{"database": "metadata", "table": "run_metadata", "is_view": false, "human_description_en": "where experiment.library_selection = \"other\" and technology = \"10x\"", "rows": [[28725, "SRR26623262", "SRX22323921", "SRS19374450", "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, "Lineage tracing rep1  cirbpb scars", "GSM7875190", null, "source name:adult brain|tissue:adult brain|cell type:mixed tissue dissociation|genotype:Tg[ubi:zebrabow M]|geo loc name:missing|collection date:missing", "Lineage tracing rep1  cirbpb scars", "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|cell type:mixed tissue dissociation|genotype:Tg[ubi:zebrabow M]", "GSM7875190", "GSM7875190: Lineage tracing rep1  cirbpb scars; Danio rerio; OTHER", "GSM7875190 r1", "GSM7875190", "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, "OTHER", "TRANSCRIPTOMIC", "other", "PAIRED", "ILLUMINA", "NextSeq 500", null, "SRP469552", null, "loader:fastq load.py", "lin1_cirbpb_scar_R1.fastq.gz lin1_cirbpb_scar_R2.fastq.gz", "fastq fastq", 157576336.0, 949552.0, "GSM7875190 r1", null, null, null, null, null, null, null, null, null, null, null, "SRX22323921", "SRS19374450", "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, 0.00141, 0.78069, 0.00053, 0.00877, 0.99857, 0.97822, 0.33536, 0.05199, 28, 120, "T", "B", "sc-like readlen", "illumina", "nextseq", "unknown", "other", "unknown", "sc", "single_cell_droplet", "10x", null, "Germany", "2023-10-31", "Adult", "Adult", "Brain", "Nervous System"], [28726, "SRR26623263", "SRX22323920", "SRS19374449", "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, "Lineage tracing rep1  cfl1 scars", "GSM7875189", null, "source name:adult brain|tissue:adult brain|cell type:mixed tissue dissociation|genotype:Tg[ubi:zebrabow M]|geo loc name:missing|collection date:missing", "Lineage tracing rep1  cfl1 scars", "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|cell type:mixed tissue dissociation|genotype:Tg[ubi:zebrabow M]", "GSM7875189", "GSM7875189: Lineage tracing rep1  cfl1 scars; Danio rerio; OTHER", "GSM7875189 r1", "GSM7875189", "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, "OTHER", "TRANSCRIPTOMIC", "other", "PAIRED", "ILLUMINA", "NextSeq 500", null, "SRP469552", null, "loader:fastq load.py", "lin1_cfl1_scar_R2.fastq.gz lin1_cfl1_scar_R1.fastq.gz", "fastq fastq", 381453696.0, 2184402.0, "GSM7875189 r1", null, null, null, null, null, null, null, null, null, null, null, "SRX22323920", "SRS19374449", "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, 0.0002, 0.91626, 0.00014, 0.00132, 0.99987, 0.99726, 0.16666, 0.56363, 28, 120, "T", "B", "sc-like readlen", "illumina", "nextseq", "unknown", "other", "unknown", "sc", "single_cell_droplet", "10x", null, "Germany", "2023-10-31", "Adult", "Adult", "Brain", "Nervous System"], [28727, "SRR26623264", "SRX22323919", "SRS19374448", "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, "Lineage tracing rep1  actb2 scars", "GSM7875188", null, "source name:adult brain|tissue:adult brain|cell type:mixed tissue dissociation|genotype:Tg[ubi:zebrabow M]|geo loc name:missing|collection date:missing", "Lineage tracing rep1  actb2 scars", "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|cell type:mixed tissue dissociation|genotype:Tg[ubi:zebrabow M]", "GSM7875188", "GSM7875188: Lineage tracing rep1  actb2 scars; Danio rerio; OTHER", "GSM7875188 r1", "GSM7875188", "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, "OTHER", "TRANSCRIPTOMIC", "other", "PAIRED", "ILLUMINA", "NextSeq 500", null, "SRP469552", null, "loader:fastq load.py", "lin1_actb2_scar_R1.fastq.gz lin1_actb2_scar_R2.fastq.gz", "fastq fastq", 491682118.0, 2851156.0, "GSM7875188 r1", null, null, null, null, null, null, null, null, null, null, null, "SRX22323919", "SRS19374448", "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, 0.00051, 0.41901, 0.00032, 0.0029, 0.99945, 0.99182, 0.57575, 0.007, 28, 120, "T", "B", "sc-like readlen", "illumina", "nextseq", "unknown", "other", "unknown", "sc", "single_cell_droplet", "10x", null, "Germany", "2023-10-31", "Adult", "Adult", "Brain", "Nervous System"], [28728, "SRR26623265", "SRX22323918", "SRS19374446", "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, "Lineage tracing rep1  actb1 scars", "GSM7875187", null, "source name:adult brain|tissue:adult brain|cell type:mixed tissue dissociation|genotype:Tg[ubi:zebrabow M]|geo loc name:missing|collection date:missing", "Lineage tracing rep1  actb1 scars", "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|cell type:mixed tissue dissociation|genotype:Tg[ubi:zebrabow M]", "GSM7875187", "GSM7875187: Lineage tracing rep1  actb1 scars; Danio rerio; OTHER", "GSM7875187 r1", "GSM7875187", "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, "OTHER", "TRANSCRIPTOMIC", "other", "PAIRED", "ILLUMINA", "NextSeq 500", null, "SRP469552", null, "loader:fastq load.py", "lin1_actb1_scar_R1.fastq.gz lin1_actb1_scar_R2.fastq.gz", "fastq fastq", 390405832.0, 2283319.0, "GSM7875187 r1", null, null, null, null, null, null, null, null, null, null, null, "SRX22323918", "SRS19374446", "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, 0.00038, 0.73548, 0.00017, 0.00057, 0.99963, 0.99571, 0.29729, 0.00243, 28, 120, "T", "B", "sc-like readlen", "illumina", "nextseq", "unknown", "other", "unknown", "sc", "single_cell_droplet", "10x", null, "Germany", "2023-10-31", "Adult", "Adult", "Brain", "Nervous System"], [28729, "SRR26623266", "SRX22323917", "SRS19374447", "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 23   telencephalon  Notch inhibition  scSLAMseq", "GSM7875186", null, "source name:adult brain|tissue:adult brain|tissue region:telencephalon|cell type:mixed tissue dissociation|genotype:wildtype|treatment:Notch inhibition DAPT|geo loc name:missing|collection date:missing", "Brain 23   telencephalon  Notch inhibition  scSLAMseq", "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. Library strategy: scSLAM seq", "adult brain", "Notch inhibiton: zebrafish were incubated in a water bath containing system water with 50 \u00b5M DAPT Gamma Secretase Inhibitor  Sigma Aldrich for 48 hours.", "The samples were prepared according to a scSLAM seq protocol  adapted from Neuschulz et al 2023. Briefly: in order to label nascent transcripts  200 mM 4sU was delivered to fish brains by intraventricular injection 6 hours prior to sample collection. The brains were then collected  and a single cell suspension was prepared by papain dissociation. The resulting cell suspension was fixed in 80% methanol and a conversion of 4sU using iodoactamide adding 111 \u00b5l 100 mM IAA to 800 \u00b5l fixed sample was done overnight. The following day  the reaction was quanched with a quenching buffer containing 100 mM DTT  post which the sample was washed with a wash buffer and filtered though a 35 \u00b5m filter. The sample was then loaded on a 10X Chromium Controller and processed according to the standard scRNA seq protocol. 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|treatment:Notch inhibition DAPT", "GSM7875186", "GSM7875186: Brain 23   telencephalon  Notch inhibition  scSLAMseq; Danio rerio; OTHER", "GSM7875186 r1", "GSM7875186", "1", "The samples were prepared according to a scSLAM seq protocol  adapted from Neuschulz et al 2023. Briefly: in order to label nascent transcripts  200 mM 4sU was delivered to fish brains by intraventricular injection 6 hours prior to sample collection. The brains were then collected  and a single cell suspension was prepared by papain dissociation. The resulting cell suspension was fixed in 80% methanol and a conversion of 4sU using iodoactamide adding 111 \u00b5l 100 mM IAA to 800 \u00b5l fixed sample was done overnight. The following day  the reaction was quanched with a quenching buffer containing 100 mM DTT  post which the sample was washed with a wash buffer and filtered though a 35 \u00b5m filter. The sample was then loaded on a 10X Chromium Controller and processed according to the standard scRNA seq protocol. 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, "OTHER", "TRANSCRIPTOMIC", "other", "PAIRED", "ILLUMINA", "Illumina NovaSeq 6000", null, "SRP469552", null, "loader:fastq load.py", "b23_ni_tre_R1.fastq.gz b23_ni_tre_R2.fastq.gz", "fastq fastq", 62392458570.0, 271271559.0, "GSM7875186 r1", "0:28 1:202", "A:18682905645;C:13783082970;G:14751373116;T:15161083446;N:14013393", 28, 202, null, null, 18682905645, 13783082970, 14751373116, 15161083446, 14013393, "SRX22323917", "SRS19374447", "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, 0.01404, 0.82351, 0.00466, 0.1026, 0.99129, 0.86774, 0.36033, 0.67345, 28, 202, "T", "B", "sc-like readlen", "illumina", "novaseq_era", "unknown", "other", "unknown", "sc", "single_cell_droplet", "10x", null, "Germany", "2023-10-31", "Adult", "Adult", "Brain", "Nervous System"], [28730, "SRR26623267", "SRX22323916", "SRS19374445", "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 22   telencephalon  control  scSLAMseq", "GSM7875185", null, "source name:adult brain|tissue:adult brain|tissue region:telencephalon|cell type:mixed tissue dissociation|genotype:wildtype|treatment:Control DMSO|geo loc name:missing|collection date:missing", "Brain 22   telencephalon  control  scSLAMseq", "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. Library strategy: scSLAM seq", "adult brain", "Control for Notch inhibition: zebrafish were incubated in a water bath containing system water with 1:200 diluted DMSO for 48 hours.", "The samples were prepared according to a scSLAM seq protocol  adapted from Neuschulz et al 2023. Briefly: in order to label nascent transcripts  200 mM 4sU was delivered to fish brains by intraventricular injection 6 hours prior to sample collection. The brains were then collected  and a single cell suspension was prepared by papain dissociation. The resulting cell suspension was fixed in 80% methanol and a conversion of 4sU using iodoactamide adding 111 \u00b5l 100 mM IAA to 800 \u00b5l fixed sample was done overnight. The following day  the reaction was quanched with a quenching buffer containing 100 mM DTT  post which the sample was washed with a wash buffer and filtered though a 35 \u00b5m filter. The sample was then loaded on a 10X Chromium Controller and processed according to the standard scRNA seq protocol. 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|treatment:Control DMSO", "GSM7875185", "GSM7875185: Brain 22   telencephalon  control  scSLAMseq; Danio rerio; OTHER", "GSM7875185 r1", "GSM7875185", "1", "The samples were prepared according to a scSLAM seq protocol  adapted from Neuschulz et al 2023. Briefly: in order to label nascent transcripts  200 mM 4sU was delivered to fish brains by intraventricular injection 6 hours prior to sample collection. The brains were then collected  and a single cell suspension was prepared by papain dissociation. The resulting cell suspension was fixed in 80% methanol and a conversion of 4sU using iodoactamide adding 111 \u00b5l 100 mM IAA to 800 \u00b5l fixed sample was done overnight. The following day  the reaction was quanched with a quenching buffer containing 100 mM DTT  post which the sample was washed with a wash buffer and filtered though a 35 \u00b5m filter. The sample was then loaded on a 10X Chromium Controller and processed according to the standard scRNA seq protocol. 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, "OTHER", "TRANSCRIPTOMIC", "other", "PAIRED", "ILLUMINA", "Illumina NovaSeq 6000", null, "SRP469552", null, "loader:fastq load.py", "b22_ni_con_R1.fastq.gz b22_ni_con_R2.fastq.gz", "fastq fastq", 59793114490.0, 259970063.0, "GSM7875185 r1", "0:28 1:202", "A:18600519530;C:12714987967;G:14147370465;T:14316863915;N:13372613", 28, 202, null, null, 18600519530, 12714987967, 14147370465, 14316863915, 13372613, "SRX22323916", "SRS19374445", "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, 0.01402, 0.79448, 0.00498, 0.11405, 0.99074, 0.8518, 0.39874, 0.67082, 28, 202, "T", "B", "sc-like readlen", "illumina", "novaseq_era", "unknown", "other", "unknown", "sc", "single_cell_droplet", "10x", null, "Germany", "2023-10-31", "Adult", "Adult", "Brain", "Nervous System"], [28754, "SRR26623291", "SRX22323897", "SRS19374426", "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, "Lineage tracing rep2  ube2e1 scars", "GSM7875196", null, "source name:adult brain|tissue:adult brain|cell type:mixed tissue dissociation|genotype:Tg[ubi:zebrabow M]|geo loc name:missing|collection date:missing", "Lineage tracing rep2  ube2e1 scars", "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|cell type:mixed tissue dissociation|genotype:Tg[ubi:zebrabow M]", "GSM7875196", "GSM7875196: Lineage tracing rep2  ube2e1 scars; Danio rerio; OTHER", "GSM7875196 r1", "GSM7875196", "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, "OTHER", "TRANSCRIPTOMIC", "other", "PAIRED", "ILLUMINA", "Illumina NovaSeq 6000", null, "SRP469552", null, "loader:fastq load.py", "lin2_ube2e1_scar_R1.fastq.gz lin2_ube2e1_scar_R2.fastq.gz", "fastq fastq", 213092161.0, 1190459.0, "GSM7875196 r1", null, null, null, null, null, null, null, null, null, null, null, "SRX22323897", "SRS19374426", "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, 0.00752, 0.90776, 0.00286, 0.01739, 0.99346, 0.95444, 0.46002, 0.96817, 28, 151, "T", "B", "sc-like readlen", "illumina", "novaseq_era", "unknown", "other", "unknown", "sc", "single_cell_droplet", "10x", null, "Germany", "2023-10-31", "Adult", "Adult", "Brain", "Nervous System"], [28755, "SRR26623292", "SRX22323896", "SRS19374425", "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, "Lineage tracing rep2  rpl39 scars", "GSM7875195", null, "source name:adult brain|tissue:adult brain|cell type:mixed tissue dissociation|genotype:Tg[ubi:zebrabow M]|geo loc name:missing|collection date:missing", "Lineage tracing rep2  rpl39 scars", "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|cell type:mixed tissue dissociation|genotype:Tg[ubi:zebrabow M]", "GSM7875195", "GSM7875195: Lineage tracing rep2  rpl39 scars; Danio rerio; OTHER", "GSM7875195 r1", "GSM7875195", "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, "OTHER", "TRANSCRIPTOMIC", "other", "PAIRED", "ILLUMINA", "Illumina NovaSeq 6000", null, "SRP469552", null, "loader:fastq load.py", "lin2_rpl39_scar_R2.fastq.gz lin2_rpl39_scar_R1.fastq.gz", "fastq fastq", 1023403502.0, 5717338.0, "GSM7875195 r1", null, null, null, null, null, null, null, null, null, null, null, "SRX22323896", "SRS19374425", "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, 0.00086, 0.83744, 0.0003, 0.00337, 0.99845, 0.97057, 0.26605, 0.40241, 28, 151, "T", "B", "sc-like readlen", "illumina", "novaseq_era", "unknown", "other", "unknown", "sc", "single_cell_droplet", "10x", null, "Germany", "2023-10-31", "Adult", "Adult", "Brain", "Nervous System"], [28756, "SRR26623293", "SRX22323895", "SRS19374424", "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, "Lineage tracing rep2  cirbpb scars", "GSM7875194", null, "source name:adult brain|tissue:adult brain|cell type:mixed tissue dissociation|genotype:Tg[ubi:zebrabow M]|geo loc name:missing|collection date:missing", "Lineage tracing rep2  cirbpb scars", "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|cell type:mixed tissue dissociation|genotype:Tg[ubi:zebrabow M]", "GSM7875194", "GSM7875194: Lineage tracing rep2  cirbpb scars; Danio rerio; OTHER", "GSM7875194 r1", "GSM7875194", "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, "OTHER", "TRANSCRIPTOMIC", "other", "PAIRED", "ILLUMINA", "Illumina NovaSeq 6000", null, "SRP469552", null, "loader:fastq load.py", "lin2_cirbpb_scar_R1.fastq.gz lin2_cirbpb_scar_R2.fastq.gz", "fastq fastq", 276120388.0, 1542572.0, "GSM7875194 r1", null, null, null, null, null, null, null, null, null, null, null, "SRX22323895", "SRS19374424", "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, 0.00206, 0.80328, 0.00078, 0.00662, 0.9975, 0.98039, 0.55421, 0.0258, 28, 151, "T", "B", "sc-like readlen", "illumina", "novaseq_era", "unknown", "other", "unknown", "sc", "single_cell_droplet", "10x", null, "Germany", "2023-10-31", "Adult", "Adult", "Brain", "Nervous System"], [28757, "SRR26623294", "SRX22323894", "SRS19374423", "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, "Lineage tracing rep2  cfl1 scars", "GSM7875193", null, "source name:adult brain|tissue:adult brain|cell type:mixed tissue dissociation|genotype:Tg[ubi:zebrabow M]|geo loc name:missing|collection date:missing", "Lineage tracing rep2  cfl1 scars", "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|cell type:mixed tissue dissociation|genotype:Tg[ubi:zebrabow M]", "GSM7875193", "GSM7875193: Lineage tracing rep2  cfl1 scars; Danio rerio; OTHER", "GSM7875193 r1", "GSM7875193", "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, "OTHER", "TRANSCRIPTOMIC", "other", "PAIRED", "ILLUMINA", "Illumina NovaSeq 6000", null, "SRP469552", null, "loader:fastq load.py", "lin2_cfl1_scar_R1.fastq.gz lin2_cfl1_scar_R2.fastq.gz", "fastq fastq", 791318009.0, 4420771.0, "GSM7875193 r1", null, null, null, null, null, null, null, null, null, null, null, "SRX22323894", "SRS19374423", "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, 0.00067, 0.46079, 0.00055, 0.16353, 0.99965, 0.99332, 0.52631, 0.44939, 28, 151, "T", "B", "sc-like readlen", "illumina", "novaseq_era", "unknown", "other", "unknown", "sc", "single_cell_droplet", "10x", null, "Germany", "2023-10-31", "Adult", "Adult", "Brain", "Nervous System"], [28758, "SRR26623295", "SRX22323893", "SRS19374421", "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, "Lineage tracing rep1  ube2e1 scars", "GSM7875192", null, "source name:adult brain|tissue:adult brain|cell type:mixed tissue dissociation|genotype:Tg[ubi:zebrabow M]|geo loc name:missing|collection date:missing", "Lineage tracing rep1  ube2e1 scars", "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|cell type:mixed tissue dissociation|genotype:Tg[ubi:zebrabow M]", "GSM7875192", "GSM7875192: Lineage tracing rep1  ube2e1 scars; Danio rerio; OTHER", "GSM7875192 r1", "GSM7875192", "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, "OTHER", "TRANSCRIPTOMIC", "other", "PAIRED", "ILLUMINA", "NextSeq 500", null, "SRP469552", null, "loader:fastq load.py", "lin1_ube2e1_scar_R1.fastq.gz lin1_ube2e1_scar_R2.fastq.gz", "fastq fastq", 99407618.0, 601781.0, "GSM7875192 r1", null, null, null, null, null, null, null, null, null, null, null, "SRX22323893", "SRS19374421", "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, 0.00657, 0.84532, 0.00205, 0.03049, 0.99537, 0.95286, 0.43306, 0.19203, 28, 150, "T", "B", "sc-like readlen", "illumina", "nextseq", "unknown", "other", "unknown", "sc", "single_cell_droplet", "10x", null, "Germany", "2023-10-31", "Adult", "Adult", "Brain", "Nervous System"], [28759, "SRR26623296", "SRX22323892", "SRS19374422", "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, "Lineage tracing rep1  rpl39 scars", "GSM7875191", null, "source name:adult brain|tissue:adult brain|cell type:mixed tissue dissociation|genotype:Tg[ubi:zebrabow M]|geo loc name:missing|collection date:missing", "Lineage tracing rep1  rpl39 scars", "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|cell type:mixed tissue dissociation|genotype:Tg[ubi:zebrabow M]", "GSM7875191", "GSM7875191: Lineage tracing rep1  rpl39 scars; Danio rerio; OTHER", "GSM7875191 r1", "GSM7875191", "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, "OTHER", "TRANSCRIPTOMIC", "other", "PAIRED", "ILLUMINA", "NextSeq 500", null, "SRP469552", null, "loader:fastq load.py", "lin1_rpl39_scar_R1.fastq.gz lin1_rpl39_scar_R2.fastq.gz", "fastq fastq", 401898718.0, 2276836.0, "GSM7875191 r1", null, null, null, null, null, null, null, null, null, null, null, "SRX22323892", "SRS19374422", "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, 0.00037, 0.2642, 0.00015, 0.00086, 0.99922, 0.99026, 0.23076, 0.42455, 28, 120, "T", "B", "sc-like readlen", "illumina", "nextseq", "unknown", "other", "unknown", "sc", "single_cell_droplet", "10x", null, "Germany", "2023-10-31", "Adult", "Adult", "Brain", "Nervous System"], [43983, "SRR6811832", "SRX3768872", "SRS3023386", "SRP121343", "PRJNA415636", "Simultaneous lineage tracing and cell type identification using CRISPR/Cas9 induced genetic scars", "GSE106121", "Other", "A key goal of developmental biology is to understand how a single cell transforms into a full grown organism consisting of many different cell types. Single cell RNA sequencing scRNA seq has become a widely used method due to its ability to identify all cell types in a tissue or organ in a systematic manner. However  a major challenge is to organize the resulting taxonomy of cell types into lineage trees revealing the developmental origin of cells. Here  we present a strategy for simultaneous lineage tracing and transcriptome profiling in thousands of single cells. By combining scRNA seq with computational analysis of lineage barcodes generated by genome editing of transgenic reporter genes  we reconstruct developmental lineage trees in zebrafish larvae and adult fish. In future analyses  LINNAEUS LINeage tracing by Nuclease Activated Editing of Ubiquitous Sequences can be used as a systematic approach for identifying the lineage origin of novel cell types  or of known cell types under different conditions. Overall design: Combining scRNA seq with computational analysis of lineage barcodes generated by genome editing of transgenic reporter genes.", null, "pubmed:29644996", null, "Larva F1 2 scar", "GSM3032175", null, "source name:Full organism|strain/background:Zebrabow M|tissue:Whole body|developmental stage:Larva", "Larva F1 2 scar", "Library strategy: Targeted amplification Scar reads have the same structure as transcript reads: they consist of a barcode  a UMI and a scar. The scar sequences were aligned using bwa mem6 to a reference of RFP. We defined a cell as a barcode with at least 500 reads. We removed reads that were unmapped  had an incorrect barcode  or did not start with the exact PCR primer we used. We truncated all scar sequences to 75 nucleotides and filtered out shorter sequences. To correct for sequencing errors  we implemented several rounds of scar filtering Supplementary Fig. 2 in publication. We started by counting the number of times each molecule was sequenced. Sequencing errors will typically have fewer reads than the actual scars they originate from. As a first filtering step  we therefore removed all molecules only seen once to reduce the complexity in the dataset for consecutive filtering steps. In the second filtering step  we aimed to remove easily recognizable sequencing errors. To this end  we consecutively considered scar sequences that have the same cellular barcode and UMI  UMIs that have the same cellular barcode and scar sequence  and cellular barcodes that have the same UMI and scar sequence. In each step  we kept only the molecule with the highest number of reads. The rationale behind this is that it is very improbable to have two valid scar sequences in the same cell with the same UMI  or to have a scar sequence with the same UMI appear in two different cells. The observation of two different UMIs for the same scar in the same cell is much more likely and corresponds to detection of multiple transcripts from the same locus  but information about scar expression levels was not required in our downstream analysis. In the third filtering step  we specifically targeted sequencing errors within each cell. We compared the scar sequences found within a cell to each other. We filtered out sequences that had a Hamming distance of 2 or less to another scar sequence in the same cell that occurred in at least eight times as many reads. Scar sequences in the same cell that were one Hamming distance apart but had a read ratio less than eight were tested on three criteria if both of them occurred at least twice in the scar library: Do both scars have more than one transcript?  Do both scars occur in cells independently from each other?  Do the UMIs of both scars have Hamming distance of two or more? If two of these criteria were true  the scars were kept and the sequences were placed on a list of validated scars that  if they occurred in the same cell in another library  did not have to be tested anymore. If one or zero criteria were true  the scar that had only one transcript  or the scar that did not occur independently  were filtered out. Genome build: N/A Supplementary files format and content: List of scar transcripts with CIGAR  cell name  cell barcode and UMI sequence.", "Full organism", null, "Single cell dissociation. 10X Genomics Chromium", null, "strain/background:Zebrabow M|tissue:Whole body|developmental stage:Larva", "GSM3032175", "GSM3032175: Larva F1 2 scar; Danio rerio; OTHER", "GSM3032175", null, "1", "Single cell dissociation. 10X Genomics Chromium", "GEO Accession:GSM3032175", "OTHER", "TRANSCRIPTOMIC", "other", "PAIRED", "ILLUMINA", "Illumina HiSeq 2500", null, "SRP121343", null, null, "F1_2_scar_R1.fastq.gz F1_2_scar_R2.fastq.gz", "fastq fastq", 9301928572.0, 75015553.0, "GSM3032175 r1", "0:26 1:98", "A:2682085389;C:3008788535;G:2093169318;T:1516220703;N:1664627", 26, 98, null, null, 2682085389, 3008788535, 2093169318, 1516220703, 1664627, "SRX3768872", "SRS3023386", "SRA623333", "GEO", "Max Delbr\u00fcck Center", 2, 0.0001, 0.00173, 4e-05, 8e-05, 0.99983, 0.99667, 0.33333, 0.531, 26, 98, "T", "T", "mates < 9% mapping rate", "illumina", "hiseq_era", "unknown", "other", "unknown", "sc", "single_cell_droplet", "10x", null, "Germany", "2018-03-06", "Larval", "Larval", "Trunk", "Surface Structure"], [43984, "SRR6811831", "SRX3768871", "SRS3023388", "SRP121343", "PRJNA415636", "Simultaneous lineage tracing and cell type identification using CRISPR/Cas9 induced genetic scars", "GSE106121", "Other", "A key goal of developmental biology is to understand how a single cell transforms into a full grown organism consisting of many different cell types. Single cell RNA sequencing scRNA seq has become a widely used method due to its ability to identify all cell types in a tissue or organ in a systematic manner. However  a major challenge is to organize the resulting taxonomy of cell types into lineage trees revealing the developmental origin of cells. Here  we present a strategy for simultaneous lineage tracing and transcriptome profiling in thousands of single cells. By combining scRNA seq with computational analysis of lineage barcodes generated by genome editing of transgenic reporter genes  we reconstruct developmental lineage trees in zebrafish larvae and adult fish. In future analyses  LINNAEUS LINeage tracing by Nuclease Activated Editing of Ubiquitous Sequences can be used as a systematic approach for identifying the lineage origin of novel cell types  or of known cell types under different conditions. Overall design: Combining scRNA seq with computational analysis of lineage barcodes generated by genome editing of transgenic reporter genes.", null, "pubmed:29644996", null, "Larva F1 1 scar", "GSM3032174", null, "source name:Full organism|strain/background:Zebrabow M|tissue:Whole body|developmental stage:Larva", "Larva F1 1 scar", "Library strategy: Targeted amplification Scar reads have the same structure as transcript reads: they consist of a barcode  a UMI and a scar. The scar sequences were aligned using bwa mem6 to a reference of RFP. We defined a cell as a barcode with at least 500 reads. We removed reads that were unmapped  had an incorrect barcode  or did not start with the exact PCR primer we used. We truncated all scar sequences to 75 nucleotides and filtered out shorter sequences. To correct for sequencing errors  we implemented several rounds of scar filtering Supplementary Fig. 2 in publication. We started by counting the number of times each molecule was sequenced. Sequencing errors will typically have fewer reads than the actual scars they originate from. As a first filtering step  we therefore removed all molecules only seen once to reduce the complexity in the dataset for consecutive filtering steps. In the second filtering step  we aimed to remove easily recognizable sequencing errors. To this end  we consecutively considered scar sequences that have the same cellular barcode and UMI  UMIs that have the same cellular barcode and scar sequence  and cellular barcodes that have the same UMI and scar sequence. In each step  we kept only the molecule with the highest number of reads. The rationale behind this is that it is very improbable to have two valid scar sequences in the same cell with the same UMI  or to have a scar sequence with the same UMI appear in two different cells. The observation of two different UMIs for the same scar in the same cell is much more likely and corresponds to detection of multiple transcripts from the same locus  but information about scar expression levels was not required in our downstream analysis. In the third filtering step  we specifically targeted sequencing errors within each cell. We compared the scar sequences found within a cell to each other. We filtered out sequences that had a Hamming distance of 2 or less to another scar sequence in the same cell that occurred in at least eight times as many reads. Scar sequences in the same cell that were one Hamming distance apart but had a read ratio less than eight were tested on three criteria if both of them occurred at least twice in the scar library: Do both scars have more than one transcript?  Do both scars occur in cells independently from each other?  Do the UMIs of both scars have Hamming distance of two or more? If two of these criteria were true  the scars were kept and the sequences were placed on a list of validated scars that  if they occurred in the same cell in another library  did not have to be tested anymore. If one or zero criteria were true  the scar that had only one transcript  or the scar that did not occur independently  were filtered out. Genome build: N/A Supplementary files format and content: List of scar transcripts with CIGAR  cell name  cell barcode and UMI sequence.", "Full organism", null, "Single cell dissociation. 10X Genomics Chromium", null, "strain/background:Zebrabow M|tissue:Whole body|developmental stage:Larva", "GSM3032174", "GSM3032174: Larva F1 1 scar; Danio rerio; OTHER", "GSM3032174", null, "1", "Single cell dissociation. 10X Genomics Chromium", "GEO Accession:GSM3032174", "OTHER", "TRANSCRIPTOMIC", "other", "PAIRED", "ILLUMINA", "Illumina HiSeq 2500", null, "SRP121343", null, null, "F1_1_scar_R1.fastq.gz F1_1_scar_R2.fastq.gz", "fastq fastq", 7772713576.0, 62683174.0, "GSM3032174 r1", "0:26 1:98", "A:2222016153;C:2555237987;G:1722216404;T:1271855931;N:1387101", 26, 98, null, null, 2222016153, 2555237987, 1722216404, 1271855931, 1387101, "SRX3768871", "SRS3023388", "SRA623333", "GEO", "Max Delbr\u00fcck Center", 2, 0.00014, 0.00171, 8e-05, 0.00012, 0.99985, 0.99642, 0.44444, 0.45454, 26, 98, "T", "T", "mates < 9% mapping rate", "illumina", "hiseq_era", "unknown", "other", "unknown", "sc", "single_cell_droplet", "10x", null, "Germany", "2018-03-06", "Larval", "Larval", "Trunk", "Surface Structure"], [43987, "SRR6811828", "SRX3768868", "SRS3023414", "SRP121343", "PRJNA415636", "Simultaneous lineage tracing and cell type identification using CRISPR/Cas9 induced genetic scars", "GSE106121", "Other", "A key goal of developmental biology is to understand how a single cell transforms into a full grown organism consisting of many different cell types. Single cell RNA sequencing scRNA seq has become a widely used method due to its ability to identify all cell types in a tissue or organ in a systematic manner. However  a major challenge is to organize the resulting taxonomy of cell types into lineage trees revealing the developmental origin of cells. Here  we present a strategy for simultaneous lineage tracing and transcriptome profiling in thousands of single cells. By combining scRNA seq with computational analysis of lineage barcodes generated by genome editing of transgenic reporter genes  we reconstruct developmental lineage trees in zebrafish larvae and adult fish. In future analyses  LINNAEUS LINeage tracing by Nuclease Activated Editing of Ubiquitous Sequences can be used as a systematic approach for identifying the lineage origin of novel cell types  or of known cell types under different conditions. Overall design: Combining scRNA seq with computational analysis of lineage barcodes generated by genome editing of transgenic reporter genes.", null, "pubmed:29644996", null, "Pancreas 3 exo scar", "GSM3032171", null, "source name:Pancreas except primary islet  liver|strain/background:Zebrabow M|tissue:Pancreas except primary islet  liver|developmental stage:Adult", "Pancreas 3 exo scar", "Library strategy: Targeted amplification Scar reads have the same structure as transcript reads: they consist of a barcode  a UMI and a scar. The scar sequences were aligned using bwa mem6 to a reference of RFP. We defined a cell as a barcode with at least 500 reads. We removed reads that were unmapped  had an incorrect barcode  or did not start with the exact PCR primer we used. We truncated all scar sequences to 75 nucleotides and filtered out shorter sequences. To correct for sequencing errors  we implemented several rounds of scar filtering Supplementary Fig. 2 in publication. We started by counting the number of times each molecule was sequenced. Sequencing errors will typically have fewer reads than the actual scars they originate from. As a first filtering step  we therefore removed all molecules only seen once to reduce the complexity in the dataset for consecutive filtering steps. In the second filtering step  we aimed to remove easily recognizable sequencing errors. To this end  we consecutively considered scar sequences that have the same cellular barcode and UMI  UMIs that have the same cellular barcode and scar sequence  and cellular barcodes that have the same UMI and scar sequence. In each step  we kept only the molecule with the highest number of reads. The rationale behind this is that it is very improbable to have two valid scar sequences in the same cell with the same UMI  or to have a scar sequence with the same UMI appear in two different cells. The observation of two different UMIs for the same scar in the same cell is much more likely and corresponds to detection of multiple transcripts from the same locus  but information about scar expression levels was not required in our downstream analysis. In the third filtering step  we specifically targeted sequencing errors within each cell. We compared the scar sequences found within a cell to each other. We filtered out sequences that had a Hamming distance of 2 or less to another scar sequence in the same cell that occurred in at least eight times as many reads. Scar sequences in the same cell that were one Hamming distance apart but had a read ratio less than eight were tested on three criteria if both of them occurred at least twice in the scar library: Do both scars have more than one transcript?  Do both scars occur in cells independently from each other?  Do the UMIs of both scars have Hamming distance of two or more? If two of these criteria were true  the scars were kept and the sequences were placed on a list of validated scars that  if they occurred in the same cell in another library  did not have to be tested anymore. If one or zero criteria were true  the scar that had only one transcript  or the scar that did not occur independently  were filtered out. Genome build: N/A Supplementary files format and content: List of scar transcripts with cell barcode  scar name  cell type  scar probability and number of organisms that have this cell.", "Pancreas except primary islet  liver", null, "Single cell dissociation. 10X Genomics Chromium", null, "strain/background:Zebrabow M|tissue:Pancreas except primary islet  liver|developmental stage:Adult", "GSM3032171", "GSM3032171: Pancreas 3 exo scar; Danio rerio; OTHER", "GSM3032171", null, "1", "Single cell dissociation. 10X Genomics Chromium", "GEO Accession:GSM3032171", "OTHER", "TRANSCRIPTOMIC", "other", "PAIRED", "ILLUMINA", "NextSeq 500", null, "SRP121343", null, null, "P7exo_scar_R1.fastq.gz P7exo_scar_R2.fastq.gz", "fastq fastq", 4291410352.0, 34608148.0, "GSM3032171 r1", "0:26 1:98", "A:1266869162;C:1345773385;G:950601988;T:726078376;N:2087441", 26, 98, null, null, 1266869162, 1345773385, 950601988, 726078376, 2087441, "SRX3768868", "SRS3023414", "SRA623333", "GEO", "Max Delbr\u00fcck Center", 2, 0.00014, 0.00241, 0.00012, 0.00024, 0.99995, 0.99691, 0.0, 0.65306, 26, 98, "T", "T", "mates < 9% mapping rate", "illumina", "nextseq", "unknown", "other", "unknown", "sc", "single_cell_droplet", "10x", null, "Germany", "2018-03-06", "Adult", "Adult", "Multi-tissue", "Multi-system"], [43988, "SRR6811827", "SRX3768867", "SRS3023384", "SRP121343", "PRJNA415636", "Simultaneous lineage tracing and cell type identification using CRISPR/Cas9 induced genetic scars", "GSE106121", "Other", "A key goal of developmental biology is to understand how a single cell transforms into a full grown organism consisting of many different cell types. Single cell RNA sequencing scRNA seq has become a widely used method due to its ability to identify all cell types in a tissue or organ in a systematic manner. However  a major challenge is to organize the resulting taxonomy of cell types into lineage trees revealing the developmental origin of cells. Here  we present a strategy for simultaneous lineage tracing and transcriptome profiling in thousands of single cells. By combining scRNA seq with computational analysis of lineage barcodes generated by genome editing of transgenic reporter genes  we reconstruct developmental lineage trees in zebrafish larvae and adult fish. In future analyses  LINNAEUS LINeage tracing by Nuclease Activated Editing of Ubiquitous Sequences can be used as a systematic approach for identifying the lineage origin of novel cell types  or of known cell types under different conditions. Overall design: Combining scRNA seq with computational analysis of lineage barcodes generated by genome editing of transgenic reporter genes.", null, "pubmed:29644996", null, "Pancreas 3 endo scar", "GSM3032170", null, "source name:Primary pancreatic islet|strain/background:Zebrabow M|tissue:Primary pancreatic islet|developmental stage:Adult", "Pancreas 3 endo scar", "Library strategy: Targeted amplification Scar reads have the same structure as transcript reads: they consist of a barcode  a UMI and a scar. The scar sequences were aligned using bwa mem6 to a reference of RFP. We defined a cell as a barcode with at least 500 reads. We removed reads that were unmapped  had an incorrect barcode  or did not start with the exact PCR primer we used. We truncated all scar sequences to 75 nucleotides and filtered out shorter sequences. To correct for sequencing errors  we implemented several rounds of scar filtering Supplementary Fig. 2 in publication. We started by counting the number of times each molecule was sequenced. Sequencing errors will typically have fewer reads than the actual scars they originate from. As a first filtering step  we therefore removed all molecules only seen once to reduce the complexity in the dataset for consecutive filtering steps. In the second filtering step  we aimed to remove easily recognizable sequencing errors. To this end  we consecutively considered scar sequences that have the same cellular barcode and UMI  UMIs that have the same cellular barcode and scar sequence  and cellular barcodes that have the same UMI and scar sequence. In each step  we kept only the molecule with the highest number of reads. The rationale behind this is that it is very improbable to have two valid scar sequences in the same cell with the same UMI  or to have a scar sequence with the same UMI appear in two different cells. The observation of two different UMIs for the same scar in the same cell is much more likely and corresponds to detection of multiple transcripts from the same locus  but information about scar expression levels was not required in our downstream analysis. In the third filtering step  we specifically targeted sequencing errors within each cell. We compared the scar sequences found within a cell to each other. We filtered out sequences that had a Hamming distance of 2 or less to another scar sequence in the same cell that occurred in at least eight times as many reads. Scar sequences in the same cell that were one Hamming distance apart but had a read ratio less than eight were tested on three criteria if both of them occurred at least twice in the scar library: Do both scars have more than one transcript?  Do both scars occur in cells independently from each other?  Do the UMIs of both scars have Hamming distance of two or more? If two of these criteria were true  the scars were kept and the sequences were placed on a list of validated scars that  if they occurred in the same cell in another library  did not have to be tested anymore. If one or zero criteria were true  the scar that had only one transcript  or the scar that did not occur independently  were filtered out. Genome build: N/A Supplementary files format and content: List of scar transcripts with cell barcode  scar name  cell type  scar probability and number of organisms that have this cell.", "Primary pancreatic islet", null, "Single cell dissociation. 10X Genomics Chromium", null, "strain/background:Zebrabow M|tissue:Primary pancreatic islet|developmental stage:Adult", "GSM3032170", "GSM3032170: Pancreas 3 endo scar; Danio rerio; OTHER", "GSM3032170", null, "1", "Single cell dissociation. 10X Genomics Chromium", "GEO Accession:GSM3032170", "OTHER", "TRANSCRIPTOMIC", "other", "PAIRED", "ILLUMINA", "NextSeq 500", null, "SRP121343", null, null, "P7endo_scar_R1.fastq.gz P7endo_scar_R2.fastq.gz", "fastq fastq", 2537781396.0, 20465979.0, "GSM3032170 r1", "0:26 1:98", "A:741745261;C:801599713;G:561531527;T:431666561;N:1238334", 26, 98, null, null, 741745261, 801599713, 561531527, 431666561, 1238334, "SRX3768867", "SRS3023384", "SRA623333", "GEO", "Max Delbr\u00fcck Center", 2, 0.00014, 0.00289, 0.00012, 0.00028, 0.99995, 0.99602, 1.0, 0.68478, 26, 98, "T", "T", "mates < 9% mapping rate", "illumina", "nextseq", "unknown", "other", "unknown", "sc", "single_cell_droplet", "10x", null, "Germany", "2018-03-06", "Adult", "Adult", "Pancreas", "Endocrine System"], [43989, "SRR6811826", "SRX3768866", "SRS3023382", "SRP121343", "PRJNA415636", "Simultaneous lineage tracing and cell type identification using CRISPR/Cas9 induced genetic scars", "GSE106121", "Other", "A key goal of developmental biology is to understand how a single cell transforms into a full grown organism consisting of many different cell types. Single cell RNA sequencing scRNA seq has become a widely used method due to its ability to identify all cell types in a tissue or organ in a systematic manner. However  a major challenge is to organize the resulting taxonomy of cell types into lineage trees revealing the developmental origin of cells. Here  we present a strategy for simultaneous lineage tracing and transcriptome profiling in thousands of single cells. By combining scRNA seq with computational analysis of lineage barcodes generated by genome editing of transgenic reporter genes  we reconstruct developmental lineage trees in zebrafish larvae and adult fish. In future analyses  LINNAEUS LINeage tracing by Nuclease Activated Editing of Ubiquitous Sequences can be used as a systematic approach for identifying the lineage origin of novel cell types  or of known cell types under different conditions. Overall design: Combining scRNA seq with computational analysis of lineage barcodes generated by genome editing of transgenic reporter genes.", null, "pubmed:29644996", null, "Heart 3 scar", "GSM3032169", null, "source name:Heart and blood|strain/background:Zebrabow M|tissue:Heart and blood|developmental stage:Adult", "Heart 3 scar", "Library strategy: Targeted amplification Scar reads have the same structure as transcript reads: they consist of a barcode  a UMI and a scar. The scar sequences were aligned using bwa mem6 to a reference of RFP. We defined a cell as a barcode with at least 500 reads. We removed reads that were unmapped  had an incorrect barcode  or did not start with the exact PCR primer we used. We truncated all scar sequences to 75 nucleotides and filtered out shorter sequences. To correct for sequencing errors  we implemented several rounds of scar filtering Supplementary Fig. 2 in publication. We started by counting the number of times each molecule was sequenced. Sequencing errors will typically have fewer reads than the actual scars they originate from. As a first filtering step  we therefore removed all molecules only seen once to reduce the complexity in the dataset for consecutive filtering steps. In the second filtering step  we aimed to remove easily recognizable sequencing errors. To this end  we consecutively considered scar sequences that have the same cellular barcode and UMI  UMIs that have the same cellular barcode and scar sequence  and cellular barcodes that have the same UMI and scar sequence. In each step  we kept only the molecule with the highest number of reads. The rationale behind this is that it is very improbable to have two valid scar sequences in the same cell with the same UMI  or to have a scar sequence with the same UMI appear in two different cells. The observation of two different UMIs for the same scar in the same cell is much more likely and corresponds to detection of multiple transcripts from the same locus  but information about scar expression levels was not required in our downstream analysis. In the third filtering step  we specifically targeted sequencing errors within each cell. We compared the scar sequences found within a cell to each other. We filtered out sequences that had a Hamming distance of 2 or less to another scar sequence in the same cell that occurred in at least eight times as many reads. Scar sequences in the same cell that were one Hamming distance apart but had a read ratio less than eight were tested on three criteria if both of them occurred at least twice in the scar library: Do both scars have more than one transcript?  Do both scars occur in cells independently from each other?  Do the UMIs of both scars have Hamming distance of two or more? If two of these criteria were true  the scars were kept and the sequences were placed on a list of validated scars that  if they occurred in the same cell in another library  did not have to be tested anymore. If one or zero criteria were true  the scar that had only one transcript  or the scar that did not occur independently  were filtered out. Genome build: N/A Supplementary files format and content: List of scar transcripts with cell barcode  scar name  cell type  scar probability and number of organisms that have this cell.", "Heart and blood", null, "Single cell dissociation. 10X Genomics Chromium", null, "strain/background:Zebrabow M|tissue:Heart and blood|developmental stage:Adult", "GSM3032169", "GSM3032169: Heart 3 scar; Danio rerio; OTHER", "GSM3032169", null, "1", "Single cell dissociation. 10X Genomics Chromium", "GEO Accession:GSM3032169", "OTHER", "TRANSCRIPTOMIC", "other", "PAIRED", "ILLUMINA", "NextSeq 500", null, "SRP121343", null, null, "H7_scar_R1.fastq.gz H7_scar_R2.fastq.gz", "fastq fastq", 3999397296.0, 32253204.0, "GSM3032169 r1", "0:26 1:98", "A:1170970784;C:1255843391;G:881154893;T:689473188;N:1955040", 26, 98, null, null, 1170970784, 1255843391, 881154893, 689473188, 1955040, "SRX3768866", "SRS3023382", "SRA623333", "GEO", "Max Delbr\u00fcck Center", 2, 0.00018, 0.00228, 0.00015, 0.00019, 0.99991, 0.99659, 0.8, 0.67235, 26, 98, "T", "T", "mates < 9% mapping rate", "illumina", "nextseq", "unknown", "other", "unknown", "sc", "single_cell_droplet", "10x", null, "Germany", "2018-03-06", "Adult", "Adult", "Multi-tissue", "Multi-system"], [43990, "SRR6811825", "SRX3768865", "SRS3023383", "SRP121343", "PRJNA415636", "Simultaneous lineage tracing and cell type identification using CRISPR/Cas9 induced genetic scars", "GSE106121", "Other", "A key goal of developmental biology is to understand how a single cell transforms into a full grown organism consisting of many different cell types. Single cell RNA sequencing scRNA seq has become a widely used method due to its ability to identify all cell types in a tissue or organ in a systematic manner. However  a major challenge is to organize the resulting taxonomy of cell types into lineage trees revealing the developmental origin of cells. Here  we present a strategy for simultaneous lineage tracing and transcriptome profiling in thousands of single cells. By combining scRNA seq with computational analysis of lineage barcodes generated by genome editing of transgenic reporter genes  we reconstruct developmental lineage trees in zebrafish larvae and adult fish. In future analyses  LINNAEUS LINeage tracing by Nuclease Activated Editing of Ubiquitous Sequences can be used as a systematic approach for identifying the lineage origin of novel cell types  or of known cell types under different conditions. Overall design: Combining scRNA seq with computational analysis of lineage barcodes generated by genome editing of transgenic reporter genes.", null, "pubmed:29644996", null, "Brain 3 scar", "GSM3032168", null, "source name:Brain|strain/background:Zebrabow M|tissue:Brain|developmental stage:Adult", "Brain 3 scar", "Library strategy: Targeted amplification Scar reads have the same structure as transcript reads: they consist of a barcode  a UMI and a scar. The scar sequences were aligned using bwa mem6 to a reference of RFP. We defined a cell as a barcode with at least 500 reads. We removed reads that were unmapped  had an incorrect barcode  or did not start with the exact PCR primer we used. We truncated all scar sequences to 75 nucleotides and filtered out shorter sequences. To correct for sequencing errors  we implemented several rounds of scar filtering Supplementary Fig. 2 in publication. We started by counting the number of times each molecule was sequenced. Sequencing errors will typically have fewer reads than the actual scars they originate from. As a first filtering step  we therefore removed all molecules only seen once to reduce the complexity in the dataset for consecutive filtering steps. In the second filtering step  we aimed to remove easily recognizable sequencing errors. To this end  we consecutively considered scar sequences that have the same cellular barcode and UMI  UMIs that have the same cellular barcode and scar sequence  and cellular barcodes that have the same UMI and scar sequence. In each step  we kept only the molecule with the highest number of reads. The rationale behind this is that it is very improbable to have two valid scar sequences in the same cell with the same UMI  or to have a scar sequence with the same UMI appear in two different cells. The observation of two different UMIs for the same scar in the same cell is much more likely and corresponds to detection of multiple transcripts from the same locus  but information about scar expression levels was not required in our downstream analysis. In the third filtering step  we specifically targeted sequencing errors within each cell. We compared the scar sequences found within a cell to each other. We filtered out sequences that had a Hamming distance of 2 or less to another scar sequence in the same cell that occurred in at least eight times as many reads. Scar sequences in the same cell that were one Hamming distance apart but had a read ratio less than eight were tested on three criteria if both of them occurred at least twice in the scar library: Do both scars have more than one transcript?  Do both scars occur in cells independently from each other?  Do the UMIs of both scars have Hamming distance of two or more? If two of these criteria were true  the scars were kept and the sequences were placed on a list of validated scars that  if they occurred in the same cell in another library  did not have to be tested anymore. If one or zero criteria were true  the scar that had only one transcript  or the scar that did not occur independently  were filtered out. Genome build: N/A Supplementary files format and content: List of scar transcripts with cell barcode  scar name  cell type  scar probability and number of organisms that have this cell.", "Brain", null, "Single cell dissociation. 10X Genomics Chromium", null, "strain/background:Zebrabow M|tissue:Brain|developmental stage:Adult", "GSM3032168", "GSM3032168: Brain 3 scar; Danio rerio; OTHER", "GSM3032168", null, "1", "Single cell dissociation. 10X Genomics Chromium", "GEO Accession:GSM3032168", "OTHER", "TRANSCRIPTOMIC", "other", "PAIRED", "ILLUMINA", "NextSeq 500", null, "SRP121343", null, null, "B7_scar_R2.fastq.gz B7_scar_R1.fastq.gz", "fastq fastq", 1359725968.0, 10965532.0, "GSM3032168 r1", "0:26 1:98", "A:397239266;C:429529094;G:288126336;T:244165622;N:665650", 26, 98, null, null, 397239266, 429529094, 288126336, 244165622, 665650, "SRX3768865", "SRS3023383", "SRA623333", "GEO", "Max Delbr\u00fcck Center", 2, 0.00032, 0.00295, 0.00026, 0.0003, 0.99991, 0.99642, 0.3, 0.64327, 26, 98, "T", "T", "mates < 9% mapping rate", "illumina", "nextseq", "unknown", "other", "unknown", "sc", "single_cell_droplet", "10x", null, "Germany", "2018-03-06", "Adult", "Adult", "Brain", "Nervous System"], [43991, "SRR6811824", "SRX3768864", "SRS3023381", "SRP121343", "PRJNA415636", "Simultaneous lineage tracing and cell type identification using CRISPR/Cas9 induced genetic scars", "GSE106121", "Other", "A key goal of developmental biology is to understand how a single cell transforms into a full grown organism consisting of many different cell types. Single cell RNA sequencing scRNA seq has become a widely used method due to its ability to identify all cell types in a tissue or organ in a systematic manner. However  a major challenge is to organize the resulting taxonomy of cell types into lineage trees revealing the developmental origin of cells. Here  we present a strategy for simultaneous lineage tracing and transcriptome profiling in thousands of single cells. By combining scRNA seq with computational analysis of lineage barcodes generated by genome editing of transgenic reporter genes  we reconstruct developmental lineage trees in zebrafish larvae and adult fish. In future analyses  LINNAEUS LINeage tracing by Nuclease Activated Editing of Ubiquitous Sequences can be used as a systematic approach for identifying the lineage origin of novel cell types  or of known cell types under different conditions. Overall design: Combining scRNA seq with computational analysis of lineage barcodes generated by genome editing of transgenic reporter genes.", null, "pubmed:29644996", null, "Larva 5 scar", "GSM3032167", null, "source name:Full organism|strain/background:Zebrabow M|tissue:Whole body|developmental stage:Larva", "Larva 5 scar", "Library strategy: Targeted amplification Scar reads have the same structure as transcript reads: they consist of a barcode  a UMI and a scar. The scar sequences were aligned using bwa mem6 to a reference of RFP. We defined a cell as a barcode with at least 500 reads. We removed reads that were unmapped  had an incorrect barcode  or did not start with the exact PCR primer we used. We truncated all scar sequences to 75 nucleotides and filtered out shorter sequences. To correct for sequencing errors  we implemented several rounds of scar filtering Supplementary Fig. 2 in publication. We started by counting the number of times each molecule was sequenced. Sequencing errors will typically have fewer reads than the actual scars they originate from. As a first filtering step  we therefore removed all molecules only seen once to reduce the complexity in the dataset for consecutive filtering steps. In the second filtering step  we aimed to remove easily recognizable sequencing errors. To this end  we consecutively considered scar sequences that have the same cellular barcode and UMI  UMIs that have the same cellular barcode and scar sequence  and cellular barcodes that have the same UMI and scar sequence. In each step  we kept only the molecule with the highest number of reads. The rationale behind this is that it is very improbable to have two valid scar sequences in the same cell with the same UMI  or to have a scar sequence with the same UMI appear in two different cells. The observation of two different UMIs for the same scar in the same cell is much more likely and corresponds to detection of multiple transcripts from the same locus  but information about scar expression levels was not required in our downstream analysis. In the third filtering step  we specifically targeted sequencing errors within each cell. We compared the scar sequences found within a cell to each other. We filtered out sequences that had a Hamming distance of 2 or less to another scar sequence in the same cell that occurred in at least eight times as many reads. Scar sequences in the same cell that were one Hamming distance apart but had a read ratio less than eight were tested on three criteria if both of them occurred at least twice in the scar library: Do both scars have more than one transcript?  Do both scars occur in cells independently from each other?  Do the UMIs of both scars have Hamming distance of two or more? If two of these criteria were true  the scars were kept and the sequences were placed on a list of validated scars that  if they occurred in the same cell in another library  did not have to be tested anymore. If one or zero criteria were true  the scar that had only one transcript  or the scar that did not occur independently  were filtered out. Genome build: N/A Supplementary files format and content: List of scar transcripts with cell barcode  scar name  cell type  scar probability and number of organisms that have this cell.", "Full organism", null, "Single cell dissociation. 10X Genomics Chromium", null, "strain/background:Zebrabow M|tissue:Whole body|developmental stage:Larva", "GSM3032167", "GSM3032167: Larva 5 scar; Danio rerio; OTHER", "GSM3032167", null, "1", "Single cell dissociation. 10X Genomics Chromium", "GEO Accession:GSM3032167", "OTHER", "TRANSCRIPTOMIC", "other", "PAIRED", "ILLUMINA", "Illumina HiSeq 2500", null, "SRP121343", null, null, "Z5_scar_R1.fastq.gz Z5_scar_R2.fastq.gz", "fastq fastq", 7672645700.0, 61876175.0, "GSM3032167 r1", "0:26 1:98", "A:2173948352;C:2439410929;G:1714624642;T:1343296377;N:1365400", 26, 98, null, null, 2173948352, 2439410929, 1714624642, 1343296377, 1365400, "SRX3768864", "SRS3023381", "SRA623333", "GEO", "Max Delbr\u00fcck Center", 2, 0.00013, 0.00164, 9e-05, 8e-05, 0.99989, 0.99669, 0.83333, 0.4918, 26, 98, "T", "T", "mates < 9% mapping rate", "illumina", "hiseq_era", "unknown", "other", "unknown", "sc", "single_cell_droplet", "10x", null, "Germany", "2018-03-06", "Larval", "Larval", "Trunk", "Surface Structure"], [43992, "SRR6811823", "SRX3768863", "SRS3023380", "SRP121343", "PRJNA415636", "Simultaneous lineage tracing and cell type identification using CRISPR/Cas9 induced genetic scars", "GSE106121", "Other", "A key goal of developmental biology is to understand how a single cell transforms into a full grown organism consisting of many different cell types. Single cell RNA sequencing scRNA seq has become a widely used method due to its ability to identify all cell types in a tissue or organ in a systematic manner. However  a major challenge is to organize the resulting taxonomy of cell types into lineage trees revealing the developmental origin of cells. Here  we present a strategy for simultaneous lineage tracing and transcriptome profiling in thousands of single cells. By combining scRNA seq with computational analysis of lineage barcodes generated by genome editing of transgenic reporter genes  we reconstruct developmental lineage trees in zebrafish larvae and adult fish. In future analyses  LINNAEUS LINeage tracing by Nuclease Activated Editing of Ubiquitous Sequences can be used as a systematic approach for identifying the lineage origin of novel cell types  or of known cell types under different conditions. Overall design: Combining scRNA seq with computational analysis of lineage barcodes generated by genome editing of transgenic reporter genes.", null, "pubmed:29644996", null, "Larva 4 scar", "GSM3032166", null, "source name:Full organism|strain/background:Zebrabow M|tissue:Whole body|developmental stage:Larva", "Larva 4 scar", "Library strategy: Targeted amplification Scar reads have the same structure as transcript reads: they consist of a barcode  a UMI and a scar. The scar sequences were aligned using bwa mem6 to a reference of RFP. We defined a cell as a barcode with at least 500 reads. We removed reads that were unmapped  had an incorrect barcode  or did not start with the exact PCR primer we used. We truncated all scar sequences to 75 nucleotides and filtered out shorter sequences. To correct for sequencing errors  we implemented several rounds of scar filtering Supplementary Fig. 2 in publication. We started by counting the number of times each molecule was sequenced. Sequencing errors will typically have fewer reads than the actual scars they originate from. As a first filtering step  we therefore removed all molecules only seen once to reduce the complexity in the dataset for consecutive filtering steps. In the second filtering step  we aimed to remove easily recognizable sequencing errors. To this end  we consecutively considered scar sequences that have the same cellular barcode and UMI  UMIs that have the same cellular barcode and scar sequence  and cellular barcodes that have the same UMI and scar sequence. In each step  we kept only the molecule with the highest number of reads. The rationale behind this is that it is very improbable to have two valid scar sequences in the same cell with the same UMI  or to have a scar sequence with the same UMI appear in two different cells. The observation of two different UMIs for the same scar in the same cell is much more likely and corresponds to detection of multiple transcripts from the same locus  but information about scar expression levels was not required in our downstream analysis. In the third filtering step  we specifically targeted sequencing errors within each cell. We compared the scar sequences found within a cell to each other. We filtered out sequences that had a Hamming distance of 2 or less to another scar sequence in the same cell that occurred in at least eight times as many reads. Scar sequences in the same cell that were one Hamming distance apart but had a read ratio less than eight were tested on three criteria if both of them occurred at least twice in the scar library: Do both scars have more than one transcript?  Do both scars occur in cells independently from each other?  Do the UMIs of both scars have Hamming distance of two or more? If two of these criteria were true  the scars were kept and the sequences were placed on a list of validated scars that  if they occurred in the same cell in another library  did not have to be tested anymore. If one or zero criteria were true  the scar that had only one transcript  or the scar that did not occur independently  were filtered out. Genome build: N/A Supplementary files format and content: List of scar transcripts with cell barcode  scar name  cell type  scar probability and number of organisms that have this cell.", "Full organism", null, "Single cell dissociation. 10X Genomics Chromium", null, "strain/background:Zebrabow M|tissue:Whole body|developmental stage:Larva", "GSM3032166", "GSM3032166: Larva 4 scar; Danio rerio; OTHER", "GSM3032166", null, "1", "Single cell dissociation. 10X Genomics Chromium", "GEO Accession:GSM3032166", "OTHER", "TRANSCRIPTOMIC", "other", "PAIRED", "ILLUMINA", "Illumina HiSeq 2500", null, "SRP121343", null, null, "Z4_scar_R1.fastq.gz Z4_scar_R2.fastq.gz", "fastq fastq", 7236183804.0, 58356321.0, "GSM3032166 r1", "0:26 1:98", "A:2080254513;C:2296727471;G:1625115068;T:1232786300;N:1300452", 26, 98, null, null, 2080254513, 2296727471, 1625115068, 1232786300, 1300452, "SRX3768863", "SRS3023380", "SRA623333", "GEO", "Max Delbr\u00fcck Center", 2, 0.00014, 0.0016, 0.00011, 7e-05, 0.99993, 0.99634, 0.33333, 0.46694, 26, 98, "T", "T", "mates < 9% mapping rate", "illumina", "hiseq_era", "unknown", "other", "unknown", "sc", "single_cell_droplet", "10x", null, "Germany", "2018-03-06", "Larval", "Larval", "Trunk", "Surface Structure"], [44008, "SRR6211484", "SRX3320759", "SRS2626332", "SRP121343", "PRJNA415636", "Simultaneous lineage tracing and cell type identification using CRISPR/Cas9 induced genetic scars", "GSE106121", "Other", "A key goal of developmental biology is to understand how a single cell transforms into a full grown organism consisting of many different cell types. Single cell RNA sequencing scRNA seq has become a widely used method due to its ability to identify all cell types in a tissue or organ in a systematic manner. However  a major challenge is to organize the resulting taxonomy of cell types into lineage trees revealing the developmental origin of cells. Here  we present a strategy for simultaneous lineage tracing and transcriptome profiling in thousands of single cells. By combining scRNA seq with computational analysis of lineage barcodes generated by genome editing of transgenic reporter genes  we reconstruct developmental lineage trees in zebrafish larvae and adult fish. In future analyses  LINNAEUS LINeage tracing by Nuclease Activated Editing of Ubiquitous Sequences can be used as a systematic approach for identifying the lineage origin of novel cell types  or of known cell types under different conditions. Overall design: Combining scRNA seq with computational analysis of lineage barcodes generated by genome editing of transgenic reporter genes.", null, "pubmed:29644996", null, "Heart 2 scar", "GSM2830055", null, "source name:Heart and blood|strain/background:Zebrabow M|tissue:Heart and blood|developmental stage:Adult", "Heart 2 scar", "Library strategy: Targeted amplification Scar reads have the same structure as transcript reads: they consist of a barcode  a UMI and a scar. The scar sequences were aligned using bwa mem6 to a reference of RFP. We defined a cell as a barcode with at least 500 reads. We removed reads that were unmapped  had an incorrect barcode  or did not start with the exact PCR primer we used. We truncated all scar sequences to 75 nucleotides and filtered out shorter sequences. To correct for sequencing errors  we implemented several rounds of scar filtering Supplementary Fig. 2 in publication. We started by counting the number of times each molecule was sequenced. Sequencing errors will typically have fewer reads than the actual scars they originate from. As a first filtering step  we therefore removed all molecules only seen once to reduce the complexity in the dataset for consecutive filtering steps. In the second filtering step  we aimed to remove easily recognizable sequencing errors. To this end  we consecutively considered scar sequences that have the same cellular barcode and UMI  UMIs that have the same cellular barcode and scar sequence  and cellular barcodes that have the same UMI and scar sequence. In each step  we kept only the molecule with the highest number of reads. The rationale behind this is that it is very improbable to have two valid scar sequences in the same cell with the same UMI  or to have a scar sequence with the same UMI appear in two different cells. The observation of two different UMIs for the same scar in the same cell is much more likely and corresponds to detection of multiple transcripts from the same locus  but information about scar expression levels was not required in our downstream analysis. In the third filtering step  we specifically targeted sequencing errors within each cell. We compared the scar sequences found within a cell to each other. We filtered out sequences that had a Hamming distance of 2 or less to another scar sequence in the same cell that occurred in at least eight times as many reads. Scar sequences in the same cell that were one Hamming distance apart but had a read ratio less than eight were tested on three criteria if both of them occurred at least twice in the scar library: Do both scars have more than one transcript?  Do both scars occur in cells independently from each other?  Do the UMIs of both scars have Hamming distance of two or more? If two of these criteria were true  the scars were kept and the sequences were placed on a list of validated scars that  if they occurred in the same cell in another library  did not have to be tested anymore. If one or zero criteria were true  the scar that had only one transcript  or the scar that did not occur independently  were filtered out. In the fourth filtering step  we determined the distribution of reads for the scars we had kept so far. Based on this distribution  we set a cut off and filtered out the scars that did not have at least this number of reads. Finally  for each cell type  we determined the distribution of different scars seen per cell and set a maximum number of scars a cell of that type can have. We filtered out cells in which we observed more than this maximum number as possible doublets. Genome build: N/A Supplementary files format and content: List of scar transcripts with cell barcode  scar name  cell type  scar probability and number of organisms that have this cell.", "Heart and blood", null, "Single cell dissociation. 10X Genomics Chromium", null, "strain/background:Zebrabow M|tissue:Heart and blood|developmental stage:Adult", "GSM2830055", "GSM2830055: Heart 2 scar; Danio rerio; OTHER", "GSM2830055", null, "1", "Single cell dissociation. 10X Genomics Chromium", "GEO Accession:GSM2830055", "OTHER", "TRANSCRIPTOMIC", "other", "PAIRED", "ILLUMINA", "NextSeq 500", null, "SRP121343", null, null, "H6_scar_R1.fastq.gz H6_scar_R2.fastq.gz", "fastq fastq", 2079107172.0, 15065994.0, "GSM2830055 r1", "0:28 1:110", "A:580640871;C:641740953;G:501505035;T:355092832;N:127481", 28, 110, null, null, 580640871, 641740953, 501505035, 355092832, 127481, "SRX3320759", "SRS2626332", "SRA623333", "GEO", "Max Delbr\u00fcck Center", 2, 5e-05, 0.00253, 4e-05, 0.00023, 1.0, 0.99778, null, 0.58419, 28, 110, "T", "T", "mates < 9% mapping rate", "illumina", "nextseq", "unknown", "other", "unknown", "sc", "single_cell_droplet", "10x", null, "Germany", "2017-10-24", "Adult", "Adult", "Multi-tissue", "Multi-system"], [44009, "SRR6211483", "SRX3320758", "SRS2626348", "SRP121343", "PRJNA415636", "Simultaneous lineage tracing and cell type identification using CRISPR/Cas9 induced genetic scars", "GSE106121", "Other", "A key goal of developmental biology is to understand how a single cell transforms into a full grown organism consisting of many different cell types. Single cell RNA sequencing scRNA seq has become a widely used method due to its ability to identify all cell types in a tissue or organ in a systematic manner. However  a major challenge is to organize the resulting taxonomy of cell types into lineage trees revealing the developmental origin of cells. Here  we present a strategy for simultaneous lineage tracing and transcriptome profiling in thousands of single cells. By combining scRNA seq with computational analysis of lineage barcodes generated by genome editing of transgenic reporter genes  we reconstruct developmental lineage trees in zebrafish larvae and adult fish. In future analyses  LINNAEUS LINeage tracing by Nuclease Activated Editing of Ubiquitous Sequences can be used as a systematic approach for identifying the lineage origin of novel cell types  or of known cell types under different conditions. Overall design: Combining scRNA seq with computational analysis of lineage barcodes generated by genome editing of transgenic reporter genes.", null, "pubmed:29644996", null, "Pancreas 2 scar", "GSM2830054", null, "source name:Pancreas and liver|strain/background:Zebrabow M|tissue:Pancreas and liver|developmental stage:Adult", "Pancreas 2 scar", "Library strategy: Targeted amplification Scar reads have the same structure as transcript reads: they consist of a barcode  a UMI and a scar. The scar sequences were aligned using bwa mem6 to a reference of RFP. We defined a cell as a barcode with at least 500 reads. We removed reads that were unmapped  had an incorrect barcode  or did not start with the exact PCR primer we used. We truncated all scar sequences to 75 nucleotides and filtered out shorter sequences. To correct for sequencing errors  we implemented several rounds of scar filtering Supplementary Fig. 2 in publication. We started by counting the number of times each molecule was sequenced. Sequencing errors will typically have fewer reads than the actual scars they originate from. As a first filtering step  we therefore removed all molecules only seen once to reduce the complexity in the dataset for consecutive filtering steps. In the second filtering step  we aimed to remove easily recognizable sequencing errors. To this end  we consecutively considered scar sequences that have the same cellular barcode and UMI  UMIs that have the same cellular barcode and scar sequence  and cellular barcodes that have the same UMI and scar sequence. In each step  we kept only the molecule with the highest number of reads. The rationale behind this is that it is very improbable to have two valid scar sequences in the same cell with the same UMI  or to have a scar sequence with the same UMI appear in two different cells. The observation of two different UMIs for the same scar in the same cell is much more likely and corresponds to detection of multiple transcripts from the same locus  but information about scar expression levels was not required in our downstream analysis. In the third filtering step  we specifically targeted sequencing errors within each cell. We compared the scar sequences found within a cell to each other. We filtered out sequences that had a Hamming distance of 2 or less to another scar sequence in the same cell that occurred in at least eight times as many reads. Scar sequences in the same cell that were one Hamming distance apart but had a read ratio less than eight were tested on three criteria if both of them occurred at least twice in the scar library: Do both scars have more than one transcript?  Do both scars occur in cells independently from each other?  Do the UMIs of both scars have Hamming distance of two or more? If two of these criteria were true  the scars were kept and the sequences were placed on a list of validated scars that  if they occurred in the same cell in another library  did not have to be tested anymore. If one or zero criteria were true  the scar that had only one transcript  or the scar that did not occur independently  were filtered out. In the fourth filtering step  we determined the distribution of reads for the scars we had kept so far. Based on this distribution  we set a cut off and filtered out the scars that did not have at least this number of reads. Finally  for each cell type  we determined the distribution of different scars seen per cell and set a maximum number of scars a cell of that type can have. We filtered out cells in which we observed more than this maximum number as possible doublets. Genome build: N/A Supplementary files format and content: List of scar transcripts with cell barcode  scar name  cell type  scar probability and number of organisms that have this cell.", "Pancreas and liver", null, "Single cell dissociation. 10X Genomics Chromium", null, "strain/background:Zebrabow M|tissue:Pancreas and liver|developmental stage:Adult", "GSM2830054", "GSM2830054: Pancreas 2 scar; Danio rerio; OTHER", "GSM2830054", null, "1", "Single cell dissociation. 10X Genomics Chromium", "GEO Accession:GSM2830054", "OTHER", "TRANSCRIPTOMIC", "other", "PAIRED", "ILLUMINA", "NextSeq 500", null, "SRP121343", null, null, "P6_scar_R1.fastq.gz P6_scar_R2.fastq.gz", "fastq fastq", 1026715860.0, 7439970.0, "GSM2830054 r1", "0:28 1:110", "A:287816843;C:320879355;G:243598630;T:174359452;N:61580", 28, 110, null, null, 287816843, 320879355, 243598630, 174359452, 61580, "SRX3320758", "SRS2626348", "SRA623333", "GEO", "Max Delbr\u00fcck Center", 2, 0.00029, 0.00123, 0.00028, 6e-05, 1.0, 0.99914, null, 0.25714, 28, 110, "T", "T", "mates < 9% mapping rate", "illumina", "nextseq", "unknown", "other", "unknown", "sc", "single_cell_droplet", "10x", null, "Germany", "2017-10-24", "Adult", "Adult", "Multi-tissue", "Multi-system"], [44010, "SRR6211482", "SRX3320757", "SRS2626331", "SRP121343", "PRJNA415636", "Simultaneous lineage tracing and cell type identification using CRISPR/Cas9 induced genetic scars", "GSE106121", "Other", "A key goal of developmental biology is to understand how a single cell transforms into a full grown organism consisting of many different cell types. Single cell RNA sequencing scRNA seq has become a widely used method due to its ability to identify all cell types in a tissue or organ in a systematic manner. However  a major challenge is to organize the resulting taxonomy of cell types into lineage trees revealing the developmental origin of cells. Here  we present a strategy for simultaneous lineage tracing and transcriptome profiling in thousands of single cells. By combining scRNA seq with computational analysis of lineage barcodes generated by genome editing of transgenic reporter genes  we reconstruct developmental lineage trees in zebrafish larvae and adult fish. In future analyses  LINNAEUS LINeage tracing by Nuclease Activated Editing of Ubiquitous Sequences can be used as a systematic approach for identifying the lineage origin of novel cell types  or of known cell types under different conditions. Overall design: Combining scRNA seq with computational analysis of lineage barcodes generated by genome editing of transgenic reporter genes.", null, "pubmed:29644996", null, "Heart 1 scar", "GSM2830053", null, "source name:Heart and blood|strain/background:Zebrabow M|tissue:Heart and blood|developmental stage:Adult", "Heart 1 scar", "Library strategy: Targeted amplification Scar reads have the same structure as transcript reads: they consist of a barcode  a UMI and a scar. The scar sequences were aligned using bwa mem6 to a reference of RFP. We defined a cell as a barcode with at least 500 reads. We removed reads that were unmapped  had an incorrect barcode  or did not start with the exact PCR primer we used. We truncated all scar sequences to 75 nucleotides and filtered out shorter sequences. To correct for sequencing errors  we implemented several rounds of scar filtering Supplementary Fig. 2 in publication. We started by counting the number of times each molecule was sequenced. Sequencing errors will typically have fewer reads than the actual scars they originate from. As a first filtering step  we therefore removed all molecules only seen once to reduce the complexity in the dataset for consecutive filtering steps. In the second filtering step  we aimed to remove easily recognizable sequencing errors. To this end  we consecutively considered scar sequences that have the same cellular barcode and UMI  UMIs that have the same cellular barcode and scar sequence  and cellular barcodes that have the same UMI and scar sequence. In each step  we kept only the molecule with the highest number of reads. The rationale behind this is that it is very improbable to have two valid scar sequences in the same cell with the same UMI  or to have a scar sequence with the same UMI appear in two different cells. The observation of two different UMIs for the same scar in the same cell is much more likely and corresponds to detection of multiple transcripts from the same locus  but information about scar expression levels was not required in our downstream analysis. In the third filtering step  we specifically targeted sequencing errors within each cell. We compared the scar sequences found within a cell to each other. We filtered out sequences that had a Hamming distance of 2 or less to another scar sequence in the same cell that occurred in at least eight times as many reads. Scar sequences in the same cell that were one Hamming distance apart but had a read ratio less than eight were tested on three criteria if both of them occurred at least twice in the scar library: Do both scars have more than one transcript?  Do both scars occur in cells independently from each other?  Do the UMIs of both scars have Hamming distance of two or more? If two of these criteria were true  the scars were kept and the sequences were placed on a list of validated scars that  if they occurred in the same cell in another library  did not have to be tested anymore. If one or zero criteria were true  the scar that had only one transcript  or the scar that did not occur independently  were filtered out. In the fourth filtering step  we determined the distribution of reads for the scars we had kept so far. Based on this distribution  we set a cut off and filtered out the scars that did not have at least this number of reads. Finally  for each cell type  we determined the distribution of different scars seen per cell and set a maximum number of scars a cell of that type can have. We filtered out cells in which we observed more than this maximum number as possible doublets. Genome build: N/A Supplementary files format and content: List of scar transcripts with cell barcode  scar name  cell type  scar probability and number of organisms that have this cell.", "Heart and blood", null, "Single cell dissociation. 10X Genomics Chromium", null, "strain/background:Zebrabow M|tissue:Heart and blood|developmental stage:Adult", "GSM2830053", "GSM2830053: Heart 1 scar; Danio rerio; OTHER", "GSM2830053", null, "1", "Single cell dissociation. 10X Genomics Chromium", "GEO Accession:GSM2830053", "OTHER", "TRANSCRIPTOMIC", "other", "PAIRED", "ILLUMINA", "NextSeq 500", null, "SRP121343", null, null, "H5_scar_R1.fastq.gz H5_scar_R2.fastq.gz", "fastq fastq", 1287546624.0, 10218624.0, "GSM2830053 r1", "0:26 1:100", "A:365272675;C:413037677;G:296495693;T:212570153;N:170426", 26, 100, null, null, 365272675, 413037677, 296495693, 212570153, 170426, "SRX3320757", "SRS2626331", "SRA623333", "GEO", "Max Delbr\u00fcck Center", 2, 0.00023, 0.00044, 0.00022, 3e-05, 1.0, 0.99955, null, 0.32653, 26, 100, "T", "T", "mates < 9% mapping rate", "illumina", "nextseq", "unknown", "other", "unknown", "sc", "single_cell_droplet", "10x", null, "Germany", "2017-10-24", "Adult", "Adult", "Multi-tissue", "Multi-system"], [44011, "SRR6211481", "SRX3320756", "SRS2626330", "SRP121343", "PRJNA415636", "Simultaneous lineage tracing and cell type identification using CRISPR/Cas9 induced genetic scars", "GSE106121", "Other", "A key goal of developmental biology is to understand how a single cell transforms into a full grown organism consisting of many different cell types. Single cell RNA sequencing scRNA seq has become a widely used method due to its ability to identify all cell types in a tissue or organ in a systematic manner. However  a major challenge is to organize the resulting taxonomy of cell types into lineage trees revealing the developmental origin of cells. Here  we present a strategy for simultaneous lineage tracing and transcriptome profiling in thousands of single cells. By combining scRNA seq with computational analysis of lineage barcodes generated by genome editing of transgenic reporter genes  we reconstruct developmental lineage trees in zebrafish larvae and adult fish. In future analyses  LINNAEUS LINeage tracing by Nuclease Activated Editing of Ubiquitous Sequences can be used as a systematic approach for identifying the lineage origin of novel cell types  or of known cell types under different conditions. Overall design: Combining scRNA seq with computational analysis of lineage barcodes generated by genome editing of transgenic reporter genes.", null, "pubmed:29644996", null, "Brain 1 scar", "GSM2830052", null, "source name:Brain|strain/background:Zebrabow M|tissue:Brain|developmental stage:Adult", "Brain 1 scar", "Library strategy: Targeted amplification Scar reads have the same structure as transcript reads: they consist of a barcode  a UMI and a scar. The scar sequences were aligned using bwa mem6 to a reference of RFP. We defined a cell as a barcode with at least 500 reads. We removed reads that were unmapped  had an incorrect barcode  or did not start with the exact PCR primer we used. We truncated all scar sequences to 75 nucleotides and filtered out shorter sequences. To correct for sequencing errors  we implemented several rounds of scar filtering Supplementary Fig. 2 in publication. We started by counting the number of times each molecule was sequenced. Sequencing errors will typically have fewer reads than the actual scars they originate from. As a first filtering step  we therefore removed all molecules only seen once to reduce the complexity in the dataset for consecutive filtering steps. In the second filtering step  we aimed to remove easily recognizable sequencing errors. To this end  we consecutively considered scar sequences that have the same cellular barcode and UMI  UMIs that have the same cellular barcode and scar sequence  and cellular barcodes that have the same UMI and scar sequence. In each step  we kept only the molecule with the highest number of reads. The rationale behind this is that it is very improbable to have two valid scar sequences in the same cell with the same UMI  or to have a scar sequence with the same UMI appear in two different cells. The observation of two different UMIs for the same scar in the same cell is much more likely and corresponds to detection of multiple transcripts from the same locus  but information about scar expression levels was not required in our downstream analysis. In the third filtering step  we specifically targeted sequencing errors within each cell. We compared the scar sequences found within a cell to each other. We filtered out sequences that had a Hamming distance of 2 or less to another scar sequence in the same cell that occurred in at least eight times as many reads. Scar sequences in the same cell that were one Hamming distance apart but had a read ratio less than eight were tested on three criteria if both of them occurred at least twice in the scar library: Do both scars have more than one transcript?  Do both scars occur in cells independently from each other?  Do the UMIs of both scars have Hamming distance of two or more? If two of these criteria were true  the scars were kept and the sequences were placed on a list of validated scars that  if they occurred in the same cell in another library  did not have to be tested anymore. If one or zero criteria were true  the scar that had only one transcript  or the scar that did not occur independently  were filtered out. In the fourth filtering step  we determined the distribution of reads for the scars we had kept so far. Based on this distribution  we set a cut off and filtered out the scars that did not have at least this number of reads. Finally  for each cell type  we determined the distribution of different scars seen per cell and set a maximum number of scars a cell of that type can have. We filtered out cells in which we observed more than this maximum number as possible doublets. Genome build: N/A Supplementary files format and content: List of scar transcripts with cell barcode  scar name  cell type  scar probability and number of organisms that have this cell.", "Brain", null, "Single cell dissociation. 10X Genomics Chromium", null, "strain/background:Zebrabow M|tissue:Brain|developmental stage:Adult", "GSM2830052", "GSM2830052: Brain 1 scar; Danio rerio; OTHER", "GSM2830052", null, "1", "Single cell dissociation. 10X Genomics Chromium", "GEO Accession:GSM2830052", "OTHER", "TRANSCRIPTOMIC", "other", "PAIRED", "ILLUMINA", "NextSeq 500", null, "SRP121343", null, null, "B5_scar_R1.fastq.gz B5_scar_R2.fastq.gz", "fastq fastq", 438883452.0, 3483202.0, "GSM2830052 r1", "0:26 1:100", "A:125829044;C:139532010;G:100613706;T:72846911;N:61781", 26, 100, null, null, 125829044, 139532010, 100613706, 72846911, 61781, "SRX3320756", "SRS2626330", "SRA623333", "GEO", "Max Delbr\u00fcck Center", 2, 0.00072, 0.0013, 0.00069, 0.00011, 0.99991, 0.99878, 0.75, 0.36879, 26, 100, "T", "T", "mates < 9% mapping rate", "illumina", "nextseq", "unknown", "other", "unknown", "sc", "single_cell_droplet", "10x", null, "Germany", "2017-10-24", "Adult", "Adult", "Brain", "Nervous System"], [44012, "SRR6211480", "SRX3320755", "SRS2626329", "SRP121343", "PRJNA415636", "Simultaneous lineage tracing and cell type identification using CRISPR/Cas9 induced genetic scars", "GSE106121", "Other", "A key goal of developmental biology is to understand how a single cell transforms into a full grown organism consisting of many different cell types. Single cell RNA sequencing scRNA seq has become a widely used method due to its ability to identify all cell types in a tissue or organ in a systematic manner. However  a major challenge is to organize the resulting taxonomy of cell types into lineage trees revealing the developmental origin of cells. Here  we present a strategy for simultaneous lineage tracing and transcriptome profiling in thousands of single cells. By combining scRNA seq with computational analysis of lineage barcodes generated by genome editing of transgenic reporter genes  we reconstruct developmental lineage trees in zebrafish larvae and adult fish. In future analyses  LINNAEUS LINeage tracing by Nuclease Activated Editing of Ubiquitous Sequences can be used as a systematic approach for identifying the lineage origin of novel cell types  or of known cell types under different conditions. Overall design: Combining scRNA seq with computational analysis of lineage barcodes generated by genome editing of transgenic reporter genes.", null, "pubmed:29644996", null, "Pancreas 1 scar", "GSM2830051", null, "source name:Pancreas and liver|strain/background:Zebrabow M|tissue:Pancreas and liver|developmental stage:Adult", "Pancreas 1 scar", "Library strategy: Targeted amplification Scar reads have the same structure as transcript reads: they consist of a barcode  a UMI and a scar. The scar sequences were aligned using bwa mem6 to a reference of RFP. We defined a cell as a barcode with at least 500 reads. We removed reads that were unmapped  had an incorrect barcode  or did not start with the exact PCR primer we used. We truncated all scar sequences to 75 nucleotides and filtered out shorter sequences. To correct for sequencing errors  we implemented several rounds of scar filtering Supplementary Fig. 2 in publication. We started by counting the number of times each molecule was sequenced. Sequencing errors will typically have fewer reads than the actual scars they originate from. As a first filtering step  we therefore removed all molecules only seen once to reduce the complexity in the dataset for consecutive filtering steps. In the second filtering step  we aimed to remove easily recognizable sequencing errors. To this end  we consecutively considered scar sequences that have the same cellular barcode and UMI  UMIs that have the same cellular barcode and scar sequence  and cellular barcodes that have the same UMI and scar sequence. In each step  we kept only the molecule with the highest number of reads. The rationale behind this is that it is very improbable to have two valid scar sequences in the same cell with the same UMI  or to have a scar sequence with the same UMI appear in two different cells. The observation of two different UMIs for the same scar in the same cell is much more likely and corresponds to detection of multiple transcripts from the same locus  but information about scar expression levels was not required in our downstream analysis. In the third filtering step  we specifically targeted sequencing errors within each cell. We compared the scar sequences found within a cell to each other. We filtered out sequences that had a Hamming distance of 2 or less to another scar sequence in the same cell that occurred in at least eight times as many reads. Scar sequences in the same cell that were one Hamming distance apart but had a read ratio less than eight were tested on three criteria if both of them occurred at least twice in the scar library: Do both scars have more than one transcript?  Do both scars occur in cells independently from each other?  Do the UMIs of both scars have Hamming distance of two or more? If two of these criteria were true  the scars were kept and the sequences were placed on a list of validated scars that  if they occurred in the same cell in another library  did not have to be tested anymore. If one or zero criteria were true  the scar that had only one transcript  or the scar that did not occur independently  were filtered out. In the fourth filtering step  we determined the distribution of reads for the scars we had kept so far. Based on this distribution  we set a cut off and filtered out the scars that did not have at least this number of reads. Finally  for each cell type  we determined the distribution of different scars seen per cell and set a maximum number of scars a cell of that type can have. We filtered out cells in which we observed more than this maximum number as possible doublets. Genome build: N/A Supplementary files format and content: List of scar transcripts with cell barcode  scar name  cell type  scar probability and number of organisms that have this cell.", "Pancreas and liver", null, "Single cell dissociation. 10X Genomics Chromium", null, "strain/background:Zebrabow M|tissue:Pancreas and liver|developmental stage:Adult", "GSM2830051", "GSM2830051: Pancreas 1 scar; Danio rerio; OTHER", "GSM2830051", null, "1", "Single cell dissociation. 10X Genomics Chromium", "GEO Accession:GSM2830051", "OTHER", "TRANSCRIPTOMIC", "other", "PAIRED", "ILLUMINA", "NextSeq 500", null, "SRP121343", null, null, "P5_scar_R1.fastq.gz P5_scar_R2.fastq.gz", "fastq fastq", 1192602600.0, 9465100.0, "GSM2830051 r1", "0:26 1:100", "A:346841760;C:383266222;G:268916933;T:193424756;N:152929", 26, 100, null, null, 346841760, 383266222, 268916933, 193424756, 152929, "SRX3320755", "SRS2626329", "SRA623333", "GEO", "Max Delbr\u00fcck Center", 2, 0.00021, 0.00091, 0.00014, 2e-05, 0.99997, 0.99892, 0.0, 0.23931, 26, 100, "T", "T", "mates < 9% mapping rate", "illumina", "nextseq", "unknown", "other", "unknown", "sc", "single_cell_droplet", "10x", null, "Germany", "2017-10-24", "Adult", "Adult", "Multi-tissue", "Multi-system"], [44013, "SRR6211477", "SRX3320753", "SRS2626327", "SRP121343", "PRJNA415636", "Simultaneous lineage tracing and cell type identification using CRISPR/Cas9 induced genetic scars", "GSE106121", "Other", "A key goal of developmental biology is to understand how a single cell transforms into a full grown organism consisting of many different cell types. Single cell RNA sequencing scRNA seq has become a widely used method due to its ability to identify all cell types in a tissue or organ in a systematic manner. However  a major challenge is to organize the resulting taxonomy of cell types into lineage trees revealing the developmental origin of cells. Here  we present a strategy for simultaneous lineage tracing and transcriptome profiling in thousands of single cells. By combining scRNA seq with computational analysis of lineage barcodes generated by genome editing of transgenic reporter genes  we reconstruct developmental lineage trees in zebrafish larvae and adult fish. In future analyses  LINNAEUS LINeage tracing by Nuclease Activated Editing of Ubiquitous Sequences can be used as a systematic approach for identifying the lineage origin of novel cell types  or of known cell types under different conditions. Overall design: Combining scRNA seq with computational analysis of lineage barcodes generated by genome editing of transgenic reporter genes.", null, "pubmed:29644996", null, "Larva 1 scar", "GSM2830049", null, "source name:Full organism|strain/background:Zebrabow M|tissue:Whole body|developmental stage:Larva", "Larva 1 scar", "Library strategy: Targeted amplification Scar reads have the same structure as transcript reads: they consist of a barcode  a UMI and a scar. The scar sequences were aligned using bwa mem6 to a reference of RFP. We defined a cell as a barcode with at least 500 reads. We removed reads that were unmapped  had an incorrect barcode  or did not start with the exact PCR primer we used. We truncated all scar sequences to 75 nucleotides and filtered out shorter sequences. To correct for sequencing errors  we implemented several rounds of scar filtering Supplementary Fig. 2 in publication. We started by counting the number of times each molecule was sequenced. Sequencing errors will typically have fewer reads than the actual scars they originate from. As a first filtering step  we therefore removed all molecules only seen once to reduce the complexity in the dataset for consecutive filtering steps. In the second filtering step  we aimed to remove easily recognizable sequencing errors. To this end  we consecutively considered scar sequences that have the same cellular barcode and UMI  UMIs that have the same cellular barcode and scar sequence  and cellular barcodes that have the same UMI and scar sequence. In each step  we kept only the molecule with the highest number of reads. The rationale behind this is that it is very improbable to have two valid scar sequences in the same cell with the same UMI  or to have a scar sequence with the same UMI appear in two different cells. The observation of two different UMIs for the same scar in the same cell is much more likely and corresponds to detection of multiple transcripts from the same locus  but information about scar expression levels was not required in our downstream analysis. In the third filtering step  we specifically targeted sequencing errors within each cell. We compared the scar sequences found within a cell to each other. We filtered out sequences that had a Hamming distance of 2 or less to another scar sequence in the same cell that occurred in at least eight times as many reads. Scar sequences in the same cell that were one Hamming distance apart but had a read ratio less than eight were tested on three criteria if both of them occurred at least twice in the scar library: Do both scars have more than one transcript?  Do both scars occur in cells independently from each other?  Do the UMIs of both scars have Hamming distance of two or more? If two of these criteria were true  the scars were kept and the sequences were placed on a list of validated scars that  if they occurred in the same cell in another library  did not have to be tested anymore. If one or zero criteria were true  the scar that had only one transcript  or the scar that did not occur independently  were filtered out. In the fourth filtering step  we determined the distribution of reads for the scars we had kept so far. Based on this distribution  we set a cut off and filtered out the scars that did not have at least this number of reads. Finally  for each cell type  we determined the distribution of different scars seen per cell and set a maximum number of scars a cell of that type can have. We filtered out cells in which we observed more than this maximum number as possible doublets. Genome build: N/A Supplementary files format and content: List of scar transcripts with cell barcode  scar name  cell type  scar probability and number of organisms that have this cell.", "Full organism", null, "Single cell dissociation. 10X Genomics Chromium", null, "strain/background:Zebrabow M|tissue:Whole body|developmental stage:Larva", "GSM2830049", "GSM2830049: Larva 1 scar; Danio rerio; OTHER", "GSM2830049", null, "1", "Single cell dissociation. 10X Genomics Chromium", "GEO Accession:GSM2830049", "OTHER", "TRANSCRIPTOMIC", "other", "PAIRED", "ILLUMINA", "Illumina HiSeq 2500", null, "SRP121343", null, null, "Z2_1_scar_R1.fastq.gz Z2_1_scar_R2.fastq.gz Z2_1_scar_R3.fastq.gz", "fastq fastq fastq", 1358915680.0, 8493223.0, "GSM2830049 r1", "0:130 1:14 2:16", "A:286107087;C:390283378;G:268551180;T:159171790;N:5555", 130, 14, 16, null, 286107087, 390283378, 268551180, 159171790, 5555, "SRX3320753", "SRS2626327", "SRA623333", "GEO", "Max Delbr\u00fcck Center", 1, 0.00515, null, 8e-05, null, 0.99959, null, 0.24561, null, 130, null, "T", null, "under 1.2% mapping rate", "illumina", "hiseq_era", "unknown", "other", "unknown", "sc", "single_cell_droplet", "10x", null, "Germany", "2017-10-24", "Larval", "Larval", "Trunk", "Surface Structure"], [44014, "SRR6211478", "SRX3320753", "SRS2626327", "SRP121343", "PRJNA415636", "Simultaneous lineage tracing and cell type identification using CRISPR/Cas9 induced genetic scars", "GSE106121", "Other", "A key goal of developmental biology is to understand how a single cell transforms into a full grown organism consisting of many different cell types. Single cell RNA sequencing scRNA seq has become a widely used method due to its ability to identify all cell types in a tissue or organ in a systematic manner. However  a major challenge is to organize the resulting taxonomy of cell types into lineage trees revealing the developmental origin of cells. Here  we present a strategy for simultaneous lineage tracing and transcriptome profiling in thousands of single cells. By combining scRNA seq with computational analysis of lineage barcodes generated by genome editing of transgenic reporter genes  we reconstruct developmental lineage trees in zebrafish larvae and adult fish. In future analyses  LINNAEUS LINeage tracing by Nuclease Activated Editing of Ubiquitous Sequences can be used as a systematic approach for identifying the lineage origin of novel cell types  or of known cell types under different conditions. Overall design: Combining scRNA seq with computational analysis of lineage barcodes generated by genome editing of transgenic reporter genes.", null, "pubmed:29644996", null, "Larva 1 scar", "GSM2830049", null, "source name:Full organism|strain/background:Zebrabow M|tissue:Whole body|developmental stage:Larva", "Larva 1 scar", "Library strategy: Targeted amplification Scar reads have the same structure as transcript reads: they consist of a barcode  a UMI and a scar. The scar sequences were aligned using bwa mem6 to a reference of RFP. We defined a cell as a barcode with at least 500 reads. We removed reads that were unmapped  had an incorrect barcode  or did not start with the exact PCR primer we used. We truncated all scar sequences to 75 nucleotides and filtered out shorter sequences. To correct for sequencing errors  we implemented several rounds of scar filtering Supplementary Fig. 2 in publication. We started by counting the number of times each molecule was sequenced. Sequencing errors will typically have fewer reads than the actual scars they originate from. As a first filtering step  we therefore removed all molecules only seen once to reduce the complexity in the dataset for consecutive filtering steps. In the second filtering step  we aimed to remove easily recognizable sequencing errors. To this end  we consecutively considered scar sequences that have the same cellular barcode and UMI  UMIs that have the same cellular barcode and scar sequence  and cellular barcodes that have the same UMI and scar sequence. In each step  we kept only the molecule with the highest number of reads. The rationale behind this is that it is very improbable to have two valid scar sequences in the same cell with the same UMI  or to have a scar sequence with the same UMI appear in two different cells. The observation of two different UMIs for the same scar in the same cell is much more likely and corresponds to detection of multiple transcripts from the same locus  but information about scar expression levels was not required in our downstream analysis. In the third filtering step  we specifically targeted sequencing errors within each cell. We compared the scar sequences found within a cell to each other. We filtered out sequences that had a Hamming distance of 2 or less to another scar sequence in the same cell that occurred in at least eight times as many reads. Scar sequences in the same cell that were one Hamming distance apart but had a read ratio less than eight were tested on three criteria if both of them occurred at least twice in the scar library: Do both scars have more than one transcript?  Do both scars occur in cells independently from each other?  Do the UMIs of both scars have Hamming distance of two or more? If two of these criteria were true  the scars were kept and the sequences were placed on a list of validated scars that  if they occurred in the same cell in another library  did not have to be tested anymore. If one or zero criteria were true  the scar that had only one transcript  or the scar that did not occur independently  were filtered out. In the fourth filtering step  we determined the distribution of reads for the scars we had kept so far. Based on this distribution  we set a cut off and filtered out the scars that did not have at least this number of reads. Finally  for each cell type  we determined the distribution of different scars seen per cell and set a maximum number of scars a cell of that type can have. We filtered out cells in which we observed more than this maximum number as possible doublets. Genome build: N/A Supplementary files format and content: List of scar transcripts with cell barcode  scar name  cell type  scar probability and number of organisms that have this cell.", "Full organism", null, "Single cell dissociation. 10X Genomics Chromium", null, "strain/background:Zebrabow M|tissue:Whole body|developmental stage:Larva", "GSM2830049", "GSM2830049: Larva 1 scar; Danio rerio; OTHER", "GSM2830049", null, "1", "Single cell dissociation. 10X Genomics Chromium", "GEO Accession:GSM2830049", "OTHER", "TRANSCRIPTOMIC", "other", "PAIRED", "ILLUMINA", "Illumina HiSeq 2500", null, "SRP121343", null, null, "Z2_2_scar_R1.fastq.gz Z2_2_scar_R2.fastq.gz Z2_2_scar_R3.fastq.gz", "fastq fastq fastq", 1103410080.0, 6896313.0, "GSM2830049 r2", "0:130 1:14 2:16", "A:232542409;C:315925727;G:218437491;T:129610895;N:4168", 130, 14, 16, null, 232542409, 315925727, 218437491, 129610895, 4168, "SRX3320753", "SRS2626327", "SRA623333", "GEO", "Max Delbr\u00fcck Center", 1, 0.00786, null, 0.0, null, 0.99922, null, 0.31764, null, 130, null, "T", null, "under 1.2% mapping rate", "illumina", "hiseq_era", "unknown", "other", "unknown", "sc", "single_cell_droplet", "10x", null, "Germany", "2017-10-24", "Larval", "Larval", "Trunk", "Surface Structure"], [66757, "SRR23717090", "SRX19578259", "SRS16961241", "SRP342737", "PRJNA773778", "Activation of lineage competence in hemogenic endothelium precedes the formation of hematopoietic stem cell heterogeneity [Zebrafish.STRT seq]", "GSE186425", "Other", "Using a combination of single cell multi omics  lineage tracing and functional assays  we show that embryonic HSPCs are originated from heterogeneous hemogenic endothelial cells HECs during zebrafish embryogenesis. Overall design: Hematopoietic stem and progenitor cells HSPCs are considered as a heterogeneous population  but where and how HSPC heterogeneity occurs remain unclear. Here  we performed scRNA seq and scATAC seq with zebrafish 36 hpf VDA derived kdrl+runx1   kdrl+runx1+  and kdrl runx1+ cells. To determine the transcriptional signatures of spi2+ lineages in zebrafish  we performed STRT seq with spi2: Gal4;UAS:GFP+ kdrl:mCherry+ HECs and spi2: Gal4;UAS:GFP+ kdrl:mCherry  hematopoietic cells at 36 hpf. To investigate the underlying molecular mechanism upon spi2 deficiency  we performed scRNA seq with the sorted ECs kdrl+runx1   HECs kdrl+runx1+ and hematopoietic cells kdrl runx1+ from spi2 morphants at 36 hpf. To determine whether spi2 can directly modulate transcriptional programs in EC/HEC  we examined genome wide spi2 binding by cut tag assay in fli1a flag spi2 EGFP+ cells sorted from trunk region of Tg fli1a flag spi2 GFP embryos at 36 hpf.", "parent bioproject:PRJNA773771", "pubmed:37016019", null, "DP1 36hpf", "GSM7083138", null, "source name:Zebrafish trunk region|tissue:Zebrafish trunk region 36hpf|cells:single spi2: Gal4;UAS:GFP+ kdrl:mCherry+ HECs and spi2: Gal4;UAS:GFP+ kdrl:mCherry  hematopoietic cells|Stage:36 hpf loc name:missing|collection date:missing", "DP1 36hpf", "For scRNA seq  and scATAC seq based on 10x Genomics\uff0craw data files were processed by Cell Ranger software suite with default mapping parameters  using the GRCz11 reference genome. For STRT seq  raw reads were first de multiplexed by barcode sequences in reads 2 to yield separate read files for individual cells  then the transcripts sequences of each cell in reads 1 were separated based on corresponding reads 2. Simultaneously  UMI sequences in reads 2 were integrated into reads 1. The template switching oligo TSO sequence  polyA sequence and the low quality reads N > 10% in reads 1 were subsequently removed by Python scripts and Trimmomatic version 0.36. Next  the clean reads were aligned to the zebrafish genome GRCz11 from Ensembl using HISAT2 version 2.1.0 with known gene annotation. Only protein coding genes were retained and the abundance of each gene were estimated by counting the reads that duplicated UMIs have been excluded. For cut&tag  reads were aligned to GRCz11 by Bowtie2. Only uniquely mapped reads with mapping quality score\u00a0\u2265 30 were kept using Samtools software. post merging replicates  MACS2 was used for the peak calling. Assembly: GRCz11 Library strategy: STRT seq", "Zebrafish trunk region", null, "For 10x Genomics based scRNA seq and scATAC seq in zebrafish  40 000 mCherry+ GFP  cells  40 000 mCherry+ GFP+ cells and 30 000 mCherry  GFP+ cells were sorted from Tg kdrl:mCherry/runx1:enGFP at 36 hpf. For STRT seq in zebrafish  single spi2: Gal4;UAS:GFP+ kdrl:mCherry+ HECs and spi2: Gal4;UAS:GFP+ kdrl:mCherry  hematopoietic cells were sorted from the trunk region of Tg spi2: Gal4;UAS:GFP/ kdrl:mCherry at 36 hpf. For scRNA seq of spi2 morphants at 36 hpf in zebrafish  ECs kdrl+runx1   HECs kdrl+runx1+ and hematopoietic cells kdrl runx1+ were sorted. For bulk CUT&TAG in zebrafish  fli1a flag spi2 EGFP+ cells were sorted from the trunk region of Tg fli1a flag spi2 GFP embryos at 36 hpf. For 10x Genomics based scRNA seq and scATAC seq in zebrafish  we loaded 20 000 cells for further 10x Genomics based scRNA seq and 90 000 cells for further 10x Genomics based scATAC seq. For scRNA seq  libraries were prepared using Single Cell 3\u2019 Library and Gel Bead Kit V3.1. Sequencing was performed on an Illumina Novaseq6000 platform to generate 150 bp paired end reads. For ATAC seq  nuclei were isolated and washed according to the methods supplied by 10x Genomics. Libraries were prepared using the Chromium Chip E Single Cell Kit and Chromium Single Cell ATAC Library & Gel Bead Kit  and further sequenced on an Illumina Novaseq6000 platform to generate 50 bp paired end reads. For 10x Genomics based scRNA seq in mice  libraries were prepared using Single Cell 3\u2019 Library and Gel Bead Kit V3.1. Sequencing was performed on an Illumina Novaseq6000 platform to generate 150 bp paired end reads. For STRT seq in zebrafish  the end repair and dA tailing of the DNA fragments and ligation of the adaptors to the DNA fragments were performed according to the KAPA Hyper Prep Kits with PCR Library Amplification/Illumina series. post the adaptor ligation step  the final PCR was performed. The libraries were sequenced on an Illumina Novaseq6000 platform to generate 150 bp paired end reads. For bulk CUT&TAG  libraries were prepared according to Hyperactive In Situ ChIP Library Prep Kit for Illumina and sequenced on an Illumina NovaSeq6000 platform to generate 150 bp paired end reads.", null, "tissue:Zebrafish trunk region 36hpf|cells:single spi2: Gal4;UAS:GFP+ kdrl:mCherry+ HECs and spi2: Gal4;UAS:GFP+ kdrl:mCherry  hematopoietic cells|Stage:36 hpf", "GSM7083138", "GSM7083138: DP1 36hpf; Danio rerio; OTHER", "GSM7083138 r1", "GSM7083138", "1", "For 10x Genomics based scRNA seq and scATAC seq in zebrafish  40 000 mCherry+ GFP  cells  40 000 mCherry+ GFP+ cells and 30 000 mCherry  GFP+ cells were sorted from Tg kdrl:mCherry/runx1:enGFP at 36 hpf. For STRT seq in zebrafish  single spi2: Gal4;UAS:GFP+ kdrl:mCherry+ HECs and spi2: Gal4;UAS:GFP+ kdrl:mCherry  hematopoietic cells were sorted from the trunk region of Tg spi2: Gal4;UAS:GFP/ kdrl:mCherry at 36 hpf. For scRNA seq of spi2 morphants at 36 hpf in zebrafish  ECs kdrl+runx1   HECs kdrl+runx1+ and hematopoietic cells kdrl runx1+ were sorted. For bulk CUT&TAG in zebrafish  fli1a flag spi2 EGFP+ cells were sorted from the trunk region of Tg fli1a flag spi2 GFP embryos at 36 hpf. For 10x Genomics based scRNA seq and scATAC seq in zebrafish  we loaded 20 000 cells for further 10x Genomics based scRNA seq and 90 000 cells for further 10x Genomics based scATAC seq. For scRNA seq  libraries were prepared using Single Cell three prime Library and Gel Bead Kit V3.1. Sequencing was performed on an Illumina Novaseq6000 platform to generate 150 bp paired end reads. For ATAC seq  nuclei were isolated and washed according to the methods supplied by 10x Genomics. Libraries were prepared using the Chromium Chip E Single Cell Kit and Chromium Single Cell ATAC Library & Gel Bead Kit  and further sequenced on an Illumina Novaseq6000 platform to generate 50 bp paired end reads. For 10x Genomics based scRNA seq in mice  libraries were prepared using Single Cell three prime Library and Gel Bead Kit V3.1. Sequencing was performed on an Illumina Novaseq6000 platform to generate 150 bp paired end reads. For STRT seq in zebrafish  the end repair and dA tailing of the DNA fragments and ligation of the adaptors to the DNA fragments were performed according to the KAPA Hyper Prep Kits with PCR Library Amplification/Illumina series. post the adaptor ligation step  the final PCR was performed. The libraries were sequenced on an Illumina Novaseq6000 platform to generate 150 bp paired end reads. For bulk CUT&TAG  libraries were prepared according to Hyperactive In Situ ChIP Library Prep Kit for Illumina and sequenced on an Illumina NovaSeq6000 platform to generate 150 bp paired end reads.", null, "OTHER", "TRANSCRIPTOMIC", "other", "PAIRED", "ILLUMINA", "Illumina NovaSeq 6000", null, "SRP342737", null, null, "36hpf-DP1_FKDL202627688-1a_1.raw.fq.gz 36hpf-DP1_FKDL202627688-1a_2.raw.fq.gz", "fastq fastq", 36641999100.0, 122139997.0, "GSM7083138 r1", "0:150 1:150", "A:11840633171;C:5285812067;G:6638992204;T:12876249105;N:312553", 150, 150, null, null, 11840633171, 5285812067, 6638992204, 12876249105, 312553, "SRX19578259", "SRS16961241", "SRA1600575", "Group of Hematopoiesis and Cardiovascular Development, INSTITUTE OF ZOOLOGY, CHINESE ACADEMY OF SCIENCES", "Group of Hematopoiesis and Cardiovascular Development, INSTITUTE OF ZOOLOGY, CHINESE ACADEMY OF SCIENCES", 2, 0.8912, 0.01571, 0.09673, 0.00653, 0.86145, 0.99849, 0.61834, 0.72033, 150, 150, "B", "T", "mate2 technical by mapping diff", "illumina", "novaseq_era", "unknown", "poly_a", "unknown", "sc", "single_cell_droplet", "10x", null, "China", "2023-03-06", "Pharyngula", "Embryo", "Trunk", "Surface Structure"], [66758, "SRR23717091", "SRX19578258", "SRS16961240", "SRP342737", "PRJNA773778", "Activation of lineage competence in hemogenic endothelium precedes the formation of hematopoietic stem cell heterogeneity [Zebrafish.STRT seq]", "GSE186425", "Other", "Using a combination of single cell multi omics  lineage tracing and functional assays  we show that embryonic HSPCs are originated from heterogeneous hemogenic endothelial cells HECs during zebrafish embryogenesis. Overall design: Hematopoietic stem and progenitor cells HSPCs are considered as a heterogeneous population  but where and how HSPC heterogeneity occurs remain unclear. Here  we performed scRNA seq and scATAC seq with zebrafish 36 hpf VDA derived kdrl+runx1   kdrl+runx1+  and kdrl runx1+ cells. To determine the transcriptional signatures of spi2+ lineages in zebrafish  we performed STRT seq with spi2: Gal4;UAS:GFP+ kdrl:mCherry+ HECs and spi2: Gal4;UAS:GFP+ kdrl:mCherry  hematopoietic cells at 36 hpf. To investigate the underlying molecular mechanism upon spi2 deficiency  we performed scRNA seq with the sorted ECs kdrl+runx1   HECs kdrl+runx1+ and hematopoietic cells kdrl runx1+ from spi2 morphants at 36 hpf. To determine whether spi2 can directly modulate transcriptional programs in EC/HEC  we examined genome wide spi2 binding by cut tag assay in fli1a flag spi2 EGFP+ cells sorted from trunk region of Tg fli1a flag spi2 GFP embryos at 36 hpf.", "parent bioproject:PRJNA773771", "pubmed:37016019", null, "DP2 36hpf", "GSM7083139", null, "source name:Zebrafish trunk region|tissue:Zebrafish trunk region 36hpf|cells:single spi2: Gal4;UAS:GFP+ kdrl:mCherry+ HECs and spi2: Gal4;UAS:GFP+ kdrl:mCherry  hematopoietic cells|Stage:36 hpf loc name:missing|collection date:missing", "DP2 36hpf", "For scRNA seq  and scATAC seq based on 10x Genomics\uff0craw data files were processed by Cell Ranger software suite with default mapping parameters  using the GRCz11 reference genome. For STRT seq  raw reads were first de multiplexed by barcode sequences in reads 2 to yield separate read files for individual cells  then the transcripts sequences of each cell in reads 1 were separated based on corresponding reads 2. Simultaneously  UMI sequences in reads 2 were integrated into reads 1. The template switching oligo TSO sequence  polyA sequence and the low quality reads N > 10% in reads 1 were subsequently removed by Python scripts and Trimmomatic version 0.36. Next  the clean reads were aligned to the zebrafish genome GRCz11 from Ensembl using HISAT2 version 2.1.0 with known gene annotation. Only protein coding genes were retained and the abundance of each gene were estimated by counting the reads that duplicated UMIs have been excluded. For cut&tag  reads were aligned to GRCz11 by Bowtie2. Only uniquely mapped reads with mapping quality score\u00a0\u2265 30 were kept using Samtools software. post merging replicates  MACS2 was used for the peak calling. Assembly: GRCz11 Library strategy: STRT seq", "Zebrafish trunk region", null, "For 10x Genomics based scRNA seq and scATAC seq in zebrafish  40 000 mCherry+ GFP  cells  40 000 mCherry+ GFP+ cells and 30 000 mCherry  GFP+ cells were sorted from Tg kdrl:mCherry/runx1:enGFP at 36 hpf. For STRT seq in zebrafish  single spi2: Gal4;UAS:GFP+ kdrl:mCherry+ HECs and spi2: Gal4;UAS:GFP+ kdrl:mCherry  hematopoietic cells were sorted from the trunk region of Tg spi2: Gal4;UAS:GFP/ kdrl:mCherry at 36 hpf. For scRNA seq of spi2 morphants at 36 hpf in zebrafish  ECs kdrl+runx1   HECs kdrl+runx1+ and hematopoietic cells kdrl runx1+ were sorted. For bulk CUT&TAG in zebrafish  fli1a flag spi2 EGFP+ cells were sorted from the trunk region of Tg fli1a flag spi2 GFP embryos at 36 hpf. For 10x Genomics based scRNA seq and scATAC seq in zebrafish  we loaded 20 000 cells for further 10x Genomics based scRNA seq and 90 000 cells for further 10x Genomics based scATAC seq. For scRNA seq  libraries were prepared using Single Cell 3\u2019 Library and Gel Bead Kit V3.1. Sequencing was performed on an Illumina Novaseq6000 platform to generate 150 bp paired end reads. For ATAC seq  nuclei were isolated and washed according to the methods supplied by 10x Genomics. Libraries were prepared using the Chromium Chip E Single Cell Kit and Chromium Single Cell ATAC Library & Gel Bead Kit  and further sequenced on an Illumina Novaseq6000 platform to generate 50 bp paired end reads. For 10x Genomics based scRNA seq in mice  libraries were prepared using Single Cell 3\u2019 Library and Gel Bead Kit V3.1. Sequencing was performed on an Illumina Novaseq6000 platform to generate 150 bp paired end reads. For STRT seq in zebrafish  the end repair and dA tailing of the DNA fragments and ligation of the adaptors to the DNA fragments were performed according to the KAPA Hyper Prep Kits with PCR Library Amplification/Illumina series. post the adaptor ligation step  the final PCR was performed. The libraries were sequenced on an Illumina Novaseq6000 platform to generate 150 bp paired end reads. For bulk CUT&TAG  libraries were prepared according to Hyperactive In Situ ChIP Library Prep Kit for Illumina and sequenced on an Illumina NovaSeq6000 platform to generate 150 bp paired end reads.", null, "tissue:Zebrafish trunk region 36hpf|cells:single spi2: Gal4;UAS:GFP+ kdrl:mCherry+ HECs and spi2: Gal4;UAS:GFP+ kdrl:mCherry  hematopoietic cells|Stage:36 hpf", "GSM7083139", "GSM7083139: DP2 36hpf; Danio rerio; OTHER", "GSM7083139 r1", "GSM7083139", "1", "For 10x Genomics based scRNA seq and scATAC seq in zebrafish  40 000 mCherry+ GFP  cells  40 000 mCherry+ GFP+ cells and 30 000 mCherry  GFP+ cells were sorted from Tg kdrl:mCherry/runx1:enGFP at 36 hpf. For STRT seq in zebrafish  single spi2: Gal4;UAS:GFP+ kdrl:mCherry+ HECs and spi2: Gal4;UAS:GFP+ kdrl:mCherry  hematopoietic cells were sorted from the trunk region of Tg spi2: Gal4;UAS:GFP/ kdrl:mCherry at 36 hpf. For scRNA seq of spi2 morphants at 36 hpf in zebrafish  ECs kdrl+runx1   HECs kdrl+runx1+ and hematopoietic cells kdrl runx1+ were sorted. For bulk CUT&TAG in zebrafish  fli1a flag spi2 EGFP+ cells were sorted from the trunk region of Tg fli1a flag spi2 GFP embryos at 36 hpf. For 10x Genomics based scRNA seq and scATAC seq in zebrafish  we loaded 20 000 cells for further 10x Genomics based scRNA seq and 90 000 cells for further 10x Genomics based scATAC seq. For scRNA seq  libraries were prepared using Single Cell three prime Library and Gel Bead Kit V3.1. Sequencing was performed on an Illumina Novaseq6000 platform to generate 150 bp paired end reads. For ATAC seq  nuclei were isolated and washed according to the methods supplied by 10x Genomics. Libraries were prepared using the Chromium Chip E Single Cell Kit and Chromium Single Cell ATAC Library & Gel Bead Kit  and further sequenced on an Illumina Novaseq6000 platform to generate 50 bp paired end reads. For 10x Genomics based scRNA seq in mice  libraries were prepared using Single Cell three prime Library and Gel Bead Kit V3.1. Sequencing was performed on an Illumina Novaseq6000 platform to generate 150 bp paired end reads. For STRT seq in zebrafish  the end repair and dA tailing of the DNA fragments and ligation of the adaptors to the DNA fragments were performed according to the KAPA Hyper Prep Kits with PCR Library Amplification/Illumina series. post the adaptor ligation step  the final PCR was performed. The libraries were sequenced on an Illumina Novaseq6000 platform to generate 150 bp paired end reads. For bulk CUT&TAG  libraries were prepared according to Hyperactive In Situ ChIP Library Prep Kit for Illumina and sequenced on an Illumina NovaSeq6000 platform to generate 150 bp paired end reads.", null, "OTHER", "TRANSCRIPTOMIC", "other", "PAIRED", "ILLUMINA", "Illumina NovaSeq 6000", null, "SRP342737", null, null, "36hpf-DP2_FKDL202627692-1a_1.raw.fq.gz 36hpf-DP2_FKDL202627692-1a_2.raw.fq.gz", "fastq fastq", 45339599100.0, 151131997.0, "GSM7083139 r1", "0:150 1:150", "A:14092451998;C:7125629842;G:9985822921;T:14135305628;N:388711", 150, 150, null, null, 14092451998, 7125629842, 9985822921, 14135305628, 388711, "SRX19578258", "SRS16961240", "SRA1600575", "Group of Hematopoiesis and Cardiovascular Development, INSTITUTE OF ZOOLOGY, CHINESE ACADEMY OF SCIENCES", "Group of Hematopoiesis and Cardiovascular Development, INSTITUTE OF ZOOLOGY, CHINESE ACADEMY OF SCIENCES", 2, 0.79702, 0.00323, 0.08737, 0.00105, 0.88663, 0.99882, 0.62618, 0.6, 150, 150, "B", "T", "mate2 technical by mapping diff", "illumina", "novaseq_era", "unknown", "poly_a", "unknown", "sc", "single_cell_droplet", "10x", null, "China", "2023-03-06", "Pharyngula", "Embryo", "Trunk", "Surface Structure"], [66759, "SRR23717092", "SRX19578257", "SRS16961239", "SRP342737", "PRJNA773778", "Activation of lineage competence in hemogenic endothelium precedes the formation of hematopoietic stem cell heterogeneity [Zebrafish.STRT seq]", "GSE186425", "Other", "Using a combination of single cell multi omics  lineage tracing and functional assays  we show that embryonic HSPCs are originated from heterogeneous hemogenic endothelial cells HECs during zebrafish embryogenesis. Overall design: Hematopoietic stem and progenitor cells HSPCs are considered as a heterogeneous population  but where and how HSPC heterogeneity occurs remain unclear. Here  we performed scRNA seq and scATAC seq with zebrafish 36 hpf VDA derived kdrl+runx1   kdrl+runx1+  and kdrl runx1+ cells. To determine the transcriptional signatures of spi2+ lineages in zebrafish  we performed STRT seq with spi2: Gal4;UAS:GFP+ kdrl:mCherry+ HECs and spi2: Gal4;UAS:GFP+ kdrl:mCherry  hematopoietic cells at 36 hpf. To investigate the underlying molecular mechanism upon spi2 deficiency  we performed scRNA seq with the sorted ECs kdrl+runx1   HECs kdrl+runx1+ and hematopoietic cells kdrl runx1+ from spi2 morphants at 36 hpf. To determine whether spi2 can directly modulate transcriptional programs in EC/HEC  we examined genome wide spi2 binding by cut tag assay in fli1a flag spi2 EGFP+ cells sorted from trunk region of Tg fli1a flag spi2 GFP embryos at 36 hpf.", "parent bioproject:PRJNA773771", "pubmed:37016019", null, "SP 36hpf", "GSM7083140", null, "source name:Zebrafish trunk region|tissue:Zebrafish trunk region 36hpf|cells:single spi2: Gal4;UAS:GFP+ kdrl:mCherry+ HECs and spi2: Gal4;UAS:GFP+ kdrl:mCherry  hematopoietic cells|Stage:36 hpf loc name:missing|collection date:missing", "SP 36hpf", "For scRNA seq  and scATAC seq based on 10x Genomics\uff0craw data files were processed by Cell Ranger software suite with default mapping parameters  using the GRCz11 reference genome. For STRT seq  raw reads were first de multiplexed by barcode sequences in reads 2 to yield separate read files for individual cells  then the transcripts sequences of each cell in reads 1 were separated based on corresponding reads 2. Simultaneously  UMI sequences in reads 2 were integrated into reads 1. The template switching oligo TSO sequence  polyA sequence and the low quality reads N > 10% in reads 1 were subsequently removed by Python scripts and Trimmomatic version 0.36. Next  the clean reads were aligned to the zebrafish genome GRCz11 from Ensembl using HISAT2 version 2.1.0 with known gene annotation. Only protein coding genes were retained and the abundance of each gene were estimated by counting the reads that duplicated UMIs have been excluded. For cut&tag  reads were aligned to GRCz11 by Bowtie2. Only uniquely mapped reads with mapping quality score\u00a0\u2265 30 were kept using Samtools software. post merging replicates  MACS2 was used for the peak calling. Assembly: GRCz11 Library strategy: STRT seq", "Zebrafish trunk region", null, "For 10x Genomics based scRNA seq and scATAC seq in zebrafish  40 000 mCherry+ GFP  cells  40 000 mCherry+ GFP+ cells and 30 000 mCherry  GFP+ cells were sorted from Tg kdrl:mCherry/runx1:enGFP at 36 hpf. For STRT seq in zebrafish  single spi2: Gal4;UAS:GFP+ kdrl:mCherry+ HECs and spi2: Gal4;UAS:GFP+ kdrl:mCherry  hematopoietic cells were sorted from the trunk region of Tg spi2: Gal4;UAS:GFP/ kdrl:mCherry at 36 hpf. For scRNA seq of spi2 morphants at 36 hpf in zebrafish  ECs kdrl+runx1   HECs kdrl+runx1+ and hematopoietic cells kdrl runx1+ were sorted. For bulk CUT&TAG in zebrafish  fli1a flag spi2 EGFP+ cells were sorted from the trunk region of Tg fli1a flag spi2 GFP embryos at 36 hpf. For 10x Genomics based scRNA seq and scATAC seq in zebrafish  we loaded 20 000 cells for further 10x Genomics based scRNA seq and 90 000 cells for further 10x Genomics based scATAC seq. For scRNA seq  libraries were prepared using Single Cell 3\u2019 Library and Gel Bead Kit V3.1. Sequencing was performed on an Illumina Novaseq6000 platform to generate 150 bp paired end reads. For ATAC seq  nuclei were isolated and washed according to the methods supplied by 10x Genomics. Libraries were prepared using the Chromium Chip E Single Cell Kit and Chromium Single Cell ATAC Library & Gel Bead Kit  and further sequenced on an Illumina Novaseq6000 platform to generate 50 bp paired end reads. For 10x Genomics based scRNA seq in mice  libraries were prepared using Single Cell 3\u2019 Library and Gel Bead Kit V3.1. Sequencing was performed on an Illumina Novaseq6000 platform to generate 150 bp paired end reads. For STRT seq in zebrafish  the end repair and dA tailing of the DNA fragments and ligation of the adaptors to the DNA fragments were performed according to the KAPA Hyper Prep Kits with PCR Library Amplification/Illumina series. post the adaptor ligation step  the final PCR was performed. The libraries were sequenced on an Illumina Novaseq6000 platform to generate 150 bp paired end reads. For bulk CUT&TAG  libraries were prepared according to Hyperactive In Situ ChIP Library Prep Kit for Illumina and sequenced on an Illumina NovaSeq6000 platform to generate 150 bp paired end reads.", null, "tissue:Zebrafish trunk region 36hpf|cells:single spi2: Gal4;UAS:GFP+ kdrl:mCherry+ HECs and spi2: Gal4;UAS:GFP+ kdrl:mCherry  hematopoietic cells|Stage:36 hpf", "GSM7083140", "GSM7083140: SP 36hpf; Danio rerio; OTHER", "GSM7083140 r1", "GSM7083140", "1", "For 10x Genomics based scRNA seq and scATAC seq in zebrafish  40 000 mCherry+ GFP  cells  40 000 mCherry+ GFP+ cells and 30 000 mCherry  GFP+ cells were sorted from Tg kdrl:mCherry/runx1:enGFP at 36 hpf. For STRT seq in zebrafish  single spi2: Gal4;UAS:GFP+ kdrl:mCherry+ HECs and spi2: Gal4;UAS:GFP+ kdrl:mCherry  hematopoietic cells were sorted from the trunk region of Tg spi2: Gal4;UAS:GFP/ kdrl:mCherry at 36 hpf. For scRNA seq of spi2 morphants at 36 hpf in zebrafish  ECs kdrl+runx1   HECs kdrl+runx1+ and hematopoietic cells kdrl runx1+ were sorted. For bulk CUT&TAG in zebrafish  fli1a flag spi2 EGFP+ cells were sorted from the trunk region of Tg fli1a flag spi2 GFP embryos at 36 hpf. For 10x Genomics based scRNA seq and scATAC seq in zebrafish  we loaded 20 000 cells for further 10x Genomics based scRNA seq and 90 000 cells for further 10x Genomics based scATAC seq. For scRNA seq  libraries were prepared using Single Cell three prime Library and Gel Bead Kit V3.1. Sequencing was performed on an Illumina Novaseq6000 platform to generate 150 bp paired end reads. For ATAC seq  nuclei were isolated and washed according to the methods supplied by 10x Genomics. Libraries were prepared using the Chromium Chip E Single Cell Kit and Chromium Single Cell ATAC Library & Gel Bead Kit  and further sequenced on an Illumina Novaseq6000 platform to generate 50 bp paired end reads. For 10x Genomics based scRNA seq in mice  libraries were prepared using Single Cell three prime Library and Gel Bead Kit V3.1. Sequencing was performed on an Illumina Novaseq6000 platform to generate 150 bp paired end reads. For STRT seq in zebrafish  the end repair and dA tailing of the DNA fragments and ligation of the adaptors to the DNA fragments were performed according to the KAPA Hyper Prep Kits with PCR Library Amplification/Illumina series. post the adaptor ligation step  the final PCR was performed. The libraries were sequenced on an Illumina Novaseq6000 platform to generate 150 bp paired end reads. For bulk CUT&TAG  libraries were prepared according to Hyperactive In Situ ChIP Library Prep Kit for Illumina and sequenced on an Illumina NovaSeq6000 platform to generate 150 bp paired end reads.", null, "OTHER", "TRANSCRIPTOMIC", "other", "PAIRED", "ILLUMINA", "Illumina NovaSeq 6000", null, "SRP342737", null, null, "36hpf-SP_FKDL202627693-1a_1.raw.fq.gz 36hpf-SP_FKDL202627693-1a_2.raw.fq.gz", "fastq fastq", 44757872400.0, 149192908.0, "GSM7083140 r1", "0:150 1:150", "A:13194662551;C:6817138207;G:9879151250;T:14866177703;N:742689", 150, 150, null, null, 13194662551, 6817138207, 9879151250, 14866177703, 742689, "SRX19578257", "SRS16961239", "SRA1600575", "Group of Hematopoiesis and Cardiovascular Development, INSTITUTE OF ZOOLOGY, CHINESE ACADEMY OF SCIENCES", "Group of Hematopoiesis and Cardiovascular Development, INSTITUTE OF ZOOLOGY, CHINESE ACADEMY OF SCIENCES", 2, 0.7854, 0.00221, 0.062, 0.00084, 0.90995, 0.99904, 0.43661, 0.67741, 150, 150, "B", "T", "mate2 technical by mapping diff", "illumina", "novaseq_era", "unknown", "poly_a", "unknown", "sc", "single_cell_droplet", "10x", null, "China", "2023-03-06", "Pharyngula", "Embryo", "Trunk", "Surface Structure"], [66926, "SRR16832335", "SRX13025545", "SRS10963312", "SRP344879", "PRJNA778402", "Atlas of lymphangiogenesis [scATAC Seq]", "GSE188340", "Other", "During development  lymphatic vasculature forms as a second and distinct network that derives from embryonic blood vessels. Transdifferentiation of venous endothelial cells into specified lymphatic endothelial cells LECs is the first step in this process. Transdifferentiation and specification of LEC fate requires Prox1  but how Prox1 regulates transdifferentiation and differentiation is not fully understood. We present a single cell transcriptomic atlas of lymphangiogenesis spanning four key developmental stages that reveals new markers and functional regulators of lymphatic development. We extend this to comprehensively profile single cell transcriptomic and chromatin changes controlled by Prox1 using zygotic prox1a mutants  which form lymphatics that then dedifferentiate. Combining this with single cell analysis of Prox1 null  double prox1a/prox1b maternal zygotic mutants  we reveal in depth the role of Prox1 in control of LEC fate specification and differentiation.  This resource reveals dual and progressive functions for Prox1  blocking blood vascular and hematopoietic fate while simultaneously up regulating a small number of early acting genes that include tspan18a/b and lgals3a/b which are essential for lymphangiogenesis. This embryonic developmental resource will serve as a baseline to better understand both developmental and pathological lymphangiogenesis in the future. [Citations in sample metadata correspond to reference numbers in the associated publication.] Overall design: Single cell ATAC profiles of endothelial cells from developing zebrafish at 4dpf in Zprox /  mutant and matched WT siblings. All code associated with this data and publication are publicly available under an open source software license at: https://atlassian.petermac.org.au/bitbucket/users/tyrone.chen/repos/hogan lab/browse/2022 Grimm Mason et al PROX1 NatCellBiol/", "parent bioproject:PRJNA778399", "pubmed:36912146", null, "20158: Zprox1ab 4dpf scATAC seq", "GSM5677991", null, "source name:embryo|tissue:embryo|transgenic:Tgfli1a:negfp;Tg 5.2lyve1b:dsRed|facs:nEGFP and dsRed2 double positive|Stage:4dpf|genotype:Zprox1ab mutant|platform:10X Chromium scATAC|facility:Peter Mac Molecular Genomics|cellranger atac pipeline:2.0.0|r version:4.0.5|internal id:20158|gap study version:ATAC 1.1", "20158: Zprox1ab 4dpf scATAC seq", "Where necessary fastq files were made using Cell Ranger [14] version 3.1.0 or 3.0.2 mkfastq.  Sequencing QC was assessed using FastQC 0.11.6 and MultiQC viewer for aggregated reports. Cell Ranger count and aggr were used to generate aggregated count files mapped to GRCz11 Ensembl 101  without xxx normalisation. Doublets were identified from the filtered aggregated count files using Scrublet [15] in Python version 3.6 and filtered from subsequent analyses.  For the MZprox1 /  mutant and Zprox1 /  mutant datasets filtered aggregated count files were processed  sc transform normalised  filtered and clustered louvain using Seurat version 2.0 [16] and 3.0 [17] respectively for R statistical software version 4.0.2. QC was evaluated before and post normalisation using plot functions in Seurat and scater 1.20.1 [18]  and all thresholds and settings are described in scripting.  Cluster solutions were evaluated using ClusTree [19]. Datasets used in the atlas of lymphangiogenesis were processed  filtered  merged and log normalised using Seurat version 3.0 [17]  with QC and settings as above.  Merged data was clustered and normalised using CSS simspec [20] and clustering and cluster evaluation performed on this object only  as described above.  For all scRNA seq datasets cluster phenotype was determined using the expression of key markers Supplementary Table  with the aid of CellXGene visualisation software [21].  All downstream analysis and plotting were performed using Seurat version 3.0 [17] using default settings. For velocity analysis  loom files containing RNA velocity information were first generated from the 10X data associated with each sample using velocyto.R 0.6. Relevant sample subsets were then combined with loompy 3.0.6. From pre computed Seurat UMAPs in scRNA seq processing and analysis  cell barcode and associated metadata were obtained and combined with the loom files. With the combined velocity scores and cell metadata  velocity maps were plotted with velocyto.R 0.6 and overlaid on the UMAPs. Genome build: GRCz11 Supplementary files format and content: peaks  barcodes and matrices output by cellranger for each sample", "embryo", "All injections were performed as previously described [10]. CRISPR genome editing for tspan18a/b was performed as previously described [11]  and all genotyping confirmed using PCR [12].  slc7a7aBAC:slc7a7a Citrineuom10 and fabp11aBAC:fabp11a Citrineuom10 recombineering was performed as previously described.", "Single cell suspensions were sorted by FACS and prepared for nuclei isolation as previously described by 10x Genomics Demonstrated Protocol for Single Cell ATAC Sequencing CG000169   Rev D. Cell suspensions were pelleted 300 x g for 5 minutes and rinsed with PBS + 0.04% BSA. Cells were resuspended in 95uL of freshly prepared lysis buffer 10mM Tris HCl pH 7.4  10\u2009mM NaCl  3\u2009mM MgCl2  0.1% Tween 20  0.1% NP40 Substitute  0.01% Digitonin  and 1% BSA and incubated on ice for 1 minute. 100uL of chilled wash buffer 10\u2009mM Tris HCl  pH 7.4  10\u2009mM NaCl  3\u2009mM MgCl2  0.1% Tween 20  1% BSA was used to neutralise the reaction  before the nuclei were pelleted 500 x g for 5 minutes and resuspended again in 7uL of 1x Nuclei Buffer 10X Genomics Cat# PN 2000153/2000207. Presence of healthy and intact nuclei was assessed by visual inspection on a brightfield microscope using Trypan Blue staining Thermo Fisher Cat# T10282 and Countess Cell Counting Chamber Slides Thermo Fisher Cat# C10228. Single nuclei suspensions were resuspended at approximately 5000 nuclei per \uf06dL before undergoing tagmentation for 60min at 37\uf0b0C. post tagmentation nuclei were partitioned and barcoded using the 10X Genomics Chromium Controller 10X Genomics and the Single Cell ATAC Reagent Kit V1.1; 10X Genomics; PN 1000176 . Tagmented nuclei were loaded onto the Chromium Single Cell Chip H 10X Genomics; PN 1000162  GEM generation  barcoding and library construction was performed according to the 10X Genomics Chromium User Guide. The resulting single cell ATAC libraries contained unique sample indices for each sample. The libraries were quantified on the Agilent BioAnalyzer 2100 using the High Sensitivity DNA Kit Agilent  5067 4626 and pooled in equimolar ratios.  Sequencing was performed on an Illumina NextSeq 500 using a 150 cycle High Output Kit as follows: 50bp Read1  8bp i7 index  16bp i5 index  50bp Read2 achieving a read depth of 25 000 read pairs per nucleus.", "Zebrafish work was conducted in compliance with animal ethics committees at University of Queensland and Peter MacCallum Cancer Centre. The uq52bh dut mutant was isolated in a previously described genetic screen [7]. The genetic mapping approach was performed as previously described [8  9].", "tissue:embryo|transgenic:Tgfli1a:negfp;Tg 5.2lyve1b:dsRed|facs:nEGFP and dsRed2 double positive|Stage:4dpf|genotype:Zprox1ab mutant|platform:10X Chromium scATAC|version:ATAC 1.1|facility:Peter Mac Molecular Genomics|cellranger atac pipeline:2.0.0|r version:4.0.5|internal id:20158", "GSM5677991", "GSM5677991: 20158: Zprox1ab 4dpf scATAC seq; Danio rerio; ATAC seq", "GSM5677991 r1", "GSM5677991", "1", "Single cell suspensions were sorted by FACS and prepared for nuclei isolation as previously described by 10x Genomics Demonstrated Protocol for Single Cell ATAC Sequencing CG000169   Rev D. Cell suspensions were pelleted 300 x g for 5 minutes and rinsed with PBS + 0.04% BSA. Cells were resuspended in 95uL of freshly prepared lysis buffer 10mM Tris HCl pH 7.4  10\u2009mM NaCl  3\u2009mM MgCl2  0.1% Tween 20  0.1% NP40 Substitute  0.01% Digitonin  and 1% BSA and incubated on ice for 1 minute. 100uL of chilled wash buffer 10\u2009mM Tris HCl  pH 7.4  10\u2009mM NaCl  3\u2009mM MgCl2  0.1% Tween 20  1% BSA was used to neutralise the reaction  before the nuclei were pelleted 500 x g for 5 minutes and resuspended again in 7uL of 1x Nuclei Buffer 10X Genomics Cat# PN 2000153/2000207. Presence of healthy and intact nuclei was assessed by visual inspection on a brightfield microscope using Trypan Blue staining Thermo Fisher Cat# T10282 and Countess Cell Counting Chamber Slides Thermo Fisher Cat# C10228. Single nuclei suspensions were resuspended at approximately 5000 nuclei per \uf06dL before undergoing tagmentation for 60min at 37\uf0b0C. post tagmentation nuclei were partitioned and barcoded using the 10X Genomics Chromium Controller 10X Genomics and the Single Cell ATAC Reagent Kit V1.1; 10X Genomics; PN 1000176 . Tagmented nuclei were loaded onto the Chromium Single Cell Chip H 10X Genomics; PN 1000162  GEM generation  barcoding and library construction was performed according to the 10X Genomics Chromium User Guide. The resulting single cell ATAC libraries contained unique sample indices for each sample. The libraries were quantified on the Agilent BioAnalyzer 2100 using the High Sensitivity DNA Kit Agilent  5067 4626 and pooled in equimolar ratios.  Sequencing was performed on an Illumina NextSeq 500 using a 150 cycle High Output Kit as follows: 50bp Read1  8bp i7 index  16bp i5 index  50bp Read2 achieving a read depth of 25 000 read pairs per nucleus.", null, "ATAC-seq", "TRANSCRIPTOMIC", "other", "PAIRED", "ILLUMINA", "NextSeq 500", null, "SRP344879", null, "loader:fastq load.py|options:  readTypes=BTB   read1PairFiles=20158 Prox Mut S7 R1 001.fastq.gz    read2PairFiles=20158 Prox Mut S7 R2 001.fastq.gz    read3PairFiles=20158 Prox Mut S7 R3 001.fastq.gz", "20158_Prox-Mut_S7_R1_001.fastq.gz 20158_Prox-Mut_S7_R2_001.fastq.gz 20158_Prox-Mut_S7_R3_001.fastq.gz", "fastq fastq fastq", 10416417336.0, 65926692.0, "GSM5677991 r1", "0:71 1:16 2:71", "A:2269946152;C:2457596815;G:2197719841;T:2435900098;N:427358", 71, 16, 71, null, 2269946152, 2457596815, 2197719841, 2435900098, 427358, "SRX13025545", "SRS10963312", "SRA1327178", "Computational Biology Lab, School of Biological Sciences, Monash University", "Computational Biology Lab, School of Biological Sciences, Monash University", 2, 0.39271, 0.39125, 0.30282, 0.30048, 0.79829, 0.79803, 0.52845, 0.52503, 71, 71, "B", "B", "biological fallback assumption", "illumina", "nextseq", "unknown", "other", "unknown", "sc", "single_cell_droplet", "10x", null, "Australia", "2021-11-06", "Larval", "Larval", "Embryo Imprecise", "All anatomical structures"], [66927, "SRR16832336", "SRX13025544", "SRS10963311", "SRP344879", "PRJNA778402", "Atlas of lymphangiogenesis [scATAC Seq]", "GSE188340", "Other", "During development  lymphatic vasculature forms as a second and distinct network that derives from embryonic blood vessels. Transdifferentiation of venous endothelial cells into specified lymphatic endothelial cells LECs is the first step in this process. Transdifferentiation and specification of LEC fate requires Prox1  but how Prox1 regulates transdifferentiation and differentiation is not fully understood. We present a single cell transcriptomic atlas of lymphangiogenesis spanning four key developmental stages that reveals new markers and functional regulators of lymphatic development. We extend this to comprehensively profile single cell transcriptomic and chromatin changes controlled by Prox1 using zygotic prox1a mutants  which form lymphatics that then dedifferentiate. Combining this with single cell analysis of Prox1 null  double prox1a/prox1b maternal zygotic mutants  we reveal in depth the role of Prox1 in control of LEC fate specification and differentiation.  This resource reveals dual and progressive functions for Prox1  blocking blood vascular and hematopoietic fate while simultaneously up regulating a small number of early acting genes that include tspan18a/b and lgals3a/b which are essential for lymphangiogenesis. This embryonic developmental resource will serve as a baseline to better understand both developmental and pathological lymphangiogenesis in the future. [Citations in sample metadata correspond to reference numbers in the associated publication.] Overall design: Single cell ATAC profiles of endothelial cells from developing zebrafish at 4dpf in Zprox /  mutant and matched WT siblings. All code associated with this data and publication are publicly available under an open source software license at: https://atlassian.petermac.org.au/bitbucket/users/tyrone.chen/repos/hogan lab/browse/2022 Grimm Mason et al PROX1 NatCellBiol/", "parent bioproject:PRJNA778399", "pubmed:36912146", null, "20157: WT 4dpf scATAC seq", "GSM5677990", null, "source name:embryo|tissue:embryo|transgenic:Tgfli1a:negfp;Tg 5.2lyve1b:dsRed|facs:nEGFP and dsRed2 double positive|Stage:4dpf|genotype:WT|platform:10X Chromium scATAC|facility:Peter Mac Molecular Genomics|cellranger atac pipeline:2.0.0|r version:4.0.5|internal id:20157|gap study version:ATAC 1.1", "20157: WT 4dpf scATAC seq", "Where necessary fastq files were made using Cell Ranger [14] version 3.1.0 or 3.0.2 mkfastq.  Sequencing QC was assessed using FastQC 0.11.6 and MultiQC viewer for aggregated reports. Cell Ranger count and aggr were used to generate aggregated count files mapped to GRCz11 Ensembl 101  without xxx normalisation. Doublets were identified from the filtered aggregated count files using Scrublet [15] in Python version 3.6 and filtered from subsequent analyses.  For the MZprox1 /  mutant and Zprox1 /  mutant datasets filtered aggregated count files were processed  sc transform normalised  filtered and clustered louvain using Seurat version 2.0 [16] and 3.0 [17] respectively for R statistical software version 4.0.2. QC was evaluated before and post normalisation using plot functions in Seurat and scater 1.20.1 [18]  and all thresholds and settings are described in scripting.  Cluster solutions were evaluated using ClusTree [19]. Datasets used in the atlas of lymphangiogenesis were processed  filtered  merged and log normalised using Seurat version 3.0 [17]  with QC and settings as above.  Merged data was clustered and normalised using CSS simspec [20] and clustering and cluster evaluation performed on this object only  as described above.  For all scRNA seq datasets cluster phenotype was determined using the expression of key markers Supplementary Table  with the aid of CellXGene visualisation software [21].  All downstream analysis and plotting were performed using Seurat version 3.0 [17] using default settings. For velocity analysis  loom files containing RNA velocity information were first generated from the 10X data associated with each sample using velocyto.R 0.6. Relevant sample subsets were then combined with loompy 3.0.6. From pre computed Seurat UMAPs in scRNA seq processing and analysis  cell barcode and associated metadata were obtained and combined with the loom files. With the combined velocity scores and cell metadata  velocity maps were plotted with velocyto.R 0.6 and overlaid on the UMAPs. Genome build: GRCz11 Supplementary files format and content: peaks  barcodes and matrices output by cellranger for each sample", "embryo", "All injections were performed as previously described [10]. CRISPR genome editing for tspan18a/b was performed as previously described [11]  and all genotyping confirmed using PCR [12].  slc7a7aBAC:slc7a7a Citrineuom10 and fabp11aBAC:fabp11a Citrineuom10 recombineering was performed as previously described.", "Single cell suspensions were sorted by FACS and prepared for nuclei isolation as previously described by 10x Genomics Demonstrated Protocol for Single Cell ATAC Sequencing CG000169   Rev D. Cell suspensions were pelleted 300 x g for 5 minutes and rinsed with PBS + 0.04% BSA. Cells were resuspended in 95uL of freshly prepared lysis buffer 10mM Tris HCl pH 7.4  10\u2009mM NaCl  3\u2009mM MgCl2  0.1% Tween 20  0.1% NP40 Substitute  0.01% Digitonin  and 1% BSA and incubated on ice for 1 minute. 100uL of chilled wash buffer 10\u2009mM Tris HCl  pH 7.4  10\u2009mM NaCl  3\u2009mM MgCl2  0.1% Tween 20  1% BSA was used to neutralise the reaction  before the nuclei were pelleted 500 x g for 5 minutes and resuspended again in 7uL of 1x Nuclei Buffer 10X Genomics Cat# PN 2000153/2000207. Presence of healthy and intact nuclei was assessed by visual inspection on a brightfield microscope using Trypan Blue staining Thermo Fisher Cat# T10282 and Countess Cell Counting Chamber Slides Thermo Fisher Cat# C10228. Single nuclei suspensions were resuspended at approximately 5000 nuclei per \uf06dL before undergoing tagmentation for 60min at 37\uf0b0C. post tagmentation nuclei were partitioned and barcoded using the 10X Genomics Chromium Controller 10X Genomics and the Single Cell ATAC Reagent Kit V1.1; 10X Genomics; PN 1000176 . Tagmented nuclei were loaded onto the Chromium Single Cell Chip H 10X Genomics; PN 1000162  GEM generation  barcoding and library construction was performed according to the 10X Genomics Chromium User Guide. The resulting single cell ATAC libraries contained unique sample indices for each sample. The libraries were quantified on the Agilent BioAnalyzer 2100 using the High Sensitivity DNA Kit Agilent  5067 4626 and pooled in equimolar ratios.  Sequencing was performed on an Illumina NextSeq 500 using a 150 cycle High Output Kit as follows: 50bp Read1  8bp i7 index  16bp i5 index  50bp Read2 achieving a read depth of 25 000 read pairs per nucleus.", "Zebrafish work was conducted in compliance with animal ethics committees at University of Queensland and Peter MacCallum Cancer Centre. The uq52bh dut mutant was isolated in a previously described genetic screen [7]. The genetic mapping approach was performed as previously described [8  9].", "tissue:embryo|transgenic:Tgfli1a:negfp;Tg 5.2lyve1b:dsRed|facs:nEGFP and dsRed2 double positive|Stage:4dpf|genotype:WT|platform:10X Chromium scATAC|version:ATAC 1.1|facility:Peter Mac Molecular Genomics|cellranger atac pipeline:2.0.0|r version:4.0.5|internal id:20157", "GSM5677990", "GSM5677990: 20157: WT 4dpf scATAC seq; Danio rerio; ATAC seq", "GSM5677990 r1", "GSM5677990", "1", "Single cell suspensions were sorted by FACS and prepared for nuclei isolation as previously described by 10x Genomics Demonstrated Protocol for Single Cell ATAC Sequencing CG000169   Rev D. Cell suspensions were pelleted 300 x g for 5 minutes and rinsed with PBS + 0.04% BSA. Cells were resuspended in 95uL of freshly prepared lysis buffer 10mM Tris HCl pH 7.4  10\u2009mM NaCl  3\u2009mM MgCl2  0.1% Tween 20  0.1% NP40 Substitute  0.01% Digitonin  and 1% BSA and incubated on ice for 1 minute. 100uL of chilled wash buffer 10\u2009mM Tris HCl  pH 7.4  10\u2009mM NaCl  3\u2009mM MgCl2  0.1% Tween 20  1% BSA was used to neutralise the reaction  before the nuclei were pelleted 500 x g for 5 minutes and resuspended again in 7uL of 1x Nuclei Buffer 10X Genomics Cat# PN 2000153/2000207. Presence of healthy and intact nuclei was assessed by visual inspection on a brightfield microscope using Trypan Blue staining Thermo Fisher Cat# T10282 and Countess Cell Counting Chamber Slides Thermo Fisher Cat# C10228. Single nuclei suspensions were resuspended at approximately 5000 nuclei per \uf06dL before undergoing tagmentation for 60min at 37\uf0b0C. post tagmentation nuclei were partitioned and barcoded using the 10X Genomics Chromium Controller 10X Genomics and the Single Cell ATAC Reagent Kit V1.1; 10X Genomics; PN 1000176 . Tagmented nuclei were loaded onto the Chromium Single Cell Chip H 10X Genomics; PN 1000162  GEM generation  barcoding and library construction was performed according to the 10X Genomics Chromium User Guide. The resulting single cell ATAC libraries contained unique sample indices for each sample. The libraries were quantified on the Agilent BioAnalyzer 2100 using the High Sensitivity DNA Kit Agilent  5067 4626 and pooled in equimolar ratios.  Sequencing was performed on an Illumina NextSeq 500 using a 150 cycle High Output Kit as follows: 50bp Read1  8bp i7 index  16bp i5 index  50bp Read2 achieving a read depth of 25 000 read pairs per nucleus.", null, "ATAC-seq", "TRANSCRIPTOMIC", "other", "PAIRED", "ILLUMINA", "NextSeq 500", null, "SRP344879", null, "loader:fastq load.py|options:  readTypes=BTB", "20157_Wild-Type_S6_R1_001.fastq.gz 20157_Wild-Type_S6_R2_001.fastq.gz 20157_Wild-Type_S6_R3_001.fastq.gz", "fastq fastq fastq", 9771147706.0, 61842707.0, "GSM5677990 r1", "0:71 1:16 2:71", "A:2361071727;C:2051725100;G:1909318228;T:2459149367;N:399972", 71, 16, 71, null, 2361071727, 2051725100, 1909318228, 2459149367, 399972, "SRX13025544", "SRS10963311", "SRA1327178", "Computational Biology Lab, School of Biological Sciences, Monash University", "Computational Biology Lab, School of Biological Sciences, Monash University", 2, 0.83384, 0.83194, 0.67607, 0.67264, 0.68014, 0.67992, 0.50904, 0.50945, 71, 71, "B", "B", "biological fallback assumption", "illumina", "nextseq", "unknown", "other", "unknown", "sc", "single_cell_droplet", "10x", null, "Australia", "2021-11-06", "Larval", "Larval", "Embryo Imprecise", "All anatomical structures"], [66962, "SRR16912780", "SRX13105209", "SRS11041502", "SRP345473", "PRJNA779441", "Single Cell RNA Sequencing Characterizes the Molecular Heterogeneity of the Larval Zebrafish Optic Tectum", "PRJNA779441", "Other", "The optic tectum OT is a multilaminated midbrain structure that acts as the primary retinorecipient in the zebrafish brain. Homologous to the mammalian superior colliculus  the OT is responsible for the reception and integration of stimuli  followed by elicitation of salient behavioral responses. While the OT has been the focus of functional experiments for decades  less is known concerning specific cell types  microcircuitry  and their individual functions within the OT. Recent efforts have contributed substantially to the knowledge of tectal cell types; however  a comprehensive cell catalog is incomplete. Here we contribute to this growing effort by applying single cell RNA sequencing scRNA seq to characterize the transcriptomic profiles of tectal cells labeled by the transgenic enhancer trap line y304Etcfos:Gal4;UAS:Kaede. We sequenced 13 320 cells  a 4X cellular coverage  and identified 25 putative OT cell populations. Within those cells  we identified several mature and developing neuronal populations  as well as non neuronal cell types including oligodendrocytes  microglia  and radial glia. Although most mature neurons demonstrate GABAergic activity  several glutamatergic populations are present  as well as one glycinergic population. We also conducted Gene Ontology analysis to identify enriched biological processes  and computed RNA velocity to infer current and future transcriptional cell states. Finally  we conducted in situ hybridization to validate our bioinformatic analyses and spatially map select clusters. In conclusion  the larval zebrafish OT is a complex structure containing at least 25 transcriptionally distinct cell populations. To our knowledge  this is the first time scRNA seq has been applied to explore the OT alone and in depth.", null, null, null, null, "Optic Tectum Run 2", null, "strain:y304Etcfos:Gal4; UAS:Kaede|age:7dpf|dev stage:Larval|sex:Unknown|tissue:Kaede+ midbrain cells|sample type:Methanol fixed FAC sorted cells|Run Number:2|BioSampleModel:Model organism or animal", null, null, null, null, null, null, null, null, "scRNA seq of danio rerio: larval optic tectum", "18798R", "18798R", "10X Genomics Next GEM Single Cell three prime Gene Expression Library prep v3.1 with UDI", null, null, "RNA-Seq", "TRANSCRIPTOMIC SINGLE CELL", "other", "PAIRED", "ILLUMINA", "Illumina NovaSeq 6000", null, "SRP345473", null, null, "18798X1_210405_A00421_0312_BHYYKVDSXY_S1_L004_I1_001.fastq.gz 18798X1_210405_A00421_0312_BHYYKVDSXY_S1_L004_I2_001.fastq.gz 18798X1_210405_A00421_0312_BHYYKVDSXY_S1_L004_R1_001.fastq.gz 18798X1_210405_A00421_0312_BHYYKVDSXY_S1_L004_R2_001.fastq.gz", "fastq fastq fastq fastq", 51241551174.0, 258795713.0, "18798X1 210405 A00421 0312 BHYYKVDSXY S1 L004 I1 001.fastq.gz", "0:10 1:10 2:28 3:150", "A:11347233208;C:8190548246;G:9193124724;T:10088035538;N:415234", 10, 10, 28, 150, 11347233208, 8190548246, 9193124724, 10088035538, 415234, "SRX13105209", "SRS11041502", "SRA1327223", "Brigham Young University|Cell Biology and Physiology", "Brigham Young University", 1, 0.87747, null, 0.31462, null, 0.80095, null, 0.57402, null, 150, null, "B", null, "usable mapping rate", "illumina", "novaseq_era", "3prime", "other", "unknown", "sc", "single_cell_droplet", "10x", null, "United States", "2021-11-10", "Larval", "Larval", "Brain", "Nervous System"], [66963, "SRR16912781", "SRX13105208", "SRS11041501", "SRP345473", "PRJNA779441", "Single Cell RNA Sequencing Characterizes the Molecular Heterogeneity of the Larval Zebrafish Optic Tectum", "PRJNA779441", "Other", "The optic tectum OT is a multilaminated midbrain structure that acts as the primary retinorecipient in the zebrafish brain. Homologous to the mammalian superior colliculus  the OT is responsible for the reception and integration of stimuli  followed by elicitation of salient behavioral responses. While the OT has been the focus of functional experiments for decades  less is known concerning specific cell types  microcircuitry  and their individual functions within the OT. Recent efforts have contributed substantially to the knowledge of tectal cell types; however  a comprehensive cell catalog is incomplete. Here we contribute to this growing effort by applying single cell RNA sequencing scRNA seq to characterize the transcriptomic profiles of tectal cells labeled by the transgenic enhancer trap line y304Etcfos:Gal4;UAS:Kaede. We sequenced 13 320 cells  a 4X cellular coverage  and identified 25 putative OT cell populations. Within those cells  we identified several mature and developing neuronal populations  as well as non neuronal cell types including oligodendrocytes  microglia  and radial glia. Although most mature neurons demonstrate GABAergic activity  several glutamatergic populations are present  as well as one glycinergic population. We also conducted Gene Ontology analysis to identify enriched biological processes  and computed RNA velocity to infer current and future transcriptional cell states. Finally  we conducted in situ hybridization to validate our bioinformatic analyses and spatially map select clusters. In conclusion  the larval zebrafish OT is a complex structure containing at least 25 transcriptionally distinct cell populations. To our knowledge  this is the first time scRNA seq has been applied to explore the OT alone and in depth.", null, null, null, null, "Optic Tectum Run 1", null, "strain:y304Etcfos:Gal4; UAS:Kaede|age:7dpf|dev stage:Larval|sex:Unknown|tissue:Kaede+ midbrain cells|sample type:Methanol fixed FAC sorted cells|Run Number:1|BioSampleModel:Model organism or animal", null, null, null, null, null, null, null, null, "scRNA seq of danio rerio: larval optic tectum", "18507R", "18507R", "10X Genomics Next GEM Single Cell three prime Gene Expression Library prep v3.1 with UDI", null, null, "RNA-Seq", "TRANSCRIPTOMIC SINGLE CELL", "other", "PAIRED", "ILLUMINA", "Illumina NovaSeq 6000", null, "SRP345473", null, null, "18507X1_201113_A00421_0256_BHLY3JDSXY_S5_L003_I1_001.fastq.gz 18507X1_201113_A00421_0256_BHLY3JDSXY_S5_L003_I2_001.fastq.gz 18507X1_201113_A00421_0256_BHLY3JDSXY_S5_L003_R1_001.fastq.gz 18507X1_201113_A00421_0256_BHLY3JDSXY_S5_L003_R2_001.fastq.gz", "fastq fastq fastq fastq", 64682907300.0, 326681350.0, "18507X1 201113 A00421 0256 BHLY3JDSXY S5 L003 I1 001.fastq.gz", "0:10 1:10 2:28 3:150", "A:14518921141;C:9982364575;G:11381145187;T:13119455374;N:316223", 10, 10, 28, 150, 14518921141, 9982364575, 11381145187, 13119455374, 316223, "SRX13105208", "SRS11041501", "SRA1327223", "Brigham Young University|Cell Biology and Physiology", "Brigham Young University", 1, 0.87166, null, 0.34827, null, 0.82298, null, 0.60301, null, 150, null, "B", null, "usable mapping rate", "illumina", "novaseq_era", "3prime", "other", "unknown", "sc", "single_cell_droplet", "10x", null, "United States", "2021-11-11", "Larval", "Larval", "Brain", "Nervous System"], [72561, "SRR22722190", "SRX18683720", "SRS16126888", "SRP412911", "PRJNA911847", "Transgenic IDH2 R172K and IDH2 R140Q zebrafish models recapitulated features of human acute myeloid leukaemia", "PRJNA911847", "Other", "Isocitrate dehydrogenase 2 IDH2 mutations occur in more than 15% of cytogenetically normal acute myeloid leukemia CN AML but comparative studies of their roles in leukemogenesis have been scarce. We generated zebrafish models of IDH2R172K and IDH2R140Q AML and reported their pathologic  functional and transcriptomic features and therapeutic responses to target therapies. Transgenic embryos co expressing FLT3ITD and IDH2 mutations showed accentuation of myelopoiesis. As these embryos were raised to maturity  full blown leukemia ensued with multi lineage dysplasia  increase in myeloblasts and marrow cellularity and splenomegaly. The leukemia cells were transplantable into primary and secondary recipients and resulted in more aggressive disease. TgRunx1:FLT3ITDIDH2R172K but not TgRunx1:FLT3ITDIDH2R140Q zebrafish showed increase in T cell development at embryonic and adult stage. Single cell transcriptomic analysis revealed increased myeloid skewing  differentiation blockade and enrichment of leukemia associated gene signatures in both zebrafish models. TgRunx1:FLT3ITDIDH2R172K but not TgRunx1:FLT3ITDIDH2R140Q zebrafish showed increase in interferon signals at adult stage. Leukemic phenotypes in both zebrafish could be ameliorated by quizartinib and enasidenib. In conclusion  the zebrafish models of IDH2 mutated AML recapitulated the morphologic  clinical  functional and transcriptomic characteristics of human diseases  and provided the prototype for developing zebrafish leukemia models of other genotypes that would become a platform for high throughput drug screening", null, null, null, null, "Transgenic FLT3 ITD IDH2 R140Q", null, "strain:TU|isolate:not applicable|breed:not applicable|cultivar:not applicable|ecotype:not applicable|age:8 month|dev stage:8 month|sex:pooled male and female|tissue:kidney marrow|genotype:Transgenic Runx1:FLT3ITDIDH2R140Q|BioSampleModel:Model organism or animal", null, null, null, null, null, null, null, null, "Single cell RNA seq of Danio rerio: Transgenic FLT3 ITD IDH2 R140Q", "ITD140Q", "ITD140Q", "Single cell RNA seq library.Single viable KM cells were collected from the transgenic double mutant n=3; pooled and WT n=3; pooled fish at 7 mpf into 0.9X PBS with 5% FBS. Their viability was examined by 0.4% Trypan blue staining under microscopy. The single cell library was constructed using the ChromiumTM Controller and ChromiumTM Next GEM Single Cell 3 Kit v3.1 10x Genomics  Pleasanton  CA. Complementary DNA cDNA was synthesized from the fragmentated RNAs using N6 random primers  followed by end repair and ligation to BGISEQ sequencer compatible adapters. Quality control of the final library was performed by checking the distribution of the fragments size using the Agilent 2100 bioanalyzer and quantification was performed by real time quantitative PCR using TaqMan probes. The final products were sequenced using the DNBSEQTM platform BGI  HK  China.", null, null, "OTHER", "TRANSCRIPTOMIC", "other", "PAIRED", "DNBSEQ", "DNBSEQ-G400", null, "SRP412911", null, "loader:fastq load.py", "ITD172KA_S1_L001_I1_001.fastq.gz ITD172KA_S1_L001_R1_001.fastq.gz ITD172KA_S1_L001_R2_001.fastq.gz", "fastq fastq fastq", 52149830422.0, 410628586.0, "ITD172KA S1 L001 I1 001.fastq.gz", "0:8 1:28 2:91", "A:10282953217;C:8459559201;G:8851285287;T:9772343870;N:1059751", 8, 28, 91, null, 10282953217, 8459559201, 8851285287, 9772343870, 1059751, "SRX18683720", "SRS16126888", "SRA1558824", "The University of Hong Kong|Department of Medicine", "The University of Hong Kong", 1, 0.88465, null, 0.13112, null, 0.81789, null, 0.55842, null, 91, null, "B", null, "usable mapping rate", "bgi", "bgi", "unknown", "random_priming", "unknown", "sc", "single_cell_droplet", "10x", null, "China", "2022-12-14", "Adult", "Adult", "Kidney", "Renal System"], [72562, "SRR22722191", "SRX18683719", "SRS16126887", "SRP412911", "PRJNA911847", "Transgenic IDH2 R172K and IDH2 R140Q zebrafish models recapitulated features of human acute myeloid leukaemia", "PRJNA911847", "Other", "Isocitrate dehydrogenase 2 IDH2 mutations occur in more than 15% of cytogenetically normal acute myeloid leukemia CN AML but comparative studies of their roles in leukemogenesis have been scarce. We generated zebrafish models of IDH2R172K and IDH2R140Q AML and reported their pathologic  functional and transcriptomic features and therapeutic responses to target therapies. Transgenic embryos co expressing FLT3ITD and IDH2 mutations showed accentuation of myelopoiesis. As these embryos were raised to maturity  full blown leukemia ensued with multi lineage dysplasia  increase in myeloblasts and marrow cellularity and splenomegaly. The leukemia cells were transplantable into primary and secondary recipients and resulted in more aggressive disease. TgRunx1:FLT3ITDIDH2R172K but not TgRunx1:FLT3ITDIDH2R140Q zebrafish showed increase in T cell development at embryonic and adult stage. Single cell transcriptomic analysis revealed increased myeloid skewing  differentiation blockade and enrichment of leukemia associated gene signatures in both zebrafish models. TgRunx1:FLT3ITDIDH2R172K but not TgRunx1:FLT3ITDIDH2R140Q zebrafish showed increase in interferon signals at adult stage. Leukemic phenotypes in both zebrafish could be ameliorated by quizartinib and enasidenib. In conclusion  the zebrafish models of IDH2 mutated AML recapitulated the morphologic  clinical  functional and transcriptomic characteristics of human diseases  and provided the prototype for developing zebrafish leukemia models of other genotypes that would become a platform for high throughput drug screening", null, null, null, null, "Transgenic FLT3 ITD IDH2 R172K", null, "strain:TU|isolate:not applicable|breed:not applicable|cultivar:not applicable|ecotype:not applicable|age:8 month|dev stage:8 month|sex:pooled male and female|tissue:kidney marrow|genotype:Transgenic Runx1:FLT3ITDIDH2R172K|BioSampleModel:Model organism or animal", null, null, null, null, null, null, null, null, "Single cell RNA seq of Danio rerio: Transgenic FLT3 ITD IDH2 R172K", "ITD172K", "ITD172K", "Single cell RNA seq library.Single viable KM cells were collected from the transgenic double mutant n=3; pooled and WT n=3; pooled fish at 7 mpf into 0.9X PBS with 5% FBS. Their viability was examined by 0.4% Trypan blue staining under microscopy. The single cell library was constructed using the ChromiumTM Controller and ChromiumTM Next GEM Single Cell 3 Kit v3.1 10x Genomics  Pleasanton  CA. Complementary DNA cDNA was synthesized from the fragmentated RNAs using N6 random primers  followed by end repair and ligation to BGISEQ sequencer compatible adapters. Quality control of the final library was performed by checking the distribution of the fragments size using the Agilent 2100 bioanalyzer and quantification was performed by real time quantitative PCR using TaqMan probes. The final products were sequenced using the DNBSEQTM platform BGI  HK  China.", null, null, "OTHER", "TRANSCRIPTOMIC", "other", "PAIRED", "DNBSEQ", "DNBSEQ-G400", null, "SRP412911", null, "loader:fastq load.py", "ITD140QA_S1_L004_I1_001.fastq.gz ITD140QA_S1_L004_R1_001.fastq.gz ITD140QA_S1_L004_R2_001.fastq.gz", "fastq fastq fastq", 28595025233.0, 225157679.0, "ITD140QA S1 L004 I1 001.fastq.gz", "0:8 1:28 2:91", "A:5628987577;C:4565848641;G:4959209904;T:5302394659;N:32908008", 8, 28, 91, null, 5628987577, 4565848641, 4959209904, 5302394659, 32908008, "SRX18683719", "SRS16126887", "SRA1558824", "The University of Hong Kong|Department of Medicine", "The University of Hong Kong", 1, 0.81404, null, 0.11315, null, 0.83327, null, 0.56222, null, 91, null, "B", null, "usable mapping rate", "bgi", "bgi", "unknown", "random_priming", "unknown", "sc", "single_cell_droplet", "10x", null, "China", "2022-12-13", "Adult", "Adult", "Kidney", "Renal System"], [72563, "SRR22722192", "SRX18683718", "SRS16126886", "SRP412911", "PRJNA911847", "Transgenic IDH2 R172K and IDH2 R140Q zebrafish models recapitulated features of human acute myeloid leukaemia", "PRJNA911847", "Other", "Isocitrate dehydrogenase 2 IDH2 mutations occur in more than 15% of cytogenetically normal acute myeloid leukemia CN AML but comparative studies of their roles in leukemogenesis have been scarce. We generated zebrafish models of IDH2R172K and IDH2R140Q AML and reported their pathologic  functional and transcriptomic features and therapeutic responses to target therapies. Transgenic embryos co expressing FLT3ITD and IDH2 mutations showed accentuation of myelopoiesis. As these embryos were raised to maturity  full blown leukemia ensued with multi lineage dysplasia  increase in myeloblasts and marrow cellularity and splenomegaly. The leukemia cells were transplantable into primary and secondary recipients and resulted in more aggressive disease. TgRunx1:FLT3ITDIDH2R172K but not TgRunx1:FLT3ITDIDH2R140Q zebrafish showed increase in T cell development at embryonic and adult stage. Single cell transcriptomic analysis revealed increased myeloid skewing  differentiation blockade and enrichment of leukemia associated gene signatures in both zebrafish models. TgRunx1:FLT3ITDIDH2R172K but not TgRunx1:FLT3ITDIDH2R140Q zebrafish showed increase in interferon signals at adult stage. Leukemic phenotypes in both zebrafish could be ameliorated by quizartinib and enasidenib. In conclusion  the zebrafish models of IDH2 mutated AML recapitulated the morphologic  clinical  functional and transcriptomic characteristics of human diseases  and provided the prototype for developing zebrafish leukemia models of other genotypes that would become a platform for high throughput drug screening", null, null, null, null, "Wild type", null, "strain:TU|isolate:not applicable|breed:not applicable|cultivar:not applicable|ecotype:not applicable|age:8 month|dev stage:8 month|sex:pooled male and female|tissue:kidney marrow|genotype:Wild type|BioSampleModel:Model organism or animal", null, null, null, null, null, null, null, null, "Single cell RNA seq of Danio rerio: Wild type", "Control", "Control", "Single cell RNA seq library.Single viable KM cells were collected from the transgenic double mutant n=3; pooled and WT n=3; pooled fish at 7 mpf into 0.9X PBS with 5% FBS. Their viability was examined by 0.4% Trypan blue staining under microscopy. The single cell library was constructed using the ChromiumTM Controller and ChromiumTM Next GEM Single Cell 3 Kit v3.1 10x Genomics  Pleasanton  CA. Complementary DNA cDNA was synthesized from the fragmentated RNAs using N6 random primers  followed by end repair and ligation to BGISEQ sequencer compatible adapters. Quality control of the final library was performed by checking the distribution of the fragments size using the Agilent 2100 bioanalyzer and quantification was performed by real time quantitative PCR using TaqMan probes. The final products were sequenced using the DNBSEQTM platform BGI  HK  China.", null, null, "OTHER", "TRANSCRIPTOMIC", "other", "PAIRED", "DNBSEQ", "DNBSEQ-G400", null, "SRP412911", null, "loader:fastq load.py", "ControlA_S1_L003_R2_001.fastq.gz ControlA_S1_L003_R1_001.fastq.gz ControlA_S1_L003_I1_001.fastq.gz", "fastq fastq fastq", 37892727491.0, 298367933.0, "ControlA S1 L003 I1 001.fastq.gz", "0:8 1:28 2:91", "A:7386602298;C:6174680460;G:6579652828;T:6955791213;N:54755104", 8, 28, 91, null, 7386602298, 6174680460, 6579652828, 6955791213, 54755104, "SRX18683718", "SRS16126886", "SRA1558824", "The University of Hong Kong|Department of Medicine", "The University of Hong Kong", 1, 0.81686, null, 0.11049, null, 0.84139, null, 0.46084, null, 91, null, "B", null, "usable mapping rate", "bgi", "bgi", "unknown", "random_priming", "unknown", "sc", "single_cell_droplet", "10x", null, "China", "2022-12-14", "Adult", "Adult", "Kidney", "Renal System"], [72833, "SRR23210472", "SRX19158411", "SRS16570832", "SRP418915", "PRJNA927004", "Differential Nodal level promotes mesendoderm cell fate segregation mediated by chromatin organization", "GSE223636", "Other", "Purpose: To investigate the mechanism of prechordal plate and anterior endoderm separation . Methods:  Nodal injected explants injected with 10pg ndr2 mRNA constructed from lft1 mutants and ndr1 morphants were harvested at xxxhpf. Libraries were prepared using Chromium Controller and Chromium Single Cell three primeLibrary & Gel Bead Kit v3 10x Genomics  PN 1000075 according to the manufacturer's protocol for 10000 cells recovery. For single cell multiomics  zebrafish embryos at 6 hpf were harvested.  Libraries were prepared using Chromium Next GEM Single Cell Multiome ATAC + Gene Expression Reagent Bundle 10x Genomics  4 rxns PN 1000285 according to the manufacturer's protocol for 10000 cells recovery. Results: A total of 10 614 single cell transcriptomes and 4 335 multiomics were collected post stringent quality control measures. Conclusions: A slight bias in Nodal signaling promotes a differential chromatin structure between prechordal plate and endoderm  which drives a differential expression of those key regulators  such as gsc and ripply1 in these two cell lineages  and further regulates mesendoderm cell fate separation. Overall design: zebrafish Nodal explants constructed from lft1 mutants and ndr1 morphants were harvested at 6hpf for scRNA seq. Zebrafish embryos were harvested at 6hpf for single cell multiomics.", null, null, null, "zebrafish embryo multiomics expression", "GSM6969677", null, "source name:zebrafish cells|strain:AB|tissue:embryonic cells|age:6hpf", "zebrafish embryo multiomics expression", "Illumina sequencing reads were aligned to the zebrafish mRNA reference genome GRCz11 using the 10x Genomics CellRanger pipeline version 6.1.2 and cellranger arc version 2.0.0 with default parameters. Assembly: GRCz11 Supplementary files format and content: tar archivr or  gzip compressed  files included filtered gene bc matrices and ATAC fragments  post running CellRanger or cellranger arc pipeline. Library strategy: scMultiome GEX", "zebrafish cells", "10pg of ndr2 mRNA was injected to one cell of embryonic animal pole at xxx cell stage. All embryos were incubated in 0.3x Danieau buffer until 1k stage  then  were transferred to Dulbecco's Modified Eagle Medium. Animal pole explants corresponding roughly to half of the blastula were incubated to 6hpf corresponding to embryonic developmental stage.", "Libraries were prepared using Chromium Controller and Chromium Single Cell 3\u2019Library & Gel Bead Kit v3 10x Genomics  PN 1000075 and Chromium Next GEM Single Cell Multiome ATAC + Gene Expression Reagent Bundle 10x Genomics  4 rxns PN 1000285 according to the manufacturer\u2019s protocol for 10000 cells recovery.", "Explants were cultured in a Petri dish coated with 1.5% agarose filled with Dulbecco's Modified Eagle Medium", "strain:AB|tissue:embryonic cells|age:6hpf", "GSM6969677", "GSM6969677: zebrafish embryo multiomics expression; Danio rerio; OTHER", "GSM6969677 r1", "GSM6969677", "1", "Libraries were prepared using Chromium Controller and Chromium Single Cell three primeLibrary & Gel Bead Kit v3 10x Genomics  PN 1000075 and Chromium Next GEM Single Cell Multiome ATAC + Gene Expression Reagent Bundle 10x Genomics  4 rxns PN 1000285 according to the manufacturer's protocol for 10000 cells recovery.", null, "OTHER", "TRANSCRIPTOMIC", "other", "PAIRED", "ILLUMINA", "Illumina NovaSeq 6000", null, "SRP418915", null, "loader:fastq load.py", "6-M-GEX-merge_S1_L001_R1_001.fastq.gz 6-M-GEX-merge_S1_L001_R2_001.fastq.gz", "fastq fastq", 67059456600.0, 223531522.0, "GSM6969677 r1", "0:150 1:150", "A:20681044309;C:12370348813;G:11664316954;T:22341885756;N:1860768", 150, 150, null, null, 20681044309, 12370348813, 11664316954, 22341885756, 1860768, "SRX19158411", "SRS16570832", "SRA1581154", "Institute of genetics, Zhejiang University", "Institute of genetics, Zhejiang University", 2, 0.45314, 0.8745, 0.16489, 0.2261, 0.99072, 0.82641, 0.79221, 0.75799, 150, 150, "T", "B", "mate1 technical by mapping diff", "illumina", "novaseq_era", "unknown", "other", "unknown", "sc", "single_cell_droplet", "10x", null, "China", "2023-01-24", "Gastrula", "Embryo", "Embryo Imprecise", "All anatomical structures"], [75288, "SRR25598371", "SRX21326033", "SRS18572181", "SRP454447", "PRJNA964500", "Danio rerio telencephalon sequencing", "PRJNA964500", "Whole Genome Sequencing", "We aimed to characterize different regions and cell types in the telencephalon of zebrafish D. rerio.", null, null, "the telencephalon of two fish was extracted and dissociated for preparing scRNA seq libraries", null, "3124R", null, "breed:wildtype|age:6 mpf|dev stage:adult|collection date:2022 03 11|geo loc name:Switzerland|sex:male|tissue:telencephalon|biomaterial provider:Rainer Friedrich|collected by:Lukas Anneser|genotype:wildtype|BioSampleModel:Model organism or animal", null, null, null, null, null, null, null, null, "scRNA Seq of Danio rerio: adult telencephalon", "3124R6", "3124R6", "Single cell suspension from adult Danio rerio telencephalon. Library prepared with 10X Genomics v3 reagents.", null, null, "RNA-Seq", "TRANSCRIPTOMIC SINGLE CELL", "other", "PAIRED", "ILLUMINA", "Illumina NovaSeq 6000", null, "SRP454447", null, null, "3124F1-5_220322_A01563_0038_AHY3YHDRXY_GCAGTATAGG-GTGCACGGAA_L002_R1_001.fastq.gz 3124F1-5_220322_A01563_0038_AHY3YHDRXY_GCAGTATAGG-GTGCACGGAA_L002_R2_001.fastq.gz", "fastq fastq", 10700303544.0, 127384566.0, "3124F1 5 220322 A01563 0038 AHY3YHDRXY GCAGTATAGG GTGCACGGAA L002 R1 001.fastq.gz", "0:28 1:56", "A:3095737110;C:2216347626;G:2244897948;T:3142928946;N:391914", 28, 56, null, null, 3095737110, 2216347626, 2244897948, 3142928946, 391914, "SRX21326033", "SRS18572181", "SRA1690085", "Friedrich Miescher Institute for Biomedical Research|Neurobiology", "Friedrich Miescher Institute for Biomedical Research", 2, 0.00785, 0.88899, 0.00393, 0.43423, 0.99017, 0.7486, 0.36074, 0.50405, 28, 56, "T", "B", "sc-like readlen", "illumina", "novaseq_era", "unknown", "other", "unknown", "sc", "single_cell_droplet", "10x", null, "Switzerland", "2023-08-10", "Adult", "Adult", "Brain", "Nervous System"], [75289, "SRR25598372", "SRX21326032", "SRS18572181", "SRP454447", "PRJNA964500", "Danio rerio telencephalon sequencing", "PRJNA964500", "Whole Genome Sequencing", "We aimed to characterize different regions and cell types in the telencephalon of zebrafish D. rerio.", null, null, "the telencephalon of two fish was extracted and dissociated for preparing scRNA seq libraries", null, "3124R", null, "breed:wildtype|age:6 mpf|dev stage:adult|collection date:2022 03 11|geo loc name:Switzerland|sex:male|tissue:telencephalon|biomaterial provider:Rainer Friedrich|collected by:Lukas Anneser|genotype:wildtype|BioSampleModel:Model organism or animal", null, null, null, null, null, null, null, null, "scRNA Seq of Danio rerio: adult telencephalon", "3124R5", "3124R5", "Single cell suspension from adult Danio rerio telencephalon. Library prepared with 10X Genomics v3 reagents.", null, null, "RNA-Seq", "TRANSCRIPTOMIC SINGLE CELL", "other", "PAIRED", "ILLUMINA", "Illumina NovaSeq 6000", null, "SRP454447", null, null, "3124F2-5_220322_A01563_0038_AHY3YHDRXY_AACCACGCAT-TAACCTGAAT_L002_R1_001.fastq.gz 3124F2-5_220322_A01563_0038_AHY3YHDRXY_AACCACGCAT-TAACCTGAAT_L002_R2_001.fastq.gz", "fastq fastq", 15103664688.0, 179805532.0, "3124F2 5 220322 A01563 0038 AHY3YHDRXY AACCACGCAT TAACCTGAAT L002 R1 001.fastq.gz", "0:28 1:56", "A:4376559806;C:3122875667;G:3166215898;T:4437453369;N:559948", 28, 56, null, null, 4376559806, 3122875667, 3166215898, 4437453369, 559948, "SRX21326032", "SRS18572181", "SRA1690085", "Friedrich Miescher Institute for Biomedical Research|Neurobiology", "Friedrich Miescher Institute for Biomedical Research", 2, 0.00779, 0.88709, 0.00398, 0.43543, 0.98973, 0.74736, 0.35685, 0.49938, 28, 56, "T", "B", "sc-like readlen", "illumina", "novaseq_era", "unknown", "other", "unknown", "sc", "single_cell_droplet", "10x", null, "Switzerland", "2023-08-10", "Adult", "Adult", "Brain", "Nervous System"], [75290, "SRR25598373", "SRX21326031", "SRS18572181", "SRP454447", "PRJNA964500", "Danio rerio telencephalon sequencing", "PRJNA964500", "Whole Genome Sequencing", "We aimed to characterize different regions and cell types in the telencephalon of zebrafish D. rerio.", null, null, "the telencephalon of two fish was extracted and dissociated for preparing scRNA seq libraries", null, "3124R", null, "breed:wildtype|age:6 mpf|dev stage:adult|collection date:2022 03 11|geo loc name:Switzerland|sex:male|tissue:telencephalon|biomaterial provider:Rainer Friedrich|collected by:Lukas Anneser|genotype:wildtype|BioSampleModel:Model organism or animal", null, null, null, null, null, null, null, null, "scRNA Seq of Danio rerio: adult telencephalon", "3124R4", "3124R4", "Single cell suspension from adult Danio rerio telencephalon. Library prepared with 10X Genomics v3 reagents.", null, null, "RNA-Seq", "TRANSCRIPTOMIC SINGLE CELL", "other", "PAIRED", "ILLUMINA", "Illumina NovaSeq 6000", null, "SRP454447", null, null, "3124F3-5_220322_A01563_0038_AHY3YHDRXY_CCCACCACAA-AAGCGGAGGT_L002_R1_001.fastq.gz 3124F3-5_220322_A01563_0038_AHY3YHDRXY_CCCACCACAA-AAGCGGAGGT_L002_R2_001.fastq.gz", "fastq fastq", 11678915220.0, 139034705.0, "3124F3 5 220322 A01563 0038 AHY3YHDRXY CCCACCACAA AAGCGGAGGT L002 R1 001.fastq.gz", "0:28 1:56", "A:3362125535;C:2431029106;G:2460479617;T:3424857950;N:423012", 28, 56, null, null, 3362125535, 2431029106, 2460479617, 3424857950, 423012, "SRX21326031", "SRS18572181", "SRA1690085", "Friedrich Miescher Institute for Biomedical Research|Neurobiology", "Friedrich Miescher Institute for Biomedical Research", 2, 0.00813, 0.89215, 0.00415, 0.42908, 0.99028, 0.74651, 0.33376, 0.49996, 28, 56, "T", "B", "sc-like readlen", "illumina", "novaseq_era", "unknown", "other", "unknown", "sc", "single_cell_droplet", "10x", null, "Switzerland", "2023-08-10", "Adult", "Adult", "Brain", "Nervous System"], [75291, "SRR25598374", "SRX21326030", "SRS18572181", "SRP454447", "PRJNA964500", "Danio rerio telencephalon sequencing", "PRJNA964500", "Whole Genome Sequencing", "We aimed to characterize different regions and cell types in the telencephalon of zebrafish D. rerio.", null, null, "the telencephalon of two fish was extracted and dissociated for preparing scRNA seq libraries", null, "3124R", null, "breed:wildtype|age:6 mpf|dev stage:adult|collection date:2022 03 11|geo loc name:Switzerland|sex:male|tissue:telencephalon|biomaterial provider:Rainer Friedrich|collected by:Lukas Anneser|genotype:wildtype|BioSampleModel:Model organism or animal", null, null, null, null, null, null, null, null, "scRNA Seq of Danio rerio: adult telencephalon", "3124R3", "3124R3", "Single cell suspension from adult Danio rerio telencephalon. Library prepared with 10X Genomics v3 reagents.", null, null, "RNA-Seq", "TRANSCRIPTOMIC SINGLE CELL", "other", "PAIRED", "ILLUMINA", "Illumina NovaSeq 6000", null, "SRP454447", null, null, "3124F1-1_220322_A01563_0038_AHY3YHDRXY_GCAGTATAGG-GTGCACGGAA_L001_R1_001.fastq.gz 3124F1-1_220322_A01563_0038_AHY3YHDRXY_GCAGTATAGG-GTGCACGGAA_L001_R2_001.fastq.gz", "fastq fastq", 22921793076.0, 272878489.0, "3124F1 1 220322 A01563 0038 AHY3YHDRXY GCAGTATAGG GTGCACGGAA L001 R1 001.fastq.gz", "0:28 1:56", "A:6639916156;C:4743377942;G:4802115704;T:6735725053;N:658221", 28, 56, null, null, 6639916156, 4743377942, 4802115704, 6735725053, 658221, "SRX21326030", "SRS18572181", "SRA1690085", "Friedrich Miescher Institute for Biomedical Research|Neurobiology", "Friedrich Miescher Institute for Biomedical Research", 2, 0.00785, 0.88789, 0.00408, 0.43253, 0.99003, 0.74722, 0.39124, 0.51078, 28, 56, "T", "B", "sc-like readlen", "illumina", "novaseq_era", "unknown", "other", "unknown", "sc", "single_cell_droplet", "10x", null, "Switzerland", "2023-08-10", "Adult", "Adult", "Brain", "Nervous System"], [75292, "SRR25598375", "SRX21326029", "SRS18572181", "SRP454447", "PRJNA964500", "Danio rerio telencephalon sequencing", "PRJNA964500", "Whole Genome Sequencing", "We aimed to characterize different regions and cell types in the telencephalon of zebrafish D. rerio.", null, null, "the telencephalon of two fish was extracted and dissociated for preparing scRNA seq libraries", null, "3124R", null, "breed:wildtype|age:6 mpf|dev stage:adult|collection date:2022 03 11|geo loc name:Switzerland|sex:male|tissue:telencephalon|biomaterial provider:Rainer Friedrich|collected by:Lukas Anneser|genotype:wildtype|BioSampleModel:Model organism or animal", null, null, null, null, null, null, null, null, "scRNA Seq of Danio rerio: adult telencephalon", "3124R2", "3124R2", "Single cell suspension from adult Danio rerio telencephalon. Library prepared with 10X Genomics v3 reagents.", null, null, "RNA-Seq", "TRANSCRIPTOMIC SINGLE CELL", "other", "PAIRED", "ILLUMINA", "Illumina NovaSeq 6000", null, "SRP454447", null, null, "3124F2-1_220322_A01563_0038_AHY3YHDRXY_AACCACGCAT-TAACCTGAAT_L001_R1_001.fastq.gz 3124F2-1_220322_A01563_0038_AHY3YHDRXY_AACCACGCAT-TAACCTGAAT_L001_R2_001.fastq.gz", "fastq fastq", 32435983776.0, 386142664.0, "3124F2 1 220322 A01563 0038 AHY3YHDRXY AACCACGCAT TAACCTGAAT L001 R1 001.fastq.gz", "0:28 1:56", "A:9417269653;C:6700846098;G:6786259472;T:9530672341;N:936212", 28, 56, null, null, 9417269653, 6700846098, 6786259472, 9530672341, 936212, "SRX21326029", "SRS18572181", "SRA1690085", "Friedrich Miescher Institute for Biomedical Research|Neurobiology", "Friedrich Miescher Institute for Biomedical Research", 2, 0.00815, 0.88581, 0.00428, 0.43686, 0.98957, 0.74704, 0.35752, 0.50656, 28, 56, "T", "B", "sc-like readlen", "illumina", "novaseq_era", "unknown", "other", "unknown", "sc", "single_cell_droplet", "10x", null, "Switzerland", "2023-08-10", "Adult", "Adult", "Brain", "Nervous System"], [75293, "SRR25598376", "SRX21326028", "SRS18572181", "SRP454447", "PRJNA964500", "Danio rerio telencephalon sequencing", "PRJNA964500", "Whole Genome Sequencing", "We aimed to characterize different regions and cell types in the telencephalon of zebrafish D. rerio.", null, null, "the telencephalon of two fish was extracted and dissociated for preparing scRNA seq libraries", null, "3124R", null, "breed:wildtype|age:6 mpf|dev stage:adult|collection date:2022 03 11|geo loc name:Switzerland|sex:male|tissue:telencephalon|biomaterial provider:Rainer Friedrich|collected by:Lukas Anneser|genotype:wildtype|BioSampleModel:Model organism or animal", null, null, null, null, null, null, null, null, "scRNA Seq of Danio rerio: adult telencephalon", "3124R1", "3124R1", "Single cell suspension from adult Danio rerio telencephalon. 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