk3  Novogene Run1  brain1  transcriptome; Danio rerio; RNA Seq", "GSM8290045 r1", "GSM8290045", "1", "Wild type and SABER x her4.3:switchCas9 zebrafish brains were harvested and dissociated at 21 dpf 23 dpf as described previously Raj et. al 2018  Nature Biotechnology  with the following modifications. All microcentrifuge tubes were treated with 1% BSA/DPBS overnight to prevent cell loss due to adhesion. Brains were dissociated with 1 mL of 20 units/ml papain in EBSS media and incubated at 37 \u00b0C for 20 min. Cells were resuspended with 150 \u03bcl ice cold 1%BSA/DPBS solution. Transcriptome libraries were processed according to the manufacturer's instructions Single Cell three prime v3 kit. Transcriptome libraries were sequenced using NovaSeq S4 300 cycle kits Novogene and Novaseq SP 100 and 200 cycle kits MedGenome. To generate SABER seq libraries  samples post cDNA amplification and prior to fragmentation were split into two parts. One part was processed for transcriptome libraries as instructed by the manufacturer. The other part was processed for SABER seq libraries as follows. To enrich for SABER barcodes  5 \u00b5l of the whole transcriptome cDNA was PCR amplified with 10X p140v1UP  GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCT CGTCGTAGATCTTCATCGTGATG and 10XPCR1F CTACACGACGCTCTTCCGATCT primers and Q5 polymerase. The reaction conditions were: 98 \u00b0C  30 s; [98 \u00b0C  10 s; 68 \u00b0C  25 s; 72 \u00b0C  15 s] x 14 cycles; 72 \u00b0C  2 min. The reaction was cleaned up with 0.8X SPRISelect beads and eluted in 20 \u00b5l elution buffer EB buffer. A second PCR was carried out using 3 \u00b5l of PCR1 product and 10XPCR1F and 10X p140v2Sa GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCT GAAACTACATGGGGTcaGCTC primers. Reaction conditions were same as PCR1 except only 13 cycles were completed. At this point  2 different clean up conditions were used: iteration 1 SABER seq v0 and iteration 2 SABER seq v1. For iteration 1  the reaction was cleaned up with 0.8X SPRISelect beads and eluted in 20 \u00b5l elution buffer EB buffer similar to PCR1.  For iteration 2  an optimization was trialed  and the reaction was cleaned up with right side size selection using SPRIselect beads 0.6X beads used initially and supernatant was rebound with 1.5X beads. The product was eluted in 20 \u00b5l EB. Finally  adapters and sample indices were incorporated in a third PCR reaction using 5 7 \u00b5l of a 1:10 dilution of PCR2 product. Reactions conditions were same as PCR1 except 9 10 cycles were completed. Primers used: 10XP5Dual Hx AATGATACGGCGACCACCGAGATCTACAC xxxxxxx xxx ACACTCTTTCCCTACACGACGCTCTTCCGATCT  10XP7Dual H5 AATGATACGGCGAC CACCGAGATCTACAC xxxxxxxxxx ACACTCTTTCCCTACACGACGCTCTTCCGATCT  where x represents index bases from the 10X Dual Index Kit TT  SetA H index entries. For iteration 1  the third PCR reaction was cleaned up with 0.8X SPRISelect beads and eluted in 20 \u00b5l elution buffer EB to generate final libraries. For iteration 2  the third PCR reaction was cleaned up with right side size selection using SPRIselect beads 0.65X beads used initially and supernatant was rebound with 1.5X beads. SABER seq libraries were sequenced using Novaseq SP 200 cycle kits MedGenome with 5 10% PhiX spike in. Sequencing parameters: Read1 28 cycles  Read2 151 cycles  Index1 10 cycles  Index2 10 cycles. Standard sequencing primers were used.", null, "RNA-Seq", "TRANSCRIPTOMIC SINGLE CELL", "cDNA", "PAIRED", "ILLUMINA", "Illumina NovaSeq 6000", null, "SRP509892", null, "loader:fastq load.py", "SABERb1_brainC1091523wk3_CKDL230038568-1A_HHVLTDSX7_S3_L003_I1_001.fastq.gz SABERb1_brainC1091523wk3_CKDL230038568-1A_HHVLTDSX7_S3_L003_I2_001.fastq.gz SABERb1_brainC1091523wk3_CKDL230038568-1A_HHVLTDSX7_S3_L003_R1_001.fastq.gz SABERb1_brainC1091523wk3_CKDL230038568-1A_HHVLTDSX7_S3_L003_R2_001.fastq.gz", "fastq fastq fastq fastq", 13715941760.0, 42862318.0, "GSM8290045 r1", "0:10 1:10 2:150 3:150", "A:3643210069;C:2019746784;G:2321631965;T:4873417599;N:688983", 10, 10, 150, 150, 3643210069, 2019746784, 2321631965, 4873417599, 688983, "SRX24701854", "SRS21429383", "SRA1989588", "University of Pennsylvania", "University of Pennsylvania", 2, 0.38679, 0.89762, 0.1285, 0.20804, 0.95881, 0.76877, 0.53381, 0.54347, 150, 150, "T", "B", "mate1 technical by mapping diff", "illumina", "novaseq_era", "full_length", "random_priming", "unknown", "sc", "single_cell_droplet", "10x", null, "United States", "2024-05-25", "Larval", "Larval", "Brain", "Nervous System"], [32395, "SRR29181704", "SRX24701853", "SRS21429382", "SRP509892", "PRJNA1116548", "Barcoding Notch signaling in the developing brain", "GSE268356", "Other", "Developmental signaling inputs are fundamental for shaping cell fates and behavior. However  traditional fluorescent based signaling reporters have limitations in scalability and molecular resolution of cell types. We present SABER seq  a CRISPR Cas molecular recorder that stores transient developmental signaling cues as permanent mutations in cellular genomes for deconstruction at later stages via single cell transcriptomics. We applied SABER seq to record Notch signaling in developing zebrafish brains. SABER seq has two components: a signaling sensor and a barcode recorder. The sensor activates Cas9 in a Notch dependent manner with inducible control while the recorder obtains mutations in ancestral cells where Notch is active. We combine SABER seq with an expanded juvenile brain atlas to identify cell types derived from Notch active founders. Our data reveals rare examples where differential Notch activity in ancestral progenitors is detected in terminally differentiated neuronal subtypes. SABER seq is a novel platform for rapid  scalable and high resolution mapping of signaling activity during development. Overall design: 10x Genomics libraries of single cell transcriptomes and SABER seq data  and genomic DNA SABER data", null, "pubmed:39575683", null, "SABERb1 brainC2  Medgenome Run2  brain1  transcriptome", "GSM8290044", null, "source name:zebrafish whole brain|tissue:zebrafish whole brain|developmental stage:21 dpf 23 dpf  Juvenile|genotype:WT/TLAB|geo loc name:missing|collection date:missing", "SABERb1 brainC2  Medgenome Run2  brain1  transcriptome", "Raw FASTQ reads were aligned against zebrafish genome  built from GRCz.109.gtf and the GRCz11.fa files  using the Cell Ranger count pipeline to generate gene by cell count matrices filtered barcode matrix. The count matrices were processed using the standard workflow of the Seurat v5.0.2 R package with modifications. Briefly  all count matrices were separately loaded into R. Seurat objects were then created with modified parameters  requiring features to be detected in at least 3 cells and cells to contain at least 300 genes. Subsequently  the Seurat objects were merged to create a single Seurat object. Cells with RNA counts between 300 and 4000 and a mitochondrial gene percentage of less than 9% were selected for downstream analysis. Gene expression values were log normalized with the default parameters and scaled for the top 3000 variable genes. The scaled data was then used to compute the 50 principal components. Cells were iteratively clustered using the Louvain algorithm post identifying neighboring cells. Differentially expressed genes for each cluster were identified using genes that are detected in at least 10% of cells in either of the two clusters and genes showing a minimum difference of 10% between the two clusters. Cell types were annotated using known marker genes.  The SABER seq FASTQ reads were processed using the Cell Ranger count pipeline  similar to the transcriptomic reads. A bash script was then used to generate a text file containing all FASTQ headers and error corrected cell barcodes for SABER seq data. post loading filtered barcode matrices into R  error corrected cell barcodes from transcriptomic reads were isolated and matched against those in the text file to identify common cell barcode that are shared between the transcriptomic and SABER seq datasets. The resulting text file contained redundant error corrected cell barcodes associated with different UMIs for the same cell. Thus  unique error corrected cell barcodes were isolated to create a text file containing unique FASTQ headers and error corrected barcodes. Subsequently  FASTQ reads matching the FASTQ headers in this file were extracted from the SABER seq raw reads using a Python script. SABER seq barcodes were analyzed using the scGESTALT pipeline available on Github as described previously7 35. To analyze differences in barcode detection and editing frequencies between cell types a binomial test was used. scRNA seq data was analyzed using the following protocol. Raw FASTQ reads were aligned against zebrafish genome  built from GRCz.109.gtf and the GRCz11.fa files  using the Cell Ranger count pipeline to generate gene by cell count matrices filtered barcode matrix. The count matrices were processed using the standard workflow of the Seurat v5.0.2 R package with modifications. Briefly  all count matrices were separately loaded into R. Seurat objects were then created with modified parameters  requiring features to be detected in at least 3 cells and cells to contain at least 300 genes. Subsequently  the Seurat objects were merged to create a single Seurat object. Cells with RNA counts between 300 and 4000 and a mitochondrial gene percentage of less than 9% were selected for downstream analysis. Gene expression values were log normalized with the default parameters and scaled for the top 3000 variable genes. The scaled data was then used to compute the 50 principal components. Cells were iteratively clustered using the Louvain algorithm post identifying neighboring cells. Differentially expressed genes for each cluster were identified using genes that are detected in at least 10% of cells in either of the two clusters and genes showing a minimum difference of 10% between the two clusters. Cell types were annotated using known marker genes. Finally  the data was visualized using UMAP. SABER seq barcode analysis was performed using the following protocol. The SABER seq FASTQ reads were processed using the Cell Ranger count pipeline.A bash script was then used to generate a text file containing all FASTQ headers and error corrected cell barcodes for SABER seq data. Error corrected cell barcodes from transcriptomic reads were isolated and matched against those in the text file to identify common cell barcode that are shared between the transcriptomic and SABER seq datasets. The resulting text file contained redundant error corrected cell barcodes associated with different UMIs for the same cell. Thus  unique error corrected cell barcodes were isolated to create a text file containing unique FASTQ headers and error corrected barcodes. Subsequently  FASTQ reads matching the FASTQ headers in this file were extracted from the SABER seq raw reads using a Python script. SABER seq barcodes were analyzed using the scGESTALT pipeline available on Github as described previously. To analyze differences in barcode detection and editing frequencies between cell types a binomial test was used. genome build/assembly: GRCz11 processed data files format and content: *.h5 files contain filtered features/genes and barcode data generated from cell ranger pipeline. processed data files format and content: LinC1 Filtered Fastq Headers CB.txt LinC2 Filtered Fastq Headers CB.txt LinD1 Filtered Fastq Headers CB.txt LinD2 Filtered Fastq Headers CB.txt LinD3 Filtered Fastq Headers CB.txt files contain filtered and unique fastq headers and cell barcodes post matching the trasncriptomic cell barcodes. processed data files format and content: SABERb1 linC1 S4 BrainC1 S6.CB.xlsx SABERb1 linC2 S5 BrainC2 S7.CB.xlsx SABERb2 linD1 S1 BrainD1 S8.CB.xlsx SABERb2 linD2 S2 BrainD2 S9.CB.xlsx SABERb2 linD3 S3 BrainD3 S10.CB.xlsx SABERb4 linB2.1 13.123.2.With.Primer.stats.xlsx SABERb4 linB2.2 14.124.2.With.Primer.stats.xlsx files contain the SABER Seq output post matching reads to the reference sequence.", "zebrafish whole brain", null, "Wild type and SABER x her4.3:switchCas9 zebrafish brains were harvested and dissociated at 21 dpf 23 dpf as described previously Raj et. al 2018  Nature Biotechnology  with the following modifications. All microcentrifuge tubes were treated with 1% BSA/DPBS overnight to prevent cell loss due to adhesion. Brains were dissociated with 1 mL of 20 units/ml papain in EBSS media and incubated at 37 \u00b0C for 20 min. Cells were resuspended with 150 \u03bcl ice cold 1%BSA/DPBS solution. Transcriptome libraries were processed according to the manufacturer\u2019s instructions Single Cell 3\u2019 v3 kit. Transcriptome libraries were sequenced using NovaSeq S4 300 cycle kits Novogene and Novaseq SP 100 and 200 cycle kits MedGenome. To generate SABER seq libraries  samples post cDNA amplification and prior to fragmentation were split into two parts. One part was processed for transcriptome libraries as instructed by the manufacturer. The other part was processed for SABER seq libraries as follows. To enrich for SABER barcodes  5 \u00b5l of the whole transcriptome cDNA was PCR amplified with 10X p140v1UP  GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCT CGTCGTAGATCTTCATCGTGATG and 10XPCR1F CTACACGACGCTCTTCCGATCT primers and Q5 polymerase. The reaction conditions were: 98 \u00b0C  30 s; [98 \u00b0C  10 s; 68 \u00b0C  25 s; 72 \u00b0C  15 s] x 14 cycles; 72 \u00b0C  2 min. The reaction was cleaned up with 0.8X SPRISelect beads and eluted in 20 \u00b5l elution buffer EB buffer. A second PCR was carried out using 3 \u00b5l of PCR1 product and 10XPCR1F and 10X p140v2Sa GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCT GAAACTACATGGGGTcaGCTC primers. Reaction conditions were same as PCR1 except only 13 cycles were completed. At this point  2 different clean up conditions were used: iteration 1 SABER seq v0 and iteration 2 SABER seq v1. For iteration 1  the reaction was cleaned up with 0.8X SPRISelect beads and eluted in 20 \u00b5l elution buffer EB buffer similar to PCR1.  For iteration 2  an optimization was trialed  and the reaction was cleaned up with right side size selection using SPRIselect beads 0.6X beads used initially and supernatant was rebound with 1.5X beads. The product was eluted in 20 \u00b5l EB. Finally  adapters and sample indices were incorporated in a third PCR reaction using 5 7 \u00b5l of a 1:10 dilution of PCR2 product. Reactions conditions were same as PCR1 except 9 10 cycles were completed. Primers used: 10XP5Dual Hx AATGATACGGCGACCACCGAGATCTACAC xxxxxxx xxx ACACTCTTTCCCTACACGACGCTCTTCCGATCT  10XP7Dual H5 AATGATACGGCGAC CACCGAGATCTACAC xxxxxxxxxx ACACTCTTTCCCTACACGACGCTCTTCCGATCT  where x represents index bases from the 10X Dual Index Kit TT  SetA H index entries. For iteration 1  the third PCR reaction was cleaned up with 0.8X SPRISelect beads and eluted in 20 \u00b5l elution buffer EB to generate final libraries. For iteration 2  the third PCR reaction was cleaned up with right side size selection using SPRIselect beads 0.65X beads used initially and supernatant was rebound with 1.5X beads. SABER seq libraries were sequenced using Novaseq SP 200 cycle kits MedGenome with 5 10% PhiX spike in. Sequencing parameters: Read1 28 cycles  Read2 151 cycles  Index1 10 cycles  Index2 10 cycles. Standard sequencing primers were used.", "This work was performed under protocol numbers 807110 and 807259  which were approved by the University of Pennsylvania\u2019s Office of Animal Welfare of Institutional Animal Care and Use Committee IACUC. All zebrafish work in this study follows the University of Pennsylvania Institutional Animal Care and Use Committee regulations.", "tissue:zebrafish whole brain|developmental stage:21 dpf 23 dpf  Juvenile|genotype:WT/TLAB", "GSM8290044", "GSM8290044: SABERb1 brainC2  Medgenome Run2  brain1  transcriptome; Danio rerio; RNA Seq", "GSM8290044 r1", "GSM8290044", "1", "Wild type and SABER x her4.3:switchCas9 zebrafish brains were harvested and dissociated at 21 dpf 23 dpf as described previously Raj et. al 2018  Nature Biotechnology  with the following modifications. All microcentrifuge tubes were treated with 1% BSA/DPBS overnight to prevent cell loss due to adhesion. Brains were dissociated with 1 mL of 20 units/ml papain in EBSS media and incubated at 37 \u00b0C for 20 min. Cells were resuspended with 150 \u03bcl ice cold 1%BSA/DPBS solution. Transcriptome libraries were processed according to the manufacturer's instructions Single Cell three prime v3 kit. Transcriptome libraries were sequenced using NovaSeq S4 300 cycle kits Novogene and Novaseq SP 100 and 200 cycle kits MedGenome. To generate SABER seq libraries  samples post cDNA amplification and prior to fragmentation were split into two parts. One part was processed for transcriptome libraries as instructed by the manufacturer. The other part was processed for SABER seq libraries as follows. To enrich for SABER barcodes  5 \u00b5l of the whole transcriptome cDNA was PCR amplified with 10X p140v1UP  GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCT CGTCGTAGATCTTCATCGTGATG and 10XPCR1F CTACACGACGCTCTTCCGATCT primers and Q5 polymerase. The reaction conditions were: 98 \u00b0C  30 s; [98 \u00b0C  10 s; 68 \u00b0C  25 s; 72 \u00b0C  15 s] x 14 cycles; 72 \u00b0C  2 min. The reaction was cleaned up with 0.8X SPRISelect beads and eluted in 20 \u00b5l elution buffer EB buffer. A second PCR was carried out using 3 \u00b5l of PCR1 product and 10XPCR1F and 10X p140v2Sa GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCT GAAACTACATGGGGTcaGCTC primers. Reaction conditions were same as PCR1 except only 13 cycles were completed. At this point  2 different clean up conditions were used: iteration 1 SABER seq v0 and iteration 2 SABER seq v1. For iteration 1  the reaction was cleaned up with 0.8X SPRISelect beads and eluted in 20 \u00b5l elution buffer EB buffer similar to PCR1.  For iteration 2  an optimization was trialed  and the reaction was cleaned up with right side size selection using SPRIselect beads 0.6X beads used initially and supernatant was rebound with 1.5X beads. The product was eluted in 20 \u00b5l EB. Finally  adapters and sample indices were incorporated in a third PCR reaction using 5 7 \u00b5l of a 1:10 dilution of PCR2 product. Reactions conditions were same as PCR1 except 9 10 cycles were completed. Primers used: 10XP5Dual Hx AATGATACGGCGACCACCGAGATCTACAC xxxxxxx xxx ACACTCTTTCCCTACACGACGCTCTTCCGATCT  10XP7Dual H5 AATGATACGGCGAC CACCGAGATCTACAC xxxxxxxxxx ACACTCTTTCCCTACACGACGCTCTTCCGATCT  where x represents index bases from the 10X Dual Index Kit TT  SetA H index entries. For iteration 1  the third PCR reaction was cleaned up with 0.8X SPRISelect beads and eluted in 20 \u00b5l elution buffer EB to generate final libraries. For iteration 2  the third PCR reaction was cleaned up with right side size selection using SPRIselect beads 0.65X beads used initially and supernatant was rebound with 1.5X beads. SABER seq libraries were sequenced using Novaseq SP 200 cycle kits MedGenome with 5 10% PhiX spike in. Sequencing parameters: Read1 28 cycles  Read2 151 cycles  Index1 10 cycles  Index2 10 cycles. Standard sequencing primers were used.", null, "RNA-Seq", "TRANSCRIPTOMIC SINGLE CELL", "cDNA", "PAIRED", "ILLUMINA", "Illumina NovaSeq 6000", null, "SRP509892", null, "loader:fastq load.py", "SABERb1_brainC2_S2_L002_I1_001.fastq.gz SABERb1_brainC2_S2_L002_I2_001.fastq.gz SABERb1_brainC2_S2_L002_R1_001.fastq.gz SABERb1_brainC2_S2_L002_R2_001.fastq.gz", "fastq fastq fastq fastq", 3635949490.0, 26157910.0, "GSM8290044 r1", "0:10 1:10 2:28 3:91", "A:726922983;C:475009000;G:515409063;T:663020855;N:7909", 10, 10, 28, 91, 726922983, 475009000, 515409063, 663020855, 7909, "SRX24701853", "SRS21429382", "SRA1989588", "University of Pennsylvania", "University of Pennsylvania", 1, 0.91216, null, 0.23091, null, 0.76495, null, 0.55384, null, 91, null, "B", null, "usable mapping rate", "illumina", "novaseq_era", "full_length", "random_priming", "unknown", "sc", "single_cell_droplet", "10x", null, "United States", "2024-05-25", "Larval", "Larval", "Brain", "Nervous System"], [32396, "SRR29181705", "SRX24701853", "SRS21429382", "SRP509892", "PRJNA1116548", "Barcoding Notch signaling in the developing brain", "GSE268356", "Other", "Developmental signaling inputs are fundamental for shaping cell fates and behavior. However  traditional fluorescent based signaling reporters have limitations in scalability and molecular resolution of cell types. We present SABER seq  a CRISPR Cas molecular recorder that stores transient developmental signaling cues as permanent mutations in cellular genomes for deconstruction at later stages via single cell transcriptomics. We applied SABER seq to record Notch signaling in developing zebrafish brains. SABER seq has two components: a signaling sensor and a barcode recorder. The sensor activates Cas9 in a Notch dependent manner with inducible control while the recorder obtains mutations in ancestral cells where Notch is active. We combine SABER seq with an expanded juvenile brain atlas to identify cell types derived from Notch active founders. Our data reveals rare examples where differential Notch activity in ancestral progenitors is detected in terminally differentiated neuronal subtypes. SABER seq is a novel platform for rapid  scalable and high resolution mapping of signaling activity during development. Overall design: 10x Genomics libraries of single cell transcriptomes and SABER seq data  and genomic DNA SABER data", null, "pubmed:39575683", null, "SABERb1 brainC2  Medgenome Run2  brain1  transcriptome", "GSM8290044", null, "source name:zebrafish whole brain|tissue:zebrafish whole brain|developmental stage:21 dpf 23 dpf  Juvenile|genotype:WT/TLAB|geo loc name:missing|collection date:missing", "SABERb1 brainC2  Medgenome Run2  brain1  transcriptome", "Raw FASTQ reads were aligned against zebrafish genome  built from GRCz.109.gtf and the GRCz11.fa files  using the Cell Ranger count pipeline to generate gene by cell count matrices filtered barcode matrix. The count matrices were processed using the standard workflow of the Seurat v5.0.2 R package with modifications. Briefly  all count matrices were separately loaded into R. Seurat objects were then created with modified parameters  requiring features to be detected in at least 3 cells and cells to contain at least 300 genes. Subsequently  the Seurat objects were merged to create a single Seurat object. Cells with RNA counts between 300 and 4000 and a mitochondrial gene percentage of less than 9% were selected for downstream analysis. Gene expression values were log normalized with the default parameters and scaled for the top 3000 variable genes. The scaled data was then used to compute the 50 principal components. Cells were iteratively clustered using the Louvain algorithm post identifying neighboring cells. Differentially expressed genes for each cluster were identified using genes that are detected in at least 10% of cells in either of the two clusters and genes showing a minimum difference of 10% between the two clusters. Cell types were annotated using known marker genes.  The SABER seq FASTQ reads were processed using the Cell Ranger count pipeline  similar to the transcriptomic reads. A bash script was then used to generate a text file containing all FASTQ headers and error corrected cell barcodes for SABER seq data. post loading filtered barcode matrices into R  error corrected cell barcodes from transcriptomic reads were isolated and matched against those in the text file to identify common cell barcode that are shared between the transcriptomic and SABER seq datasets. The resulting text file contained redundant error corrected cell barcodes associated with different UMIs for the same cell. Thus  unique error corrected cell barcodes were isolated to create a text file containing unique FASTQ headers and error corrected barcodes. Subsequently  FASTQ reads matching the FASTQ headers in this file were extracted from the SABER seq raw reads using a Python script. SABER seq barcodes were analyzed using the scGESTALT pipeline available on Github as described previously7 35. To analyze differences in barcode detection and editing frequencies between cell types a binomial test was used. scRNA seq data was analyzed using the following protocol. Raw FASTQ reads were aligned against zebrafish genome  built from GRCz.109.gtf and the GRCz11.fa files  using the Cell Ranger count pipeline to generate gene by cell count matrices filtered barcode matrix. The count matrices were processed using the standard workflow of the Seurat v5.0.2 R package with modifications. Briefly  all count matrices were separately loaded into R. Seurat objects were then created with modified parameters  requiring features to be detected in at least 3 cells and cells to contain at least 300 genes. Subsequently  the Seurat objects were merged to create a single Seurat object. Cells with RNA counts between 300 and 4000 and a mitochondrial gene percentage of less than 9% were selected for downstream analysis. Gene expression values were log normalized with the default parameters and scaled for the top 3000 variable genes. The scaled data was then used to compute the 50 principal components. Cells were iteratively clustered using the Louvain algorithm post identifying neighboring cells. Differentially expressed genes for each cluster were identified using genes that are detected in at least 10% of cells in either of the two clusters and genes showing a minimum difference of 10% between the two clusters. Cell types were annotated using known marker genes. Finally  the data was visualized using UMAP. SABER seq barcode analysis was performed using the following protocol. The SABER seq FASTQ reads were processed using the Cell Ranger count pipeline.A bash script was then used to generate a text file containing all FASTQ headers and error corrected cell barcodes for SABER seq data. Error corrected cell barcodes from transcriptomic reads were isolated and matched against those in the text file to identify common cell barcode that are shared between the transcriptomic and SABER seq datasets. The resulting text file contained redundant error corrected cell barcodes associated with different UMIs for the same cell. Thus  unique error corrected cell barcodes were isolated to create a text file containing unique FASTQ headers and error corrected barcodes. Subsequently  FASTQ reads matching the FASTQ headers in this file were extracted from the SABER seq raw reads using a Python script. SABER seq barcodes were analyzed using the scGESTALT pipeline available on Github as described previously. To analyze differences in barcode detection and editing frequencies between cell types a binomial test was used. genome build/assembly: GRCz11 processed data files format and content: *.h5 files contain filtered features/genes and barcode data generated from cell ranger pipeline. processed data files format and content: LinC1 Filtered Fastq Headers CB.txt LinC2 Filtered Fastq Headers CB.txt LinD1 Filtered Fastq Headers CB.txt LinD2 Filtered Fastq Headers CB.txt LinD3 Filtered Fastq Headers CB.txt files contain filtered and unique fastq headers and cell barcodes post matching the trasncriptomic cell barcodes. processed data files format and content: SABERb1 linC1 S4 BrainC1 S6.CB.xlsx SABERb1 linC2 S5 BrainC2 S7.CB.xlsx SABERb2 linD1 S1 BrainD1 S8.CB.xlsx SABERb2 linD2 S2 BrainD2 S9.CB.xlsx SABERb2 linD3 S3 BrainD3 S10.CB.xlsx SABERb4 linB2.1 13.123.2.With.Primer.stats.xlsx SABERb4 linB2.2 14.124.2.With.Primer.stats.xlsx files contain the SABER Seq output post matching reads to the reference sequence.", "zebrafish whole brain", null, "Wild type and SABER x her4.3:switchCas9 zebrafish brains were harvested and dissociated at 21 dpf 23 dpf as described previously Raj et. al 2018  Nature Biotechnology  with the following modifications. All microcentrifuge tubes were treated with 1% BSA/DPBS overnight to prevent cell loss due to adhesion. Brains were dissociated with 1 mL of 20 units/ml papain in EBSS media and incubated at 37 \u00b0C for 20 min. Cells were resuspended with 150 \u03bcl ice cold 1%BSA/DPBS solution. Transcriptome libraries were processed according to the manufacturer\u2019s instructions Single Cell 3\u2019 v3 kit. Transcriptome libraries were sequenced using NovaSeq S4 300 cycle kits Novogene and Novaseq SP 100 and 200 cycle kits MedGenome. To generate SABER seq libraries  samples post cDNA amplification and prior to fragmentation were split into two parts. One part was processed for transcriptome libraries as instructed by the manufacturer. The other part was processed for SABER seq libraries as follows. To enrich for SABER barcodes  5 \u00b5l of the whole transcriptome cDNA was PCR amplified with 10X p140v1UP  GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCT CGTCGTAGATCTTCATCGTGATG and 10XPCR1F CTACACGACGCTCTTCCGATCT primers and Q5 polymerase. The reaction conditions were: 98 \u00b0C  30 s; [98 \u00b0C  10 s; 68 \u00b0C  25 s; 72 \u00b0C  15 s] x 14 cycles; 72 \u00b0C  2 min. The reaction was cleaned up with 0.8X SPRISelect beads and eluted in 20 \u00b5l elution buffer EB buffer. A second PCR was carried out using 3 \u00b5l of PCR1 product and 10XPCR1F and 10X p140v2Sa GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCT GAAACTACATGGGGTcaGCTC primers. Reaction conditions were same as PCR1 except only 13 cycles were completed. At this point  2 different clean up conditions were used: iteration 1 SABER seq v0 and iteration 2 SABER seq v1. For iteration 1  the reaction was cleaned up with 0.8X SPRISelect beads and eluted in 20 \u00b5l elution buffer EB buffer similar to PCR1.  For iteration 2  an optimization was trialed  and the reaction was cleaned up with right side size selection using SPRIselect beads 0.6X beads used initially and supernatant was rebound with 1.5X beads. The product was eluted in 20 \u00b5l EB. Finally  adapters and sample indices were incorporated in a third PCR reaction using 5 7 \u00b5l of a 1:10 dilution of PCR2 product. Reactions conditions were same as PCR1 except 9 10 cycles were completed. Primers used: 10XP5Dual Hx AATGATACGGCGACCACCGAGATCTACAC xxxxxxx xxx ACACTCTTTCCCTACACGACGCTCTTCCGATCT  10XP7Dual H5 AATGATACGGCGAC CACCGAGATCTACAC xxxxxxxxxx ACACTCTTTCCCTACACGACGCTCTTCCGATCT  where x represents index bases from the 10X Dual Index Kit TT  SetA H index entries. For iteration 1  the third PCR reaction was cleaned up with 0.8X SPRISelect beads and eluted in 20 \u00b5l elution buffer EB to generate final libraries. For iteration 2  the third PCR reaction was cleaned up with right side size selection using SPRIselect beads 0.65X beads used initially and supernatant was rebound with 1.5X beads. SABER seq libraries were sequenced using Novaseq SP 200 cycle kits MedGenome with 5 10% PhiX spike in. Sequencing parameters: Read1 28 cycles  Read2 151 cycles  Index1 10 cycles  Index2 10 cycles. Standard sequencing primers were used.", "This work was performed under protocol numbers 807110 and 807259  which were approved by the University of Pennsylvania\u2019s Office of Animal Welfare of Institutional Animal Care and Use Committee IACUC. All zebrafish work in this study follows the University of Pennsylvania Institutional Animal Care and Use Committee regulations.", "tissue:zebrafish whole brain|developmental stage:21 dpf 23 dpf  Juvenile|genotype:WT/TLAB", "GSM8290044", "GSM8290044: SABERb1 brainC2  Medgenome Run2  brain1  transcriptome; Danio rerio; RNA Seq", "GSM8290044 r1", "GSM8290044", "1", "Wild type and SABER x her4.3:switchCas9 zebrafish brains were harvested and dissociated at 21 dpf 23 dpf as described previously Raj et. al 2018  Nature Biotechnology  with the following modifications. All microcentrifuge tubes were treated with 1% BSA/DPBS overnight to prevent cell loss due to adhesion. Brains were dissociated with 1 mL of 20 units/ml papain in EBSS media and incubated at 37 \u00b0C for 20 min. Cells were resuspended with 150 \u03bcl ice cold 1%BSA/DPBS solution. Transcriptome libraries were processed according to the manufacturer's instructions Single Cell three prime v3 kit. Transcriptome libraries were sequenced using NovaSeq S4 300 cycle kits Novogene and Novaseq SP 100 and 200 cycle kits MedGenome. To generate SABER seq libraries  samples post cDNA amplification and prior to fragmentation were split into two parts. One part was processed for transcriptome libraries as instructed by the manufacturer. The other part was processed for SABER seq libraries as follows. To enrich for SABER barcodes  5 \u00b5l of the whole transcriptome cDNA was PCR amplified with 10X p140v1UP  GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCT CGTCGTAGATCTTCATCGTGATG and 10XPCR1F CTACACGACGCTCTTCCGATCT primers and Q5 polymerase. The reaction conditions were: 98 \u00b0C  30 s; [98 \u00b0C  10 s; 68 \u00b0C  25 s; 72 \u00b0C  15 s] x 14 cycles; 72 \u00b0C  2 min. The reaction was cleaned up with 0.8X SPRISelect beads and eluted in 20 \u00b5l elution buffer EB buffer. A second PCR was carried out using 3 \u00b5l of PCR1 product and 10XPCR1F and 10X p140v2Sa GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCT GAAACTACATGGGGTcaGCTC primers. Reaction conditions were same as PCR1 except only 13 cycles were completed. At this point  2 different clean up conditions were used: iteration 1 SABER seq v0 and iteration 2 SABER seq v1. For iteration 1  the reaction was cleaned up with 0.8X SPRISelect beads and eluted in 20 \u00b5l elution buffer EB buffer similar to PCR1.  For iteration 2  an optimization was trialed  and the reaction was cleaned up with right side size selection using SPRIselect beads 0.6X beads used initially and supernatant was rebound with 1.5X beads. The product was eluted in 20 \u00b5l EB. Finally  adapters and sample indices were incorporated in a third PCR reaction using 5 7 \u00b5l of a 1:10 dilution of PCR2 product. Reactions conditions were same as PCR1 except 9 10 cycles were completed. Primers used: 10XP5Dual Hx AATGATACGGCGACCACCGAGATCTACAC xxxxxxx xxx ACACTCTTTCCCTACACGACGCTCTTCCGATCT  10XP7Dual H5 AATGATACGGCGAC CACCGAGATCTACAC xxxxxxxxxx ACACTCTTTCCCTACACGACGCTCTTCCGATCT  where x represents index bases from the 10X Dual Index Kit TT  SetA H index entries. For iteration 1  the third PCR reaction was cleaned up with 0.8X SPRISelect beads and eluted in 20 \u00b5l elution buffer EB to generate final libraries. For iteration 2  the third PCR reaction was cleaned up with right side size selection using SPRIselect beads 0.65X beads used initially and supernatant was rebound with 1.5X beads. SABER seq libraries were sequenced using Novaseq SP 200 cycle kits MedGenome with 5 10% PhiX spike in. Sequencing parameters: Read1 28 cycles  Read2 151 cycles  Index1 10 cycles  Index2 10 cycles. Standard sequencing primers were used.", null, "RNA-Seq", "TRANSCRIPTOMIC SINGLE CELL", "cDNA", "PAIRED", "ILLUMINA", "Illumina NovaSeq 6000", null, "SRP509892", null, "loader:fastq load.py", "SABERb1_brainC2_S7_L002_I1_001.fastq.gz SABERb1_brainC2_S7_L002_I2_001.fastq.gz SABERb1_brainC2_S7_L002_R1_001.fastq.gz SABERb1_brainC2_S7_L002_R2_001.fastq.gz", "fastq fastq fastq fastq", 11551358978.0, 83103302.0, "GSM8290044 r2", "0:10 1:10 2:28 3:91", "A:2291886666;C:1518484627;G:1649680085;T:2102329631;N:19473", 10, 10, 28, 91, 2291886666, 1518484627, 1649680085, 2102329631, 19473, "SRX24701853", "SRS21429382", "SRA1989588", "University of Pennsylvania", "University of Pennsylvania", 1, 0.91457, null, 0.23286, null, 0.76674, null, 0.5505, null, 91, null, "B", null, "usable mapping rate", "illumina", "novaseq_era", "full_length", "random_priming", "unknown", "sc", "single_cell_droplet", "10x", null, "United States", "2024-05-25", "Larval", "Larval", "Brain", "Nervous System"], [32397, "SRR29181702", "SRX24701852", "SRS21429381", "SRP509892", "PRJNA1116548", "Barcoding Notch signaling in the developing brain", "GSE268356", "Other", "Developmental signaling inputs are fundamental for shaping cell fates and behavior. However  traditional fluorescent based signaling reporters have limitations in scalability and molecular resolution of cell types. We present SABER seq  a CRISPR Cas molecular recorder that stores transient developmental signaling cues as permanent mutations in cellular genomes for deconstruction at later stages via single cell transcriptomics. We applied SABER seq to record Notch signaling in developing zebrafish brains. SABER seq has two components: a signaling sensor and a barcode recorder. The sensor activates Cas9 in a Notch dependent manner with inducible control while the recorder obtains mutations in ancestral cells where Notch is active. We combine SABER seq with an expanded juvenile brain atlas to identify cell types derived from Notch active founders. Our data reveals rare examples where differential Notch activity in ancestral progenitors is detected in terminally differentiated neuronal subtypes. SABER seq is a novel platform for rapid  scalable and high resolution mapping of signaling activity during development. Overall design: 10x Genomics libraries of single cell transcriptomes and SABER seq data  and genomic DNA SABER data", null, "pubmed:39575683", null, "SABERb1 brainC1  Medgenome Run2  brain1  transcriptome", "GSM8290043", null, "source name:zebrafish whole brain|tissue:zebrafish whole brain|developmental stage:21 dpf 23 dpf  Juvenile|genotype:WT/TLAB|geo loc name:missing|collection date:missing", "SABERb1 brainC1  Medgenome Run2  brain1  transcriptome", "Raw FASTQ reads were aligned against zebrafish genome  built from GRCz.109.gtf and the GRCz11.fa files  using the Cell Ranger count pipeline to generate gene by cell count matrices filtered barcode matrix. The count matrices were processed using the standard workflow of the Seurat v5.0.2 R package with modifications. Briefly  all count matrices were separately loaded into R. Seurat objects were then created with modified parameters  requiring features to be detected in at least 3 cells and cells to contain at least 300 genes. Subsequently  the Seurat objects were merged to create a single Seurat object. Cells with RNA counts between 300 and 4000 and a mitochondrial gene percentage of less than 9% were selected for downstream analysis. Gene expression values were log normalized with the default parameters and scaled for the top 3000 variable genes. The scaled data was then used to compute the 50 principal components. Cells were iteratively clustered using the Louvain algorithm post identifying neighboring cells. Differentially expressed genes for each cluster were identified using genes that are detected in at least 10% of cells in either of the two clusters and genes showing a minimum difference of 10% between the two clusters. Cell types were annotated using known marker genes.  The SABER seq FASTQ reads were processed using the Cell Ranger count pipeline  similar to the transcriptomic reads. A bash script was then used to generate a text file containing all FASTQ headers and error corrected cell barcodes for SABER seq data. post loading filtered barcode matrices into R  error corrected cell barcodes from transcriptomic reads were isolated and matched against those in the text file to identify common cell barcode that are shared between the transcriptomic and SABER seq datasets. The resulting text file contained redundant error corrected cell barcodes associated with different UMIs for the same cell. Thus  unique error corrected cell barcodes were isolated to create a text file containing unique FASTQ headers and error corrected barcodes. Subsequently  FASTQ reads matching the FASTQ headers in this file were extracted from the SABER seq raw reads using a Python script. SABER seq barcodes were analyzed using the scGESTALT pipeline available on Github as described previously7 35. To analyze differences in barcode detection and editing frequencies between cell types a binomial test was used. scRNA seq data was analyzed using the following protocol. Raw FASTQ reads were aligned against zebrafish genome  built from GRCz.109.gtf and the GRCz11.fa files  using the Cell Ranger count pipeline to generate gene by cell count matrices filtered barcode matrix. The count matrices were processed using the standard workflow of the Seurat v5.0.2 R package with modifications. Briefly  all count matrices were separately loaded into R. Seurat objects were then created with modified parameters  requiring features to be detected in at least 3 cells and cells to contain at least 300 genes. Subsequently  the Seurat objects were merged to create a single Seurat object. Cells with RNA counts between 300 and 4000 and a mitochondrial gene percentage of less than 9% were selected for downstream analysis. Gene expression values were log normalized with the default parameters and scaled for the top 3000 variable genes. The scaled data was then used to compute the 50 principal components. Cells were iteratively clustered using the Louvain algorithm post identifying neighboring cells. Differentially expressed genes for each cluster were identified using genes that are detected in at least 10% of cells in either of the two clusters and genes showing a minimum difference of 10% between the two clusters. Cell types were annotated using known marker genes. Finally  the data was visualized using UMAP. SABER seq barcode analysis was performed using the following protocol. The SABER seq FASTQ reads were processed using the Cell Ranger count pipeline.A bash script was then used to generate a text file containing all FASTQ headers and error corrected cell barcodes for SABER seq data. Error corrected cell barcodes from transcriptomic reads were isolated and matched against those in the text file to identify common cell barcode that are shared between the transcriptomic and SABER seq datasets. The resulting text file contained redundant error corrected cell barcodes associated with different UMIs for the same cell. Thus  unique error corrected cell barcodes were isolated to create a text file containing unique FASTQ headers and error corrected barcodes. Subsequently  FASTQ reads matching the FASTQ headers in this file were extracted from the SABER seq raw reads using a Python script. SABER seq barcodes were analyzed using the scGESTALT pipeline available on Github as described previously. To analyze differences in barcode detection and editing frequencies between cell types a binomial test was used. genome build/assembly: GRCz11 processed data files format and content: *.h5 files contain filtered features/genes and barcode data generated from cell ranger pipeline. processed data files format and content: LinC1 Filtered Fastq Headers CB.txt LinC2 Filtered Fastq Headers CB.txt LinD1 Filtered Fastq Headers CB.txt LinD2 Filtered Fastq Headers CB.txt LinD3 Filtered Fastq Headers CB.txt files contain filtered and unique fastq headers and cell barcodes post matching the trasncriptomic cell barcodes. processed data files format and content: SABERb1 linC1 S4 BrainC1 S6.CB.xlsx SABERb1 linC2 S5 BrainC2 S7.CB.xlsx SABERb2 linD1 S1 BrainD1 S8.CB.xlsx SABERb2 linD2 S2 BrainD2 S9.CB.xlsx SABERb2 linD3 S3 BrainD3 S10.CB.xlsx SABERb4 linB2.1 13.123.2.With.Primer.stats.xlsx SABERb4 linB2.2 14.124.2.With.Primer.stats.xlsx files contain the SABER Seq output post matching reads to the reference sequence.", "zebrafish whole brain", null, "Wild type and SABER x her4.3:switchCas9 zebrafish brains were harvested and dissociated at 21 dpf 23 dpf as described previously Raj et. al 2018  Nature Biotechnology  with the following modifications. All microcentrifuge tubes were treated with 1% BSA/DPBS overnight to prevent cell loss due to adhesion. Brains were dissociated with 1 mL of 20 units/ml papain in EBSS media and incubated at 37 \u00b0C for 20 min. Cells were resuspended with 150 \u03bcl ice cold 1%BSA/DPBS solution. Transcriptome libraries were processed according to the manufacturer\u2019s instructions Single Cell 3\u2019 v3 kit. Transcriptome libraries were sequenced using NovaSeq S4 300 cycle kits Novogene and Novaseq SP 100 and 200 cycle kits MedGenome. To generate SABER seq libraries  samples post cDNA amplification and prior to fragmentation were split into two parts. One part was processed for transcriptome libraries as instructed by the manufacturer. The other part was processed for SABER seq libraries as follows. To enrich for SABER barcodes  5 \u00b5l of the whole transcriptome cDNA was PCR amplified with 10X p140v1UP  GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCT CGTCGTAGATCTTCATCGTGATG and 10XPCR1F CTACACGACGCTCTTCCGATCT primers and Q5 polymerase. The reaction conditions were: 98 \u00b0C  30 s; [98 \u00b0C  10 s; 68 \u00b0C  25 s; 72 \u00b0C  15 s] x 14 cycles; 72 \u00b0C  2 min. The reaction was cleaned up with 0.8X SPRISelect beads and eluted in 20 \u00b5l elution buffer EB buffer. A second PCR was carried out using 3 \u00b5l of PCR1 product and 10XPCR1F and 10X p140v2Sa GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCT GAAACTACATGGGGTcaGCTC primers. Reaction conditions were same as PCR1 except only 13 cycles were completed. At this point  2 different clean up conditions were used: iteration 1 SABER seq v0 and iteration 2 SABER seq v1. For iteration 1  the reaction was cleaned up with 0.8X SPRISelect beads and eluted in 20 \u00b5l elution buffer EB buffer similar to PCR1.  For iteration 2  an optimization was trialed  and the reaction was cleaned up with right side size selection using SPRIselect beads 0.6X beads used initially and supernatant was rebound with 1.5X beads. The product was eluted in 20 \u00b5l EB. Finally  adapters and sample indices were incorporated in a third PCR reaction using 5 7 \u00b5l of a 1:10 dilution of PCR2 product. Reactions conditions were same as PCR1 except 9 10 cycles were completed. Primers used: 10XP5Dual Hx AATGATACGGCGACCACCGAGATCTACAC xxxxxxx xxx ACACTCTTTCCCTACACGACGCTCTTCCGATCT  10XP7Dual H5 AATGATACGGCGAC CACCGAGATCTACAC xxxxxxxxxx ACACTCTTTCCCTACACGACGCTCTTCCGATCT  where x represents index bases from the 10X Dual Index Kit TT  SetA H index entries. For iteration 1  the third PCR reaction was cleaned up with 0.8X SPRISelect beads and eluted in 20 \u00b5l elution buffer EB to generate final libraries. For iteration 2  the third PCR reaction was cleaned up with right side size selection using SPRIselect beads 0.65X beads used initially and supernatant was rebound with 1.5X beads. SABER seq libraries were sequenced using Novaseq SP 200 cycle kits MedGenome with 5 10% PhiX spike in. Sequencing parameters: Read1 28 cycles  Read2 151 cycles  Index1 10 cycles  Index2 10 cycles. Standard sequencing primers were used.", "This work was performed under protocol numbers 807110 and 807259  which were approved by the University of Pennsylvania\u2019s Office of Animal Welfare of Institutional Animal Care and Use Committee IACUC. All zebrafish work in this study follows the University of Pennsylvania Institutional Animal Care and Use Committee regulations.", "tissue:zebrafish whole brain|developmental stage:21 dpf 23 dpf  Juvenile|genotype:WT/TLAB", "GSM8290043", "GSM8290043: SABERb1 brainC1  Medgenome Run2  brain1  transcriptome; Danio rerio; RNA Seq", "GSM8290043 r1", "GSM8290043", "1", "Wild type and SABER x her4.3:switchCas9 zebrafish brains were harvested and dissociated at 21 dpf 23 dpf as described previously Raj et. al 2018  Nature Biotechnology  with the following modifications. All microcentrifuge tubes were treated with 1% BSA/DPBS overnight to prevent cell loss due to adhesion. Brains were dissociated with 1 mL of 20 units/ml papain in EBSS media and incubated at 37 \u00b0C for 20 min. Cells were resuspended with 150 \u03bcl ice cold 1%BSA/DPBS solution. Transcriptome libraries were processed according to the manufacturer's instructions Single Cell three prime v3 kit. Transcriptome libraries were sequenced using NovaSeq S4 300 cycle kits Novogene and Novaseq SP 100 and 200 cycle kits MedGenome. To generate SABER seq libraries  samples post cDNA amplification and prior to fragmentation were split into two parts. One part was processed for transcriptome libraries as instructed by the manufacturer. The other part was processed for SABER seq libraries as follows. To enrich for SABER barcodes  5 \u00b5l of the whole transcriptome cDNA was PCR amplified with 10X p140v1UP  GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCT CGTCGTAGATCTTCATCGTGATG and 10XPCR1F CTACACGACGCTCTTCCGATCT primers and Q5 polymerase. The reaction conditions were: 98 \u00b0C  30 s; [98 \u00b0C  10 s; 68 \u00b0C  25 s; 72 \u00b0C  15 s] x 14 cycles; 72 \u00b0C  2 min. The reaction was cleaned up with 0.8X SPRISelect beads and eluted in 20 \u00b5l elution buffer EB buffer. A second PCR was carried out using 3 \u00b5l of PCR1 product and 10XPCR1F and 10X p140v2Sa GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCT GAAACTACATGGGGTcaGCTC primers. Reaction conditions were same as PCR1 except only 13 cycles were completed. At this point  2 different clean up conditions were used: iteration 1 SABER seq v0 and iteration 2 SABER seq v1. For iteration 1  the reaction was cleaned up with 0.8X SPRISelect beads and eluted in 20 \u00b5l elution buffer EB buffer similar to PCR1.  For iteration 2  an optimization was trialed  and the reaction was cleaned up with right side size selection using SPRIselect beads 0.6X beads used initially and supernatant was rebound with 1.5X beads. The product was eluted in 20 \u00b5l EB. Finally  adapters and sample indices were incorporated in a third PCR reaction using 5 7 \u00b5l of a 1:10 dilution of PCR2 product. Reactions conditions were same as PCR1 except 9 10 cycles were completed. Primers used: 10XP5Dual Hx AATGATACGGCGACCACCGAGATCTACAC xxxxxxx xxx ACACTCTTTCCCTACACGACGCTCTTCCGATCT  10XP7Dual H5 AATGATACGGCGAC CACCGAGATCTACAC xxxxxxxxxx ACACTCTTTCCCTACACGACGCTCTTCCGATCT  where x represents index bases from the 10X Dual Index Kit TT  SetA H index entries. For iteration 1  the third PCR reaction was cleaned up with 0.8X SPRISelect beads and eluted in 20 \u00b5l elution buffer EB to generate final libraries. For iteration 2  the third PCR reaction was cleaned up with right side size selection using SPRIselect beads 0.65X beads used initially and supernatant was rebound with 1.5X beads. SABER seq libraries were sequenced using Novaseq SP 200 cycle kits MedGenome with 5 10% PhiX spike in. Sequencing parameters: Read1 28 cycles  Read2 151 cycles  Index1 10 cycles  Index2 10 cycles. Standard sequencing primers were used.", null, "RNA-Seq", "TRANSCRIPTOMIC SINGLE CELL", "cDNA", "PAIRED", "ILLUMINA", "Illumina NovaSeq 6000", null, "SRP509892", null, "loader:fastq load.py", "SABERb1_brainC1_S1_L002_I1_001.fastq.gz SABERb1_brainC1_S1_L002_I2_001.fastq.gz SABERb1_brainC1_S1_L002_R1_001.fastq.gz SABERb1_brainC1_S1_L002_R2_001.fastq.gz", "fastq fastq fastq fastq", 3575922201.0, 25726059.0, "GSM8290043 r1", "0:10 1:10 2:28 3:91", "A:706764997;C:473784835;G:514720645;T:645793304;N:7588", 10, 10, 28, 91, 706764997, 473784835, 514720645, 645793304, 7588, "SRX24701852", "SRS21429381", "SRA1989588", "University of Pennsylvania", "University of Pennsylvania", 1, 0.91627, null, 0.22683, null, 0.76469, null, 0.54848, null, 91, null, "B", null, "usable mapping rate", "illumina", "novaseq_era", "full_length", "random_priming", "unknown", "sc", "single_cell_droplet", "10x", null, "United States", "2024-05-25", "Larval", "Larval", "Brain", "Nervous System"], [32398, "SRR29181703", "SRX24701852", "SRS21429381", "SRP509892", "PRJNA1116548", "Barcoding Notch signaling in the developing brain", "GSE268356", "Other", "Developmental signaling inputs are fundamental for shaping cell fates and behavior. However  traditional fluorescent based signaling reporters have limitations in scalability and molecular resolution of cell types. We present SABER seq  a CRISPR Cas molecular recorder that stores transient developmental signaling cues as permanent mutations in cellular genomes for deconstruction at later stages via single cell transcriptomics. We applied SABER seq to record Notch signaling in developing zebrafish brains. SABER seq has two components: a signaling sensor and a barcode recorder. The sensor activates Cas9 in a Notch dependent manner with inducible control while the recorder obtains mutations in ancestral cells where Notch is active. We combine SABER seq with an expanded juvenile brain atlas to identify cell types derived from Notch active founders. Our data reveals rare examples where differential Notch activity in ancestral progenitors is detected in terminally differentiated neuronal subtypes. SABER seq is a novel platform for rapid  scalable and high resolution mapping of signaling activity during development. Overall design: 10x Genomics libraries of single cell transcriptomes and SABER seq data  and genomic DNA SABER data", null, "pubmed:39575683", null, "SABERb1 brainC1  Medgenome Run2  brain1  transcriptome", "GSM8290043", null, "source name:zebrafish whole brain|tissue:zebrafish whole brain|developmental stage:21 dpf 23 dpf  Juvenile|genotype:WT/TLAB|geo loc name:missing|collection date:missing", "SABERb1 brainC1  Medgenome Run2  brain1  transcriptome", "Raw FASTQ reads were aligned against zebrafish genome  built from GRCz.109.gtf and the GRCz11.fa files  using the Cell Ranger count pipeline to generate gene by cell count matrices filtered barcode matrix. The count matrices were processed using the standard workflow of the Seurat v5.0.2 R package with modifications. Briefly  all count matrices were separately loaded into R. Seurat objects were then created with modified parameters  requiring features to be detected in at least 3 cells and cells to contain at least 300 genes. Subsequently  the Seurat objects were merged to create a single Seurat object. Cells with RNA counts between 300 and 4000 and a mitochondrial gene percentage of less than 9% were selected for downstream analysis. Gene expression values were log normalized with the default parameters and scaled for the top 3000 variable genes. The scaled data was then used to compute the 50 principal components. Cells were iteratively clustered using the Louvain algorithm post identifying neighboring cells. Differentially expressed genes for each cluster were identified using genes that are detected in at least 10% of cells in either of the two clusters and genes showing a minimum difference of 10% between the two clusters. Cell types were annotated using known marker genes.  The SABER seq FASTQ reads were processed using the Cell Ranger count pipeline  similar to the transcriptomic reads. A bash script was then used to generate a text file containing all FASTQ headers and error corrected cell barcodes for SABER seq data. post loading filtered barcode matrices into R  error corrected cell barcodes from transcriptomic reads were isolated and matched against those in the text file to identify common cell barcode that are shared between the transcriptomic and SABER seq datasets. The resulting text file contained redundant error corrected cell barcodes associated with different UMIs for the same cell. Thus  unique error corrected cell barcodes were isolated to create a text file containing unique FASTQ headers and error corrected barcodes. Subsequently  FASTQ reads matching the FASTQ headers in this file were extracted from the SABER seq raw reads using a Python script. SABER seq barcodes were analyzed using the scGESTALT pipeline available on Github as described previously7 35. To analyze differences in barcode detection and editing frequencies between cell types a binomial test was used. scRNA seq data was analyzed using the following protocol. Raw FASTQ reads were aligned against zebrafish genome  built from GRCz.109.gtf and the GRCz11.fa files  using the Cell Ranger count pipeline to generate gene by cell count matrices filtered barcode matrix. The count matrices were processed using the standard workflow of the Seurat v5.0.2 R package with modifications. Briefly  all count matrices were separately loaded into R. Seurat objects were then created with modified parameters  requiring features to be detected in at least 3 cells and cells to contain at least 300 genes. Subsequently  the Seurat objects were merged to create a single Seurat object. Cells with RNA counts between 300 and 4000 and a mitochondrial gene percentage of less than 9% were selected for downstream analysis. Gene expression values were log normalized with the default parameters and scaled for the top 3000 variable genes. The scaled data was then used to compute the 50 principal components. Cells were iteratively clustered using the Louvain algorithm post identifying neighboring cells. Differentially expressed genes for each cluster were identified using genes that are detected in at least 10% of cells in either of the two clusters and genes showing a minimum difference of 10% between the two clusters. Cell types were annotated using known marker genes. Finally  the data was visualized using UMAP. SABER seq barcode analysis was performed using the following protocol. The SABER seq FASTQ reads were processed using the Cell Ranger count pipeline.A bash script was then used to generate a text file containing all FASTQ headers and error corrected cell barcodes for SABER seq data. Error corrected cell barcodes from transcriptomic reads were isolated and matched against those in the text file to identify common cell barcode that are shared between the transcriptomic and SABER seq datasets. The resulting text file contained redundant error corrected cell barcodes associated with different UMIs for the same cell. Thus  unique error corrected cell barcodes were isolated to create a text file containing unique FASTQ headers and error corrected barcodes. Subsequently  FASTQ reads matching the FASTQ headers in this file were extracted from the SABER seq raw reads using a Python script. SABER seq barcodes were analyzed using the scGESTALT pipeline available on Github as described previously. To analyze differences in barcode detection and editing frequencies between cell types a binomial test was used. genome build/assembly: GRCz11 processed data files format and content: *.h5 files contain filtered features/genes and barcode data generated from cell ranger pipeline. processed data files format and content: LinC1 Filtered Fastq Headers CB.txt LinC2 Filtered Fastq Headers CB.txt LinD1 Filtered Fastq Headers CB.txt LinD2 Filtered Fastq Headers CB.txt LinD3 Filtered Fastq Headers CB.txt files contain filtered and unique fastq headers and cell barcodes post matching the trasncriptomic cell barcodes. processed data files format and content: SABERb1 linC1 S4 BrainC1 S6.CB.xlsx SABERb1 linC2 S5 BrainC2 S7.CB.xlsx SABERb2 linD1 S1 BrainD1 S8.CB.xlsx SABERb2 linD2 S2 BrainD2 S9.CB.xlsx SABERb2 linD3 S3 BrainD3 S10.CB.xlsx SABERb4 linB2.1 13.123.2.With.Primer.stats.xlsx SABERb4 linB2.2 14.124.2.With.Primer.stats.xlsx files contain the SABER Seq output post matching reads to the reference sequence.", "zebrafish whole brain", null, "Wild type and SABER x her4.3:switchCas9 zebrafish brains were harvested and dissociated at 21 dpf 23 dpf as described previously Raj et. al 2018  Nature Biotechnology  with the following modifications. All microcentrifuge tubes were treated with 1% BSA/DPBS overnight to prevent cell loss due to adhesion. Brains were dissociated with 1 mL of 20 units/ml papain in EBSS media and incubated at 37 \u00b0C for 20 min. Cells were resuspended with 150 \u03bcl ice cold 1%BSA/DPBS solution. Transcriptome libraries were processed according to the manufacturer\u2019s instructions Single Cell 3\u2019 v3 kit. Transcriptome libraries were sequenced using NovaSeq S4 300 cycle kits Novogene and Novaseq SP 100 and 200 cycle kits MedGenome. To generate SABER seq libraries  samples post cDNA amplification and prior to fragmentation were split into two parts. One part was processed for transcriptome libraries as instructed by the manufacturer. The other part was processed for SABER seq libraries as follows. To enrich for SABER barcodes  5 \u00b5l of the whole transcriptome cDNA was PCR amplified with 10X p140v1UP  GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCT CGTCGTAGATCTTCATCGTGATG and 10XPCR1F CTACACGACGCTCTTCCGATCT primers and Q5 polymerase. The reaction conditions were: 98 \u00b0C  30 s; [98 \u00b0C  10 s; 68 \u00b0C  25 s; 72 \u00b0C  15 s] x 14 cycles; 72 \u00b0C  2 min. The reaction was cleaned up with 0.8X SPRISelect beads and eluted in 20 \u00b5l elution buffer EB buffer. A second PCR was carried out using 3 \u00b5l of PCR1 product and 10XPCR1F and 10X p140v2Sa GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCT GAAACTACATGGGGTcaGCTC primers. Reaction conditions were same as PCR1 except only 13 cycles were completed. At this point  2 different clean up conditions were used: iteration 1 SABER seq v0 and iteration 2 SABER seq v1. For iteration 1  the reaction was cleaned up with 0.8X SPRISelect beads and eluted in 20 \u00b5l elution buffer EB buffer similar to PCR1.  For iteration 2  an optimization was trialed  and the reaction was cleaned up with right side size selection using SPRIselect beads 0.6X beads used initially and supernatant was rebound with 1.5X beads. The product was eluted in 20 \u00b5l EB. Finally  adapters and sample indices were incorporated in a third PCR reaction using 5 7 \u00b5l of a 1:10 dilution of PCR2 product. Reactions conditions were same as PCR1 except 9 10 cycles were completed. Primers used: 10XP5Dual Hx AATGATACGGCGACCACCGAGATCTACAC xxxxxxx xxx ACACTCTTTCCCTACACGACGCTCTTCCGATCT  10XP7Dual H5 AATGATACGGCGAC CACCGAGATCTACAC xxxxxxxxxx ACACTCTTTCCCTACACGACGCTCTTCCGATCT  where x represents index bases from the 10X Dual Index Kit TT  SetA H index entries. For iteration 1  the third PCR reaction was cleaned up with 0.8X SPRISelect beads and eluted in 20 \u00b5l elution buffer EB to generate final libraries. For iteration 2  the third PCR reaction was cleaned up with right side size selection using SPRIselect beads 0.65X beads used initially and supernatant was rebound with 1.5X beads. SABER seq libraries were sequenced using Novaseq SP 200 cycle kits MedGenome with 5 10% PhiX spike in. Sequencing parameters: Read1 28 cycles  Read2 151 cycles  Index1 10 cycles  Index2 10 cycles. Standard sequencing primers were used.", "This work was performed under protocol numbers 807110 and 807259  which were approved by the University of Pennsylvania\u2019s Office of Animal Welfare of Institutional Animal Care and Use Committee IACUC. All zebrafish work in this study follows the University of Pennsylvania Institutional Animal Care and Use Committee regulations.", "tissue:zebrafish whole brain|developmental stage:21 dpf 23 dpf  Juvenile|genotype:WT/TLAB", "GSM8290043", "GSM8290043: SABERb1 brainC1  Medgenome Run2  brain1  transcriptome; Danio rerio; RNA Seq", "GSM8290043 r1", "GSM8290043", "1", "Wild type and SABER x her4.3:switchCas9 zebrafish brains were harvested and dissociated at 21 dpf 23 dpf as described previously Raj et. al 2018  Nature Biotechnology  with the following modifications. All microcentrifuge tubes were treated with 1% BSA/DPBS overnight to prevent cell loss due to adhesion. Brains were dissociated with 1 mL of 20 units/ml papain in EBSS media and incubated at 37 \u00b0C for 20 min. Cells were resuspended with 150 \u03bcl ice cold 1%BSA/DPBS solution. Transcriptome libraries were processed according to the manufacturer's instructions Single Cell three prime v3 kit. Transcriptome libraries were sequenced using NovaSeq S4 300 cycle kits Novogene and Novaseq SP 100 and 200 cycle kits MedGenome. To generate SABER seq libraries  samples post cDNA amplification and prior to fragmentation were split into two parts. One part was processed for transcriptome libraries as instructed by the manufacturer. The other part was processed for SABER seq libraries as follows. To enrich for SABER barcodes  5 \u00b5l of the whole transcriptome cDNA was PCR amplified with 10X p140v1UP  GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCT CGTCGTAGATCTTCATCGTGATG and 10XPCR1F CTACACGACGCTCTTCCGATCT primers and Q5 polymerase. The reaction conditions were: 98 \u00b0C  30 s; [98 \u00b0C  10 s; 68 \u00b0C  25 s; 72 \u00b0C  15 s] x 14 cycles; 72 \u00b0C  2 min. The reaction was cleaned up with 0.8X SPRISelect beads and eluted in 20 \u00b5l elution buffer EB buffer. A second PCR was carried out using 3 \u00b5l of PCR1 product and 10XPCR1F and 10X p140v2Sa GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCT GAAACTACATGGGGTcaGCTC primers. Reaction conditions were same as PCR1 except only 13 cycles were completed. At this point  2 different clean up conditions were used: iteration 1 SABER seq v0 and iteration 2 SABER seq v1. For iteration 1  the reaction was cleaned up with 0.8X SPRISelect beads and eluted in 20 \u00b5l elution buffer EB buffer similar to PCR1.  For iteration 2  an optimization was trialed  and the reaction was cleaned up with right side size selection using SPRIselect beads 0.6X beads used initially and supernatant was rebound with 1.5X beads. The product was eluted in 20 \u00b5l EB. Finally  adapters and sample indices were incorporated in a third PCR reaction using 5 7 \u00b5l of a 1:10 dilution of PCR2 product. Reactions conditions were same as PCR1 except 9 10 cycles were completed. Primers used: 10XP5Dual Hx AATGATACGGCGACCACCGAGATCTACAC xxxxxxx xxx ACACTCTTTCCCTACACGACGCTCTTCCGATCT  10XP7Dual H5 AATGATACGGCGAC CACCGAGATCTACAC xxxxxxxxxx ACACTCTTTCCCTACACGACGCTCTTCCGATCT  where x represents index bases from the 10X Dual Index Kit TT  SetA H index entries. For iteration 1  the third PCR reaction was cleaned up with 0.8X SPRISelect beads and eluted in 20 \u00b5l elution buffer EB to generate final libraries. For iteration 2  the third PCR reaction was cleaned up with right side size selection using SPRIselect beads 0.65X beads used initially and supernatant was rebound with 1.5X beads. SABER seq libraries were sequenced using Novaseq SP 200 cycle kits MedGenome with 5 10% PhiX spike in. Sequencing parameters: Read1 28 cycles  Read2 151 cycles  Index1 10 cycles  Index2 10 cycles. Standard sequencing primers were used.", null, "RNA-Seq", "TRANSCRIPTOMIC SINGLE CELL", "cDNA", "PAIRED", "ILLUMINA", "Illumina NovaSeq 6000", null, "SRP509892", null, "loader:fastq load.py", "SABERb1_brainC1_S6_L002_R2_001.fastq.gz SABERb1_brainC1_S6_L002_R1_001.fastq.gz SABERb1_brainC1_S6_L002_I2_001.fastq.gz SABERb1_brainC1_S6_L002_I1_001.fastq.gz", "fastq fastq fastq fastq", 9218042845.0, 66316855.0, "GSM8290043 r2", "0:10 1:10 2:28 3:91", "A:1809678331;C:1230188279;G:1336832583;T:1658118873;N:15739", 10, 10, 28, 91, 1809678331, 1230188279, 1336832583, 1658118873, 15739, "SRX24701852", "SRS21429381", "SRA1989588", "University of Pennsylvania", "University of Pennsylvania", 1, 0.91802, null, 0.22377, null, 0.76254, null, 0.54853, null, 91, null, "B", null, "usable mapping rate", "illumina", "novaseq_era", "full_length", "random_priming", "unknown", "sc", "single_cell_droplet", "10x", null, "United States", "2024-05-25", "Larval", "Larval", "Brain", "Nervous System"], [32399, "SRR29181701", "SRX24701851", "SRS21429380", "SRP509892", "PRJNA1116548", "Barcoding Notch signaling in the developing brain", "GSE268356", "Other", "Developmental signaling inputs are fundamental for shaping cell fates and behavior. However  traditional fluorescent based signaling reporters have limitations in scalability and molecular resolution of cell types. We present SABER seq  a CRISPR Cas molecular recorder that stores transient developmental signaling cues as permanent mutations in cellular genomes for deconstruction at later stages via single cell transcriptomics. We applied SABER seq to record Notch signaling in developing zebrafish brains. SABER seq has two components: a signaling sensor and a barcode recorder. The sensor activates Cas9 in a Notch dependent manner with inducible control while the recorder obtains mutations in ancestral cells where Notch is active. We combine SABER seq with an expanded juvenile brain atlas to identify cell types derived from Notch active founders. Our data reveals rare examples where differential Notch activity in ancestral progenitors is detected in terminally differentiated neuronal subtypes. SABER seq is a novel platform for rapid  scalable and high resolution mapping of signaling activity during development. Overall design: 10x Genomics libraries of single cell transcriptomes and SABER seq data  and genomic DNA SABER data", null, "pubmed:39575683", null, "zBrHb C1hind021023wk3  hindbrain  transcriptome", "GSM8290042", null, "source name:zebrafish hindbrain|tissue:zebrafish hindbrain|developmental stage:21 dpf 23 dpf  Juvenile|genotype:WT/TLAB|geo loc name:missing|collection date:missing", "zBrHb C1hind021023wk3  hindbrain  transcriptome", "Raw FASTQ reads were aligned against zebrafish genome  built from GRCz.109.gtf and the GRCz11.fa files  using the Cell Ranger count pipeline to generate gene by cell count matrices filtered barcode matrix. The count matrices were processed using the standard workflow of the Seurat v5.0.2 R package with modifications. Briefly  all count matrices were separately loaded into R. Seurat objects were then created with modified parameters  requiring features to be detected in at least 3 cells and cells to contain at least 300 genes. Subsequently  the Seurat objects were merged to create a single Seurat object. Cells with RNA counts between 300 and 4000 and a mitochondrial gene percentage of less than 9% were selected for downstream analysis. Gene expression values were log normalized with the default parameters and scaled for the top 3000 variable genes. The scaled data was then used to compute the 50 principal components. Cells were iteratively clustered using the Louvain algorithm post identifying neighboring cells. Differentially expressed genes for each cluster were identified using genes that are detected in at least 10% of cells in either of the two clusters and genes showing a minimum difference of 10% between the two clusters. Cell types were annotated using known marker genes.  The SABER seq FASTQ reads were processed using the Cell Ranger count pipeline  similar to the transcriptomic reads. A bash script was then used to generate a text file containing all FASTQ headers and error corrected cell barcodes for SABER seq data. post loading filtered barcode matrices into R  error corrected cell barcodes from transcriptomic reads were isolated and matched against those in the text file to identify common cell barcode that are shared between the transcriptomic and SABER seq datasets. The resulting text file contained redundant error corrected cell barcodes associated with different UMIs for the same cell. Thus  unique error corrected cell barcodes were isolated to create a text file containing unique FASTQ headers and error corrected barcodes. Subsequently  FASTQ reads matching the FASTQ headers in this file were extracted from the SABER seq raw reads using a Python script. SABER seq barcodes were analyzed using the scGESTALT pipeline available on Github as described previously7 35. To analyze differences in barcode detection and editing frequencies between cell types a binomial test was used. scRNA seq data was analyzed using the following protocol. Raw FASTQ reads were aligned against zebrafish genome  built from GRCz.109.gtf and the GRCz11.fa files  using the Cell Ranger count pipeline to generate gene by cell count matrices filtered barcode matrix. The count matrices were processed using the standard workflow of the Seurat v5.0.2 R package with modifications. Briefly  all count matrices were separately loaded into R. Seurat objects were then created with modified parameters  requiring features to be detected in at least 3 cells and cells to contain at least 300 genes. Subsequently  the Seurat objects were merged to create a single Seurat object. Cells with RNA counts between 300 and 4000 and a mitochondrial gene percentage of less than 9% were selected for downstream analysis. Gene expression values were log normalized with the default parameters and scaled for the top 3000 variable genes. The scaled data was then used to compute the 50 principal components. Cells were iteratively clustered using the Louvain algorithm post identifying neighboring cells. Differentially expressed genes for each cluster were identified using genes that are detected in at least 10% of cells in either of the two clusters and genes showing a minimum difference of 10% between the two clusters. Cell types were annotated using known marker genes. Finally  the data was visualized using UMAP. SABER seq barcode analysis was performed using the following protocol. The SABER seq FASTQ reads were processed using the Cell Ranger count pipeline.A bash script was then used to generate a text file containing all FASTQ headers and error corrected cell barcodes for SABER seq data. Error corrected cell barcodes from transcriptomic reads were isolated and matched against those in the text file to identify common cell barcode that are shared between the transcriptomic and SABER seq datasets. The resulting text file contained redundant error corrected cell barcodes associated with different UMIs for the same cell. Thus  unique error corrected cell barcodes were isolated to create a text file containing unique FASTQ headers and error corrected barcodes. Subsequently  FASTQ reads matching the FASTQ headers in this file were extracted from the SABER seq raw reads using a Python script. SABER seq barcodes were analyzed using the scGESTALT pipeline available on Github as described previously. To analyze differences in barcode detection and editing frequencies between cell types a binomial test was used. genome build/assembly: GRCz11 processed data files format and content: *.h5 files contain filtered features/genes and barcode data generated from cell ranger pipeline. processed data files format and content: LinC1 Filtered Fastq Headers CB.txt LinC2 Filtered Fastq Headers CB.txt LinD1 Filtered Fastq Headers CB.txt LinD2 Filtered Fastq Headers CB.txt LinD3 Filtered Fastq Headers CB.txt files contain filtered and unique fastq headers and cell barcodes post matching the trasncriptomic cell barcodes. processed data files format and content: SABERb1 linC1 S4 BrainC1 S6.CB.xlsx SABERb1 linC2 S5 BrainC2 S7.CB.xlsx SABERb2 linD1 S1 BrainD1 S8.CB.xlsx SABERb2 linD2 S2 BrainD2 S9.CB.xlsx SABERb2 linD3 S3 BrainD3 S10.CB.xlsx SABERb4 linB2.1 13.123.2.With.Primer.stats.xlsx SABERb4 linB2.2 14.124.2.With.Primer.stats.xlsx files contain the SABER Seq output post matching reads to the reference sequence.", "zebrafish hindbrain", null, "Wild type and SABER x her4.3:switchCas9 zebrafish brains were harvested and dissociated at 21 dpf 23 dpf as described previously Raj et. al 2018  Nature Biotechnology  with the following modifications. All microcentrifuge tubes were treated with 1% BSA/DPBS overnight to prevent cell loss due to adhesion. Brains were dissociated with 1 mL of 20 units/ml papain in EBSS media and incubated at 37 \u00b0C for 20 min. Cells were resuspended with 150 \u03bcl ice cold 1%BSA/DPBS solution. Transcriptome libraries were processed according to the manufacturer\u2019s instructions Single Cell 3\u2019 v3 kit. Transcriptome libraries were sequenced using NovaSeq S4 300 cycle kits Novogene and Novaseq SP 100 and 200 cycle kits MedGenome. To generate SABER seq libraries  samples post cDNA amplification and prior to fragmentation were split into two parts. One part was processed for transcriptome libraries as instructed by the manufacturer. The other part was processed for SABER seq libraries as follows. To enrich for SABER barcodes  5 \u00b5l of the whole transcriptome cDNA was PCR amplified with 10X p140v1UP  GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCT CGTCGTAGATCTTCATCGTGATG and 10XPCR1F CTACACGACGCTCTTCCGATCT primers and Q5 polymerase. The reaction conditions were: 98 \u00b0C  30 s; [98 \u00b0C  10 s; 68 \u00b0C  25 s; 72 \u00b0C  15 s] x 14 cycles; 72 \u00b0C  2 min. The reaction was cleaned up with 0.8X SPRISelect beads and eluted in 20 \u00b5l elution buffer EB buffer. A second PCR was carried out using 3 \u00b5l of PCR1 product and 10XPCR1F and 10X p140v2Sa GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCT GAAACTACATGGGGTcaGCTC primers. Reaction conditions were same as PCR1 except only 13 cycles were completed. At this point  2 different clean up conditions were used: iteration 1 SABER seq v0 and iteration 2 SABER seq v1. For iteration 1  the reaction was cleaned up with 0.8X SPRISelect beads and eluted in 20 \u00b5l elution buffer EB buffer similar to PCR1.  For iteration 2  an optimization was trialed  and the reaction was cleaned up with right side size selection using SPRIselect beads 0.6X beads used initially and supernatant was rebound with 1.5X beads. The product was eluted in 20 \u00b5l EB. Finally  adapters and sample indices were incorporated in a third PCR reaction using 5 7 \u00b5l of a 1:10 dilution of PCR2 product. Reactions conditions were same as PCR1 except 9 10 cycles were completed. Primers used: 10XP5Dual Hx AATGATACGGCGACCACCGAGATCTACAC xxxxxxx xxx ACACTCTTTCCCTACACGACGCTCTTCCGATCT  10XP7Dual H5 AATGATACGGCGAC CACCGAGATCTACAC xxxxxxxxxx ACACTCTTTCCCTACACGACGCTCTTCCGATCT  where x represents index bases from the 10X Dual Index Kit TT  SetA H index entries. For iteration 1  the third PCR reaction was cleaned up with 0.8X SPRISelect beads and eluted in 20 \u00b5l elution buffer EB to generate final libraries. For iteration 2  the third PCR reaction was cleaned up with right side size selection using SPRIselect beads 0.65X beads used initially and supernatant was rebound with 1.5X beads. SABER seq libraries were sequenced using Novaseq SP 200 cycle kits MedGenome with 5 10% PhiX spike in. Sequencing parameters: Read1 28 cycles  Read2 151 cycles  Index1 10 cycles  Index2 10 cycles. Standard sequencing primers were used.", "This work was performed under protocol numbers 807110 and 807259  which were approved by the University of Pennsylvania\u2019s Office of Animal Welfare of Institutional Animal Care and Use Committee IACUC. All zebrafish work in this study follows the University of Pennsylvania Institutional Animal Care and Use Committee regulations.", "tissue:zebrafish hindbrain|developmental stage:21 dpf 23 dpf  Juvenile|genotype:WT/TLAB", "GSM8290042", "GSM8290042: zBrHb C1hind021023wk3  hindbrain  transcriptome; Danio rerio; RNA Seq", "GSM8290042 r1", "GSM8290042", "1", "Wild type and SABER x her4.3:switchCas9 zebrafish brains were harvested and dissociated at 21 dpf 23 dpf as described previously Raj et. al 2018  Nature Biotechnology  with the following modifications. All microcentrifuge tubes were treated with 1% BSA/DPBS overnight to prevent cell loss due to adhesion. Brains were dissociated with 1 mL of 20 units/ml papain in EBSS media and incubated at 37 \u00b0C for 20 min. Cells were resuspended with 150 \u03bcl ice cold 1%BSA/DPBS solution. Transcriptome libraries were processed according to the manufacturer's instructions Single Cell three prime v3 kit. Transcriptome libraries were sequenced using NovaSeq S4 300 cycle kits Novogene and Novaseq SP 100 and 200 cycle kits MedGenome. To generate SABER seq libraries  samples post cDNA amplification and prior to fragmentation were split into two parts. One part was processed for transcriptome libraries as instructed by the manufacturer. The other part was processed for SABER seq libraries as follows. To enrich for SABER barcodes  5 \u00b5l of the whole transcriptome cDNA was PCR amplified with 10X p140v1UP  GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCT CGTCGTAGATCTTCATCGTGATG and 10XPCR1F CTACACGACGCTCTTCCGATCT primers and Q5 polymerase. The reaction conditions were: 98 \u00b0C  30 s; [98 \u00b0C  10 s; 68 \u00b0C  25 s; 72 \u00b0C  15 s] x 14 cycles; 72 \u00b0C  2 min. The reaction was cleaned up with 0.8X SPRISelect beads and eluted in 20 \u00b5l elution buffer EB buffer. A second PCR was carried out using 3 \u00b5l of PCR1 product and 10XPCR1F and 10X p140v2Sa GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCT GAAACTACATGGGGTcaGCTC primers. Reaction conditions were same as PCR1 except only 13 cycles were completed. At this point  2 different clean up conditions were used: iteration 1 SABER seq v0 and iteration 2 SABER seq v1. For iteration 1  the reaction was cleaned up with 0.8X SPRISelect beads and eluted in 20 \u00b5l elution buffer EB buffer similar to PCR1.  For iteration 2  an optimization was trialed  and the reaction was cleaned up with right side size selection using SPRIselect beads 0.6X beads used initially and supernatant was rebound with 1.5X beads. The product was eluted in 20 \u00b5l EB. Finally  adapters and sample indices were incorporated in a third PCR reaction using 5 7 \u00b5l of a 1:10 dilution of PCR2 product. Reactions conditions were same as PCR1 except 9 10 cycles were completed. Primers used: 10XP5Dual Hx AATGATACGGCGACCACCGAGATCTACAC xxxxxxx xxx ACACTCTTTCCCTACACGACGCTCTTCCGATCT  10XP7Dual H5 AATGATACGGCGAC CACCGAGATCTACAC xxxxxxxxxx ACACTCTTTCCCTACACGACGCTCTTCCGATCT  where x represents index bases from the 10X Dual Index Kit TT  SetA H index entries. For iteration 1  the third PCR reaction was cleaned up with 0.8X SPRISelect beads and eluted in 20 \u00b5l elution buffer EB to generate final libraries. For iteration 2  the third PCR reaction was cleaned up with right side size selection using SPRIselect beads 0.65X beads used initially and supernatant was rebound with 1.5X beads. SABER seq libraries were sequenced using Novaseq SP 200 cycle kits MedGenome with 5 10% PhiX spike in. Sequencing parameters: Read1 28 cycles  Read2 151 cycles  Index1 10 cycles  Index2 10 cycles. Standard sequencing primers were used.", null, "RNA-Seq", "TRANSCRIPTOMIC SINGLE CELL", "cDNA", "PAIRED", "ILLUMINA", "Illumina NovaSeq 6000", null, "SRP509892", null, "loader:fastq load.py", "zBrHb_C2hind021023wk3_CKDL230006230-1A_HW3J5DSX5_S2_L004_I1_001.fastq.gz zBrHb_C2hind021023wk3_CKDL230006230-1A_HW3J5DSX5_S2_L004_I2_001.fastq.gz zBrHb_C2hind021023wk3_CKDL230006230-1A_HW3J5DSX5_S2_L004_R1_001.fastq.gz zBrHb_C2hind021023wk3_CKDL230006230-1A_HW3J5DSX5_S2_L004_R2_001.fastq.gz", "fastq fastq fastq fastq", 64546948800.0, 201709215.0, "GSM8290042 r2", "0:10 1:10 2:150 3:150", "A:19999216483;C:7881744410;G:8397001445;T:24234036869;N:765293", 10, 10, 150, 150, 19999216483, 7881744410, 8397001445, 24234036869, 765293, "SRX24701851", "SRS21429380", "SRA1989588", "University of Pennsylvania", "University of Pennsylvania", 2, 0.16904, 0.87311, 0.05961, 0.20305, 0.98169, 0.771, 0.4964, 0.52096, 150, 150, "T", "B", "mate1 technical by mapping diff", "illumina", "novaseq_era", "full_length", "random_priming", "unknown", "sc", "single_cell_droplet", "10x", null, "United States", "2024-05-25", "Larval", "Larval", "Brain", "Nervous System"], [32400, "SRR29181710", "SRX24701851", "SRS21429380", "SRP509892", "PRJNA1116548", "Barcoding Notch signaling in the developing brain", "GSE268356", "Other", "Developmental signaling inputs are fundamental for shaping cell fates and behavior. However  traditional fluorescent based signaling reporters have limitations in scalability and molecular resolution of cell types. We present SABER seq  a CRISPR Cas molecular recorder that stores transient developmental signaling cues as permanent mutations in cellular genomes for deconstruction at later stages via single cell transcriptomics. We applied SABER seq to record Notch signaling in developing zebrafish brains. SABER seq has two components: a signaling sensor and a barcode recorder. The sensor activates Cas9 in a Notch dependent manner with inducible control while the recorder obtains mutations in ancestral cells where Notch is active. We combine SABER seq with an expanded juvenile brain atlas to identify cell types derived from Notch active founders. Our data reveals rare examples where differential Notch activity in ancestral progenitors is detected in terminally differentiated neuronal subtypes. SABER seq is a novel platform for rapid  scalable and high resolution mapping of signaling activity during development. Overall design: 10x Genomics libraries of single cell transcriptomes and SABER seq data  and genomic DNA SABER data", null, "pubmed:39575683", null, "zBrHb C1hind021023wk3  hindbrain  transcriptome", "GSM8290042", null, "source name:zebrafish hindbrain|tissue:zebrafish hindbrain|developmental stage:21 dpf 23 dpf  Juvenile|genotype:WT/TLAB|geo loc name:missing|collection date:missing", "zBrHb C1hind021023wk3  hindbrain  transcriptome", "Raw FASTQ reads were aligned against zebrafish genome  built from GRCz.109.gtf and the GRCz11.fa files  using the Cell Ranger count pipeline to generate gene by cell count matrices filtered barcode matrix. The count matrices were processed using the standard workflow of the Seurat v5.0.2 R package with modifications. Briefly  all count matrices were separately loaded into R. Seurat objects were then created with modified parameters  requiring features to be detected in at least 3 cells and cells to contain at least 300 genes. Subsequently  the Seurat objects were merged to create a single Seurat object. Cells with RNA counts between 300 and 4000 and a mitochondrial gene percentage of less than 9% were selected for downstream analysis. Gene expression values were log normalized with the default parameters and scaled for the top 3000 variable genes. The scaled data was then used to compute the 50 principal components. Cells were iteratively clustered using the Louvain algorithm post identifying neighboring cells. Differentially expressed genes for each cluster were identified using genes that are detected in at least 10% of cells in either of the two clusters and genes showing a minimum difference of 10% between the two clusters. Cell types were annotated using known marker genes.  The SABER seq FASTQ reads were processed using the Cell Ranger count pipeline  similar to the transcriptomic reads. A bash script was then used to generate a text file containing all FASTQ headers and error corrected cell barcodes for SABER seq data. post loading filtered barcode matrices into R  error corrected cell barcodes from transcriptomic reads were isolated and matched against those in the text file to identify common cell barcode that are shared between the transcriptomic and SABER seq datasets. The resulting text file contained redundant error corrected cell barcodes associated with different UMIs for the same cell. Thus  unique error corrected cell barcodes were isolated to create a text file containing unique FASTQ headers and error corrected barcodes. Subsequently  FASTQ reads matching the FASTQ headers in this file were extracted from the SABER seq raw reads using a Python script. SABER seq barcodes were analyzed using the scGESTALT pipeline available on Github as described previously7 35. To analyze differences in barcode detection and editing frequencies between cell types a binomial test was used. scRNA seq data was analyzed using the following protocol. Raw FASTQ reads were aligned against zebrafish genome  built from GRCz.109.gtf and the GRCz11.fa files  using the Cell Ranger count pipeline to generate gene by cell count matrices filtered barcode matrix. The count matrices were processed using the standard workflow of the Seurat v5.0.2 R package with modifications. Briefly  all count matrices were separately loaded into R. Seurat objects were then created with modified parameters  requiring features to be detected in at least 3 cells and cells to contain at least 300 genes. Subsequently  the Seurat objects were merged to create a single Seurat object. Cells with RNA counts between 300 and 4000 and a mitochondrial gene percentage of less than 9% were selected for downstream analysis. Gene expression values were log normalized with the default parameters and scaled for the top 3000 variable genes. The scaled data was then used to compute the 50 principal components. Cells were iteratively clustered using the Louvain algorithm post identifying neighboring cells. Differentially expressed genes for each cluster were identified using genes that are detected in at least 10% of cells in either of the two clusters and genes showing a minimum difference of 10% between the two clusters. Cell types were annotated using known marker genes. Finally  the data was visualized using UMAP. SABER seq barcode analysis was performed using the following protocol. The SABER seq FASTQ reads were processed using the Cell Ranger count pipeline.A bash script was then used to generate a text file containing all FASTQ headers and error corrected cell barcodes for SABER seq data. Error corrected cell barcodes from transcriptomic reads were isolated and matched against those in the text file to identify common cell barcode that are shared between the transcriptomic and SABER seq datasets. The resulting text file contained redundant error corrected cell barcodes associated with different UMIs for the same cell. Thus  unique error corrected cell barcodes were isolated to create a text file containing unique FASTQ headers and error corrected barcodes. Subsequently  FASTQ reads matching the FASTQ headers in this file were extracted from the SABER seq raw reads using a Python script. SABER seq barcodes were analyzed using the scGESTALT pipeline available on Github as described previously. To analyze differences in barcode detection and editing frequencies between cell types a binomial test was used. genome build/assembly: GRCz11 processed data files format and content: *.h5 files contain filtered features/genes and barcode data generated from cell ranger pipeline. processed data files format and content: LinC1 Filtered Fastq Headers CB.txt LinC2 Filtered Fastq Headers CB.txt LinD1 Filtered Fastq Headers CB.txt LinD2 Filtered Fastq Headers CB.txt LinD3 Filtered Fastq Headers CB.txt files contain filtered and unique fastq headers and cell barcodes post matching the trasncriptomic cell barcodes. processed data files format and content: SABERb1 linC1 S4 BrainC1 S6.CB.xlsx SABERb1 linC2 S5 BrainC2 S7.CB.xlsx SABERb2 linD1 S1 BrainD1 S8.CB.xlsx SABERb2 linD2 S2 BrainD2 S9.CB.xlsx SABERb2 linD3 S3 BrainD3 S10.CB.xlsx SABERb4 linB2.1 13.123.2.With.Primer.stats.xlsx SABERb4 linB2.2 14.124.2.With.Primer.stats.xlsx files contain the SABER Seq output post matching reads to the reference sequence.", "zebrafish hindbrain", null, "Wild type and SABER x her4.3:switchCas9 zebrafish brains were harvested and dissociated at 21 dpf 23 dpf as described previously Raj et. al 2018  Nature Biotechnology  with the following modifications. All microcentrifuge tubes were treated with 1% BSA/DPBS overnight to prevent cell loss due to adhesion. Brains were dissociated with 1 mL of 20 units/ml papain in EBSS media and incubated at 37 \u00b0C for 20 min. Cells were resuspended with 150 \u03bcl ice cold 1%BSA/DPBS solution. Transcriptome libraries were processed according to the manufacturer\u2019s instructions Single Cell 3\u2019 v3 kit. Transcriptome libraries were sequenced using NovaSeq S4 300 cycle kits Novogene and Novaseq SP 100 and 200 cycle kits MedGenome. To generate SABER seq libraries  samples post cDNA amplification and prior to fragmentation were split into two parts. One part was processed for transcriptome libraries as instructed by the manufacturer. The other part was processed for SABER seq libraries as follows. To enrich for SABER barcodes  5 \u00b5l of the whole transcriptome cDNA was PCR amplified with 10X p140v1UP  GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCT CGTCGTAGATCTTCATCGTGATG and 10XPCR1F CTACACGACGCTCTTCCGATCT primers and Q5 polymerase. The reaction conditions were: 98 \u00b0C  30 s; [98 \u00b0C  10 s; 68 \u00b0C  25 s; 72 \u00b0C  15 s] x 14 cycles; 72 \u00b0C  2 min. The reaction was cleaned up with 0.8X SPRISelect beads and eluted in 20 \u00b5l elution buffer EB buffer. A second PCR was carried out using 3 \u00b5l of PCR1 product and 10XPCR1F and 10X p140v2Sa GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCT GAAACTACATGGGGTcaGCTC primers. Reaction conditions were same as PCR1 except only 13 cycles were completed. At this point  2 different clean up conditions were used: iteration 1 SABER seq v0 and iteration 2 SABER seq v1. For iteration 1  the reaction was cleaned up with 0.8X SPRISelect beads and eluted in 20 \u00b5l elution buffer EB buffer similar to PCR1.  For iteration 2  an optimization was trialed  and the reaction was cleaned up with right side size selection using SPRIselect beads 0.6X beads used initially and supernatant was rebound with 1.5X beads. The product was eluted in 20 \u00b5l EB. Finally  adapters and sample indices were incorporated in a third PCR reaction using 5 7 \u00b5l of a 1:10 dilution of PCR2 product. Reactions conditions were same as PCR1 except 9 10 cycles were completed. Primers used: 10XP5Dual Hx AATGATACGGCGACCACCGAGATCTACAC xxxxxxx xxx ACACTCTTTCCCTACACGACGCTCTTCCGATCT  10XP7Dual H5 AATGATACGGCGAC CACCGAGATCTACAC xxxxxxxxxx ACACTCTTTCCCTACACGACGCTCTTCCGATCT  where x represents index bases from the 10X Dual Index Kit TT  SetA H index entries. For iteration 1  the third PCR reaction was cleaned up with 0.8X SPRISelect beads and eluted in 20 \u00b5l elution buffer EB to generate final libraries. For iteration 2  the third PCR reaction was cleaned up with right side size selection using SPRIselect beads 0.65X beads used initially and supernatant was rebound with 1.5X beads. SABER seq libraries were sequenced using Novaseq SP 200 cycle kits MedGenome with 5 10% PhiX spike in. Sequencing parameters: Read1 28 cycles  Read2 151 cycles  Index1 10 cycles  Index2 10 cycles. Standard sequencing primers were used.", "This work was performed under protocol numbers 807110 and 807259  which were approved by the University of Pennsylvania\u2019s Office of Animal Welfare of Institutional Animal Care and Use Committee IACUC. All zebrafish work in this study follows the University of Pennsylvania Institutional Animal Care and Use Committee regulations.", "tissue:zebrafish hindbrain|developmental stage:21 dpf 23 dpf  Juvenile|genotype:WT/TLAB", "GSM8290042", "GSM8290042: zBrHb C1hind021023wk3  hindbrain  transcriptome; Danio rerio; RNA Seq", "GSM8290042 r1", "GSM8290042", "1", "Wild type and SABER x her4.3:switchCas9 zebrafish brains were harvested and dissociated at 21 dpf 23 dpf as described previously Raj et. al 2018  Nature Biotechnology  with the following modifications. All microcentrifuge tubes were treated with 1% BSA/DPBS overnight to prevent cell loss due to adhesion. Brains were dissociated with 1 mL of 20 units/ml papain in EBSS media and incubated at 37 \u00b0C for 20 min. Cells were resuspended with 150 \u03bcl ice cold 1%BSA/DPBS solution. Transcriptome libraries were processed according to the manufacturer's instructions Single Cell three prime v3 kit. Transcriptome libraries were sequenced using NovaSeq S4 300 cycle kits Novogene and Novaseq SP 100 and 200 cycle kits MedGenome. To generate SABER seq libraries  samples post cDNA amplification and prior to fragmentation were split into two parts. One part was processed for transcriptome libraries as instructed by the manufacturer. The other part was processed for SABER seq libraries as follows. To enrich for SABER barcodes  5 \u00b5l of the whole transcriptome cDNA was PCR amplified with 10X p140v1UP  GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCT CGTCGTAGATCTTCATCGTGATG and 10XPCR1F CTACACGACGCTCTTCCGATCT primers and Q5 polymerase. The reaction conditions were: 98 \u00b0C  30 s; [98 \u00b0C  10 s; 68 \u00b0C  25 s; 72 \u00b0C  15 s] x 14 cycles; 72 \u00b0C  2 min. The reaction was cleaned up with 0.8X SPRISelect beads and eluted in 20 \u00b5l elution buffer EB buffer. A second PCR was carried out using 3 \u00b5l of PCR1 product and 10XPCR1F and 10X p140v2Sa GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCT GAAACTACATGGGGTcaGCTC primers. Reaction conditions were same as PCR1 except only 13 cycles were completed. At this point  2 different clean up conditions were used: iteration 1 SABER seq v0 and iteration 2 SABER seq v1. For iteration 1  the reaction was cleaned up with 0.8X SPRISelect beads and eluted in 20 \u00b5l elution buffer EB buffer similar to PCR1.  For iteration 2  an optimization was trialed  and the reaction was cleaned up with right side size selection using SPRIselect beads 0.6X beads used initially and supernatant was rebound with 1.5X beads. The product was eluted in 20 \u00b5l EB. Finally  adapters and sample indices were incorporated in a third PCR reaction using 5 7 \u00b5l of a 1:10 dilution of PCR2 product. Reactions conditions were same as PCR1 except 9 10 cycles were completed. Primers used: 10XP5Dual Hx AATGATACGGCGACCACCGAGATCTACAC xxxxxxx xxx ACACTCTTTCCCTACACGACGCTCTTCCGATCT  10XP7Dual H5 AATGATACGGCGAC CACCGAGATCTACAC xxxxxxxxxx ACACTCTTTCCCTACACGACGCTCTTCCGATCT  where x represents index bases from the 10X Dual Index Kit TT  SetA H index entries. For iteration 1  the third PCR reaction was cleaned up with 0.8X SPRISelect beads and eluted in 20 \u00b5l elution buffer EB to generate final libraries. For iteration 2  the third PCR reaction was cleaned up with right side size selection using SPRIselect beads 0.65X beads used initially and supernatant was rebound with 1.5X beads. SABER seq libraries were sequenced using Novaseq SP 200 cycle kits MedGenome with 5 10% PhiX spike in. Sequencing parameters: Read1 28 cycles  Read2 151 cycles  Index1 10 cycles  Index2 10 cycles. Standard sequencing primers were used.", null, "RNA-Seq", "TRANSCRIPTOMIC SINGLE CELL", "cDNA", "PAIRED", "ILLUMINA", "Illumina NovaSeq 6000", null, "SRP509892", null, "loader:fastq load.py", "zBrHb_C1hind021023wk3_CKDL230006230-1A_HW3J5DSX5_S4_L004_R2_001.fastq.gz zBrHb_C1hind021023wk3_CKDL230006230-1A_HW3J5DSX5_S4_L004_R1_001.fastq.gz zBrHb_C1hind021023wk3_CKDL230006230-1A_HW3J5DSX5_S4_L004_I2_001.fastq.gz zBrHb_C1hind021023wk3_CKDL230006230-1A_HW3J5DSX5_S4_L004_I1_001.fastq.gz", "fastq fastq fastq fastq", 65651641280.0, 205161379.0, "GSM8290042 r1", "0:10 1:10 2:150 3:150", "A:20160289471;C:8227800878;G:8724732321;T:24434818500;N:772530", 10, 10, 150, 150, 20160289471, 8227800878, 8724732321, 24434818500, 772530, "SRX24701851", "SRS21429380", "SRA1989588", "University of Pennsylvania", "University of Pennsylvania", 2, 0.20153, 0.88953, 0.07095, 0.20427, 0.97944, 0.77007, 0.47788, 0.5233, 150, 150, "T", "B", "mate1 technical by mapping diff", "illumina", "novaseq_era", "full_length", "random_priming", "unknown", "sc", "single_cell_droplet", "10x", null, "United States", "2024-05-25", "Larval", "Larval", "Brain", "Nervous System"], [32401, "SRR29181708", "SRX24701850", "SRS21429379", "SRP509892", "PRJNA1116548", "Barcoding Notch signaling in the developing brain", "GSE268356", "Other", "Developmental signaling inputs are fundamental for shaping cell fates and behavior. However  traditional fluorescent based signaling reporters have limitations in scalability and molecular resolution of cell types. We present SABER seq  a CRISPR Cas molecular recorder that stores transient developmental signaling cues as permanent mutations in cellular genomes for deconstruction at later stages via single cell transcriptomics. We applied SABER seq to record Notch signaling in developing zebrafish brains. SABER seq has two components: a signaling sensor and a barcode recorder. The sensor activates Cas9 in a Notch dependent manner with inducible control while the recorder obtains mutations in ancestral cells where Notch is active. We combine SABER seq with an expanded juvenile brain atlas to identify cell types derived from Notch active founders. Our data reveals rare examples where differential Notch activity in ancestral progenitors is detected in terminally differentiated neuronal subtypes. SABER seq is a novel platform for rapid  scalable and high resolution mapping of signaling activity during development. Overall design: 10x Genomics libraries of single cell transcriptomes and SABER seq data  and genomic DNA SABER data", null, "pubmed:39575683", null, "zBrMb B1mid021023wk3  midbrain  transcriptome", "GSM8290041", null, "source name:zebrafish midbrain|tissue:zebrafish midbrain|developmental stage:21 dpf 23 dpf  Juvenile|genotype:WT/TLAB|geo loc name:missing|collection date:missing", "zBrMb B1mid021023wk3  midbrain  transcriptome", "Raw FASTQ reads were aligned against zebrafish genome  built from GRCz.109.gtf and the GRCz11.fa files  using the Cell Ranger count pipeline to generate gene by cell count matrices filtered barcode matrix. The count matrices were processed using the standard workflow of the Seurat v5.0.2 R package with modifications. Briefly  all count matrices were separately loaded into R. Seurat objects were then created with modified parameters  requiring features to be detected in at least 3 cells and cells to contain at least 300 genes. Subsequently  the Seurat objects were merged to create a single Seurat object. Cells with RNA counts between 300 and 4000 and a mitochondrial gene percentage of less than 9% were selected for downstream analysis. Gene expression values were log normalized with the default parameters and scaled for the top 3000 variable genes. The scaled data was then used to compute the 50 principal components. Cells were iteratively clustered using the Louvain algorithm post identifying neighboring cells. Differentially expressed genes for each cluster were identified using genes that are detected in at least 10% of cells in either of the two clusters and genes showing a minimum difference of 10% between the two clusters. Cell types were annotated using known marker genes.  The SABER seq FASTQ reads were processed using the Cell Ranger count pipeline  similar to the transcriptomic reads. A bash script was then used to generate a text file containing all FASTQ headers and error corrected cell barcodes for SABER seq data. post loading filtered barcode matrices into R  error corrected cell barcodes from transcriptomic reads were isolated and matched against those in the text file to identify common cell barcode that are shared between the transcriptomic and SABER seq datasets. The resulting text file contained redundant error corrected cell barcodes associated with different UMIs for the same cell. Thus  unique error corrected cell barcodes were isolated to create a text file containing unique FASTQ headers and error corrected barcodes. Subsequently  FASTQ reads matching the FASTQ headers in this file were extracted from the SABER seq raw reads using a Python script. SABER seq barcodes were analyzed using the scGESTALT pipeline available on Github as described previously7 35. To analyze differences in barcode detection and editing frequencies between cell types a binomial test was used. scRNA seq data was analyzed using the following protocol. Raw FASTQ reads were aligned against zebrafish genome  built from GRCz.109.gtf and the GRCz11.fa files  using the Cell Ranger count pipeline to generate gene by cell count matrices filtered barcode matrix. The count matrices were processed using the standard workflow of the Seurat v5.0.2 R package with modifications. Briefly  all count matrices were separately loaded into R. Seurat objects were then created with modified parameters  requiring features to be detected in at least 3 cells and cells to contain at least 300 genes. Subsequently  the Seurat objects were merged to create a single Seurat object. Cells with RNA counts between 300 and 4000 and a mitochondrial gene percentage of less than 9% were selected for downstream analysis. Gene expression values were log normalized with the default parameters and scaled for the top 3000 variable genes. The scaled data was then used to compute the 50 principal components. Cells were iteratively clustered using the Louvain algorithm post identifying neighboring cells. Differentially expressed genes for each cluster were identified using genes that are detected in at least 10% of cells in either of the two clusters and genes showing a minimum difference of 10% between the two clusters. Cell types were annotated using known marker genes. Finally  the data was visualized using UMAP. SABER seq barcode analysis was performed using the following protocol. The SABER seq FASTQ reads were processed using the Cell Ranger count pipeline.A bash script was then used to generate a text file containing all FASTQ headers and error corrected cell barcodes for SABER seq data. Error corrected cell barcodes from transcriptomic reads were isolated and matched against those in the text file to identify common cell barcode that are shared between the transcriptomic and SABER seq datasets. The resulting text file contained redundant error corrected cell barcodes associated with different UMIs for the same cell. Thus  unique error corrected cell barcodes were isolated to create a text file containing unique FASTQ headers and error corrected barcodes. Subsequently  FASTQ reads matching the FASTQ headers in this file were extracted from the SABER seq raw reads using a Python script. SABER seq barcodes were analyzed using the scGESTALT pipeline available on Github as described previously. To analyze differences in barcode detection and editing frequencies between cell types a binomial test was used. genome build/assembly: GRCz11 processed data files format and content: *.h5 files contain filtered features/genes and barcode data generated from cell ranger pipeline. processed data files format and content: LinC1 Filtered Fastq Headers CB.txt LinC2 Filtered Fastq Headers CB.txt LinD1 Filtered Fastq Headers CB.txt LinD2 Filtered Fastq Headers CB.txt LinD3 Filtered Fastq Headers CB.txt files contain filtered and unique fastq headers and cell barcodes post matching the trasncriptomic cell barcodes. processed data files format and content: SABERb1 linC1 S4 BrainC1 S6.CB.xlsx SABERb1 linC2 S5 BrainC2 S7.CB.xlsx SABERb2 linD1 S1 BrainD1 S8.CB.xlsx SABERb2 linD2 S2 BrainD2 S9.CB.xlsx SABERb2 linD3 S3 BrainD3 S10.CB.xlsx SABERb4 linB2.1 13.123.2.With.Primer.stats.xlsx SABERb4 linB2.2 14.124.2.With.Primer.stats.xlsx files contain the SABER Seq output post matching reads to the reference sequence.", "zebrafish midbrain", null, "Wild type and SABER x her4.3:switchCas9 zebrafish brains were harvested and dissociated at 21 dpf 23 dpf as described previously Raj et. al 2018  Nature Biotechnology  with the following modifications. All microcentrifuge tubes were treated with 1% BSA/DPBS overnight to prevent cell loss due to adhesion. Brains were dissociated with 1 mL of 20 units/ml papain in EBSS media and incubated at 37 \u00b0C for 20 min. Cells were resuspended with 150 \u03bcl ice cold 1%BSA/DPBS solution. Transcriptome libraries were processed according to the manufacturer\u2019s instructions Single Cell 3\u2019 v3 kit. Transcriptome libraries were sequenced using NovaSeq S4 300 cycle kits Novogene and Novaseq SP 100 and 200 cycle kits MedGenome. To generate SABER seq libraries  samples post cDNA amplification and prior to fragmentation were split into two parts. One part was processed for transcriptome libraries as instructed by the manufacturer. The other part was processed for SABER seq libraries as follows. To enrich for SABER barcodes  5 \u00b5l of the whole transcriptome cDNA was PCR amplified with 10X p140v1UP  GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCT CGTCGTAGATCTTCATCGTGATG and 10XPCR1F CTACACGACGCTCTTCCGATCT primers and Q5 polymerase. The reaction conditions were: 98 \u00b0C  30 s; [98 \u00b0C  10 s; 68 \u00b0C  25 s; 72 \u00b0C  15 s] x 14 cycles; 72 \u00b0C  2 min. The reaction was cleaned up with 0.8X SPRISelect beads and eluted in 20 \u00b5l elution buffer EB buffer. A second PCR was carried out using 3 \u00b5l of PCR1 product and 10XPCR1F and 10X p140v2Sa GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCT GAAACTACATGGGGTcaGCTC primers. Reaction conditions were same as PCR1 except only 13 cycles were completed. At this point  2 different clean up conditions were used: iteration 1 SABER seq v0 and iteration 2 SABER seq v1. For iteration 1  the reaction was cleaned up with 0.8X SPRISelect beads and eluted in 20 \u00b5l elution buffer EB buffer similar to PCR1.  For iteration 2  an optimization was trialed  and the reaction was cleaned up with right side size selection using SPRIselect beads 0.6X beads used initially and supernatant was rebound with 1.5X beads. The product was eluted in 20 \u00b5l EB. Finally  adapters and sample indices were incorporated in a third PCR reaction using 5 7 \u00b5l of a 1:10 dilution of PCR2 product. Reactions conditions were same as PCR1 except 9 10 cycles were completed. Primers used: 10XP5Dual Hx AATGATACGGCGACCACCGAGATCTACAC xxxxxxx xxx ACACTCTTTCCCTACACGACGCTCTTCCGATCT  10XP7Dual H5 AATGATACGGCGAC CACCGAGATCTACAC xxxxxxxxxx ACACTCTTTCCCTACACGACGCTCTTCCGATCT  where x represents index bases from the 10X Dual Index Kit TT  SetA H index entries. For iteration 1  the third PCR reaction was cleaned up with 0.8X SPRISelect beads and eluted in 20 \u00b5l elution buffer EB to generate final libraries. For iteration 2  the third PCR reaction was cleaned up with right side size selection using SPRIselect beads 0.65X beads used initially and supernatant was rebound with 1.5X beads. SABER seq libraries were sequenced using Novaseq SP 200 cycle kits MedGenome with 5 10% PhiX spike in. Sequencing parameters: Read1 28 cycles  Read2 151 cycles  Index1 10 cycles  Index2 10 cycles. Standard sequencing primers were used.", "This work was performed under protocol numbers 807110 and 807259  which were approved by the University of Pennsylvania\u2019s Office of Animal Welfare of Institutional Animal Care and Use Committee IACUC. All zebrafish work in this study follows the University of Pennsylvania Institutional Animal Care and Use Committee regulations.", "tissue:zebrafish midbrain|developmental stage:21 dpf 23 dpf  Juvenile|genotype:WT/TLAB", "GSM8290041", "GSM8290041: zBrMb B1mid021023wk3  midbrain  transcriptome; Danio rerio; RNA Seq", "GSM8290041 r1", "GSM8290041", "1", "Wild type and SABER x her4.3:switchCas9 zebrafish brains were harvested and dissociated at 21 dpf 23 dpf as described previously Raj et. al 2018  Nature Biotechnology  with the following modifications. All microcentrifuge tubes were treated with 1% BSA/DPBS overnight to prevent cell loss due to adhesion. Brains were dissociated with 1 mL of 20 units/ml papain in EBSS media and incubated at 37 \u00b0C for 20 min. Cells were resuspended with 150 \u03bcl ice cold 1%BSA/DPBS solution. Transcriptome libraries were processed according to the manufacturer's instructions Single Cell three prime v3 kit. Transcriptome libraries were sequenced using NovaSeq S4 300 cycle kits Novogene and Novaseq SP 100 and 200 cycle kits MedGenome. To generate SABER seq libraries  samples post cDNA amplification and prior to fragmentation were split into two parts. One part was processed for transcriptome libraries as instructed by the manufacturer. The other part was processed for SABER seq libraries as follows. To enrich for SABER barcodes  5 \u00b5l of the whole transcriptome cDNA was PCR amplified with 10X p140v1UP  GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCT CGTCGTAGATCTTCATCGTGATG and 10XPCR1F CTACACGACGCTCTTCCGATCT primers and Q5 polymerase. The reaction conditions were: 98 \u00b0C  30 s; [98 \u00b0C  10 s; 68 \u00b0C  25 s; 72 \u00b0C  15 s] x 14 cycles; 72 \u00b0C  2 min. The reaction was cleaned up with 0.8X SPRISelect beads and eluted in 20 \u00b5l elution buffer EB buffer. A second PCR was carried out using 3 \u00b5l of PCR1 product and 10XPCR1F and 10X p140v2Sa GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCT GAAACTACATGGGGTcaGCTC primers. Reaction conditions were same as PCR1 except only 13 cycles were completed. At this point  2 different clean up conditions were used: iteration 1 SABER seq v0 and iteration 2 SABER seq v1. For iteration 1  the reaction was cleaned up with 0.8X SPRISelect beads and eluted in 20 \u00b5l elution buffer EB buffer similar to PCR1.  For iteration 2  an optimization was trialed  and the reaction was cleaned up with right side size selection using SPRIselect beads 0.6X beads used initially and supernatant was rebound with 1.5X beads. The product was eluted in 20 \u00b5l EB. Finally  adapters and sample indices were incorporated in a third PCR reaction using 5 7 \u00b5l of a 1:10 dilution of PCR2 product. Reactions conditions were same as PCR1 except 9 10 cycles were completed. Primers used: 10XP5Dual Hx AATGATACGGCGACCACCGAGATCTACAC xxxxxxx xxx ACACTCTTTCCCTACACGACGCTCTTCCGATCT  10XP7Dual H5 AATGATACGGCGAC CACCGAGATCTACAC xxxxxxxxxx ACACTCTTTCCCTACACGACGCTCTTCCGATCT  where x represents index bases from the 10X Dual Index Kit TT  SetA H index entries. For iteration 1  the third PCR reaction was cleaned up with 0.8X SPRISelect beads and eluted in 20 \u00b5l elution buffer EB to generate final libraries. For iteration 2  the third PCR reaction was cleaned up with right side size selection using SPRIselect beads 0.65X beads used initially and supernatant was rebound with 1.5X beads. SABER seq libraries were sequenced using Novaseq SP 200 cycle kits MedGenome with 5 10% PhiX spike in. Sequencing parameters: Read1 28 cycles  Read2 151 cycles  Index1 10 cycles  Index2 10 cycles. Standard sequencing primers were used.", null, "RNA-Seq", "TRANSCRIPTOMIC SINGLE CELL", "cDNA", "PAIRED", "ILLUMINA", "Illumina NovaSeq 6000", null, "SRP509892", null, "loader:fastq load.py", "zBrMb_B1mid021023wk3_CKDL230006230-1A_HW3J5DSX5_S1_L004_I1_001.fastq.gz zBrMb_B1mid021023wk3_CKDL230006230-1A_HW3J5DSX5_S1_L004_I2_001.fastq.gz zBrMb_B1mid021023wk3_CKDL230006230-1A_HW3J5DSX5_S1_L004_R1_001.fastq.gz zBrMb_B1mid021023wk3_CKDL230006230-1A_HW3J5DSX5_S1_L004_R2_001.fastq.gz", "fastq fastq fastq fastq", 57396728320.0, 179364776.0, "GSM8290041 r1", "0:10 1:10 2:150 3:150", "A:17624133070;C:7302355189;G:7764883537;T:21117377617;N:683387", 10, 10, 150, 150, 17624133070, 7302355189, 7764883537, 21117377617, 683387, "SRX24701850", "SRS21429379", "SRA1989588", "University of Pennsylvania", "University of Pennsylvania", 2, 0.17325, 0.90421, 0.06608, 0.18333, 0.98508, 0.78303, 0.51396, 0.52463, 150, 150, "T", "B", "mate1 technical by mapping diff", "illumina", "novaseq_era", "full_length", "random_priming", "unknown", "sc", "single_cell_droplet", "10x", null, "United States", "2024-05-25", "Larval", "Larval", "Brain", "Nervous System"], [32402, "SRR29181709", "SRX24701850", "SRS21429379", "SRP509892", "PRJNA1116548", "Barcoding Notch signaling in the developing brain", "GSE268356", "Other", "Developmental signaling inputs are fundamental for shaping cell fates and behavior. However  traditional fluorescent based signaling reporters have limitations in scalability and molecular resolution of cell types. We present SABER seq  a CRISPR Cas molecular recorder that stores transient developmental signaling cues as permanent mutations in cellular genomes for deconstruction at later stages via single cell transcriptomics. We applied SABER seq to record Notch signaling in developing zebrafish brains. SABER seq has two components: a signaling sensor and a barcode recorder. The sensor activates Cas9 in a Notch dependent manner with inducible control while the recorder obtains mutations in ancestral cells where Notch is active. We combine SABER seq with an expanded juvenile brain atlas to identify cell types derived from Notch active founders. Our data reveals rare examples where differential Notch activity in ancestral progenitors is detected in terminally differentiated neuronal subtypes. SABER seq is a novel platform for rapid  scalable and high resolution mapping of signaling activity during development. Overall design: 10x Genomics libraries of single cell transcriptomes and SABER seq data  and genomic DNA SABER data", null, "pubmed:39575683", null, "zBrMb B1mid021023wk3  midbrain  transcriptome", "GSM8290041", null, "source name:zebrafish midbrain|tissue:zebrafish midbrain|developmental stage:21 dpf 23 dpf  Juvenile|genotype:WT/TLAB|geo loc name:missing|collection date:missing", "zBrMb B1mid021023wk3  midbrain  transcriptome", "Raw FASTQ reads were aligned against zebrafish genome  built from GRCz.109.gtf and the GRCz11.fa files  using the Cell Ranger count pipeline to generate gene by cell count matrices filtered barcode matrix. The count matrices were processed using the standard workflow of the Seurat v5.0.2 R package with modifications. Briefly  all count matrices were separately loaded into R. Seurat objects were then created with modified parameters  requiring features to be detected in at least 3 cells and cells to contain at least 300 genes. Subsequently  the Seurat objects were merged to create a single Seurat object. Cells with RNA counts between 300 and 4000 and a mitochondrial gene percentage of less than 9% were selected for downstream analysis. Gene expression values were log normalized with the default parameters and scaled for the top 3000 variable genes. The scaled data was then used to compute the 50 principal components. Cells were iteratively clustered using the Louvain algorithm post identifying neighboring cells. Differentially expressed genes for each cluster were identified using genes that are detected in at least 10% of cells in either of the two clusters and genes showing a minimum difference of 10% between the two clusters. Cell types were annotated using known marker genes.  The SABER seq FASTQ reads were processed using the Cell Ranger count pipeline  similar to the transcriptomic reads. A bash script was then used to generate a text file containing all FASTQ headers and error corrected cell barcodes for SABER seq data. post loading filtered barcode matrices into R  error corrected cell barcodes from transcriptomic reads were isolated and matched against those in the text file to identify common cell barcode that are shared between the transcriptomic and SABER seq datasets. The resulting text file contained redundant error corrected cell barcodes associated with different UMIs for the same cell. Thus  unique error corrected cell barcodes were isolated to create a text file containing unique FASTQ headers and error corrected barcodes. Subsequently  FASTQ reads matching the FASTQ headers in this file were extracted from the SABER seq raw reads using a Python script. SABER seq barcodes were analyzed using the scGESTALT pipeline available on Github as described previously7 35. To analyze differences in barcode detection and editing frequencies between cell types a binomial test was used. scRNA seq data was analyzed using the following protocol. Raw FASTQ reads were aligned against zebrafish genome  built from GRCz.109.gtf and the GRCz11.fa files  using the Cell Ranger count pipeline to generate gene by cell count matrices filtered barcode matrix. The count matrices were processed using the standard workflow of the Seurat v5.0.2 R package with modifications. Briefly  all count matrices were separately loaded into R. Seurat objects were then created with modified parameters  requiring features to be detected in at least 3 cells and cells to contain at least 300 genes. Subsequently  the Seurat objects were merged to create a single Seurat object. Cells with RNA counts between 300 and 4000 and a mitochondrial gene percentage of less than 9% were selected for downstream analysis. Gene expression values were log normalized with the default parameters and scaled for the top 3000 variable genes. The scaled data was then used to compute the 50 principal components. Cells were iteratively clustered using the Louvain algorithm post identifying neighboring cells. Differentially expressed genes for each cluster were identified using genes that are detected in at least 10% of cells in either of the two clusters and genes showing a minimum difference of 10% between the two clusters. Cell types were annotated using known marker genes. Finally  the data was visualized using UMAP. SABER seq barcode analysis was performed using the following protocol. The SABER seq FASTQ reads were processed using the Cell Ranger count pipeline.A bash script was then used to generate a text file containing all FASTQ headers and error corrected cell barcodes for SABER seq data. Error corrected cell barcodes from transcriptomic reads were isolated and matched against those in the text file to identify common cell barcode that are shared between the transcriptomic and SABER seq datasets. The resulting text file contained redundant error corrected cell barcodes associated with different UMIs for the same cell. Thus  unique error corrected cell barcodes were isolated to create a text file containing unique FASTQ headers and error corrected barcodes. Subsequently  FASTQ reads matching the FASTQ headers in this file were extracted from the SABER seq raw reads using a Python script. SABER seq barcodes were analyzed using the scGESTALT pipeline available on Github as described previously. To analyze differences in barcode detection and editing frequencies between cell types a binomial test was used. genome build/assembly: GRCz11 processed data files format and content: *.h5 files contain filtered features/genes and barcode data generated from cell ranger pipeline. processed data files format and content: LinC1 Filtered Fastq Headers CB.txt LinC2 Filtered Fastq Headers CB.txt LinD1 Filtered Fastq Headers CB.txt LinD2 Filtered Fastq Headers CB.txt LinD3 Filtered Fastq Headers CB.txt files contain filtered and unique fastq headers and cell barcodes post matching the trasncriptomic cell barcodes. processed data files format and content: SABERb1 linC1 S4 BrainC1 S6.CB.xlsx SABERb1 linC2 S5 BrainC2 S7.CB.xlsx SABERb2 linD1 S1 BrainD1 S8.CB.xlsx SABERb2 linD2 S2 BrainD2 S9.CB.xlsx SABERb2 linD3 S3 BrainD3 S10.CB.xlsx SABERb4 linB2.1 13.123.2.With.Primer.stats.xlsx SABERb4 linB2.2 14.124.2.With.Primer.stats.xlsx files contain the SABER Seq output post matching reads to the reference sequence.", "zebrafish midbrain", null, "Wild type and SABER x her4.3:switchCas9 zebrafish brains were harvested and dissociated at 21 dpf 23 dpf as described previously Raj et. al 2018  Nature Biotechnology  with the following modifications. All microcentrifuge tubes were treated with 1% BSA/DPBS overnight to prevent cell loss due to adhesion. Brains were dissociated with 1 mL of 20 units/ml papain in EBSS media and incubated at 37 \u00b0C for 20 min. Cells were resuspended with 150 \u03bcl ice cold 1%BSA/DPBS solution. Transcriptome libraries were processed according to the manufacturer\u2019s instructions Single Cell 3\u2019 v3 kit. Transcriptome libraries were sequenced using NovaSeq S4 300 cycle kits Novogene and Novaseq SP 100 and 200 cycle kits MedGenome. To generate SABER seq libraries  samples post cDNA amplification and prior to fragmentation were split into two parts. One part was processed for transcriptome libraries as instructed by the manufacturer. The other part was processed for SABER seq libraries as follows. To enrich for SABER barcodes  5 \u00b5l of the whole transcriptome cDNA was PCR amplified with 10X p140v1UP  GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCT CGTCGTAGATCTTCATCGTGATG and 10XPCR1F CTACACGACGCTCTTCCGATCT primers and Q5 polymerase. The reaction conditions were: 98 \u00b0C  30 s; [98 \u00b0C  10 s; 68 \u00b0C  25 s; 72 \u00b0C  15 s] x 14 cycles; 72 \u00b0C  2 min. The reaction was cleaned up with 0.8X SPRISelect beads and eluted in 20 \u00b5l elution buffer EB buffer. A second PCR was carried out using 3 \u00b5l of PCR1 product and 10XPCR1F and 10X p140v2Sa GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCT GAAACTACATGGGGTcaGCTC primers. Reaction conditions were same as PCR1 except only 13 cycles were completed. At this point  2 different clean up conditions were used: iteration 1 SABER seq v0 and iteration 2 SABER seq v1. For iteration 1  the reaction was cleaned up with 0.8X SPRISelect beads and eluted in 20 \u00b5l elution buffer EB buffer similar to PCR1.  For iteration 2  an optimization was trialed  and the reaction was cleaned up with right side size selection using SPRIselect beads 0.6X beads used initially and supernatant was rebound with 1.5X beads. The product was eluted in 20 \u00b5l EB. Finally  adapters and sample indices were incorporated in a third PCR reaction using 5 7 \u00b5l of a 1:10 dilution of PCR2 product. Reactions conditions were same as PCR1 except 9 10 cycles were completed. Primers used: 10XP5Dual Hx AATGATACGGCGACCACCGAGATCTACAC xxxxxxx xxx ACACTCTTTCCCTACACGACGCTCTTCCGATCT  10XP7Dual H5 AATGATACGGCGAC CACCGAGATCTACAC xxxxxxxxxx ACACTCTTTCCCTACACGACGCTCTTCCGATCT  where x represents index bases from the 10X Dual Index Kit TT  SetA H index entries. For iteration 1  the third PCR reaction was cleaned up with 0.8X SPRISelect beads and eluted in 20 \u00b5l elution buffer EB to generate final libraries. For iteration 2  the third PCR reaction was cleaned up with right side size selection using SPRIselect beads 0.65X beads used initially and supernatant was rebound with 1.5X beads. SABER seq libraries were sequenced using Novaseq SP 200 cycle kits MedGenome with 5 10% PhiX spike in. Sequencing parameters: Read1 28 cycles  Read2 151 cycles  Index1 10 cycles  Index2 10 cycles. Standard sequencing primers were used.", "This work was performed under protocol numbers 807110 and 807259  which were approved by the University of Pennsylvania\u2019s Office of Animal Welfare of Institutional Animal Care and Use Committee IACUC. All zebrafish work in this study follows the University of Pennsylvania Institutional Animal Care and Use Committee regulations.", "tissue:zebrafish midbrain|developmental stage:21 dpf 23 dpf  Juvenile|genotype:WT/TLAB", "GSM8290041", "GSM8290041: zBrMb B1mid021023wk3  midbrain  transcriptome; Danio rerio; RNA Seq", "GSM8290041 r1", "GSM8290041", "1", "Wild type and SABER x her4.3:switchCas9 zebrafish brains were harvested and dissociated at 21 dpf 23 dpf as described previously Raj et. al 2018  Nature Biotechnology  with the following modifications. All microcentrifuge tubes were treated with 1% BSA/DPBS overnight to prevent cell loss due to adhesion. Brains were dissociated with 1 mL of 20 units/ml papain in EBSS media and incubated at 37 \u00b0C for 20 min. Cells were resuspended with 150 \u03bcl ice cold 1%BSA/DPBS solution. Transcriptome libraries were processed according to the manufacturer's instructions Single Cell three prime v3 kit. Transcriptome libraries were sequenced using NovaSeq S4 300 cycle kits Novogene and Novaseq SP 100 and 200 cycle kits MedGenome. To generate SABER seq libraries  samples post cDNA amplification and prior to fragmentation were split into two parts. One part was processed for transcriptome libraries as instructed by the manufacturer. The other part was processed for SABER seq libraries as follows. To enrich for SABER barcodes  5 \u00b5l of the whole transcriptome cDNA was PCR amplified with 10X p140v1UP  GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCT CGTCGTAGATCTTCATCGTGATG and 10XPCR1F CTACACGACGCTCTTCCGATCT primers and Q5 polymerase. The reaction conditions were: 98 \u00b0C  30 s; [98 \u00b0C  10 s; 68 \u00b0C  25 s; 72 \u00b0C  15 s] x 14 cycles; 72 \u00b0C  2 min. The reaction was cleaned up with 0.8X SPRISelect beads and eluted in 20 \u00b5l elution buffer EB buffer. A second PCR was carried out using 3 \u00b5l of PCR1 product and 10XPCR1F and 10X p140v2Sa GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCT GAAACTACATGGGGTcaGCTC primers. Reaction conditions were same as PCR1 except only 13 cycles were completed. At this point  2 different clean up conditions were used: iteration 1 SABER seq v0 and iteration 2 SABER seq v1. For iteration 1  the reaction was cleaned up with 0.8X SPRISelect beads and eluted in 20 \u00b5l elution buffer EB buffer similar to PCR1.  For iteration 2  an optimization was trialed  and the reaction was cleaned up with right side size selection using SPRIselect beads 0.6X beads used initially and supernatant was rebound with 1.5X beads. The product was eluted in 20 \u00b5l EB. Finally  adapters and sample indices were incorporated in a third PCR reaction using 5 7 \u00b5l of a 1:10 dilution of PCR2 product. Reactions conditions were same as PCR1 except 9 10 cycles were completed. Primers used: 10XP5Dual Hx AATGATACGGCGACCACCGAGATCTACAC xxxxxxx xxx ACACTCTTTCCCTACACGACGCTCTTCCGATCT  10XP7Dual H5 AATGATACGGCGAC CACCGAGATCTACAC xxxxxxxxxx ACACTCTTTCCCTACACGACGCTCTTCCGATCT  where x represents index bases from the 10X Dual Index Kit TT  SetA H index entries. For iteration 1  the third PCR reaction was cleaned up with 0.8X SPRISelect beads and eluted in 20 \u00b5l elution buffer EB to generate final libraries. For iteration 2  the third PCR reaction was cleaned up with right side size selection using SPRIselect beads 0.65X beads used initially and supernatant was rebound with 1.5X beads. SABER seq libraries were sequenced using Novaseq SP 200 cycle kits MedGenome with 5 10% PhiX spike in. Sequencing parameters: Read1 28 cycles  Read2 151 cycles  Index1 10 cycles  Index2 10 cycles. Standard sequencing primers were used.", null, "RNA-Seq", "TRANSCRIPTOMIC SINGLE CELL", "cDNA", "PAIRED", "ILLUMINA", "Illumina NovaSeq 6000", null, "SRP509892", null, "loader:fastq load.py", "zBrMb_B2mid021023wk3_CKDL230006230-1A_HW3J5DSX5_S6_L004_R2_001.fastq.gz zBrMb_B2mid021023wk3_CKDL230006230-1A_HW3J5DSX5_S6_L004_R1_001.fastq.gz zBrMb_B2mid021023wk3_CKDL230006230-1A_HW3J5DSX5_S6_L004_I2_001.fastq.gz zBrMb_B2mid021023wk3_CKDL230006230-1A_HW3J5DSX5_S6_L004_I1_001.fastq.gz", "fastq fastq fastq fastq", 61698264000.0, 192807075.0, "GSM8290041 r2", "0:10 1:10 2:150 3:150", "A:18951447819;C:7712374364;G:8225043540;T:22952522047;N:734730", 10, 10, 150, 150, 18951447819, 7712374364, 8225043540, 22952522047, 734730, "SRX24701850", "SRS21429379", "SRA1989588", "University of Pennsylvania", "University of Pennsylvania", 2, 0.18731, 0.88885, 0.07427, 0.21742, 0.98088, 0.77662, 0.50039, 0.51547, 150, 150, "T", "B", "mate1 technical by mapping diff", "illumina", "novaseq_era", "full_length", "random_priming", "unknown", "sc", "single_cell_droplet", "10x", null, "United States", "2024-05-25", "Larval", "Larval", "Brain", "Nervous System"], [32403, "SRR29181706", "SRX24701849", "SRS21429378", "SRP509892", "PRJNA1116548", "Barcoding Notch signaling in the developing brain", "GSE268356", "Other", "Developmental signaling inputs are fundamental for shaping cell fates and behavior. However  traditional fluorescent based signaling reporters have limitations in scalability and molecular resolution of cell types. We present SABER seq  a CRISPR Cas molecular recorder that stores transient developmental signaling cues as permanent mutations in cellular genomes for deconstruction at later stages via single cell transcriptomics. We applied SABER seq to record Notch signaling in developing zebrafish brains. SABER seq has two components: a signaling sensor and a barcode recorder. The sensor activates Cas9 in a Notch dependent manner with inducible control while the recorder obtains mutations in ancestral cells where Notch is active. We combine SABER seq with an expanded juvenile brain atlas to identify cell types derived from Notch active founders. Our data reveals rare examples where differential Notch activity in ancestral progenitors is detected in terminally differentiated neuronal subtypes. SABER seq is a novel platform for rapid  scalable and high resolution mapping of signaling activity during development. Overall design: 10x Genomics libraries of single cell transcriptomes and SABER seq data  and genomic DNA SABER data", null, "pubmed:39575683", null, "zBrFb A1fore021023wk3  forebrain  transcriptome", "GSM8290040", null, "source name:zebrafish forebrain|tissue:zebrafish forebrain|developmental stage:21 dpf 23 dpf  Juvenile|genotype:WT/TLAB|geo loc name:missing|collection date:missing", "zBrFb A1fore021023wk3  forebrain  transcriptome", "Raw FASTQ reads were aligned against zebrafish genome  built from GRCz.109.gtf and the GRCz11.fa files  using the Cell Ranger count pipeline to generate gene by cell count matrices filtered barcode matrix. The count matrices were processed using the standard workflow of the Seurat v5.0.2 R package with modifications. Briefly  all count matrices were separately loaded into R. Seurat objects were then created with modified parameters  requiring features to be detected in at least 3 cells and cells to contain at least 300 genes. Subsequently  the Seurat objects were merged to create a single Seurat object. Cells with RNA counts between 300 and 4000 and a mitochondrial gene percentage of less than 9% were selected for downstream analysis. Gene expression values were log normalized with the default parameters and scaled for the top 3000 variable genes. The scaled data was then used to compute the 50 principal components. Cells were iteratively clustered using the Louvain algorithm post identifying neighboring cells. Differentially expressed genes for each cluster were identified using genes that are detected in at least 10% of cells in either of the two clusters and genes showing a minimum difference of 10% between the two clusters. Cell types were annotated using known marker genes.  The SABER seq FASTQ reads were processed using the Cell Ranger count pipeline  similar to the transcriptomic reads. A bash script was then used to generate a text file containing all FASTQ headers and error corrected cell barcodes for SABER seq data. post loading filtered barcode matrices into R  error corrected cell barcodes from transcriptomic reads were isolated and matched against those in the text file to identify common cell barcode that are shared between the transcriptomic and SABER seq datasets. The resulting text file contained redundant error corrected cell barcodes associated with different UMIs for the same cell. Thus  unique error corrected cell barcodes were isolated to create a text file containing unique FASTQ headers and error corrected barcodes. Subsequently  FASTQ reads matching the FASTQ headers in this file were extracted from the SABER seq raw reads using a Python script. SABER seq barcodes were analyzed using the scGESTALT pipeline available on Github as described previously7 35. To analyze differences in barcode detection and editing frequencies between cell types a binomial test was used. scRNA seq data was analyzed using the following protocol. Raw FASTQ reads were aligned against zebrafish genome  built from GRCz.109.gtf and the GRCz11.fa files  using the Cell Ranger count pipeline to generate gene by cell count matrices filtered barcode matrix. The count matrices were processed using the standard workflow of the Seurat v5.0.2 R package with modifications. Briefly  all count matrices were separately loaded into R. Seurat objects were then created with modified parameters  requiring features to be detected in at least 3 cells and cells to contain at least 300 genes. Subsequently  the Seurat objects were merged to create a single Seurat object. Cells with RNA counts between 300 and 4000 and a mitochondrial gene percentage of less than 9% were selected for downstream analysis. Gene expression values were log normalized with the default parameters and scaled for the top 3000 variable genes. The scaled data was then used to compute the 50 principal components. Cells were iteratively clustered using the Louvain algorithm post identifying neighboring cells. Differentially expressed genes for each cluster were identified using genes that are detected in at least 10% of cells in either of the two clusters and genes showing a minimum difference of 10% between the two clusters. Cell types were annotated using known marker genes. Finally  the data was visualized using UMAP. SABER seq barcode analysis was performed using the following protocol. The SABER seq FASTQ reads were processed using the Cell Ranger count pipeline.A bash script was then used to generate a text file containing all FASTQ headers and error corrected cell barcodes for SABER seq data. Error corrected cell barcodes from transcriptomic reads were isolated and matched against those in the text file to identify common cell barcode that are shared between the transcriptomic and SABER seq datasets. The resulting text file contained redundant error corrected cell barcodes associated with different UMIs for the same cell. Thus  unique error corrected cell barcodes were isolated to create a text file containing unique FASTQ headers and error corrected barcodes. Subsequently  FASTQ reads matching the FASTQ headers in this file were extracted from the SABER seq raw reads using a Python script. SABER seq barcodes were analyzed using the scGESTALT pipeline available on Github as described previously. To analyze differences in barcode detection and editing frequencies between cell types a binomial test was used. genome build/assembly: GRCz11 processed data files format and content: *.h5 files contain filtered features/genes and barcode data generated from cell ranger pipeline. processed data files format and content: LinC1 Filtered Fastq Headers CB.txt LinC2 Filtered Fastq Headers CB.txt LinD1 Filtered Fastq Headers CB.txt LinD2 Filtered Fastq Headers CB.txt LinD3 Filtered Fastq Headers CB.txt files contain filtered and unique fastq headers and cell barcodes post matching the trasncriptomic cell barcodes. processed data files format and content: SABERb1 linC1 S4 BrainC1 S6.CB.xlsx SABERb1 linC2 S5 BrainC2 S7.CB.xlsx SABERb2 linD1 S1 BrainD1 S8.CB.xlsx SABERb2 linD2 S2 BrainD2 S9.CB.xlsx SABERb2 linD3 S3 BrainD3 S10.CB.xlsx SABERb4 linB2.1 13.123.2.With.Primer.stats.xlsx SABERb4 linB2.2 14.124.2.With.Primer.stats.xlsx files contain the SABER Seq output post matching reads to the reference sequence.", "zebrafish forebrain", null, "Wild type and SABER x her4.3:switchCas9 zebrafish brains were harvested and dissociated at 21 dpf 23 dpf as described previously Raj et. al 2018  Nature Biotechnology  with the following modifications. All microcentrifuge tubes were treated with 1% BSA/DPBS overnight to prevent cell loss due to adhesion. Brains were dissociated with 1 mL of 20 units/ml papain in EBSS media and incubated at 37 \u00b0C for 20 min. Cells were resuspended with 150 \u03bcl ice cold 1%BSA/DPBS solution. Transcriptome libraries were processed according to the manufacturer\u2019s instructions Single Cell 3\u2019 v3 kit. Transcriptome libraries were sequenced using NovaSeq S4 300 cycle kits Novogene and Novaseq SP 100 and 200 cycle kits MedGenome. To generate SABER seq libraries  samples post cDNA amplification and prior to fragmentation were split into two parts. One part was processed for transcriptome libraries as instructed by the manufacturer. The other part was processed for SABER seq libraries as follows. To enrich for SABER barcodes  5 \u00b5l of the whole transcriptome cDNA was PCR amplified with 10X p140v1UP  GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCT CGTCGTAGATCTTCATCGTGATG and 10XPCR1F CTACACGACGCTCTTCCGATCT primers and Q5 polymerase. The reaction conditions were: 98 \u00b0C  30 s; [98 \u00b0C  10 s; 68 \u00b0C  25 s; 72 \u00b0C  15 s] x 14 cycles; 72 \u00b0C  2 min. The reaction was cleaned up with 0.8X SPRISelect beads and eluted in 20 \u00b5l elution buffer EB buffer. A second PCR was carried out using 3 \u00b5l of PCR1 product and 10XPCR1F and 10X p140v2Sa GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCT GAAACTACATGGGGTcaGCTC primers. Reaction conditions were same as PCR1 except only 13 cycles were completed. At this point  2 different clean up conditions were used: iteration 1 SABER seq v0 and iteration 2 SABER seq v1. For iteration 1  the reaction was cleaned up with 0.8X SPRISelect beads and eluted in 20 \u00b5l elution buffer EB buffer similar to PCR1.  For iteration 2  an optimization was trialed  and the reaction was cleaned up with right side size selection using SPRIselect beads 0.6X beads used initially and supernatant was rebound with 1.5X beads. The product was eluted in 20 \u00b5l EB. Finally  adapters and sample indices were incorporated in a third PCR reaction using 5 7 \u00b5l of a 1:10 dilution of PCR2 product. Reactions conditions were same as PCR1 except 9 10 cycles were completed. Primers used: 10XP5Dual Hx AATGATACGGCGACCACCGAGATCTACAC xxxxxxx xxx ACACTCTTTCCCTACACGACGCTCTTCCGATCT  10XP7Dual H5 AATGATACGGCGAC CACCGAGATCTACAC xxxxxxxxxx ACACTCTTTCCCTACACGACGCTCTTCCGATCT  where x represents index bases from the 10X Dual Index Kit TT  SetA H index entries. For iteration 1  the third PCR reaction was cleaned up with 0.8X SPRISelect beads and eluted in 20 \u00b5l elution buffer EB to generate final libraries. For iteration 2  the third PCR reaction was cleaned up with right side size selection using SPRIselect beads 0.65X beads used initially and supernatant was rebound with 1.5X beads. SABER seq libraries were sequenced using Novaseq SP 200 cycle kits MedGenome with 5 10% PhiX spike in. Sequencing parameters: Read1 28 cycles  Read2 151 cycles  Index1 10 cycles  Index2 10 cycles. Standard sequencing primers were used.", "This work was performed under protocol numbers 807110 and 807259  which were approved by the University of Pennsylvania\u2019s Office of Animal Welfare of Institutional Animal Care and Use Committee IACUC. All zebrafish work in this study follows the University of Pennsylvania Institutional Animal Care and Use Committee regulations.", "tissue:zebrafish forebrain|developmental stage:21 dpf 23 dpf  Juvenile|genotype:WT/TLAB", "GSM8290040", "GSM8290040: zBrFb A1fore021023wk3  forebrain  transcriptome; Danio rerio; RNA Seq", "GSM8290040 r1", "GSM8290040", "1", "Wild type and SABER x her4.3:switchCas9 zebrafish brains were harvested and dissociated at 21 dpf 23 dpf as described previously Raj et. al 2018  Nature Biotechnology  with the following modifications. All microcentrifuge tubes were treated with 1% BSA/DPBS overnight to prevent cell loss due to adhesion. Brains were dissociated with 1 mL of 20 units/ml papain in EBSS media and incubated at 37 \u00b0C for 20 min. Cells were resuspended with 150 \u03bcl ice cold 1%BSA/DPBS solution. Transcriptome libraries were processed according to the manufacturer's instructions Single Cell three prime v3 kit. Transcriptome libraries were sequenced using NovaSeq S4 300 cycle kits Novogene and Novaseq SP 100 and 200 cycle kits MedGenome. To generate SABER seq libraries  samples post cDNA amplification and prior to fragmentation were split into two parts. One part was processed for transcriptome libraries as instructed by the manufacturer. The other part was processed for SABER seq libraries as follows. To enrich for SABER barcodes  5 \u00b5l of the whole transcriptome cDNA was PCR amplified with 10X p140v1UP  GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCT CGTCGTAGATCTTCATCGTGATG and 10XPCR1F CTACACGACGCTCTTCCGATCT primers and Q5 polymerase. The reaction conditions were: 98 \u00b0C  30 s; [98 \u00b0C  10 s; 68 \u00b0C  25 s; 72 \u00b0C  15 s] x 14 cycles; 72 \u00b0C  2 min. The reaction was cleaned up with 0.8X SPRISelect beads and eluted in 20 \u00b5l elution buffer EB buffer. A second PCR was carried out using 3 \u00b5l of PCR1 product and 10XPCR1F and 10X p140v2Sa GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCT GAAACTACATGGGGTcaGCTC primers. Reaction conditions were same as PCR1 except only 13 cycles were completed. At this point  2 different clean up conditions were used: iteration 1 SABER seq v0 and iteration 2 SABER seq v1. For iteration 1  the reaction was cleaned up with 0.8X SPRISelect beads and eluted in 20 \u00b5l elution buffer EB buffer similar to PCR1.  For iteration 2  an optimization was trialed  and the reaction was cleaned up with right side size selection using SPRIselect beads 0.6X beads used initially and supernatant was rebound with 1.5X beads. The product was eluted in 20 \u00b5l EB. Finally  adapters and sample indices were incorporated in a third PCR reaction using 5 7 \u00b5l of a 1:10 dilution of PCR2 product. Reactions conditions were same as PCR1 except 9 10 cycles were completed. Primers used: 10XP5Dual Hx AATGATACGGCGACCACCGAGATCTACAC xxxxxxx xxx ACACTCTTTCCCTACACGACGCTCTTCCGATCT  10XP7Dual H5 AATGATACGGCGAC CACCGAGATCTACAC xxxxxxxxxx ACACTCTTTCCCTACACGACGCTCTTCCGATCT  where x represents index bases from the 10X Dual Index Kit TT  SetA H index entries. For iteration 1  the third PCR reaction was cleaned up with 0.8X SPRISelect beads and eluted in 20 \u00b5l elution buffer EB to generate final libraries. For iteration 2  the third PCR reaction was cleaned up with right side size selection using SPRIselect beads 0.65X beads used initially and supernatant was rebound with 1.5X beads. SABER seq libraries were sequenced using Novaseq SP 200 cycle kits MedGenome with 5 10% PhiX spike in. Sequencing parameters: Read1 28 cycles  Read2 151 cycles  Index1 10 cycles  Index2 10 cycles. Standard sequencing primers were used.", null, "RNA-Seq", "TRANSCRIPTOMIC SINGLE CELL", "cDNA", "PAIRED", "ILLUMINA", "Illumina NovaSeq 6000", null, "SRP509892", null, "loader:fastq load.py", "zBrFb_A1fore021023wk3_CKDL230006230-1A_HW3J5DSX5_S5_L004_R2_001.fastq.gz zBrFb_A1fore021023wk3_CKDL230006230-1A_HW3J5DSX5_S5_L004_R1_001.fastq.gz zBrFb_A1fore021023wk3_CKDL230006230-1A_HW3J5DSX5_S5_L004_I2_001.fastq.gz zBrFb_A1fore021023wk3_CKDL230006230-1A_HW3J5DSX5_S5_L004_I1_001.fastq.gz", "fastq fastq fastq fastq", 60327497600.0, 188523430.0, "GSM8290040 r1", "0:10 1:10 2:150 3:150", "A:18406517456;C:7463455362;G:7981015888;T:22705319066;N:721228", 10, 10, 150, 150, 18406517456, 7463455362, 7981015888, 22705319066, 721228, "SRX24701849", "SRS21429378", "SRA1989588", "University of Pennsylvania", "University of Pennsylvania", 2, 0.17216, 0.88979, 0.06088, 0.22548, 0.98206, 0.76654, 0.50967, 0.49721, 150, 150, "T", "B", "mate1 technical by mapping diff", "illumina", "novaseq_era", "full_length", "random_priming", "unknown", "sc", "single_cell_droplet", "10x", null, "United States", "2024-05-25", "Larval", "Larval", "Brain", "Nervous System"], [32404, "SRR29181707", "SRX24701849", "SRS21429378", "SRP509892", "PRJNA1116548", "Barcoding Notch signaling in the developing brain", "GSE268356", "Other", "Developmental signaling inputs are fundamental for shaping cell fates and behavior. However  traditional fluorescent based signaling reporters have limitations in scalability and molecular resolution of cell types. We present SABER seq  a CRISPR Cas molecular recorder that stores transient developmental signaling cues as permanent mutations in cellular genomes for deconstruction at later stages via single cell transcriptomics. We applied SABER seq to record Notch signaling in developing zebrafish brains. SABER seq has two components: a signaling sensor and a barcode recorder. The sensor activates Cas9 in a Notch dependent manner with inducible control while the recorder obtains mutations in ancestral cells where Notch is active. We combine SABER seq with an expanded juvenile brain atlas to identify cell types derived from Notch active founders. Our data reveals rare examples where differential Notch activity in ancestral progenitors is detected in terminally differentiated neuronal subtypes. SABER seq is a novel platform for rapid  scalable and high resolution mapping of signaling activity during development. Overall design: 10x Genomics libraries of single cell transcriptomes and SABER seq data  and genomic DNA SABER data", null, "pubmed:39575683", null, "zBrFb A1fore021023wk3  forebrain  transcriptome", "GSM8290040", null, "source name:zebrafish forebrain|tissue:zebrafish forebrain|developmental stage:21 dpf 23 dpf  Juvenile|genotype:WT/TLAB|geo loc name:missing|collection date:missing", "zBrFb A1fore021023wk3  forebrain  transcriptome", "Raw FASTQ reads were aligned against zebrafish genome  built from GRCz.109.gtf and the GRCz11.fa files  using the Cell Ranger count pipeline to generate gene by cell count matrices filtered barcode matrix. The count matrices were processed using the standard workflow of the Seurat v5.0.2 R package with modifications. Briefly  all count matrices were separately loaded into R. Seurat objects were then created with modified parameters  requiring features to be detected in at least 3 cells and cells to contain at least 300 genes. Subsequently  the Seurat objects were merged to create a single Seurat object. Cells with RNA counts between 300 and 4000 and a mitochondrial gene percentage of less than 9% were selected for downstream analysis. Gene expression values were log normalized with the default parameters and scaled for the top 3000 variable genes. The scaled data was then used to compute the 50 principal components. Cells were iteratively clustered using the Louvain algorithm post identifying neighboring cells. Differentially expressed genes for each cluster were identified using genes that are detected in at least 10% of cells in either of the two clusters and genes showing a minimum difference of 10% between the two clusters. Cell types were annotated using known marker genes.  The SABER seq FASTQ reads were processed using the Cell Ranger count pipeline  similar to the transcriptomic reads. A bash script was then used to generate a text file containing all FASTQ headers and error corrected cell barcodes for SABER seq data. post loading filtered barcode matrices into R  error corrected cell barcodes from transcriptomic reads were isolated and matched against those in the text file to identify common cell barcode that are shared between the transcriptomic and SABER seq datasets. The resulting text file contained redundant error corrected cell barcodes associated with different UMIs for the same cell. Thus  unique error corrected cell barcodes were isolated to create a text file containing unique FASTQ headers and error corrected barcodes. Subsequently  FASTQ reads matching the FASTQ headers in this file were extracted from the SABER seq raw reads using a Python script. SABER seq barcodes were analyzed using the scGESTALT pipeline available on Github as described previously7 35. To analyze differences in barcode detection and editing frequencies between cell types a binomial test was used. scRNA seq data was analyzed using the following protocol. Raw FASTQ reads were aligned against zebrafish genome  built from GRCz.109.gtf and the GRCz11.fa files  using the Cell Ranger count pipeline to generate gene by cell count matrices filtered barcode matrix. The count matrices were processed using the standard workflow of the Seurat v5.0.2 R package with modifications. Briefly  all count matrices were separately loaded into R. Seurat objects were then created with modified parameters  requiring features to be detected in at least 3 cells and cells to contain at least 300 genes. Subsequently  the Seurat objects were merged to create a single Seurat object. Cells with RNA counts between 300 and 4000 and a mitochondrial gene percentage of less than 9% were selected for downstream analysis. Gene expression values were log normalized with the default parameters and scaled for the top 3000 variable genes. The scaled data was then used to compute the 50 principal components. Cells were iteratively clustered using the Louvain algorithm post identifying neighboring cells. Differentially expressed genes for each cluster were identified using genes that are detected in at least 10% of cells in either of the two clusters and genes showing a minimum difference of 10% between the two clusters. Cell types were annotated using known marker genes. Finally  the data was visualized using UMAP. SABER seq barcode analysis was performed using the following protocol. The SABER seq FASTQ reads were processed using the Cell Ranger count pipeline.A bash script was then used to generate a text file containing all FASTQ headers and error corrected cell barcodes for SABER seq data. Error corrected cell barcodes from transcriptomic reads were isolated and matched against those in the text file to identify common cell barcode that are shared between the transcriptomic and SABER seq datasets. The resulting text file contained redundant error corrected cell barcodes associated with different UMIs for the same cell. Thus  unique error corrected cell barcodes were isolated to create a text file containing unique FASTQ headers and error corrected barcodes. Subsequently  FASTQ reads matching the FASTQ headers in this file were extracted from the SABER seq raw reads using a Python script. SABER seq barcodes were analyzed using the scGESTALT pipeline available on Github as described previously. To analyze differences in barcode detection and editing frequencies between cell types a binomial test was used. genome build/assembly: GRCz11 processed data files format and content: *.h5 files contain filtered features/genes and barcode data generated from cell ranger pipeline. processed data files format and content: LinC1 Filtered Fastq Headers CB.txt LinC2 Filtered Fastq Headers CB.txt LinD1 Filtered Fastq Headers CB.txt LinD2 Filtered Fastq Headers CB.txt LinD3 Filtered Fastq Headers CB.txt files contain filtered and unique fastq headers and cell barcodes post matching the trasncriptomic cell barcodes. processed data files format and content: SABERb1 linC1 S4 BrainC1 S6.CB.xlsx SABERb1 linC2 S5 BrainC2 S7.CB.xlsx SABERb2 linD1 S1 BrainD1 S8.CB.xlsx SABERb2 linD2 S2 BrainD2 S9.CB.xlsx SABERb2 linD3 S3 BrainD3 S10.CB.xlsx SABERb4 linB2.1 13.123.2.With.Primer.stats.xlsx SABERb4 linB2.2 14.124.2.With.Primer.stats.xlsx files contain the SABER Seq output post matching reads to the reference sequence.", "zebrafish forebrain", null, "Wild type and SABER x her4.3:switchCas9 zebrafish brains were harvested and dissociated at 21 dpf 23 dpf as described previously Raj et. al 2018  Nature Biotechnology  with the following modifications. All microcentrifuge tubes were treated with 1% BSA/DPBS overnight to prevent cell loss due to adhesion. Brains were dissociated with 1 mL of 20 units/ml papain in EBSS media and incubated at 37 \u00b0C for 20 min. Cells were resuspended with 150 \u03bcl ice cold 1%BSA/DPBS solution. Transcriptome libraries were processed according to the manufacturer\u2019s instructions Single Cell 3\u2019 v3 kit. Transcriptome libraries were sequenced using NovaSeq S4 300 cycle kits Novogene and Novaseq SP 100 and 200 cycle kits MedGenome. To generate SABER seq libraries  samples post cDNA amplification and prior to fragmentation were split into two parts. One part was processed for transcriptome libraries as instructed by the manufacturer. The other part was processed for SABER seq libraries as follows. To enrich for SABER barcodes  5 \u00b5l of the whole transcriptome cDNA was PCR amplified with 10X p140v1UP  GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCT CGTCGTAGATCTTCATCGTGATG and 10XPCR1F CTACACGACGCTCTTCCGATCT primers and Q5 polymerase. The reaction conditions were: 98 \u00b0C  30 s; [98 \u00b0C  10 s; 68 \u00b0C  25 s; 72 \u00b0C  15 s] x 14 cycles; 72 \u00b0C  2 min. The reaction was cleaned up with 0.8X SPRISelect beads and eluted in 20 \u00b5l elution buffer EB buffer. A second PCR was carried out using 3 \u00b5l of PCR1 product and 10XPCR1F and 10X p140v2Sa GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCT GAAACTACATGGGGTcaGCTC primers. Reaction conditions were same as PCR1 except only 13 cycles were completed. At this point  2 different clean up conditions were used: iteration 1 SABER seq v0 and iteration 2 SABER seq v1. For iteration 1  the reaction was cleaned up with 0.8X SPRISelect beads and eluted in 20 \u00b5l elution buffer EB buffer similar to PCR1.  For iteration 2  an optimization was trialed  and the reaction was cleaned up with right side size selection using SPRIselect beads 0.6X beads used initially and supernatant was rebound with 1.5X beads. The product was eluted in 20 \u00b5l EB. Finally  adapters and sample indices were incorporated in a third PCR reaction using 5 7 \u00b5l of a 1:10 dilution of PCR2 product. Reactions conditions were same as PCR1 except 9 10 cycles were completed. Primers used: 10XP5Dual Hx AATGATACGGCGACCACCGAGATCTACAC xxxxxxx xxx ACACTCTTTCCCTACACGACGCTCTTCCGATCT  10XP7Dual H5 AATGATACGGCGAC CACCGAGATCTACAC xxxxxxxxxx ACACTCTTTCCCTACACGACGCTCTTCCGATCT  where x represents index bases from the 10X Dual Index Kit TT  SetA H index entries. For iteration 1  the third PCR reaction was cleaned up with 0.8X SPRISelect beads and eluted in 20 \u00b5l elution buffer EB to generate final libraries. For iteration 2  the third PCR reaction was cleaned up with right side size selection using SPRIselect beads 0.65X beads used initially and supernatant was rebound with 1.5X beads. SABER seq libraries were sequenced using Novaseq SP 200 cycle kits MedGenome with 5 10% PhiX spike in. Sequencing parameters: Read1 28 cycles  Read2 151 cycles  Index1 10 cycles  Index2 10 cycles. Standard sequencing primers were used.", "This work was performed under protocol numbers 807110 and 807259  which were approved by the University of Pennsylvania\u2019s Office of Animal Welfare of Institutional Animal Care and Use Committee IACUC. All zebrafish work in this study follows the University of Pennsylvania Institutional Animal Care and Use Committee regulations.", "tissue:zebrafish forebrain|developmental stage:21 dpf 23 dpf  Juvenile|genotype:WT/TLAB", "GSM8290040", "GSM8290040: zBrFb A1fore021023wk3  forebrain  transcriptome; Danio rerio; RNA Seq", "GSM8290040 r1", "GSM8290040", "1", "Wild type and SABER x her4.3:switchCas9 zebrafish brains were harvested and dissociated at 21 dpf 23 dpf as described previously Raj et. al 2018  Nature Biotechnology  with the following modifications. All microcentrifuge tubes were treated with 1% BSA/DPBS overnight to prevent cell loss due to adhesion. Brains were dissociated with 1 mL of 20 units/ml papain in EBSS media and incubated at 37 \u00b0C for 20 min. Cells were resuspended with 150 \u03bcl ice cold 1%BSA/DPBS solution. Transcriptome libraries were processed according to the manufacturer's instructions Single Cell three prime v3 kit. Transcriptome libraries were sequenced using NovaSeq S4 300 cycle kits Novogene and Novaseq SP 100 and 200 cycle kits MedGenome. To generate SABER seq libraries  samples post cDNA amplification and prior to fragmentation were split into two parts. One part was processed for transcriptome libraries as instructed by the manufacturer. The other part was processed for SABER seq libraries as follows. To enrich for SABER barcodes  5 \u00b5l of the whole transcriptome cDNA was PCR amplified with 10X p140v1UP  GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCT CGTCGTAGATCTTCATCGTGATG and 10XPCR1F CTACACGACGCTCTTCCGATCT primers and Q5 polymerase. The reaction conditions were: 98 \u00b0C  30 s; [98 \u00b0C  10 s; 68 \u00b0C  25 s; 72 \u00b0C  15 s] x 14 cycles; 72 \u00b0C  2 min. The reaction was cleaned up with 0.8X SPRISelect beads and eluted in 20 \u00b5l elution buffer EB buffer. A second PCR was carried out using 3 \u00b5l of PCR1 product and 10XPCR1F and 10X p140v2Sa GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCT GAAACTACATGGGGTcaGCTC primers. Reaction conditions were same as PCR1 except only 13 cycles were completed. At this point  2 different clean up conditions were used: iteration 1 SABER seq v0 and iteration 2 SABER seq v1. For iteration 1  the reaction was cleaned up with 0.8X SPRISelect beads and eluted in 20 \u00b5l elution buffer EB buffer similar to PCR1.  For iteration 2  an optimization was trialed  and the reaction was cleaned up with right side size selection using SPRIselect beads 0.6X beads used initially and supernatant was rebound with 1.5X beads. The product was eluted in 20 \u00b5l EB. Finally  adapters and sample indices were incorporated in a third PCR reaction using 5 7 \u00b5l of a 1:10 dilution of PCR2 product. Reactions conditions were same as PCR1 except 9 10 cycles were completed. Primers used: 10XP5Dual Hx AATGATACGGCGACCACCGAGATCTACAC xxxxxxx xxx ACACTCTTTCCCTACACGACGCTCTTCCGATCT  10XP7Dual H5 AATGATACGGCGAC CACCGAGATCTACAC xxxxxxxxxx ACACTCTTTCCCTACACGACGCTCTTCCGATCT  where x represents index bases from the 10X Dual Index Kit TT  SetA H index entries. For iteration 1  the third PCR reaction was cleaned up with 0.8X SPRISelect beads and eluted in 20 \u00b5l elution buffer EB to generate final libraries. For iteration 2  the third PCR reaction was cleaned up with right side size selection using SPRIselect beads 0.65X beads used initially and supernatant was rebound with 1.5X beads. SABER seq libraries were sequenced using Novaseq SP 200 cycle kits MedGenome with 5 10% PhiX spike in. Sequencing parameters: Read1 28 cycles  Read2 151 cycles  Index1 10 cycles  Index2 10 cycles. Standard sequencing primers were used.", null, "RNA-Seq", "TRANSCRIPTOMIC SINGLE CELL", "cDNA", "PAIRED", "ILLUMINA", "Illumina NovaSeq 6000", null, "SRP509892", null, "loader:fastq load.py", "zBrFb_A2fore021023wk3_CKDL230006230-1A_HW3J5DSX5_S3_L004_I1_001.fastq.gz zBrFb_A2fore021023wk3_CKDL230006230-1A_HW3J5DSX5_S3_L004_R1_001.fastq.gz zBrFb_A2fore021023wk3_CKDL230006230-1A_HW3J5DSX5_S3_L004_R2_001.fastq.gz zBrFb_A2fore021023wk3_CKDL230006230-1A_HW3J5DSX5_S3_L004_I2_001.fastq.gz", "fastq fastq fastq fastq", 68647176320.0, 214522426.0, "GSM8290040 r2", "0:10 1:10 2:150 3:150", "A:21091878705;C:8438830095;G:8989975904;T:25835224348;N:818748", 10, 10, 150, 150, 21091878705, 8438830095, 8989975904, 25835224348, 818748, "SRX24701849", "SRS21429378", "SRA1989588", "University of Pennsylvania", "University of Pennsylvania", 2, 0.18856, 0.87876, 0.06876, 0.22256, 0.9809, 0.77151, 0.51907, 0.53004, 150, 150, "T", "B", "mate1 technical by mapping diff", "illumina", "novaseq_era", "full_length", "random_priming", "unknown", "sc", "single_cell_droplet", "10x", null, "United States", "2024-05-25", "Larval", "Larval", "Brain", "Nervous System"], [32553, "SRR30792563", "SRX26194054", "SRS22736395", "SRP512205", "PRJNA1120771", "Transcriptomic neuron types vary topographically in function and morphology", "GSE269232", "Transcriptome Analysis", "We transcriptionally profiled the neuronal types of the zebrafish larvae optic tectum and matched them with their functional and morphological properties. Overall design: Wild type larvae were raised until 6 or 7 dpf. The optic tectum and torus longitudinalis of 6 dpf and 7 dpf larvae were then carefully dissected under a stereoscope followed by cell dissociation and sequencing. Please note that batches 5  6  and 8 the raw data with technical replica i.e. replica 1  replica 2 were processed together  and the resulting processed data is linked to the corresponding replica 1 sample records.", null, "pubmed:39939759", null, "optic tectum batch 12  scRNAseq  additional sequencing", "GSM8536814", null, "source name:optic tectum|tissue:optic tectum|age:7 dpf type:all cells|genotype:wild type|treatment:no treatment|geo loc name:missing|collection date:missing", "optic tectum batch 12  scRNAseq  additional sequencing", "Demultiplexing  barcoded processing  gene counting and aggregation were made using the Cell Ranger software v7.1.0 Assembly: GRCz11 genome assembly Ensembl release 98 Supplementary files format and content: Tab separated values files and matrix files", "optic tectum", null, "The optic tectum and torus longitudinalis of 6 dpf and 7 dpf larvae were carefully dissected under a stereoscope. Cell dissociation was performed using the Papain Dissociation System Worthington Biochemical Corporation. libraries according to the manufacturer\u2019s instructions Chromium Single Cell 3\u2032 Reagent Kit v3  10x Genomics. single cell 3\u2032 barcoded cDNA", null, "tissue:optic tectum|age:7 dpf type:all cells|genotype:wild type|treatment:no treatment", "GSM8536814", "GSM8536814: optic tectum batch 12  scRNAseq  additional sequencing; Danio rerio; RNA Seq", "GSM8536814 r1", "GSM8536814", "1", "The optic tectum and torus longitudinalis of 6 dpf and 7 dpf larvae were carefully dissected under a stereoscope. Cell dissociation was performed using the Papain Dissociation System Worthington Biochemical Corporation. libraries according to the manufacturer's instructions Chromium Single Cell 3\u2032 Reagent Kit v3  10x Genomics. single cell 3\u2032 barcoded cDNA", null, "RNA-Seq", "TRANSCRIPTOMIC SINGLE CELL", "cDNA", "PAIRED", "ILLUMINA", "Illumina NovaSeq 6000", null, "SRP512205", null, "loader:fastq load.py", "dr_7_dpf_tectum12_S1_b_L001_I1_001.fastq.gz dr_7_dpf_tectum12_S1_b_L001_I2_001.fastq.gz dr_7_dpf_tectum12_S1_b_L001_R1_001.fastq.gz dr_7_dpf_tectum12_S1_b_L001_R2_001.fastq.gz", "fastq fastq fastq fastq", 8950711602.0, 64860229.0, "GSM8536814 r1", "0:10 1:10 2:28 3:90", "A:1849918393;C:1118618358;G:1252858411;T:1615952915;N:72533", 10, 10, 28, 90, 1849918393, 1118618358, 1252858411, 1615952915, 72533, "SRX26194054", "SRS22736395", "SRA1978976", "Max Planck Institute for Biological Intelligence", "Max Planck Institute for Biological Intelligence", null, null, null, null, null, null, null, null, null, null, null, "B", null, "usable mapping rate", "illumina", "novaseq_era", "unknown", "cdna_unspecified", "unknown", "sc", "single_cell_droplet", "10x", null, "Germany", "2024-09-25", "Larval", "Larval", "Brain", "Nervous System"], [32554, "SRR30792564", "SRX26194054", "SRS22736395", "SRP512205", "PRJNA1120771", "Transcriptomic neuron types vary topographically in function and morphology", "GSE269232", "Transcriptome Analysis", "We transcriptionally profiled the neuronal types of the zebrafish larvae optic tectum and matched them with their functional and morphological properties. 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