{"database": "metadata", "table": "run_metadata", "is_view": false, "human_description_en": "where experiment.library_selection = \"cDNA\", experiment.library_source = \"TRANSCRIPTOMIC\" and technology = \"indrops\"", "rows": [[42599, "SRR5810682", "SRX2989237", "SRS2341160", "SRP111339", "PRJNA393429", "Dissecting hematopoietic and renal cell heterogeneity in adult zebrafish at single cell resolution using RNA sequencing [inDrops]", "GSE100910", "Transcriptome Analysis", "Recent advances in single cell transcriptomic profiling have provided unprecedented access to investigate cell heterogeneity during tissue and organ development. Here  we utilized massively parallel single cell RNA sequencing to define cell heterogeneity within the zebrafish kidney marrow  constructing a comprehensive molecular atlas of definitive hematopoiesis and functionally distinct renal cells found in adult zebrafish. Because our method analyzed blood and kidney cells in an unbiased manner  our approach was useful in characterizing immune cell deficiencies within prkdcD3612fs  il2rgaY91fs and double homozygous mutant fish  identifying blood cell losses in T  B  and natural killer cells within specific genetic mutants.  Our analysis also uncovered novel cell types including two classes of natural killer immune cells  classically defined and erythroid primed hematopoietic stem and progenitor cells  mucin secreting kidney cells  and kidney stem/progenitor cells. In total  our work provides the first comprehensive single cell transcriptomic analysis of kidney and marrow cells in the adult zebrafish. Overall design: The goal of our study is to establish the transcriptional profiles of hematopoietic and kidney cell lineages residing in the zebrafish whole kidney marrow. Firstly  we performed single cell RNA sequencing by a modified Smart seq2 protocol on sorted single cells from fluorescent transgenic zebrafish lines  which label distinct blood cell types n = 246 cells total. Secondly  we utilized droplet based single cell RNA sequencing inDrop to investigate unmarked  comprehensive hematopoietic lineage structure within wild type  casper strain zebrafish N=3 animals  n=3 782 cells total. From this  we identified ten distinct hematopoietic groups of blood and immune identities. Thirdly  we confirmed blood lineage interpretations by comparing hematopoietic lineages within wild type fish with mutant zebrafish with known immunodeficiencies  including prkdcD3612fs N=3 animals  n=3 201 cells  il2rgaY91fs N=2 animals  n=2 068 cells and prkdcD3612fs  il2rgaY91fs double compound mutant fish N=2 animals  n=2 276 cells. Lastly  we identified seven structural and functional cell lineages of kidney identities in the whole kidney marrow n=1 699 kidney cells.", "parent bioproject:PRJNA393415", "pubmed:28878000", null, "inDrop sequencing of multiple samples 2", "GSM2696104", null, "source name:Whole kidney marrow|indrop version:inDrop V3 protocol|tissue:kidney marrow|genotype:multiple genotypes", "inDrop sequencing of multiple samples 2", "Reads were processed using the indrops pipelinehttps://github.com/indrops/indrops. Briefly  reads were filtered according to structure and quality criteria. Filtered reads were then they were sorted by barcode. Demultiplexed reads were aligned to the GRCz10 transcriptome using Bowtie. Please see file \"inDrop GEO supplementary.xlsx \" available on the series record for information on de multiplexing and barcodes for individual samples and cells. Genome build: GRCz10 Supplementary files format and content: text file with read counts.", "Whole kidney marrow", null, "RNA from individual cells were reverse transcribed  barcoded  and unique transcripts are imprinted with UMI  all within the droplet. RNA from 1 500 cells of each animal were pooled and subsequently processed together. cDNA products were pre amplified to produce a cDNA library compatible with the Illumina Nextseq platform. Three sequencing runs were completed using the Nextseq 500 High Output V2 kit 75 cycles Illumina on a NextSeq 500 platform Illumina. Zebrafish cells are isolated from the kidney marrow into single cell suspensions  and treated with 15% Optiprep Sigma. Single cells are microfluidically sorted into droplets containing reverse transcription and barcoding reagents.  This method is outlined in Klein et al. PMID 26000487 and Zilionis et al. 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Here  we utilized massively parallel single cell RNA sequencing to define cell heterogeneity within the zebrafish kidney marrow  constructing a comprehensive molecular atlas of definitive hematopoiesis and functionally distinct renal cells found in adult zebrafish. Because our method analyzed blood and kidney cells in an unbiased manner  our approach was useful in characterizing immune cell deficiencies within prkdcD3612fs  il2rgaY91fs and double homozygous mutant fish  identifying blood cell losses in T  B  and natural killer cells within specific genetic mutants.  Our analysis also uncovered novel cell types including two classes of natural killer immune cells  classically defined and erythroid primed hematopoietic stem and progenitor cells  mucin secreting kidney cells  and kidney stem/progenitor cells. In total  our work provides the first comprehensive single cell transcriptomic analysis of kidney and marrow cells in the adult zebrafish. Overall design: The goal of our study is to establish the transcriptional profiles of hematopoietic and kidney cell lineages residing in the zebrafish whole kidney marrow. Firstly  we performed single cell RNA sequencing by a modified Smart seq2 protocol on sorted single cells from fluorescent transgenic zebrafish lines  which label distinct blood cell types n = 246 cells total. Secondly  we utilized droplet based single cell RNA sequencing inDrop to investigate unmarked  comprehensive hematopoietic lineage structure within wild type  casper strain zebrafish N=3 animals  n=3 782 cells total. From this  we identified ten distinct hematopoietic groups of blood and immune identities. Thirdly  we confirmed blood lineage interpretations by comparing hematopoietic lineages within wild type fish with mutant zebrafish with known immunodeficiencies  including prkdcD3612fs N=3 animals  n=3 201 cells  il2rgaY91fs N=2 animals  n=2 068 cells and prkdcD3612fs  il2rgaY91fs double compound mutant fish N=2 animals  n=2 276 cells. Lastly  we identified seven structural and functional cell lineages of kidney identities in the whole kidney marrow n=1 699 kidney cells.", "parent bioproject:PRJNA393415", "pubmed:28878000", null, "inDrop sequencing of multiple samples 2", "GSM2696104", null, "source name:Whole kidney marrow|indrop version:inDrop V3 protocol|tissue:kidney marrow|genotype:multiple genotypes", "inDrop sequencing of multiple samples 2", "Reads were processed using the indrops pipelinehttps://github.com/indrops/indrops. Briefly  reads were filtered according to structure and quality criteria. Filtered reads were then they were sorted by barcode. Demultiplexed reads were aligned to the GRCz10 transcriptome using Bowtie. Please see file \"inDrop GEO supplementary.xlsx \" available on the series record for information on de multiplexing and barcodes for individual samples and cells. Genome build: GRCz10 Supplementary files format and content: text file with read counts.", "Whole kidney marrow", null, "RNA from individual cells were reverse transcribed  barcoded  and unique transcripts are imprinted with UMI  all within the droplet. RNA from 1 500 cells of each animal were pooled and subsequently processed together. cDNA products were pre amplified to produce a cDNA library compatible with the Illumina Nextseq platform. Three sequencing runs were completed using the Nextseq 500 High Output V2 kit 75 cycles Illumina on a NextSeq 500 platform Illumina. Zebrafish cells are isolated from the kidney marrow into single cell suspensions  and treated with 15% Optiprep Sigma. Single cells are microfluidically sorted into droplets containing reverse transcription and barcoding reagents.  This method is outlined in Klein et al. PMID 26000487 and Zilionis et al. PMID 27929523.", null, "indrop version:inDrop V3 protocol|tissue:kidney marrow|genotype:multiple genotypes", "GSM2696104", "GSM2696104: inDrop sequencing of multiple samples 2; Danio rerio; RNA Seq", "GSM2696104", null, "1", "RNA from individual cells were reverse transcribed  barcoded  and unique transcripts are imprinted with UMI  all within the droplet. RNA from 1 500 cells of each animal were pooled and subsequently processed together. cDNA products were pre amplified to produce a cDNA library compatible with the Illumina Nextseq platform. Three sequencing runs were completed using the Nextseq 500 High Output V2 kit 75 cycles Illumina on a NextSeq 500 platform Illumina. Zebrafish cells are isolated from the kidney marrow into single cell suspensions  and treated with 15% Optiprep Sigma. 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Here  we utilized massively parallel single cell RNA sequencing to define cell heterogeneity within the zebrafish kidney marrow  constructing a comprehensive molecular atlas of definitive hematopoiesis and functionally distinct renal cells found in adult zebrafish. Because our method analyzed blood and kidney cells in an unbiased manner  our approach was useful in characterizing immune cell deficiencies within prkdcD3612fs  il2rgaY91fs and double homozygous mutant fish  identifying blood cell losses in T  B  and natural killer cells within specific genetic mutants.  Our analysis also uncovered novel cell types including two classes of natural killer immune cells  classically defined and erythroid primed hematopoietic stem and progenitor cells  mucin secreting kidney cells  and kidney stem/progenitor cells. In total  our work provides the first comprehensive single cell transcriptomic analysis of kidney and marrow cells in the adult zebrafish. Overall design: The goal of our study is to establish the transcriptional profiles of hematopoietic and kidney cell lineages residing in the zebrafish whole kidney marrow. Firstly  we performed single cell RNA sequencing by a modified Smart seq2 protocol on sorted single cells from fluorescent transgenic zebrafish lines  which label distinct blood cell types n = 246 cells total. Secondly  we utilized droplet based single cell RNA sequencing inDrop to investigate unmarked  comprehensive hematopoietic lineage structure within wild type  casper strain zebrafish N=3 animals  n=3 782 cells total. From this  we identified ten distinct hematopoietic groups of blood and immune identities. Thirdly  we confirmed blood lineage interpretations by comparing hematopoietic lineages within wild type fish with mutant zebrafish with known immunodeficiencies  including prkdcD3612fs N=3 animals  n=3 201 cells  il2rgaY91fs N=2 animals  n=2 068 cells and prkdcD3612fs  il2rgaY91fs double compound mutant fish N=2 animals  n=2 276 cells. Lastly  we identified seven structural and functional cell lineages of kidney identities in the whole kidney marrow n=1 699 kidney cells.", "parent bioproject:PRJNA393415", "pubmed:28878000", null, "inDrop sequencing of multiple samples 2", "GSM2696104", null, "source name:Whole kidney marrow|indrop version:inDrop V3 protocol|tissue:kidney marrow|genotype:multiple genotypes", "inDrop sequencing of multiple samples 2", "Reads were processed using the indrops pipelinehttps://github.com/indrops/indrops. Briefly  reads were filtered according to structure and quality criteria. Filtered reads were then they were sorted by barcode. Demultiplexed reads were aligned to the GRCz10 transcriptome using Bowtie. Please see file \"inDrop GEO supplementary.xlsx \" available on the series record for information on de multiplexing and barcodes for individual samples and cells. Genome build: GRCz10 Supplementary files format and content: text file with read counts.", "Whole kidney marrow", null, "RNA from individual cells were reverse transcribed  barcoded  and unique transcripts are imprinted with UMI  all within the droplet. RNA from 1 500 cells of each animal were pooled and subsequently processed together. cDNA products were pre amplified to produce a cDNA library compatible with the Illumina Nextseq platform. Three sequencing runs were completed using the Nextseq 500 High Output V2 kit 75 cycles Illumina on a NextSeq 500 platform Illumina. Zebrafish cells are isolated from the kidney marrow into single cell suspensions  and treated with 15% Optiprep Sigma. Single cells are microfluidically sorted into droplets containing reverse transcription and barcoding reagents.  This method is outlined in Klein et al. PMID 26000487 and Zilionis et al. PMID 27929523.", null, "indrop version:inDrop V3 protocol|tissue:kidney marrow|genotype:multiple genotypes", "GSM2696104", "GSM2696104: inDrop sequencing of multiple samples 2; Danio rerio; RNA Seq", "GSM2696104", null, "1", "RNA from individual cells were reverse transcribed  barcoded  and unique transcripts are imprinted with UMI  all within the droplet. RNA from 1 500 cells of each animal were pooled and subsequently processed together. cDNA products were pre amplified to produce a cDNA library compatible with the Illumina Nextseq platform. Three sequencing runs were completed using the Nextseq 500 High Output V2 kit 75 cycles Illumina on a NextSeq 500 platform Illumina. Zebrafish cells are isolated from the kidney marrow into single cell suspensions  and treated with 15% Optiprep Sigma. Single cells are microfluidically sorted into droplets containing reverse transcription and barcoding reagents.  This method is outlined in Klein et al. PMID 26000487 and Zilionis et al. 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Here  we utilized massively parallel single cell RNA sequencing to define cell heterogeneity within the zebrafish kidney marrow  constructing a comprehensive molecular atlas of definitive hematopoiesis and functionally distinct renal cells found in adult zebrafish. Because our method analyzed blood and kidney cells in an unbiased manner  our approach was useful in characterizing immune cell deficiencies within prkdcD3612fs  il2rgaY91fs and double homozygous mutant fish  identifying blood cell losses in T  B  and natural killer cells within specific genetic mutants.  Our analysis also uncovered novel cell types including two classes of natural killer immune cells  classically defined and erythroid primed hematopoietic stem and progenitor cells  mucin secreting kidney cells  and kidney stem/progenitor cells. In total  our work provides the first comprehensive single cell transcriptomic analysis of kidney and marrow cells in the adult zebrafish. Overall design: The goal of our study is to establish the transcriptional profiles of hematopoietic and kidney cell lineages residing in the zebrafish whole kidney marrow. Firstly  we performed single cell RNA sequencing by a modified Smart seq2 protocol on sorted single cells from fluorescent transgenic zebrafish lines  which label distinct blood cell types n = 246 cells total. Secondly  we utilized droplet based single cell RNA sequencing inDrop to investigate unmarked  comprehensive hematopoietic lineage structure within wild type  casper strain zebrafish N=3 animals  n=3 782 cells total. From this  we identified ten distinct hematopoietic groups of blood and immune identities. Thirdly  we confirmed blood lineage interpretations by comparing hematopoietic lineages within wild type fish with mutant zebrafish with known immunodeficiencies  including prkdcD3612fs N=3 animals  n=3 201 cells  il2rgaY91fs N=2 animals  n=2 068 cells and prkdcD3612fs  il2rgaY91fs double compound mutant fish N=2 animals  n=2 276 cells. Lastly  we identified seven structural and functional cell lineages of kidney identities in the whole kidney marrow n=1 699 kidney cells.", "parent bioproject:PRJNA393415", "pubmed:28878000", null, "inDrop sequencing of multiple samples 2", "GSM2696104", null, "source name:Whole kidney marrow|indrop version:inDrop V3 protocol|tissue:kidney marrow|genotype:multiple genotypes", "inDrop sequencing of multiple samples 2", "Reads were processed using the indrops pipelinehttps://github.com/indrops/indrops. Briefly  reads were filtered according to structure and quality criteria. Filtered reads were then they were sorted by barcode. Demultiplexed reads were aligned to the GRCz10 transcriptome using Bowtie. Please see file \"inDrop GEO supplementary.xlsx \" available on the series record for information on de multiplexing and barcodes for individual samples and cells. Genome build: GRCz10 Supplementary files format and content: text file with read counts.", "Whole kidney marrow", null, "RNA from individual cells were reverse transcribed  barcoded  and unique transcripts are imprinted with UMI  all within the droplet. RNA from 1 500 cells of each animal were pooled and subsequently processed together. cDNA products were pre amplified to produce a cDNA library compatible with the Illumina Nextseq platform. Three sequencing runs were completed using the Nextseq 500 High Output V2 kit 75 cycles Illumina on a NextSeq 500 platform Illumina. Zebrafish cells are isolated from the kidney marrow into single cell suspensions  and treated with 15% Optiprep Sigma. Single cells are microfluidically sorted into droplets containing reverse transcription and barcoding reagents.  This method is outlined in Klein et al. PMID 26000487 and Zilionis et al. PMID 27929523.", null, "indrop version:inDrop V3 protocol|tissue:kidney marrow|genotype:multiple genotypes", "GSM2696104", "GSM2696104: inDrop sequencing of multiple samples 2; Danio rerio; RNA Seq", "GSM2696104", null, "1", "RNA from individual cells were reverse transcribed  barcoded  and unique transcripts are imprinted with UMI  all within the droplet. RNA from 1 500 cells of each animal were pooled and subsequently processed together. cDNA products were pre amplified to produce a cDNA library compatible with the Illumina Nextseq platform. Three sequencing runs were completed using the Nextseq 500 High Output V2 kit 75 cycles Illumina on a NextSeq 500 platform Illumina. Zebrafish cells are isolated from the kidney marrow into single cell suspensions  and treated with 15% Optiprep Sigma. Single cells are microfluidically sorted into droplets containing reverse transcription and barcoding reagents.  This method is outlined in Klein et al. PMID 26000487 and Zilionis et al. 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Here  we utilized massively parallel single cell RNA sequencing to define cell heterogeneity within the zebrafish kidney marrow  constructing a comprehensive molecular atlas of definitive hematopoiesis and functionally distinct renal cells found in adult zebrafish. Because our method analyzed blood and kidney cells in an unbiased manner  our approach was useful in characterizing immune cell deficiencies within prkdcD3612fs  il2rgaY91fs and double homozygous mutant fish  identifying blood cell losses in T  B  and natural killer cells within specific genetic mutants.  Our analysis also uncovered novel cell types including two classes of natural killer immune cells  classically defined and erythroid primed hematopoietic stem and progenitor cells  mucin secreting kidney cells  and kidney stem/progenitor cells. In total  our work provides the first comprehensive single cell transcriptomic analysis of kidney and marrow cells in the adult zebrafish. Overall design: The goal of our study is to establish the transcriptional profiles of hematopoietic and kidney cell lineages residing in the zebrafish whole kidney marrow. Firstly  we performed single cell RNA sequencing by a modified Smart seq2 protocol on sorted single cells from fluorescent transgenic zebrafish lines  which label distinct blood cell types n = 246 cells total. Secondly  we utilized droplet based single cell RNA sequencing inDrop to investigate unmarked  comprehensive hematopoietic lineage structure within wild type  casper strain zebrafish N=3 animals  n=3 782 cells total. From this  we identified ten distinct hematopoietic groups of blood and immune identities. Thirdly  we confirmed blood lineage interpretations by comparing hematopoietic lineages within wild type fish with mutant zebrafish with known immunodeficiencies  including prkdcD3612fs N=3 animals  n=3 201 cells  il2rgaY91fs N=2 animals  n=2 068 cells and prkdcD3612fs  il2rgaY91fs double compound mutant fish N=2 animals  n=2 276 cells. Lastly  we identified seven structural and functional cell lineages of kidney identities in the whole kidney marrow n=1 699 kidney cells.", "parent bioproject:PRJNA393415", "pubmed:28878000", null, "inDrop sequencing of multiple samples 1", "GSM2696103", null, "source name:Whole kidney marrow|indrop version:inDrop V3 protocol|tissue:kidney marrow|genotype:multiple genotypes", "inDrop sequencing of multiple samples 1", "Reads were processed using the indrops pipelinehttps://github.com/indrops/indrops. Briefly  reads were filtered according to structure and quality criteria. Filtered reads were then they were sorted by barcode. Demultiplexed reads were aligned to the GRCz10 transcriptome using Bowtie. Please see file \"inDrop GEO supplementary.xlsx \" available on the series record for information on de multiplexing and barcodes for individual samples and cells. Genome build: GRCz10 Supplementary files format and content: text file with read counts.", "Whole kidney marrow", null, "RNA from individual cells were reverse transcribed  barcoded  and unique transcripts are imprinted with UMI  all within the droplet. RNA from 1 500 cells of each animal were pooled and subsequently processed together. cDNA products were pre amplified to produce a cDNA library compatible with the Illumina Nextseq platform. Three sequencing runs were completed using the Nextseq 500 High Output V2 kit 75 cycles Illumina on a NextSeq 500 platform Illumina. Zebrafish cells are isolated from the kidney marrow into single cell suspensions  and treated with 15% Optiprep Sigma. Single cells are microfluidically sorted into droplets containing reverse transcription and barcoding reagents.  This method is outlined in Klein et al. PMID 26000487 and Zilionis et al. PMID 27929523.", null, "indrop version:inDrop V3 protocol|tissue:kidney marrow|genotype:multiple genotypes", "GSM2696103", "GSM2696103: inDrop sequencing of multiple samples 1; Danio rerio; RNA Seq", "GSM2696103", null, "1", "RNA from individual cells were reverse transcribed  barcoded  and unique transcripts are imprinted with UMI  all within the droplet. RNA from 1 500 cells of each animal were pooled and subsequently processed together. cDNA products were pre amplified to produce a cDNA library compatible with the Illumina Nextseq platform. Three sequencing runs were completed using the Nextseq 500 High Output V2 kit 75 cycles Illumina on a NextSeq 500 platform Illumina. Zebrafish cells are isolated from the kidney marrow into single cell suspensions  and treated with 15% Optiprep Sigma. Single cells are microfluidically sorted into droplets containing reverse transcription and barcoding reagents.  This method is outlined in Klein et al. PMID 26000487 and Zilionis et al. 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Here  we utilized massively parallel single cell RNA sequencing to define cell heterogeneity within the zebrafish kidney marrow  constructing a comprehensive molecular atlas of definitive hematopoiesis and functionally distinct renal cells found in adult zebrafish. Because our method analyzed blood and kidney cells in an unbiased manner  our approach was useful in characterizing immune cell deficiencies within prkdcD3612fs  il2rgaY91fs and double homozygous mutant fish  identifying blood cell losses in T  B  and natural killer cells within specific genetic mutants.  Our analysis also uncovered novel cell types including two classes of natural killer immune cells  classically defined and erythroid primed hematopoietic stem and progenitor cells  mucin secreting kidney cells  and kidney stem/progenitor cells. In total  our work provides the first comprehensive single cell transcriptomic analysis of kidney and marrow cells in the adult zebrafish. Overall design: The goal of our study is to establish the transcriptional profiles of hematopoietic and kidney cell lineages residing in the zebrafish whole kidney marrow. Firstly  we performed single cell RNA sequencing by a modified Smart seq2 protocol on sorted single cells from fluorescent transgenic zebrafish lines  which label distinct blood cell types n = 246 cells total. Secondly  we utilized droplet based single cell RNA sequencing inDrop to investigate unmarked  comprehensive hematopoietic lineage structure within wild type  casper strain zebrafish N=3 animals  n=3 782 cells total. From this  we identified ten distinct hematopoietic groups of blood and immune identities. Thirdly  we confirmed blood lineage interpretations by comparing hematopoietic lineages within wild type fish with mutant zebrafish with known immunodeficiencies  including prkdcD3612fs N=3 animals  n=3 201 cells  il2rgaY91fs N=2 animals  n=2 068 cells and prkdcD3612fs  il2rgaY91fs double compound mutant fish N=2 animals  n=2 276 cells. Lastly  we identified seven structural and functional cell lineages of kidney identities in the whole kidney marrow n=1 699 kidney cells.", "parent bioproject:PRJNA393415", "pubmed:28878000", null, "inDrop sequencing of multiple samples 1", "GSM2696103", null, "source name:Whole kidney marrow|indrop version:inDrop V3 protocol|tissue:kidney marrow|genotype:multiple genotypes", "inDrop sequencing of multiple samples 1", "Reads were processed using the indrops pipelinehttps://github.com/indrops/indrops. Briefly  reads were filtered according to structure and quality criteria. Filtered reads were then they were sorted by barcode. Demultiplexed reads were aligned to the GRCz10 transcriptome using Bowtie. Please see file \"inDrop GEO supplementary.xlsx \" available on the series record for information on de multiplexing and barcodes for individual samples and cells. Genome build: GRCz10 Supplementary files format and content: text file with read counts.", "Whole kidney marrow", null, "RNA from individual cells were reverse transcribed  barcoded  and unique transcripts are imprinted with UMI  all within the droplet. RNA from 1 500 cells of each animal were pooled and subsequently processed together. cDNA products were pre amplified to produce a cDNA library compatible with the Illumina Nextseq platform. Three sequencing runs were completed using the Nextseq 500 High Output V2 kit 75 cycles Illumina on a NextSeq 500 platform Illumina. Zebrafish cells are isolated from the kidney marrow into single cell suspensions  and treated with 15% Optiprep Sigma. Single cells are microfluidically sorted into droplets containing reverse transcription and barcoding reagents.  This method is outlined in Klein et al. PMID 26000487 and Zilionis et al. PMID 27929523.", null, "indrop version:inDrop V3 protocol|tissue:kidney marrow|genotype:multiple genotypes", "GSM2696103", "GSM2696103: inDrop sequencing of multiple samples 1; Danio rerio; RNA Seq", "GSM2696103", null, "1", "RNA from individual cells were reverse transcribed  barcoded  and unique transcripts are imprinted with UMI  all within the droplet. RNA from 1 500 cells of each animal were pooled and subsequently processed together. cDNA products were pre amplified to produce a cDNA library compatible with the Illumina Nextseq platform. Three sequencing runs were completed using the Nextseq 500 High Output V2 kit 75 cycles Illumina on a NextSeq 500 platform Illumina. Zebrafish cells are isolated from the kidney marrow into single cell suspensions  and treated with 15% Optiprep Sigma. Single cells are microfluidically sorted into droplets containing reverse transcription and barcoding reagents.  This method is outlined in Klein et al. PMID 26000487 and Zilionis et al. PMID 27929523.", "GEO Accession:GSM2696103", "RNA-Seq", "TRANSCRIPTOMIC", "cDNA", "SINGLE", "ILLUMINA", "NextSeq 500", null, "SRP111339", null, null, "inDrop_v3_run1_R2.fastq.gz", "fastq", 3523720792.0, 440465099.0, "GSM2696103 r2", "0:0 1:8", "A:892841722;C:781074758;G:1176856476;T:672376743;N:571093", 0, 8, null, null, 892841722, 781074758, 1176856476, 672376743, 571093, "SRX2989236", "SRS2341159", "SRA584587", "GEO", "Pathology, Massachusetts General Hospital", 1, 0.0, null, 0.0, null, 1.0, null, null, null, 8, null, "T", null, "under 1.2% mapping rate", "illumina", "nextseq", "unknown", "random_priming", "unknown", "sc", "single_cell_droplet", "indrops", null, "United States", "2017-07-07", "Undetermined", "Adult", "Kidney", "Renal System"], [42605, "SRR5810680", "SRX2989236", "SRS2341159", "SRP111339", "PRJNA393429", "Dissecting hematopoietic and renal cell heterogeneity in adult zebrafish at single cell resolution using RNA sequencing [inDrops]", "GSE100910", "Transcriptome Analysis", "Recent advances in single cell transcriptomic profiling have provided unprecedented access to investigate cell heterogeneity during tissue and organ development. Here  we utilized massively parallel single cell RNA sequencing to define cell heterogeneity within the zebrafish kidney marrow  constructing a comprehensive molecular atlas of definitive hematopoiesis and functionally distinct renal cells found in adult zebrafish. Because our method analyzed blood and kidney cells in an unbiased manner  our approach was useful in characterizing immune cell deficiencies within prkdcD3612fs  il2rgaY91fs and double homozygous mutant fish  identifying blood cell losses in T  B  and natural killer cells within specific genetic mutants.  Our analysis also uncovered novel cell types including two classes of natural killer immune cells  classically defined and erythroid primed hematopoietic stem and progenitor cells  mucin secreting kidney cells  and kidney stem/progenitor cells. In total  our work provides the first comprehensive single cell transcriptomic analysis of kidney and marrow cells in the adult zebrafish. Overall design: The goal of our study is to establish the transcriptional profiles of hematopoietic and kidney cell lineages residing in the zebrafish whole kidney marrow. Firstly  we performed single cell RNA sequencing by a modified Smart seq2 protocol on sorted single cells from fluorescent transgenic zebrafish lines  which label distinct blood cell types n = 246 cells total. Secondly  we utilized droplet based single cell RNA sequencing inDrop to investigate unmarked  comprehensive hematopoietic lineage structure within wild type  casper strain zebrafish N=3 animals  n=3 782 cells total. From this  we identified ten distinct hematopoietic groups of blood and immune identities. Thirdly  we confirmed blood lineage interpretations by comparing hematopoietic lineages within wild type fish with mutant zebrafish with known immunodeficiencies  including prkdcD3612fs N=3 animals  n=3 201 cells  il2rgaY91fs N=2 animals  n=2 068 cells and prkdcD3612fs  il2rgaY91fs double compound mutant fish N=2 animals  n=2 276 cells. Lastly  we identified seven structural and functional cell lineages of kidney identities in the whole kidney marrow n=1 699 kidney cells.", "parent bioproject:PRJNA393415", "pubmed:28878000", null, "inDrop sequencing of multiple samples 1", "GSM2696103", null, "source name:Whole kidney marrow|indrop version:inDrop V3 protocol|tissue:kidney marrow|genotype:multiple genotypes", "inDrop sequencing of multiple samples 1", "Reads were processed using the indrops pipelinehttps://github.com/indrops/indrops. Briefly  reads were filtered according to structure and quality criteria. Filtered reads were then they were sorted by barcode. Demultiplexed reads were aligned to the GRCz10 transcriptome using Bowtie. Please see file \"inDrop GEO supplementary.xlsx \" available on the series record for information on de multiplexing and barcodes for individual samples and cells. Genome build: GRCz10 Supplementary files format and content: text file with read counts.", "Whole kidney marrow", null, "RNA from individual cells were reverse transcribed  barcoded  and unique transcripts are imprinted with UMI  all within the droplet. RNA from 1 500 cells of each animal were pooled and subsequently processed together. cDNA products were pre amplified to produce a cDNA library compatible with the Illumina Nextseq platform. Three sequencing runs were completed using the Nextseq 500 High Output V2 kit 75 cycles Illumina on a NextSeq 500 platform Illumina. Zebrafish cells are isolated from the kidney marrow into single cell suspensions  and treated with 15% Optiprep Sigma. Single cells are microfluidically sorted into droplets containing reverse transcription and barcoding reagents.  This method is outlined in Klein et al. PMID 26000487 and Zilionis et al. PMID 27929523.", null, "indrop version:inDrop V3 protocol|tissue:kidney marrow|genotype:multiple genotypes", "GSM2696103", "GSM2696103: inDrop sequencing of multiple samples 1; Danio rerio; RNA Seq", "GSM2696103", null, "1", "RNA from individual cells were reverse transcribed  barcoded  and unique transcripts are imprinted with UMI  all within the droplet. RNA from 1 500 cells of each animal were pooled and subsequently processed together. cDNA products were pre amplified to produce a cDNA library compatible with the Illumina Nextseq platform. Three sequencing runs were completed using the Nextseq 500 High Output V2 kit 75 cycles Illumina on a NextSeq 500 platform Illumina. Zebrafish cells are isolated from the kidney marrow into single cell suspensions  and treated with 15% Optiprep Sigma. Single cells are microfluidically sorted into droplets containing reverse transcription and barcoding reagents.  This method is outlined in Klein et al. PMID 26000487 and Zilionis et al. PMID 27929523.", "GEO Accession:GSM2696103", "RNA-Seq", "TRANSCRIPTOMIC", "cDNA", "SINGLE", "ILLUMINA", "NextSeq 500", null, "SRP111339", null, null, "inDrop_v3_run1_R3.fastq.gz", "fastq", 3523720792.0, 440465099.0, "GSM2696103 r3", "0:8", "A:1016854893;C:682256486;G:759860179;T:1063129659;N:1619575", 8, null, null, null, 1016854893, 682256486, 759860179, 1063129659, 1619575, "SRX2989236", "SRS2341159", "SRA584587", "GEO", "Pathology, Massachusetts General Hospital", 1, 0.0, null, 0.0, null, 1.0, null, null, null, 8, null, "T", null, "under 1.2% mapping rate", "illumina", "nextseq", "unknown", "random_priming", "unknown", "sc", "single_cell_droplet", "indrops", null, "United States", "2017-07-07", "Undetermined", "Adult", "Kidney", "Renal System"], [42606, "SRR5810681", "SRX2989236", "SRS2341159", "SRP111339", "PRJNA393429", "Dissecting hematopoietic and renal cell heterogeneity in adult zebrafish at single cell resolution using RNA sequencing [inDrops]", "GSE100910", "Transcriptome Analysis", "Recent advances in single cell transcriptomic profiling have provided unprecedented access to investigate cell heterogeneity during tissue and organ development. Here  we utilized massively parallel single cell RNA sequencing to define cell heterogeneity within the zebrafish kidney marrow  constructing a comprehensive molecular atlas of definitive hematopoiesis and functionally distinct renal cells found in adult zebrafish. Because our method analyzed blood and kidney cells in an unbiased manner  our approach was useful in characterizing immune cell deficiencies within prkdcD3612fs  il2rgaY91fs and double homozygous mutant fish  identifying blood cell losses in T  B  and natural killer cells within specific genetic mutants.  Our analysis also uncovered novel cell types including two classes of natural killer immune cells  classically defined and erythroid primed hematopoietic stem and progenitor cells  mucin secreting kidney cells  and kidney stem/progenitor cells. In total  our work provides the first comprehensive single cell transcriptomic analysis of kidney and marrow cells in the adult zebrafish. Overall design: The goal of our study is to establish the transcriptional profiles of hematopoietic and kidney cell lineages residing in the zebrafish whole kidney marrow. Firstly  we performed single cell RNA sequencing by a modified Smart seq2 protocol on sorted single cells from fluorescent transgenic zebrafish lines  which label distinct blood cell types n = 246 cells total. Secondly  we utilized droplet based single cell RNA sequencing inDrop to investigate unmarked  comprehensive hematopoietic lineage structure within wild type  casper strain zebrafish N=3 animals  n=3 782 cells total. From this  we identified ten distinct hematopoietic groups of blood and immune identities. Thirdly  we confirmed blood lineage interpretations by comparing hematopoietic lineages within wild type fish with mutant zebrafish with known immunodeficiencies  including prkdcD3612fs N=3 animals  n=3 201 cells  il2rgaY91fs N=2 animals  n=2 068 cells and prkdcD3612fs  il2rgaY91fs double compound mutant fish N=2 animals  n=2 276 cells. Lastly  we identified seven structural and functional cell lineages of kidney identities in the whole kidney marrow n=1 699 kidney cells.", "parent bioproject:PRJNA393415", "pubmed:28878000", null, "inDrop sequencing of multiple samples 1", "GSM2696103", null, "source name:Whole kidney marrow|indrop version:inDrop V3 protocol|tissue:kidney marrow|genotype:multiple genotypes", "inDrop sequencing of multiple samples 1", "Reads were processed using the indrops pipelinehttps://github.com/indrops/indrops. Briefly  reads were filtered according to structure and quality criteria. Filtered reads were then they were sorted by barcode. Demultiplexed reads were aligned to the GRCz10 transcriptome using Bowtie. Please see file \"inDrop GEO supplementary.xlsx \" available on the series record for information on de multiplexing and barcodes for individual samples and cells. Genome build: GRCz10 Supplementary files format and content: text file with read counts.", "Whole kidney marrow", null, "RNA from individual cells were reverse transcribed  barcoded  and unique transcripts are imprinted with UMI  all within the droplet. RNA from 1 500 cells of each animal were pooled and subsequently processed together. cDNA products were pre amplified to produce a cDNA library compatible with the Illumina Nextseq platform. Three sequencing runs were completed using the Nextseq 500 High Output V2 kit 75 cycles Illumina on a NextSeq 500 platform Illumina. Zebrafish cells are isolated from the kidney marrow into single cell suspensions  and treated with 15% Optiprep Sigma. Single cells are microfluidically sorted into droplets containing reverse transcription and barcoding reagents.  This method is outlined in Klein et al. PMID 26000487 and Zilionis et al. PMID 27929523.", null, "indrop version:inDrop V3 protocol|tissue:kidney marrow|genotype:multiple genotypes", "GSM2696103", "GSM2696103: inDrop sequencing of multiple samples 1; Danio rerio; RNA Seq", "GSM2696103", null, "1", "RNA from individual cells were reverse transcribed  barcoded  and unique transcripts are imprinted with UMI  all within the droplet. RNA from 1 500 cells of each animal were pooled and subsequently processed together. cDNA products were pre amplified to produce a cDNA library compatible with the Illumina Nextseq platform. Three sequencing runs were completed using the Nextseq 500 High Output V2 kit 75 cycles Illumina on a NextSeq 500 platform Illumina. Zebrafish cells are isolated from the kidney marrow into single cell suspensions  and treated with 15% Optiprep Sigma. Single cells are microfluidically sorted into droplets containing reverse transcription and barcoding reagents.  This method is outlined in Klein et al. PMID 26000487 and Zilionis et al. PMID 27929523.", "GEO Accession:GSM2696103", "RNA-Seq", "TRANSCRIPTOMIC", "cDNA", "SINGLE", "ILLUMINA", "NextSeq 500", null, "SRP111339", null, null, "inDrop_v3_run1_R4.fastq.gz", "fastq", 6166511386.0, 440465099.0, "GSM2696103 r4", "0:14", "A:1513770161;C:1469643118;G:1669791951;T:1509862301;N:3443855", 14, null, null, null, 1513770161, 1469643118, 1669791951, 1509862301, 3443855, "SRX2989236", "SRS2341159", "SRA584587", "GEO", "Pathology, Massachusetts General Hospital", 1, 0.0, null, 0.0, null, 1.0, null, null, null, 14, null, "T", null, "under 1.2% mapping rate", "illumina", "nextseq", "unknown", "random_priming", "unknown", "sc", "single_cell_droplet", "indrops", null, "United States", "2017-07-07", "Undetermined", "Adult", "Kidney", "Renal System"], [42607, "SRR5810676", "SRX2989235", "SRS2341158", "SRP111339", "PRJNA393429", "Dissecting hematopoietic and renal cell heterogeneity in adult zebrafish at single cell resolution using RNA sequencing [inDrops]", "GSE100910", "Transcriptome Analysis", "Recent advances in single cell transcriptomic profiling have provided unprecedented access to investigate cell heterogeneity during tissue and organ development. Here  we utilized massively parallel single cell RNA sequencing to define cell heterogeneity within the zebrafish kidney marrow  constructing a comprehensive molecular atlas of definitive hematopoiesis and functionally distinct renal cells found in adult zebrafish. Because our method analyzed blood and kidney cells in an unbiased manner  our approach was useful in characterizing immune cell deficiencies within prkdcD3612fs  il2rgaY91fs and double homozygous mutant fish  identifying blood cell losses in T  B  and natural killer cells within specific genetic mutants.  Our analysis also uncovered novel cell types including two classes of natural killer immune cells  classically defined and erythroid primed hematopoietic stem and progenitor cells  mucin secreting kidney cells  and kidney stem/progenitor cells. In total  our work provides the first comprehensive single cell transcriptomic analysis of kidney and marrow cells in the adult zebrafish. Overall design: The goal of our study is to establish the transcriptional profiles of hematopoietic and kidney cell lineages residing in the zebrafish whole kidney marrow. Firstly  we performed single cell RNA sequencing by a modified Smart seq2 protocol on sorted single cells from fluorescent transgenic zebrafish lines  which label distinct blood cell types n = 246 cells total. Secondly  we utilized droplet based single cell RNA sequencing inDrop to investigate unmarked  comprehensive hematopoietic lineage structure within wild type  casper strain zebrafish N=3 animals  n=3 782 cells total. From this  we identified ten distinct hematopoietic groups of blood and immune identities. Thirdly  we confirmed blood lineage interpretations by comparing hematopoietic lineages within wild type fish with mutant zebrafish with known immunodeficiencies  including prkdcD3612fs N=3 animals  n=3 201 cells  il2rgaY91fs N=2 animals  n=2 068 cells and prkdcD3612fs  il2rgaY91fs double compound mutant fish N=2 animals  n=2 276 cells. Lastly  we identified seven structural and functional cell lineages of kidney identities in the whole kidney marrow n=1 699 kidney cells.", "parent bioproject:PRJNA393415", "pubmed:28878000", null, "inDrop sequencing of prkdcD3612fs homozygous mutant zebrafish sample animal #2", "GSM2696102", null, "source name:Whole kidney marrow|indrop version:inDrop V2 protocol|tissue:kidney marrow|genotype:prkdcD3612fs", "inDrop sequencing of prkdcD3612fs homozygous mutant zebrafish sample animal #2", "Reads were processed using the indrops pipelinehttps://github.com/indrops/indrops. Briefly  reads were filtered according to structure and quality criteria. Filtered reads were then they were sorted by barcode. Demultiplexed reads were aligned to the GRCz10 transcriptome using Bowtie. Please see file \"inDrop GEO supplementary.xlsx \" available on the series record for information on de multiplexing and barcodes for individual samples and cells. Genome build: GRCz10 Supplementary files format and content: text file with read counts.", "Whole kidney marrow", null, "RNA from individual cells were reverse transcribed  barcoded  and unique transcripts are imprinted with UMI  all within the droplet. RNA from 1 500 cells of each animal were pooled and subsequently processed together. cDNA products were pre amplified to produce a cDNA library compatible with the Illumina Nextseq platform. Three sequencing runs were completed using the Nextseq 500 High Output V2 kit 75 cycles Illumina on a NextSeq 500 platform Illumina. Zebrafish cells are isolated from the kidney marrow into single cell suspensions  and treated with 15% Optiprep Sigma. Single cells are microfluidically sorted into droplets containing reverse transcription and barcoding reagents.  This method is outlined in Klein et al. PMID 26000487 and Zilionis et al. PMID 27929523.", null, "indrop version:inDrop V2 protocol|tissue:kidney marrow|genotype:prkdcD3612fs", "GSM2696102", "GSM2696102: inDrop sequencing of prkdcD3612fs homozygous mutant zebrafish sample animal #2; Danio rerio; RNA Seq", "GSM2696102", null, "1", "RNA from individual cells were reverse transcribed  barcoded  and unique transcripts are imprinted with UMI  all within the droplet. RNA from 1 500 cells of each animal were pooled and subsequently processed together. cDNA products were pre amplified to produce a cDNA library compatible with the Illumina Nextseq platform. Three sequencing runs were completed using the Nextseq 500 High Output V2 kit 75 cycles Illumina on a NextSeq 500 platform Illumina. Zebrafish cells are isolated from the kidney marrow into single cell suspensions  and treated with 15% Optiprep Sigma. Single cells are microfluidically sorted into droplets containing reverse transcription and barcoding reagents.  This method is outlined in Klein et al. PMID 26000487 and Zilionis et al. PMID 27929523.", "GEO Accession:GSM2696102", "RNA-Seq", "TRANSCRIPTOMIC", "cDNA", "SINGLE", "ILLUMINA", "NextSeq 500", null, "SRP111339", null, null, "PRKDC2_R1.fastq.gz", "fastq", 1987929396.0, 55220261.0, "GSM2696102 r1", "0:36 1:0", "A:506372087;C:413396954;G:415448050;T:652683387;N:28918", 36, 0, null, null, 506372087, 413396954, 415448050, 652683387, 28918, "SRX2989235", "SRS2341158", "SRA584587", "GEO", "Pathology, Massachusetts General Hospital", 1, 0.86587, null, 0.12447, null, 0.80336, null, 0.54126, null, 36, null, "B", null, "usable mapping rate", "illumina", "nextseq", "unknown", "random_priming", "unknown", "sc", "single_cell_droplet", "indrops", null, "United States", "2017-07-07", "Undetermined", "Adult", "Kidney", "Renal System"], [42608, "SRR5810677", "SRX2989235", "SRS2341158", "SRP111339", "PRJNA393429", "Dissecting hematopoietic and renal cell heterogeneity in adult zebrafish at single cell resolution using RNA sequencing [inDrops]", "GSE100910", "Transcriptome Analysis", "Recent advances in single cell transcriptomic profiling have provided unprecedented access to investigate cell heterogeneity during tissue and organ development. Here  we utilized massively parallel single cell RNA sequencing to define cell heterogeneity within the zebrafish kidney marrow  constructing a comprehensive molecular atlas of definitive hematopoiesis and functionally distinct renal cells found in adult zebrafish. Because our method analyzed blood and kidney cells in an unbiased manner  our approach was useful in characterizing immune cell deficiencies within prkdcD3612fs  il2rgaY91fs and double homozygous mutant fish  identifying blood cell losses in T  B  and natural killer cells within specific genetic mutants.  Our analysis also uncovered novel cell types including two classes of natural killer immune cells  classically defined and erythroid primed hematopoietic stem and progenitor cells  mucin secreting kidney cells  and kidney stem/progenitor cells. In total  our work provides the first comprehensive single cell transcriptomic analysis of kidney and marrow cells in the adult zebrafish. Overall design: The goal of our study is to establish the transcriptional profiles of hematopoietic and kidney cell lineages residing in the zebrafish whole kidney marrow. Firstly  we performed single cell RNA sequencing by a modified Smart seq2 protocol on sorted single cells from fluorescent transgenic zebrafish lines  which label distinct blood cell types n = 246 cells total. Secondly  we utilized droplet based single cell RNA sequencing inDrop to investigate unmarked  comprehensive hematopoietic lineage structure within wild type  casper strain zebrafish N=3 animals  n=3 782 cells total. From this  we identified ten distinct hematopoietic groups of blood and immune identities. Thirdly  we confirmed blood lineage interpretations by comparing hematopoietic lineages within wild type fish with mutant zebrafish with known immunodeficiencies  including prkdcD3612fs N=3 animals  n=3 201 cells  il2rgaY91fs N=2 animals  n=2 068 cells and prkdcD3612fs  il2rgaY91fs double compound mutant fish N=2 animals  n=2 276 cells. Lastly  we identified seven structural and functional cell lineages of kidney identities in the whole kidney marrow n=1 699 kidney cells.", "parent bioproject:PRJNA393415", "pubmed:28878000", null, "inDrop sequencing of prkdcD3612fs homozygous mutant zebrafish sample animal #2", "GSM2696102", null, "source name:Whole kidney marrow|indrop version:inDrop V2 protocol|tissue:kidney marrow|genotype:prkdcD3612fs", "inDrop sequencing of prkdcD3612fs homozygous mutant zebrafish sample animal #2", "Reads were processed using the indrops pipelinehttps://github.com/indrops/indrops. Briefly  reads were filtered according to structure and quality criteria. Filtered reads were then they were sorted by barcode. Demultiplexed reads were aligned to the GRCz10 transcriptome using Bowtie. Please see file \"inDrop GEO supplementary.xlsx \" available on the series record for information on de multiplexing and barcodes for individual samples and cells. Genome build: GRCz10 Supplementary files format and content: text file with read counts.", "Whole kidney marrow", null, "RNA from individual cells were reverse transcribed  barcoded  and unique transcripts are imprinted with UMI  all within the droplet. RNA from 1 500 cells of each animal were pooled and subsequently processed together. cDNA products were pre amplified to produce a cDNA library compatible with the Illumina Nextseq platform. Three sequencing runs were completed using the Nextseq 500 High Output V2 kit 75 cycles Illumina on a NextSeq 500 platform Illumina. Zebrafish cells are isolated from the kidney marrow into single cell suspensions  and treated with 15% Optiprep Sigma. Single cells are microfluidically sorted into droplets containing reverse transcription and barcoding reagents.  This method is outlined in Klein et al. PMID 26000487 and Zilionis et al. PMID 27929523.", null, "indrop version:inDrop V2 protocol|tissue:kidney marrow|genotype:prkdcD3612fs", "GSM2696102", "GSM2696102: inDrop sequencing of prkdcD3612fs homozygous mutant zebrafish sample animal #2; Danio rerio; RNA Seq", "GSM2696102", null, "1", "RNA from individual cells were reverse transcribed  barcoded  and unique transcripts are imprinted with UMI  all within the droplet. RNA from 1 500 cells of each animal were pooled and subsequently processed together. cDNA products were pre amplified to produce a cDNA library compatible with the Illumina Nextseq platform. Three sequencing runs were completed using the Nextseq 500 High Output V2 kit 75 cycles Illumina on a NextSeq 500 platform Illumina. Zebrafish cells are isolated from the kidney marrow into single cell suspensions  and treated with 15% Optiprep Sigma. Single cells are microfluidically sorted into droplets containing reverse transcription and barcoding reagents.  This method is outlined in Klein et al. PMID 26000487 and Zilionis et al. PMID 27929523.", "GEO Accession:GSM2696102", "RNA-Seq", "TRANSCRIPTOMIC", "cDNA", "SINGLE", "ILLUMINA", "NextSeq 500", null, "SRP111339", null, null, "PRKDC2_R2.fastq.gz", "fastq", 2761013050.0, 55220261.0, "GSM2696102 r2", "0:0 1:50", "A:554483675;C:506568509;G:721419849;T:974751566;N:3789451", 0, 50, null, null, 554483675, 506568509, 721419849, 974751566, 3789451, "SRX2989235", "SRS2341158", "SRA584587", "GEO", "Pathology, Massachusetts General Hospital", 1, 0.00796, null, 0.0077, null, 0.99961, null, 0.78947, null, 50, null, "T", null, "under 1.2% mapping rate", "illumina", "nextseq", "unknown", "random_priming", "unknown", "sc", "single_cell_droplet", "indrops", null, "United States", "2017-07-07", "Undetermined", "Adult", "Kidney", "Renal System"], [42609, "SRR5810674", "SRX2989234", "SRS2341157", "SRP111339", "PRJNA393429", "Dissecting hematopoietic and renal cell heterogeneity in adult zebrafish at single cell resolution using RNA sequencing [inDrops]", "GSE100910", "Transcriptome Analysis", "Recent advances in single cell transcriptomic profiling have provided unprecedented access to investigate cell heterogeneity during tissue and organ development. Here  we utilized massively parallel single cell RNA sequencing to define cell heterogeneity within the zebrafish kidney marrow  constructing a comprehensive molecular atlas of definitive hematopoiesis and functionally distinct renal cells found in adult zebrafish. Because our method analyzed blood and kidney cells in an unbiased manner  our approach was useful in characterizing immune cell deficiencies within prkdcD3612fs  il2rgaY91fs and double homozygous mutant fish  identifying blood cell losses in T  B  and natural killer cells within specific genetic mutants.  Our analysis also uncovered novel cell types including two classes of natural killer immune cells  classically defined and erythroid primed hematopoietic stem and progenitor cells  mucin secreting kidney cells  and kidney stem/progenitor cells. In total  our work provides the first comprehensive single cell transcriptomic analysis of kidney and marrow cells in the adult zebrafish. Overall design: The goal of our study is to establish the transcriptional profiles of hematopoietic and kidney cell lineages residing in the zebrafish whole kidney marrow. Firstly  we performed single cell RNA sequencing by a modified Smart seq2 protocol on sorted single cells from fluorescent transgenic zebrafish lines  which label distinct blood cell types n = 246 cells total. Secondly  we utilized droplet based single cell RNA sequencing inDrop to investigate unmarked  comprehensive hematopoietic lineage structure within wild type  casper strain zebrafish N=3 animals  n=3 782 cells total. From this  we identified ten distinct hematopoietic groups of blood and immune identities. Thirdly  we confirmed blood lineage interpretations by comparing hematopoietic lineages within wild type fish with mutant zebrafish with known immunodeficiencies  including prkdcD3612fs N=3 animals  n=3 201 cells  il2rgaY91fs N=2 animals  n=2 068 cells and prkdcD3612fs  il2rgaY91fs double compound mutant fish N=2 animals  n=2 276 cells. Lastly  we identified seven structural and functional cell lineages of kidney identities in the whole kidney marrow n=1 699 kidney cells.", "parent bioproject:PRJNA393415", "pubmed:28878000", null, "inDrop sequencing of prkdcD3612fs homozygous mutant zebrafish sample animal #1", "GSM2696101", null, "source name:Whole kidney marrow|indrop version:inDrop V2 protocol|tissue:kidney marrow|genotype:prkdcD3612fs", "inDrop sequencing of prkdcD3612fs homozygous mutant zebrafish sample animal #1", "Reads were processed using the indrops pipelinehttps://github.com/indrops/indrops. Briefly  reads were filtered according to structure and quality criteria. Filtered reads were then they were sorted by barcode. Demultiplexed reads were aligned to the GRCz10 transcriptome using Bowtie. Please see file \"inDrop GEO supplementary.xlsx \" available on the series record for information on de multiplexing and barcodes for individual samples and cells. Genome build: GRCz10 Supplementary files format and content: text file with read counts.", "Whole kidney marrow", null, "RNA from individual cells were reverse transcribed  barcoded  and unique transcripts are imprinted with UMI  all within the droplet. RNA from 1 500 cells of each animal were pooled and subsequently processed together. cDNA products were pre amplified to produce a cDNA library compatible with the Illumina Nextseq platform. Three sequencing runs were completed using the Nextseq 500 High Output V2 kit 75 cycles Illumina on a NextSeq 500 platform Illumina. Zebrafish cells are isolated from the kidney marrow into single cell suspensions  and treated with 15% Optiprep Sigma. Single cells are microfluidically sorted into droplets containing reverse transcription and barcoding reagents.  This method is outlined in Klein et al. PMID 26000487 and Zilionis et al. PMID 27929523.", null, "indrop version:inDrop V2 protocol|tissue:kidney marrow|genotype:prkdcD3612fs", "GSM2696101", "GSM2696101: inDrop sequencing of prkdcD3612fs homozygous mutant zebrafish sample animal #1; Danio rerio; RNA Seq", "GSM2696101", null, "1", "RNA from individual cells were reverse transcribed  barcoded  and unique transcripts are imprinted with UMI  all within the droplet. RNA from 1 500 cells of each animal were pooled and subsequently processed together. cDNA products were pre amplified to produce a cDNA library compatible with the Illumina Nextseq platform. Three sequencing runs were completed using the Nextseq 500 High Output V2 kit 75 cycles Illumina on a NextSeq 500 platform Illumina. Zebrafish cells are isolated from the kidney marrow into single cell suspensions  and treated with 15% Optiprep Sigma. Single cells are microfluidically sorted into droplets containing reverse transcription and barcoding reagents.  This method is outlined in Klein et al. PMID 26000487 and Zilionis et al. PMID 27929523.", "GEO Accession:GSM2696101", "RNA-Seq", "TRANSCRIPTOMIC", "cDNA", "SINGLE", "ILLUMINA", "NextSeq 500", null, "SRP111339", null, null, "PRKDC1_R1.fastq.gz", "fastq", 1549442736.0, 43040076.0, "GSM2696101 r1", "0:36 1:0", "A:402261661;C:326108405;G:319637981;T:501413963;N:20726", 36, 0, null, null, 402261661, 326108405, 319637981, 501413963, 20726, "SRX2989234", "SRS2341157", "SRA584587", "GEO", "Pathology, Massachusetts General Hospital", 1, 0.84263, null, 0.11449, null, 0.79464, null, 0.57001, null, 36, null, "B", null, "usable mapping rate", "illumina", "nextseq", "unknown", "random_priming", "unknown", "sc", "single_cell_droplet", "indrops", null, "United States", "2017-07-07", "Undetermined", "Adult", "Kidney", "Renal System"], [42610, "SRR5810675", "SRX2989234", "SRS2341157", "SRP111339", "PRJNA393429", "Dissecting hematopoietic and renal cell heterogeneity in adult zebrafish at single cell resolution using RNA sequencing [inDrops]", "GSE100910", "Transcriptome Analysis", "Recent advances in single cell transcriptomic profiling have provided unprecedented access to investigate cell heterogeneity during tissue and organ development. Here  we utilized massively parallel single cell RNA sequencing to define cell heterogeneity within the zebrafish kidney marrow  constructing a comprehensive molecular atlas of definitive hematopoiesis and functionally distinct renal cells found in adult zebrafish. Because our method analyzed blood and kidney cells in an unbiased manner  our approach was useful in characterizing immune cell deficiencies within prkdcD3612fs  il2rgaY91fs and double homozygous mutant fish  identifying blood cell losses in T  B  and natural killer cells within specific genetic mutants.  Our analysis also uncovered novel cell types including two classes of natural killer immune cells  classically defined and erythroid primed hematopoietic stem and progenitor cells  mucin secreting kidney cells  and kidney stem/progenitor cells. In total  our work provides the first comprehensive single cell transcriptomic analysis of kidney and marrow cells in the adult zebrafish. Overall design: The goal of our study is to establish the transcriptional profiles of hematopoietic and kidney cell lineages residing in the zebrafish whole kidney marrow. Firstly  we performed single cell RNA sequencing by a modified Smart seq2 protocol on sorted single cells from fluorescent transgenic zebrafish lines  which label distinct blood cell types n = 246 cells total. Secondly  we utilized droplet based single cell RNA sequencing inDrop to investigate unmarked  comprehensive hematopoietic lineage structure within wild type  casper strain zebrafish N=3 animals  n=3 782 cells total. From this  we identified ten distinct hematopoietic groups of blood and immune identities. Thirdly  we confirmed blood lineage interpretations by comparing hematopoietic lineages within wild type fish with mutant zebrafish with known immunodeficiencies  including prkdcD3612fs N=3 animals  n=3 201 cells  il2rgaY91fs N=2 animals  n=2 068 cells and prkdcD3612fs  il2rgaY91fs double compound mutant fish N=2 animals  n=2 276 cells. Lastly  we identified seven structural and functional cell lineages of kidney identities in the whole kidney marrow n=1 699 kidney cells.", "parent bioproject:PRJNA393415", "pubmed:28878000", null, "inDrop sequencing of prkdcD3612fs homozygous mutant zebrafish sample animal #1", "GSM2696101", null, "source name:Whole kidney marrow|indrop version:inDrop V2 protocol|tissue:kidney marrow|genotype:prkdcD3612fs", "inDrop sequencing of prkdcD3612fs homozygous mutant zebrafish sample animal #1", "Reads were processed using the indrops pipelinehttps://github.com/indrops/indrops. Briefly  reads were filtered according to structure and quality criteria. Filtered reads were then they were sorted by barcode. Demultiplexed reads were aligned to the GRCz10 transcriptome using Bowtie. Please see file \"inDrop GEO supplementary.xlsx \" available on the series record for information on de multiplexing and barcodes for individual samples and cells. Genome build: GRCz10 Supplementary files format and content: text file with read counts.", "Whole kidney marrow", null, "RNA from individual cells were reverse transcribed  barcoded  and unique transcripts are imprinted with UMI  all within the droplet. RNA from 1 500 cells of each animal were pooled and subsequently processed together. cDNA products were pre amplified to produce a cDNA library compatible with the Illumina Nextseq platform. Three sequencing runs were completed using the Nextseq 500 High Output V2 kit 75 cycles Illumina on a NextSeq 500 platform Illumina. Zebrafish cells are isolated from the kidney marrow into single cell suspensions  and treated with 15% Optiprep Sigma. Single cells are microfluidically sorted into droplets containing reverse transcription and barcoding reagents.  This method is outlined in Klein et al. PMID 26000487 and Zilionis et al. PMID 27929523.", null, "indrop version:inDrop V2 protocol|tissue:kidney marrow|genotype:prkdcD3612fs", "GSM2696101", "GSM2696101: inDrop sequencing of prkdcD3612fs homozygous mutant zebrafish sample animal #1; Danio rerio; RNA Seq", "GSM2696101", null, "1", "RNA from individual cells were reverse transcribed  barcoded  and unique transcripts are imprinted with UMI  all within the droplet. RNA from 1 500 cells of each animal were pooled and subsequently processed together. cDNA products were pre amplified to produce a cDNA library compatible with the Illumina Nextseq platform. Three sequencing runs were completed using the Nextseq 500 High Output V2 kit 75 cycles Illumina on a NextSeq 500 platform Illumina. Zebrafish cells are isolated from the kidney marrow into single cell suspensions  and treated with 15% Optiprep Sigma. Single cells are microfluidically sorted into droplets containing reverse transcription and barcoding reagents.  This method is outlined in Klein et al. PMID 26000487 and Zilionis et al. PMID 27929523.", "GEO Accession:GSM2696101", "RNA-Seq", "TRANSCRIPTOMIC", "cDNA", "SINGLE", "ILLUMINA", "NextSeq 500", null, "SRP111339", null, null, "PRKDC1_R2.fastq.gz", "fastq", 2152003800.0, 43040076.0, "GSM2696101 r2", "0:0 1:50", "A:432547886;C:403759182;G:561234660;T:751564536;N:2897536", 0, 50, null, null, 432547886, 403759182, 561234660, 751564536, 2897536, "SRX2989234", "SRS2341157", "SRA584587", "GEO", "Pathology, Massachusetts General Hospital", 1, 0.00796, null, 0.00773, null, 0.99971, null, 0.82758, null, 50, null, "T", null, "under 1.2% mapping rate", "illumina", "nextseq", "unknown", "random_priming", "unknown", "sc", "single_cell_droplet", "indrops", null, "United States", "2017-07-07", "Undetermined", "Adult", "Kidney", "Renal System"], [42611, "SRR5810672", "SRX2989233", "SRS2341156", "SRP111339", "PRJNA393429", "Dissecting hematopoietic and renal cell heterogeneity in adult zebrafish at single cell resolution using RNA sequencing [inDrops]", "GSE100910", "Transcriptome Analysis", "Recent advances in single cell transcriptomic profiling have provided unprecedented access to investigate cell heterogeneity during tissue and organ development. Here  we utilized massively parallel single cell RNA sequencing to define cell heterogeneity within the zebrafish kidney marrow  constructing a comprehensive molecular atlas of definitive hematopoiesis and functionally distinct renal cells found in adult zebrafish. Because our method analyzed blood and kidney cells in an unbiased manner  our approach was useful in characterizing immune cell deficiencies within prkdcD3612fs  il2rgaY91fs and double homozygous mutant fish  identifying blood cell losses in T  B  and natural killer cells within specific genetic mutants.  Our analysis also uncovered novel cell types including two classes of natural killer immune cells  classically defined and erythroid primed hematopoietic stem and progenitor cells  mucin secreting kidney cells  and kidney stem/progenitor cells. In total  our work provides the first comprehensive single cell transcriptomic analysis of kidney and marrow cells in the adult zebrafish. Overall design: The goal of our study is to establish the transcriptional profiles of hematopoietic and kidney cell lineages residing in the zebrafish whole kidney marrow. Firstly  we performed single cell RNA sequencing by a modified Smart seq2 protocol on sorted single cells from fluorescent transgenic zebrafish lines  which label distinct blood cell types n = 246 cells total. Secondly  we utilized droplet based single cell RNA sequencing inDrop to investigate unmarked  comprehensive hematopoietic lineage structure within wild type  casper strain zebrafish N=3 animals  n=3 782 cells total. From this  we identified ten distinct hematopoietic groups of blood and immune identities. Thirdly  we confirmed blood lineage interpretations by comparing hematopoietic lineages within wild type fish with mutant zebrafish with known immunodeficiencies  including prkdcD3612fs N=3 animals  n=3 201 cells  il2rgaY91fs N=2 animals  n=2 068 cells and prkdcD3612fs  il2rgaY91fs double compound mutant fish N=2 animals  n=2 276 cells. Lastly  we identified seven structural and functional cell lineages of kidney identities in the whole kidney marrow n=1 699 kidney cells.", "parent bioproject:PRJNA393415", "pubmed:28878000", null, "inDrop sequencing of WT sample animal #2", "GSM2696100", null, "source name:Whole kidney marrow|indrop version:inDrop V2 protocol|tissue:kidney marrow|genotype:wildtype", "inDrop sequencing of WT sample animal #2", "Reads were processed using the indrops pipelinehttps://github.com/indrops/indrops. Briefly  reads were filtered according to structure and quality criteria. Filtered reads were then they were sorted by barcode. Demultiplexed reads were aligned to the GRCz10 transcriptome using Bowtie. Please see file \"inDrop GEO supplementary.xlsx \" available on the series record for information on de multiplexing and barcodes for individual samples and cells. Genome build: GRCz10 Supplementary files format and content: text file with read counts.", "Whole kidney marrow", null, "RNA from individual cells were reverse transcribed  barcoded  and unique transcripts are imprinted with UMI  all within the droplet. RNA from 1 500 cells of each animal were pooled and subsequently processed together. cDNA products were pre amplified to produce a cDNA library compatible with the Illumina Nextseq platform. Three sequencing runs were completed using the Nextseq 500 High Output V2 kit 75 cycles Illumina on a NextSeq 500 platform Illumina. Zebrafish cells are isolated from the kidney marrow into single cell suspensions  and treated with 15% Optiprep Sigma. Single cells are microfluidically sorted into droplets containing reverse transcription and barcoding reagents.  This method is outlined in Klein et al. PMID 26000487 and Zilionis et al. PMID 27929523.", null, "indrop version:inDrop V2 protocol|tissue:kidney marrow|genotype:wildtype", "GSM2696100", "GSM2696100: inDrop sequencing of WT sample animal #2; Danio rerio; RNA Seq", "GSM2696100", null, "1", "RNA from individual cells were reverse transcribed  barcoded  and unique transcripts are imprinted with UMI  all within the droplet. RNA from 1 500 cells of each animal were pooled and subsequently processed together. cDNA products were pre amplified to produce a cDNA library compatible with the Illumina Nextseq platform. Three sequencing runs were completed using the Nextseq 500 High Output V2 kit 75 cycles Illumina on a NextSeq 500 platform Illumina. Zebrafish cells are isolated from the kidney marrow into single cell suspensions  and treated with 15% Optiprep Sigma. Single cells are microfluidically sorted into droplets containing reverse transcription and barcoding reagents.  This method is outlined in Klein et al. PMID 26000487 and Zilionis et al. PMID 27929523.", "GEO Accession:GSM2696100", "RNA-Seq", "TRANSCRIPTOMIC", "cDNA", "SINGLE", "ILLUMINA", "NextSeq 500", null, "SRP111339", null, null, "WT2_R1.fastq.gz", "fastq", 3202042500.0, 88945625.0, "GSM2696100 r1", "0:36 1:0", "A:821476951;C:657776656;G:665293293;T:1057448635;N:46965", 36, 0, null, null, 821476951, 657776656, 665293293, 1057448635, 46965, "SRX2989233", "SRS2341156", "SRA584587", "GEO", "Pathology, Massachusetts General Hospital", 1, 0.84222, null, 0.12969, null, 0.80683, null, 0.53542, null, 36, null, "B", null, "usable mapping rate", "illumina", "nextseq", "unknown", "random_priming", "unknown", "sc", "single_cell_droplet", "indrops", null, "United States", "2017-07-07", "Undetermined", "Adult", "Kidney", "Renal System"], [42612, "SRR5810673", "SRX2989233", "SRS2341156", "SRP111339", "PRJNA393429", "Dissecting hematopoietic and renal cell heterogeneity in adult zebrafish at single cell resolution using RNA sequencing [inDrops]", "GSE100910", "Transcriptome Analysis", "Recent advances in single cell transcriptomic profiling have provided unprecedented access to investigate cell heterogeneity during tissue and organ development. Here  we utilized massively parallel single cell RNA sequencing to define cell heterogeneity within the zebrafish kidney marrow  constructing a comprehensive molecular atlas of definitive hematopoiesis and functionally distinct renal cells found in adult zebrafish. Because our method analyzed blood and kidney cells in an unbiased manner  our approach was useful in characterizing immune cell deficiencies within prkdcD3612fs  il2rgaY91fs and double homozygous mutant fish  identifying blood cell losses in T  B  and natural killer cells within specific genetic mutants.  Our analysis also uncovered novel cell types including two classes of natural killer immune cells  classically defined and erythroid primed hematopoietic stem and progenitor cells  mucin secreting kidney cells  and kidney stem/progenitor cells. In total  our work provides the first comprehensive single cell transcriptomic analysis of kidney and marrow cells in the adult zebrafish. Overall design: The goal of our study is to establish the transcriptional profiles of hematopoietic and kidney cell lineages residing in the zebrafish whole kidney marrow. Firstly  we performed single cell RNA sequencing by a modified Smart seq2 protocol on sorted single cells from fluorescent transgenic zebrafish lines  which label distinct blood cell types n = 246 cells total. Secondly  we utilized droplet based single cell RNA sequencing inDrop to investigate unmarked  comprehensive hematopoietic lineage structure within wild type  casper strain zebrafish N=3 animals  n=3 782 cells total. From this  we identified ten distinct hematopoietic groups of blood and immune identities. Thirdly  we confirmed blood lineage interpretations by comparing hematopoietic lineages within wild type fish with mutant zebrafish with known immunodeficiencies  including prkdcD3612fs N=3 animals  n=3 201 cells  il2rgaY91fs N=2 animals  n=2 068 cells and prkdcD3612fs  il2rgaY91fs double compound mutant fish N=2 animals  n=2 276 cells. Lastly  we identified seven structural and functional cell lineages of kidney identities in the whole kidney marrow n=1 699 kidney cells.", "parent bioproject:PRJNA393415", "pubmed:28878000", null, "inDrop sequencing of WT sample animal #2", "GSM2696100", null, "source name:Whole kidney marrow|indrop version:inDrop V2 protocol|tissue:kidney marrow|genotype:wildtype", "inDrop sequencing of WT sample animal #2", "Reads were processed using the indrops pipelinehttps://github.com/indrops/indrops. Briefly  reads were filtered according to structure and quality criteria. Filtered reads were then they were sorted by barcode. Demultiplexed reads were aligned to the GRCz10 transcriptome using Bowtie. Please see file \"inDrop GEO supplementary.xlsx \" available on the series record for information on de multiplexing and barcodes for individual samples and cells. Genome build: GRCz10 Supplementary files format and content: text file with read counts.", "Whole kidney marrow", null, "RNA from individual cells were reverse transcribed  barcoded  and unique transcripts are imprinted with UMI  all within the droplet. RNA from 1 500 cells of each animal were pooled and subsequently processed together. cDNA products were pre amplified to produce a cDNA library compatible with the Illumina Nextseq platform. Three sequencing runs were completed using the Nextseq 500 High Output V2 kit 75 cycles Illumina on a NextSeq 500 platform Illumina. Zebrafish cells are isolated from the kidney marrow into single cell suspensions  and treated with 15% Optiprep Sigma. Single cells are microfluidically sorted into droplets containing reverse transcription and barcoding reagents.  This method is outlined in Klein et al. PMID 26000487 and Zilionis et al. PMID 27929523.", null, "indrop version:inDrop V2 protocol|tissue:kidney marrow|genotype:wildtype", "GSM2696100", "GSM2696100: inDrop sequencing of WT sample animal #2; Danio rerio; RNA Seq", "GSM2696100", null, "1", "RNA from individual cells were reverse transcribed  barcoded  and unique transcripts are imprinted with UMI  all within the droplet. RNA from 1 500 cells of each animal were pooled and subsequently processed together. cDNA products were pre amplified to produce a cDNA library compatible with the Illumina Nextseq platform. Three sequencing runs were completed using the Nextseq 500 High Output V2 kit 75 cycles Illumina on a NextSeq 500 platform Illumina. Zebrafish cells are isolated from the kidney marrow into single cell suspensions  and treated with 15% Optiprep Sigma. Single cells are microfluidically sorted into droplets containing reverse transcription and barcoding reagents.  This method is outlined in Klein et al. PMID 26000487 and Zilionis et al. PMID 27929523.", "GEO Accession:GSM2696100", "RNA-Seq", "TRANSCRIPTOMIC", "cDNA", "SINGLE", "ILLUMINA", "NextSeq 500", null, "SRP111339", null, null, "WT2_R2.fastq.gz", "fastq", 4447281250.0, 88945625.0, "GSM2696100 r2", "0:0 1:50", "A:880464480;C:827380868;G:1171423978;T:1561925150;N:6086774", 0, 50, null, null, 880464480, 827380868, 1171423978, 1561925150, 6086774, "SRX2989233", "SRS2341156", "SRA584587", "GEO", "Pathology, Massachusetts General Hospital", 1, 0.00782, null, 0.0075, null, 0.99947, null, 0.91666, null, 50, null, "T", null, "under 1.2% mapping rate", "illumina", "nextseq", "unknown", "random_priming", "unknown", "sc", "single_cell_droplet", "indrops", null, "United States", "2017-07-07", "Undetermined", "Adult", "Kidney", "Renal System"], [42613, "SRR5810670", "SRX2989232", "SRS2341155", "SRP111339", "PRJNA393429", "Dissecting hematopoietic and renal cell heterogeneity in adult zebrafish at single cell resolution using RNA sequencing [inDrops]", "GSE100910", "Transcriptome Analysis", "Recent advances in single cell transcriptomic profiling have provided unprecedented access to investigate cell heterogeneity during tissue and organ development. Here  we utilized massively parallel single cell RNA sequencing to define cell heterogeneity within the zebrafish kidney marrow  constructing a comprehensive molecular atlas of definitive hematopoiesis and functionally distinct renal cells found in adult zebrafish. Because our method analyzed blood and kidney cells in an unbiased manner  our approach was useful in characterizing immune cell deficiencies within prkdcD3612fs  il2rgaY91fs and double homozygous mutant fish  identifying blood cell losses in T  B  and natural killer cells within specific genetic mutants.  Our analysis also uncovered novel cell types including two classes of natural killer immune cells  classically defined and erythroid primed hematopoietic stem and progenitor cells  mucin secreting kidney cells  and kidney stem/progenitor cells. In total  our work provides the first comprehensive single cell transcriptomic analysis of kidney and marrow cells in the adult zebrafish. Overall design: The goal of our study is to establish the transcriptional profiles of hematopoietic and kidney cell lineages residing in the zebrafish whole kidney marrow. Firstly  we performed single cell RNA sequencing by a modified Smart seq2 protocol on sorted single cells from fluorescent transgenic zebrafish lines  which label distinct blood cell types n = 246 cells total. Secondly  we utilized droplet based single cell RNA sequencing inDrop to investigate unmarked  comprehensive hematopoietic lineage structure within wild type  casper strain zebrafish N=3 animals  n=3 782 cells total. From this  we identified ten distinct hematopoietic groups of blood and immune identities. Thirdly  we confirmed blood lineage interpretations by comparing hematopoietic lineages within wild type fish with mutant zebrafish with known immunodeficiencies  including prkdcD3612fs N=3 animals  n=3 201 cells  il2rgaY91fs N=2 animals  n=2 068 cells and prkdcD3612fs  il2rgaY91fs double compound mutant fish N=2 animals  n=2 276 cells. Lastly  we identified seven structural and functional cell lineages of kidney identities in the whole kidney marrow n=1 699 kidney cells.", "parent bioproject:PRJNA393415", "pubmed:28878000", null, "inDrop sequencing of WT sample animal #1", "GSM2696099", null, "source name:Whole kidney marrow|indrop version:inDrop V2 protocol|tissue:kidney marrow|genotype:wildtype", "inDrop sequencing of WT sample animal #1", "Reads were processed using the indrops pipelinehttps://github.com/indrops/indrops. Briefly  reads were filtered according to structure and quality criteria. Filtered reads were then they were sorted by barcode. Demultiplexed reads were aligned to the GRCz10 transcriptome using Bowtie. Please see file \"inDrop GEO supplementary.xlsx \" available on the series record for information on de multiplexing and barcodes for individual samples and cells. Genome build: GRCz10 Supplementary files format and content: text file with read counts.", "Whole kidney marrow", null, "RNA from individual cells were reverse transcribed  barcoded  and unique transcripts are imprinted with UMI  all within the droplet. RNA from 1 500 cells of each animal were pooled and subsequently processed together. cDNA products were pre amplified to produce a cDNA library compatible with the Illumina Nextseq platform. Three sequencing runs were completed using the Nextseq 500 High Output V2 kit 75 cycles Illumina on a NextSeq 500 platform Illumina. Zebrafish cells are isolated from the kidney marrow into single cell suspensions  and treated with 15% Optiprep Sigma. Single cells are microfluidically sorted into droplets containing reverse transcription and barcoding reagents.  This method is outlined in Klein et al. PMID 26000487 and Zilionis et al. PMID 27929523.", null, "indrop version:inDrop V2 protocol|tissue:kidney marrow|genotype:wildtype", "GSM2696099", "GSM2696099: inDrop sequencing of WT sample animal #1; Danio rerio; RNA Seq", "GSM2696099", null, "1", "RNA from individual cells were reverse transcribed  barcoded  and unique transcripts are imprinted with UMI  all within the droplet. RNA from 1 500 cells of each animal were pooled and subsequently processed together. cDNA products were pre amplified to produce a cDNA library compatible with the Illumina Nextseq platform. Three sequencing runs were completed using the Nextseq 500 High Output V2 kit 75 cycles Illumina on a NextSeq 500 platform Illumina. Zebrafish cells are isolated from the kidney marrow into single cell suspensions  and treated with 15% Optiprep Sigma. Single cells are microfluidically sorted into droplets containing reverse transcription and barcoding reagents.  This method is outlined in Klein et al. PMID 26000487 and Zilionis et al. PMID 27929523.", "GEO Accession:GSM2696099", "RNA-Seq", "TRANSCRIPTOMIC", "cDNA", "SINGLE", "ILLUMINA", "NextSeq 500", null, "SRP111339", null, null, "WT1_R1.fastq.gz", "fastq", 3313205496.0, 92033486.0, "GSM2696099 r1", "0:36 1:0", "A:858288917;C:693848400;G:675650809;T:1085369048;N:48322", 36, 0, null, null, 858288917, 693848400, 675650809, 1085369048, 48322, "SRX2989232", "SRS2341155", "SRA584587", "GEO", "Pathology, Massachusetts General Hospital", 1, 0.8771, null, 0.13918, null, 0.81115, null, 0.59004, null, 36, null, "B", null, "usable mapping rate", "illumina", "nextseq", "unknown", "random_priming", "unknown", "sc", "single_cell_droplet", "indrops", null, "United States", "2017-07-07", "Undetermined", "Adult", "Kidney", "Renal System"], [42614, "SRR5810671", "SRX2989232", "SRS2341155", "SRP111339", "PRJNA393429", "Dissecting hematopoietic and renal cell heterogeneity in adult zebrafish at single cell resolution using RNA sequencing [inDrops]", "GSE100910", "Transcriptome Analysis", "Recent advances in single cell transcriptomic profiling have provided unprecedented access to investigate cell heterogeneity during tissue and organ development. Here  we utilized massively parallel single cell RNA sequencing to define cell heterogeneity within the zebrafish kidney marrow  constructing a comprehensive molecular atlas of definitive hematopoiesis and functionally distinct renal cells found in adult zebrafish. Because our method analyzed blood and kidney cells in an unbiased manner  our approach was useful in characterizing immune cell deficiencies within prkdcD3612fs  il2rgaY91fs and double homozygous mutant fish  identifying blood cell losses in T  B  and natural killer cells within specific genetic mutants.  Our analysis also uncovered novel cell types including two classes of natural killer immune cells  classically defined and erythroid primed hematopoietic stem and progenitor cells  mucin secreting kidney cells  and kidney stem/progenitor cells. In total  our work provides the first comprehensive single cell transcriptomic analysis of kidney and marrow cells in the adult zebrafish. Overall design: The goal of our study is to establish the transcriptional profiles of hematopoietic and kidney cell lineages residing in the zebrafish whole kidney marrow. Firstly  we performed single cell RNA sequencing by a modified Smart seq2 protocol on sorted single cells from fluorescent transgenic zebrafish lines  which label distinct blood cell types n = 246 cells total. Secondly  we utilized droplet based single cell RNA sequencing inDrop to investigate unmarked  comprehensive hematopoietic lineage structure within wild type  casper strain zebrafish N=3 animals  n=3 782 cells total. From this  we identified ten distinct hematopoietic groups of blood and immune identities. Thirdly  we confirmed blood lineage interpretations by comparing hematopoietic lineages within wild type fish with mutant zebrafish with known immunodeficiencies  including prkdcD3612fs N=3 animals  n=3 201 cells  il2rgaY91fs N=2 animals  n=2 068 cells and prkdcD3612fs  il2rgaY91fs double compound mutant fish N=2 animals  n=2 276 cells. Lastly  we identified seven structural and functional cell lineages of kidney identities in the whole kidney marrow n=1 699 kidney cells.", "parent bioproject:PRJNA393415", "pubmed:28878000", null, "inDrop sequencing of WT sample animal #1", "GSM2696099", null, "source name:Whole kidney marrow|indrop version:inDrop V2 protocol|tissue:kidney marrow|genotype:wildtype", "inDrop sequencing of WT sample animal #1", "Reads were processed using the indrops pipelinehttps://github.com/indrops/indrops. Briefly  reads were filtered according to structure and quality criteria. Filtered reads were then they were sorted by barcode. Demultiplexed reads were aligned to the GRCz10 transcriptome using Bowtie. Please see file \"inDrop GEO supplementary.xlsx \" available on the series record for information on de multiplexing and barcodes for individual samples and cells. Genome build: GRCz10 Supplementary files format and content: text file with read counts.", "Whole kidney marrow", null, "RNA from individual cells were reverse transcribed  barcoded  and unique transcripts are imprinted with UMI  all within the droplet. RNA from 1 500 cells of each animal were pooled and subsequently processed together. cDNA products were pre amplified to produce a cDNA library compatible with the Illumina Nextseq platform. Three sequencing runs were completed using the Nextseq 500 High Output V2 kit 75 cycles Illumina on a NextSeq 500 platform Illumina. Zebrafish cells are isolated from the kidney marrow into single cell suspensions  and treated with 15% Optiprep Sigma. Single cells are microfluidically sorted into droplets containing reverse transcription and barcoding reagents.  This method is outlined in Klein et al. PMID 26000487 and Zilionis et al. PMID 27929523.", null, "indrop version:inDrop V2 protocol|tissue:kidney marrow|genotype:wildtype", "GSM2696099", "GSM2696099: inDrop sequencing of WT sample animal #1; Danio rerio; RNA Seq", "GSM2696099", null, "1", "RNA from individual cells were reverse transcribed  barcoded  and unique transcripts are imprinted with UMI  all within the droplet. RNA from 1 500 cells of each animal were pooled and subsequently processed together. cDNA products were pre amplified to produce a cDNA library compatible with the Illumina Nextseq platform. Three sequencing runs were completed using the Nextseq 500 High Output V2 kit 75 cycles Illumina on a NextSeq 500 platform Illumina. Zebrafish cells are isolated from the kidney marrow into single cell suspensions  and treated with 15% Optiprep Sigma. Single cells are microfluidically sorted into droplets containing reverse transcription and barcoding reagents.  This method is outlined in Klein et al. PMID 26000487 and Zilionis et al. PMID 27929523.", "GEO Accession:GSM2696099", "RNA-Seq", "TRANSCRIPTOMIC", "cDNA", "SINGLE", "ILLUMINA", "NextSeq 500", null, "SRP111339", null, null, "WT1_R2.fastq.gz", "fastq", 4601674300.0, 92033486.0, "GSM2696099 r2", "0:0 1:50", "A:914884703;C:856837987;G:1192418010;T:1631240786;N:6292814", 0, 50, null, null, 914884703, 856837987, 1192418010, 1631240786, 6292814, "SRX2989232", "SRS2341155", "SRA584587", "GEO", "Pathology, Massachusetts General Hospital", 1, 0.0063, null, 0.00598, null, 0.99959, null, 0.8913, null, 50, null, "T", null, "under 1.2% mapping rate", "illumina", "nextseq", "unknown", "random_priming", "unknown", "sc", "single_cell_droplet", "indrops", null, "United States", "2017-07-07", "Undetermined", "Adult", "Kidney", "Renal System"], [43845, "SRR6176746", "SRX3287413", "SRS2596886", "SRP120009", "PRJNA414416", "Simultaneous single cell profiling of lineages and cell types in the vertebrate brain", "GSE105010", "Other", "The lineage relationships among the hundreds of cell types generated during development are difficult to reconstruct. A recent method  GESTALT  used CRISPR\u2013Cas9 barcode editing for large scale lineage tracing  but was restricted to early development and did not identify cell types. Here we present scGESTALT  which combines the lineage recording capabilities of GESTALT with cell type identification by single cell RNA sequencing. The method relies on an inducible system that enables barcodes to be edited at multiple time points  capturing lineage information from later stages of development. Sequencing of 60 000 transcriptomes from the juvenile zebrafish brain identified >100 cell types and marker genes. Using these data  we generate lineage trees with hundreds of branches that help uncover restrictions at the level of cell types  brain regions  and gene expression cascades during differentiation. scGESTALT can be applied to other multicellular organisms to simultaneously characterize molecular identities and lineage histories of thousands of cells during development and disease. Overall design: inDrops libraries of single cell transcriptomes and scGESTALT barcodes  and genomic DNA GESTALT libraries", null, "pubmed:29608178", null, "DEW157 mid", "GSM2813983", null, "source name:zebrafish brain|tissue:brain|developmental stage:23 25dpf", "DEW157 mid", "Single cell RNA Sequencing data FASTQ files were processed using the inDrops.py bioinformatics pipeline available at https://github.com/indrops/indrops. Transcriptome libraries were mapped to a zebrafish reference built from a custom GTF file and the zebrafish GRCz10 release 86 genome assembly. Bowtie version1.1.1 was used with parameter \u2013e 200; UMI quantification was used with parameter \u2013u 2 counts were ignored from UMIs split between more than 2 genes. genomic DNA GESTALT and scGESTALT libraries were processed using a custom pipeline available at https://github.com/shendurelab/Cas9FateMapping Genome build: GRCz10 CSV files for transcriptome data were generated using the inDrops pipeline. Each column in the CSV files contains a cell identifier and each row contains expression values for genes. Txt files for genomic DNA GESTALT libraries *allReadCounts contain lineage barcode sequences HMID column for each cell and their proportion in the sequenced libraries. scGESTALT data *GestMaster.txt contains the inDrops cell identifiers CellBarcode and BarcodeKey that were used to match barcodes to transcriptomes. They also contain lineage barcode sequences HMID column for each cell with a corresponding inDrops single cell gene expression profile  as well as the the t SNE cluster membership number ClusterIdent column Txt files ending in *stats.txt contain information about the each individually captured UMI or cell per sample. The barcode sequence aligned to a reference unedited sequence mergedRead column  mutations at each target site target[X] columns and the edited sequences at each target site sequence[X] columns were used for downstream analysis. inDropsExpMatrix noQ txt file is the gene expression matrix for the full dataset. Columns are individual cells from different batches of whole brains f1   f2   f3   f4   f5   f6  or brain regions fore   mid   hind . fall.inDrops.Robj is the processed Seurat R object  which can be loaded into R and explored.", "zebrafish brain", null, "Single cell suspensions were processed through inDrops to generate single cell cDNA libraries. cDNAs were in vitro transcribed and fragmented. The three prime fragments were reverse transcribed and prepared for sequencing Libraries were prepared as described in Zilionis et al.  2017  Nature Protocols PMID = 27929523", null, "tissue:brain|developmental stage:23 25dpf", "GSM2813983", "GSM2813983: DEW157 mid; Danio rerio; RNA Seq", "GSM2813983", null, "1", "Single cell suspensions were processed through inDrops to generate single cell cDNA libraries. cDNAs were in vitro transcribed and fragmented. The three prime fragments were reverse transcribed and prepared for sequencing Libraries were prepared as described in Zilionis et al.  2017  Nature Protocols PMID = 27929523", "GEO Accession:GSM2813983", "RNA-Seq", "TRANSCRIPTOMIC", "cDNA", "SINGLE", "ILLUMINA", "NextSeq 500", null, "SRP120009", null, "loader:fastq load.py|options:  appendBCtoName", "DEW157_Lane1.sorted.fastq.gz", "fastq", 3544126799.0, 61301471.0, "GSM2813983 r1", "0:57.81", "A:974428815;C:692114455;G:739762074;T:1137804675;N:16780", 57, null, null, null, 974428815, 692114455, 739762074, 1137804675, 16780, "SRX3287413", "SRS2596886", "SRA619743", "GEO", "Harvard University", 1, 0.89639, null, 0.20572, null, 0.77784, null, 0.5325, null, 61, null, "B", null, "usable mapping rate", "illumina", "nextseq", "unknown", "cdna_unspecified", "unknown", "sc", "single_cell_droplet", "indrops", null, "United States", "2017-10-16", "Larval", "Larval", "Brain", "Nervous System"], [43846, "SRR6176747", "SRX3287413", "SRS2596886", "SRP120009", "PRJNA414416", "Simultaneous single cell profiling of lineages and cell types in the vertebrate brain", "GSE105010", "Other", "The lineage relationships among the hundreds of cell types generated during development are difficult to reconstruct. A recent method  GESTALT  used CRISPR\u2013Cas9 barcode editing for large scale lineage tracing  but was restricted to early development and did not identify cell types. Here we present scGESTALT  which combines the lineage recording capabilities of GESTALT with cell type identification by single cell RNA sequencing. The method relies on an inducible system that enables barcodes to be edited at multiple time points  capturing lineage information from later stages of development. Sequencing of 60 000 transcriptomes from the juvenile zebrafish brain identified >100 cell types and marker genes. Using these data  we generate lineage trees with hundreds of branches that help uncover restrictions at the level of cell types  brain regions  and gene expression cascades during differentiation. scGESTALT can be applied to other multicellular organisms to simultaneously characterize molecular identities and lineage histories of thousands of cells during development and disease. Overall design: inDrops libraries of single cell transcriptomes and scGESTALT barcodes  and genomic DNA GESTALT libraries", null, "pubmed:29608178", null, "DEW157 mid", "GSM2813983", null, "source name:zebrafish brain|tissue:brain|developmental stage:23 25dpf", "DEW157 mid", "Single cell RNA Sequencing data FASTQ files were processed using the inDrops.py bioinformatics pipeline available at https://github.com/indrops/indrops. Transcriptome libraries were mapped to a zebrafish reference built from a custom GTF file and the zebrafish GRCz10 release 86 genome assembly. Bowtie version1.1.1 was used with parameter \u2013e 200; UMI quantification was used with parameter \u2013u 2 counts were ignored from UMIs split between more than 2 genes. genomic DNA GESTALT and scGESTALT libraries were processed using a custom pipeline available at https://github.com/shendurelab/Cas9FateMapping Genome build: GRCz10 CSV files for transcriptome data were generated using the inDrops pipeline. Each column in the CSV files contains a cell identifier and each row contains expression values for genes. Txt files for genomic DNA GESTALT libraries *allReadCounts contain lineage barcode sequences HMID column for each cell and their proportion in the sequenced libraries. scGESTALT data *GestMaster.txt contains the inDrops cell identifiers CellBarcode and BarcodeKey that were used to match barcodes to transcriptomes. They also contain lineage barcode sequences HMID column for each cell with a corresponding inDrops single cell gene expression profile  as well as the the t SNE cluster membership number ClusterIdent column Txt files ending in *stats.txt contain information about the each individually captured UMI or cell per sample. The barcode sequence aligned to a reference unedited sequence mergedRead column  mutations at each target site target[X] columns and the edited sequences at each target site sequence[X] columns were used for downstream analysis. inDropsExpMatrix noQ txt file is the gene expression matrix for the full dataset. Columns are individual cells from different batches of whole brains f1   f2   f3   f4   f5   f6  or brain regions fore   mid   hind . fall.inDrops.Robj is the processed Seurat R object  which can be loaded into R and explored.", "zebrafish brain", null, "Single cell suspensions were processed through inDrops to generate single cell cDNA libraries. cDNAs were in vitro transcribed and fragmented. The three prime fragments were reverse transcribed and prepared for sequencing Libraries were prepared as described in Zilionis et al.  2017  Nature Protocols PMID = 27929523", null, "tissue:brain|developmental stage:23 25dpf", "GSM2813983", "GSM2813983: DEW157 mid; Danio rerio; RNA Seq", "GSM2813983", null, "1", "Single cell suspensions were processed through inDrops to generate single cell cDNA libraries. cDNAs were in vitro transcribed and fragmented. The three prime fragments were reverse transcribed and prepared for sequencing Libraries were prepared as described in Zilionis et al.  2017  Nature Protocols PMID = 27929523", "GEO Accession:GSM2813983", "RNA-Seq", "TRANSCRIPTOMIC", "cDNA", "SINGLE", "ILLUMINA", "NextSeq 500", null, "SRP120009", null, "loader:fastq load.py|options:  appendBCtoName", "DEW157_Lane2.sorted.fastq.gz", "fastq", 2352087459.0, 41062625.0, "GSM2813983 r2", "0:57.28", "A:645085555;C:458868888;G:493735966;T:754390615;N:6435", 57, null, null, null, 645085555, 458868888, 493735966, 754390615, 6435, "SRX3287413", "SRS2596886", "SRA619743", "GEO", "Harvard University", 1, 0.89716, null, 0.2057, null, 0.7782, null, 0.5213, null, 61, null, "B", null, "usable mapping rate", "illumina", "nextseq", "unknown", "cdna_unspecified", "unknown", "sc", "single_cell_droplet", "indrops", null, "United States", "2017-10-16", "Larval", "Larval", "Brain", "Nervous System"], [43847, "SRR6176744", "SRX3287412", "SRS2596885", "SRP120009", "PRJNA414416", "Simultaneous single cell profiling of lineages and cell types in the vertebrate brain", "GSE105010", "Other", "The lineage relationships among the hundreds of cell types generated during development are difficult to reconstruct. A recent method  GESTALT  used CRISPR\u2013Cas9 barcode editing for large scale lineage tracing  but was restricted to early development and did not identify cell types. Here we present scGESTALT  which combines the lineage recording capabilities of GESTALT with cell type identification by single cell RNA sequencing. The method relies on an inducible system that enables barcodes to be edited at multiple time points  capturing lineage information from later stages of development. Sequencing of 60 000 transcriptomes from the juvenile zebrafish brain identified >100 cell types and marker genes. Using these data  we generate lineage trees with hundreds of branches that help uncover restrictions at the level of cell types  brain regions  and gene expression cascades during differentiation. scGESTALT can be applied to other multicellular organisms to simultaneously characterize molecular identities and lineage histories of thousands of cells during development and disease. Overall design: inDrops libraries of single cell transcriptomes and scGESTALT barcodes  and genomic DNA GESTALT libraries", null, "pubmed:29608178", null, "DEW156 mid", "GSM2813982", null, "source name:zebrafish brain|tissue:brain|developmental stage:23 25dpf", "DEW156 mid", "Single cell RNA Sequencing data FASTQ files were processed using the inDrops.py bioinformatics pipeline available at https://github.com/indrops/indrops. Transcriptome libraries were mapped to a zebrafish reference built from a custom GTF file and the zebrafish GRCz10 release 86 genome assembly. Bowtie version1.1.1 was used with parameter \u2013e 200; UMI quantification was used with parameter \u2013u 2 counts were ignored from UMIs split between more than 2 genes. genomic DNA GESTALT and scGESTALT libraries were processed using a custom pipeline available at https://github.com/shendurelab/Cas9FateMapping Genome build: GRCz10 CSV files for transcriptome data were generated using the inDrops pipeline. Each column in the CSV files contains a cell identifier and each row contains expression values for genes. Txt files for genomic DNA GESTALT libraries *allReadCounts contain lineage barcode sequences HMID column for each cell and their proportion in the sequenced libraries. scGESTALT data *GestMaster.txt contains the inDrops cell identifiers CellBarcode and BarcodeKey that were used to match barcodes to transcriptomes. They also contain lineage barcode sequences HMID column for each cell with a corresponding inDrops single cell gene expression profile  as well as the the t SNE cluster membership number ClusterIdent column Txt files ending in *stats.txt contain information about the each individually captured UMI or cell per sample. The barcode sequence aligned to a reference unedited sequence mergedRead column  mutations at each target site target[X] columns and the edited sequences at each target site sequence[X] columns were used for downstream analysis. inDropsExpMatrix noQ txt file is the gene expression matrix for the full dataset. Columns are individual cells from different batches of whole brains f1   f2   f3   f4   f5   f6  or brain regions fore   mid   hind . fall.inDrops.Robj is the processed Seurat R object  which can be loaded into R and explored.", "zebrafish brain", null, "Single cell suspensions were processed through inDrops to generate single cell cDNA libraries. cDNAs were in vitro transcribed and fragmented. The three prime fragments were reverse transcribed and prepared for sequencing Libraries were prepared as described in Zilionis et al.  2017  Nature Protocols PMID = 27929523", null, "tissue:brain|developmental stage:23 25dpf", "GSM2813982", "GSM2813982: DEW156 mid; Danio rerio; RNA Seq", "GSM2813982", null, "1", "Single cell suspensions were processed through inDrops to generate single cell cDNA libraries. cDNAs were in vitro transcribed and fragmented. The three prime fragments were reverse transcribed and prepared for sequencing Libraries were prepared as described in Zilionis et al.  2017  Nature Protocols PMID = 27929523", "GEO Accession:GSM2813982", "RNA-Seq", "TRANSCRIPTOMIC", "cDNA", "SINGLE", "ILLUMINA", "NextSeq 500", null, "SRP120009", null, "loader:fastq load.py|options:  appendBCtoName", "DEW156_Lane1.sorted.fastq.gz", "fastq", 2214394241.0, 38215249.0, "GSM2813982 r1", "0:57.95", "A:607624943;C:431025702;G:463801442;T:711931460;N:10694", 57, null, null, null, 607624943, 431025702, 463801442, 711931460, 10694, "SRX3287412", "SRS2596885", "SRA619743", "GEO", "Harvard University", 1, 0.89922, null, 0.20797, null, 0.77626, null, 0.52363, null, 61, null, "B", null, "usable mapping rate", "illumina", "nextseq", "unknown", "cdna_unspecified", "unknown", "sc", "single_cell_droplet", "indrops", null, "United States", "2017-10-16", "Larval", "Larval", "Brain", "Nervous System"], [43848, "SRR6176745", "SRX3287412", "SRS2596885", "SRP120009", "PRJNA414416", "Simultaneous single cell profiling of lineages and cell types in the vertebrate brain", "GSE105010", "Other", "The lineage relationships among the hundreds of cell types generated during development are difficult to reconstruct. A recent method  GESTALT  used CRISPR\u2013Cas9 barcode editing for large scale lineage tracing  but was restricted to early development and did not identify cell types. Here we present scGESTALT  which combines the lineage recording capabilities of GESTALT with cell type identification by single cell RNA sequencing. The method relies on an inducible system that enables barcodes to be edited at multiple time points  capturing lineage information from later stages of development. Sequencing of 60 000 transcriptomes from the juvenile zebrafish brain identified >100 cell types and marker genes. Using these data  we generate lineage trees with hundreds of branches that help uncover restrictions at the level of cell types  brain regions  and gene expression cascades during differentiation. scGESTALT can be applied to other multicellular organisms to simultaneously characterize molecular identities and lineage histories of thousands of cells during development and disease. Overall design: inDrops libraries of single cell transcriptomes and scGESTALT barcodes  and genomic DNA GESTALT libraries", null, "pubmed:29608178", null, "DEW156 mid", "GSM2813982", null, "source name:zebrafish brain|tissue:brain|developmental stage:23 25dpf", "DEW156 mid", "Single cell RNA Sequencing data FASTQ files were processed using the inDrops.py bioinformatics pipeline available at https://github.com/indrops/indrops. Transcriptome libraries were mapped to a zebrafish reference built from a custom GTF file and the zebrafish GRCz10 release 86 genome assembly. Bowtie version1.1.1 was used with parameter \u2013e 200; UMI quantification was used with parameter \u2013u 2 counts were ignored from UMIs split between more than 2 genes. genomic DNA GESTALT and scGESTALT libraries were processed using a custom pipeline available at https://github.com/shendurelab/Cas9FateMapping Genome build: GRCz10 CSV files for transcriptome data were generated using the inDrops pipeline. Each column in the CSV files contains a cell identifier and each row contains expression values for genes. Txt files for genomic DNA GESTALT libraries *allReadCounts contain lineage barcode sequences HMID column for each cell and their proportion in the sequenced libraries. scGESTALT data *GestMaster.txt contains the inDrops cell identifiers CellBarcode and BarcodeKey that were used to match barcodes to transcriptomes. They also contain lineage barcode sequences HMID column for each cell with a corresponding inDrops single cell gene expression profile  as well as the the t SNE cluster membership number ClusterIdent column Txt files ending in *stats.txt contain information about the each individually captured UMI or cell per sample. The barcode sequence aligned to a reference unedited sequence mergedRead column  mutations at each target site target[X] columns and the edited sequences at each target site sequence[X] columns were used for downstream analysis. inDropsExpMatrix noQ txt file is the gene expression matrix for the full dataset. Columns are individual cells from different batches of whole brains f1   f2   f3   f4   f5   f6  or brain regions fore   mid   hind . fall.inDrops.Robj is the processed Seurat R object  which can be loaded into R and explored.", "zebrafish brain", null, "Single cell suspensions were processed through inDrops to generate single cell cDNA libraries. cDNAs were in vitro transcribed and fragmented. The three prime fragments were reverse transcribed and prepared for sequencing Libraries were prepared as described in Zilionis et al.  2017  Nature Protocols PMID = 27929523", null, "tissue:brain|developmental stage:23 25dpf", "GSM2813982", "GSM2813982: DEW156 mid; Danio rerio; RNA Seq", "GSM2813982", null, "1", "Single cell suspensions were processed through inDrops to generate single cell cDNA libraries. cDNAs were in vitro transcribed and fragmented. The three prime fragments were reverse transcribed and prepared for sequencing Libraries were prepared as described in Zilionis et al.  2017  Nature Protocols PMID = 27929523", "GEO Accession:GSM2813982", "RNA-Seq", "TRANSCRIPTOMIC", "cDNA", "SINGLE", "ILLUMINA", "NextSeq 500", null, "SRP120009", null, "loader:fastq load.py|options:  appendBCtoName", "DEW156_Lane2.sorted.fastq.gz", "fastq", 1448314433.0, 25210929.0, "GSM2813982 r2", "0:57.45", "A:396430204;C:281605460;G:305048301;T:465226358;N:4110", 57, null, null, null, 396430204, 281605460, 305048301, 465226358, 4110, "SRX3287412", "SRS2596885", "SRA619743", "GEO", "Harvard University", 1, 0.90054, null, 0.21045, null, 0.77508, null, 0.51559, null, 61, null, "B", null, "usable mapping rate", "illumina", "nextseq", "unknown", "cdna_unspecified", "unknown", "sc", "single_cell_droplet", "indrops", null, "United States", "2017-10-16", "Larval", "Larval", "Brain", "Nervous System"], [43849, "SRR6176742", "SRX3287411", "SRS2596902", "SRP120009", "PRJNA414416", "Simultaneous single cell profiling of lineages and cell types in the vertebrate brain", "GSE105010", "Other", "The lineage relationships among the hundreds of cell types generated during development are difficult to reconstruct. A recent method  GESTALT  used CRISPR\u2013Cas9 barcode editing for large scale lineage tracing  but was restricted to early development and did not identify cell types. Here we present scGESTALT  which combines the lineage recording capabilities of GESTALT with cell type identification by single cell RNA sequencing. The method relies on an inducible system that enables barcodes to be edited at multiple time points  capturing lineage information from later stages of development. Sequencing of 60 000 transcriptomes from the juvenile zebrafish brain identified >100 cell types and marker genes. Using these data  we generate lineage trees with hundreds of branches that help uncover restrictions at the level of cell types  brain regions  and gene expression cascades during differentiation. scGESTALT can be applied to other multicellular organisms to simultaneously characterize molecular identities and lineage histories of thousands of cells during development and disease. Overall design: inDrops libraries of single cell transcriptomes and scGESTALT barcodes  and genomic DNA GESTALT libraries", null, "pubmed:29608178", null, "DEW155 mid", "GSM2813981", null, "source name:zebrafish brain|tissue:brain|developmental stage:23 25dpf", "DEW155 mid", "Single cell RNA Sequencing data FASTQ files were processed using the inDrops.py bioinformatics pipeline available at https://github.com/indrops/indrops. Transcriptome libraries were mapped to a zebrafish reference built from a custom GTF file and the zebrafish GRCz10 release 86 genome assembly. Bowtie version1.1.1 was used with parameter \u2013e 200; UMI quantification was used with parameter \u2013u 2 counts were ignored from UMIs split between more than 2 genes. genomic DNA GESTALT and scGESTALT libraries were processed using a custom pipeline available at https://github.com/shendurelab/Cas9FateMapping Genome build: GRCz10 CSV files for transcriptome data were generated using the inDrops pipeline. Each column in the CSV files contains a cell identifier and each row contains expression values for genes. Txt files for genomic DNA GESTALT libraries *allReadCounts contain lineage barcode sequences HMID column for each cell and their proportion in the sequenced libraries. scGESTALT data *GestMaster.txt contains the inDrops cell identifiers CellBarcode and BarcodeKey that were used to match barcodes to transcriptomes. They also contain lineage barcode sequences HMID column for each cell with a corresponding inDrops single cell gene expression profile  as well as the the t SNE cluster membership number ClusterIdent column Txt files ending in *stats.txt contain information about the each individually captured UMI or cell per sample. The barcode sequence aligned to a reference unedited sequence mergedRead column  mutations at each target site target[X] columns and the edited sequences at each target site sequence[X] columns were used for downstream analysis. inDropsExpMatrix noQ txt file is the gene expression matrix for the full dataset. Columns are individual cells from different batches of whole brains f1   f2   f3   f4   f5   f6  or brain regions fore   mid   hind . fall.inDrops.Robj is the processed Seurat R object  which can be loaded into R and explored.", "zebrafish brain", null, "Single cell suspensions were processed through inDrops to generate single cell cDNA libraries. cDNAs were in vitro transcribed and fragmented. The three prime fragments were reverse transcribed and prepared for sequencing Libraries were prepared as described in Zilionis et al.  2017  Nature Protocols PMID = 27929523", null, "tissue:brain|developmental stage:23 25dpf", "GSM2813981", "GSM2813981: DEW155 mid; Danio rerio; RNA Seq", "GSM2813981", null, "1", "Single cell suspensions were processed through inDrops to generate single cell cDNA libraries. cDNAs were in vitro transcribed and fragmented. The three prime fragments were reverse transcribed and prepared for sequencing Libraries were prepared as described in Zilionis et al.  2017  Nature Protocols PMID = 27929523", "GEO Accession:GSM2813981", "RNA-Seq", "TRANSCRIPTOMIC", "cDNA", "SINGLE", "ILLUMINA", "NextSeq 500", null, "SRP120009", null, "loader:fastq load.py|options:  appendBCtoName", "DEW155_Lane1.sorted.fastq.gz", "fastq", 1378927876.0, 24382489.0, "GSM2813981 r1", "0:56.55", "A:382023525;C:266729277;G:288053480;T:442115668;N:5926", 56, null, null, null, 382023525, 266729277, 288053480, 442115668, 5926, "SRX3287411", "SRS2596902", "SRA619743", "GEO", "Harvard University", 1, 0.89076, null, 0.20521, null, 0.78281, null, 0.53104, null, 61, null, "B", null, "usable mapping rate", "illumina", "nextseq", "unknown", "cdna_unspecified", "unknown", "sc", "single_cell_droplet", "indrops", null, "United States", "2017-10-16", "Larval", "Larval", "Brain", "Nervous System"], [43850, "SRR6176743", "SRX3287411", "SRS2596902", "SRP120009", "PRJNA414416", "Simultaneous single cell profiling of lineages and cell types in the vertebrate brain", "GSE105010", "Other", "The lineage relationships among the hundreds of cell types generated during development are difficult to reconstruct. A recent method  GESTALT  used CRISPR\u2013Cas9 barcode editing for large scale lineage tracing  but was restricted to early development and did not identify cell types. Here we present scGESTALT  which combines the lineage recording capabilities of GESTALT with cell type identification by single cell RNA sequencing. The method relies on an inducible system that enables barcodes to be edited at multiple time points  capturing lineage information from later stages of development. Sequencing of 60 000 transcriptomes from the juvenile zebrafish brain identified >100 cell types and marker genes. Using these data  we generate lineage trees with hundreds of branches that help uncover restrictions at the level of cell types  brain regions  and gene expression cascades during differentiation. scGESTALT can be applied to other multicellular organisms to simultaneously characterize molecular identities and lineage histories of thousands of cells during development and disease. Overall design: inDrops libraries of single cell transcriptomes and scGESTALT barcodes  and genomic DNA GESTALT libraries", null, "pubmed:29608178", null, "DEW155 mid", "GSM2813981", null, "source name:zebrafish brain|tissue:brain|developmental stage:23 25dpf", "DEW155 mid", "Single cell RNA Sequencing data FASTQ files were processed using the inDrops.py bioinformatics pipeline available at https://github.com/indrops/indrops. Transcriptome libraries were mapped to a zebrafish reference built from a custom GTF file and the zebrafish GRCz10 release 86 genome assembly. Bowtie version1.1.1 was used with parameter \u2013e 200; UMI quantification was used with parameter \u2013u 2 counts were ignored from UMIs split between more than 2 genes. genomic DNA GESTALT and scGESTALT libraries were processed using a custom pipeline available at https://github.com/shendurelab/Cas9FateMapping Genome build: GRCz10 CSV files for transcriptome data were generated using the inDrops pipeline. Each column in the CSV files contains a cell identifier and each row contains expression values for genes. Txt files for genomic DNA GESTALT libraries *allReadCounts contain lineage barcode sequences HMID column for each cell and their proportion in the sequenced libraries. scGESTALT data *GestMaster.txt contains the inDrops cell identifiers CellBarcode and BarcodeKey that were used to match barcodes to transcriptomes. They also contain lineage barcode sequences HMID column for each cell with a corresponding inDrops single cell gene expression profile  as well as the the t SNE cluster membership number ClusterIdent column Txt files ending in *stats.txt contain information about the each individually captured UMI or cell per sample. The barcode sequence aligned to a reference unedited sequence mergedRead column  mutations at each target site target[X] columns and the edited sequences at each target site sequence[X] columns were used for downstream analysis. inDropsExpMatrix noQ txt file is the gene expression matrix for the full dataset. Columns are individual cells from different batches of whole brains f1   f2   f3   f4   f5   f6  or brain regions fore   mid   hind . fall.inDrops.Robj is the processed Seurat R object  which can be loaded into R and explored.", "zebrafish brain", null, "Single cell suspensions were processed through inDrops to generate single cell cDNA libraries. cDNAs were in vitro transcribed and fragmented. The three prime fragments were reverse transcribed and prepared for sequencing Libraries were prepared as described in Zilionis et al.  2017  Nature Protocols PMID = 27929523", null, "tissue:brain|developmental stage:23 25dpf", "GSM2813981", "GSM2813981: DEW155 mid; Danio rerio; RNA Seq", "GSM2813981", null, "1", "Single cell suspensions were processed through inDrops to generate single cell cDNA libraries. cDNAs were in vitro transcribed and fragmented. The three prime fragments were reverse transcribed and prepared for sequencing Libraries were prepared as described in Zilionis et al.  2017  Nature Protocols PMID = 27929523", "GEO Accession:GSM2813981", "RNA-Seq", "TRANSCRIPTOMIC", "cDNA", "SINGLE", "ILLUMINA", "NextSeq 500", null, "SRP120009", null, "loader:fastq load.py|options:  appendBCtoName", "DEW155_Lane2.sorted.fastq.gz", "fastq", 978765898.0, 17410876.0, "GSM2813981 r2", "0:56.22", "A:270631925;C:189594639;G:205361829;T:313174989;N:2516", 56, null, null, null, 270631925, 189594639, 205361829, 313174989, 2516, "SRX3287411", "SRS2596902", "SRA619743", "GEO", "Harvard University", 1, 0.89018, null, 0.20474, null, 0.78198, null, 0.5019, null, 46, null, "B", null, "usable mapping rate", "illumina", "nextseq", "unknown", "cdna_unspecified", "unknown", "sc", "single_cell_droplet", "indrops", null, "United States", "2017-10-16", "Larval", "Larval", "Brain", "Nervous System"], [43851, "SRR6176740", "SRX3287410", "SRS2596884", "SRP120009", "PRJNA414416", "Simultaneous single cell profiling of lineages and cell types in the vertebrate brain", "GSE105010", "Other", "The lineage relationships among the hundreds of cell types generated during development are difficult to reconstruct. A recent method  GESTALT  used CRISPR\u2013Cas9 barcode editing for large scale lineage tracing  but was restricted to early development and did not identify cell types. Here we present scGESTALT  which combines the lineage recording capabilities of GESTALT with cell type identification by single cell RNA sequencing. The method relies on an inducible system that enables barcodes to be edited at multiple time points  capturing lineage information from later stages of development. Sequencing of 60 000 transcriptomes from the juvenile zebrafish brain identified >100 cell types and marker genes. Using these data  we generate lineage trees with hundreds of branches that help uncover restrictions at the level of cell types  brain regions  and gene expression cascades during differentiation. scGESTALT can be applied to other multicellular organisms to simultaneously characterize molecular identities and lineage histories of thousands of cells during development and disease. Overall design: inDrops libraries of single cell transcriptomes and scGESTALT barcodes  and genomic DNA GESTALT libraries", null, "pubmed:29608178", null, "DEW154 hind", "GSM2813980", null, "source name:zebrafish brain|tissue:brain|developmental stage:23 25dpf", "DEW154 hind", "Single cell RNA Sequencing data FASTQ files were processed using the inDrops.py bioinformatics pipeline available at https://github.com/indrops/indrops. Transcriptome libraries were mapped to a zebrafish reference built from a custom GTF file and the zebrafish GRCz10 release 86 genome assembly. Bowtie version1.1.1 was used with parameter \u2013e 200; UMI quantification was used with parameter \u2013u 2 counts were ignored from UMIs split between more than 2 genes. genomic DNA GESTALT and scGESTALT libraries were processed using a custom pipeline available at https://github.com/shendurelab/Cas9FateMapping Genome build: GRCz10 CSV files for transcriptome data were generated using the inDrops pipeline. Each column in the CSV files contains a cell identifier and each row contains expression values for genes. Txt files for genomic DNA GESTALT libraries *allReadCounts contain lineage barcode sequences HMID column for each cell and their proportion in the sequenced libraries. scGESTALT data *GestMaster.txt contains the inDrops cell identifiers CellBarcode and BarcodeKey that were used to match barcodes to transcriptomes. They also contain lineage barcode sequences HMID column for each cell with a corresponding inDrops single cell gene expression profile  as well as the the t SNE cluster membership number ClusterIdent column Txt files ending in *stats.txt contain information about the each individually captured UMI or cell per sample. The barcode sequence aligned to a reference unedited sequence mergedRead column  mutations at each target site target[X] columns and the edited sequences at each target site sequence[X] columns were used for downstream analysis. inDropsExpMatrix noQ txt file is the gene expression matrix for the full dataset. Columns are individual cells from different batches of whole brains f1   f2   f3   f4   f5   f6  or brain regions fore   mid   hind . fall.inDrops.Robj is the processed Seurat R object  which can be loaded into R and explored.", "zebrafish brain", null, "Single cell suspensions were processed through inDrops to generate single cell cDNA libraries. cDNAs were in vitro transcribed and fragmented. The three prime fragments were reverse transcribed and prepared for sequencing Libraries were prepared as described in Zilionis et al.  2017  Nature Protocols PMID = 27929523", null, "tissue:brain|developmental stage:23 25dpf", "GSM2813980", "GSM2813980: DEW154 hind; Danio rerio; RNA Seq", "GSM2813980", null, "1", "Single cell suspensions were processed through inDrops to generate single cell cDNA libraries. cDNAs were in vitro transcribed and fragmented. The three prime fragments were reverse transcribed and prepared for sequencing Libraries were prepared as described in Zilionis et al.  2017  Nature Protocols PMID = 27929523", "GEO Accession:GSM2813980", "RNA-Seq", "TRANSCRIPTOMIC", "cDNA", "SINGLE", "ILLUMINA", "NextSeq 500", null, "SRP120009", null, "loader:fastq load.py|options:  appendBCtoName", "DEW154_Lane1.sorted.fastq.gz", "fastq", 191741626.0, 3624944.0, "GSM2813980 r1", "0:52.90", "A:54153798;C:35006191;G:40959293;T:61621602;N:742", 52, null, null, null, 54153798, 35006191, 40959293, 61621602, 742, "SRX3287410", "SRS2596884", "SRA619743", "GEO", "Harvard University", 1, 0.86682, null, 0.2127, null, 0.79695, null, 0.52028, null, 61, null, "B", null, "usable mapping rate", "illumina", "nextseq", "unknown", "cdna_unspecified", "unknown", "sc", "single_cell_droplet", "indrops", null, "United States", "2017-10-16", "Larval", "Larval", "Brain", "Nervous System"], [43852, "SRR6176741", "SRX3287410", "SRS2596884", "SRP120009", "PRJNA414416", "Simultaneous single cell profiling of lineages and cell types in the vertebrate brain", "GSE105010", "Other", "The lineage relationships among the hundreds of cell types generated during development are difficult to reconstruct. A recent method  GESTALT  used CRISPR\u2013Cas9 barcode editing for large scale lineage tracing  but was restricted to early development and did not identify cell types. Here we present scGESTALT  which combines the lineage recording capabilities of GESTALT with cell type identification by single cell RNA sequencing. The method relies on an inducible system that enables barcodes to be edited at multiple time points  capturing lineage information from later stages of development. Sequencing of 60 000 transcriptomes from the juvenile zebrafish brain identified >100 cell types and marker genes. Using these data  we generate lineage trees with hundreds of branches that help uncover restrictions at the level of cell types  brain regions  and gene expression cascades during differentiation. scGESTALT can be applied to other multicellular organisms to simultaneously characterize molecular identities and lineage histories of thousands of cells during development and disease. Overall design: inDrops libraries of single cell transcriptomes and scGESTALT barcodes  and genomic DNA GESTALT libraries", null, "pubmed:29608178", null, "DEW154 hind", "GSM2813980", null, "source name:zebrafish brain|tissue:brain|developmental stage:23 25dpf", "DEW154 hind", "Single cell RNA Sequencing data FASTQ files were processed using the inDrops.py bioinformatics pipeline available at https://github.com/indrops/indrops. Transcriptome libraries were mapped to a zebrafish reference built from a custom GTF file and the zebrafish GRCz10 release 86 genome assembly. Bowtie version1.1.1 was used with parameter \u2013e 200; UMI quantification was used with parameter \u2013u 2 counts were ignored from UMIs split between more than 2 genes. genomic DNA GESTALT and scGESTALT libraries were processed using a custom pipeline available at https://github.com/shendurelab/Cas9FateMapping Genome build: GRCz10 CSV files for transcriptome data were generated using the inDrops pipeline. Each column in the CSV files contains a cell identifier and each row contains expression values for genes. Txt files for genomic DNA GESTALT libraries *allReadCounts contain lineage barcode sequences HMID column for each cell and their proportion in the sequenced libraries. scGESTALT data *GestMaster.txt contains the inDrops cell identifiers CellBarcode and BarcodeKey that were used to match barcodes to transcriptomes. They also contain lineage barcode sequences HMID column for each cell with a corresponding inDrops single cell gene expression profile  as well as the the t SNE cluster membership number ClusterIdent column Txt files ending in *stats.txt contain information about the each individually captured UMI or cell per sample. The barcode sequence aligned to a reference unedited sequence mergedRead column  mutations at each target site target[X] columns and the edited sequences at each target site sequence[X] columns were used for downstream analysis. inDropsExpMatrix noQ txt file is the gene expression matrix for the full dataset. Columns are individual cells from different batches of whole brains f1   f2   f3   f4   f5   f6  or brain regions fore   mid   hind . fall.inDrops.Robj is the processed Seurat R object  which can be loaded into R and explored.", "zebrafish brain", null, "Single cell suspensions were processed through inDrops to generate single cell cDNA libraries. cDNAs were in vitro transcribed and fragmented. The three prime fragments were reverse transcribed and prepared for sequencing Libraries were prepared as described in Zilionis et al.  2017  Nature Protocols PMID = 27929523", null, "tissue:brain|developmental stage:23 25dpf", "GSM2813980", "GSM2813980: DEW154 hind; Danio rerio; RNA Seq", "GSM2813980", null, "1", "Single cell suspensions were processed through inDrops to generate single cell cDNA libraries. cDNAs were in vitro transcribed and fragmented. The three prime fragments were reverse transcribed and prepared for sequencing Libraries were prepared as described in Zilionis et al.  2017  Nature Protocols PMID = 27929523", "GEO Accession:GSM2813980", "RNA-Seq", "TRANSCRIPTOMIC", "cDNA", "SINGLE", "ILLUMINA", "NextSeq 500", null, "SRP120009", null, "loader:fastq load.py|options:  appendBCtoName", "DEW154_Lane2.sorted.fastq.gz", "fastq", 174428391.0, 3464500.0, "GSM2813980 r2", "0:50.35", "A:49679029;C:31732468;G:36877474;T:56139060;N:360", 50, null, null, null, 49679029, 31732468, 36877474, 56139060, 360, "SRX3287410", "SRS2596884", "SRA619743", "GEO", "Harvard University", 1, 0.85562, null, 0.21627, null, 0.79839, null, 0.52737, null, 61, null, "B", null, "usable mapping rate", "illumina", "nextseq", "unknown", "cdna_unspecified", "unknown", "sc", "single_cell_droplet", "indrops", null, "United States", "2017-10-16", "Larval", "Larval", "Brain", "Nervous System"], [43853, "SRR6176738", "SRX3287409", "SRS2596883", "SRP120009", "PRJNA414416", "Simultaneous single cell profiling of lineages and cell types in the vertebrate brain", "GSE105010", "Other", "The lineage relationships among the hundreds of cell types generated during development are difficult to reconstruct. A recent method  GESTALT  used CRISPR\u2013Cas9 barcode editing for large scale lineage tracing  but was restricted to early development and did not identify cell types. Here we present scGESTALT  which combines the lineage recording capabilities of GESTALT with cell type identification by single cell RNA sequencing. The method relies on an inducible system that enables barcodes to be edited at multiple time points  capturing lineage information from later stages of development. Sequencing of 60 000 transcriptomes from the juvenile zebrafish brain identified >100 cell types and marker genes. Using these data  we generate lineage trees with hundreds of branches that help uncover restrictions at the level of cell types  brain regions  and gene expression cascades during differentiation. scGESTALT can be applied to other multicellular organisms to simultaneously characterize molecular identities and lineage histories of thousands of cells during development and disease. Overall design: inDrops libraries of single cell transcriptomes and scGESTALT barcodes  and genomic DNA GESTALT libraries", null, "pubmed:29608178", null, "DEW153 hind", "GSM2813979", null, "source name:zebrafish brain|tissue:brain|developmental stage:23 25dpf", "DEW153 hind", "Single cell RNA Sequencing data FASTQ files were processed using the inDrops.py bioinformatics pipeline available at https://github.com/indrops/indrops. Transcriptome libraries were mapped to a zebrafish reference built from a custom GTF file and the zebrafish GRCz10 release 86 genome assembly. Bowtie version1.1.1 was used with parameter \u2013e 200; UMI quantification was used with parameter \u2013u 2 counts were ignored from UMIs split between more than 2 genes. genomic DNA GESTALT and scGESTALT libraries were processed using a custom pipeline available at https://github.com/shendurelab/Cas9FateMapping Genome build: GRCz10 CSV files for transcriptome data were generated using the inDrops pipeline. Each column in the CSV files contains a cell identifier and each row contains expression values for genes. Txt files for genomic DNA GESTALT libraries *allReadCounts contain lineage barcode sequences HMID column for each cell and their proportion in the sequenced libraries. scGESTALT data *GestMaster.txt contains the inDrops cell identifiers CellBarcode and BarcodeKey that were used to match barcodes to transcriptomes. They also contain lineage barcode sequences HMID column for each cell with a corresponding inDrops single cell gene expression profile  as well as the the t SNE cluster membership number ClusterIdent column Txt files ending in *stats.txt contain information about the each individually captured UMI or cell per sample. The barcode sequence aligned to a reference unedited sequence mergedRead column  mutations at each target site target[X] columns and the edited sequences at each target site sequence[X] columns were used for downstream analysis. inDropsExpMatrix noQ txt file is the gene expression matrix for the full dataset. Columns are individual cells from different batches of whole brains f1   f2   f3   f4   f5   f6  or brain regions fore   mid   hind . fall.inDrops.Robj is the processed Seurat R object  which can be loaded into R and explored.", "zebrafish brain", null, "Single cell suspensions were processed through inDrops to generate single cell cDNA libraries. cDNAs were in vitro transcribed and fragmented. The three prime fragments were reverse transcribed and prepared for sequencing Libraries were prepared as described in Zilionis et al.  2017  Nature Protocols PMID = 27929523", null, "tissue:brain|developmental stage:23 25dpf", "GSM2813979", "GSM2813979: DEW153 hind; Danio rerio; RNA Seq", "GSM2813979", null, "1", "Single cell suspensions were processed through inDrops to generate single cell cDNA libraries. cDNAs were in vitro transcribed and fragmented. The three prime fragments were reverse transcribed and prepared for sequencing Libraries were prepared as described in Zilionis et al.  2017  Nature Protocols PMID = 27929523", "GEO Accession:GSM2813979", "RNA-Seq", "TRANSCRIPTOMIC", "cDNA", "SINGLE", "ILLUMINA", "NextSeq 500", null, "SRP120009", null, "loader:fastq load.py|options:  appendBCtoName", "DEW153_Lane1.sorted.fastq.gz", "fastq", 2206528171.0, 38115978.0, "GSM2813979 r1", "0:57.89", "A:607831752;C:435134646;G:462618869;T:700932389;N:10515", 57, null, null, null, 607831752, 435134646, 462618869, 700932389, 10515, "SRX3287409", "SRS2596883", "SRA619743", "GEO", "Harvard University", 1, 0.89527, null, 0.20046, null, 0.78259, null, 0.50876, null, 60, null, "B", null, "usable mapping rate", "illumina", "nextseq", "unknown", "cdna_unspecified", "unknown", "sc", "single_cell_droplet", "indrops", null, "United States", "2017-10-16", "Larval", "Larval", "Brain", "Nervous System"], [43854, "SRR6176739", "SRX3287409", "SRS2596883", "SRP120009", "PRJNA414416", "Simultaneous single cell profiling of lineages and cell types in the vertebrate brain", "GSE105010", "Other", "The lineage relationships among the hundreds of cell types generated during development are difficult to reconstruct. A recent method  GESTALT  used CRISPR\u2013Cas9 barcode editing for large scale lineage tracing  but was restricted to early development and did not identify cell types. Here we present scGESTALT  which combines the lineage recording capabilities of GESTALT with cell type identification by single cell RNA sequencing. The method relies on an inducible system that enables barcodes to be edited at multiple time points  capturing lineage information from later stages of development. Sequencing of 60 000 transcriptomes from the juvenile zebrafish brain identified >100 cell types and marker genes. Using these data  we generate lineage trees with hundreds of branches that help uncover restrictions at the level of cell types  brain regions  and gene expression cascades during differentiation. scGESTALT can be applied to other multicellular organisms to simultaneously characterize molecular identities and lineage histories of thousands of cells during development and disease. Overall design: inDrops libraries of single cell transcriptomes and scGESTALT barcodes  and genomic DNA GESTALT libraries", null, "pubmed:29608178", null, "DEW153 hind", "GSM2813979", null, "source name:zebrafish brain|tissue:brain|developmental stage:23 25dpf", "DEW153 hind", "Single cell RNA Sequencing data FASTQ files were processed using the inDrops.py bioinformatics pipeline available at https://github.com/indrops/indrops. Transcriptome libraries were mapped to a zebrafish reference built from a custom GTF file and the zebrafish GRCz10 release 86 genome assembly. Bowtie version1.1.1 was used with parameter \u2013e 200; UMI quantification was used with parameter \u2013u 2 counts were ignored from UMIs split between more than 2 genes. genomic DNA GESTALT and scGESTALT libraries were processed using a custom pipeline available at https://github.com/shendurelab/Cas9FateMapping Genome build: GRCz10 CSV files for transcriptome data were generated using the inDrops pipeline. Each column in the CSV files contains a cell identifier and each row contains expression values for genes. Txt files for genomic DNA GESTALT libraries *allReadCounts contain lineage barcode sequences HMID column for each cell and their proportion in the sequenced libraries. scGESTALT data *GestMaster.txt contains the inDrops cell identifiers CellBarcode and BarcodeKey that were used to match barcodes to transcriptomes. They also contain lineage barcode sequences HMID column for each cell with a corresponding inDrops single cell gene expression profile  as well as the the t SNE cluster membership number ClusterIdent column Txt files ending in *stats.txt contain information about the each individually captured UMI or cell per sample. The barcode sequence aligned to a reference unedited sequence mergedRead column  mutations at each target site target[X] columns and the edited sequences at each target site sequence[X] columns were used for downstream analysis. inDropsExpMatrix noQ txt file is the gene expression matrix for the full dataset. Columns are individual cells from different batches of whole brains f1   f2   f3   f4   f5   f6  or brain regions fore   mid   hind . fall.inDrops.Robj is the processed Seurat R object  which can be loaded into R and explored.", "zebrafish brain", null, "Single cell suspensions were processed through inDrops to generate single cell cDNA libraries. cDNAs were in vitro transcribed and fragmented. The three prime fragments were reverse transcribed and prepared for sequencing Libraries were prepared as described in Zilionis et al.  2017  Nature Protocols PMID = 27929523", null, "tissue:brain|developmental stage:23 25dpf", "GSM2813979", "GSM2813979: DEW153 hind; Danio rerio; RNA Seq", "GSM2813979", null, "1", "Single cell suspensions were processed through inDrops to generate single cell cDNA libraries. cDNAs were in vitro transcribed and fragmented. The three prime fragments were reverse transcribed and prepared for sequencing Libraries were prepared as described in Zilionis et al.  2017  Nature Protocols PMID = 27929523", "GEO Accession:GSM2813979", "RNA-Seq", "TRANSCRIPTOMIC", "cDNA", "SINGLE", "ILLUMINA", "NextSeq 500", null, "SRP120009", null, "loader:fastq load.py|options:  appendBCtoName", "DEW153_Lane2.sorted.fastq.gz", "fastq", 1450777748.0, 25293610.0, "GSM2813979 r2", "0:57.36", "A:398797215;C:285454365;G:305822323;T:460699598;N:4247", 57, null, null, null, 398797215, 285454365, 305822323, 460699598, 4247, "SRX3287409", "SRS2596883", "SRA619743", "GEO", "Harvard University", 1, 0.89512, null, 0.20047, null, 0.78222, null, 0.53369, null, 60, null, "B", null, "usable mapping rate", "illumina", "nextseq", "unknown", "cdna_unspecified", "unknown", "sc", "single_cell_droplet", "indrops", null, "United States", "2017-10-16", "Larval", "Larval", "Brain", "Nervous System"], [43855, "SRR6176736", "SRX3287408", "SRS2596882", "SRP120009", "PRJNA414416", "Simultaneous single cell profiling of lineages and cell types in the vertebrate brain", "GSE105010", "Other", "The lineage relationships among the hundreds of cell types generated during development are difficult to reconstruct. A recent method  GESTALT  used CRISPR\u2013Cas9 barcode editing for large scale lineage tracing  but was restricted to early development and did not identify cell types. Here we present scGESTALT  which combines the lineage recording capabilities of GESTALT with cell type identification by single cell RNA sequencing. The method relies on an inducible system that enables barcodes to be edited at multiple time points  capturing lineage information from later stages of development. Sequencing of 60 000 transcriptomes from the juvenile zebrafish brain identified >100 cell types and marker genes. Using these data  we generate lineage trees with hundreds of branches that help uncover restrictions at the level of cell types  brain regions  and gene expression cascades during differentiation. scGESTALT can be applied to other multicellular organisms to simultaneously characterize molecular identities and lineage histories of thousands of cells during development and disease. Overall design: inDrops libraries of single cell transcriptomes and scGESTALT barcodes  and genomic DNA GESTALT libraries", null, "pubmed:29608178", null, "DEW152 hind", "GSM2813978", null, "source name:zebrafish brain|tissue:brain|developmental stage:23 25dpf", "DEW152 hind", "Single cell RNA Sequencing data FASTQ files were processed using the inDrops.py bioinformatics pipeline available at https://github.com/indrops/indrops. Transcriptome libraries were mapped to a zebrafish reference built from a custom GTF file and the zebrafish GRCz10 release 86 genome assembly. Bowtie version1.1.1 was used with parameter \u2013e 200; UMI quantification was used with parameter \u2013u 2 counts were ignored from UMIs split between more than 2 genes. genomic DNA GESTALT and scGESTALT libraries were processed using a custom pipeline available at https://github.com/shendurelab/Cas9FateMapping Genome build: GRCz10 CSV files for transcriptome data were generated using the inDrops pipeline. Each column in the CSV files contains a cell identifier and each row contains expression values for genes. Txt files for genomic DNA GESTALT libraries *allReadCounts contain lineage barcode sequences HMID column for each cell and their proportion in the sequenced libraries. scGESTALT data *GestMaster.txt contains the inDrops cell identifiers CellBarcode and BarcodeKey that were used to match barcodes to transcriptomes. They also contain lineage barcode sequences HMID column for each cell with a corresponding inDrops single cell gene expression profile  as well as the the t SNE cluster membership number ClusterIdent column Txt files ending in *stats.txt contain information about the each individually captured UMI or cell per sample. The barcode sequence aligned to a reference unedited sequence mergedRead column  mutations at each target site target[X] columns and the edited sequences at each target site sequence[X] columns were used for downstream analysis. inDropsExpMatrix noQ txt file is the gene expression matrix for the full dataset. Columns are individual cells from different batches of whole brains f1   f2   f3   f4   f5   f6  or brain regions fore   mid   hind . fall.inDrops.Robj is the processed Seurat R object  which can be loaded into R and explored.", "zebrafish brain", null, "Single cell suspensions were processed through inDrops to generate single cell cDNA libraries. cDNAs were in vitro transcribed and fragmented. The three prime fragments were reverse transcribed and prepared for sequencing Libraries were prepared as described in Zilionis et al.  2017  Nature Protocols PMID = 27929523", null, "tissue:brain|developmental stage:23 25dpf", "GSM2813978", "GSM2813978: DEW152 hind; Danio rerio; RNA Seq", "GSM2813978", null, "1", "Single cell suspensions were processed through inDrops to generate single cell cDNA libraries. cDNAs were in vitro transcribed and fragmented. The three prime fragments were reverse transcribed and prepared for sequencing Libraries were prepared as described in Zilionis et al.  2017  Nature Protocols PMID = 27929523", "GEO Accession:GSM2813978", "RNA-Seq", "TRANSCRIPTOMIC", "cDNA", "SINGLE", "ILLUMINA", "NextSeq 500", null, "SRP120009", null, "loader:fastq load.py|options:  appendBCtoName", "DEW152_Lane1.sorted.fastq.gz", "fastq", 2005997266.0, 34574429.0, "GSM2813978 r1", "0:58.02", "A:552258277;C:393949059;G:416248897;T:643531466;N:9567", 58, null, null, null, 552258277, 393949059, 416248897, 643531466, 9567, "SRX3287408", "SRS2596882", "SRA619743", "GEO", "Harvard University", 1, 0.89818, null, 0.20396, null, 0.78001, null, 0.5176, null, 61, null, "B", null, "usable mapping rate", "illumina", "nextseq", "unknown", "cdna_unspecified", "unknown", "sc", "single_cell_droplet", "indrops", null, "United States", "2017-10-16", "Larval", "Larval", "Brain", "Nervous System"], [43856, "SRR6176737", "SRX3287408", "SRS2596882", "SRP120009", "PRJNA414416", "Simultaneous single cell profiling of lineages and cell types in the vertebrate brain", "GSE105010", "Other", "The lineage relationships among the hundreds of cell types generated during development are difficult to reconstruct. A recent method  GESTALT  used CRISPR\u2013Cas9 barcode editing for large scale lineage tracing  but was restricted to early development and did not identify cell types. Here we present scGESTALT  which combines the lineage recording capabilities of GESTALT with cell type identification by single cell RNA sequencing. The method relies on an inducible system that enables barcodes to be edited at multiple time points  capturing lineage information from later stages of development. Sequencing of 60 000 transcriptomes from the juvenile zebrafish brain identified >100 cell types and marker genes. Using these data  we generate lineage trees with hundreds of branches that help uncover restrictions at the level of cell types  brain regions  and gene expression cascades during differentiation. scGESTALT can be applied to other multicellular organisms to simultaneously characterize molecular identities and lineage histories of thousands of cells during development and disease. Overall design: inDrops libraries of single cell transcriptomes and scGESTALT barcodes  and genomic DNA GESTALT libraries", null, "pubmed:29608178", null, "DEW152 hind", "GSM2813978", null, "source name:zebrafish brain|tissue:brain|developmental stage:23 25dpf", "DEW152 hind", "Single cell RNA Sequencing data FASTQ files were processed using the inDrops.py bioinformatics pipeline available at https://github.com/indrops/indrops. Transcriptome libraries were mapped to a zebrafish reference built from a custom GTF file and the zebrafish GRCz10 release 86 genome assembly. Bowtie version1.1.1 was used with parameter \u2013e 200; UMI quantification was used with parameter \u2013u 2 counts were ignored from UMIs split between more than 2 genes. genomic DNA GESTALT and scGESTALT libraries were processed using a custom pipeline available at https://github.com/shendurelab/Cas9FateMapping Genome build: GRCz10 CSV files for transcriptome data were generated using the inDrops pipeline. Each column in the CSV files contains a cell identifier and each row contains expression values for genes. Txt files for genomic DNA GESTALT libraries *allReadCounts contain lineage barcode sequences HMID column for each cell and their proportion in the sequenced libraries. scGESTALT data *GestMaster.txt contains the inDrops cell identifiers CellBarcode and BarcodeKey that were used to match barcodes to transcriptomes. They also contain lineage barcode sequences HMID column for each cell with a corresponding inDrops single cell gene expression profile  as well as the the t SNE cluster membership number ClusterIdent column Txt files ending in *stats.txt contain information about the each individually captured UMI or cell per sample. The barcode sequence aligned to a reference unedited sequence mergedRead column  mutations at each target site target[X] columns and the edited sequences at each target site sequence[X] columns were used for downstream analysis. inDropsExpMatrix noQ txt file is the gene expression matrix for the full dataset. Columns are individual cells from different batches of whole brains f1   f2   f3   f4   f5   f6  or brain regions fore   mid   hind . fall.inDrops.Robj is the processed Seurat R object  which can be loaded into R and explored.", "zebrafish brain", null, "Single cell suspensions were processed through inDrops to generate single cell cDNA libraries. cDNAs were in vitro transcribed and fragmented. The three prime fragments were reverse transcribed and prepared for sequencing Libraries were prepared as described in Zilionis et al.  2017  Nature Protocols PMID = 27929523", null, "tissue:brain|developmental stage:23 25dpf", "GSM2813978", "GSM2813978: DEW152 hind; Danio rerio; RNA Seq", "GSM2813978", null, "1", "Single cell suspensions were processed through inDrops to generate single cell cDNA libraries. cDNAs were in vitro transcribed and fragmented. The three prime fragments were reverse transcribed and prepared for sequencing Libraries were prepared as described in Zilionis et al.  2017  Nature Protocols PMID = 27929523", "GEO Accession:GSM2813978", "RNA-Seq", "TRANSCRIPTOMIC", "cDNA", "SINGLE", "ILLUMINA", "NextSeq 500", null, "SRP120009", null, "loader:fastq load.py|options:  appendBCtoName", "DEW152_Lane2.sorted.fastq.gz", "fastq", 1306624373.0, 22721229.0, "GSM2813978 r2", "0:57.51", "A:358953147;C:256408571;G:272527726;T:418731243;N:3686", 57, null, null, null, 358953147, 256408571, 272527726, 418731243, 3686, "SRX3287408", "SRS2596882", "SRA619743", "GEO", "Harvard University", 1, 0.89769, null, 0.20479, null, 0.78204, null, 0.51518, null, 61, null, "B", null, "usable mapping rate", "illumina", "nextseq", "unknown", "cdna_unspecified", "unknown", "sc", "single_cell_droplet", "indrops", null, "United States", "2017-10-16", "Larval", "Larval", "Brain", "Nervous System"], [43857, "SRR6176734", "SRX3287407", "SRS2596880", "SRP120009", "PRJNA414416", "Simultaneous single cell profiling of lineages and cell types in the vertebrate brain", "GSE105010", "Other", "The lineage relationships among the hundreds of cell types generated during development are difficult to reconstruct. A recent method  GESTALT  used CRISPR\u2013Cas9 barcode editing for large scale lineage tracing  but was restricted to early development and did not identify cell types. Here we present scGESTALT  which combines the lineage recording capabilities of GESTALT with cell type identification by single cell RNA sequencing. The method relies on an inducible system that enables barcodes to be edited at multiple time points  capturing lineage information from later stages of development. Sequencing of 60 000 transcriptomes from the juvenile zebrafish brain identified >100 cell types and marker genes. Using these data  we generate lineage trees with hundreds of branches that help uncover restrictions at the level of cell types  brain regions  and gene expression cascades during differentiation. scGESTALT can be applied to other multicellular organisms to simultaneously characterize molecular identities and lineage histories of thousands of cells during development and disease. Overall design: inDrops libraries of single cell transcriptomes and scGESTALT barcodes  and genomic DNA GESTALT libraries", null, "pubmed:29608178", null, "DEW151 fore", "GSM2813977", null, "source name:zebrafish brain|tissue:brain|developmental stage:23 25dpf", "DEW151 fore", "Single cell RNA Sequencing data FASTQ files were processed using the inDrops.py bioinformatics pipeline available at https://github.com/indrops/indrops. Transcriptome libraries were mapped to a zebrafish reference built from a custom GTF file and the zebrafish GRCz10 release 86 genome assembly. Bowtie version1.1.1 was used with parameter \u2013e 200; UMI quantification was used with parameter \u2013u 2 counts were ignored from UMIs split between more than 2 genes. genomic DNA GESTALT and scGESTALT libraries were processed using a custom pipeline available at https://github.com/shendurelab/Cas9FateMapping Genome build: GRCz10 CSV files for transcriptome data were generated using the inDrops pipeline. Each column in the CSV files contains a cell identifier and each row contains expression values for genes. Txt files for genomic DNA GESTALT libraries *allReadCounts contain lineage barcode sequences HMID column for each cell and their proportion in the sequenced libraries. scGESTALT data *GestMaster.txt contains the inDrops cell identifiers CellBarcode and BarcodeKey that were used to match barcodes to transcriptomes. They also contain lineage barcode sequences HMID column for each cell with a corresponding inDrops single cell gene expression profile  as well as the the t SNE cluster membership number ClusterIdent column Txt files ending in *stats.txt contain information about the each individually captured UMI or cell per sample. The barcode sequence aligned to a reference unedited sequence mergedRead column  mutations at each target site target[X] columns and the edited sequences at each target site sequence[X] columns were used for downstream analysis. inDropsExpMatrix noQ txt file is the gene expression matrix for the full dataset. Columns are individual cells from different batches of whole brains f1   f2   f3   f4   f5   f6  or brain regions fore   mid   hind . fall.inDrops.Robj is the processed Seurat R object  which can be loaded into R and explored.", "zebrafish brain", null, "Single cell suspensions were processed through inDrops to generate single cell cDNA libraries. cDNAs were in vitro transcribed and fragmented. The three prime fragments were reverse transcribed and prepared for sequencing Libraries were prepared as described in Zilionis et al.  2017  Nature Protocols PMID = 27929523", null, "tissue:brain|developmental stage:23 25dpf", "GSM2813977", "GSM2813977: DEW151 fore; Danio rerio; RNA Seq", "GSM2813977", null, "1", "Single cell suspensions were processed through inDrops to generate single cell cDNA libraries. cDNAs were in vitro transcribed and fragmented. The three prime fragments were reverse transcribed and prepared for sequencing Libraries were prepared as described in Zilionis et al.  2017  Nature Protocols PMID = 27929523", "GEO Accession:GSM2813977", "RNA-Seq", "TRANSCRIPTOMIC", "cDNA", "SINGLE", "ILLUMINA", "NextSeq 500", null, "SRP120009", null, "loader:fastq load.py|options:  appendBCtoName", "DEW151_Lane1.sorted.fastq.gz", "fastq", 1781584043.0, 30810377.0, "GSM2813977 r1", "0:57.82", "A:494848942;C:336194488;G:366298578;T:584233757;N:8278", 57, null, null, null, 494848942, 336194488, 366298578, 584233757, 8278, "SRX3287407", "SRS2596880", "SRA619743", "GEO", "Harvard University", 1, 0.88247, null, 0.27771, null, 0.78311, null, 0.52819, null, 61, null, "B", null, "usable mapping rate", "illumina", "nextseq", "unknown", "cdna_unspecified", "unknown", "sc", "single_cell_droplet", "indrops", null, "United States", "2017-10-16", "Larval", "Larval", "Brain", "Nervous System"], [43858, "SRR6176735", "SRX3287407", "SRS2596880", "SRP120009", "PRJNA414416", "Simultaneous single cell profiling of lineages and cell types in the vertebrate brain", "GSE105010", "Other", "The lineage relationships among the hundreds of cell types generated during development are difficult to reconstruct. A recent method  GESTALT  used CRISPR\u2013Cas9 barcode editing for large scale lineage tracing  but was restricted to early development and did not identify cell types. Here we present scGESTALT  which combines the lineage recording capabilities of GESTALT with cell type identification by single cell RNA sequencing. The method relies on an inducible system that enables barcodes to be edited at multiple time points  capturing lineage information from later stages of development. Sequencing of 60 000 transcriptomes from the juvenile zebrafish brain identified >100 cell types and marker genes. Using these data  we generate lineage trees with hundreds of branches that help uncover restrictions at the level of cell types  brain regions  and gene expression cascades during differentiation. scGESTALT can be applied to other multicellular organisms to simultaneously characterize molecular identities and lineage histories of thousands of cells during development and disease. Overall design: inDrops libraries of single cell transcriptomes and scGESTALT barcodes  and genomic DNA GESTALT libraries", null, "pubmed:29608178", null, "DEW151 fore", "GSM2813977", null, "source name:zebrafish brain|tissue:brain|developmental stage:23 25dpf", "DEW151 fore", "Single cell RNA Sequencing data FASTQ files were processed using the inDrops.py bioinformatics pipeline available at https://github.com/indrops/indrops. Transcriptome libraries were mapped to a zebrafish reference built from a custom GTF file and the zebrafish GRCz10 release 86 genome assembly. Bowtie version1.1.1 was used with parameter \u2013e 200; UMI quantification was used with parameter \u2013u 2 counts were ignored from UMIs split between more than 2 genes. genomic DNA GESTALT and scGESTALT libraries were processed using a custom pipeline available at https://github.com/shendurelab/Cas9FateMapping Genome build: GRCz10 CSV files for transcriptome data were generated using the inDrops pipeline. Each column in the CSV files contains a cell identifier and each row contains expression values for genes. Txt files for genomic DNA GESTALT libraries *allReadCounts contain lineage barcode sequences HMID column for each cell and their proportion in the sequenced libraries. scGESTALT data *GestMaster.txt contains the inDrops cell identifiers CellBarcode and BarcodeKey that were used to match barcodes to transcriptomes. They also contain lineage barcode sequences HMID column for each cell with a corresponding inDrops single cell gene expression profile  as well as the the t SNE cluster membership number ClusterIdent column Txt files ending in *stats.txt contain information about the each individually captured UMI or cell per sample. The barcode sequence aligned to a reference unedited sequence mergedRead column  mutations at each target site target[X] columns and the edited sequences at each target site sequence[X] columns were used for downstream analysis. inDropsExpMatrix noQ txt file is the gene expression matrix for the full dataset. Columns are individual cells from different batches of whole brains f1   f2   f3   f4   f5   f6  or brain regions fore   mid   hind . fall.inDrops.Robj is the processed Seurat R object  which can be loaded into R and explored.", "zebrafish brain", null, "Single cell suspensions were processed through inDrops to generate single cell cDNA libraries. cDNAs were in vitro transcribed and fragmented. The three prime fragments were reverse transcribed and prepared for sequencing Libraries were prepared as described in Zilionis et al.  2017  Nature Protocols PMID = 27929523", null, "tissue:brain|developmental stage:23 25dpf", "GSM2813977", "GSM2813977: DEW151 fore; Danio rerio; RNA Seq", "GSM2813977", null, "1", "Single cell suspensions were processed through inDrops to generate single cell cDNA libraries. cDNAs were in vitro transcribed and fragmented. The three prime fragments were reverse transcribed and prepared for sequencing Libraries were prepared as described in Zilionis et al.  2017  Nature Protocols PMID = 27929523", "GEO Accession:GSM2813977", "RNA-Seq", "TRANSCRIPTOMIC", "cDNA", "SINGLE", "ILLUMINA", "NextSeq 500", null, "SRP120009", null, "loader:fastq load.py|options:  appendBCtoName", "DEW151_Lane2.sorted.fastq.gz", "fastq", 1157526748.0, 20213966.0, "GSM2813977 r2", "0:57.26", "A:320886308;C:218034938;G:239168032;T:379434373;N:3097", 57, null, null, null, 320886308, 218034938, 239168032, 379434373, 3097, "SRX3287407", "SRS2596880", "SRA619743", "GEO", "Harvard University", 1, 0.88331, null, 0.27805, null, 0.78599, null, 0.52469, null, 60, null, "B", null, "usable mapping rate", "illumina", "nextseq", "unknown", "cdna_unspecified", "unknown", "sc", "single_cell_droplet", "indrops", null, "United States", "2017-10-16", "Larval", "Larval", "Brain", "Nervous System"], [43859, "SRR6176732", "SRX3287406", "SRS2596881", "SRP120009", "PRJNA414416", "Simultaneous single cell profiling of lineages and cell types in the vertebrate brain", "GSE105010", "Other", "The lineage relationships among the hundreds of cell types generated during development are difficult to reconstruct. A recent method  GESTALT  used CRISPR\u2013Cas9 barcode editing for large scale lineage tracing  but was restricted to early development and did not identify cell types. Here we present scGESTALT  which combines the lineage recording capabilities of GESTALT with cell type identification by single cell RNA sequencing. The method relies on an inducible system that enables barcodes to be edited at multiple time points  capturing lineage information from later stages of development. Sequencing of 60 000 transcriptomes from the juvenile zebrafish brain identified >100 cell types and marker genes. Using these data  we generate lineage trees with hundreds of branches that help uncover restrictions at the level of cell types  brain regions  and gene expression cascades during differentiation. scGESTALT can be applied to other multicellular organisms to simultaneously characterize molecular identities and lineage histories of thousands of cells during development and disease. Overall design: inDrops libraries of single cell transcriptomes and scGESTALT barcodes  and genomic DNA GESTALT libraries", null, "pubmed:29608178", null, "DEW150 fore", "GSM2813976", null, "source name:zebrafish brain|tissue:brain|developmental stage:23 25dpf", "DEW150 fore", "Single cell RNA Sequencing data FASTQ files were processed using the inDrops.py bioinformatics pipeline available at https://github.com/indrops/indrops. Transcriptome libraries were mapped to a zebrafish reference built from a custom GTF file and the zebrafish GRCz10 release 86 genome assembly. Bowtie version1.1.1 was used with parameter \u2013e 200; UMI quantification was used with parameter \u2013u 2 counts were ignored from UMIs split between more than 2 genes. genomic DNA GESTALT and scGESTALT libraries were processed using a custom pipeline available at https://github.com/shendurelab/Cas9FateMapping Genome build: GRCz10 CSV files for transcriptome data were generated using the inDrops pipeline. Each column in the CSV files contains a cell identifier and each row contains expression values for genes. Txt files for genomic DNA GESTALT libraries *allReadCounts contain lineage barcode sequences HMID column for each cell and their proportion in the sequenced libraries. scGESTALT data *GestMaster.txt contains the inDrops cell identifiers CellBarcode and BarcodeKey that were used to match barcodes to transcriptomes. They also contain lineage barcode sequences HMID column for each cell with a corresponding inDrops single cell gene expression profile  as well as the the t SNE cluster membership number ClusterIdent column Txt files ending in *stats.txt contain information about the each individually captured UMI or cell per sample. The barcode sequence aligned to a reference unedited sequence mergedRead column  mutations at each target site target[X] columns and the edited sequences at each target site sequence[X] columns were used for downstream analysis. inDropsExpMatrix noQ txt file is the gene expression matrix for the full dataset. Columns are individual cells from different batches of whole brains f1   f2   f3   f4   f5   f6  or brain regions fore   mid   hind . fall.inDrops.Robj is the processed Seurat R object  which can be loaded into R and explored.", "zebrafish brain", null, "Single cell suspensions were processed through inDrops to generate single cell cDNA libraries. cDNAs were in vitro transcribed and fragmented. The three prime fragments were reverse transcribed and prepared for sequencing Libraries were prepared as described in Zilionis et al.  2017  Nature Protocols PMID = 27929523", null, "tissue:brain|developmental stage:23 25dpf", "GSM2813976", "GSM2813976: DEW150 fore; Danio rerio; RNA Seq", "GSM2813976", null, "1", "Single cell suspensions were processed through inDrops to generate single cell cDNA libraries. cDNAs were in vitro transcribed and fragmented. The three prime fragments were reverse transcribed and prepared for sequencing Libraries were prepared as described in Zilionis et al.  2017  Nature Protocols PMID = 27929523", "GEO Accession:GSM2813976", "RNA-Seq", "TRANSCRIPTOMIC", "cDNA", "SINGLE", "ILLUMINA", "NextSeq 500", null, "SRP120009", null, "loader:fastq load.py|options:  appendBCtoName", "DEW150_Lane1.sorted.fastq.gz", "fastq", 2032276612.0, 35123226.0, "GSM2813976 r1", "0:57.86", "A:563752104;C:383031134;G:429113539;T:656370389;N:9446", 57, null, null, null, 563752104, 383031134, 429113539, 656370389, 9446, "SRX3287406", "SRS2596881", "SRA619743", "GEO", "Harvard University", 1, 0.8788, null, 0.27566, null, 0.78995, null, 0.521, null, 61, null, "B", null, "usable mapping rate", "illumina", "nextseq", "unknown", "cdna_unspecified", "unknown", "sc", "single_cell_droplet", "indrops", null, "United States", "2017-10-16", "Larval", "Larval", "Brain", "Nervous System"], [43860, "SRR6176733", "SRX3287406", "SRS2596881", "SRP120009", "PRJNA414416", "Simultaneous single cell profiling of lineages and cell types in the vertebrate brain", "GSE105010", "Other", "The lineage relationships among the hundreds of cell types generated during development are difficult to reconstruct. A recent method  GESTALT  used CRISPR\u2013Cas9 barcode editing for large scale lineage tracing  but was restricted to early development and did not identify cell types. Here we present scGESTALT  which combines the lineage recording capabilities of GESTALT with cell type identification by single cell RNA sequencing. The method relies on an inducible system that enables barcodes to be edited at multiple time points  capturing lineage information from later stages of development. Sequencing of 60 000 transcriptomes from the juvenile zebrafish brain identified >100 cell types and marker genes. Using these data  we generate lineage trees with hundreds of branches that help uncover restrictions at the level of cell types  brain regions  and gene expression cascades during differentiation. scGESTALT can be applied to other multicellular organisms to simultaneously characterize molecular identities and lineage histories of thousands of cells during development and disease. Overall design: inDrops libraries of single cell transcriptomes and scGESTALT barcodes  and genomic DNA GESTALT libraries", null, "pubmed:29608178", null, "DEW150 fore", "GSM2813976", null, "source name:zebrafish brain|tissue:brain|developmental stage:23 25dpf", "DEW150 fore", "Single cell RNA Sequencing data FASTQ files were processed using the inDrops.py bioinformatics pipeline available at https://github.com/indrops/indrops. Transcriptome libraries were mapped to a zebrafish reference built from a custom GTF file and the zebrafish GRCz10 release 86 genome assembly. Bowtie version1.1.1 was used with parameter \u2013e 200; UMI quantification was used with parameter \u2013u 2 counts were ignored from UMIs split between more than 2 genes. genomic DNA GESTALT and scGESTALT libraries were processed using a custom pipeline available at https://github.com/shendurelab/Cas9FateMapping Genome build: GRCz10 CSV files for transcriptome data were generated using the inDrops pipeline. Each column in the CSV files contains a cell identifier and each row contains expression values for genes. Txt files for genomic DNA GESTALT libraries *allReadCounts contain lineage barcode sequences HMID column for each cell and their proportion in the sequenced libraries. scGESTALT data *GestMaster.txt contains the inDrops cell identifiers CellBarcode and BarcodeKey that were used to match barcodes to transcriptomes. They also contain lineage barcode sequences HMID column for each cell with a corresponding inDrops single cell gene expression profile  as well as the the t SNE cluster membership number ClusterIdent column Txt files ending in *stats.txt contain information about the each individually captured UMI or cell per sample. The barcode sequence aligned to a reference unedited sequence mergedRead column  mutations at each target site target[X] columns and the edited sequences at each target site sequence[X] columns were used for downstream analysis. inDropsExpMatrix noQ txt file is the gene expression matrix for the full dataset. Columns are individual cells from different batches of whole brains f1   f2   f3   f4   f5   f6  or brain regions fore   mid   hind . fall.inDrops.Robj is the processed Seurat R object  which can be loaded into R and explored.", "zebrafish brain", null, "Single cell suspensions were processed through inDrops to generate single cell cDNA libraries. cDNAs were in vitro transcribed and fragmented. The three prime fragments were reverse transcribed and prepared for sequencing Libraries were prepared as described in Zilionis et al.  2017  Nature Protocols PMID = 27929523", null, "tissue:brain|developmental stage:23 25dpf", "GSM2813976", "GSM2813976: DEW150 fore; Danio rerio; RNA Seq", "GSM2813976", null, "1", "Single cell suspensions were processed through inDrops to generate single cell cDNA libraries. cDNAs were in vitro transcribed and fragmented. The three prime fragments were reverse transcribed and prepared for sequencing Libraries were prepared as described in Zilionis et al.  2017  Nature Protocols PMID = 27929523", "GEO Accession:GSM2813976", "RNA-Seq", "TRANSCRIPTOMIC", "cDNA", "SINGLE", "ILLUMINA", "NextSeq 500", null, "SRP120009", null, "loader:fastq load.py|options:  appendBCtoName", "DEW150_Lane2.sorted.fastq.gz", "fastq", 1315848593.0, 22939860.0, "GSM2813976 r2", "0:57.36", "A:364153526;C:247636153;G:279420005;T:424635138;N:3771", 57, null, null, null, 364153526, 247636153, 279420005, 424635138, 3771, "SRX3287406", "SRS2596881", "SRA619743", "GEO", "Harvard University", 1, 0.87913, null, 0.27404, null, 0.78666, null, 0.52726, null, 61, null, "B", null, "usable mapping rate", "illumina", "nextseq", "unknown", "cdna_unspecified", "unknown", "sc", "single_cell_droplet", "indrops", null, "United States", "2017-10-16", "Larval", "Larval", "Brain", "Nervous System"], [43861, "SRR6176730", "SRX3287405", "SRS2596879", "SRP120009", "PRJNA414416", "Simultaneous single cell profiling of lineages and cell types in the vertebrate brain", "GSE105010", "Other", "The lineage relationships among the hundreds of cell types generated during development are difficult to reconstruct. A recent method  GESTALT  used CRISPR\u2013Cas9 barcode editing for large scale lineage tracing  but was restricted to early development and did not identify cell types. Here we present scGESTALT  which combines the lineage recording capabilities of GESTALT with cell type identification by single cell RNA sequencing. The method relies on an inducible system that enables barcodes to be edited at multiple time points  capturing lineage information from later stages of development. Sequencing of 60 000 transcriptomes from the juvenile zebrafish brain identified >100 cell types and marker genes. Using these data  we generate lineage trees with hundreds of branches that help uncover restrictions at the level of cell types  brain regions  and gene expression cascades during differentiation. scGESTALT can be applied to other multicellular organisms to simultaneously characterize molecular identities and lineage histories of thousands of cells during development and disease. Overall design: inDrops libraries of single cell transcriptomes and scGESTALT barcodes  and genomic DNA GESTALT libraries", null, "pubmed:29608178", null, "DEW149 fore", "GSM2813975", null, "source name:zebrafish brain|tissue:brain|developmental stage:23 25dpf", "DEW149 fore", "Single cell RNA Sequencing data FASTQ files were processed using the inDrops.py bioinformatics pipeline available at https://github.com/indrops/indrops. Transcriptome libraries were mapped to a zebrafish reference built from a custom GTF file and the zebrafish GRCz10 release 86 genome assembly. Bowtie version1.1.1 was used with parameter \u2013e 200; UMI quantification was used with parameter \u2013u 2 counts were ignored from UMIs split between more than 2 genes. genomic DNA GESTALT and scGESTALT libraries were processed using a custom pipeline available at https://github.com/shendurelab/Cas9FateMapping Genome build: GRCz10 CSV files for transcriptome data were generated using the inDrops pipeline. Each column in the CSV files contains a cell identifier and each row contains expression values for genes. Txt files for genomic DNA GESTALT libraries *allReadCounts contain lineage barcode sequences HMID column for each cell and their proportion in the sequenced libraries. scGESTALT data *GestMaster.txt contains the inDrops cell identifiers CellBarcode and BarcodeKey that were used to match barcodes to transcriptomes. They also contain lineage barcode sequences HMID column for each cell with a corresponding inDrops single cell gene expression profile  as well as the the t SNE cluster membership number ClusterIdent column Txt files ending in *stats.txt contain information about the each individually captured UMI or cell per sample. The barcode sequence aligned to a reference unedited sequence mergedRead column  mutations at each target site target[X] columns and the edited sequences at each target site sequence[X] columns were used for downstream analysis. inDropsExpMatrix noQ txt file is the gene expression matrix for the full dataset. Columns are individual cells from different batches of whole brains f1   f2   f3   f4   f5   f6  or brain regions fore   mid   hind . fall.inDrops.Robj is the processed Seurat R object  which can be loaded into R and explored.", "zebrafish brain", null, "Single cell suspensions were processed through inDrops to generate single cell cDNA libraries. cDNAs were in vitro transcribed and fragmented. The three prime fragments were reverse transcribed and prepared for sequencing Libraries were prepared as described in Zilionis et al.  2017  Nature Protocols PMID = 27929523", null, "tissue:brain|developmental stage:23 25dpf", "GSM2813975", "GSM2813975: DEW149 fore; Danio rerio; RNA Seq", "GSM2813975", null, "1", "Single cell suspensions were processed through inDrops to generate single cell cDNA libraries. cDNAs were in vitro transcribed and fragmented. The three prime fragments were reverse transcribed and prepared for sequencing Libraries were prepared as described in Zilionis et al.  2017  Nature Protocols PMID = 27929523", "GEO Accession:GSM2813975", "RNA-Seq", "TRANSCRIPTOMIC", "cDNA", "SINGLE", "ILLUMINA", "NextSeq 500", null, "SRP120009", null, "loader:fastq load.py|options:  appendBCtoName", "DEW149_Lane1.sorted.fastq.gz", "fastq", 2040984134.0, 35277532.0, "GSM2813975 r1", "0:57.86", "A:567524269;C:385217324;G:426974572;T:661258382;N:9587", 57, null, null, null, 567524269, 385217324, 426974572, 661258382, 9587, "SRX3287405", "SRS2596879", "SRA619743", "GEO", "Harvard University", 1, 0.8811, null, 0.27464, null, 0.78654, null, 0.52635, null, 61, null, "B", null, "usable mapping rate", "illumina", "nextseq", "unknown", "cdna_unspecified", "unknown", "sc", "single_cell_droplet", "indrops", null, "United States", "2017-10-16", "Larval", "Larval", "Brain", "Nervous System"], [43862, "SRR6176731", "SRX3287405", "SRS2596879", "SRP120009", "PRJNA414416", "Simultaneous single cell profiling of lineages and cell types in the vertebrate brain", "GSE105010", "Other", "The lineage relationships among the hundreds of cell types generated during development are difficult to reconstruct. A recent method  GESTALT  used CRISPR\u2013Cas9 barcode editing for large scale lineage tracing  but was restricted to early development and did not identify cell types. Here we present scGESTALT  which combines the lineage recording capabilities of GESTALT with cell type identification by single cell RNA sequencing. The method relies on an inducible system that enables barcodes to be edited at multiple time points  capturing lineage information from later stages of development. Sequencing of 60 000 transcriptomes from the juvenile zebrafish brain identified >100 cell types and marker genes. Using these data  we generate lineage trees with hundreds of branches that help uncover restrictions at the level of cell types  brain regions  and gene expression cascades during differentiation. scGESTALT can be applied to other multicellular organisms to simultaneously characterize molecular identities and lineage histories of thousands of cells during development and disease. Overall design: inDrops libraries of single cell transcriptomes and scGESTALT barcodes  and genomic DNA GESTALT libraries", null, "pubmed:29608178", null, "DEW149 fore", "GSM2813975", null, "source name:zebrafish brain|tissue:brain|developmental stage:23 25dpf", "DEW149 fore", "Single cell RNA Sequencing data FASTQ files were processed using the inDrops.py bioinformatics pipeline available at https://github.com/indrops/indrops. Transcriptome libraries were mapped to a zebrafish reference built from a custom GTF file and the zebrafish GRCz10 release 86 genome assembly. Bowtie version1.1.1 was used with parameter \u2013e 200; UMI quantification was used with parameter \u2013u 2 counts were ignored from UMIs split between more than 2 genes. genomic DNA GESTALT and scGESTALT libraries were processed using a custom pipeline available at https://github.com/shendurelab/Cas9FateMapping Genome build: GRCz10 CSV files for transcriptome data were generated using the inDrops pipeline. Each column in the CSV files contains a cell identifier and each row contains expression values for genes. Txt files for genomic DNA GESTALT libraries *allReadCounts contain lineage barcode sequences HMID column for each cell and their proportion in the sequenced libraries. scGESTALT data *GestMaster.txt contains the inDrops cell identifiers CellBarcode and BarcodeKey that were used to match barcodes to transcriptomes. They also contain lineage barcode sequences HMID column for each cell with a corresponding inDrops single cell gene expression profile  as well as the the t SNE cluster membership number ClusterIdent column Txt files ending in *stats.txt contain information about the each individually captured UMI or cell per sample. The barcode sequence aligned to a reference unedited sequence mergedRead column  mutations at each target site target[X] columns and the edited sequences at each target site sequence[X] columns were used for downstream analysis. inDropsExpMatrix noQ txt file is the gene expression matrix for the full dataset. Columns are individual cells from different batches of whole brains f1   f2   f3   f4   f5   f6  or brain regions fore   mid   hind . fall.inDrops.Robj is the processed Seurat R object  which can be loaded into R and explored.", "zebrafish brain", null, "Single cell suspensions were processed through inDrops to generate single cell cDNA libraries. cDNAs were in vitro transcribed and fragmented. The three prime fragments were reverse transcribed and prepared for sequencing Libraries were prepared as described in Zilionis et al.  2017  Nature Protocols PMID = 27929523", null, "tissue:brain|developmental stage:23 25dpf", "GSM2813975", "GSM2813975: DEW149 fore; Danio rerio; RNA Seq", "GSM2813975", null, "1", "Single cell suspensions were processed through inDrops to generate single cell cDNA libraries. cDNAs were in vitro transcribed and fragmented. The three prime fragments were reverse transcribed and prepared for sequencing Libraries were prepared as described in Zilionis et al.  2017  Nature Protocols PMID = 27929523", "GEO Accession:GSM2813975", "RNA-Seq", "TRANSCRIPTOMIC", "cDNA", "SINGLE", "ILLUMINA", "NextSeq 500", null, "SRP120009", null, "loader:fastq load.py|options:  appendBCtoName", "DEW149_Lane2.sorted.fastq.gz", "fastq", 1324869432.0, 23093917.0, "GSM2813975 r2", "0:57.37", "A:367652083;C:249666726;G:278649220;T:428897643;N:3760", 57, null, null, null, 367652083, 249666726, 278649220, 428897643, 3760, "SRX3287405", "SRS2596879", "SRA619743", "GEO", "Harvard University", 1, 0.88017, null, 0.27604, null, 0.78658, null, 0.53022, null, 60, null, "B", null, "usable mapping rate", "illumina", "nextseq", "unknown", "cdna_unspecified", "unknown", "sc", "single_cell_droplet", "indrops", null, "United States", "2017-10-16", "Larval", "Larval", "Brain", "Nervous System"], [43863, "SRR6176728", "SRX3287404", "SRS2596878", "SRP120009", "PRJNA414416", "Simultaneous single cell profiling of lineages and cell types in the vertebrate brain", "GSE105010", "Other", "The lineage relationships among the hundreds of cell types generated during development are difficult to reconstruct. A recent method  GESTALT  used CRISPR\u2013Cas9 barcode editing for large scale lineage tracing  but was restricted to early development and did not identify cell types. Here we present scGESTALT  which combines the lineage recording capabilities of GESTALT with cell type identification by single cell RNA sequencing. The method relies on an inducible system that enables barcodes to be edited at multiple time points  capturing lineage information from later stages of development. Sequencing of 60 000 transcriptomes from the juvenile zebrafish brain identified >100 cell types and marker genes. Using these data  we generate lineage trees with hundreds of branches that help uncover restrictions at the level of cell types  brain regions  and gene expression cascades during differentiation. scGESTALT can be applied to other multicellular organisms to simultaneously characterize molecular identities and lineage histories of thousands of cells during development and disease. Overall design: inDrops libraries of single cell transcriptomes and scGESTALT barcodes  and genomic DNA GESTALT libraries", null, "pubmed:29608178", null, "DEW148 f6", "GSM2813974", null, "source name:zebrafish brain|tissue:brain|developmental stage:23 25dpf", "DEW148 f6", "Single cell RNA Sequencing data FASTQ files were processed using the inDrops.py bioinformatics pipeline available at https://github.com/indrops/indrops. Transcriptome libraries were mapped to a zebrafish reference built from a custom GTF file and the zebrafish GRCz10 release 86 genome assembly. Bowtie version1.1.1 was used with parameter \u2013e 200; UMI quantification was used with parameter \u2013u 2 counts were ignored from UMIs split between more than 2 genes. genomic DNA GESTALT and scGESTALT libraries were processed using a custom pipeline available at https://github.com/shendurelab/Cas9FateMapping Genome build: GRCz10 CSV files for transcriptome data were generated using the inDrops pipeline. Each column in the CSV files contains a cell identifier and each row contains expression values for genes. Txt files for genomic DNA GESTALT libraries *allReadCounts contain lineage barcode sequences HMID column for each cell and their proportion in the sequenced libraries. scGESTALT data *GestMaster.txt contains the inDrops cell identifiers CellBarcode and BarcodeKey that were used to match barcodes to transcriptomes. They also contain lineage barcode sequences HMID column for each cell with a corresponding inDrops single cell gene expression profile  as well as the the t SNE cluster membership number ClusterIdent column Txt files ending in *stats.txt contain information about the each individually captured UMI or cell per sample. The barcode sequence aligned to a reference unedited sequence mergedRead column  mutations at each target site target[X] columns and the edited sequences at each target site sequence[X] columns were used for downstream analysis. inDropsExpMatrix noQ txt file is the gene expression matrix for the full dataset. Columns are individual cells from different batches of whole brains f1   f2   f3   f4   f5   f6  or brain regions fore   mid   hind . fall.inDrops.Robj is the processed Seurat R object  which can be loaded into R and explored.", "zebrafish brain", null, "Single cell suspensions were processed through inDrops to generate single cell cDNA libraries. cDNAs were in vitro transcribed and fragmented. The three prime fragments were reverse transcribed and prepared for sequencing Libraries were prepared as described in Zilionis et al.  2017  Nature Protocols PMID = 27929523", null, "tissue:brain|developmental stage:23 25dpf", "GSM2813974", "GSM2813974: DEW148 f6; Danio rerio; RNA Seq", "GSM2813974", null, "1", "Single cell suspensions were processed through inDrops to generate single cell cDNA libraries. cDNAs were in vitro transcribed and fragmented. The three prime fragments were reverse transcribed and prepared for sequencing Libraries were prepared as described in Zilionis et al.  2017  Nature Protocols PMID = 27929523", "GEO Accession:GSM2813974", "RNA-Seq", "TRANSCRIPTOMIC", "cDNA", "SINGLE", "ILLUMINA", "NextSeq 500", null, "SRP120009", null, "loader:fastq load.py|options:  appendBCtoName", "DEW148_Lane1.sorted.fastq.gz", "fastq", 1783746165.0, 32647019.0, "GSM2813974 r1", "0:54.64", "A:492007833;C:343654151;G:370938043;T:577142407;N:3731", 54, null, null, null, 492007833, 343654151, 370938043, 577142407, 3731, "SRX3287404", "SRS2596878", "SRA619743", "GEO", "Harvard University", 1, 0.86946, null, 0.22807, null, 0.79462, null, 0.51961, null, 61, null, "B", null, "usable mapping rate", "illumina", "nextseq", "unknown", "cdna_unspecified", "unknown", "sc", "single_cell_droplet", "indrops", null, "United States", "2017-10-16", "Larval", "Larval", "Brain", "Nervous System"], [43864, "SRR6176729", "SRX3287404", "SRS2596878", "SRP120009", "PRJNA414416", "Simultaneous single cell profiling of lineages and cell types in the vertebrate brain", "GSE105010", "Other", "The lineage relationships among the hundreds of cell types generated during development are difficult to reconstruct. A recent method  GESTALT  used CRISPR\u2013Cas9 barcode editing for large scale lineage tracing  but was restricted to early development and did not identify cell types. Here we present scGESTALT  which combines the lineage recording capabilities of GESTALT with cell type identification by single cell RNA sequencing. The method relies on an inducible system that enables barcodes to be edited at multiple time points  capturing lineage information from later stages of development. Sequencing of 60 000 transcriptomes from the juvenile zebrafish brain identified >100 cell types and marker genes. Using these data  we generate lineage trees with hundreds of branches that help uncover restrictions at the level of cell types  brain regions  and gene expression cascades during differentiation. scGESTALT can be applied to other multicellular organisms to simultaneously characterize molecular identities and lineage histories of thousands of cells during development and disease. Overall design: inDrops libraries of single cell transcriptomes and scGESTALT barcodes  and genomic DNA GESTALT libraries", null, "pubmed:29608178", null, "DEW148 f6", "GSM2813974", null, "source name:zebrafish brain|tissue:brain|developmental stage:23 25dpf", "DEW148 f6", "Single cell RNA Sequencing data FASTQ files were processed using the inDrops.py bioinformatics pipeline available at https://github.com/indrops/indrops. Transcriptome libraries were mapped to a zebrafish reference built from a custom GTF file and the zebrafish GRCz10 release 86 genome assembly. Bowtie version1.1.1 was used with parameter \u2013e 200; UMI quantification was used with parameter \u2013u 2 counts were ignored from UMIs split between more than 2 genes. genomic DNA GESTALT and scGESTALT libraries were processed using a custom pipeline available at https://github.com/shendurelab/Cas9FateMapping Genome build: GRCz10 CSV files for transcriptome data were generated using the inDrops pipeline. Each column in the CSV files contains a cell identifier and each row contains expression values for genes. Txt files for genomic DNA GESTALT libraries *allReadCounts contain lineage barcode sequences HMID column for each cell and their proportion in the sequenced libraries. scGESTALT data *GestMaster.txt contains the inDrops cell identifiers CellBarcode and BarcodeKey that were used to match barcodes to transcriptomes. They also contain lineage barcode sequences HMID column for each cell with a corresponding inDrops single cell gene expression profile  as well as the the t SNE cluster membership number ClusterIdent column Txt files ending in *stats.txt contain information about the each individually captured UMI or cell per sample. The barcode sequence aligned to a reference unedited sequence mergedRead column  mutations at each target site target[X] columns and the edited sequences at each target site sequence[X] columns were used for downstream analysis. inDropsExpMatrix noQ txt file is the gene expression matrix for the full dataset. Columns are individual cells from different batches of whole brains f1   f2   f3   f4   f5   f6  or brain regions fore   mid   hind . fall.inDrops.Robj is the processed Seurat R object  which can be loaded into R and explored.", "zebrafish brain", null, "Single cell suspensions were processed through inDrops to generate single cell cDNA libraries. cDNAs were in vitro transcribed and fragmented. The three prime fragments were reverse transcribed and prepared for sequencing Libraries were prepared as described in Zilionis et al.  2017  Nature Protocols PMID = 27929523", null, "tissue:brain|developmental stage:23 25dpf", "GSM2813974", "GSM2813974: DEW148 f6; Danio rerio; RNA Seq", "GSM2813974", null, "1", "Single cell suspensions were processed through inDrops to generate single cell cDNA libraries. cDNAs were in vitro transcribed and fragmented. The three prime fragments were reverse transcribed and prepared for sequencing Libraries were prepared as described in Zilionis et al.  2017  Nature Protocols PMID = 27929523", "GEO Accession:GSM2813974", "RNA-Seq", "TRANSCRIPTOMIC", "cDNA", "SINGLE", "ILLUMINA", "NextSeq 500", null, "SRP120009", null, "loader:fastq load.py|options:  appendBCtoName", "DEW148_Lane2.sorted.fastq.gz", "fastq", 2321976093.0, 39701872.0, "GSM2813974 r2", "0:58.49", "A:644394074;C:446370620;G:489186161;T:742017510;N:7728", 58, null, null, null, 644394074, 446370620, 489186161, 742017510, 7728, "SRX3287404", "SRS2596878", "SRA619743", "GEO", "Harvard University", 1, 0.8902, null, 0.22908, null, 0.78689, null, 0.52036, null, 61, null, "B", null, "usable mapping rate", "illumina", "nextseq", "unknown", "cdna_unspecified", "unknown", "sc", "single_cell_droplet", "indrops", null, "United States", "2017-10-16", "Larval", "Larval", "Brain", "Nervous System"], [43865, "SRR6176726", "SRX3287403", "SRS2596877", "SRP120009", "PRJNA414416", "Simultaneous single cell profiling of lineages and cell types in the vertebrate brain", "GSE105010", "Other", "The lineage relationships among the hundreds of cell types generated during development are difficult to reconstruct. A recent method  GESTALT  used CRISPR\u2013Cas9 barcode editing for large scale lineage tracing  but was restricted to early development and did not identify cell types. Here we present scGESTALT  which combines the lineage recording capabilities of GESTALT with cell type identification by single cell RNA sequencing. The method relies on an inducible system that enables barcodes to be edited at multiple time points  capturing lineage information from later stages of development. Sequencing of 60 000 transcriptomes from the juvenile zebrafish brain identified >100 cell types and marker genes. Using these data  we generate lineage trees with hundreds of branches that help uncover restrictions at the level of cell types  brain regions  and gene expression cascades during differentiation. scGESTALT can be applied to other multicellular organisms to simultaneously characterize molecular identities and lineage histories of thousands of cells during development and disease. Overall design: inDrops libraries of single cell transcriptomes and scGESTALT barcodes  and genomic DNA GESTALT libraries", null, "pubmed:29608178", null, "DEW147 f6", "GSM2813973", null, "source name:zebrafish brain|tissue:brain|developmental stage:23 25dpf", "DEW147 f6", "Single cell RNA Sequencing data FASTQ files were processed using the inDrops.py bioinformatics pipeline available at https://github.com/indrops/indrops. Transcriptome libraries were mapped to a zebrafish reference built from a custom GTF file and the zebrafish GRCz10 release 86 genome assembly. Bowtie version1.1.1 was used with parameter \u2013e 200; UMI quantification was used with parameter \u2013u 2 counts were ignored from UMIs split between more than 2 genes. genomic DNA GESTALT and scGESTALT libraries were processed using a custom pipeline available at https://github.com/shendurelab/Cas9FateMapping Genome build: GRCz10 CSV files for transcriptome data were generated using the inDrops pipeline. Each column in the CSV files contains a cell identifier and each row contains expression values for genes. Txt files for genomic DNA GESTALT libraries *allReadCounts contain lineage barcode sequences HMID column for each cell and their proportion in the sequenced libraries. scGESTALT data *GestMaster.txt contains the inDrops cell identifiers CellBarcode and BarcodeKey that were used to match barcodes to transcriptomes. They also contain lineage barcode sequences HMID column for each cell with a corresponding inDrops single cell gene expression profile  as well as the the t SNE cluster membership number ClusterIdent column Txt files ending in *stats.txt contain information about the each individually captured UMI or cell per sample. The barcode sequence aligned to a reference unedited sequence mergedRead column  mutations at each target site target[X] columns and the edited sequences at each target site sequence[X] columns were used for downstream analysis. inDropsExpMatrix noQ txt file is the gene expression matrix for the full dataset. Columns are individual cells from different batches of whole brains f1   f2   f3   f4   f5   f6  or brain regions fore   mid   hind . fall.inDrops.Robj is the processed Seurat R object  which can be loaded into R and explored.", "zebrafish brain", null, "Single cell suspensions were processed through inDrops to generate single cell cDNA libraries. cDNAs were in vitro transcribed and fragmented. The three prime fragments were reverse transcribed and prepared for sequencing Libraries were prepared as described in Zilionis et al.  2017  Nature Protocols PMID = 27929523", null, "tissue:brain|developmental stage:23 25dpf", "GSM2813973", "GSM2813973: DEW147 f6; Danio rerio; RNA Seq", "GSM2813973", null, "1", "Single cell suspensions were processed through inDrops to generate single cell cDNA libraries. cDNAs were in vitro transcribed and fragmented. The three prime fragments were reverse transcribed and prepared for sequencing Libraries were prepared as described in Zilionis et al.  2017  Nature Protocols PMID = 27929523", "GEO Accession:GSM2813973", "RNA-Seq", "TRANSCRIPTOMIC", "cDNA", "SINGLE", "ILLUMINA", "NextSeq 500", null, "SRP120009", null, "loader:fastq load.py|options:  appendBCtoName", "DEW147_Lane1.sorted.fastq.gz", "fastq", 1861151726.0, 33960188.0, "GSM2813973 r1", "0:54.80", "A:514475433;C:356385455;G:385168377;T:605118287;N:4174", 54, null, null, null, 514475433, 356385455, 385168377, 605118287, 4174, "SRX3287403", "SRS2596877", "SRA619743", "GEO", "Harvard University", 1, 0.86477, null, 0.23182, null, 0.79454, null, 0.51967, null, 41, null, "B", null, "usable mapping rate", "illumina", "nextseq", "unknown", "cdna_unspecified", "unknown", "sc", "single_cell_droplet", "indrops", null, "United States", "2017-10-16", "Larval", "Larval", "Brain", "Nervous System"], [43866, "SRR6176727", "SRX3287403", "SRS2596877", "SRP120009", "PRJNA414416", "Simultaneous single cell profiling of lineages and cell types in the vertebrate brain", "GSE105010", "Other", "The lineage relationships among the hundreds of cell types generated during development are difficult to reconstruct. A recent method  GESTALT  used CRISPR\u2013Cas9 barcode editing for large scale lineage tracing  but was restricted to early development and did not identify cell types. Here we present scGESTALT  which combines the lineage recording capabilities of GESTALT with cell type identification by single cell RNA sequencing. The method relies on an inducible system that enables barcodes to be edited at multiple time points  capturing lineage information from later stages of development. Sequencing of 60 000 transcriptomes from the juvenile zebrafish brain identified >100 cell types and marker genes. Using these data  we generate lineage trees with hundreds of branches that help uncover restrictions at the level of cell types  brain regions  and gene expression cascades during differentiation. scGESTALT can be applied to other multicellular organisms to simultaneously characterize molecular identities and lineage histories of thousands of cells during development and disease. Overall design: inDrops libraries of single cell transcriptomes and scGESTALT barcodes  and genomic DNA GESTALT libraries", null, "pubmed:29608178", null, "DEW147 f6", "GSM2813973", null, "source name:zebrafish brain|tissue:brain|developmental stage:23 25dpf", "DEW147 f6", "Single cell RNA Sequencing data FASTQ files were processed using the inDrops.py bioinformatics pipeline available at https://github.com/indrops/indrops. Transcriptome libraries were mapped to a zebrafish reference built from a custom GTF file and the zebrafish GRCz10 release 86 genome assembly. Bowtie version1.1.1 was used with parameter \u2013e 200; UMI quantification was used with parameter \u2013u 2 counts were ignored from UMIs split between more than 2 genes. genomic DNA GESTALT and scGESTALT libraries were processed using a custom pipeline available at https://github.com/shendurelab/Cas9FateMapping Genome build: GRCz10 CSV files for transcriptome data were generated using the inDrops pipeline. Each column in the CSV files contains a cell identifier and each row contains expression values for genes. Txt files for genomic DNA GESTALT libraries *allReadCounts contain lineage barcode sequences HMID column for each cell and their proportion in the sequenced libraries. scGESTALT data *GestMaster.txt contains the inDrops cell identifiers CellBarcode and BarcodeKey that were used to match barcodes to transcriptomes. They also contain lineage barcode sequences HMID column for each cell with a corresponding inDrops single cell gene expression profile  as well as the the t SNE cluster membership number ClusterIdent column Txt files ending in *stats.txt contain information about the each individually captured UMI or cell per sample. The barcode sequence aligned to a reference unedited sequence mergedRead column  mutations at each target site target[X] columns and the edited sequences at each target site sequence[X] columns were used for downstream analysis. inDropsExpMatrix noQ txt file is the gene expression matrix for the full dataset. Columns are individual cells from different batches of whole brains f1   f2   f3   f4   f5   f6  or brain regions fore   mid   hind . fall.inDrops.Robj is the processed Seurat R object  which can be loaded into R and explored.", "zebrafish brain", null, "Single cell suspensions were processed through inDrops to generate single cell cDNA libraries. cDNAs were in vitro transcribed and fragmented. The three prime fragments were reverse transcribed and prepared for sequencing Libraries were prepared as described in Zilionis et al.  2017  Nature Protocols PMID = 27929523", null, "tissue:brain|developmental stage:23 25dpf", "GSM2813973", "GSM2813973: DEW147 f6; Danio rerio; RNA Seq", "GSM2813973", null, "1", "Single cell suspensions were processed through inDrops to generate single cell cDNA libraries. cDNAs were in vitro transcribed and fragmented. The three prime fragments were reverse transcribed and prepared for sequencing Libraries were prepared as described in Zilionis et al.  2017  Nature Protocols PMID = 27929523", "GEO Accession:GSM2813973", "RNA-Seq", "TRANSCRIPTOMIC", "cDNA", "SINGLE", "ILLUMINA", "NextSeq 500", null, "SRP120009", null, "loader:fastq load.py|options:  appendBCtoName", "DEW147_Lane2.sorted.fastq.gz", "fastq", 2384082430.0, 40711661.0, "GSM2813973 r2", "0:58.56", "A:662878220;C:455374350;G:499509285;T:766312566;N:8009", 58, null, null, null, 662878220, 455374350, 499509285, 766312566, 8009, "SRX3287403", "SRS2596877", "SRA619743", "GEO", "Harvard University", 1, 0.88512, null, 0.23293, null, 0.7878, null, 0.51475, null, 61, null, "B", null, "usable mapping rate", "illumina", "nextseq", "unknown", "cdna_unspecified", "unknown", "sc", "single_cell_droplet", "indrops", null, "United States", "2017-10-16", "Larval", "Larval", "Brain", "Nervous System"], [43867, "SRR6176724", "SRX3287402", "SRS2596876", "SRP120009", "PRJNA414416", "Simultaneous single cell profiling of lineages and cell types in the vertebrate brain", "GSE105010", "Other", "The lineage relationships among the hundreds of cell types generated during development are difficult to reconstruct. A recent method  GESTALT  used CRISPR\u2013Cas9 barcode editing for large scale lineage tracing  but was restricted to early development and did not identify cell types. Here we present scGESTALT  which combines the lineage recording capabilities of GESTALT with cell type identification by single cell RNA sequencing. The method relies on an inducible system that enables barcodes to be edited at multiple time points  capturing lineage information from later stages of development. Sequencing of 60 000 transcriptomes from the juvenile zebrafish brain identified >100 cell types and marker genes. Using these data  we generate lineage trees with hundreds of branches that help uncover restrictions at the level of cell types  brain regions  and gene expression cascades during differentiation. scGESTALT can be applied to other multicellular organisms to simultaneously characterize molecular identities and lineage histories of thousands of cells during development and disease. Overall design: inDrops libraries of single cell transcriptomes and scGESTALT barcodes  and genomic DNA GESTALT libraries", null, "pubmed:29608178", null, "DEW146 f6", "GSM2813972", null, "source name:zebrafish brain|tissue:brain|developmental stage:23 25dpf", "DEW146 f6", "Single cell RNA Sequencing data FASTQ files were processed using the inDrops.py bioinformatics pipeline available at https://github.com/indrops/indrops. Transcriptome libraries were mapped to a zebrafish reference built from a custom GTF file and the zebrafish GRCz10 release 86 genome assembly. Bowtie version1.1.1 was used with parameter \u2013e 200; UMI quantification was used with parameter \u2013u 2 counts were ignored from UMIs split between more than 2 genes. genomic DNA GESTALT and scGESTALT libraries were processed using a custom pipeline available at https://github.com/shendurelab/Cas9FateMapping Genome build: GRCz10 CSV files for transcriptome data were generated using the inDrops pipeline. Each column in the CSV files contains a cell identifier and each row contains expression values for genes. Txt files for genomic DNA GESTALT libraries *allReadCounts contain lineage barcode sequences HMID column for each cell and their proportion in the sequenced libraries. scGESTALT data *GestMaster.txt contains the inDrops cell identifiers CellBarcode and BarcodeKey that were used to match barcodes to transcriptomes. They also contain lineage barcode sequences HMID column for each cell with a corresponding inDrops single cell gene expression profile  as well as the the t SNE cluster membership number ClusterIdent column Txt files ending in *stats.txt contain information about the each individually captured UMI or cell per sample. The barcode sequence aligned to a reference unedited sequence mergedRead column  mutations at each target site target[X] columns and the edited sequences at each target site sequence[X] columns were used for downstream analysis. inDropsExpMatrix noQ txt file is the gene expression matrix for the full dataset. Columns are individual cells from different batches of whole brains f1   f2   f3   f4   f5   f6  or brain regions fore   mid   hind . fall.inDrops.Robj is the processed Seurat R object  which can be loaded into R and explored.", "zebrafish brain", null, "Single cell suspensions were processed through inDrops to generate single cell cDNA libraries. cDNAs were in vitro transcribed and fragmented. The three prime fragments were reverse transcribed and prepared for sequencing Libraries were prepared as described in Zilionis et al.  2017  Nature Protocols PMID = 27929523", null, "tissue:brain|developmental stage:23 25dpf", "GSM2813972", "GSM2813972: DEW146 f6; Danio rerio; RNA Seq", "GSM2813972", null, "1", "Single cell suspensions were processed through inDrops to generate single cell cDNA libraries. cDNAs were in vitro transcribed and fragmented. The three prime fragments were reverse transcribed and prepared for sequencing Libraries were prepared as described in Zilionis et al.  2017  Nature Protocols PMID = 27929523", "GEO Accession:GSM2813972", "RNA-Seq", "TRANSCRIPTOMIC", "cDNA", "SINGLE", "ILLUMINA", "NextSeq 500", null, "SRP120009", null, "loader:fastq load.py|options:  appendBCtoName", "DEW146_Lane1.sorted.fastq.gz", "fastq", 1879106765.0, 34463595.0, "GSM2813972 r1", "0:54.52", "A:518564123;C:360410554;G:392630549;T:607497754;N:3785", 54, null, null, null, 518564123, 360410554, 392630549, 607497754, 3785, "SRX3287402", "SRS2596876", "SRA619743", "GEO", "Harvard University", 1, 0.868, null, 0.22669, null, 0.79506, null, 0.50915, null, 55, null, "B", null, "usable mapping rate", "illumina", "nextseq", "unknown", "cdna_unspecified", "unknown", "sc", "single_cell_droplet", "indrops", null, "United States", "2017-10-16", "Larval", "Larval", "Brain", "Nervous System"], [43868, "SRR6176725", "SRX3287402", "SRS2596876", "SRP120009", "PRJNA414416", "Simultaneous single cell profiling of lineages and cell types in the vertebrate brain", "GSE105010", "Other", "The lineage relationships among the hundreds of cell types generated during development are difficult to reconstruct. A recent method  GESTALT  used CRISPR\u2013Cas9 barcode editing for large scale lineage tracing  but was restricted to early development and did not identify cell types. Here we present scGESTALT  which combines the lineage recording capabilities of GESTALT with cell type identification by single cell RNA sequencing. The method relies on an inducible system that enables barcodes to be edited at multiple time points  capturing lineage information from later stages of development. Sequencing of 60 000 transcriptomes from the juvenile zebrafish brain identified >100 cell types and marker genes. Using these data  we generate lineage trees with hundreds of branches that help uncover restrictions at the level of cell types  brain regions  and gene expression cascades during differentiation. scGESTALT can be applied to other multicellular organisms to simultaneously characterize molecular identities and lineage histories of thousands of cells during development and disease. Overall design: inDrops libraries of single cell transcriptomes and scGESTALT barcodes  and genomic DNA GESTALT libraries", null, "pubmed:29608178", null, "DEW146 f6", "GSM2813972", null, "source name:zebrafish brain|tissue:brain|developmental stage:23 25dpf", "DEW146 f6", "Single cell RNA Sequencing data FASTQ files were processed using the inDrops.py bioinformatics pipeline available at https://github.com/indrops/indrops. Transcriptome libraries were mapped to a zebrafish reference built from a custom GTF file and the zebrafish GRCz10 release 86 genome assembly. Bowtie version1.1.1 was used with parameter \u2013e 200; UMI quantification was used with parameter \u2013u 2 counts were ignored from UMIs split between more than 2 genes. genomic DNA GESTALT and scGESTALT libraries were processed using a custom pipeline available at https://github.com/shendurelab/Cas9FateMapping Genome build: GRCz10 CSV files for transcriptome data were generated using the inDrops pipeline. Each column in the CSV files contains a cell identifier and each row contains expression values for genes. Txt files for genomic DNA GESTALT libraries *allReadCounts contain lineage barcode sequences HMID column for each cell and their proportion in the sequenced libraries. scGESTALT data *GestMaster.txt contains the inDrops cell identifiers CellBarcode and BarcodeKey that were used to match barcodes to transcriptomes. They also contain lineage barcode sequences HMID column for each cell with a corresponding inDrops single cell gene expression profile  as well as the the t SNE cluster membership number ClusterIdent column Txt files ending in *stats.txt contain information about the each individually captured UMI or cell per sample. The barcode sequence aligned to a reference unedited sequence mergedRead column  mutations at each target site target[X] columns and the edited sequences at each target site sequence[X] columns were used for downstream analysis. inDropsExpMatrix noQ txt file is the gene expression matrix for the full dataset. Columns are individual cells from different batches of whole brains f1   f2   f3   f4   f5   f6  or brain regions fore   mid   hind . fall.inDrops.Robj is the processed Seurat R object  which can be loaded into R and explored.", "zebrafish brain", null, "Single cell suspensions were processed through inDrops to generate single cell cDNA libraries. cDNAs were in vitro transcribed and fragmented. The three prime fragments were reverse transcribed and prepared for sequencing Libraries were prepared as described in Zilionis et al.  2017  Nature Protocols PMID = 27929523", null, "tissue:brain|developmental stage:23 25dpf", "GSM2813972", "GSM2813972: DEW146 f6; Danio rerio; RNA Seq", "GSM2813972", null, "1", "Single cell suspensions were processed through inDrops to generate single cell cDNA libraries. cDNAs were in vitro transcribed and fragmented. The three prime fragments were reverse transcribed and prepared for sequencing Libraries were prepared as described in Zilionis et al.  2017  Nature Protocols PMID = 27929523", "GEO Accession:GSM2813972", "RNA-Seq", "TRANSCRIPTOMIC", "cDNA", "SINGLE", "ILLUMINA", "NextSeq 500", null, "SRP120009", null, "loader:fastq load.py|options:  appendBCtoName", "DEW146_Lane2.sorted.fastq.gz", "fastq", 2501035007.0, 42768522.0, "GSM2813972 r2", "0:58.48", "A:694138768;C:479060448;G:529998089;T:797829519;N:8183", 58, null, null, null, 694138768, 479060448, 529998089, 797829519, 8183, "SRX3287402", "SRS2596876", "SRA619743", "GEO", "Harvard University", 1, 0.8925, null, 0.2282, null, 0.78739, null, 0.49912, null, 45, null, "B", null, "usable mapping rate", "illumina", "nextseq", "unknown", "cdna_unspecified", "unknown", "sc", "single_cell_droplet", "indrops", null, "United States", "2017-10-16", "Larval", "Larval", "Brain", "Nervous System"], [43869, "SRR6176722", "SRX3287401", "SRS2596875", "SRP120009", "PRJNA414416", "Simultaneous single cell profiling of lineages and cell types in the vertebrate brain", "GSE105010", "Other", "The lineage relationships among the hundreds of cell types generated during development are difficult to reconstruct. A recent method  GESTALT  used CRISPR\u2013Cas9 barcode editing for large scale lineage tracing  but was restricted to early development and did not identify cell types. Here we present scGESTALT  which combines the lineage recording capabilities of GESTALT with cell type identification by single cell RNA sequencing. The method relies on an inducible system that enables barcodes to be edited at multiple time points  capturing lineage information from later stages of development. Sequencing of 60 000 transcriptomes from the juvenile zebrafish brain identified >100 cell types and marker genes. Using these data  we generate lineage trees with hundreds of branches that help uncover restrictions at the level of cell types  brain regions  and gene expression cascades during differentiation. scGESTALT can be applied to other multicellular organisms to simultaneously characterize molecular identities and lineage histories of thousands of cells during development and disease. Overall design: inDrops libraries of single cell transcriptomes and scGESTALT barcodes  and genomic DNA GESTALT libraries", null, "pubmed:29608178", null, "DEW145 f6", "GSM2813971", null, "source name:zebrafish brain|tissue:brain|developmental stage:23 25dpf", "DEW145 f6", "Single cell RNA Sequencing data FASTQ files were processed using the inDrops.py bioinformatics pipeline available at https://github.com/indrops/indrops. Transcriptome libraries were mapped to a zebrafish reference built from a custom GTF file and the zebrafish GRCz10 release 86 genome assembly. Bowtie version1.1.1 was used with parameter \u2013e 200; UMI quantification was used with parameter \u2013u 2 counts were ignored from UMIs split between more than 2 genes. genomic DNA GESTALT and scGESTALT libraries were processed using a custom pipeline available at https://github.com/shendurelab/Cas9FateMapping Genome build: GRCz10 CSV files for transcriptome data were generated using the inDrops pipeline. Each column in the CSV files contains a cell identifier and each row contains expression values for genes. Txt files for genomic DNA GESTALT libraries *allReadCounts contain lineage barcode sequences HMID column for each cell and their proportion in the sequenced libraries. scGESTALT data *GestMaster.txt contains the inDrops cell identifiers CellBarcode and BarcodeKey that were used to match barcodes to transcriptomes. They also contain lineage barcode sequences HMID column for each cell with a corresponding inDrops single cell gene expression profile  as well as the the t SNE cluster membership number ClusterIdent column Txt files ending in *stats.txt contain information about the each individually captured UMI or cell per sample. The barcode sequence aligned to a reference unedited sequence mergedRead column  mutations at each target site target[X] columns and the edited sequences at each target site sequence[X] columns were used for downstream analysis. inDropsExpMatrix noQ txt file is the gene expression matrix for the full dataset. Columns are individual cells from different batches of whole brains f1   f2   f3   f4   f5   f6  or brain regions fore   mid   hind . fall.inDrops.Robj is the processed Seurat R object  which can be loaded into R and explored.", "zebrafish brain", null, "Single cell suspensions were processed through inDrops to generate single cell cDNA libraries. cDNAs were in vitro transcribed and fragmented. The three prime fragments were reverse transcribed and prepared for sequencing Libraries were prepared as described in Zilionis et al.  2017  Nature Protocols PMID = 27929523", null, "tissue:brain|developmental stage:23 25dpf", "GSM2813971", "GSM2813971: DEW145 f6; Danio rerio; RNA Seq", "GSM2813971", null, "1", "Single cell suspensions were processed through inDrops to generate single cell cDNA libraries. cDNAs were in vitro transcribed and fragmented. The three prime fragments were reverse transcribed and prepared for sequencing Libraries were prepared as described in Zilionis et al.  2017  Nature Protocols PMID = 27929523", "GEO Accession:GSM2813971", "RNA-Seq", "TRANSCRIPTOMIC", "cDNA", "SINGLE", "ILLUMINA", "NextSeq 500", null, "SRP120009", null, "loader:fastq load.py|options:  appendBCtoName", "DEW145_Lane1.sorted.fastq.gz", "fastq", 1936158206.0, 35348080.0, "GSM2813971 r1", "0:54.77", "A:532782579;C:372215991;G:405559545;T:625595853;N:4238", 54, null, null, null, 532782579, 372215991, 405559545, 625595853, 4238, "SRX3287401", "SRS2596875", "SRA619743", "GEO", "Harvard University", 1, 0.87324, null, 0.22959, null, 0.79399, null, 0.50829, null, 61, null, "B", null, "usable mapping rate", "illumina", "nextseq", "unknown", "cdna_unspecified", "unknown", "sc", "single_cell_droplet", "indrops", null, "United States", "2017-10-16", "Larval", "Larval", "Brain", "Nervous System"], [43870, "SRR6176723", "SRX3287401", "SRS2596875", "SRP120009", "PRJNA414416", "Simultaneous single cell profiling of lineages and cell types in the vertebrate brain", "GSE105010", "Other", "The lineage relationships among the hundreds of cell types generated during development are difficult to reconstruct. A recent method  GESTALT  used CRISPR\u2013Cas9 barcode editing for large scale lineage tracing  but was restricted to early development and did not identify cell types. Here we present scGESTALT  which combines the lineage recording capabilities of GESTALT with cell type identification by single cell RNA sequencing. The method relies on an inducible system that enables barcodes to be edited at multiple time points  capturing lineage information from later stages of development. Sequencing of 60 000 transcriptomes from the juvenile zebrafish brain identified >100 cell types and marker genes. Using these data  we generate lineage trees with hundreds of branches that help uncover restrictions at the level of cell types  brain regions  and gene expression cascades during differentiation. scGESTALT can be applied to other multicellular organisms to simultaneously characterize molecular identities and lineage histories of thousands of cells during development and disease. Overall design: inDrops libraries of single cell transcriptomes and scGESTALT barcodes  and genomic DNA GESTALT libraries", null, "pubmed:29608178", null, "DEW145 f6", "GSM2813971", null, "source name:zebrafish brain|tissue:brain|developmental stage:23 25dpf", "DEW145 f6", "Single cell RNA Sequencing data FASTQ files were processed using the inDrops.py bioinformatics pipeline available at https://github.com/indrops/indrops. Transcriptome libraries were mapped to a zebrafish reference built from a custom GTF file and the zebrafish GRCz10 release 86 genome assembly. Bowtie version1.1.1 was used with parameter \u2013e 200; UMI quantification was used with parameter \u2013u 2 counts were ignored from UMIs split between more than 2 genes. genomic DNA GESTALT and scGESTALT libraries were processed using a custom pipeline available at https://github.com/shendurelab/Cas9FateMapping Genome build: GRCz10 CSV files for transcriptome data were generated using the inDrops pipeline. Each column in the CSV files contains a cell identifier and each row contains expression values for genes. Txt files for genomic DNA GESTALT libraries *allReadCounts contain lineage barcode sequences HMID column for each cell and their proportion in the sequenced libraries. scGESTALT data *GestMaster.txt contains the inDrops cell identifiers CellBarcode and BarcodeKey that were used to match barcodes to transcriptomes. They also contain lineage barcode sequences HMID column for each cell with a corresponding inDrops single cell gene expression profile  as well as the the t SNE cluster membership number ClusterIdent column Txt files ending in *stats.txt contain information about the each individually captured UMI or cell per sample. The barcode sequence aligned to a reference unedited sequence mergedRead column  mutations at each target site target[X] columns and the edited sequences at each target site sequence[X] columns were used for downstream analysis. inDropsExpMatrix noQ txt file is the gene expression matrix for the full dataset. Columns are individual cells from different batches of whole brains f1   f2   f3   f4   f5   f6  or brain regions fore   mid   hind . fall.inDrops.Robj is the processed Seurat R object  which can be loaded into R and explored.", "zebrafish brain", null, "Single cell suspensions were processed through inDrops to generate single cell cDNA libraries. cDNAs were in vitro transcribed and fragmented. The three prime fragments were reverse transcribed and prepared for sequencing Libraries were prepared as described in Zilionis et al.  2017  Nature Protocols PMID = 27929523", null, "tissue:brain|developmental stage:23 25dpf", "GSM2813971", "GSM2813971: DEW145 f6; Danio rerio; RNA Seq", "GSM2813971", null, "1", "Single cell suspensions were processed through inDrops to generate single cell cDNA libraries. cDNAs were in vitro transcribed and fragmented. The three prime fragments were reverse transcribed and prepared for sequencing Libraries were prepared as described in Zilionis et al.  2017  Nature Protocols PMID = 27929523", "GEO Accession:GSM2813971", "RNA-Seq", "TRANSCRIPTOMIC", "cDNA", "SINGLE", "ILLUMINA", "NextSeq 500", null, "SRP120009", null, "loader:fastq load.py|options:  appendBCtoName", "DEW145_Lane2.sorted.fastq.gz", "fastq", 2511357553.0, 42895488.0, "GSM2813971 r2", "0:58.55", "A:694942009;C:481699208;G:532793934;T:801914020;N:8382", 58, null, null, null, 694942009, 481699208, 532793934, 801914020, 8382, "SRX3287401", "SRS2596875", "SRA619743", "GEO", "Harvard University", 1, 0.89493, null, 0.23125, null, 0.78837, null, 0.52183, null, 61, null, "B", null, "usable mapping rate", "illumina", "nextseq", "unknown", "cdna_unspecified", "unknown", "sc", "single_cell_droplet", "indrops", null, "United States", "2017-10-16", "Larval", "Larval", "Brain", "Nervous System"], [43871, "SRR6176720", "SRX3287400", "SRS2596874", "SRP120009", "PRJNA414416", "Simultaneous single cell profiling of lineages and cell types in the vertebrate brain", "GSE105010", "Other", "The lineage relationships among the hundreds of cell types generated during development are difficult to reconstruct. A recent method  GESTALT  used CRISPR\u2013Cas9 barcode editing for large scale lineage tracing  but was restricted to early development and did not identify cell types. Here we present scGESTALT  which combines the lineage recording capabilities of GESTALT with cell type identification by single cell RNA sequencing. The method relies on an inducible system that enables barcodes to be edited at multiple time points  capturing lineage information from later stages of development. Sequencing of 60 000 transcriptomes from the juvenile zebrafish brain identified >100 cell types and marker genes. Using these data  we generate lineage trees with hundreds of branches that help uncover restrictions at the level of cell types  brain regions  and gene expression cascades during differentiation. scGESTALT can be applied to other multicellular organisms to simultaneously characterize molecular identities and lineage histories of thousands of cells during development and disease. Overall design: inDrops libraries of single cell transcriptomes and scGESTALT barcodes  and genomic DNA GESTALT libraries", null, "pubmed:29608178", null, "DEW144 f6", "GSM2813970", null, "source name:zebrafish brain|tissue:brain|developmental stage:23 25dpf", "DEW144 f6", "Single cell RNA Sequencing data FASTQ files were processed using the inDrops.py bioinformatics pipeline available at https://github.com/indrops/indrops. Transcriptome libraries were mapped to a zebrafish reference built from a custom GTF file and the zebrafish GRCz10 release 86 genome assembly. Bowtie version1.1.1 was used with parameter \u2013e 200; UMI quantification was used with parameter \u2013u 2 counts were ignored from UMIs split between more than 2 genes. genomic DNA GESTALT and scGESTALT libraries were processed using a custom pipeline available at https://github.com/shendurelab/Cas9FateMapping Genome build: GRCz10 CSV files for transcriptome data were generated using the inDrops pipeline. Each column in the CSV files contains a cell identifier and each row contains expression values for genes. Txt files for genomic DNA GESTALT libraries *allReadCounts contain lineage barcode sequences HMID column for each cell and their proportion in the sequenced libraries. scGESTALT data *GestMaster.txt contains the inDrops cell identifiers CellBarcode and BarcodeKey that were used to match barcodes to transcriptomes. They also contain lineage barcode sequences HMID column for each cell with a corresponding inDrops single cell gene expression profile  as well as the the t SNE cluster membership number ClusterIdent column Txt files ending in *stats.txt contain information about the each individually captured UMI or cell per sample. The barcode sequence aligned to a reference unedited sequence mergedRead column  mutations at each target site target[X] columns and the edited sequences at each target site sequence[X] columns were used for downstream analysis. inDropsExpMatrix noQ txt file is the gene expression matrix for the full dataset. Columns are individual cells from different batches of whole brains f1   f2   f3   f4   f5   f6  or brain regions fore   mid   hind . fall.inDrops.Robj is the processed Seurat R object  which can be loaded into R and explored.", "zebrafish brain", null, "Single cell suspensions were processed through inDrops to generate single cell cDNA libraries. cDNAs were in vitro transcribed and fragmented. The three prime fragments were reverse transcribed and prepared for sequencing Libraries were prepared as described in Zilionis et al.  2017  Nature Protocols PMID = 27929523", null, "tissue:brain|developmental stage:23 25dpf", "GSM2813970", "GSM2813970: DEW144 f6; Danio rerio; RNA Seq", "GSM2813970", null, "1", "Single cell suspensions were processed through inDrops to generate single cell cDNA libraries. cDNAs were in vitro transcribed and fragmented. The three prime fragments were reverse transcribed and prepared for sequencing Libraries were prepared as described in Zilionis et al.  2017  Nature Protocols PMID = 27929523", "GEO Accession:GSM2813970", "RNA-Seq", "TRANSCRIPTOMIC", "cDNA", "SINGLE", "ILLUMINA", "NextSeq 500", null, "SRP120009", null, "loader:fastq load.py|options:  appendBCtoName", "DEW144_Lane1.sorted.fastq.gz", "fastq", 1994827685.0, 36418133.0, "GSM2813970 r1", "0:54.78", "A:549834507;C:383404208;G:413619246;T:647965404;N:4320", 54, null, null, null, 549834507, 383404208, 413619246, 647965404, 4320, "SRX3287400", "SRS2596874", "SRA619743", "GEO", "Harvard University", 1, 0.87127, null, 0.23087, null, 0.79235, null, 0.51641, null, 38, null, "B", null, "usable mapping rate", "illumina", "nextseq", "unknown", "cdna_unspecified", "unknown", "sc", "single_cell_droplet", "indrops", null, "United States", "2017-10-16", "Larval", "Larval", "Brain", "Nervous System"], [43872, "SRR6176721", "SRX3287400", "SRS2596874", "SRP120009", "PRJNA414416", "Simultaneous single cell profiling of lineages and cell types in the vertebrate brain", "GSE105010", "Other", "The lineage relationships among the hundreds of cell types generated during development are difficult to reconstruct. A recent method  GESTALT  used CRISPR\u2013Cas9 barcode editing for large scale lineage tracing  but was restricted to early development and did not identify cell types. Here we present scGESTALT  which combines the lineage recording capabilities of GESTALT with cell type identification by single cell RNA sequencing. The method relies on an inducible system that enables barcodes to be edited at multiple time points  capturing lineage information from later stages of development. Sequencing of 60 000 transcriptomes from the juvenile zebrafish brain identified >100 cell types and marker genes. Using these data  we generate lineage trees with hundreds of branches that help uncover restrictions at the level of cell types  brain regions  and gene expression cascades during differentiation. scGESTALT can be applied to other multicellular organisms to simultaneously characterize molecular identities and lineage histories of thousands of cells during development and disease. Overall design: inDrops libraries of single cell transcriptomes and scGESTALT barcodes  and genomic DNA GESTALT libraries", null, "pubmed:29608178", null, "DEW144 f6", "GSM2813970", null, "source name:zebrafish brain|tissue:brain|developmental stage:23 25dpf", "DEW144 f6", "Single cell RNA Sequencing data FASTQ files were processed using the inDrops.py bioinformatics pipeline available at https://github.com/indrops/indrops. Transcriptome libraries were mapped to a zebrafish reference built from a custom GTF file and the zebrafish GRCz10 release 86 genome assembly. Bowtie version1.1.1 was used with parameter \u2013e 200; UMI quantification was used with parameter \u2013u 2 counts were ignored from UMIs split between more than 2 genes. genomic DNA GESTALT and scGESTALT libraries were processed using a custom pipeline available at https://github.com/shendurelab/Cas9FateMapping Genome build: GRCz10 CSV files for transcriptome data were generated using the inDrops pipeline. Each column in the CSV files contains a cell identifier and each row contains expression values for genes. Txt files for genomic DNA GESTALT libraries *allReadCounts contain lineage barcode sequences HMID column for each cell and their proportion in the sequenced libraries. scGESTALT data *GestMaster.txt contains the inDrops cell identifiers CellBarcode and BarcodeKey that were used to match barcodes to transcriptomes. They also contain lineage barcode sequences HMID column for each cell with a corresponding inDrops single cell gene expression profile  as well as the the t SNE cluster membership number ClusterIdent column Txt files ending in *stats.txt contain information about the each individually captured UMI or cell per sample. The barcode sequence aligned to a reference unedited sequence mergedRead column  mutations at each target site target[X] columns and the edited sequences at each target site sequence[X] columns were used for downstream analysis. inDropsExpMatrix noQ txt file is the gene expression matrix for the full dataset. Columns are individual cells from different batches of whole brains f1   f2   f3   f4   f5   f6  or brain regions fore   mid   hind . fall.inDrops.Robj is the processed Seurat R object  which can be loaded into R and explored.", "zebrafish brain", null, "Single cell suspensions were processed through inDrops to generate single cell cDNA libraries. cDNAs were in vitro transcribed and fragmented. The three prime fragments were reverse transcribed and prepared for sequencing Libraries were prepared as described in Zilionis et al.  2017  Nature Protocols PMID = 27929523", null, "tissue:brain|developmental stage:23 25dpf", "GSM2813970", "GSM2813970: DEW144 f6; Danio rerio; RNA Seq", "GSM2813970", null, "1", "Single cell suspensions were processed through inDrops to generate single cell cDNA libraries. cDNAs were in vitro transcribed and fragmented. The three prime fragments were reverse transcribed and prepared for sequencing Libraries were prepared as described in Zilionis et al.  2017  Nature Protocols PMID = 27929523", "GEO Accession:GSM2813970", "RNA-Seq", "TRANSCRIPTOMIC", "cDNA", "SINGLE", "ILLUMINA", "NextSeq 500", null, "SRP120009", null, "loader:fastq load.py|options:  appendBCtoName", "DEW144_Lane2.sorted.fastq.gz", "fastq", 2576418895.0, 43999832.0, "GSM2813970 r2", "0:58.56", "A:714324521;C:494519068;G:541009379;T:826557189;N:8738", 58, null, null, null, 714324521, 494519068, 541009379, 826557189, 8738, "SRX3287400", "SRS2596874", "SRA619743", "GEO", "Harvard University", 1, 0.89423, null, 0.23084, null, 0.78644, null, 0.50488, null, 61, null, "B", null, "usable mapping rate", "illumina", "nextseq", "unknown", "cdna_unspecified", "unknown", "sc", "single_cell_droplet", "indrops", null, "United States", "2017-10-16", "Larval", "Larval", "Brain", "Nervous System"], [43873, "SRR6176718", "SRX3287399", "SRS2596873", "SRP120009", "PRJNA414416", "Simultaneous single cell profiling of lineages and cell types in the vertebrate brain", "GSE105010", "Other", "The lineage relationships among the hundreds of cell types generated during development are difficult to reconstruct. A recent method  GESTALT  used CRISPR\u2013Cas9 barcode editing for large scale lineage tracing  but was restricted to early development and did not identify cell types. Here we present scGESTALT  which combines the lineage recording capabilities of GESTALT with cell type identification by single cell RNA sequencing. The method relies on an inducible system that enables barcodes to be edited at multiple time points  capturing lineage information from later stages of development. Sequencing of 60 000 transcriptomes from the juvenile zebrafish brain identified >100 cell types and marker genes. Using these data  we generate lineage trees with hundreds of branches that help uncover restrictions at the level of cell types  brain regions  and gene expression cascades during differentiation. scGESTALT can be applied to other multicellular organisms to simultaneously characterize molecular identities and lineage histories of thousands of cells during development and disease. Overall design: inDrops libraries of single cell transcriptomes and scGESTALT barcodes  and genomic DNA GESTALT libraries", null, "pubmed:29608178", null, "DEW143 f6", "GSM2813969", null, "source name:zebrafish brain|tissue:brain|developmental stage:23 25dpf", "DEW143 f6", "Single cell RNA Sequencing data FASTQ files were processed using the inDrops.py bioinformatics pipeline available at https://github.com/indrops/indrops. Transcriptome libraries were mapped to a zebrafish reference built from a custom GTF file and the zebrafish GRCz10 release 86 genome assembly. Bowtie version1.1.1 was used with parameter \u2013e 200; UMI quantification was used with parameter \u2013u 2 counts were ignored from UMIs split between more than 2 genes. genomic DNA GESTALT and scGESTALT libraries were processed using a custom pipeline available at https://github.com/shendurelab/Cas9FateMapping Genome build: GRCz10 CSV files for transcriptome data were generated using the inDrops pipeline. Each column in the CSV files contains a cell identifier and each row contains expression values for genes. Txt files for genomic DNA GESTALT libraries *allReadCounts contain lineage barcode sequences HMID column for each cell and their proportion in the sequenced libraries. scGESTALT data *GestMaster.txt contains the inDrops cell identifiers CellBarcode and BarcodeKey that were used to match barcodes to transcriptomes. They also contain lineage barcode sequences HMID column for each cell with a corresponding inDrops single cell gene expression profile  as well as the the t SNE cluster membership number ClusterIdent column Txt files ending in *stats.txt contain information about the each individually captured UMI or cell per sample. The barcode sequence aligned to a reference unedited sequence mergedRead column  mutations at each target site target[X] columns and the edited sequences at each target site sequence[X] columns were used for downstream analysis. inDropsExpMatrix noQ txt file is the gene expression matrix for the full dataset. Columns are individual cells from different batches of whole brains f1   f2   f3   f4   f5   f6  or brain regions fore   mid   hind . fall.inDrops.Robj is the processed Seurat R object  which can be loaded into R and explored.", "zebrafish brain", null, "Single cell suspensions were processed through inDrops to generate single cell cDNA libraries. cDNAs were in vitro transcribed and fragmented. The three prime fragments were reverse transcribed and prepared for sequencing Libraries were prepared as described in Zilionis et al.  2017  Nature Protocols PMID = 27929523", null, "tissue:brain|developmental stage:23 25dpf", "GSM2813969", "GSM2813969: DEW143 f6; Danio rerio; RNA Seq", "GSM2813969", null, "1", "Single cell suspensions were processed through inDrops to generate single cell cDNA libraries. cDNAs were in vitro transcribed and fragmented. The three prime fragments were reverse transcribed and prepared for sequencing Libraries were prepared as described in Zilionis et al.  2017  Nature Protocols PMID = 27929523", "GEO Accession:GSM2813969", "RNA-Seq", "TRANSCRIPTOMIC", "cDNA", "SINGLE", "ILLUMINA", "NextSeq 500", null, "SRP120009", null, "loader:fastq load.py|options:  appendBCtoName", "DEW143_Lane1.sorted.fastq.gz", "fastq", 1853910108.0, 34085903.0, "GSM2813969 r1", "0:54.39", "A:510615110;C:356714130;G:381836358;T:604740730;N:3780", 54, null, null, null, 510615110, 356714130, 381836358, 604740730, 3780, "SRX3287399", "SRS2596873", "SRA619743", "GEO", "Harvard University", 1, 0.87029, null, 0.22942, null, 0.7948, null, 0.52291, null, 61, null, "B", null, "usable mapping rate", "illumina", "nextseq", "unknown", "cdna_unspecified", "unknown", "sc", "single_cell_droplet", "indrops", null, "United States", "2017-10-16", "Larval", "Larval", "Brain", "Nervous System"], [43874, "SRR6176719", "SRX3287399", "SRS2596873", "SRP120009", "PRJNA414416", "Simultaneous single cell profiling of lineages and cell types in the vertebrate brain", "GSE105010", "Other", "The lineage relationships among the hundreds of cell types generated during development are difficult to reconstruct. A recent method  GESTALT  used CRISPR\u2013Cas9 barcode editing for large scale lineage tracing  but was restricted to early development and did not identify cell types. Here we present scGESTALT  which combines the lineage recording capabilities of GESTALT with cell type identification by single cell RNA sequencing. The method relies on an inducible system that enables barcodes to be edited at multiple time points  capturing lineage information from later stages of development. Sequencing of 60 000 transcriptomes from the juvenile zebrafish brain identified >100 cell types and marker genes. Using these data  we generate lineage trees with hundreds of branches that help uncover restrictions at the level of cell types  brain regions  and gene expression cascades during differentiation. scGESTALT can be applied to other multicellular organisms to simultaneously characterize molecular identities and lineage histories of thousands of cells during development and disease. Overall design: inDrops libraries of single cell transcriptomes and scGESTALT barcodes  and genomic DNA GESTALT libraries", null, "pubmed:29608178", null, "DEW143 f6", "GSM2813969", null, "source name:zebrafish brain|tissue:brain|developmental stage:23 25dpf", "DEW143 f6", "Single cell RNA Sequencing data FASTQ files were processed using the inDrops.py bioinformatics pipeline available at https://github.com/indrops/indrops. Transcriptome libraries were mapped to a zebrafish reference built from a custom GTF file and the zebrafish GRCz10 release 86 genome assembly. Bowtie version1.1.1 was used with parameter \u2013e 200; UMI quantification was used with parameter \u2013u 2 counts were ignored from UMIs split between more than 2 genes. genomic DNA GESTALT and scGESTALT libraries were processed using a custom pipeline available at https://github.com/shendurelab/Cas9FateMapping Genome build: GRCz10 CSV files for transcriptome data were generated using the inDrops pipeline. Each column in the CSV files contains a cell identifier and each row contains expression values for genes. Txt files for genomic DNA GESTALT libraries *allReadCounts contain lineage barcode sequences HMID column for each cell and their proportion in the sequenced libraries. scGESTALT data *GestMaster.txt contains the inDrops cell identifiers CellBarcode and BarcodeKey that were used to match barcodes to transcriptomes. They also contain lineage barcode sequences HMID column for each cell with a corresponding inDrops single cell gene expression profile  as well as the the t SNE cluster membership number ClusterIdent column Txt files ending in *stats.txt contain information about the each individually captured UMI or cell per sample. The barcode sequence aligned to a reference unedited sequence mergedRead column  mutations at each target site target[X] columns and the edited sequences at each target site sequence[X] columns were used for downstream analysis. inDropsExpMatrix noQ txt file is the gene expression matrix for the full dataset. Columns are individual cells from different batches of whole brains f1   f2   f3   f4   f5   f6  or brain regions fore   mid   hind . fall.inDrops.Robj is the processed Seurat R object  which can be loaded into R and explored.", "zebrafish brain", null, "Single cell suspensions were processed through inDrops to generate single cell cDNA libraries. cDNAs were in vitro transcribed and fragmented. The three prime fragments were reverse transcribed and prepared for sequencing Libraries were prepared as described in Zilionis et al.  2017  Nature Protocols PMID = 27929523", null, "tissue:brain|developmental stage:23 25dpf", "GSM2813969", "GSM2813969: DEW143 f6; Danio rerio; RNA Seq", "GSM2813969", null, "1", "Single cell suspensions were processed through inDrops to generate single cell cDNA libraries. cDNAs were in vitro transcribed and fragmented. The three prime fragments were reverse transcribed and prepared for sequencing Libraries were prepared as described in Zilionis et al.  2017  Nature Protocols PMID = 27929523", "GEO Accession:GSM2813969", "RNA-Seq", "TRANSCRIPTOMIC", "cDNA", "SINGLE", "ILLUMINA", "NextSeq 500", null, "SRP120009", null, "loader:fastq load.py|options:  appendBCtoName", "DEW143_Lane2.sorted.fastq.gz", "fastq", 2449871749.0, 41929623.0, "GSM2813969 r2", "0:58.43", "A:678158197;C:470881649;G:512160662;T:788663100;N:8141", 58, null, null, null, 678158197, 470881649, 512160662, 788663100, 8141, "SRX3287399", "SRS2596873", "SRA619743", "GEO", "Harvard University", 1, 0.8929, null, 0.22935, null, 0.78772, null, 0.50436, null, 61, null, "B", null, "usable mapping rate", "illumina", "nextseq", "unknown", "cdna_unspecified", "unknown", "sc", "single_cell_droplet", "indrops", null, "United States", "2017-10-16", "Larval", "Larval", "Brain", "Nervous System"], [43875, "SRR6176716", "SRX3287398", "SRS2596872", "SRP120009", "PRJNA414416", "Simultaneous single cell profiling of lineages and cell types in the vertebrate brain", "GSE105010", "Other", "The lineage relationships among the hundreds of cell types generated during development are difficult to reconstruct. A recent method  GESTALT  used CRISPR\u2013Cas9 barcode editing for large scale lineage tracing  but was restricted to early development and did not identify cell types. Here we present scGESTALT  which combines the lineage recording capabilities of GESTALT with cell type identification by single cell RNA sequencing. The method relies on an inducible system that enables barcodes to be edited at multiple time points  capturing lineage information from later stages of development. Sequencing of 60 000 transcriptomes from the juvenile zebrafish brain identified >100 cell types and marker genes. Using these data  we generate lineage trees with hundreds of branches that help uncover restrictions at the level of cell types  brain regions  and gene expression cascades during differentiation. scGESTALT can be applied to other multicellular organisms to simultaneously characterize molecular identities and lineage histories of thousands of cells during development and disease. Overall design: inDrops libraries of single cell transcriptomes and scGESTALT barcodes  and genomic DNA GESTALT libraries", null, "pubmed:29608178", null, "DEW142 f6", "GSM2813968", null, "source name:zebrafish brain|tissue:brain|developmental stage:23 25dpf", "DEW142 f6", "Single cell RNA Sequencing data FASTQ files were processed using the inDrops.py bioinformatics pipeline available at https://github.com/indrops/indrops. Transcriptome libraries were mapped to a zebrafish reference built from a custom GTF file and the zebrafish GRCz10 release 86 genome assembly. Bowtie version1.1.1 was used with parameter \u2013e 200; UMI quantification was used with parameter \u2013u 2 counts were ignored from UMIs split between more than 2 genes. genomic DNA GESTALT and scGESTALT libraries were processed using a custom pipeline available at https://github.com/shendurelab/Cas9FateMapping Genome build: GRCz10 CSV files for transcriptome data were generated using the inDrops pipeline. Each column in the CSV files contains a cell identifier and each row contains expression values for genes. Txt files for genomic DNA GESTALT libraries *allReadCounts contain lineage barcode sequences HMID column for each cell and their proportion in the sequenced libraries. scGESTALT data *GestMaster.txt contains the inDrops cell identifiers CellBarcode and BarcodeKey that were used to match barcodes to transcriptomes. They also contain lineage barcode sequences HMID column for each cell with a corresponding inDrops single cell gene expression profile  as well as the the t SNE cluster membership number ClusterIdent column Txt files ending in *stats.txt contain information about the each individually captured UMI or cell per sample. The barcode sequence aligned to a reference unedited sequence mergedRead column  mutations at each target site target[X] columns and the edited sequences at each target site sequence[X] columns were used for downstream analysis. inDropsExpMatrix noQ txt file is the gene expression matrix for the full dataset. Columns are individual cells from different batches of whole brains f1   f2   f3   f4   f5   f6  or brain regions fore   mid   hind . fall.inDrops.Robj is the processed Seurat R object  which can be loaded into R and explored.", "zebrafish brain", null, "Single cell suspensions were processed through inDrops to generate single cell cDNA libraries. cDNAs were in vitro transcribed and fragmented. The three prime fragments were reverse transcribed and prepared for sequencing Libraries were prepared as described in Zilionis et al.  2017  Nature Protocols PMID = 27929523", null, "tissue:brain|developmental stage:23 25dpf", "GSM2813968", "GSM2813968: DEW142 f6; Danio rerio; RNA Seq", "GSM2813968", null, "1", "Single cell suspensions were processed through inDrops to generate single cell cDNA libraries. cDNAs were in vitro transcribed and fragmented. The three prime fragments were reverse transcribed and prepared for sequencing Libraries were prepared as described in Zilionis et al.  2017  Nature Protocols PMID = 27929523", "GEO Accession:GSM2813968", "RNA-Seq", "TRANSCRIPTOMIC", "cDNA", "SINGLE", "ILLUMINA", "NextSeq 500", null, "SRP120009", null, "loader:fastq load.py|options:  appendBCtoName", "DEW142_Lane1.sorted.fastq.gz", "fastq", 1762629861.0, 32268172.0, "GSM2813968 r1", "0:54.62", "A:488608268;C:339649107;G:361663399;T:572705563;N:3524", 54, null, null, null, 488608268, 339649107, 361663399, 572705563, 3524, "SRX3287398", "SRS2596872", "SRA619743", "GEO", "Harvard University", 1, 0.86716, null, 0.22745, null, 0.79847, null, 0.52104, null, 57, null, "B", null, "usable mapping rate", "illumina", "nextseq", "unknown", "cdna_unspecified", "unknown", "sc", "single_cell_droplet", "indrops", null, "United States", "2017-10-16", "Larval", "Larval", "Brain", "Nervous System"], [43876, "SRR6176717", "SRX3287398", "SRS2596872", "SRP120009", "PRJNA414416", "Simultaneous single cell profiling of lineages and cell types in the vertebrate brain", "GSE105010", "Other", "The lineage relationships among the hundreds of cell types generated during development are difficult to reconstruct. A recent method  GESTALT  used CRISPR\u2013Cas9 barcode editing for large scale lineage tracing  but was restricted to early development and did not identify cell types. Here we present scGESTALT  which combines the lineage recording capabilities of GESTALT with cell type identification by single cell RNA sequencing. The method relies on an inducible system that enables barcodes to be edited at multiple time points  capturing lineage information from later stages of development. Sequencing of 60 000 transcriptomes from the juvenile zebrafish brain identified >100 cell types and marker genes. Using these data  we generate lineage trees with hundreds of branches that help uncover restrictions at the level of cell types  brain regions  and gene expression cascades during differentiation. scGESTALT can be applied to other multicellular organisms to simultaneously characterize molecular identities and lineage histories of thousands of cells during development and disease. Overall design: inDrops libraries of single cell transcriptomes and scGESTALT barcodes  and genomic DNA GESTALT libraries", null, "pubmed:29608178", null, "DEW142 f6", "GSM2813968", null, "source name:zebrafish brain|tissue:brain|developmental stage:23 25dpf", "DEW142 f6", "Single cell RNA Sequencing data FASTQ files were processed using the inDrops.py bioinformatics pipeline available at https://github.com/indrops/indrops. Transcriptome libraries were mapped to a zebrafish reference built from a custom GTF file and the zebrafish GRCz10 release 86 genome assembly. Bowtie version1.1.1 was used with parameter \u2013e 200; UMI quantification was used with parameter \u2013u 2 counts were ignored from UMIs split between more than 2 genes. genomic DNA GESTALT and scGESTALT libraries were processed using a custom pipeline available at https://github.com/shendurelab/Cas9FateMapping Genome build: GRCz10 CSV files for transcriptome data were generated using the inDrops pipeline. Each column in the CSV files contains a cell identifier and each row contains expression values for genes. Txt files for genomic DNA GESTALT libraries *allReadCounts contain lineage barcode sequences HMID column for each cell and their proportion in the sequenced libraries. scGESTALT data *GestMaster.txt contains the inDrops cell identifiers CellBarcode and BarcodeKey that were used to match barcodes to transcriptomes. They also contain lineage barcode sequences HMID column for each cell with a corresponding inDrops single cell gene expression profile  as well as the the t SNE cluster membership number ClusterIdent column Txt files ending in *stats.txt contain information about the each individually captured UMI or cell per sample. The barcode sequence aligned to a reference unedited sequence mergedRead column  mutations at each target site target[X] columns and the edited sequences at each target site sequence[X] columns were used for downstream analysis. inDropsExpMatrix noQ txt file is the gene expression matrix for the full dataset. Columns are individual cells from different batches of whole brains f1   f2   f3   f4   f5   f6  or brain regions fore   mid   hind . fall.inDrops.Robj is the processed Seurat R object  which can be loaded into R and explored.", "zebrafish brain", null, "Single cell suspensions were processed through inDrops to generate single cell cDNA libraries. cDNAs were in vitro transcribed and fragmented. The three prime fragments were reverse transcribed and prepared for sequencing Libraries were prepared as described in Zilionis et al.  2017  Nature Protocols PMID = 27929523", null, "tissue:brain|developmental stage:23 25dpf", "GSM2813968", "GSM2813968: DEW142 f6; Danio rerio; RNA Seq", "GSM2813968", null, "1", "Single cell suspensions were processed through inDrops to generate single cell cDNA libraries. cDNAs were in vitro transcribed and fragmented. The three prime fragments were reverse transcribed and prepared for sequencing Libraries were prepared as described in Zilionis et al.  2017  Nature Protocols PMID = 27929523", "GEO Accession:GSM2813968", "RNA-Seq", "TRANSCRIPTOMIC", "cDNA", "SINGLE", "ILLUMINA", "NextSeq 500", null, "SRP120009", null, "loader:fastq load.py|options:  appendBCtoName", "DEW142_Lane2.sorted.fastq.gz", "fastq", 2346864256.0, 40110005.0, "GSM2813968 r2", "0:58.51", "A:654034260;C:451467879;G:488256208;T:753098041;N:7868", 58, null, null, null, 654034260, 451467879, 488256208, 753098041, 7868, "SRX3287398", "SRS2596872", "SRA619743", "GEO", "Harvard University", 1, 0.88889, null, 0.22997, null, 0.79241, null, 0.51721, null, 61, null, "B", null, "usable mapping rate", "illumina", "nextseq", "unknown", "cdna_unspecified", "unknown", "sc", "single_cell_droplet", "indrops", null, "United States", "2017-10-16", "Larval", "Larval", "Brain", "Nervous System"], [43877, "SRR6176714", "SRX3287397", "SRS2596871", "SRP120009", "PRJNA414416", "Simultaneous single cell profiling of lineages and cell types in the vertebrate brain", "GSE105010", "Other", "The lineage relationships among the hundreds of cell types generated during development are difficult to reconstruct. A recent method  GESTALT  used CRISPR\u2013Cas9 barcode editing for large scale lineage tracing  but was restricted to early development and did not identify cell types. Here we present scGESTALT  which combines the lineage recording capabilities of GESTALT with cell type identification by single cell RNA sequencing. The method relies on an inducible system that enables barcodes to be edited at multiple time points  capturing lineage information from later stages of development. Sequencing of 60 000 transcriptomes from the juvenile zebrafish brain identified >100 cell types and marker genes. Using these data  we generate lineage trees with hundreds of branches that help uncover restrictions at the level of cell types  brain regions  and gene expression cascades during differentiation. scGESTALT can be applied to other multicellular organisms to simultaneously characterize molecular identities and lineage histories of thousands of cells during development and disease. Overall design: inDrops libraries of single cell transcriptomes and scGESTALT barcodes  and genomic DNA GESTALT libraries", null, "pubmed:29608178", null, "DEW141 f5", "GSM2813967", null, "source name:zebrafish brain|tissue:brain|developmental stage:23 25dpf", "DEW141 f5", "Single cell RNA Sequencing data FASTQ files were processed using the inDrops.py bioinformatics pipeline available at https://github.com/indrops/indrops. Transcriptome libraries were mapped to a zebrafish reference built from a custom GTF file and the zebrafish GRCz10 release 86 genome assembly. Bowtie version1.1.1 was used with parameter \u2013e 200; UMI quantification was used with parameter \u2013u 2 counts were ignored from UMIs split between more than 2 genes. genomic DNA GESTALT and scGESTALT libraries were processed using a custom pipeline available at https://github.com/shendurelab/Cas9FateMapping Genome build: GRCz10 CSV files for transcriptome data were generated using the inDrops pipeline. Each column in the CSV files contains a cell identifier and each row contains expression values for genes. Txt files for genomic DNA GESTALT libraries *allReadCounts contain lineage barcode sequences HMID column for each cell and their proportion in the sequenced libraries. scGESTALT data *GestMaster.txt contains the inDrops cell identifiers CellBarcode and BarcodeKey that were used to match barcodes to transcriptomes. They also contain lineage barcode sequences HMID column for each cell with a corresponding inDrops single cell gene expression profile  as well as the the t SNE cluster membership number ClusterIdent column Txt files ending in *stats.txt contain information about the each individually captured UMI or cell per sample. The barcode sequence aligned to a reference unedited sequence mergedRead column  mutations at each target site target[X] columns and the edited sequences at each target site sequence[X] columns were used for downstream analysis. inDropsExpMatrix noQ txt file is the gene expression matrix for the full dataset. Columns are individual cells from different batches of whole brains f1   f2   f3   f4   f5   f6  or brain regions fore   mid   hind . fall.inDrops.Robj is the processed Seurat R object  which can be loaded into R and explored.", "zebrafish brain", null, "Single cell suspensions were processed through inDrops to generate single cell cDNA libraries. cDNAs were in vitro transcribed and fragmented. The three prime fragments were reverse transcribed and prepared for sequencing Libraries were prepared as described in Zilionis et al.  2017  Nature Protocols PMID = 27929523", null, "tissue:brain|developmental stage:23 25dpf", "GSM2813967", "GSM2813967: DEW141 f5; Danio rerio; RNA Seq", "GSM2813967", null, "1", "Single cell suspensions were processed through inDrops to generate single cell cDNA libraries. cDNAs were in vitro transcribed and fragmented. The three prime fragments were reverse transcribed and prepared for sequencing Libraries were prepared as described in Zilionis et al.  2017  Nature Protocols PMID = 27929523", "GEO Accession:GSM2813967", "RNA-Seq", "TRANSCRIPTOMIC", "cDNA", "SINGLE", "ILLUMINA", "NextSeq 500", null, "SRP120009", null, "loader:fastq load.py|options:  appendBCtoName", "DEW141_Lane1.sorted.fastq.gz", "fastq", 2797102577.0, 48311826.0, "GSM2813967 r1", "0:57.90", "A:778811258;C:528303302;G:570412822;T:919568474;N:6721", 57, null, null, null, 778811258, 528303302, 570412822, 919568474, 6721, "SRX3287397", "SRS2596871", "SRA619743", "GEO", "Harvard University", 1, 0.89023, null, 0.23795, null, 0.782, null, 0.50945, null, 61, null, "B", null, "usable mapping rate", "illumina", "nextseq", "unknown", "cdna_unspecified", "unknown", "sc", "single_cell_droplet", "indrops", null, "United States", "2017-10-16", "Larval", "Larval", "Brain", "Nervous System"], [43878, "SRR6176715", "SRX3287397", "SRS2596871", "SRP120009", "PRJNA414416", "Simultaneous single cell profiling of lineages and cell types in the vertebrate brain", "GSE105010", "Other", "The lineage relationships among the hundreds of cell types generated during development are difficult to reconstruct. A recent method  GESTALT  used CRISPR\u2013Cas9 barcode editing for large scale lineage tracing  but was restricted to early development and did not identify cell types. Here we present scGESTALT  which combines the lineage recording capabilities of GESTALT with cell type identification by single cell RNA sequencing. The method relies on an inducible system that enables barcodes to be edited at multiple time points  capturing lineage information from later stages of development. Sequencing of 60 000 transcriptomes from the juvenile zebrafish brain identified >100 cell types and marker genes. Using these data  we generate lineage trees with hundreds of branches that help uncover restrictions at the level of cell types  brain regions  and gene expression cascades during differentiation. scGESTALT can be applied to other multicellular organisms to simultaneously characterize molecular identities and lineage histories of thousands of cells during development and disease. Overall design: inDrops libraries of single cell transcriptomes and scGESTALT barcodes  and genomic DNA GESTALT libraries", null, "pubmed:29608178", null, "DEW141 f5", "GSM2813967", null, "source name:zebrafish brain|tissue:brain|developmental stage:23 25dpf", "DEW141 f5", "Single cell RNA Sequencing data FASTQ files were processed using the inDrops.py bioinformatics pipeline available at https://github.com/indrops/indrops. Transcriptome libraries were mapped to a zebrafish reference built from a custom GTF file and the zebrafish GRCz10 release 86 genome assembly. Bowtie version1.1.1 was used with parameter \u2013e 200; UMI quantification was used with parameter \u2013u 2 counts were ignored from UMIs split between more than 2 genes. genomic DNA GESTALT and scGESTALT libraries were processed using a custom pipeline available at https://github.com/shendurelab/Cas9FateMapping Genome build: GRCz10 CSV files for transcriptome data were generated using the inDrops pipeline. Each column in the CSV files contains a cell identifier and each row contains expression values for genes. Txt files for genomic DNA GESTALT libraries *allReadCounts contain lineage barcode sequences HMID column for each cell and their proportion in the sequenced libraries. scGESTALT data *GestMaster.txt contains the inDrops cell identifiers CellBarcode and BarcodeKey that were used to match barcodes to transcriptomes. They also contain lineage barcode sequences HMID column for each cell with a corresponding inDrops single cell gene expression profile  as well as the the t SNE cluster membership number ClusterIdent column Txt files ending in *stats.txt contain information about the each individually captured UMI or cell per sample. The barcode sequence aligned to a reference unedited sequence mergedRead column  mutations at each target site target[X] columns and the edited sequences at each target site sequence[X] columns were used for downstream analysis. inDropsExpMatrix noQ txt file is the gene expression matrix for the full dataset. Columns are individual cells from different batches of whole brains f1   f2   f3   f4   f5   f6  or brain regions fore   mid   hind . fall.inDrops.Robj is the processed Seurat R object  which can be loaded into R and explored.", "zebrafish brain", null, "Single cell suspensions were processed through inDrops to generate single cell cDNA libraries. cDNAs were in vitro transcribed and fragmented. The three prime fragments were reverse transcribed and prepared for sequencing Libraries were prepared as described in Zilionis et al.  2017  Nature Protocols PMID = 27929523", null, "tissue:brain|developmental stage:23 25dpf", "GSM2813967", "GSM2813967: DEW141 f5; Danio rerio; RNA Seq", "GSM2813967", null, "1", "Single cell suspensions were processed through inDrops to generate single cell cDNA libraries. cDNAs were in vitro transcribed and fragmented. The three prime fragments were reverse transcribed and prepared for sequencing Libraries were prepared as described in Zilionis et al.  2017  Nature Protocols PMID = 27929523", "GEO Accession:GSM2813967", "RNA-Seq", "TRANSCRIPTOMIC", "cDNA", "SINGLE", "ILLUMINA", "NextSeq 500", null, "SRP120009", null, "loader:fastq load.py|options:  appendBCtoName", "DEW141_Lane2.sorted.fastq.gz", "fastq", 3577808104.0, 60730645.0, "GSM2813967 r2", "0:58.91", "A:997648014;C:674533198;G:734148175;T:1171466042;N:12675", 58, null, null, null, 997648014, 674533198, 734148175, 1171466042, 12675, "SRX3287397", "SRS2596871", "SRA619743", "GEO", "Harvard University", 1, 0.89059, null, 0.23311, null, 0.78025, null, 0.4954, null, 60, null, "B", null, "usable mapping rate", "illumina", "nextseq", "unknown", "cdna_unspecified", "unknown", "sc", "single_cell_droplet", "indrops", null, "United States", "2017-10-16", "Larval", "Larval", "Brain", "Nervous System"], [43879, "SRR6176712", "SRX3287396", "SRS2596870", "SRP120009", "PRJNA414416", "Simultaneous single cell profiling of lineages and cell types in the vertebrate brain", "GSE105010", "Other", "The lineage relationships among the hundreds of cell types generated during development are difficult to reconstruct. A recent method  GESTALT  used CRISPR\u2013Cas9 barcode editing for large scale lineage tracing  but was restricted to early development and did not identify cell types. Here we present scGESTALT  which combines the lineage recording capabilities of GESTALT with cell type identification by single cell RNA sequencing. The method relies on an inducible system that enables barcodes to be edited at multiple time points  capturing lineage information from later stages of development. Sequencing of 60 000 transcriptomes from the juvenile zebrafish brain identified >100 cell types and marker genes. Using these data  we generate lineage trees with hundreds of branches that help uncover restrictions at the level of cell types  brain regions  and gene expression cascades during differentiation. scGESTALT can be applied to other multicellular organisms to simultaneously characterize molecular identities and lineage histories of thousands of cells during development and disease. Overall design: inDrops libraries of single cell transcriptomes and scGESTALT barcodes  and genomic DNA GESTALT libraries", null, "pubmed:29608178", null, "DEW140 f5", "GSM2813966", null, "source name:zebrafish brain|tissue:brain|developmental stage:23 25dpf", "DEW140 f5", "Single cell RNA Sequencing data FASTQ files were processed using the inDrops.py bioinformatics pipeline available at https://github.com/indrops/indrops. Transcriptome libraries were mapped to a zebrafish reference built from a custom GTF file and the zebrafish GRCz10 release 86 genome assembly. Bowtie version1.1.1 was used with parameter \u2013e 200; UMI quantification was used with parameter \u2013u 2 counts were ignored from UMIs split between more than 2 genes. genomic DNA GESTALT and scGESTALT libraries were processed using a custom pipeline available at https://github.com/shendurelab/Cas9FateMapping Genome build: GRCz10 CSV files for transcriptome data were generated using the inDrops pipeline. Each column in the CSV files contains a cell identifier and each row contains expression values for genes. Txt files for genomic DNA GESTALT libraries *allReadCounts contain lineage barcode sequences HMID column for each cell and their proportion in the sequenced libraries. scGESTALT data *GestMaster.txt contains the inDrops cell identifiers CellBarcode and BarcodeKey that were used to match barcodes to transcriptomes. They also contain lineage barcode sequences HMID column for each cell with a corresponding inDrops single cell gene expression profile  as well as the the t SNE cluster membership number ClusterIdent column Txt files ending in *stats.txt contain information about the each individually captured UMI or cell per sample. The barcode sequence aligned to a reference unedited sequence mergedRead column  mutations at each target site target[X] columns and the edited sequences at each target site sequence[X] columns were used for downstream analysis. inDropsExpMatrix noQ txt file is the gene expression matrix for the full dataset. Columns are individual cells from different batches of whole brains f1   f2   f3   f4   f5   f6  or brain regions fore   mid   hind . fall.inDrops.Robj is the processed Seurat R object  which can be loaded into R and explored.", "zebrafish brain", null, "Single cell suspensions were processed through inDrops to generate single cell cDNA libraries. cDNAs were in vitro transcribed and fragmented. The three prime fragments were reverse transcribed and prepared for sequencing Libraries were prepared as described in Zilionis et al.  2017  Nature Protocols PMID = 27929523", null, "tissue:brain|developmental stage:23 25dpf", "GSM2813966", "GSM2813966: DEW140 f5; Danio rerio; RNA Seq", "GSM2813966", null, "1", "Single cell suspensions were processed through inDrops to generate single cell cDNA libraries. cDNAs were in vitro transcribed and fragmented. The three prime fragments were reverse transcribed and prepared for sequencing Libraries were prepared as described in Zilionis et al.  2017  Nature Protocols PMID = 27929523", "GEO Accession:GSM2813966", "RNA-Seq", "TRANSCRIPTOMIC", "cDNA", "SINGLE", "ILLUMINA", "NextSeq 500", null, "SRP120009", null, "loader:fastq load.py|options:  appendBCtoName", "DEW140_Lane1.sorted.fastq.gz", "fastq", 2917746792.0, 50350610.0, "GSM2813966 r1", "0:57.95", "A:812726490;C:548270217;G:594090392;T:962652776;N:6917", 57, null, null, null, 812726490, 548270217, 594090392, 962652776, 6917, "SRX3287396", "SRS2596870", "SRA619743", "GEO", "Harvard University", 1, 0.89062, null, 0.23582, null, 0.78403, null, 0.5019, null, 61, null, "B", null, "usable mapping rate", "illumina", "nextseq", "unknown", "cdna_unspecified", "unknown", "sc", "single_cell_droplet", "indrops", null, "United States", "2017-10-16", "Larval", "Larval", "Brain", "Nervous System"], [43880, "SRR6176713", "SRX3287396", "SRS2596870", "SRP120009", "PRJNA414416", "Simultaneous single cell profiling of lineages and cell types in the vertebrate brain", "GSE105010", "Other", "The lineage relationships among the hundreds of cell types generated during development are difficult to reconstruct. A recent method  GESTALT  used CRISPR\u2013Cas9 barcode editing for large scale lineage tracing  but was restricted to early development and did not identify cell types. Here we present scGESTALT  which combines the lineage recording capabilities of GESTALT with cell type identification by single cell RNA sequencing. The method relies on an inducible system that enables barcodes to be edited at multiple time points  capturing lineage information from later stages of development. Sequencing of 60 000 transcriptomes from the juvenile zebrafish brain identified >100 cell types and marker genes. Using these data  we generate lineage trees with hundreds of branches that help uncover restrictions at the level of cell types  brain regions  and gene expression cascades during differentiation. scGESTALT can be applied to other multicellular organisms to simultaneously characterize molecular identities and lineage histories of thousands of cells during development and disease. Overall design: inDrops libraries of single cell transcriptomes and scGESTALT barcodes  and genomic DNA GESTALT libraries", null, "pubmed:29608178", null, "DEW140 f5", "GSM2813966", null, "source name:zebrafish brain|tissue:brain|developmental stage:23 25dpf", "DEW140 f5", "Single cell RNA Sequencing data FASTQ files were processed using the inDrops.py bioinformatics pipeline available at https://github.com/indrops/indrops. Transcriptome libraries were mapped to a zebrafish reference built from a custom GTF file and the zebrafish GRCz10 release 86 genome assembly. Bowtie version1.1.1 was used with parameter \u2013e 200; UMI quantification was used with parameter \u2013u 2 counts were ignored from UMIs split between more than 2 genes. genomic DNA GESTALT and scGESTALT libraries were processed using a custom pipeline available at https://github.com/shendurelab/Cas9FateMapping Genome build: GRCz10 CSV files for transcriptome data were generated using the inDrops pipeline. Each column in the CSV files contains a cell identifier and each row contains expression values for genes. Txt files for genomic DNA GESTALT libraries *allReadCounts contain lineage barcode sequences HMID column for each cell and their proportion in the sequenced libraries. scGESTALT data *GestMaster.txt contains the inDrops cell identifiers CellBarcode and BarcodeKey that were used to match barcodes to transcriptomes. They also contain lineage barcode sequences HMID column for each cell with a corresponding inDrops single cell gene expression profile  as well as the the t SNE cluster membership number ClusterIdent column Txt files ending in *stats.txt contain information about the each individually captured UMI or cell per sample. The barcode sequence aligned to a reference unedited sequence mergedRead column  mutations at each target site target[X] columns and the edited sequences at each target site sequence[X] columns were used for downstream analysis. inDropsExpMatrix noQ txt file is the gene expression matrix for the full dataset. Columns are individual cells from different batches of whole brains f1   f2   f3   f4   f5   f6  or brain regions fore   mid   hind . fall.inDrops.Robj is the processed Seurat R object  which can be loaded into R and explored.", "zebrafish brain", null, "Single cell suspensions were processed through inDrops to generate single cell cDNA libraries. cDNAs were in vitro transcribed and fragmented. The three prime fragments were reverse transcribed and prepared for sequencing Libraries were prepared as described in Zilionis et al.  2017  Nature Protocols PMID = 27929523", null, "tissue:brain|developmental stage:23 25dpf", "GSM2813966", "GSM2813966: DEW140 f5; Danio rerio; RNA Seq", "GSM2813966", null, "1", "Single cell suspensions were processed through inDrops to generate single cell cDNA libraries. cDNAs were in vitro transcribed and fragmented. The three prime fragments were reverse transcribed and prepared for sequencing Libraries were prepared as described in Zilionis et al.  2017  Nature Protocols PMID = 27929523", "GEO Accession:GSM2813966", "RNA-Seq", "TRANSCRIPTOMIC", "cDNA", "SINGLE", "ILLUMINA", "NextSeq 500", null, "SRP120009", null, "loader:fastq load.py|options:  appendBCtoName", "DEW140_Lane2.sorted.fastq.gz", "fastq", 3718402181.0, 63086820.0, "GSM2813966 r2", "0:58.94", "A:1037269475;C:697925461;G:761607666;T:1221586424;N:13155", 58, null, null, null, 1037269475, 697925461, 761607666, 1221586424, 13155, "SRX3287396", "SRS2596870", "SRA619743", "GEO", "Harvard University", 1, 0.89177, null, 0.23224, null, 0.78216, null, 0.51101, null, 61, null, "B", null, "usable mapping rate", "illumina", "nextseq", "unknown", "cdna_unspecified", "unknown", "sc", "single_cell_droplet", "indrops", null, "United States", "2017-10-16", "Larval", "Larval", "Brain", "Nervous System"], [43881, "SRR6176710", "SRX3287395", "SRS2596869", "SRP120009", "PRJNA414416", "Simultaneous single cell profiling of lineages and cell types in the vertebrate brain", "GSE105010", "Other", "The lineage relationships among the hundreds of cell types generated during development are difficult to reconstruct. A recent method  GESTALT  used CRISPR\u2013Cas9 barcode editing for large scale lineage tracing  but was restricted to early development and did not identify cell types. Here we present scGESTALT  which combines the lineage recording capabilities of GESTALT with cell type identification by single cell RNA sequencing. The method relies on an inducible system that enables barcodes to be edited at multiple time points  capturing lineage information from later stages of development. Sequencing of 60 000 transcriptomes from the juvenile zebrafish brain identified >100 cell types and marker genes. Using these data  we generate lineage trees with hundreds of branches that help uncover restrictions at the level of cell types  brain regions  and gene expression cascades during differentiation. scGESTALT can be applied to other multicellular organisms to simultaneously characterize molecular identities and lineage histories of thousands of cells during development and disease. Overall design: inDrops libraries of single cell transcriptomes and scGESTALT barcodes  and genomic DNA GESTALT libraries", null, "pubmed:29608178", null, "DEW139 f5", "GSM2813965", null, "source name:zebrafish brain|tissue:brain|developmental stage:23 25dpf", "DEW139 f5", "Single cell RNA Sequencing data FASTQ files were processed using the inDrops.py bioinformatics pipeline available at https://github.com/indrops/indrops. Transcriptome libraries were mapped to a zebrafish reference built from a custom GTF file and the zebrafish GRCz10 release 86 genome assembly. Bowtie version1.1.1 was used with parameter \u2013e 200; UMI quantification was used with parameter \u2013u 2 counts were ignored from UMIs split between more than 2 genes. genomic DNA GESTALT and scGESTALT libraries were processed using a custom pipeline available at https://github.com/shendurelab/Cas9FateMapping Genome build: GRCz10 CSV files for transcriptome data were generated using the inDrops pipeline. Each column in the CSV files contains a cell identifier and each row contains expression values for genes. Txt files for genomic DNA GESTALT libraries *allReadCounts contain lineage barcode sequences HMID column for each cell and their proportion in the sequenced libraries. scGESTALT data *GestMaster.txt contains the inDrops cell identifiers CellBarcode and BarcodeKey that were used to match barcodes to transcriptomes. They also contain lineage barcode sequences HMID column for each cell with a corresponding inDrops single cell gene expression profile  as well as the the t SNE cluster membership number ClusterIdent column Txt files ending in *stats.txt contain information about the each individually captured UMI or cell per sample. The barcode sequence aligned to a reference unedited sequence mergedRead column  mutations at each target site target[X] columns and the edited sequences at each target site sequence[X] columns were used for downstream analysis. inDropsExpMatrix noQ txt file is the gene expression matrix for the full dataset. Columns are individual cells from different batches of whole brains f1   f2   f3   f4   f5   f6  or brain regions fore   mid   hind . fall.inDrops.Robj is the processed Seurat R object  which can be loaded into R and explored.", "zebrafish brain", null, "Single cell suspensions were processed through inDrops to generate single cell cDNA libraries. cDNAs were in vitro transcribed and fragmented. The three prime fragments were reverse transcribed and prepared for sequencing Libraries were prepared as described in Zilionis et al.  2017  Nature Protocols PMID = 27929523", null, "tissue:brain|developmental stage:23 25dpf", "GSM2813965", "GSM2813965: DEW139 f5; Danio rerio; RNA Seq", "GSM2813965", null, "1", "Single cell suspensions were processed through inDrops to generate single cell cDNA libraries. cDNAs were in vitro transcribed and fragmented. The three prime fragments were reverse transcribed and prepared for sequencing Libraries were prepared as described in Zilionis et al.  2017  Nature Protocols PMID = 27929523", "GEO Accession:GSM2813965", "RNA-Seq", "TRANSCRIPTOMIC", "cDNA", "SINGLE", "ILLUMINA", "NextSeq 500", null, "SRP120009", null, "loader:fastq load.py|options:  appendBCtoName", "DEW139_Lane1.sorted.fastq.gz", "fastq", 2905049784.0, 50110696.0, "GSM2813965 r1", "0:57.97", "A:805337279;C:546305611;G:596251042;T:957149051;N:6801", 57, null, null, null, 805337279, 546305611, 596251042, 957149051, 6801, "SRX3287395", "SRS2596869", "SRA619743", "GEO", "Harvard University", 1, 0.89282, null, 0.23416, null, 0.78281, null, 0.50737, null, 61, null, "B", null, "usable mapping rate", "illumina", "nextseq", "unknown", "cdna_unspecified", "unknown", "sc", "single_cell_droplet", "indrops", null, "United States", "2017-10-16", "Larval", "Larval", "Brain", "Nervous System"], [43882, "SRR6176711", "SRX3287395", "SRS2596869", "SRP120009", "PRJNA414416", "Simultaneous single cell profiling of lineages and cell types in the vertebrate brain", "GSE105010", "Other", "The lineage relationships among the hundreds of cell types generated during development are difficult to reconstruct. A recent method  GESTALT  used CRISPR\u2013Cas9 barcode editing for large scale lineage tracing  but was restricted to early development and did not identify cell types. Here we present scGESTALT  which combines the lineage recording capabilities of GESTALT with cell type identification by single cell RNA sequencing. The method relies on an inducible system that enables barcodes to be edited at multiple time points  capturing lineage information from later stages of development. Sequencing of 60 000 transcriptomes from the juvenile zebrafish brain identified >100 cell types and marker genes. Using these data  we generate lineage trees with hundreds of branches that help uncover restrictions at the level of cell types  brain regions  and gene expression cascades during differentiation. scGESTALT can be applied to other multicellular organisms to simultaneously characterize molecular identities and lineage histories of thousands of cells during development and disease. Overall design: inDrops libraries of single cell transcriptomes and scGESTALT barcodes  and genomic DNA GESTALT libraries", null, "pubmed:29608178", null, "DEW139 f5", "GSM2813965", null, "source name:zebrafish brain|tissue:brain|developmental stage:23 25dpf", "DEW139 f5", "Single cell RNA Sequencing data FASTQ files were processed using the inDrops.py bioinformatics pipeline available at https://github.com/indrops/indrops. Transcriptome libraries were mapped to a zebrafish reference built from a custom GTF file and the zebrafish GRCz10 release 86 genome assembly. Bowtie version1.1.1 was used with parameter \u2013e 200; UMI quantification was used with parameter \u2013u 2 counts were ignored from UMIs split between more than 2 genes. genomic DNA GESTALT and scGESTALT libraries were processed using a custom pipeline available at https://github.com/shendurelab/Cas9FateMapping Genome build: GRCz10 CSV files for transcriptome data were generated using the inDrops pipeline. Each column in the CSV files contains a cell identifier and each row contains expression values for genes. Txt files for genomic DNA GESTALT libraries *allReadCounts contain lineage barcode sequences HMID column for each cell and their proportion in the sequenced libraries. scGESTALT data *GestMaster.txt contains the inDrops cell identifiers CellBarcode and BarcodeKey that were used to match barcodes to transcriptomes. They also contain lineage barcode sequences HMID column for each cell with a corresponding inDrops single cell gene expression profile  as well as the the t SNE cluster membership number ClusterIdent column Txt files ending in *stats.txt contain information about the each individually captured UMI or cell per sample. The barcode sequence aligned to a reference unedited sequence mergedRead column  mutations at each target site target[X] columns and the edited sequences at each target site sequence[X] columns were used for downstream analysis. inDropsExpMatrix noQ txt file is the gene expression matrix for the full dataset. Columns are individual cells from different batches of whole brains f1   f2   f3   f4   f5   f6  or brain regions fore   mid   hind . fall.inDrops.Robj is the processed Seurat R object  which can be loaded into R and explored.", "zebrafish brain", null, "Single cell suspensions were processed through inDrops to generate single cell cDNA libraries. cDNAs were in vitro transcribed and fragmented. The three prime fragments were reverse transcribed and prepared for sequencing Libraries were prepared as described in Zilionis et al.  2017  Nature Protocols PMID = 27929523", null, "tissue:brain|developmental stage:23 25dpf", "GSM2813965", "GSM2813965: DEW139 f5; Danio rerio; RNA Seq", "GSM2813965", null, "1", "Single cell suspensions were processed through inDrops to generate single cell cDNA libraries. cDNAs were in vitro transcribed and fragmented. The three prime fragments were reverse transcribed and prepared for sequencing Libraries were prepared as described in Zilionis et al.  2017  Nature Protocols PMID = 27929523", "GEO Accession:GSM2813965", "RNA-Seq", "TRANSCRIPTOMIC", "cDNA", "SINGLE", "ILLUMINA", "NextSeq 500", null, "SRP120009", null, "loader:fastq load.py|options:  appendBCtoName", "DEW139_Lane2.sorted.fastq.gz", "fastq", 3735432281.0, 63376689.0, "GSM2813965 r2", "0:58.94", "A:1037172797;C:701422244;G:771367652;T:1225456437;N:13151", 58, null, null, null, 1037172797, 701422244, 771367652, 1225456437, 13151, "SRX3287395", "SRS2596869", "SRA619743", "GEO", "Harvard University", 1, 0.89351, null, 0.23166, null, 0.78259, null, 0.50069, null, 33, null, "B", null, "usable mapping rate", "illumina", "nextseq", "unknown", "cdna_unspecified", "unknown", "sc", "single_cell_droplet", "indrops", null, "United States", "2017-10-16", "Larval", "Larval", "Brain", "Nervous System"], [43883, "SRR6176708", "SRX3287394", "SRS2596868", "SRP120009", "PRJNA414416", "Simultaneous single cell profiling of lineages and cell types in the vertebrate brain", "GSE105010", "Other", "The lineage relationships among the hundreds of cell types generated during development are difficult to reconstruct. A recent method  GESTALT  used CRISPR\u2013Cas9 barcode editing for large scale lineage tracing  but was restricted to early development and did not identify cell types. Here we present scGESTALT  which combines the lineage recording capabilities of GESTALT with cell type identification by single cell RNA sequencing. The method relies on an inducible system that enables barcodes to be edited at multiple time points  capturing lineage information from later stages of development. Sequencing of 60 000 transcriptomes from the juvenile zebrafish brain identified >100 cell types and marker genes. Using these data  we generate lineage trees with hundreds of branches that help uncover restrictions at the level of cell types  brain regions  and gene expression cascades during differentiation. scGESTALT can be applied to other multicellular organisms to simultaneously characterize molecular identities and lineage histories of thousands of cells during development and disease. Overall design: inDrops libraries of single cell transcriptomes and scGESTALT barcodes  and genomic DNA GESTALT libraries", null, "pubmed:29608178", null, "DEW138 f5", "GSM2813964", null, "source name:zebrafish brain|tissue:brain|developmental stage:23 25dpf", "DEW138 f5", "Single cell RNA Sequencing data FASTQ files were processed using the inDrops.py bioinformatics pipeline available at https://github.com/indrops/indrops. Transcriptome libraries were mapped to a zebrafish reference built from a custom GTF file and the zebrafish GRCz10 release 86 genome assembly. Bowtie version1.1.1 was used with parameter \u2013e 200; UMI quantification was used with parameter \u2013u 2 counts were ignored from UMIs split between more than 2 genes. genomic DNA GESTALT and scGESTALT libraries were processed using a custom pipeline available at https://github.com/shendurelab/Cas9FateMapping Genome build: GRCz10 CSV files for transcriptome data were generated using the inDrops pipeline. Each column in the CSV files contains a cell identifier and each row contains expression values for genes. Txt files for genomic DNA GESTALT libraries *allReadCounts contain lineage barcode sequences HMID column for each cell and their proportion in the sequenced libraries. scGESTALT data *GestMaster.txt contains the inDrops cell identifiers CellBarcode and BarcodeKey that were used to match barcodes to transcriptomes. They also contain lineage barcode sequences HMID column for each cell with a corresponding inDrops single cell gene expression profile  as well as the the t SNE cluster membership number ClusterIdent column Txt files ending in *stats.txt contain information about the each individually captured UMI or cell per sample. The barcode sequence aligned to a reference unedited sequence mergedRead column  mutations at each target site target[X] columns and the edited sequences at each target site sequence[X] columns were used for downstream analysis. inDropsExpMatrix noQ txt file is the gene expression matrix for the full dataset. Columns are individual cells from different batches of whole brains f1   f2   f3   f4   f5   f6  or brain regions fore   mid   hind . fall.inDrops.Robj is the processed Seurat R object  which can be loaded into R and explored.", "zebrafish brain", null, "Single cell suspensions were processed through inDrops to generate single cell cDNA libraries. cDNAs were in vitro transcribed and fragmented. The three prime fragments were reverse transcribed and prepared for sequencing Libraries were prepared as described in Zilionis et al.  2017  Nature Protocols PMID = 27929523", null, "tissue:brain|developmental stage:23 25dpf", "GSM2813964", "GSM2813964: DEW138 f5; Danio rerio; RNA Seq", "GSM2813964", null, "1", "Single cell suspensions were processed through inDrops to generate single cell cDNA libraries. cDNAs were in vitro transcribed and fragmented. The three prime fragments were reverse transcribed and prepared for sequencing Libraries were prepared as described in Zilionis et al.  2017  Nature Protocols PMID = 27929523", "GEO Accession:GSM2813964", "RNA-Seq", "TRANSCRIPTOMIC", "cDNA", "SINGLE", "ILLUMINA", "NextSeq 500", null, "SRP120009", null, "loader:fastq load.py|options:  appendBCtoName", "DEW138_Lane1.sorted.fastq.gz", "fastq", 2692846427.0, 46459407.0, "GSM2813964 r1", "0:57.96", "A:742846652;C:514118077;G:553047856;T:882827383;N:6459", 57, null, null, null, 742846652, 514118077, 553047856, 882827383, 6459, "SRX3287394", "SRS2596868", "SRA619743", "GEO", "Harvard University", 1, 0.89246, null, 0.23163, null, 0.78228, null, 0.51357, null, 61, null, "B", null, "usable mapping rate", "illumina", "nextseq", "unknown", "cdna_unspecified", "unknown", "sc", "single_cell_droplet", "indrops", null, "United States", "2017-10-16", "Larval", "Larval", "Brain", "Nervous System"], [43884, "SRR6176709", "SRX3287394", "SRS2596868", "SRP120009", "PRJNA414416", "Simultaneous single cell profiling of lineages and cell types in the vertebrate brain", "GSE105010", "Other", "The lineage relationships among the hundreds of cell types generated during development are difficult to reconstruct. A recent method  GESTALT  used CRISPR\u2013Cas9 barcode editing for large scale lineage tracing  but was restricted to early development and did not identify cell types. Here we present scGESTALT  which combines the lineage recording capabilities of GESTALT with cell type identification by single cell RNA sequencing. The method relies on an inducible system that enables barcodes to be edited at multiple time points  capturing lineage information from later stages of development. Sequencing of 60 000 transcriptomes from the juvenile zebrafish brain identified >100 cell types and marker genes. Using these data  we generate lineage trees with hundreds of branches that help uncover restrictions at the level of cell types  brain regions  and gene expression cascades during differentiation. scGESTALT can be applied to other multicellular organisms to simultaneously characterize molecular identities and lineage histories of thousands of cells during development and disease. Overall design: inDrops libraries of single cell transcriptomes and scGESTALT barcodes  and genomic DNA GESTALT libraries", null, "pubmed:29608178", null, "DEW138 f5", "GSM2813964", null, "source name:zebrafish brain|tissue:brain|developmental stage:23 25dpf", "DEW138 f5", "Single cell RNA Sequencing data FASTQ files were processed using the inDrops.py bioinformatics pipeline available at https://github.com/indrops/indrops. Transcriptome libraries were mapped to a zebrafish reference built from a custom GTF file and the zebrafish GRCz10 release 86 genome assembly. Bowtie version1.1.1 was used with parameter \u2013e 200; UMI quantification was used with parameter \u2013u 2 counts were ignored from UMIs split between more than 2 genes. genomic DNA GESTALT and scGESTALT libraries were processed using a custom pipeline available at https://github.com/shendurelab/Cas9FateMapping Genome build: GRCz10 CSV files for transcriptome data were generated using the inDrops pipeline. Each column in the CSV files contains a cell identifier and each row contains expression values for genes. Txt files for genomic DNA GESTALT libraries *allReadCounts contain lineage barcode sequences HMID column for each cell and their proportion in the sequenced libraries. scGESTALT data *GestMaster.txt contains the inDrops cell identifiers CellBarcode and BarcodeKey that were used to match barcodes to transcriptomes. They also contain lineage barcode sequences HMID column for each cell with a corresponding inDrops single cell gene expression profile  as well as the the t SNE cluster membership number ClusterIdent column Txt files ending in *stats.txt contain information about the each individually captured UMI or cell per sample. The barcode sequence aligned to a reference unedited sequence mergedRead column  mutations at each target site target[X] columns and the edited sequences at each target site sequence[X] columns were used for downstream analysis. inDropsExpMatrix noQ txt file is the gene expression matrix for the full dataset. Columns are individual cells from different batches of whole brains f1   f2   f3   f4   f5   f6  or brain regions fore   mid   hind . fall.inDrops.Robj is the processed Seurat R object  which can be loaded into R and explored.", "zebrafish brain", null, "Single cell suspensions were processed through inDrops to generate single cell cDNA libraries. cDNAs were in vitro transcribed and fragmented. The three prime fragments were reverse transcribed and prepared for sequencing Libraries were prepared as described in Zilionis et al.  2017  Nature Protocols PMID = 27929523", null, "tissue:brain|developmental stage:23 25dpf", "GSM2813964", "GSM2813964: DEW138 f5; Danio rerio; RNA Seq", "GSM2813964", null, "1", "Single cell suspensions were processed through inDrops to generate single cell cDNA libraries. cDNAs were in vitro transcribed and fragmented. The three prime fragments were reverse transcribed and prepared for sequencing Libraries were prepared as described in Zilionis et al.  2017  Nature Protocols PMID = 27929523", "GEO Accession:GSM2813964", "RNA-Seq", "TRANSCRIPTOMIC", "cDNA", "SINGLE", "ILLUMINA", "NextSeq 500", null, "SRP120009", null, "loader:fastq load.py|options:  appendBCtoName", "DEW138_Lane2.sorted.fastq.gz", "fastq", 3462981407.0, 58755893.0, "GSM2813964 r2", "0:58.94", "A:956805112;C:660226337;G:715337930;T:1130599701;N:12327", 58, null, null, null, 956805112, 660226337, 715337930, 1130599701, 12327, "SRX3287394", "SRS2596868", "SRA619743", "GEO", "Harvard University", 1, 0.89374, null, 0.22971, null, 0.78214, null, 0.51135, null, 60, null, "B", null, "usable mapping rate", "illumina", "nextseq", "unknown", "cdna_unspecified", "unknown", "sc", "single_cell_droplet", "indrops", null, "United States", "2017-10-16", "Larval", "Larval", "Brain", "Nervous System"], [43885, "SRR6176706", "SRX3287393", "SRS2596867", "SRP120009", "PRJNA414416", "Simultaneous single cell profiling of lineages and cell types in the vertebrate brain", "GSE105010", "Other", "The lineage relationships among the hundreds of cell types generated during development are difficult to reconstruct. A recent method  GESTALT  used CRISPR\u2013Cas9 barcode editing for large scale lineage tracing  but was restricted to early development and did not identify cell types. Here we present scGESTALT  which combines the lineage recording capabilities of GESTALT with cell type identification by single cell RNA sequencing. The method relies on an inducible system that enables barcodes to be edited at multiple time points  capturing lineage information from later stages of development. Sequencing of 60 000 transcriptomes from the juvenile zebrafish brain identified >100 cell types and marker genes. Using these data  we generate lineage trees with hundreds of branches that help uncover restrictions at the level of cell types  brain regions  and gene expression cascades during differentiation. scGESTALT can be applied to other multicellular organisms to simultaneously characterize molecular identities and lineage histories of thousands of cells during development and disease. Overall design: inDrops libraries of single cell transcriptomes and scGESTALT barcodes  and genomic DNA GESTALT libraries", null, "pubmed:29608178", null, "DEW137 f5", "GSM2813963", null, "source name:zebrafish brain|tissue:brain|developmental stage:23 25dpf", "DEW137 f5", "Single cell RNA Sequencing data FASTQ files were processed using the inDrops.py bioinformatics pipeline available at https://github.com/indrops/indrops. Transcriptome libraries were mapped to a zebrafish reference built from a custom GTF file and the zebrafish GRCz10 release 86 genome assembly. Bowtie version1.1.1 was used with parameter \u2013e 200; UMI quantification was used with parameter \u2013u 2 counts were ignored from UMIs split between more than 2 genes. genomic DNA GESTALT and scGESTALT libraries were processed using a custom pipeline available at https://github.com/shendurelab/Cas9FateMapping Genome build: GRCz10 CSV files for transcriptome data were generated using the inDrops pipeline. Each column in the CSV files contains a cell identifier and each row contains expression values for genes. Txt files for genomic DNA GESTALT libraries *allReadCounts contain lineage barcode sequences HMID column for each cell and their proportion in the sequenced libraries. scGESTALT data *GestMaster.txt contains the inDrops cell identifiers CellBarcode and BarcodeKey that were used to match barcodes to transcriptomes. They also contain lineage barcode sequences HMID column for each cell with a corresponding inDrops single cell gene expression profile  as well as the the t SNE cluster membership number ClusterIdent column Txt files ending in *stats.txt contain information about the each individually captured UMI or cell per sample. The barcode sequence aligned to a reference unedited sequence mergedRead column  mutations at each target site target[X] columns and the edited sequences at each target site sequence[X] columns were used for downstream analysis. inDropsExpMatrix noQ txt file is the gene expression matrix for the full dataset. Columns are individual cells from different batches of whole brains f1   f2   f3   f4   f5   f6  or brain regions fore   mid   hind . fall.inDrops.Robj is the processed Seurat R object  which can be loaded into R and explored.", "zebrafish brain", null, "Single cell suspensions were processed through inDrops to generate single cell cDNA libraries. cDNAs were in vitro transcribed and fragmented. The three prime fragments were reverse transcribed and prepared for sequencing Libraries were prepared as described in Zilionis et al.  2017  Nature Protocols PMID = 27929523", null, "tissue:brain|developmental stage:23 25dpf", "GSM2813963", "GSM2813963: DEW137 f5; Danio rerio; RNA Seq", "GSM2813963", null, "1", "Single cell suspensions were processed through inDrops to generate single cell cDNA libraries. cDNAs were in vitro transcribed and fragmented. The three prime fragments were reverse transcribed and prepared for sequencing Libraries were prepared as described in Zilionis et al.  2017  Nature Protocols PMID = 27929523", "GEO Accession:GSM2813963", "RNA-Seq", "TRANSCRIPTOMIC", "cDNA", "SINGLE", "ILLUMINA", "NextSeq 500", null, "SRP120009", null, "loader:fastq load.py|options:  appendBCtoName", "DEW137_Lane1.sorted.fastq.gz", "fastq", 3250376466.0, 56202961.0, "GSM2813963 r1", "0:57.83", "A:899541071;C:615022999;G:668853277;T:1066951383;N:7736", 57, null, null, null, 899541071, 615022999, 668853277, 1066951383, 7736, "SRX3287393", "SRS2596867", "SRA619743", "GEO", "Harvard University", 1, 0.8922, null, 0.22976, null, 0.7823, null, 0.49602, null, 61, null, "B", null, "usable mapping rate", "illumina", "nextseq", "unknown", "cdna_unspecified", "unknown", "sc", "single_cell_droplet", "indrops", null, "United States", "2017-10-16", "Larval", "Larval", "Brain", "Nervous System"], [43886, "SRR6176707", "SRX3287393", "SRS2596867", "SRP120009", "PRJNA414416", "Simultaneous single cell profiling of lineages and cell types in the vertebrate brain", "GSE105010", "Other", "The lineage relationships among the hundreds of cell types generated during development are difficult to reconstruct. A recent method  GESTALT  used CRISPR\u2013Cas9 barcode editing for large scale lineage tracing  but was restricted to early development and did not identify cell types. Here we present scGESTALT  which combines the lineage recording capabilities of GESTALT with cell type identification by single cell RNA sequencing. The method relies on an inducible system that enables barcodes to be edited at multiple time points  capturing lineage information from later stages of development. Sequencing of 60 000 transcriptomes from the juvenile zebrafish brain identified >100 cell types and marker genes. Using these data  we generate lineage trees with hundreds of branches that help uncover restrictions at the level of cell types  brain regions  and gene expression cascades during differentiation. scGESTALT can be applied to other multicellular organisms to simultaneously characterize molecular identities and lineage histories of thousands of cells during development and disease. Overall design: inDrops libraries of single cell transcriptomes and scGESTALT barcodes  and genomic DNA GESTALT libraries", null, "pubmed:29608178", null, "DEW137 f5", "GSM2813963", null, "source name:zebrafish brain|tissue:brain|developmental stage:23 25dpf", "DEW137 f5", "Single cell RNA Sequencing data FASTQ files were processed using the inDrops.py bioinformatics pipeline available at https://github.com/indrops/indrops. Transcriptome libraries were mapped to a zebrafish reference built from a custom GTF file and the zebrafish GRCz10 release 86 genome assembly. Bowtie version1.1.1 was used with parameter \u2013e 200; UMI quantification was used with parameter \u2013u 2 counts were ignored from UMIs split between more than 2 genes. genomic DNA GESTALT and scGESTALT libraries were processed using a custom pipeline available at https://github.com/shendurelab/Cas9FateMapping Genome build: GRCz10 CSV files for transcriptome data were generated using the inDrops pipeline. Each column in the CSV files contains a cell identifier and each row contains expression values for genes. Txt files for genomic DNA GESTALT libraries *allReadCounts contain lineage barcode sequences HMID column for each cell and their proportion in the sequenced libraries. scGESTALT data *GestMaster.txt contains the inDrops cell identifiers CellBarcode and BarcodeKey that were used to match barcodes to transcriptomes. They also contain lineage barcode sequences HMID column for each cell with a corresponding inDrops single cell gene expression profile  as well as the the t SNE cluster membership number ClusterIdent column Txt files ending in *stats.txt contain information about the each individually captured UMI or cell per sample. The barcode sequence aligned to a reference unedited sequence mergedRead column  mutations at each target site target[X] columns and the edited sequences at each target site sequence[X] columns were used for downstream analysis. inDropsExpMatrix noQ txt file is the gene expression matrix for the full dataset. Columns are individual cells from different batches of whole brains f1   f2   f3   f4   f5   f6  or brain regions fore   mid   hind . fall.inDrops.Robj is the processed Seurat R object  which can be loaded into R and explored.", "zebrafish brain", null, "Single cell suspensions were processed through inDrops to generate single cell cDNA libraries. cDNAs were in vitro transcribed and fragmented. The three prime fragments were reverse transcribed and prepared for sequencing Libraries were prepared as described in Zilionis et al.  2017  Nature Protocols PMID = 27929523", null, "tissue:brain|developmental stage:23 25dpf", "GSM2813963", "GSM2813963: DEW137 f5; Danio rerio; RNA Seq", "GSM2813963", null, "1", "Single cell suspensions were processed through inDrops to generate single cell cDNA libraries. cDNAs were in vitro transcribed and fragmented. The three prime fragments were reverse transcribed and prepared for sequencing Libraries were prepared as described in Zilionis et al.  2017  Nature Protocols PMID = 27929523", "GEO Accession:GSM2813963", "RNA-Seq", "TRANSCRIPTOMIC", "cDNA", "SINGLE", "ILLUMINA", "NextSeq 500", null, "SRP120009", null, "loader:fastq load.py|options:  appendBCtoName", "DEW137_Lane2.sorted.fastq.gz", "fastq", 4147022050.0, 70410733.0, "GSM2813963 r2", "0:58.90", "A:1149440282;C:783582822;G:858546692;T:1355437992;N:14262", 58, null, null, null, 1149440282, 783582822, 858546692, 1355437992, 14262, "SRX3287393", "SRS2596867", "SRA619743", "GEO", "Harvard University", 1, 0.89401, null, 0.2275, null, 0.78046, null, 0.49962, null, 61, null, "B", null, "usable mapping rate", "illumina", "nextseq", "unknown", "cdna_unspecified", "unknown", "sc", "single_cell_droplet", "indrops", null, "United States", "2017-10-16", "Larval", "Larval", "Brain", "Nervous System"], [43887, "SRR6176704", "SRX3287392", "SRS2596866", "SRP120009", "PRJNA414416", "Simultaneous single cell profiling of lineages and cell types in the vertebrate brain", "GSE105010", "Other", "The lineage relationships among the hundreds of cell types generated during development are difficult to reconstruct. A recent method  GESTALT  used CRISPR\u2013Cas9 barcode editing for large scale lineage tracing  but was restricted to early development and did not identify cell types. Here we present scGESTALT  which combines the lineage recording capabilities of GESTALT with cell type identification by single cell RNA sequencing. The method relies on an inducible system that enables barcodes to be edited at multiple time points  capturing lineage information from later stages of development. Sequencing of 60 000 transcriptomes from the juvenile zebrafish brain identified >100 cell types and marker genes. Using these data  we generate lineage trees with hundreds of branches that help uncover restrictions at the level of cell types  brain regions  and gene expression cascades during differentiation. scGESTALT can be applied to other multicellular organisms to simultaneously characterize molecular identities and lineage histories of thousands of cells during development and disease. Overall design: inDrops libraries of single cell transcriptomes and scGESTALT barcodes  and genomic DNA GESTALT libraries", null, "pubmed:29608178", null, "DEW136 f5", "GSM2813962", null, "source name:zebrafish brain|tissue:brain|developmental stage:23 25dpf", "DEW136 f5", "Single cell RNA Sequencing data FASTQ files were processed using the inDrops.py bioinformatics pipeline available at https://github.com/indrops/indrops. Transcriptome libraries were mapped to a zebrafish reference built from a custom GTF file and the zebrafish GRCz10 release 86 genome assembly. Bowtie version1.1.1 was used with parameter \u2013e 200; UMI quantification was used with parameter \u2013u 2 counts were ignored from UMIs split between more than 2 genes. genomic DNA GESTALT and scGESTALT libraries were processed using a custom pipeline available at https://github.com/shendurelab/Cas9FateMapping Genome build: GRCz10 CSV files for transcriptome data were generated using the inDrops pipeline. Each column in the CSV files contains a cell identifier and each row contains expression values for genes. Txt files for genomic DNA GESTALT libraries *allReadCounts contain lineage barcode sequences HMID column for each cell and their proportion in the sequenced libraries. scGESTALT data *GestMaster.txt contains the inDrops cell identifiers CellBarcode and BarcodeKey that were used to match barcodes to transcriptomes. They also contain lineage barcode sequences HMID column for each cell with a corresponding inDrops single cell gene expression profile  as well as the the t SNE cluster membership number ClusterIdent column Txt files ending in *stats.txt contain information about the each individually captured UMI or cell per sample. The barcode sequence aligned to a reference unedited sequence mergedRead column  mutations at each target site target[X] columns and the edited sequences at each target site sequence[X] columns were used for downstream analysis. inDropsExpMatrix noQ txt file is the gene expression matrix for the full dataset. Columns are individual cells from different batches of whole brains f1   f2   f3   f4   f5   f6  or brain regions fore   mid   hind . fall.inDrops.Robj is the processed Seurat R object  which can be loaded into R and explored.", "zebrafish brain", null, "Single cell suspensions were processed through inDrops to generate single cell cDNA libraries. cDNAs were in vitro transcribed and fragmented. The three prime fragments were reverse transcribed and prepared for sequencing Libraries were prepared as described in Zilionis et al.  2017  Nature Protocols PMID = 27929523", null, "tissue:brain|developmental stage:23 25dpf", "GSM2813962", "GSM2813962: DEW136 f5; Danio rerio; RNA Seq", "GSM2813962", null, "1", "Single cell suspensions were processed through inDrops to generate single cell cDNA libraries. cDNAs were in vitro transcribed and fragmented. The three prime fragments were reverse transcribed and prepared for sequencing Libraries were prepared as described in Zilionis et al.  2017  Nature Protocols PMID = 27929523", "GEO Accession:GSM2813962", "RNA-Seq", "TRANSCRIPTOMIC", "cDNA", "SINGLE", "ILLUMINA", "NextSeq 500", null, "SRP120009", null, "loader:fastq load.py|options:  appendBCtoName", "DEW136_Lane1.sorted.fastq.gz", "fastq", 2681039910.0, 46262097.0, "GSM2813962 r1", "0:57.95", "A:742027813;C:505890516;G:549783741;T:883331533;N:6307", 57, null, null, null, 742027813, 505890516, 549783741, 883331533, 6307, "SRX3287392", "SRS2596866", "SRA619743", "GEO", "Harvard University", 1, 0.89063, null, 0.23313, null, 0.78466, null, 0.50689, null, 16, null, "B", null, "usable mapping rate", "illumina", "nextseq", "unknown", "cdna_unspecified", "unknown", "sc", "single_cell_droplet", "indrops", null, "United States", "2017-10-16", "Larval", "Larval", "Brain", "Nervous System"], [43888, "SRR6176705", "SRX3287392", "SRS2596866", "SRP120009", "PRJNA414416", "Simultaneous single cell profiling of lineages and cell types in the vertebrate brain", "GSE105010", "Other", "The lineage relationships among the hundreds of cell types generated during development are difficult to reconstruct. A recent method  GESTALT  used CRISPR\u2013Cas9 barcode editing for large scale lineage tracing  but was restricted to early development and did not identify cell types. Here we present scGESTALT  which combines the lineage recording capabilities of GESTALT with cell type identification by single cell RNA sequencing. The method relies on an inducible system that enables barcodes to be edited at multiple time points  capturing lineage information from later stages of development. Sequencing of 60 000 transcriptomes from the juvenile zebrafish brain identified >100 cell types and marker genes. Using these data  we generate lineage trees with hundreds of branches that help uncover restrictions at the level of cell types  brain regions  and gene expression cascades during differentiation. scGESTALT can be applied to other multicellular organisms to simultaneously characterize molecular identities and lineage histories of thousands of cells during development and disease. Overall design: inDrops libraries of single cell transcriptomes and scGESTALT barcodes  and genomic DNA GESTALT libraries", null, "pubmed:29608178", null, "DEW136 f5", "GSM2813962", null, "source name:zebrafish brain|tissue:brain|developmental stage:23 25dpf", "DEW136 f5", "Single cell RNA Sequencing data FASTQ files were processed using the inDrops.py bioinformatics pipeline available at https://github.com/indrops/indrops. Transcriptome libraries were mapped to a zebrafish reference built from a custom GTF file and the zebrafish GRCz10 release 86 genome assembly. Bowtie version1.1.1 was used with parameter \u2013e 200; UMI quantification was used with parameter \u2013u 2 counts were ignored from UMIs split between more than 2 genes. genomic DNA GESTALT and scGESTALT libraries were processed using a custom pipeline available at https://github.com/shendurelab/Cas9FateMapping Genome build: GRCz10 CSV files for transcriptome data were generated using the inDrops pipeline. Each column in the CSV files contains a cell identifier and each row contains expression values for genes. Txt files for genomic DNA GESTALT libraries *allReadCounts contain lineage barcode sequences HMID column for each cell and their proportion in the sequenced libraries. scGESTALT data *GestMaster.txt contains the inDrops cell identifiers CellBarcode and BarcodeKey that were used to match barcodes to transcriptomes. They also contain lineage barcode sequences HMID column for each cell with a corresponding inDrops single cell gene expression profile  as well as the the t SNE cluster membership number ClusterIdent column Txt files ending in *stats.txt contain information about the each individually captured UMI or cell per sample. The barcode sequence aligned to a reference unedited sequence mergedRead column  mutations at each target site target[X] columns and the edited sequences at each target site sequence[X] columns were used for downstream analysis. inDropsExpMatrix noQ txt file is the gene expression matrix for the full dataset. Columns are individual cells from different batches of whole brains f1   f2   f3   f4   f5   f6  or brain regions fore   mid   hind . fall.inDrops.Robj is the processed Seurat R object  which can be loaded into R and explored.", "zebrafish brain", null, "Single cell suspensions were processed through inDrops to generate single cell cDNA libraries. cDNAs were in vitro transcribed and fragmented. The three prime fragments were reverse transcribed and prepared for sequencing Libraries were prepared as described in Zilionis et al.  2017  Nature Protocols PMID = 27929523", null, "tissue:brain|developmental stage:23 25dpf", "GSM2813962", "GSM2813962: DEW136 f5; Danio rerio; RNA Seq", "GSM2813962", null, "1", "Single cell suspensions were processed through inDrops to generate single cell cDNA libraries. cDNAs were in vitro transcribed and fragmented. The three prime fragments were reverse transcribed and prepared for sequencing Libraries were prepared as described in Zilionis et al.  2017  Nature Protocols PMID = 27929523", "GEO Accession:GSM2813962", "RNA-Seq", "TRANSCRIPTOMIC", "cDNA", "SINGLE", "ILLUMINA", "NextSeq 500", null, "SRP120009", null, "loader:fastq load.py|options:  appendBCtoName", "DEW136_Lane2.sorted.fastq.gz", "fastq", 3452108683.0, 58556706.0, "GSM2813962 r2", "0:58.95", "A:956852778;C:650048613;G:712593784;T:1132601522;N:11986", 58, null, null, null, 956852778, 650048613, 712593784, 1132601522, 11986, "SRX3287392", "SRS2596866", "SRA619743", "GEO", "Harvard University", 1, 0.89419, null, 0.23225, null, 0.77981, null, 0.49604, null, 55, null, "B", null, "usable mapping rate", "illumina", "nextseq", "unknown", "cdna_unspecified", "unknown", "sc", "single_cell_droplet", "indrops", null, "United States", "2017-10-16", "Larval", "Larval", "Brain", "Nervous System"], [43889, "SRR6176702", "SRX3287391", "SRS2596865", "SRP120009", "PRJNA414416", "Simultaneous single cell profiling of lineages and cell types in the vertebrate brain", "GSE105010", "Other", "The lineage relationships among the hundreds of cell types generated during development are difficult to reconstruct. A recent method  GESTALT  used CRISPR\u2013Cas9 barcode editing for large scale lineage tracing  but was restricted to early development and did not identify cell types. Here we present scGESTALT  which combines the lineage recording capabilities of GESTALT with cell type identification by single cell RNA sequencing. The method relies on an inducible system that enables barcodes to be edited at multiple time points  capturing lineage information from later stages of development. Sequencing of 60 000 transcriptomes from the juvenile zebrafish brain identified >100 cell types and marker genes. Using these data  we generate lineage trees with hundreds of branches that help uncover restrictions at the level of cell types  brain regions  and gene expression cascades during differentiation. scGESTALT can be applied to other multicellular organisms to simultaneously characterize molecular identities and lineage histories of thousands of cells during development and disease. Overall design: inDrops libraries of single cell transcriptomes and scGESTALT barcodes  and genomic DNA GESTALT libraries", null, "pubmed:29608178", null, "DEW135 f5", "GSM2813961", null, "source name:zebrafish brain|tissue:brain|developmental stage:23 25dpf", "DEW135 f5", "Single cell RNA Sequencing data FASTQ files were processed using the inDrops.py bioinformatics pipeline available at https://github.com/indrops/indrops. Transcriptome libraries were mapped to a zebrafish reference built from a custom GTF file and the zebrafish GRCz10 release 86 genome assembly. Bowtie version1.1.1 was used with parameter \u2013e 200; UMI quantification was used with parameter \u2013u 2 counts were ignored from UMIs split between more than 2 genes. genomic DNA GESTALT and scGESTALT libraries were processed using a custom pipeline available at https://github.com/shendurelab/Cas9FateMapping Genome build: GRCz10 CSV files for transcriptome data were generated using the inDrops pipeline. Each column in the CSV files contains a cell identifier and each row contains expression values for genes. Txt files for genomic DNA GESTALT libraries *allReadCounts contain lineage barcode sequences HMID column for each cell and their proportion in the sequenced libraries. scGESTALT data *GestMaster.txt contains the inDrops cell identifiers CellBarcode and BarcodeKey that were used to match barcodes to transcriptomes. They also contain lineage barcode sequences HMID column for each cell with a corresponding inDrops single cell gene expression profile  as well as the the t SNE cluster membership number ClusterIdent column Txt files ending in *stats.txt contain information about the each individually captured UMI or cell per sample. The barcode sequence aligned to a reference unedited sequence mergedRead column  mutations at each target site target[X] columns and the edited sequences at each target site sequence[X] columns were used for downstream analysis. inDropsExpMatrix noQ txt file is the gene expression matrix for the full dataset. Columns are individual cells from different batches of whole brains f1   f2   f3   f4   f5   f6  or brain regions fore   mid   hind . fall.inDrops.Robj is the processed Seurat R object  which can be loaded into R and explored.", "zebrafish brain", null, "Single cell suspensions were processed through inDrops to generate single cell cDNA libraries. cDNAs were in vitro transcribed and fragmented. The three prime fragments were reverse transcribed and prepared for sequencing Libraries were prepared as described in Zilionis et al.  2017  Nature Protocols PMID = 27929523", null, "tissue:brain|developmental stage:23 25dpf", "GSM2813961", "GSM2813961: DEW135 f5; Danio rerio; RNA Seq", "GSM2813961", null, "1", "Single cell suspensions were processed through inDrops to generate single cell cDNA libraries. cDNAs were in vitro transcribed and fragmented. The three prime fragments were reverse transcribed and prepared for sequencing Libraries were prepared as described in Zilionis et al.  2017  Nature Protocols PMID = 27929523", "GEO Accession:GSM2813961", "RNA-Seq", "TRANSCRIPTOMIC", "cDNA", "SINGLE", "ILLUMINA", "NextSeq 500", null, "SRP120009", null, "loader:fastq load.py|options:  appendBCtoName", "DEW135_Lane1.sorted.fastq.gz", "fastq", 2557513556.0, 44159247.0, "GSM2813961 r1", "0:57.92", "A:709801542;C:479379107;G:526974755;T:841352077;N:6075", 57, null, null, null, 709801542, 479379107, 526974755, 841352077, 6075, "SRX3287391", "SRS2596865", "SRA619743", "GEO", "Harvard University", 1, 0.88899, null, 0.23784, null, 0.78348, null, 0.50227, null, 61, null, "B", null, "usable mapping rate", "illumina", "nextseq", "unknown", "cdna_unspecified", "unknown", "sc", "single_cell_droplet", "indrops", null, "United States", "2017-10-16", "Larval", "Larval", "Brain", "Nervous System"], [43890, "SRR6176703", "SRX3287391", "SRS2596865", "SRP120009", "PRJNA414416", "Simultaneous single cell profiling of lineages and cell types in the vertebrate brain", "GSE105010", "Other", "The lineage relationships among the hundreds of cell types generated during development are difficult to reconstruct. A recent method  GESTALT  used CRISPR\u2013Cas9 barcode editing for large scale lineage tracing  but was restricted to early development and did not identify cell types. Here we present scGESTALT  which combines the lineage recording capabilities of GESTALT with cell type identification by single cell RNA sequencing. The method relies on an inducible system that enables barcodes to be edited at multiple time points  capturing lineage information from later stages of development. Sequencing of 60 000 transcriptomes from the juvenile zebrafish brain identified >100 cell types and marker genes. Using these data  we generate lineage trees with hundreds of branches that help uncover restrictions at the level of cell types  brain regions  and gene expression cascades during differentiation. scGESTALT can be applied to other multicellular organisms to simultaneously characterize molecular identities and lineage histories of thousands of cells during development and disease. Overall design: inDrops libraries of single cell transcriptomes and scGESTALT barcodes  and genomic DNA GESTALT libraries", null, "pubmed:29608178", null, "DEW135 f5", "GSM2813961", null, "source name:zebrafish brain|tissue:brain|developmental stage:23 25dpf", "DEW135 f5", "Single cell RNA Sequencing data FASTQ files were processed using the inDrops.py bioinformatics pipeline available at https://github.com/indrops/indrops. Transcriptome libraries were mapped to a zebrafish reference built from a custom GTF file and the zebrafish GRCz10 release 86 genome assembly. Bowtie version1.1.1 was used with parameter \u2013e 200; UMI quantification was used with parameter \u2013u 2 counts were ignored from UMIs split between more than 2 genes. genomic DNA GESTALT and scGESTALT libraries were processed using a custom pipeline available at https://github.com/shendurelab/Cas9FateMapping Genome build: GRCz10 CSV files for transcriptome data were generated using the inDrops pipeline. Each column in the CSV files contains a cell identifier and each row contains expression values for genes. Txt files for genomic DNA GESTALT libraries *allReadCounts contain lineage barcode sequences HMID column for each cell and their proportion in the sequenced libraries. scGESTALT data *GestMaster.txt contains the inDrops cell identifiers CellBarcode and BarcodeKey that were used to match barcodes to transcriptomes. They also contain lineage barcode sequences HMID column for each cell with a corresponding inDrops single cell gene expression profile  as well as the the t SNE cluster membership number ClusterIdent column Txt files ending in *stats.txt contain information about the each individually captured UMI or cell per sample. The barcode sequence aligned to a reference unedited sequence mergedRead column  mutations at each target site target[X] columns and the edited sequences at each target site sequence[X] columns were used for downstream analysis. inDropsExpMatrix noQ txt file is the gene expression matrix for the full dataset. Columns are individual cells from different batches of whole brains f1   f2   f3   f4   f5   f6  or brain regions fore   mid   hind . fall.inDrops.Robj is the processed Seurat R object  which can be loaded into R and explored.", "zebrafish brain", null, "Single cell suspensions were processed through inDrops to generate single cell cDNA libraries. cDNAs were in vitro transcribed and fragmented. The three prime fragments were reverse transcribed and prepared for sequencing Libraries were prepared as described in Zilionis et al.  2017  Nature Protocols PMID = 27929523", null, "tissue:brain|developmental stage:23 25dpf", "GSM2813961", "GSM2813961: DEW135 f5; Danio rerio; RNA Seq", "GSM2813961", null, "1", "Single cell suspensions were processed through inDrops to generate single cell cDNA libraries. cDNAs were in vitro transcribed and fragmented. The three prime fragments were reverse transcribed and prepared for sequencing Libraries were prepared as described in Zilionis et al.  2017  Nature Protocols PMID = 27929523", "GEO Accession:GSM2813961", "RNA-Seq", "TRANSCRIPTOMIC", "cDNA", "SINGLE", "ILLUMINA", "NextSeq 500", null, "SRP120009", null, "loader:fastq load.py|options:  appendBCtoName", "DEW135_Lane2.sorted.fastq.gz", "fastq", 3290706830.0, 55845160.0, "GSM2813961 r2", "0:58.93", "A:914468451;C:615561913;G:682649039;T:1078015822;N:11605", 58, null, null, null, 914468451, 615561913, 682649039, 1078015822, 11605, "SRX3287391", "SRS2596865", "SRA619743", "GEO", "Harvard University", 1, 0.89195, null, 0.23748, null, 0.78025, null, 0.48835, null, 61, null, "B", null, "usable mapping rate", "illumina", "nextseq", "unknown", "cdna_unspecified", "unknown", "sc", "single_cell_droplet", "indrops", null, "United States", "2017-10-16", "Larval", "Larval", "Brain", "Nervous System"], [43891, "SRR6176700", "SRX3287390", "SRS2596864", "SRP120009", "PRJNA414416", "Simultaneous single cell profiling of lineages and cell types in the vertebrate brain", "GSE105010", "Other", "The lineage relationships among the hundreds of cell types generated during development are difficult to reconstruct. A recent method  GESTALT  used CRISPR\u2013Cas9 barcode editing for large scale lineage tracing  but was restricted to early development and did not identify cell types. Here we present scGESTALT  which combines the lineage recording capabilities of GESTALT with cell type identification by single cell RNA sequencing. The method relies on an inducible system that enables barcodes to be edited at multiple time points  capturing lineage information from later stages of development. Sequencing of 60 000 transcriptomes from the juvenile zebrafish brain identified >100 cell types and marker genes. Using these data  we generate lineage trees with hundreds of branches that help uncover restrictions at the level of cell types  brain regions  and gene expression cascades during differentiation. scGESTALT can be applied to other multicellular organisms to simultaneously characterize molecular identities and lineage histories of thousands of cells during development and disease. Overall design: inDrops libraries of single cell transcriptomes and scGESTALT barcodes  and genomic DNA GESTALT libraries", null, "pubmed:29608178", null, "DEW134 f4", "GSM2813960", null, "source name:zebrafish brain|tissue:brain|developmental stage:23 25dpf", "DEW134 f4", "Single cell RNA Sequencing data FASTQ files were processed using the inDrops.py bioinformatics pipeline available at https://github.com/indrops/indrops. Transcriptome libraries were mapped to a zebrafish reference built from a custom GTF file and the zebrafish GRCz10 release 86 genome assembly. Bowtie version1.1.1 was used with parameter \u2013e 200; UMI quantification was used with parameter \u2013u 2 counts were ignored from UMIs split between more than 2 genes. genomic DNA GESTALT and scGESTALT libraries were processed using a custom pipeline available at https://github.com/shendurelab/Cas9FateMapping Genome build: GRCz10 CSV files for transcriptome data were generated using the inDrops pipeline. Each column in the CSV files contains a cell identifier and each row contains expression values for genes. Txt files for genomic DNA GESTALT libraries *allReadCounts contain lineage barcode sequences HMID column for each cell and their proportion in the sequenced libraries. scGESTALT data *GestMaster.txt contains the inDrops cell identifiers CellBarcode and BarcodeKey that were used to match barcodes to transcriptomes. They also contain lineage barcode sequences HMID column for each cell with a corresponding inDrops single cell gene expression profile  as well as the the t SNE cluster membership number ClusterIdent column Txt files ending in *stats.txt contain information about the each individually captured UMI or cell per sample. The barcode sequence aligned to a reference unedited sequence mergedRead column  mutations at each target site target[X] columns and the edited sequences at each target site sequence[X] columns were used for downstream analysis. inDropsExpMatrix noQ txt file is the gene expression matrix for the full dataset. Columns are individual cells from different batches of whole brains f1   f2   f3   f4   f5   f6  or brain regions fore   mid   hind . fall.inDrops.Robj is the processed Seurat R object  which can be loaded into R and explored.", "zebrafish brain", null, "Single cell suspensions were processed through inDrops to generate single cell cDNA libraries. cDNAs were in vitro transcribed and fragmented. The three prime fragments were reverse transcribed and prepared for sequencing Libraries were prepared as described in Zilionis et al.  2017  Nature Protocols PMID = 27929523", null, "tissue:brain|developmental stage:23 25dpf", "GSM2813960", "GSM2813960: DEW134 f4; Danio rerio; RNA Seq", "GSM2813960", null, "1", "Single cell suspensions were processed through inDrops to generate single cell cDNA libraries. cDNAs were in vitro transcribed and fragmented. The three prime fragments were reverse transcribed and prepared for sequencing Libraries were prepared as described in Zilionis et al.  2017  Nature Protocols PMID = 27929523", "GEO Accession:GSM2813960", "RNA-Seq", "TRANSCRIPTOMIC", "cDNA", "SINGLE", "ILLUMINA", "NextSeq 500", null, "SRP120009", null, "loader:fastq load.py|options:  appendBCtoName", "DEW134_Lane1.sorted.fastq.gz", "fastq", 2032554623.0, 34929201.0, "GSM2813960 r1", "0:58.19", "A:581873993;C:401061575;G:406205462;T:643409873;N:3720", 58, null, null, null, 581873993, 401061575, 406205462, 643409873, 3720, "SRX3287390", "SRS2596864", "SRA619743", "GEO", "Harvard University", 1, 0.89159, null, 0.24077, null, 0.79052, null, 0.59755, null, 61, null, "B", null, "usable mapping rate", "illumina", "nextseq", "unknown", "cdna_unspecified", "unknown", "sc", "single_cell_droplet", "indrops", null, "United States", "2017-10-16", "Larval", "Larval", "Brain", "Nervous System"], [43892, "SRR6176701", "SRX3287390", "SRS2596864", "SRP120009", "PRJNA414416", "Simultaneous single cell profiling of lineages and cell types in the vertebrate brain", "GSE105010", "Other", "The lineage relationships among the hundreds of cell types generated during development are difficult to reconstruct. A recent method  GESTALT  used CRISPR\u2013Cas9 barcode editing for large scale lineage tracing  but was restricted to early development and did not identify cell types. Here we present scGESTALT  which combines the lineage recording capabilities of GESTALT with cell type identification by single cell RNA sequencing. The method relies on an inducible system that enables barcodes to be edited at multiple time points  capturing lineage information from later stages of development. Sequencing of 60 000 transcriptomes from the juvenile zebrafish brain identified >100 cell types and marker genes. Using these data  we generate lineage trees with hundreds of branches that help uncover restrictions at the level of cell types  brain regions  and gene expression cascades during differentiation. scGESTALT can be applied to other multicellular organisms to simultaneously characterize molecular identities and lineage histories of thousands of cells during development and disease. Overall design: inDrops libraries of single cell transcriptomes and scGESTALT barcodes  and genomic DNA GESTALT libraries", null, "pubmed:29608178", null, "DEW134 f4", "GSM2813960", null, "source name:zebrafish brain|tissue:brain|developmental stage:23 25dpf", "DEW134 f4", "Single cell RNA Sequencing data FASTQ files were processed using the inDrops.py bioinformatics pipeline available at https://github.com/indrops/indrops. Transcriptome libraries were mapped to a zebrafish reference built from a custom GTF file and the zebrafish GRCz10 release 86 genome assembly. Bowtie version1.1.1 was used with parameter \u2013e 200; UMI quantification was used with parameter \u2013u 2 counts were ignored from UMIs split between more than 2 genes. genomic DNA GESTALT and scGESTALT libraries were processed using a custom pipeline available at https://github.com/shendurelab/Cas9FateMapping Genome build: GRCz10 CSV files for transcriptome data were generated using the inDrops pipeline. Each column in the CSV files contains a cell identifier and each row contains expression values for genes. Txt files for genomic DNA GESTALT libraries *allReadCounts contain lineage barcode sequences HMID column for each cell and their proportion in the sequenced libraries. scGESTALT data *GestMaster.txt contains the inDrops cell identifiers CellBarcode and BarcodeKey that were used to match barcodes to transcriptomes. They also contain lineage barcode sequences HMID column for each cell with a corresponding inDrops single cell gene expression profile  as well as the the t SNE cluster membership number ClusterIdent column Txt files ending in *stats.txt contain information about the each individually captured UMI or cell per sample. The barcode sequence aligned to a reference unedited sequence mergedRead column  mutations at each target site target[X] columns and the edited sequences at each target site sequence[X] columns were used for downstream analysis. inDropsExpMatrix noQ txt file is the gene expression matrix for the full dataset. Columns are individual cells from different batches of whole brains f1   f2   f3   f4   f5   f6  or brain regions fore   mid   hind . fall.inDrops.Robj is the processed Seurat R object  which can be loaded into R and explored.", "zebrafish brain", null, "Single cell suspensions were processed through inDrops to generate single cell cDNA libraries. cDNAs were in vitro transcribed and fragmented. The three prime fragments were reverse transcribed and prepared for sequencing Libraries were prepared as described in Zilionis et al.  2017  Nature Protocols PMID = 27929523", null, "tissue:brain|developmental stage:23 25dpf", "GSM2813960", "GSM2813960: DEW134 f4; Danio rerio; RNA Seq", "GSM2813960", null, "1", "Single cell suspensions were processed through inDrops to generate single cell cDNA libraries. cDNAs were in vitro transcribed and fragmented. The three prime fragments were reverse transcribed and prepared for sequencing Libraries were prepared as described in Zilionis et al.  2017  Nature Protocols PMID = 27929523", "GEO Accession:GSM2813960", "RNA-Seq", "TRANSCRIPTOMIC", "cDNA", "SINGLE", "ILLUMINA", "NextSeq 500", null, "SRP120009", null, "loader:fastq load.py|options:  appendBCtoName", "DEW134_Lane2.sorted.fastq.gz", "fastq", 2303180024.0, 39120881.0, "GSM2813960 r2", "0:58.87", "A:658586250;C:454192659;G:461779091;T:728613596;N:8428", 58, null, null, null, 658586250, 454192659, 461779091, 728613596, 8428, "SRX3287390", "SRS2596864", "SRA619743", "GEO", "Harvard University", 1, 0.89876, null, 0.24328, null, 0.79233, null, 0.5978, null, 61, null, "B", null, "usable mapping rate", "illumina", "nextseq", "unknown", "cdna_unspecified", "unknown", "sc", "single_cell_droplet", "indrops", null, "United States", "2017-10-16", "Larval", "Larval", "Brain", "Nervous System"], [43893, "SRR6176698", "SRX3287389", "SRS2596863", "SRP120009", "PRJNA414416", "Simultaneous single cell profiling of lineages and cell types in the vertebrate brain", "GSE105010", "Other", "The lineage relationships among the hundreds of cell types generated during development are difficult to reconstruct. A recent method  GESTALT  used CRISPR\u2013Cas9 barcode editing for large scale lineage tracing  but was restricted to early development and did not identify cell types. Here we present scGESTALT  which combines the lineage recording capabilities of GESTALT with cell type identification by single cell RNA sequencing. The method relies on an inducible system that enables barcodes to be edited at multiple time points  capturing lineage information from later stages of development. Sequencing of 60 000 transcriptomes from the juvenile zebrafish brain identified >100 cell types and marker genes. Using these data  we generate lineage trees with hundreds of branches that help uncover restrictions at the level of cell types  brain regions  and gene expression cascades during differentiation. scGESTALT can be applied to other multicellular organisms to simultaneously characterize molecular identities and lineage histories of thousands of cells during development and disease. Overall design: inDrops libraries of single cell transcriptomes and scGESTALT barcodes  and genomic DNA GESTALT libraries", null, "pubmed:29608178", null, "DEW133 f4", "GSM2813959", null, "source name:zebrafish brain|tissue:brain|developmental stage:23 25dpf", "DEW133 f4", "Single cell RNA Sequencing data FASTQ files were processed using the inDrops.py bioinformatics pipeline available at https://github.com/indrops/indrops. Transcriptome libraries were mapped to a zebrafish reference built from a custom GTF file and the zebrafish GRCz10 release 86 genome assembly. Bowtie version1.1.1 was used with parameter \u2013e 200; UMI quantification was used with parameter \u2013u 2 counts were ignored from UMIs split between more than 2 genes. genomic DNA GESTALT and scGESTALT libraries were processed using a custom pipeline available at https://github.com/shendurelab/Cas9FateMapping Genome build: GRCz10 CSV files for transcriptome data were generated using the inDrops pipeline. Each column in the CSV files contains a cell identifier and each row contains expression values for genes. Txt files for genomic DNA GESTALT libraries *allReadCounts contain lineage barcode sequences HMID column for each cell and their proportion in the sequenced libraries. scGESTALT data *GestMaster.txt contains the inDrops cell identifiers CellBarcode and BarcodeKey that were used to match barcodes to transcriptomes. They also contain lineage barcode sequences HMID column for each cell with a corresponding inDrops single cell gene expression profile  as well as the the t SNE cluster membership number ClusterIdent column Txt files ending in *stats.txt contain information about the each individually captured UMI or cell per sample. The barcode sequence aligned to a reference unedited sequence mergedRead column  mutations at each target site target[X] columns and the edited sequences at each target site sequence[X] columns were used for downstream analysis. inDropsExpMatrix noQ txt file is the gene expression matrix for the full dataset. Columns are individual cells from different batches of whole brains f1   f2   f3   f4   f5   f6  or brain regions fore   mid   hind . fall.inDrops.Robj is the processed Seurat R object  which can be loaded into R and explored.", "zebrafish brain", null, "Single cell suspensions were processed through inDrops to generate single cell cDNA libraries. cDNAs were in vitro transcribed and fragmented. The three prime fragments were reverse transcribed and prepared for sequencing Libraries were prepared as described in Zilionis et al.  2017  Nature Protocols PMID = 27929523", null, "tissue:brain|developmental stage:23 25dpf", "GSM2813959", "GSM2813959: DEW133 f4; Danio rerio; RNA Seq", "GSM2813959", null, "1", "Single cell suspensions were processed through inDrops to generate single cell cDNA libraries. cDNAs were in vitro transcribed and fragmented. The three prime fragments were reverse transcribed and prepared for sequencing Libraries were prepared as described in Zilionis et al.  2017  Nature Protocols PMID = 27929523", "GEO Accession:GSM2813959", "RNA-Seq", "TRANSCRIPTOMIC", "cDNA", "SINGLE", "ILLUMINA", "NextSeq 500", null, "SRP120009", null, "loader:fastq load.py|options:  appendBCtoName", "DEW133_Lane1.sorted.fastq.gz", "fastq", 1480262161.0, 25443752.0, "GSM2813959 r1", "0:58.18", "A:422761649;C:294239860;G:298842946;T:464414920;N:2786", 58, null, null, null, 422761649, 294239860, 298842946, 464414920, 2786, "SRX3287389", "SRS2596863", "SRA619743", "GEO", "Harvard University", 1, 0.89359, null, 0.23759, null, 0.79162, null, 0.5992, null, 61, null, "B", null, "usable mapping rate", "illumina", "nextseq", "unknown", "cdna_unspecified", "unknown", "sc", "single_cell_droplet", "indrops", null, "United States", "2017-10-16", "Larval", "Larval", "Brain", "Nervous System"], [43894, "SRR6176699", "SRX3287389", "SRS2596863", "SRP120009", "PRJNA414416", "Simultaneous single cell profiling of lineages and cell types in the vertebrate brain", "GSE105010", "Other", "The lineage relationships among the hundreds of cell types generated during development are difficult to reconstruct. A recent method  GESTALT  used CRISPR\u2013Cas9 barcode editing for large scale lineage tracing  but was restricted to early development and did not identify cell types. Here we present scGESTALT  which combines the lineage recording capabilities of GESTALT with cell type identification by single cell RNA sequencing. The method relies on an inducible system that enables barcodes to be edited at multiple time points  capturing lineage information from later stages of development. Sequencing of 60 000 transcriptomes from the juvenile zebrafish brain identified >100 cell types and marker genes. Using these data  we generate lineage trees with hundreds of branches that help uncover restrictions at the level of cell types  brain regions  and gene expression cascades during differentiation. scGESTALT can be applied to other multicellular organisms to simultaneously characterize molecular identities and lineage histories of thousands of cells during development and disease. Overall design: inDrops libraries of single cell transcriptomes and scGESTALT barcodes  and genomic DNA GESTALT libraries", null, "pubmed:29608178", null, "DEW133 f4", "GSM2813959", null, "source name:zebrafish brain|tissue:brain|developmental stage:23 25dpf", "DEW133 f4", "Single cell RNA Sequencing data FASTQ files were processed using the inDrops.py bioinformatics pipeline available at https://github.com/indrops/indrops. Transcriptome libraries were mapped to a zebrafish reference built from a custom GTF file and the zebrafish GRCz10 release 86 genome assembly. Bowtie version1.1.1 was used with parameter \u2013e 200; UMI quantification was used with parameter \u2013u 2 counts were ignored from UMIs split between more than 2 genes. genomic DNA GESTALT and scGESTALT libraries were processed using a custom pipeline available at https://github.com/shendurelab/Cas9FateMapping Genome build: GRCz10 CSV files for transcriptome data were generated using the inDrops pipeline. Each column in the CSV files contains a cell identifier and each row contains expression values for genes. Txt files for genomic DNA GESTALT libraries *allReadCounts contain lineage barcode sequences HMID column for each cell and their proportion in the sequenced libraries. scGESTALT data *GestMaster.txt contains the inDrops cell identifiers CellBarcode and BarcodeKey that were used to match barcodes to transcriptomes. They also contain lineage barcode sequences HMID column for each cell with a corresponding inDrops single cell gene expression profile  as well as the the t SNE cluster membership number ClusterIdent column Txt files ending in *stats.txt contain information about the each individually captured UMI or cell per sample. The barcode sequence aligned to a reference unedited sequence mergedRead column  mutations at each target site target[X] columns and the edited sequences at each target site sequence[X] columns were used for downstream analysis. inDropsExpMatrix noQ txt file is the gene expression matrix for the full dataset. Columns are individual cells from different batches of whole brains f1   f2   f3   f4   f5   f6  or brain regions fore   mid   hind . fall.inDrops.Robj is the processed Seurat R object  which can be loaded into R and explored.", "zebrafish brain", null, "Single cell suspensions were processed through inDrops to generate single cell cDNA libraries. cDNAs were in vitro transcribed and fragmented. The three prime fragments were reverse transcribed and prepared for sequencing Libraries were prepared as described in Zilionis et al.  2017  Nature Protocols PMID = 27929523", null, "tissue:brain|developmental stage:23 25dpf", "GSM2813959", "GSM2813959: DEW133 f4; Danio rerio; RNA Seq", "GSM2813959", null, "1", "Single cell suspensions were processed through inDrops to generate single cell cDNA libraries. cDNAs were in vitro transcribed and fragmented. The three prime fragments were reverse transcribed and prepared for sequencing Libraries were prepared as described in Zilionis et al.  2017  Nature Protocols PMID = 27929523", "GEO Accession:GSM2813959", "RNA-Seq", "TRANSCRIPTOMIC", "cDNA", "SINGLE", "ILLUMINA", "NextSeq 500", null, "SRP120009", null, "loader:fastq load.py|options:  appendBCtoName", "DEW133_Lane2.sorted.fastq.gz", "fastq", 1657538573.0, 28178142.0, "GSM2813959 r2", "0:58.82", "A:472962184;C:329028443;G:335649862;T:519891706;N:6378", 58, null, null, null, 472962184, 329028443, 335649862, 519891706, 6378, "SRX3287389", "SRS2596863", "SRA619743", "GEO", "Harvard University", 1, 0.89934, null, 0.23901, null, 0.79287, null, 0.59481, null, 61, null, "B", null, "usable mapping rate", "illumina", "nextseq", "unknown", "cdna_unspecified", "unknown", "sc", "single_cell_droplet", "indrops", null, "United States", "2017-10-16", "Larval", "Larval", "Brain", "Nervous System"], [43895, "SRR6176696", "SRX3287388", "SRS2596862", "SRP120009", "PRJNA414416", "Simultaneous single cell profiling of lineages and cell types in the vertebrate brain", "GSE105010", "Other", "The lineage relationships among the hundreds of cell types generated during development are difficult to reconstruct. A recent method  GESTALT  used CRISPR\u2013Cas9 barcode editing for large scale lineage tracing  but was restricted to early development and did not identify cell types. Here we present scGESTALT  which combines the lineage recording capabilities of GESTALT with cell type identification by single cell RNA sequencing. The method relies on an inducible system that enables barcodes to be edited at multiple time points  capturing lineage information from later stages of development. Sequencing of 60 000 transcriptomes from the juvenile zebrafish brain identified >100 cell types and marker genes. Using these data  we generate lineage trees with hundreds of branches that help uncover restrictions at the level of cell types  brain regions  and gene expression cascades during differentiation. scGESTALT can be applied to other multicellular organisms to simultaneously characterize molecular identities and lineage histories of thousands of cells during development and disease. Overall design: inDrops libraries of single cell transcriptomes and scGESTALT barcodes  and genomic DNA GESTALT libraries", null, "pubmed:29608178", null, "DEW132 f4", "GSM2813958", null, "source name:zebrafish brain|tissue:brain|developmental stage:23 25dpf", "DEW132 f4", "Single cell RNA Sequencing data FASTQ files were processed using the inDrops.py bioinformatics pipeline available at https://github.com/indrops/indrops. Transcriptome libraries were mapped to a zebrafish reference built from a custom GTF file and the zebrafish GRCz10 release 86 genome assembly. Bowtie version1.1.1 was used with parameter \u2013e 200; UMI quantification was used with parameter \u2013u 2 counts were ignored from UMIs split between more than 2 genes. genomic DNA GESTALT and scGESTALT libraries were processed using a custom pipeline available at https://github.com/shendurelab/Cas9FateMapping Genome build: GRCz10 CSV files for transcriptome data were generated using the inDrops pipeline. Each column in the CSV files contains a cell identifier and each row contains expression values for genes. Txt files for genomic DNA GESTALT libraries *allReadCounts contain lineage barcode sequences HMID column for each cell and their proportion in the sequenced libraries. scGESTALT data *GestMaster.txt contains the inDrops cell identifiers CellBarcode and BarcodeKey that were used to match barcodes to transcriptomes. They also contain lineage barcode sequences HMID column for each cell with a corresponding inDrops single cell gene expression profile  as well as the the t SNE cluster membership number ClusterIdent column Txt files ending in *stats.txt contain information about the each individually captured UMI or cell per sample. The barcode sequence aligned to a reference unedited sequence mergedRead column  mutations at each target site target[X] columns and the edited sequences at each target site sequence[X] columns were used for downstream analysis. inDropsExpMatrix noQ txt file is the gene expression matrix for the full dataset. Columns are individual cells from different batches of whole brains f1   f2   f3   f4   f5   f6  or brain regions fore   mid   hind . fall.inDrops.Robj is the processed Seurat R object  which can be loaded into R and explored.", "zebrafish brain", null, "Single cell suspensions were processed through inDrops to generate single cell cDNA libraries. cDNAs were in vitro transcribed and fragmented. The three prime fragments were reverse transcribed and prepared for sequencing Libraries were prepared as described in Zilionis et al.  2017  Nature Protocols PMID = 27929523", null, "tissue:brain|developmental stage:23 25dpf", "GSM2813958", "GSM2813958: DEW132 f4; Danio rerio; RNA Seq", "GSM2813958", null, "1", "Single cell suspensions were processed through inDrops to generate single cell cDNA libraries. cDNAs were in vitro transcribed and fragmented. The three prime fragments were reverse transcribed and prepared for sequencing Libraries were prepared as described in Zilionis et al.  2017  Nature Protocols PMID = 27929523", "GEO Accession:GSM2813958", "RNA-Seq", "TRANSCRIPTOMIC", "cDNA", "SINGLE", "ILLUMINA", "NextSeq 500", null, "SRP120009", null, "loader:fastq load.py|options:  appendBCtoName", "DEW132_Lane1.sorted.fastq.gz", "fastq", 3242469018.0, 55736345.0, "GSM2813958 r1", "0:58.18", "A:925435996;C:642008408;G:651239986;T:1023778585;N:6043", 58, null, null, null, 925435996, 642008408, 651239986, 1023778585, 6043, "SRX3287388", "SRS2596862", "SRA619743", "GEO", "Harvard University", 1, 0.89535, null, 0.23494, null, 0.78995, null, 0.57979, null, 61, null, "B", null, "usable mapping rate", "illumina", "nextseq", "unknown", "cdna_unspecified", "unknown", "sc", "single_cell_droplet", "indrops", null, "United States", "2017-10-16", "Larval", "Larval", "Brain", "Nervous System"], [43896, "SRR6176697", "SRX3287388", "SRS2596862", "SRP120009", "PRJNA414416", "Simultaneous single cell profiling of lineages and cell types in the vertebrate brain", "GSE105010", "Other", "The lineage relationships among the hundreds of cell types generated during development are difficult to reconstruct. A recent method  GESTALT  used CRISPR\u2013Cas9 barcode editing for large scale lineage tracing  but was restricted to early development and did not identify cell types. Here we present scGESTALT  which combines the lineage recording capabilities of GESTALT with cell type identification by single cell RNA sequencing. The method relies on an inducible system that enables barcodes to be edited at multiple time points  capturing lineage information from later stages of development. Sequencing of 60 000 transcriptomes from the juvenile zebrafish brain identified >100 cell types and marker genes. Using these data  we generate lineage trees with hundreds of branches that help uncover restrictions at the level of cell types  brain regions  and gene expression cascades during differentiation. scGESTALT can be applied to other multicellular organisms to simultaneously characterize molecular identities and lineage histories of thousands of cells during development and disease. Overall design: inDrops libraries of single cell transcriptomes and scGESTALT barcodes  and genomic DNA GESTALT libraries", null, "pubmed:29608178", null, "DEW132 f4", "GSM2813958", null, "source name:zebrafish brain|tissue:brain|developmental stage:23 25dpf", "DEW132 f4", "Single cell RNA Sequencing data FASTQ files were processed using the inDrops.py bioinformatics pipeline available at https://github.com/indrops/indrops. Transcriptome libraries were mapped to a zebrafish reference built from a custom GTF file and the zebrafish GRCz10 release 86 genome assembly. Bowtie version1.1.1 was used with parameter \u2013e 200; UMI quantification was used with parameter \u2013u 2 counts were ignored from UMIs split between more than 2 genes. genomic DNA GESTALT and scGESTALT libraries were processed using a custom pipeline available at https://github.com/shendurelab/Cas9FateMapping Genome build: GRCz10 CSV files for transcriptome data were generated using the inDrops pipeline. Each column in the CSV files contains a cell identifier and each row contains expression values for genes. Txt files for genomic DNA GESTALT libraries *allReadCounts contain lineage barcode sequences HMID column for each cell and their proportion in the sequenced libraries. scGESTALT data *GestMaster.txt contains the inDrops cell identifiers CellBarcode and BarcodeKey that were used to match barcodes to transcriptomes. They also contain lineage barcode sequences HMID column for each cell with a corresponding inDrops single cell gene expression profile  as well as the the t SNE cluster membership number ClusterIdent column Txt files ending in *stats.txt contain information about the each individually captured UMI or cell per sample. The barcode sequence aligned to a reference unedited sequence mergedRead column  mutations at each target site target[X] columns and the edited sequences at each target site sequence[X] columns were used for downstream analysis. inDropsExpMatrix noQ txt file is the gene expression matrix for the full dataset. Columns are individual cells from different batches of whole brains f1   f2   f3   f4   f5   f6  or brain regions fore   mid   hind . fall.inDrops.Robj is the processed Seurat R object  which can be loaded into R and explored.", "zebrafish brain", null, "Single cell suspensions were processed through inDrops to generate single cell cDNA libraries. cDNAs were in vitro transcribed and fragmented. The three prime fragments were reverse transcribed and prepared for sequencing Libraries were prepared as described in Zilionis et al.  2017  Nature Protocols PMID = 27929523", null, "tissue:brain|developmental stage:23 25dpf", "GSM2813958", "GSM2813958: DEW132 f4; Danio rerio; RNA Seq", "GSM2813958", null, "1", "Single cell suspensions were processed through inDrops to generate single cell cDNA libraries. cDNAs were in vitro transcribed and fragmented. The three prime fragments were reverse transcribed and prepared for sequencing Libraries were prepared as described in Zilionis et al.  2017  Nature Protocols PMID = 27929523", "GEO Accession:GSM2813958", "RNA-Seq", "TRANSCRIPTOMIC", "cDNA", "SINGLE", "ILLUMINA", "NextSeq 500", null, "SRP120009", null, "loader:fastq load.py|options:  appendBCtoName", "DEW132_Lane2.sorted.fastq.gz", "fastq", 3665495067.0, 62275136.0, "GSM2813958 r2", "0:58.86", "A:1044846621;C:725295067;G:738582256;T:1156757558;N:13565", 58, null, null, null, 1044846621, 725295067, 738582256, 1156757558, 13565, "SRX3287388", "SRS2596862", "SRA619743", "GEO", "Harvard University", 1, 0.89933, null, 0.23593, null, 0.78794, null, 0.59183, null, 61, null, "B", null, "usable mapping rate", "illumina", "nextseq", "unknown", "cdna_unspecified", "unknown", "sc", "single_cell_droplet", "indrops", null, "United States", "2017-10-16", "Larval", "Larval", "Brain", "Nervous System"], [43897, "SRR6176694", "SRX3287387", "SRS2596861", "SRP120009", "PRJNA414416", "Simultaneous single cell profiling of lineages and cell types in the vertebrate brain", "GSE105010", "Other", "The lineage relationships among the hundreds of cell types generated during development are difficult to reconstruct. A recent method  GESTALT  used CRISPR\u2013Cas9 barcode editing for large scale lineage tracing  but was restricted to early development and did not identify cell types. Here we present scGESTALT  which combines the lineage recording capabilities of GESTALT with cell type identification by single cell RNA sequencing. The method relies on an inducible system that enables barcodes to be edited at multiple time points  capturing lineage information from later stages of development. Sequencing of 60 000 transcriptomes from the juvenile zebrafish brain identified >100 cell types and marker genes. Using these data  we generate lineage trees with hundreds of branches that help uncover restrictions at the level of cell types  brain regions  and gene expression cascades during differentiation. scGESTALT can be applied to other multicellular organisms to simultaneously characterize molecular identities and lineage histories of thousands of cells during development and disease. Overall design: inDrops libraries of single cell transcriptomes and scGESTALT barcodes  and genomic DNA GESTALT libraries", null, "pubmed:29608178", null, "DEW131 f4", "GSM2813957", null, "source name:zebrafish brain|tissue:brain|developmental stage:23 25dpf", "DEW131 f4", "Single cell RNA Sequencing data FASTQ files were processed using the inDrops.py bioinformatics pipeline available at https://github.com/indrops/indrops. Transcriptome libraries were mapped to a zebrafish reference built from a custom GTF file and the zebrafish GRCz10 release 86 genome assembly. Bowtie version1.1.1 was used with parameter \u2013e 200; UMI quantification was used with parameter \u2013u 2 counts were ignored from UMIs split between more than 2 genes. genomic DNA GESTALT and scGESTALT libraries were processed using a custom pipeline available at https://github.com/shendurelab/Cas9FateMapping Genome build: GRCz10 CSV files for transcriptome data were generated using the inDrops pipeline. Each column in the CSV files contains a cell identifier and each row contains expression values for genes. Txt files for genomic DNA GESTALT libraries *allReadCounts contain lineage barcode sequences HMID column for each cell and their proportion in the sequenced libraries. scGESTALT data *GestMaster.txt contains the inDrops cell identifiers CellBarcode and BarcodeKey that were used to match barcodes to transcriptomes. They also contain lineage barcode sequences HMID column for each cell with a corresponding inDrops single cell gene expression profile  as well as the the t SNE cluster membership number ClusterIdent column Txt files ending in *stats.txt contain information about the each individually captured UMI or cell per sample. The barcode sequence aligned to a reference unedited sequence mergedRead column  mutations at each target site target[X] columns and the edited sequences at each target site sequence[X] columns were used for downstream analysis. inDropsExpMatrix noQ txt file is the gene expression matrix for the full dataset. Columns are individual cells from different batches of whole brains f1   f2   f3   f4   f5   f6  or brain regions fore   mid   hind . fall.inDrops.Robj is the processed Seurat R object  which can be loaded into R and explored.", "zebrafish brain", null, "Single cell suspensions were processed through inDrops to generate single cell cDNA libraries. cDNAs were in vitro transcribed and fragmented. The three prime fragments were reverse transcribed and prepared for sequencing Libraries were prepared as described in Zilionis et al.  2017  Nature Protocols PMID = 27929523", null, "tissue:brain|developmental stage:23 25dpf", "GSM2813957", "GSM2813957: DEW131 f4; Danio rerio; RNA Seq", "GSM2813957", null, "1", "Single cell suspensions were processed through inDrops to generate single cell cDNA libraries. cDNAs were in vitro transcribed and fragmented. The three prime fragments were reverse transcribed and prepared for sequencing Libraries were prepared as described in Zilionis et al.  2017  Nature Protocols PMID = 27929523", "GEO Accession:GSM2813957", "RNA-Seq", "TRANSCRIPTOMIC", "cDNA", "SINGLE", "ILLUMINA", "NextSeq 500", null, "SRP120009", null, "loader:fastq load.py|options:  appendBCtoName", "DEW131_Lane1.sorted.fastq.gz", "fastq", 1429323388.0, 24668535.0, "GSM2813957 r1", "0:57.94", "A:409174223;C:280349860;G:288337035;T:451459797;N:2473", 57, null, null, null, 409174223, 280349860, 288337035, 451459797, 2473, "SRX3287387", "SRS2596861", "SRA619743", "GEO", "Harvard University", 1, 0.89191, null, 0.24025, null, 0.78806, null, 0.59636, null, 61, null, "B", null, "usable mapping rate", "illumina", "nextseq", "unknown", "cdna_unspecified", "unknown", "sc", "single_cell_droplet", "indrops", null, "United States", "2017-10-16", "Larval", "Larval", "Brain", "Nervous System"], [43898, "SRR6176695", "SRX3287387", "SRS2596861", "SRP120009", "PRJNA414416", "Simultaneous single cell profiling of lineages and cell types in the vertebrate brain", "GSE105010", "Other", "The lineage relationships among the hundreds of cell types generated during development are difficult to reconstruct. A recent method  GESTALT  used CRISPR\u2013Cas9 barcode editing for large scale lineage tracing  but was restricted to early development and did not identify cell types. Here we present scGESTALT  which combines the lineage recording capabilities of GESTALT with cell type identification by single cell RNA sequencing. The method relies on an inducible system that enables barcodes to be edited at multiple time points  capturing lineage information from later stages of development. Sequencing of 60 000 transcriptomes from the juvenile zebrafish brain identified >100 cell types and marker genes. Using these data  we generate lineage trees with hundreds of branches that help uncover restrictions at the level of cell types  brain regions  and gene expression cascades during differentiation. scGESTALT can be applied to other multicellular organisms to simultaneously characterize molecular identities and lineage histories of thousands of cells during development and disease. Overall design: inDrops libraries of single cell transcriptomes and scGESTALT barcodes  and genomic DNA GESTALT libraries", null, "pubmed:29608178", null, "DEW131 f4", "GSM2813957", null, "source name:zebrafish brain|tissue:brain|developmental stage:23 25dpf", "DEW131 f4", "Single cell RNA Sequencing data FASTQ files were processed using the inDrops.py bioinformatics pipeline available at https://github.com/indrops/indrops. Transcriptome libraries were mapped to a zebrafish reference built from a custom GTF file and the zebrafish GRCz10 release 86 genome assembly. Bowtie version1.1.1 was used with parameter \u2013e 200; UMI quantification was used with parameter \u2013u 2 counts were ignored from UMIs split between more than 2 genes. genomic DNA GESTALT and scGESTALT libraries were processed using a custom pipeline available at https://github.com/shendurelab/Cas9FateMapping Genome build: GRCz10 CSV files for transcriptome data were generated using the inDrops pipeline. Each column in the CSV files contains a cell identifier and each row contains expression values for genes. Txt files for genomic DNA GESTALT libraries *allReadCounts contain lineage barcode sequences HMID column for each cell and their proportion in the sequenced libraries. scGESTALT data *GestMaster.txt contains the inDrops cell identifiers CellBarcode and BarcodeKey that were used to match barcodes to transcriptomes. They also contain lineage barcode sequences HMID column for each cell with a corresponding inDrops single cell gene expression profile  as well as the the t SNE cluster membership number ClusterIdent column Txt files ending in *stats.txt contain information about the each individually captured UMI or cell per sample. The barcode sequence aligned to a reference unedited sequence mergedRead column  mutations at each target site target[X] columns and the edited sequences at each target site sequence[X] columns were used for downstream analysis. inDropsExpMatrix noQ txt file is the gene expression matrix for the full dataset. Columns are individual cells from different batches of whole brains f1   f2   f3   f4   f5   f6  or brain regions fore   mid   hind . fall.inDrops.Robj is the processed Seurat R object  which can be loaded into R and explored.", "zebrafish brain", null, "Single cell suspensions were processed through inDrops to generate single cell cDNA libraries. cDNAs were in vitro transcribed and fragmented. The three prime fragments were reverse transcribed and prepared for sequencing Libraries were prepared as described in Zilionis et al.  2017  Nature Protocols PMID = 27929523", null, "tissue:brain|developmental stage:23 25dpf", "GSM2813957", "GSM2813957: DEW131 f4; Danio rerio; RNA Seq", "GSM2813957", null, "1", "Single cell suspensions were processed through inDrops to generate single cell cDNA libraries. cDNAs were in vitro transcribed and fragmented. The three prime fragments were reverse transcribed and prepared for sequencing Libraries were prepared as described in Zilionis et al.  2017  Nature Protocols PMID = 27929523", "GEO Accession:GSM2813957", "RNA-Seq", "TRANSCRIPTOMIC", "cDNA", "SINGLE", "ILLUMINA", "NextSeq 500", null, "SRP120009", null, "loader:fastq load.py|options:  appendBCtoName", "DEW131_Lane2.sorted.fastq.gz", "fastq", 1333331762.0, 22830073.0, "GSM2813957 r2", "0:58.40", "A:382643211;C:258086557;G:269989349;T:422608190;N:4455", 58, null, null, null, 382643211, 258086557, 269989349, 422608190, 4455, "SRX3287387", "SRS2596861", "SRA619743", "GEO", "Harvard University", 1, 0.89439, null, 0.24514, null, 0.78918, null, 0.59083, null, 61, null, "B", null, "usable mapping rate", "illumina", "nextseq", "unknown", "cdna_unspecified", "unknown", "sc", "single_cell_droplet", "indrops", null, "United States", "2017-10-16", "Larval", "Larval", "Brain", "Nervous System"], [43899, "SRR6176692", "SRX3287386", "SRS2596860", "SRP120009", "PRJNA414416", "Simultaneous single cell profiling of lineages and cell types in the vertebrate brain", "GSE105010", "Other", "The lineage relationships among the hundreds of cell types generated during development are difficult to reconstruct. A recent method  GESTALT  used CRISPR\u2013Cas9 barcode editing for large scale lineage tracing  but was restricted to early development and did not identify cell types. Here we present scGESTALT  which combines the lineage recording capabilities of GESTALT with cell type identification by single cell RNA sequencing. The method relies on an inducible system that enables barcodes to be edited at multiple time points  capturing lineage information from later stages of development. Sequencing of 60 000 transcriptomes from the juvenile zebrafish brain identified >100 cell types and marker genes. Using these data  we generate lineage trees with hundreds of branches that help uncover restrictions at the level of cell types  brain regions  and gene expression cascades during differentiation. scGESTALT can be applied to other multicellular organisms to simultaneously characterize molecular identities and lineage histories of thousands of cells during development and disease. Overall design: inDrops libraries of single cell transcriptomes and scGESTALT barcodes  and genomic DNA GESTALT libraries", null, "pubmed:29608178", null, "DEW130 f4", "GSM2813956", null, "source name:zebrafish brain|tissue:brain|developmental stage:23 25dpf", "DEW130 f4", "Single cell RNA Sequencing data FASTQ files were processed using the inDrops.py bioinformatics pipeline available at https://github.com/indrops/indrops. Transcriptome libraries were mapped to a zebrafish reference built from a custom GTF file and the zebrafish GRCz10 release 86 genome assembly. Bowtie version1.1.1 was used with parameter \u2013e 200; UMI quantification was used with parameter \u2013u 2 counts were ignored from UMIs split between more than 2 genes. genomic DNA GESTALT and scGESTALT libraries were processed using a custom pipeline available at https://github.com/shendurelab/Cas9FateMapping Genome build: GRCz10 CSV files for transcriptome data were generated using the inDrops pipeline. Each column in the CSV files contains a cell identifier and each row contains expression values for genes. Txt files for genomic DNA GESTALT libraries *allReadCounts contain lineage barcode sequences HMID column for each cell and their proportion in the sequenced libraries. scGESTALT data *GestMaster.txt contains the inDrops cell identifiers CellBarcode and BarcodeKey that were used to match barcodes to transcriptomes. They also contain lineage barcode sequences HMID column for each cell with a corresponding inDrops single cell gene expression profile  as well as the the t SNE cluster membership number ClusterIdent column Txt files ending in *stats.txt contain information about the each individually captured UMI or cell per sample. The barcode sequence aligned to a reference unedited sequence mergedRead column  mutations at each target site target[X] columns and the edited sequences at each target site sequence[X] columns were used for downstream analysis. inDropsExpMatrix noQ txt file is the gene expression matrix for the full dataset. Columns are individual cells from different batches of whole brains f1   f2   f3   f4   f5   f6  or brain regions fore   mid   hind . fall.inDrops.Robj is the processed Seurat R object  which can be loaded into R and explored.", "zebrafish brain", null, "Single cell suspensions were processed through inDrops to generate single cell cDNA libraries. cDNAs were in vitro transcribed and fragmented. The three prime fragments were reverse transcribed and prepared for sequencing Libraries were prepared as described in Zilionis et al.  2017  Nature Protocols PMID = 27929523", null, "tissue:brain|developmental stage:23 25dpf", "GSM2813956", "GSM2813956: DEW130 f4; Danio rerio; RNA Seq", "GSM2813956", null, "1", "Single cell suspensions were processed through inDrops to generate single cell cDNA libraries. cDNAs were in vitro transcribed and fragmented. The three prime fragments were reverse transcribed and prepared for sequencing Libraries were prepared as described in Zilionis et al.  2017  Nature Protocols PMID = 27929523", "GEO Accession:GSM2813956", "RNA-Seq", "TRANSCRIPTOMIC", "cDNA", "SINGLE", "ILLUMINA", "NextSeq 500", null, "SRP120009", null, "loader:fastq load.py|options:  appendBCtoName", "DEW130_Lane1.sorted.fastq.gz", "fastq", 802325365.0, 14453629.0, "GSM2813956 r1", "0:55.51", "A:232201519;C:150952153;G:168770024;T:250400724;N:945", 55, null, null, null, 232201519, 150952153, 168770024, 250400724, 945, "SRX3287386", "SRS2596860", "SRA619743", "GEO", "Harvard University", 1, 0.87297, null, 0.2473, null, 0.79494, null, 0.56771, null, 61, null, "B", null, "usable mapping rate", "illumina", "nextseq", "unknown", "cdna_unspecified", "unknown", "sc", "single_cell_droplet", "indrops", null, "United States", "2017-10-16", "Larval", "Larval", "Brain", "Nervous System"], [43900, "SRR6176693", "SRX3287386", "SRS2596860", "SRP120009", "PRJNA414416", "Simultaneous single cell profiling of lineages and cell types in the vertebrate brain", "GSE105010", "Other", "The lineage relationships among the hundreds of cell types generated during development are difficult to reconstruct. A recent method  GESTALT  used CRISPR\u2013Cas9 barcode editing for large scale lineage tracing  but was restricted to early development and did not identify cell types. Here we present scGESTALT  which combines the lineage recording capabilities of GESTALT with cell type identification by single cell RNA sequencing. The method relies on an inducible system that enables barcodes to be edited at multiple time points  capturing lineage information from later stages of development. Sequencing of 60 000 transcriptomes from the juvenile zebrafish brain identified >100 cell types and marker genes. Using these data  we generate lineage trees with hundreds of branches that help uncover restrictions at the level of cell types  brain regions  and gene expression cascades during differentiation. scGESTALT can be applied to other multicellular organisms to simultaneously characterize molecular identities and lineage histories of thousands of cells during development and disease. Overall design: inDrops libraries of single cell transcriptomes and scGESTALT barcodes  and genomic DNA GESTALT libraries", null, "pubmed:29608178", null, "DEW130 f4", "GSM2813956", null, "source name:zebrafish brain|tissue:brain|developmental stage:23 25dpf", "DEW130 f4", "Single cell RNA Sequencing data FASTQ files were processed using the inDrops.py bioinformatics pipeline available at https://github.com/indrops/indrops. Transcriptome libraries were mapped to a zebrafish reference built from a custom GTF file and the zebrafish GRCz10 release 86 genome assembly. Bowtie version1.1.1 was used with parameter \u2013e 200; UMI quantification was used with parameter \u2013u 2 counts were ignored from UMIs split between more than 2 genes. genomic DNA GESTALT and scGESTALT libraries were processed using a custom pipeline available at https://github.com/shendurelab/Cas9FateMapping Genome build: GRCz10 CSV files for transcriptome data were generated using the inDrops pipeline. Each column in the CSV files contains a cell identifier and each row contains expression values for genes. Txt files for genomic DNA GESTALT libraries *allReadCounts contain lineage barcode sequences HMID column for each cell and their proportion in the sequenced libraries. scGESTALT data *GestMaster.txt contains the inDrops cell identifiers CellBarcode and BarcodeKey that were used to match barcodes to transcriptomes. They also contain lineage barcode sequences HMID column for each cell with a corresponding inDrops single cell gene expression profile  as well as the the t SNE cluster membership number ClusterIdent column Txt files ending in *stats.txt contain information about the each individually captured UMI or cell per sample. The barcode sequence aligned to a reference unedited sequence mergedRead column  mutations at each target site target[X] columns and the edited sequences at each target site sequence[X] columns were used for downstream analysis. inDropsExpMatrix noQ txt file is the gene expression matrix for the full dataset. Columns are individual cells from different batches of whole brains f1   f2   f3   f4   f5   f6  or brain regions fore   mid   hind . fall.inDrops.Robj is the processed Seurat R object  which can be loaded into R and explored.", "zebrafish brain", null, "Single cell suspensions were processed through inDrops to generate single cell cDNA libraries. cDNAs were in vitro transcribed and fragmented. The three prime fragments were reverse transcribed and prepared for sequencing Libraries were prepared as described in Zilionis et al.  2017  Nature Protocols PMID = 27929523", null, "tissue:brain|developmental stage:23 25dpf", "GSM2813956", "GSM2813956: DEW130 f4; Danio rerio; RNA Seq", "GSM2813956", null, "1", "Single cell suspensions were processed through inDrops to generate single cell cDNA libraries. cDNAs were in vitro transcribed and fragmented. The three prime fragments were reverse transcribed and prepared for sequencing Libraries were prepared as described in Zilionis et al.  2017  Nature Protocols PMID = 27929523", "GEO Accession:GSM2813956", "RNA-Seq", "TRANSCRIPTOMIC", "cDNA", "SINGLE", "ILLUMINA", "NextSeq 500", null, "SRP120009", null, "loader:fastq load.py|options:  appendBCtoName", "DEW130_Lane2.sorted.fastq.gz", "fastq", 217595433.0, 3897192.0, "GSM2813956 r2", "0:55.83", "A:63499390;C:38493171;G:47755138;T:67847146;N:588", 55, null, null, null, 63499390, 38493171, 47755138, 67847146, 588, "SRX3287386", "SRS2596860", "SRA619743", "GEO", "Harvard University", 1, 0.8761, null, 0.25865, null, 0.79575, null, 0.55816, null, 61, null, "B", null, "usable mapping rate", "illumina", "nextseq", "unknown", "cdna_unspecified", "unknown", "sc", "single_cell_droplet", "indrops", null, "United States", "2017-10-16", "Larval", "Larval", "Brain", "Nervous System"], [43901, "SRR6176690", "SRX3287385", "SRS2596859", "SRP120009", "PRJNA414416", "Simultaneous single cell profiling of lineages and cell types in the vertebrate brain", "GSE105010", "Other", "The lineage relationships among the hundreds of cell types generated during development are difficult to reconstruct. A recent method  GESTALT  used CRISPR\u2013Cas9 barcode editing for large scale lineage tracing  but was restricted to early development and did not identify cell types. Here we present scGESTALT  which combines the lineage recording capabilities of GESTALT with cell type identification by single cell RNA sequencing. The method relies on an inducible system that enables barcodes to be edited at multiple time points  capturing lineage information from later stages of development. Sequencing of 60 000 transcriptomes from the juvenile zebrafish brain identified >100 cell types and marker genes. Using these data  we generate lineage trees with hundreds of branches that help uncover restrictions at the level of cell types  brain regions  and gene expression cascades during differentiation. scGESTALT can be applied to other multicellular organisms to simultaneously characterize molecular identities and lineage histories of thousands of cells during development and disease. Overall design: inDrops libraries of single cell transcriptomes and scGESTALT barcodes  and genomic DNA GESTALT libraries", null, "pubmed:29608178", null, "DEW129 f4", "GSM2813955", null, "source name:zebrafish brain|tissue:brain|developmental stage:23 25dpf", "DEW129 f4", "Single cell RNA Sequencing data FASTQ files were processed using the inDrops.py bioinformatics pipeline available at https://github.com/indrops/indrops. Transcriptome libraries were mapped to a zebrafish reference built from a custom GTF file and the zebrafish GRCz10 release 86 genome assembly. Bowtie version1.1.1 was used with parameter \u2013e 200; UMI quantification was used with parameter \u2013u 2 counts were ignored from UMIs split between more than 2 genes. genomic DNA GESTALT and scGESTALT libraries were processed using a custom pipeline available at https://github.com/shendurelab/Cas9FateMapping Genome build: GRCz10 CSV files for transcriptome data were generated using the inDrops pipeline. Each column in the CSV files contains a cell identifier and each row contains expression values for genes. Txt files for genomic DNA GESTALT libraries *allReadCounts contain lineage barcode sequences HMID column for each cell and their proportion in the sequenced libraries. scGESTALT data *GestMaster.txt contains the inDrops cell identifiers CellBarcode and BarcodeKey that were used to match barcodes to transcriptomes. They also contain lineage barcode sequences HMID column for each cell with a corresponding inDrops single cell gene expression profile  as well as the the t SNE cluster membership number ClusterIdent column Txt files ending in *stats.txt contain information about the each individually captured UMI or cell per sample. The barcode sequence aligned to a reference unedited sequence mergedRead column  mutations at each target site target[X] columns and the edited sequences at each target site sequence[X] columns were used for downstream analysis. inDropsExpMatrix noQ txt file is the gene expression matrix for the full dataset. Columns are individual cells from different batches of whole brains f1   f2   f3   f4   f5   f6  or brain regions fore   mid   hind . fall.inDrops.Robj is the processed Seurat R object  which can be loaded into R and explored.", "zebrafish brain", null, "Single cell suspensions were processed through inDrops to generate single cell cDNA libraries. cDNAs were in vitro transcribed and fragmented. The three prime fragments were reverse transcribed and prepared for sequencing Libraries were prepared as described in Zilionis et al.  2017  Nature Protocols PMID = 27929523", null, "tissue:brain|developmental stage:23 25dpf", "GSM2813955", "GSM2813955: DEW129 f4; Danio rerio; RNA Seq", "GSM2813955", null, "1", "Single cell suspensions were processed through inDrops to generate single cell cDNA libraries. cDNAs were in vitro transcribed and fragmented. The three prime fragments were reverse transcribed and prepared for sequencing Libraries were prepared as described in Zilionis et al.  2017  Nature Protocols PMID = 27929523", "GEO Accession:GSM2813955", "RNA-Seq", "TRANSCRIPTOMIC", "cDNA", "SINGLE", "ILLUMINA", "NextSeq 500", null, "SRP120009", null, "loader:fastq load.py|options:  appendBCtoName", "DEW129_Lane1.sorted.fastq.gz", "fastq", 1608523647.0, 27633786.0, "GSM2813955 r1", "0:58.21", "A:459523325;C:318570707;G:331034880;T:499391844;N:2891", 58, null, null, null, 459523325, 318570707, 331034880, 499391844, 2891, "SRX3287385", "SRS2596859", "SRA619743", "GEO", "Harvard University", 1, 0.89225, null, 0.23911, null, 0.79444, null, 0.59646, null, 60, null, "B", null, "usable mapping rate", "illumina", "nextseq", "unknown", "cdna_unspecified", "unknown", "sc", "single_cell_droplet", "indrops", null, "United States", "2017-10-16", "Larval", "Larval", "Brain", "Nervous System"], [43902, "SRR6176691", "SRX3287385", "SRS2596859", "SRP120009", "PRJNA414416", "Simultaneous single cell profiling of lineages and cell types in the vertebrate brain", "GSE105010", "Other", "The lineage relationships among the hundreds of cell types generated during development are difficult to reconstruct. A recent method  GESTALT  used CRISPR\u2013Cas9 barcode editing for large scale lineage tracing  but was restricted to early development and did not identify cell types. Here we present scGESTALT  which combines the lineage recording capabilities of GESTALT with cell type identification by single cell RNA sequencing. The method relies on an inducible system that enables barcodes to be edited at multiple time points  capturing lineage information from later stages of development. Sequencing of 60 000 transcriptomes from the juvenile zebrafish brain identified >100 cell types and marker genes. Using these data  we generate lineage trees with hundreds of branches that help uncover restrictions at the level of cell types  brain regions  and gene expression cascades during differentiation. scGESTALT can be applied to other multicellular organisms to simultaneously characterize molecular identities and lineage histories of thousands of cells during development and disease. Overall design: inDrops libraries of single cell transcriptomes and scGESTALT barcodes  and genomic DNA GESTALT libraries", null, "pubmed:29608178", null, "DEW129 f4", "GSM2813955", null, "source name:zebrafish brain|tissue:brain|developmental stage:23 25dpf", "DEW129 f4", "Single cell RNA Sequencing data FASTQ files were processed using the inDrops.py bioinformatics pipeline available at https://github.com/indrops/indrops. Transcriptome libraries were mapped to a zebrafish reference built from a custom GTF file and the zebrafish GRCz10 release 86 genome assembly. Bowtie version1.1.1 was used with parameter \u2013e 200; UMI quantification was used with parameter \u2013u 2 counts were ignored from UMIs split between more than 2 genes. genomic DNA GESTALT and scGESTALT libraries were processed using a custom pipeline available at https://github.com/shendurelab/Cas9FateMapping Genome build: GRCz10 CSV files for transcriptome data were generated using the inDrops pipeline. Each column in the CSV files contains a cell identifier and each row contains expression values for genes. Txt files for genomic DNA GESTALT libraries *allReadCounts contain lineage barcode sequences HMID column for each cell and their proportion in the sequenced libraries. scGESTALT data *GestMaster.txt contains the inDrops cell identifiers CellBarcode and BarcodeKey that were used to match barcodes to transcriptomes. They also contain lineage barcode sequences HMID column for each cell with a corresponding inDrops single cell gene expression profile  as well as the the t SNE cluster membership number ClusterIdent column Txt files ending in *stats.txt contain information about the each individually captured UMI or cell per sample. The barcode sequence aligned to a reference unedited sequence mergedRead column  mutations at each target site target[X] columns and the edited sequences at each target site sequence[X] columns were used for downstream analysis. inDropsExpMatrix noQ txt file is the gene expression matrix for the full dataset. Columns are individual cells from different batches of whole brains f1   f2   f3   f4   f5   f6  or brain regions fore   mid   hind . fall.inDrops.Robj is the processed Seurat R object  which can be loaded into R and explored.", "zebrafish brain", null, "Single cell suspensions were processed through inDrops to generate single cell cDNA libraries. cDNAs were in vitro transcribed and fragmented. The three prime fragments were reverse transcribed and prepared for sequencing Libraries were prepared as described in Zilionis et al.  2017  Nature Protocols PMID = 27929523", null, "tissue:brain|developmental stage:23 25dpf", "GSM2813955", "GSM2813955: DEW129 f4; Danio rerio; RNA Seq", "GSM2813955", null, "1", "Single cell suspensions were processed through inDrops to generate single cell cDNA libraries. cDNAs were in vitro transcribed and fragmented. The three prime fragments were reverse transcribed and prepared for sequencing Libraries were prepared as described in Zilionis et al.  2017  Nature Protocols PMID = 27929523", "GEO Accession:GSM2813955", "RNA-Seq", "TRANSCRIPTOMIC", "cDNA", "SINGLE", "ILLUMINA", "NextSeq 500", null, "SRP120009", null, "loader:fastq load.py|options:  appendBCtoName", "DEW129_Lane2.sorted.fastq.gz", "fastq", 1816963632.0, 30863531.0, "GSM2813955 r2", "0:58.87", "A:518689812;C:359842055;G:374765615;T:563659156;N:6994", 58, null, null, null, 518689812, 359842055, 374765615, 563659156, 6994, "SRX3287385", "SRS2596859", "SRA619743", "GEO", "Harvard University", 1, 0.89748, null, 0.24157, null, 0.79632, null, 0.59646, null, 60, null, "B", null, "usable mapping rate", "illumina", "nextseq", "unknown", "cdna_unspecified", "unknown", "sc", "single_cell_droplet", "indrops", null, "United States", "2017-10-16", "Larval", "Larval", "Brain", "Nervous System"], [43903, "SRR6176688", "SRX3287384", "SRS2596858", "SRP120009", "PRJNA414416", "Simultaneous single cell profiling of lineages and cell types in the vertebrate brain", "GSE105010", "Other", "The lineage relationships among the hundreds of cell types generated during development are difficult to reconstruct. A recent method  GESTALT  used CRISPR\u2013Cas9 barcode editing for large scale lineage tracing  but was restricted to early development and did not identify cell types. Here we present scGESTALT  which combines the lineage recording capabilities of GESTALT with cell type identification by single cell RNA sequencing. The method relies on an inducible system that enables barcodes to be edited at multiple time points  capturing lineage information from later stages of development. Sequencing of 60 000 transcriptomes from the juvenile zebrafish brain identified >100 cell types and marker genes. Using these data  we generate lineage trees with hundreds of branches that help uncover restrictions at the level of cell types  brain regions  and gene expression cascades during differentiation. scGESTALT can be applied to other multicellular organisms to simultaneously characterize molecular identities and lineage histories of thousands of cells during development and disease. Overall design: inDrops libraries of single cell transcriptomes and scGESTALT barcodes  and genomic DNA GESTALT libraries", null, "pubmed:29608178", null, "DEW128 f4", "GSM2813954", null, "source name:zebrafish brain|tissue:brain|developmental stage:23 25dpf", "DEW128 f4", "Single cell RNA Sequencing data FASTQ files were processed using the inDrops.py bioinformatics pipeline available at https://github.com/indrops/indrops. Transcriptome libraries were mapped to a zebrafish reference built from a custom GTF file and the zebrafish GRCz10 release 86 genome assembly. Bowtie version1.1.1 was used with parameter \u2013e 200; UMI quantification was used with parameter \u2013u 2 counts were ignored from UMIs split between more than 2 genes. genomic DNA GESTALT and scGESTALT libraries were processed using a custom pipeline available at https://github.com/shendurelab/Cas9FateMapping Genome build: GRCz10 CSV files for transcriptome data were generated using the inDrops pipeline. Each column in the CSV files contains a cell identifier and each row contains expression values for genes. Txt files for genomic DNA GESTALT libraries *allReadCounts contain lineage barcode sequences HMID column for each cell and their proportion in the sequenced libraries. scGESTALT data *GestMaster.txt contains the inDrops cell identifiers CellBarcode and BarcodeKey that were used to match barcodes to transcriptomes. They also contain lineage barcode sequences HMID column for each cell with a corresponding inDrops single cell gene expression profile  as well as the the t SNE cluster membership number ClusterIdent column Txt files ending in *stats.txt contain information about the each individually captured UMI or cell per sample. The barcode sequence aligned to a reference unedited sequence mergedRead column  mutations at each target site target[X] columns and the edited sequences at each target site sequence[X] columns were used for downstream analysis. inDropsExpMatrix noQ txt file is the gene expression matrix for the full dataset. Columns are individual cells from different batches of whole brains f1   f2   f3   f4   f5   f6  or brain regions fore   mid   hind . fall.inDrops.Robj is the processed Seurat R object  which can be loaded into R and explored.", "zebrafish brain", null, "Single cell suspensions were processed through inDrops to generate single cell cDNA libraries. cDNAs were in vitro transcribed and fragmented. The three prime fragments were reverse transcribed and prepared for sequencing Libraries were prepared as described in Zilionis et al.  2017  Nature Protocols PMID = 27929523", null, "tissue:brain|developmental stage:23 25dpf", "GSM2813954", "GSM2813954: DEW128 f4; Danio rerio; RNA Seq", "GSM2813954", null, "1", "Single cell suspensions were processed through inDrops to generate single cell cDNA libraries. cDNAs were in vitro transcribed and fragmented. The three prime fragments were reverse transcribed and prepared for sequencing Libraries were prepared as described in Zilionis et al.  2017  Nature Protocols PMID = 27929523", "GEO Accession:GSM2813954", "RNA-Seq", "TRANSCRIPTOMIC", "cDNA", "SINGLE", "ILLUMINA", "NextSeq 500", null, "SRP120009", null, "loader:fastq load.py|options:  appendBCtoName", "DEW128_Lane1.sorted.fastq.gz", "fastq", 1529479909.0, 26263477.0, "GSM2813954 r1", "0:58.24", "A:433396829;C:297731146;G:312171109;T:486178018;N:2807", 58, null, null, null, 433396829, 297731146, 312171109, 486178018, 2807, "SRX3287384", "SRS2596858", "SRA619743", "GEO", "Harvard University", 1, 0.8923, null, 0.24087, null, 0.79048, null, 0.56588, null, 61, null, "B", null, "usable mapping rate", "illumina", "nextseq", "unknown", "cdna_unspecified", "unknown", "sc", "single_cell_droplet", "indrops", null, "United States", "2017-10-16", "Larval", "Larval", "Brain", "Nervous System"], [43904, "SRR6176689", "SRX3287384", "SRS2596858", "SRP120009", "PRJNA414416", "Simultaneous single cell profiling of lineages and cell types in the vertebrate brain", "GSE105010", "Other", "The lineage relationships among the hundreds of cell types generated during development are difficult to reconstruct. A recent method  GESTALT  used CRISPR\u2013Cas9 barcode editing for large scale lineage tracing  but was restricted to early development and did not identify cell types. Here we present scGESTALT  which combines the lineage recording capabilities of GESTALT with cell type identification by single cell RNA sequencing. The method relies on an inducible system that enables barcodes to be edited at multiple time points  capturing lineage information from later stages of development. Sequencing of 60 000 transcriptomes from the juvenile zebrafish brain identified >100 cell types and marker genes. Using these data  we generate lineage trees with hundreds of branches that help uncover restrictions at the level of cell types  brain regions  and gene expression cascades during differentiation. scGESTALT can be applied to other multicellular organisms to simultaneously characterize molecular identities and lineage histories of thousands of cells during development and disease. Overall design: inDrops libraries of single cell transcriptomes and scGESTALT barcodes  and genomic DNA GESTALT libraries", null, "pubmed:29608178", null, "DEW128 f4", "GSM2813954", null, "source name:zebrafish brain|tissue:brain|developmental stage:23 25dpf", "DEW128 f4", "Single cell RNA Sequencing data FASTQ files were processed using the inDrops.py bioinformatics pipeline available at https://github.com/indrops/indrops. Transcriptome libraries were mapped to a zebrafish reference built from a custom GTF file and the zebrafish GRCz10 release 86 genome assembly. Bowtie version1.1.1 was used with parameter \u2013e 200; UMI quantification was used with parameter \u2013u 2 counts were ignored from UMIs split between more than 2 genes. genomic DNA GESTALT and scGESTALT libraries were processed using a custom pipeline available at https://github.com/shendurelab/Cas9FateMapping Genome build: GRCz10 CSV files for transcriptome data were generated using the inDrops pipeline. Each column in the CSV files contains a cell identifier and each row contains expression values for genes. Txt files for genomic DNA GESTALT libraries *allReadCounts contain lineage barcode sequences HMID column for each cell and their proportion in the sequenced libraries. scGESTALT data *GestMaster.txt contains the inDrops cell identifiers CellBarcode and BarcodeKey that were used to match barcodes to transcriptomes. They also contain lineage barcode sequences HMID column for each cell with a corresponding inDrops single cell gene expression profile  as well as the the t SNE cluster membership number ClusterIdent column Txt files ending in *stats.txt contain information about the each individually captured UMI or cell per sample. The barcode sequence aligned to a reference unedited sequence mergedRead column  mutations at each target site target[X] columns and the edited sequences at each target site sequence[X] columns were used for downstream analysis. inDropsExpMatrix noQ txt file is the gene expression matrix for the full dataset. Columns are individual cells from different batches of whole brains f1   f2   f3   f4   f5   f6  or brain regions fore   mid   hind . fall.inDrops.Robj is the processed Seurat R object  which can be loaded into R and explored.", "zebrafish brain", null, "Single cell suspensions were processed through inDrops to generate single cell cDNA libraries. cDNAs were in vitro transcribed and fragmented. The three prime fragments were reverse transcribed and prepared for sequencing Libraries were prepared as described in Zilionis et al.  2017  Nature Protocols PMID = 27929523", null, "tissue:brain|developmental stage:23 25dpf", "GSM2813954", "GSM2813954: DEW128 f4; Danio rerio; RNA Seq", "GSM2813954", null, "1", "Single cell suspensions were processed through inDrops to generate single cell cDNA libraries. cDNAs were in vitro transcribed and fragmented. The three prime fragments were reverse transcribed and prepared for sequencing Libraries were prepared as described in Zilionis et al.  2017  Nature Protocols PMID = 27929523", "GEO Accession:GSM2813954", "RNA-Seq", "TRANSCRIPTOMIC", "cDNA", "SINGLE", "ILLUMINA", "NextSeq 500", null, "SRP120009", null, "loader:fastq load.py|options:  appendBCtoName", "DEW128_Lane2.sorted.fastq.gz", "fastq", 1722831702.0, 29239401.0, "GSM2813954 r2", "0:58.92", "A:487868018;C:335153495;G:352552183;T:547251517;N:6489", 58, null, null, null, 487868018, 335153495, 352552183, 547251517, 6489, "SRX3287384", "SRS2596858", "SRA619743", "GEO", "Harvard University", 1, 0.89701, null, 0.24322, null, 0.78912, null, 0.58095, null, 61, null, "B", null, "usable mapping rate", "illumina", "nextseq", "unknown", "cdna_unspecified", "unknown", "sc", "single_cell_droplet", "indrops", null, "United States", "2017-10-16", "Larval", "Larval", "Brain", "Nervous System"], [43905, "SRR6176686", "SRX3287383", "SRS2596857", "SRP120009", "PRJNA414416", "Simultaneous single cell profiling of lineages and cell types in the vertebrate brain", "GSE105010", "Other", "The lineage relationships among the hundreds of cell types generated during development are difficult to reconstruct. A recent method  GESTALT  used CRISPR\u2013Cas9 barcode editing for large scale lineage tracing  but was restricted to early development and did not identify cell types. Here we present scGESTALT  which combines the lineage recording capabilities of GESTALT with cell type identification by single cell RNA sequencing. The method relies on an inducible system that enables barcodes to be edited at multiple time points  capturing lineage information from later stages of development. Sequencing of 60 000 transcriptomes from the juvenile zebrafish brain identified >100 cell types and marker genes. Using these data  we generate lineage trees with hundreds of branches that help uncover restrictions at the level of cell types  brain regions  and gene expression cascades during differentiation. scGESTALT can be applied to other multicellular organisms to simultaneously characterize molecular identities and lineage histories of thousands of cells during development and disease. Overall design: inDrops libraries of single cell transcriptomes and scGESTALT barcodes  and genomic DNA GESTALT libraries", null, "pubmed:29608178", null, "DEW127 f4", "GSM2813953", null, "source name:zebrafish brain|tissue:brain|developmental stage:23 25dpf", "DEW127 f4", "Single cell RNA Sequencing data FASTQ files were processed using the inDrops.py bioinformatics pipeline available at https://github.com/indrops/indrops. Transcriptome libraries were mapped to a zebrafish reference built from a custom GTF file and the zebrafish GRCz10 release 86 genome assembly. Bowtie version1.1.1 was used with parameter \u2013e 200; UMI quantification was used with parameter \u2013u 2 counts were ignored from UMIs split between more than 2 genes. genomic DNA GESTALT and scGESTALT libraries were processed using a custom pipeline available at https://github.com/shendurelab/Cas9FateMapping Genome build: GRCz10 CSV files for transcriptome data were generated using the inDrops pipeline. Each column in the CSV files contains a cell identifier and each row contains expression values for genes. Txt files for genomic DNA GESTALT libraries *allReadCounts contain lineage barcode sequences HMID column for each cell and their proportion in the sequenced libraries. scGESTALT data *GestMaster.txt contains the inDrops cell identifiers CellBarcode and BarcodeKey that were used to match barcodes to transcriptomes. They also contain lineage barcode sequences HMID column for each cell with a corresponding inDrops single cell gene expression profile  as well as the the t SNE cluster membership number ClusterIdent column Txt files ending in *stats.txt contain information about the each individually captured UMI or cell per sample. The barcode sequence aligned to a reference unedited sequence mergedRead column  mutations at each target site target[X] columns and the edited sequences at each target site sequence[X] columns were used for downstream analysis. inDropsExpMatrix noQ txt file is the gene expression matrix for the full dataset. Columns are individual cells from different batches of whole brains f1   f2   f3   f4   f5   f6  or brain regions fore   mid   hind . fall.inDrops.Robj is the processed Seurat R object  which can be loaded into R and explored.", "zebrafish brain", null, "Single cell suspensions were processed through inDrops to generate single cell cDNA libraries. cDNAs were in vitro transcribed and fragmented. The three prime fragments were reverse transcribed and prepared for sequencing Libraries were prepared as described in Zilionis et al.  2017  Nature Protocols PMID = 27929523", null, "tissue:brain|developmental stage:23 25dpf", "GSM2813953", "GSM2813953: DEW127 f4; Danio rerio; RNA Seq", "GSM2813953", null, "1", "Single cell suspensions were processed through inDrops to generate single cell cDNA libraries. cDNAs were in vitro transcribed and fragmented. The three prime fragments were reverse transcribed and prepared for sequencing Libraries were prepared as described in Zilionis et al.  2017  Nature Protocols PMID = 27929523", "GEO Accession:GSM2813953", "RNA-Seq", "TRANSCRIPTOMIC", "cDNA", "SINGLE", "ILLUMINA", "NextSeq 500", null, "SRP120009", null, "loader:fastq load.py|options:  appendBCtoName", "DEW127_Lane1.sorted.fastq.gz", "fastq", 1289436644.0, 22188276.0, "GSM2813953 r1", "0:58.11", "A:365922709;C:250482345;G:264361533;T:408667760;N:2297", 58, null, null, null, 365922709, 250482345, 264361533, 408667760, 2297, "SRX3287383", "SRS2596857", "SRA619743", "GEO", "Harvard University", 1, 0.88934, null, 0.24432, null, 0.79312, null, 0.57617, null, 61, null, "B", null, "usable mapping rate", "illumina", "nextseq", "unknown", "cdna_unspecified", "unknown", "sc", "single_cell_droplet", "indrops", null, "United States", "2017-10-16", "Larval", "Larval", "Brain", "Nervous System"], [43906, "SRR6176687", "SRX3287383", "SRS2596857", "SRP120009", "PRJNA414416", "Simultaneous single cell profiling of lineages and cell types in the vertebrate brain", "GSE105010", "Other", "The lineage relationships among the hundreds of cell types generated during development are difficult to reconstruct. A recent method  GESTALT  used CRISPR\u2013Cas9 barcode editing for large scale lineage tracing  but was restricted to early development and did not identify cell types. Here we present scGESTALT  which combines the lineage recording capabilities of GESTALT with cell type identification by single cell RNA sequencing. The method relies on an inducible system that enables barcodes to be edited at multiple time points  capturing lineage information from later stages of development. Sequencing of 60 000 transcriptomes from the juvenile zebrafish brain identified >100 cell types and marker genes. Using these data  we generate lineage trees with hundreds of branches that help uncover restrictions at the level of cell types  brain regions  and gene expression cascades during differentiation. scGESTALT can be applied to other multicellular organisms to simultaneously characterize molecular identities and lineage histories of thousands of cells during development and disease. Overall design: inDrops libraries of single cell transcriptomes and scGESTALT barcodes  and genomic DNA GESTALT libraries", null, "pubmed:29608178", null, "DEW127 f4", "GSM2813953", null, "source name:zebrafish brain|tissue:brain|developmental stage:23 25dpf", "DEW127 f4", "Single cell RNA Sequencing data FASTQ files were processed using the inDrops.py bioinformatics pipeline available at https://github.com/indrops/indrops. Transcriptome libraries were mapped to a zebrafish reference built from a custom GTF file and the zebrafish GRCz10 release 86 genome assembly. Bowtie version1.1.1 was used with parameter \u2013e 200; UMI quantification was used with parameter \u2013u 2 counts were ignored from UMIs split between more than 2 genes. genomic DNA GESTALT and scGESTALT libraries were processed using a custom pipeline available at https://github.com/shendurelab/Cas9FateMapping Genome build: GRCz10 CSV files for transcriptome data were generated using the inDrops pipeline. Each column in the CSV files contains a cell identifier and each row contains expression values for genes. Txt files for genomic DNA GESTALT libraries *allReadCounts contain lineage barcode sequences HMID column for each cell and their proportion in the sequenced libraries. scGESTALT data *GestMaster.txt contains the inDrops cell identifiers CellBarcode and BarcodeKey that were used to match barcodes to transcriptomes. They also contain lineage barcode sequences HMID column for each cell with a corresponding inDrops single cell gene expression profile  as well as the the t SNE cluster membership number ClusterIdent column Txt files ending in *stats.txt contain information about the each individually captured UMI or cell per sample. The barcode sequence aligned to a reference unedited sequence mergedRead column  mutations at each target site target[X] columns and the edited sequences at each target site sequence[X] columns were used for downstream analysis. inDropsExpMatrix noQ txt file is the gene expression matrix for the full dataset. Columns are individual cells from different batches of whole brains f1   f2   f3   f4   f5   f6  or brain regions fore   mid   hind . fall.inDrops.Robj is the processed Seurat R object  which can be loaded into R and explored.", "zebrafish brain", null, "Single cell suspensions were processed through inDrops to generate single cell cDNA libraries. cDNAs were in vitro transcribed and fragmented. The three prime fragments were reverse transcribed and prepared for sequencing Libraries were prepared as described in Zilionis et al.  2017  Nature Protocols PMID = 27929523", null, "tissue:brain|developmental stage:23 25dpf", "GSM2813953", "GSM2813953: DEW127 f4; Danio rerio; RNA Seq", "GSM2813953", null, "1", "Single cell suspensions were processed through inDrops to generate single cell cDNA libraries. cDNAs were in vitro transcribed and fragmented. The three prime fragments were reverse transcribed and prepared for sequencing Libraries were prepared as described in Zilionis et al.  2017  Nature Protocols PMID = 27929523", "GEO Accession:GSM2813953", "RNA-Seq", "TRANSCRIPTOMIC", "cDNA", "SINGLE", "ILLUMINA", "NextSeq 500", null, "SRP120009", null, "loader:fastq load.py|options:  appendBCtoName", "DEW127_Lane2.sorted.fastq.gz", "fastq", 1451386988.0, 24679845.0, "GSM2813953 r2", "0:58.81", "A:411699390;C:281768037;G:298187448;T:459726810;N:5303", 58, null, null, null, 411699390, 281768037, 298187448, 459726810, 5303, "SRX3287383", "SRS2596857", "SRA619743", "GEO", "Harvard University", 1, 0.89514, null, 0.24484, null, 0.792, null, 0.58002, null, 60, null, "B", null, "usable mapping rate", "illumina", "nextseq", "unknown", "cdna_unspecified", "unknown", "sc", "single_cell_droplet", "indrops", null, "United States", "2017-10-16", "Larval", "Larval", "Brain", "Nervous System"], [43907, "SRR6176684", "SRX3287382", "SRS2596856", "SRP120009", "PRJNA414416", "Simultaneous single cell profiling of lineages and cell types in the vertebrate brain", "GSE105010", "Other", "The lineage relationships among the hundreds of cell types generated during development are difficult to reconstruct. A recent method  GESTALT  used CRISPR\u2013Cas9 barcode editing for large scale lineage tracing  but was restricted to early development and did not identify cell types. Here we present scGESTALT  which combines the lineage recording capabilities of GESTALT with cell type identification by single cell RNA sequencing. The method relies on an inducible system that enables barcodes to be edited at multiple time points  capturing lineage information from later stages of development. Sequencing of 60 000 transcriptomes from the juvenile zebrafish brain identified >100 cell types and marker genes. Using these data  we generate lineage trees with hundreds of branches that help uncover restrictions at the level of cell types  brain regions  and gene expression cascades during differentiation. scGESTALT can be applied to other multicellular organisms to simultaneously characterize molecular identities and lineage histories of thousands of cells during development and disease. Overall design: inDrops libraries of single cell transcriptomes and scGESTALT barcodes  and genomic DNA GESTALT libraries", null, "pubmed:29608178", null, "DEW126 f4", "GSM2813952", null, "source name:zebrafish brain|tissue:brain|developmental stage:23 25dpf", "DEW126 f4", "Single cell RNA Sequencing data FASTQ files were processed using the inDrops.py bioinformatics pipeline available at https://github.com/indrops/indrops. Transcriptome libraries were mapped to a zebrafish reference built from a custom GTF file and the zebrafish GRCz10 release 86 genome assembly. Bowtie version1.1.1 was used with parameter \u2013e 200; UMI quantification was used with parameter \u2013u 2 counts were ignored from UMIs split between more than 2 genes. genomic DNA GESTALT and scGESTALT libraries were processed using a custom pipeline available at https://github.com/shendurelab/Cas9FateMapping Genome build: GRCz10 CSV files for transcriptome data were generated using the inDrops pipeline. Each column in the CSV files contains a cell identifier and each row contains expression values for genes. Txt files for genomic DNA GESTALT libraries *allReadCounts contain lineage barcode sequences HMID column for each cell and their proportion in the sequenced libraries. scGESTALT data *GestMaster.txt contains the inDrops cell identifiers CellBarcode and BarcodeKey that were used to match barcodes to transcriptomes. They also contain lineage barcode sequences HMID column for each cell with a corresponding inDrops single cell gene expression profile  as well as the the t SNE cluster membership number ClusterIdent column Txt files ending in *stats.txt contain information about the each individually captured UMI or cell per sample. The barcode sequence aligned to a reference unedited sequence mergedRead column  mutations at each target site target[X] columns and the edited sequences at each target site sequence[X] columns were used for downstream analysis. inDropsExpMatrix noQ txt file is the gene expression matrix for the full dataset. Columns are individual cells from different batches of whole brains f1   f2   f3   f4   f5   f6  or brain regions fore   mid   hind . fall.inDrops.Robj is the processed Seurat R object  which can be loaded into R and explored.", "zebrafish brain", null, "Single cell suspensions were processed through inDrops to generate single cell cDNA libraries. cDNAs were in vitro transcribed and fragmented. The three prime fragments were reverse transcribed and prepared for sequencing Libraries were prepared as described in Zilionis et al.  2017  Nature Protocols PMID = 27929523", null, "tissue:brain|developmental stage:23 25dpf", "GSM2813952", "GSM2813952: DEW126 f4; Danio rerio; RNA Seq", "GSM2813952", null, "1", "Single cell suspensions were processed through inDrops to generate single cell cDNA libraries. cDNAs were in vitro transcribed and fragmented. The three prime fragments were reverse transcribed and prepared for sequencing Libraries were prepared as described in Zilionis et al.  2017  Nature Protocols PMID = 27929523", "GEO Accession:GSM2813952", "RNA-Seq", "TRANSCRIPTOMIC", "cDNA", "SINGLE", "ILLUMINA", "NextSeq 500", null, "SRP120009", null, "loader:fastq load.py|options:  appendBCtoName", "DEW126_Lane1.sorted.fastq.gz", "fastq", 1732228908.0, 29740686.0, "GSM2813952 r1", "0:58.24", "A:491786960;C:337694973;G:345595607;T:557148186;N:3182", 58, null, null, null, 491786960, 337694973, 345595607, 557148186, 3182, "SRX3287382", "SRS2596856", "SRA619743", "GEO", "Harvard University", 1, 0.89381, null, 0.23387, null, 0.78845, null, 0.58208, null, 59, null, "B", null, "usable mapping rate", "illumina", "nextseq", "unknown", "cdna_unspecified", "unknown", "sc", "single_cell_droplet", "indrops", null, "United States", "2017-10-16", "Larval", "Larval", "Brain", "Nervous System"], [43908, "SRR6176685", "SRX3287382", "SRS2596856", "SRP120009", "PRJNA414416", "Simultaneous single cell profiling of lineages and cell types in the vertebrate brain", "GSE105010", "Other", "The lineage relationships among the hundreds of cell types generated during development are difficult to reconstruct. A recent method  GESTALT  used CRISPR\u2013Cas9 barcode editing for large scale lineage tracing  but was restricted to early development and did not identify cell types. Here we present scGESTALT  which combines the lineage recording capabilities of GESTALT with cell type identification by single cell RNA sequencing. The method relies on an inducible system that enables barcodes to be edited at multiple time points  capturing lineage information from later stages of development. Sequencing of 60 000 transcriptomes from the juvenile zebrafish brain identified >100 cell types and marker genes. Using these data  we generate lineage trees with hundreds of branches that help uncover restrictions at the level of cell types  brain regions  and gene expression cascades during differentiation. scGESTALT can be applied to other multicellular organisms to simultaneously characterize molecular identities and lineage histories of thousands of cells during development and disease. Overall design: inDrops libraries of single cell transcriptomes and scGESTALT barcodes  and genomic DNA GESTALT libraries", null, "pubmed:29608178", null, "DEW126 f4", "GSM2813952", null, "source name:zebrafish brain|tissue:brain|developmental stage:23 25dpf", "DEW126 f4", "Single cell RNA Sequencing data FASTQ files were processed using the inDrops.py bioinformatics pipeline available at https://github.com/indrops/indrops. Transcriptome libraries were mapped to a zebrafish reference built from a custom GTF file and the zebrafish GRCz10 release 86 genome assembly. Bowtie version1.1.1 was used with parameter \u2013e 200; UMI quantification was used with parameter \u2013u 2 counts were ignored from UMIs split between more than 2 genes. genomic DNA GESTALT and scGESTALT libraries were processed using a custom pipeline available at https://github.com/shendurelab/Cas9FateMapping Genome build: GRCz10 CSV files for transcriptome data were generated using the inDrops pipeline. Each column in the CSV files contains a cell identifier and each row contains expression values for genes. Txt files for genomic DNA GESTALT libraries *allReadCounts contain lineage barcode sequences HMID column for each cell and their proportion in the sequenced libraries. scGESTALT data *GestMaster.txt contains the inDrops cell identifiers CellBarcode and BarcodeKey that were used to match barcodes to transcriptomes. They also contain lineage barcode sequences HMID column for each cell with a corresponding inDrops single cell gene expression profile  as well as the the t SNE cluster membership number ClusterIdent column Txt files ending in *stats.txt contain information about the each individually captured UMI or cell per sample. The barcode sequence aligned to a reference unedited sequence mergedRead column  mutations at each target site target[X] columns and the edited sequences at each target site sequence[X] columns were used for downstream analysis. inDropsExpMatrix noQ txt file is the gene expression matrix for the full dataset. Columns are individual cells from different batches of whole brains f1   f2   f3   f4   f5   f6  or brain regions fore   mid   hind . fall.inDrops.Robj is the processed Seurat R object  which can be loaded into R and explored.", "zebrafish brain", null, "Single cell suspensions were processed through inDrops to generate single cell cDNA libraries. cDNAs were in vitro transcribed and fragmented. The three prime fragments were reverse transcribed and prepared for sequencing Libraries were prepared as described in Zilionis et al.  2017  Nature Protocols PMID = 27929523", null, "tissue:brain|developmental stage:23 25dpf", "GSM2813952", "GSM2813952: DEW126 f4; Danio rerio; RNA Seq", "GSM2813952", null, "1", "Single cell suspensions were processed through inDrops to generate single cell cDNA libraries. cDNAs were in vitro transcribed and fragmented. The three prime fragments were reverse transcribed and prepared for sequencing Libraries were prepared as described in Zilionis et al.  2017  Nature Protocols PMID = 27929523", "GEO Accession:GSM2813952", "RNA-Seq", "TRANSCRIPTOMIC", "cDNA", "SINGLE", "ILLUMINA", "NextSeq 500", null, "SRP120009", null, "loader:fastq load.py|options:  appendBCtoName", "DEW126_Lane2.sorted.fastq.gz", "fastq", 1956974114.0, 33233968.0, "GSM2813952 r2", "0:58.88", "A:555108782;C:381036712;G:391375322;T:629446011;N:7287", 58, null, null, null, 555108782, 381036712, 391375322, 629446011, 7287, "SRX3287382", "SRS2596856", "SRA619743", "GEO", "Harvard University", 1, 0.89893, null, 0.23409, null, 0.78784, null, 0.57241, null, 60, null, "B", null, "usable mapping rate", "illumina", "nextseq", "unknown", "cdna_unspecified", "unknown", "sc", "single_cell_droplet", "indrops", null, "United States", "2017-10-16", "Larval", "Larval", "Brain", "Nervous System"], [43909, "SRR6176682", "SRX3287381", "SRS2596855", "SRP120009", "PRJNA414416", "Simultaneous single cell profiling of lineages and cell types in the vertebrate brain", "GSE105010", "Other", "The lineage relationships among the hundreds of cell types generated during development are difficult to reconstruct. A recent method  GESTALT  used CRISPR\u2013Cas9 barcode editing for large scale lineage tracing  but was restricted to early development and did not identify cell types. Here we present scGESTALT  which combines the lineage recording capabilities of GESTALT with cell type identification by single cell RNA sequencing. The method relies on an inducible system that enables barcodes to be edited at multiple time points  capturing lineage information from later stages of development. Sequencing of 60 000 transcriptomes from the juvenile zebrafish brain identified >100 cell types and marker genes. Using these data  we generate lineage trees with hundreds of branches that help uncover restrictions at the level of cell types  brain regions  and gene expression cascades during differentiation. scGESTALT can be applied to other multicellular organisms to simultaneously characterize molecular identities and lineage histories of thousands of cells during development and disease. Overall design: inDrops libraries of single cell transcriptomes and scGESTALT barcodes  and genomic DNA GESTALT libraries", null, "pubmed:29608178", null, "DEW125 f4", "GSM2813951", null, "source name:zebrafish brain|tissue:brain|developmental stage:23 25dpf", "DEW125 f4", "Single cell RNA Sequencing data FASTQ files were processed using the inDrops.py bioinformatics pipeline available at https://github.com/indrops/indrops. Transcriptome libraries were mapped to a zebrafish reference built from a custom GTF file and the zebrafish GRCz10 release 86 genome assembly. Bowtie version1.1.1 was used with parameter \u2013e 200; UMI quantification was used with parameter \u2013u 2 counts were ignored from UMIs split between more than 2 genes. genomic DNA GESTALT and scGESTALT libraries were processed using a custom pipeline available at https://github.com/shendurelab/Cas9FateMapping Genome build: GRCz10 CSV files for transcriptome data were generated using the inDrops pipeline. Each column in the CSV files contains a cell identifier and each row contains expression values for genes. Txt files for genomic DNA GESTALT libraries *allReadCounts contain lineage barcode sequences HMID column for each cell and their proportion in the sequenced libraries. scGESTALT data *GestMaster.txt contains the inDrops cell identifiers CellBarcode and BarcodeKey that were used to match barcodes to transcriptomes. They also contain lineage barcode sequences HMID column for each cell with a corresponding inDrops single cell gene expression profile  as well as the the t SNE cluster membership number ClusterIdent column Txt files ending in *stats.txt contain information about the each individually captured UMI or cell per sample. The barcode sequence aligned to a reference unedited sequence mergedRead column  mutations at each target site target[X] columns and the edited sequences at each target site sequence[X] columns were used for downstream analysis. inDropsExpMatrix noQ txt file is the gene expression matrix for the full dataset. Columns are individual cells from different batches of whole brains f1   f2   f3   f4   f5   f6  or brain regions fore   mid   hind . fall.inDrops.Robj is the processed Seurat R object  which can be loaded into R and explored.", "zebrafish brain", null, "Single cell suspensions were processed through inDrops to generate single cell cDNA libraries. cDNAs were in vitro transcribed and fragmented. The three prime fragments were reverse transcribed and prepared for sequencing Libraries were prepared as described in Zilionis et al.  2017  Nature Protocols PMID = 27929523", null, "tissue:brain|developmental stage:23 25dpf", "GSM2813951", "GSM2813951: DEW125 f4; Danio rerio; RNA Seq", "GSM2813951", null, "1", "Single cell suspensions were processed through inDrops to generate single cell cDNA libraries. cDNAs were in vitro transcribed and fragmented. The three prime fragments were reverse transcribed and prepared for sequencing Libraries were prepared as described in Zilionis et al.  2017  Nature Protocols PMID = 27929523", "GEO Accession:GSM2813951", "RNA-Seq", "TRANSCRIPTOMIC", "cDNA", "SINGLE", "ILLUMINA", "NextSeq 500", null, "SRP120009", null, "loader:fastq load.py|options:  appendBCtoName", "DEW125_Lane1.sorted.fastq.gz", "fastq", 2052135970.0, 35281452.0, "GSM2813951 r1", "0:58.16", "A:584737414;C:400546905;G:408496810;T:658351017;N:3824", 58, null, null, null, 584737414, 400546905, 408496810, 658351017, 3824, "SRX3287381", "SRS2596855", "SRA619743", "GEO", "Harvard University", 1, 0.89325, null, 0.2332, null, 0.78752, null, 0.59224, null, 61, null, "B", null, "usable mapping rate", "illumina", "nextseq", "unknown", "cdna_unspecified", "unknown", "sc", "single_cell_droplet", "indrops", null, "United States", "2017-10-16", "Larval", "Larval", "Brain", "Nervous System"], [43910, "SRR6176683", "SRX3287381", "SRS2596855", "SRP120009", "PRJNA414416", "Simultaneous single cell profiling of lineages and cell types in the vertebrate brain", "GSE105010", "Other", "The lineage relationships among the hundreds of cell types generated during development are difficult to reconstruct. A recent method  GESTALT  used CRISPR\u2013Cas9 barcode editing for large scale lineage tracing  but was restricted to early development and did not identify cell types. Here we present scGESTALT  which combines the lineage recording capabilities of GESTALT with cell type identification by single cell RNA sequencing. The method relies on an inducible system that enables barcodes to be edited at multiple time points  capturing lineage information from later stages of development. Sequencing of 60 000 transcriptomes from the juvenile zebrafish brain identified >100 cell types and marker genes. Using these data  we generate lineage trees with hundreds of branches that help uncover restrictions at the level of cell types  brain regions  and gene expression cascades during differentiation. scGESTALT can be applied to other multicellular organisms to simultaneously characterize molecular identities and lineage histories of thousands of cells during development and disease. Overall design: inDrops libraries of single cell transcriptomes and scGESTALT barcodes  and genomic DNA GESTALT libraries", null, "pubmed:29608178", null, "DEW125 f4", "GSM2813951", null, "source name:zebrafish brain|tissue:brain|developmental stage:23 25dpf", "DEW125 f4", "Single cell RNA Sequencing data FASTQ files were processed using the inDrops.py bioinformatics pipeline available at https://github.com/indrops/indrops. Transcriptome libraries were mapped to a zebrafish reference built from a custom GTF file and the zebrafish GRCz10 release 86 genome assembly. Bowtie version1.1.1 was used with parameter \u2013e 200; UMI quantification was used with parameter \u2013u 2 counts were ignored from UMIs split between more than 2 genes. genomic DNA GESTALT and scGESTALT libraries were processed using a custom pipeline available at https://github.com/shendurelab/Cas9FateMapping Genome build: GRCz10 CSV files for transcriptome data were generated using the inDrops pipeline. Each column in the CSV files contains a cell identifier and each row contains expression values for genes. Txt files for genomic DNA GESTALT libraries *allReadCounts contain lineage barcode sequences HMID column for each cell and their proportion in the sequenced libraries. scGESTALT data *GestMaster.txt contains the inDrops cell identifiers CellBarcode and BarcodeKey that were used to match barcodes to transcriptomes. They also contain lineage barcode sequences HMID column for each cell with a corresponding inDrops single cell gene expression profile  as well as the the t SNE cluster membership number ClusterIdent column Txt files ending in *stats.txt contain information about the each individually captured UMI or cell per sample. The barcode sequence aligned to a reference unedited sequence mergedRead column  mutations at each target site target[X] columns and the edited sequences at each target site sequence[X] columns were used for downstream analysis. inDropsExpMatrix noQ txt file is the gene expression matrix for the full dataset. Columns are individual cells from different batches of whole brains f1   f2   f3   f4   f5   f6  or brain regions fore   mid   hind . fall.inDrops.Robj is the processed Seurat R object  which can be loaded into R and explored.", "zebrafish brain", null, "Single cell suspensions were processed through inDrops to generate single cell cDNA libraries. cDNAs were in vitro transcribed and fragmented. The three prime fragments were reverse transcribed and prepared for sequencing Libraries were prepared as described in Zilionis et al.  2017  Nature Protocols PMID = 27929523", null, "tissue:brain|developmental stage:23 25dpf", "GSM2813951", "GSM2813951: DEW125 f4; Danio rerio; RNA Seq", "GSM2813951", null, "1", "Single cell suspensions were processed through inDrops to generate single cell cDNA libraries. cDNAs were in vitro transcribed and fragmented. The three prime fragments were reverse transcribed and prepared for sequencing Libraries were prepared as described in Zilionis et al.  2017  Nature Protocols PMID = 27929523", "GEO Accession:GSM2813951", "RNA-Seq", "TRANSCRIPTOMIC", "cDNA", "SINGLE", "ILLUMINA", "NextSeq 500", null, "SRP120009", null, "loader:fastq load.py|options:  appendBCtoName", "DEW125_Lane2.sorted.fastq.gz", "fastq", 2317293534.0, 39373296.0, "GSM2813951 r2", "0:58.85", "A:659601063;C:451827371;G:462587835;T:743268509;N:8756", 58, null, null, null, 659601063, 451827371, 462587835, 743268509, 8756, "SRX3287381", "SRS2596855", "SRA619743", "GEO", "Harvard University", 1, 0.89719, null, 0.23578, null, 0.78622, null, 0.58561, null, 61, null, "B", null, "usable mapping rate", "illumina", "nextseq", "unknown", "cdna_unspecified", "unknown", "sc", "single_cell_droplet", "indrops", null, "United States", "2017-10-16", "Larval", "Larval", "Brain", "Nervous System"], [43911, "SRR6176680", "SRX3287380", "SRS2596854", "SRP120009", "PRJNA414416", "Simultaneous single cell profiling of lineages and cell types in the vertebrate brain", "GSE105010", "Other", "The lineage relationships among the hundreds of cell types generated during development are difficult to reconstruct. A recent method  GESTALT  used CRISPR\u2013Cas9 barcode editing for large scale lineage tracing  but was restricted to early development and did not identify cell types. Here we present scGESTALT  which combines the lineage recording capabilities of GESTALT with cell type identification by single cell RNA sequencing. The method relies on an inducible system that enables barcodes to be edited at multiple time points  capturing lineage information from later stages of development. Sequencing of 60 000 transcriptomes from the juvenile zebrafish brain identified >100 cell types and marker genes. Using these data  we generate lineage trees with hundreds of branches that help uncover restrictions at the level of cell types  brain regions  and gene expression cascades during differentiation. scGESTALT can be applied to other multicellular organisms to simultaneously characterize molecular identities and lineage histories of thousands of cells during development and disease. Overall design: inDrops libraries of single cell transcriptomes and scGESTALT barcodes  and genomic DNA GESTALT libraries", null, "pubmed:29608178", null, "DEW124 f4", "GSM2813950", null, "source name:zebrafish brain|tissue:brain|developmental stage:23 25dpf", "DEW124 f4", "Single cell RNA Sequencing data FASTQ files were processed using the inDrops.py bioinformatics pipeline available at https://github.com/indrops/indrops. Transcriptome libraries were mapped to a zebrafish reference built from a custom GTF file and the zebrafish GRCz10 release 86 genome assembly. Bowtie version1.1.1 was used with parameter \u2013e 200; UMI quantification was used with parameter \u2013u 2 counts were ignored from UMIs split between more than 2 genes. genomic DNA GESTALT and scGESTALT libraries were processed using a custom pipeline available at https://github.com/shendurelab/Cas9FateMapping Genome build: GRCz10 CSV files for transcriptome data were generated using the inDrops pipeline. Each column in the CSV files contains a cell identifier and each row contains expression values for genes. Txt files for genomic DNA GESTALT libraries *allReadCounts contain lineage barcode sequences HMID column for each cell and their proportion in the sequenced libraries. scGESTALT data *GestMaster.txt contains the inDrops cell identifiers CellBarcode and BarcodeKey that were used to match barcodes to transcriptomes. They also contain lineage barcode sequences HMID column for each cell with a corresponding inDrops single cell gene expression profile  as well as the the t SNE cluster membership number ClusterIdent column Txt files ending in *stats.txt contain information about the each individually captured UMI or cell per sample. The barcode sequence aligned to a reference unedited sequence mergedRead column  mutations at each target site target[X] columns and the edited sequences at each target site sequence[X] columns were used for downstream analysis. inDropsExpMatrix noQ txt file is the gene expression matrix for the full dataset. Columns are individual cells from different batches of whole brains f1   f2   f3   f4   f5   f6  or brain regions fore   mid   hind . fall.inDrops.Robj is the processed Seurat R object  which can be loaded into R and explored.", "zebrafish brain", null, "Single cell suspensions were processed through inDrops to generate single cell cDNA libraries. cDNAs were in vitro transcribed and fragmented. The three prime fragments were reverse transcribed and prepared for sequencing Libraries were prepared as described in Zilionis et al.  2017  Nature Protocols PMID = 27929523", null, "tissue:brain|developmental stage:23 25dpf", "GSM2813950", "GSM2813950: DEW124 f4; Danio rerio; RNA Seq", "GSM2813950", null, "1", "Single cell suspensions were processed through inDrops to generate single cell cDNA libraries. cDNAs were in vitro transcribed and fragmented. The three prime fragments were reverse transcribed and prepared for sequencing Libraries were prepared as described in Zilionis et al.  2017  Nature Protocols PMID = 27929523", "GEO Accession:GSM2813950", "RNA-Seq", "TRANSCRIPTOMIC", "cDNA", "SINGLE", "ILLUMINA", "NextSeq 500", null, "SRP120009", null, "loader:fastq load.py|options:  appendBCtoName", "DEW124_Lane1.sorted.fastq.gz", "fastq", 1779081927.0, 30568871.0, "GSM2813950 r1", "0:58.20", "A:505313337;C:347024008;G:355651877;T:571089434;N:3271", 58, null, null, null, 505313337, 347024008, 355651877, 571089434, 3271, "SRX3287380", "SRS2596854", "SRA619743", "GEO", "Harvard University", 1, 0.89472, null, 0.23434, null, 0.78904, null, 0.55858, null, 61, null, "B", null, "usable mapping rate", "illumina", "nextseq", "unknown", "cdna_unspecified", "unknown", "sc", "single_cell_droplet", "indrops", null, "United States", "2017-10-16", "Larval", "Larval", "Brain", "Nervous System"], [43912, "SRR6176681", "SRX3287380", "SRS2596854", "SRP120009", "PRJNA414416", "Simultaneous single cell profiling of lineages and cell types in the vertebrate brain", "GSE105010", "Other", "The lineage relationships among the hundreds of cell types generated during development are difficult to reconstruct. A recent method  GESTALT  used CRISPR\u2013Cas9 barcode editing for large scale lineage tracing  but was restricted to early development and did not identify cell types. Here we present scGESTALT  which combines the lineage recording capabilities of GESTALT with cell type identification by single cell RNA sequencing. The method relies on an inducible system that enables barcodes to be edited at multiple time points  capturing lineage information from later stages of development. Sequencing of 60 000 transcriptomes from the juvenile zebrafish brain identified >100 cell types and marker genes. Using these data  we generate lineage trees with hundreds of branches that help uncover restrictions at the level of cell types  brain regions  and gene expression cascades during differentiation. scGESTALT can be applied to other multicellular organisms to simultaneously characterize molecular identities and lineage histories of thousands of cells during development and disease. Overall design: inDrops libraries of single cell transcriptomes and scGESTALT barcodes  and genomic DNA GESTALT libraries", null, "pubmed:29608178", null, "DEW124 f4", "GSM2813950", null, "source name:zebrafish brain|tissue:brain|developmental stage:23 25dpf", "DEW124 f4", "Single cell RNA Sequencing data FASTQ files were processed using the inDrops.py bioinformatics pipeline available at https://github.com/indrops/indrops. Transcriptome libraries were mapped to a zebrafish reference built from a custom GTF file and the zebrafish GRCz10 release 86 genome assembly. Bowtie version1.1.1 was used with parameter \u2013e 200; UMI quantification was used with parameter \u2013u 2 counts were ignored from UMIs split between more than 2 genes. genomic DNA GESTALT and scGESTALT libraries were processed using a custom pipeline available at https://github.com/shendurelab/Cas9FateMapping Genome build: GRCz10 CSV files for transcriptome data were generated using the inDrops pipeline. Each column in the CSV files contains a cell identifier and each row contains expression values for genes. Txt files for genomic DNA GESTALT libraries *allReadCounts contain lineage barcode sequences HMID column for each cell and their proportion in the sequenced libraries. scGESTALT data *GestMaster.txt contains the inDrops cell identifiers CellBarcode and BarcodeKey that were used to match barcodes to transcriptomes. They also contain lineage barcode sequences HMID column for each cell with a corresponding inDrops single cell gene expression profile  as well as the the t SNE cluster membership number ClusterIdent column Txt files ending in *stats.txt contain information about the each individually captured UMI or cell per sample. The barcode sequence aligned to a reference unedited sequence mergedRead column  mutations at each target site target[X] columns and the edited sequences at each target site sequence[X] columns were used for downstream analysis. inDropsExpMatrix noQ txt file is the gene expression matrix for the full dataset. Columns are individual cells from different batches of whole brains f1   f2   f3   f4   f5   f6  or brain regions fore   mid   hind . fall.inDrops.Robj is the processed Seurat R object  which can be loaded into R and explored.", "zebrafish brain", null, "Single cell suspensions were processed through inDrops to generate single cell cDNA libraries. cDNAs were in vitro transcribed and fragmented. The three prime fragments were reverse transcribed and prepared for sequencing Libraries were prepared as described in Zilionis et al.  2017  Nature Protocols PMID = 27929523", null, "tissue:brain|developmental stage:23 25dpf", "GSM2813950", "GSM2813950: DEW124 f4; Danio rerio; RNA Seq", "GSM2813950", null, "1", "Single cell suspensions were processed through inDrops to generate single cell cDNA libraries. cDNAs were in vitro transcribed and fragmented. The three prime fragments were reverse transcribed and prepared for sequencing Libraries were prepared as described in Zilionis et al.  2017  Nature Protocols PMID = 27929523", "GEO Accession:GSM2813950", "RNA-Seq", "TRANSCRIPTOMIC", "cDNA", "SINGLE", "ILLUMINA", "NextSeq 500", null, "SRP120009", null, "loader:fastq load.py|options:  appendBCtoName", "DEW124_Lane2.sorted.fastq.gz", "fastq", 2007451773.0, 34089979.0, "GSM2813950 r2", "0:58.89", "A:569364072;C:391252006;G:402616764;T:644211365;N:7566", 58, null, null, null, 569364072, 391252006, 402616764, 644211365, 7566, "SRX3287380", "SRS2596854", "SRA619743", "GEO", "Harvard University", 1, 0.89984, null, 0.23537, null, 0.78737, null, 0.58513, null, 61, null, "B", null, "usable mapping rate", "illumina", "nextseq", "unknown", "cdna_unspecified", "unknown", "sc", "single_cell_droplet", "indrops", null, "United States", "2017-10-16", "Larval", "Larval", "Brain", "Nervous System"], [43913, "SRR6176678", "SRX3287379", "SRS2596853", "SRP120009", "PRJNA414416", "Simultaneous single cell profiling of lineages and cell types in the vertebrate brain", "GSE105010", "Other", "The lineage relationships among the hundreds of cell types generated during development are difficult to reconstruct. A recent method  GESTALT  used CRISPR\u2013Cas9 barcode editing for large scale lineage tracing  but was restricted to early development and did not identify cell types. Here we present scGESTALT  which combines the lineage recording capabilities of GESTALT with cell type identification by single cell RNA sequencing. The method relies on an inducible system that enables barcodes to be edited at multiple time points  capturing lineage information from later stages of development. Sequencing of 60 000 transcriptomes from the juvenile zebrafish brain identified >100 cell types and marker genes. Using these data  we generate lineage trees with hundreds of branches that help uncover restrictions at the level of cell types  brain regions  and gene expression cascades during differentiation. scGESTALT can be applied to other multicellular organisms to simultaneously characterize molecular identities and lineage histories of thousands of cells during development and disease. Overall design: inDrops libraries of single cell transcriptomes and scGESTALT barcodes  and genomic DNA GESTALT libraries", null, "pubmed:29608178", null, "DEW123 f4", "GSM2813949", null, "source name:zebrafish brain|tissue:brain|developmental stage:23 25dpf", "DEW123 f4", "Single cell RNA Sequencing data FASTQ files were processed using the inDrops.py bioinformatics pipeline available at https://github.com/indrops/indrops. Transcriptome libraries were mapped to a zebrafish reference built from a custom GTF file and the zebrafish GRCz10 release 86 genome assembly. Bowtie version1.1.1 was used with parameter \u2013e 200; UMI quantification was used with parameter \u2013u 2 counts were ignored from UMIs split between more than 2 genes. genomic DNA GESTALT and scGESTALT libraries were processed using a custom pipeline available at https://github.com/shendurelab/Cas9FateMapping Genome build: GRCz10 CSV files for transcriptome data were generated using the inDrops pipeline. Each column in the CSV files contains a cell identifier and each row contains expression values for genes. Txt files for genomic DNA GESTALT libraries *allReadCounts contain lineage barcode sequences HMID column for each cell and their proportion in the sequenced libraries. scGESTALT data *GestMaster.txt contains the inDrops cell identifiers CellBarcode and BarcodeKey that were used to match barcodes to transcriptomes. They also contain lineage barcode sequences HMID column for each cell with a corresponding inDrops single cell gene expression profile  as well as the the t SNE cluster membership number ClusterIdent column Txt files ending in *stats.txt contain information about the each individually captured UMI or cell per sample. The barcode sequence aligned to a reference unedited sequence mergedRead column  mutations at each target site target[X] columns and the edited sequences at each target site sequence[X] columns were used for downstream analysis. inDropsExpMatrix noQ txt file is the gene expression matrix for the full dataset. Columns are individual cells from different batches of whole brains f1   f2   f3   f4   f5   f6  or brain regions fore   mid   hind . fall.inDrops.Robj is the processed Seurat R object  which can be loaded into R and explored.", "zebrafish brain", null, "Single cell suspensions were processed through inDrops to generate single cell cDNA libraries. cDNAs were in vitro transcribed and fragmented. The three prime fragments were reverse transcribed and prepared for sequencing Libraries were prepared as described in Zilionis et al.  2017  Nature Protocols PMID = 27929523", null, "tissue:brain|developmental stage:23 25dpf", "GSM2813949", "GSM2813949: DEW123 f4; Danio rerio; RNA Seq", "GSM2813949", null, "1", "Single cell suspensions were processed through inDrops to generate single cell cDNA libraries. cDNAs were in vitro transcribed and fragmented. The three prime fragments were reverse transcribed and prepared for sequencing Libraries were prepared as described in Zilionis et al.  2017  Nature Protocols PMID = 27929523", "GEO Accession:GSM2813949", "RNA-Seq", "TRANSCRIPTOMIC", "cDNA", "SINGLE", "ILLUMINA", "NextSeq 500", null, "SRP120009", null, "loader:fastq load.py|options:  appendBCtoName", "DEW123_Lane1.sorted.fastq.gz", "fastq", 2081583990.0, 35717514.0, "GSM2813949 r1", "0:58.28", "A:590232722;C:406414232;G:415274944;T:669658374;N:3718", 58, null, null, null, 590232722, 406414232, 415274944, 669658374, 3718, "SRX3287379", "SRS2596853", "SRA619743", "GEO", "Harvard University", 1, 0.89541, null, 0.23231, null, 0.78705, null, 0.58604, null, 61, null, "B", null, "usable mapping rate", "illumina", "nextseq", "unknown", "cdna_unspecified", "unknown", "sc", "single_cell_droplet", "indrops", null, "United States", "2017-10-16", "Larval", "Larval", "Brain", "Nervous System"], [43914, "SRR6176679", "SRX3287379", "SRS2596853", "SRP120009", "PRJNA414416", "Simultaneous single cell profiling of lineages and cell types in the vertebrate brain", "GSE105010", "Other", "The lineage relationships among the hundreds of cell types generated during development are difficult to reconstruct. A recent method  GESTALT  used CRISPR\u2013Cas9 barcode editing for large scale lineage tracing  but was restricted to early development and did not identify cell types. Here we present scGESTALT  which combines the lineage recording capabilities of GESTALT with cell type identification by single cell RNA sequencing. The method relies on an inducible system that enables barcodes to be edited at multiple time points  capturing lineage information from later stages of development. Sequencing of 60 000 transcriptomes from the juvenile zebrafish brain identified >100 cell types and marker genes. Using these data  we generate lineage trees with hundreds of branches that help uncover restrictions at the level of cell types  brain regions  and gene expression cascades during differentiation. scGESTALT can be applied to other multicellular organisms to simultaneously characterize molecular identities and lineage histories of thousands of cells during development and disease. Overall design: inDrops libraries of single cell transcriptomes and scGESTALT barcodes  and genomic DNA GESTALT libraries", null, "pubmed:29608178", null, "DEW123 f4", "GSM2813949", null, "source name:zebrafish brain|tissue:brain|developmental stage:23 25dpf", "DEW123 f4", "Single cell RNA Sequencing data FASTQ files were processed using the inDrops.py bioinformatics pipeline available at https://github.com/indrops/indrops. Transcriptome libraries were mapped to a zebrafish reference built from a custom GTF file and the zebrafish GRCz10 release 86 genome assembly. Bowtie version1.1.1 was used with parameter \u2013e 200; UMI quantification was used with parameter \u2013u 2 counts were ignored from UMIs split between more than 2 genes. genomic DNA GESTALT and scGESTALT libraries were processed using a custom pipeline available at https://github.com/shendurelab/Cas9FateMapping Genome build: GRCz10 CSV files for transcriptome data were generated using the inDrops pipeline. Each column in the CSV files contains a cell identifier and each row contains expression values for genes. Txt files for genomic DNA GESTALT libraries *allReadCounts contain lineage barcode sequences HMID column for each cell and their proportion in the sequenced libraries. scGESTALT data *GestMaster.txt contains the inDrops cell identifiers CellBarcode and BarcodeKey that were used to match barcodes to transcriptomes. They also contain lineage barcode sequences HMID column for each cell with a corresponding inDrops single cell gene expression profile  as well as the the t SNE cluster membership number ClusterIdent column Txt files ending in *stats.txt contain information about the each individually captured UMI or cell per sample. The barcode sequence aligned to a reference unedited sequence mergedRead column  mutations at each target site target[X] columns and the edited sequences at each target site sequence[X] columns were used for downstream analysis. inDropsExpMatrix noQ txt file is the gene expression matrix for the full dataset. Columns are individual cells from different batches of whole brains f1   f2   f3   f4   f5   f6  or brain regions fore   mid   hind . fall.inDrops.Robj is the processed Seurat R object  which can be loaded into R and explored.", "zebrafish brain", null, "Single cell suspensions were processed through inDrops to generate single cell cDNA libraries. cDNAs were in vitro transcribed and fragmented. The three prime fragments were reverse transcribed and prepared for sequencing Libraries were prepared as described in Zilionis et al.  2017  Nature Protocols PMID = 27929523", null, "tissue:brain|developmental stage:23 25dpf", "GSM2813949", "GSM2813949: DEW123 f4; Danio rerio; RNA Seq", "GSM2813949", null, "1", "Single cell suspensions were processed through inDrops to generate single cell cDNA libraries. cDNAs were in vitro transcribed and fragmented. The three prime fragments were reverse transcribed and prepared for sequencing Libraries were prepared as described in Zilionis et al.  2017  Nature Protocols PMID = 27929523", "GEO Accession:GSM2813949", "RNA-Seq", "TRANSCRIPTOMIC", "cDNA", "SINGLE", "ILLUMINA", "NextSeq 500", null, "SRP120009", null, "loader:fastq load.py|options:  appendBCtoName", "DEW123_Lane2.sorted.fastq.gz", "fastq", 2346671825.0, 39816492.0, "GSM2813949 r2", "0:58.94", "A:664664451;C:457682624;G:469409290;T:754906648;N:8812", 58, null, null, null, 664664451, 457682624, 469409290, 754906648, 8812, "SRX3287379", "SRS2596853", "SRA619743", "GEO", "Harvard University", 1, 0.90006, null, 0.23442, null, 0.78464, null, 0.5855, null, 61, null, "B", null, "usable mapping rate", "illumina", "nextseq", "unknown", "cdna_unspecified", "unknown", "sc", "single_cell_droplet", "indrops", null, "United States", "2017-10-16", "Larval", "Larval", "Brain", "Nervous System"], [43915, "SRR6176676", "SRX3287378", "SRS2596852", "SRP120009", "PRJNA414416", "Simultaneous single cell profiling of lineages and cell types in the vertebrate brain", "GSE105010", "Other", "The lineage relationships among the hundreds of cell types generated during development are difficult to reconstruct. A recent method  GESTALT  used CRISPR\u2013Cas9 barcode editing for large scale lineage tracing  but was restricted to early development and did not identify cell types. Here we present scGESTALT  which combines the lineage recording capabilities of GESTALT with cell type identification by single cell RNA sequencing. The method relies on an inducible system that enables barcodes to be edited at multiple time points  capturing lineage information from later stages of development. Sequencing of 60 000 transcriptomes from the juvenile zebrafish brain identified >100 cell types and marker genes. Using these data  we generate lineage trees with hundreds of branches that help uncover restrictions at the level of cell types  brain regions  and gene expression cascades during differentiation. scGESTALT can be applied to other multicellular organisms to simultaneously characterize molecular identities and lineage histories of thousands of cells during development and disease. Overall design: inDrops libraries of single cell transcriptomes and scGESTALT barcodes  and genomic DNA GESTALT libraries", null, "pubmed:29608178", null, "DEW122 f3", "GSM2813948", null, "source name:zebrafish brain|tissue:brain|developmental stage:23 25dpf", "DEW122 f3", "Single cell RNA Sequencing data FASTQ files were processed using the inDrops.py bioinformatics pipeline available at https://github.com/indrops/indrops. Transcriptome libraries were mapped to a zebrafish reference built from a custom GTF file and the zebrafish GRCz10 release 86 genome assembly. Bowtie version1.1.1 was used with parameter \u2013e 200; UMI quantification was used with parameter \u2013u 2 counts were ignored from UMIs split between more than 2 genes. genomic DNA GESTALT and scGESTALT libraries were processed using a custom pipeline available at https://github.com/shendurelab/Cas9FateMapping Genome build: GRCz10 CSV files for transcriptome data were generated using the inDrops pipeline. Each column in the CSV files contains a cell identifier and each row contains expression values for genes. Txt files for genomic DNA GESTALT libraries *allReadCounts contain lineage barcode sequences HMID column for each cell and their proportion in the sequenced libraries. scGESTALT data *GestMaster.txt contains the inDrops cell identifiers CellBarcode and BarcodeKey that were used to match barcodes to transcriptomes. They also contain lineage barcode sequences HMID column for each cell with a corresponding inDrops single cell gene expression profile  as well as the the t SNE cluster membership number ClusterIdent column Txt files ending in *stats.txt contain information about the each individually captured UMI or cell per sample. The barcode sequence aligned to a reference unedited sequence mergedRead column  mutations at each target site target[X] columns and the edited sequences at each target site sequence[X] columns were used for downstream analysis. inDropsExpMatrix noQ txt file is the gene expression matrix for the full dataset. Columns are individual cells from different batches of whole brains f1   f2   f3   f4   f5   f6  or brain regions fore   mid   hind . fall.inDrops.Robj is the processed Seurat R object  which can be loaded into R and explored.", "zebrafish brain", null, "Single cell suspensions were processed through inDrops to generate single cell cDNA libraries. cDNAs were in vitro transcribed and fragmented. The three prime fragments were reverse transcribed and prepared for sequencing Libraries were prepared as described in Zilionis et al.  2017  Nature Protocols PMID = 27929523", null, "tissue:brain|developmental stage:23 25dpf", "GSM2813948", "GSM2813948: DEW122 f3; Danio rerio; RNA Seq", "GSM2813948", null, "1", "Single cell suspensions were processed through inDrops to generate single cell cDNA libraries. cDNAs were in vitro transcribed and fragmented. The three prime fragments were reverse transcribed and prepared for sequencing Libraries were prepared as described in Zilionis et al.  2017  Nature Protocols PMID = 27929523", "GEO Accession:GSM2813948", "RNA-Seq", "TRANSCRIPTOMIC", "cDNA", "SINGLE", "ILLUMINA", "NextSeq 500", null, "SRP120009", null, "loader:fastq load.py|options:  appendBCtoName", "DEW122_Lane1.sorted.fastq.gz", "fastq", 1562577527.0, 26378748.0, "GSM2813948 r1", "0:59.24", "A:437298698;C:301001398;G:315561940;T:508707589;N:7902", 59, null, null, null, 437298698, 301001398, 315561940, 508707589, 7902, "SRX3287378", "SRS2596852", "SRA619743", "GEO", "Harvard University", 1, 0.90139, null, 0.23545, null, 0.78468, null, 0.55319, null, 61, null, "B", null, "usable mapping rate", "illumina", "nextseq", "unknown", "cdna_unspecified", "unknown", "sc", "single_cell_droplet", "indrops", null, "United States", "2017-10-16", "Larval", "Larval", "Brain", "Nervous System"], [43916, "SRR6176677", "SRX3287378", "SRS2596852", "SRP120009", "PRJNA414416", "Simultaneous single cell profiling of lineages and cell types in the vertebrate brain", "GSE105010", "Other", "The lineage relationships among the hundreds of cell types generated during development are difficult to reconstruct. A recent method  GESTALT  used CRISPR\u2013Cas9 barcode editing for large scale lineage tracing  but was restricted to early development and did not identify cell types. Here we present scGESTALT  which combines the lineage recording capabilities of GESTALT with cell type identification by single cell RNA sequencing. The method relies on an inducible system that enables barcodes to be edited at multiple time points  capturing lineage information from later stages of development. Sequencing of 60 000 transcriptomes from the juvenile zebrafish brain identified >100 cell types and marker genes. Using these data  we generate lineage trees with hundreds of branches that help uncover restrictions at the level of cell types  brain regions  and gene expression cascades during differentiation. scGESTALT can be applied to other multicellular organisms to simultaneously characterize molecular identities and lineage histories of thousands of cells during development and disease. Overall design: inDrops libraries of single cell transcriptomes and scGESTALT barcodes  and genomic DNA GESTALT libraries", null, "pubmed:29608178", null, "DEW122 f3", "GSM2813948", null, "source name:zebrafish brain|tissue:brain|developmental stage:23 25dpf", "DEW122 f3", "Single cell RNA Sequencing data FASTQ files were processed using the inDrops.py bioinformatics pipeline available at https://github.com/indrops/indrops. Transcriptome libraries were mapped to a zebrafish reference built from a custom GTF file and the zebrafish GRCz10 release 86 genome assembly. Bowtie version1.1.1 was used with parameter \u2013e 200; UMI quantification was used with parameter \u2013u 2 counts were ignored from UMIs split between more than 2 genes. genomic DNA GESTALT and scGESTALT libraries were processed using a custom pipeline available at https://github.com/shendurelab/Cas9FateMapping Genome build: GRCz10 CSV files for transcriptome data were generated using the inDrops pipeline. Each column in the CSV files contains a cell identifier and each row contains expression values for genes. Txt files for genomic DNA GESTALT libraries *allReadCounts contain lineage barcode sequences HMID column for each cell and their proportion in the sequenced libraries. scGESTALT data *GestMaster.txt contains the inDrops cell identifiers CellBarcode and BarcodeKey that were used to match barcodes to transcriptomes. They also contain lineage barcode sequences HMID column for each cell with a corresponding inDrops single cell gene expression profile  as well as the the t SNE cluster membership number ClusterIdent column Txt files ending in *stats.txt contain information about the each individually captured UMI or cell per sample. The barcode sequence aligned to a reference unedited sequence mergedRead column  mutations at each target site target[X] columns and the edited sequences at each target site sequence[X] columns were used for downstream analysis. inDropsExpMatrix noQ txt file is the gene expression matrix for the full dataset. Columns are individual cells from different batches of whole brains f1   f2   f3   f4   f5   f6  or brain regions fore   mid   hind . fall.inDrops.Robj is the processed Seurat R object  which can be loaded into R and explored.", "zebrafish brain", null, "Single cell suspensions were processed through inDrops to generate single cell cDNA libraries. cDNAs were in vitro transcribed and fragmented. The three prime fragments were reverse transcribed and prepared for sequencing Libraries were prepared as described in Zilionis et al.  2017  Nature Protocols PMID = 27929523", null, "tissue:brain|developmental stage:23 25dpf", "GSM2813948", "GSM2813948: DEW122 f3; Danio rerio; RNA Seq", "GSM2813948", null, "1", "Single cell suspensions were processed through inDrops to generate single cell cDNA libraries. cDNAs were in vitro transcribed and fragmented. The three prime fragments were reverse transcribed and prepared for sequencing Libraries were prepared as described in Zilionis et al.  2017  Nature Protocols PMID = 27929523", "GEO Accession:GSM2813948", "RNA-Seq", "TRANSCRIPTOMIC", "cDNA", "SINGLE", "ILLUMINA", "NextSeq 500", null, "SRP120009", null, "loader:fastq load.py|options:  appendBCtoName", "DEW122_Lane2.sorted.fastq.gz", "fastq", 1088184272.0, 18471582.0, "GSM2813948 r2", "0:58.91", "A:303730851;C:209482142;G:220498036;T:354466632;N:6611", 58, null, null, null, 303730851, 209482142, 220498036, 354466632, 6611, "SRX3287378", "SRS2596852", "SRA619743", "GEO", "Harvard University", 1, 0.90138, null, 0.23552, null, 0.78563, null, 0.55162, null, 61, null, "B", null, "usable mapping rate", "illumina", "nextseq", "unknown", "cdna_unspecified", "unknown", "sc", "single_cell_droplet", "indrops", null, "United States", "2017-10-16", "Larval", "Larval", "Brain", "Nervous System"], [43917, "SRR6176674", "SRX3287377", "SRS2596851", "SRP120009", "PRJNA414416", "Simultaneous single cell profiling of lineages and cell types in the vertebrate brain", "GSE105010", "Other", "The lineage relationships among the hundreds of cell types generated during development are difficult to reconstruct. A recent method  GESTALT  used CRISPR\u2013Cas9 barcode editing for large scale lineage tracing  but was restricted to early development and did not identify cell types. Here we present scGESTALT  which combines the lineage recording capabilities of GESTALT with cell type identification by single cell RNA sequencing. The method relies on an inducible system that enables barcodes to be edited at multiple time points  capturing lineage information from later stages of development. Sequencing of 60 000 transcriptomes from the juvenile zebrafish brain identified >100 cell types and marker genes. Using these data  we generate lineage trees with hundreds of branches that help uncover restrictions at the level of cell types  brain regions  and gene expression cascades during differentiation. scGESTALT can be applied to other multicellular organisms to simultaneously characterize molecular identities and lineage histories of thousands of cells during development and disease. Overall design: inDrops libraries of single cell transcriptomes and scGESTALT barcodes  and genomic DNA GESTALT libraries", null, "pubmed:29608178", null, "DEW121 f3", "GSM2813947", null, "source name:zebrafish brain|tissue:brain|developmental stage:23 25dpf", "DEW121 f3", "Single cell RNA Sequencing data FASTQ files were processed using the inDrops.py bioinformatics pipeline available at https://github.com/indrops/indrops. Transcriptome libraries were mapped to a zebrafish reference built from a custom GTF file and the zebrafish GRCz10 release 86 genome assembly. Bowtie version1.1.1 was used with parameter \u2013e 200; UMI quantification was used with parameter \u2013u 2 counts were ignored from UMIs split between more than 2 genes. genomic DNA GESTALT and scGESTALT libraries were processed using a custom pipeline available at https://github.com/shendurelab/Cas9FateMapping Genome build: GRCz10 CSV files for transcriptome data were generated using the inDrops pipeline. Each column in the CSV files contains a cell identifier and each row contains expression values for genes. Txt files for genomic DNA GESTALT libraries *allReadCounts contain lineage barcode sequences HMID column for each cell and their proportion in the sequenced libraries. scGESTALT data *GestMaster.txt contains the inDrops cell identifiers CellBarcode and BarcodeKey that were used to match barcodes to transcriptomes. They also contain lineage barcode sequences HMID column for each cell with a corresponding inDrops single cell gene expression profile  as well as the the t SNE cluster membership number ClusterIdent column Txt files ending in *stats.txt contain information about the each individually captured UMI or cell per sample. The barcode sequence aligned to a reference unedited sequence mergedRead column  mutations at each target site target[X] columns and the edited sequences at each target site sequence[X] columns were used for downstream analysis. inDropsExpMatrix noQ txt file is the gene expression matrix for the full dataset. Columns are individual cells from different batches of whole brains f1   f2   f3   f4   f5   f6  or brain regions fore   mid   hind . fall.inDrops.Robj is the processed Seurat R object  which can be loaded into R and explored.", "zebrafish brain", null, "Single cell suspensions were processed through inDrops to generate single cell cDNA libraries. cDNAs were in vitro transcribed and fragmented. The three prime fragments were reverse transcribed and prepared for sequencing Libraries were prepared as described in Zilionis et al.  2017  Nature Protocols PMID = 27929523", null, "tissue:brain|developmental stage:23 25dpf", "GSM2813947", "GSM2813947: DEW121 f3; Danio rerio; RNA Seq", "GSM2813947", null, "1", "Single cell suspensions were processed through inDrops to generate single cell cDNA libraries. cDNAs were in vitro transcribed and fragmented. The three prime fragments were reverse transcribed and prepared for sequencing Libraries were prepared as described in Zilionis et al.  2017  Nature Protocols PMID = 27929523", "GEO Accession:GSM2813947", "RNA-Seq", "TRANSCRIPTOMIC", "cDNA", "SINGLE", "ILLUMINA", "NextSeq 500", null, "SRP120009", null, "loader:fastq load.py|options:  appendBCtoName", "DEW121_Lane1.sorted.fastq.gz", "fastq", 1889291521.0, 31858948.0, "GSM2813947 r1", "0:59.30", "A:526886888;C:368493310;G:381057562;T:612844022;N:9739", 59, null, null, null, 526886888, 368493310, 381057562, 612844022, 9739, "SRX3287377", "SRS2596851", "SRA619743", "GEO", "Harvard University", 1, 0.90379, null, 0.22768, null, 0.78622, null, 0.56039, null, 61, null, "B", null, "usable mapping rate", "illumina", "nextseq", "unknown", "cdna_unspecified", "unknown", "sc", "single_cell_droplet", "indrops", null, "United States", "2017-10-16", "Larval", "Larval", "Brain", "Nervous System"], [43918, "SRR6176675", "SRX3287377", "SRS2596851", "SRP120009", "PRJNA414416", "Simultaneous single cell profiling of lineages and cell types in the vertebrate brain", "GSE105010", "Other", "The lineage relationships among the hundreds of cell types generated during development are difficult to reconstruct. A recent method  GESTALT  used CRISPR\u2013Cas9 barcode editing for large scale lineage tracing  but was restricted to early development and did not identify cell types. Here we present scGESTALT  which combines the lineage recording capabilities of GESTALT with cell type identification by single cell RNA sequencing. The method relies on an inducible system that enables barcodes to be edited at multiple time points  capturing lineage information from later stages of development. Sequencing of 60 000 transcriptomes from the juvenile zebrafish brain identified >100 cell types and marker genes. Using these data  we generate lineage trees with hundreds of branches that help uncover restrictions at the level of cell types  brain regions  and gene expression cascades during differentiation. scGESTALT can be applied to other multicellular organisms to simultaneously characterize molecular identities and lineage histories of thousands of cells during development and disease. Overall design: inDrops libraries of single cell transcriptomes and scGESTALT barcodes  and genomic DNA GESTALT libraries", null, "pubmed:29608178", null, "DEW121 f3", "GSM2813947", null, "source name:zebrafish brain|tissue:brain|developmental stage:23 25dpf", "DEW121 f3", "Single cell RNA Sequencing data FASTQ files were processed using the inDrops.py bioinformatics pipeline available at https://github.com/indrops/indrops. Transcriptome libraries were mapped to a zebrafish reference built from a custom GTF file and the zebrafish GRCz10 release 86 genome assembly. Bowtie version1.1.1 was used with parameter \u2013e 200; UMI quantification was used with parameter \u2013u 2 counts were ignored from UMIs split between more than 2 genes. genomic DNA GESTALT and scGESTALT libraries were processed using a custom pipeline available at https://github.com/shendurelab/Cas9FateMapping Genome build: GRCz10 CSV files for transcriptome data were generated using the inDrops pipeline. Each column in the CSV files contains a cell identifier and each row contains expression values for genes. Txt files for genomic DNA GESTALT libraries *allReadCounts contain lineage barcode sequences HMID column for each cell and their proportion in the sequenced libraries. scGESTALT data *GestMaster.txt contains the inDrops cell identifiers CellBarcode and BarcodeKey that were used to match barcodes to transcriptomes. They also contain lineage barcode sequences HMID column for each cell with a corresponding inDrops single cell gene expression profile  as well as the the t SNE cluster membership number ClusterIdent column Txt files ending in *stats.txt contain information about the each individually captured UMI or cell per sample. The barcode sequence aligned to a reference unedited sequence mergedRead column  mutations at each target site target[X] columns and the edited sequences at each target site sequence[X] columns were used for downstream analysis. inDropsExpMatrix noQ txt file is the gene expression matrix for the full dataset. Columns are individual cells from different batches of whole brains f1   f2   f3   f4   f5   f6  or brain regions fore   mid   hind . fall.inDrops.Robj is the processed Seurat R object  which can be loaded into R and explored.", "zebrafish brain", null, "Single cell suspensions were processed through inDrops to generate single cell cDNA libraries. cDNAs were in vitro transcribed and fragmented. The three prime fragments were reverse transcribed and prepared for sequencing Libraries were prepared as described in Zilionis et al.  2017  Nature Protocols PMID = 27929523", null, "tissue:brain|developmental stage:23 25dpf", "GSM2813947", "GSM2813947: DEW121 f3; Danio rerio; RNA Seq", "GSM2813947", null, "1", "Single cell suspensions were processed through inDrops to generate single cell cDNA libraries. cDNAs were in vitro transcribed and fragmented. The three prime fragments were reverse transcribed and prepared for sequencing Libraries were prepared as described in Zilionis et al.  2017  Nature Protocols PMID = 27929523", "GEO Accession:GSM2813947", "RNA-Seq", "TRANSCRIPTOMIC", "cDNA", "SINGLE", "ILLUMINA", "NextSeq 500", null, "SRP120009", null, "loader:fastq load.py|options:  appendBCtoName", "DEW121_Lane2.sorted.fastq.gz", "fastq", 1352587920.0, 22937513.0, "GSM2813947 r2", "0:58.97", "A:376141462;C:263880457;G:273962700;T:438594952;N:8349", 58, null, null, null, 376141462, 263880457, 273962700, 438594952, 8349, "SRX3287377", "SRS2596851", "SRA619743", "GEO", "Harvard University", 1, 0.90519, null, 0.22909, null, 0.78614, null, 0.55263, null, 61, null, "B", null, "usable mapping rate", "illumina", "nextseq", "unknown", "cdna_unspecified", "unknown", "sc", "single_cell_droplet", "indrops", null, "United States", "2017-10-16", "Larval", "Larval", "Brain", "Nervous System"], [43919, "SRR6176672", "SRX3287376", "SRS2596850", "SRP120009", "PRJNA414416", "Simultaneous single cell profiling of lineages and cell types in the vertebrate brain", "GSE105010", "Other", "The lineage relationships among the hundreds of cell types generated during development are difficult to reconstruct. A recent method  GESTALT  used CRISPR\u2013Cas9 barcode editing for large scale lineage tracing  but was restricted to early development and did not identify cell types. Here we present scGESTALT  which combines the lineage recording capabilities of GESTALT with cell type identification by single cell RNA sequencing. The method relies on an inducible system that enables barcodes to be edited at multiple time points  capturing lineage information from later stages of development. Sequencing of 60 000 transcriptomes from the juvenile zebrafish brain identified >100 cell types and marker genes. Using these data  we generate lineage trees with hundreds of branches that help uncover restrictions at the level of cell types  brain regions  and gene expression cascades during differentiation. scGESTALT can be applied to other multicellular organisms to simultaneously characterize molecular identities and lineage histories of thousands of cells during development and disease. Overall design: inDrops libraries of single cell transcriptomes and scGESTALT barcodes  and genomic DNA GESTALT libraries", null, "pubmed:29608178", null, "DEW120 f3", "GSM2813946", null, "source name:zebrafish brain|tissue:brain|developmental stage:23 25dpf", "DEW120 f3", "Single cell RNA Sequencing data FASTQ files were processed using the inDrops.py bioinformatics pipeline available at https://github.com/indrops/indrops. Transcriptome libraries were mapped to a zebrafish reference built from a custom GTF file and the zebrafish GRCz10 release 86 genome assembly. Bowtie version1.1.1 was used with parameter \u2013e 200; UMI quantification was used with parameter \u2013u 2 counts were ignored from UMIs split between more than 2 genes. genomic DNA GESTALT and scGESTALT libraries were processed using a custom pipeline available at https://github.com/shendurelab/Cas9FateMapping Genome build: GRCz10 CSV files for transcriptome data were generated using the inDrops pipeline. Each column in the CSV files contains a cell identifier and each row contains expression values for genes. Txt files for genomic DNA GESTALT libraries *allReadCounts contain lineage barcode sequences HMID column for each cell and their proportion in the sequenced libraries. scGESTALT data *GestMaster.txt contains the inDrops cell identifiers CellBarcode and BarcodeKey that were used to match barcodes to transcriptomes. They also contain lineage barcode sequences HMID column for each cell with a corresponding inDrops single cell gene expression profile  as well as the the t SNE cluster membership number ClusterIdent column Txt files ending in *stats.txt contain information about the each individually captured UMI or cell per sample. The barcode sequence aligned to a reference unedited sequence mergedRead column  mutations at each target site target[X] columns and the edited sequences at each target site sequence[X] columns were used for downstream analysis. inDropsExpMatrix noQ txt file is the gene expression matrix for the full dataset. Columns are individual cells from different batches of whole brains f1   f2   f3   f4   f5   f6  or brain regions fore   mid   hind . fall.inDrops.Robj is the processed Seurat R object  which can be loaded into R and explored.", "zebrafish brain", null, "Single cell suspensions were processed through inDrops to generate single cell cDNA libraries. cDNAs were in vitro transcribed and fragmented. The three prime fragments were reverse transcribed and prepared for sequencing Libraries were prepared as described in Zilionis et al.  2017  Nature Protocols PMID = 27929523", null, "tissue:brain|developmental stage:23 25dpf", "GSM2813946", "GSM2813946: DEW120 f3; Danio rerio; RNA Seq", "GSM2813946", null, "1", "Single cell suspensions were processed through inDrops to generate single cell cDNA libraries. cDNAs were in vitro transcribed and fragmented. The three prime fragments were reverse transcribed and prepared for sequencing Libraries were prepared as described in Zilionis et al.  2017  Nature Protocols PMID = 27929523", "GEO Accession:GSM2813946", "RNA-Seq", "TRANSCRIPTOMIC", "cDNA", "SINGLE", "ILLUMINA", "NextSeq 500", null, "SRP120009", null, "loader:fastq load.py|options:  appendBCtoName", "DEW120_Lane1.sorted.fastq.gz", "fastq", 1634812660.0, 27562292.0, "GSM2813946 r1", "0:59.31", "A:457777447;C:315526292;G:328034539;T:533465836;N:8546", 59, null, null, null, 457777447, 315526292, 328034539, 533465836, 8546, "SRX3287376", "SRS2596850", "SRA619743", "GEO", "Harvard University", 1, 0.90285, null, 0.23468, null, 0.7833, null, 0.53639, null, 61, null, "B", null, "usable mapping rate", "illumina", "nextseq", "unknown", "cdna_unspecified", "unknown", "sc", "single_cell_droplet", "indrops", null, "United States", "2017-10-16", "Larval", "Larval", "Brain", "Nervous System"], [43920, "SRR6176673", "SRX3287376", "SRS2596850", "SRP120009", "PRJNA414416", "Simultaneous single cell profiling of lineages and cell types in the vertebrate brain", "GSE105010", "Other", "The lineage relationships among the hundreds of cell types generated during development are difficult to reconstruct. A recent method  GESTALT  used CRISPR\u2013Cas9 barcode editing for large scale lineage tracing  but was restricted to early development and did not identify cell types. Here we present scGESTALT  which combines the lineage recording capabilities of GESTALT with cell type identification by single cell RNA sequencing. The method relies on an inducible system that enables barcodes to be edited at multiple time points  capturing lineage information from later stages of development. Sequencing of 60 000 transcriptomes from the juvenile zebrafish brain identified >100 cell types and marker genes. Using these data  we generate lineage trees with hundreds of branches that help uncover restrictions at the level of cell types  brain regions  and gene expression cascades during differentiation. scGESTALT can be applied to other multicellular organisms to simultaneously characterize molecular identities and lineage histories of thousands of cells during development and disease. Overall design: inDrops libraries of single cell transcriptomes and scGESTALT barcodes  and genomic DNA GESTALT libraries", null, "pubmed:29608178", null, "DEW120 f3", "GSM2813946", null, "source name:zebrafish brain|tissue:brain|developmental stage:23 25dpf", "DEW120 f3", "Single cell RNA Sequencing data FASTQ files were processed using the inDrops.py bioinformatics pipeline available at https://github.com/indrops/indrops. Transcriptome libraries were mapped to a zebrafish reference built from a custom GTF file and the zebrafish GRCz10 release 86 genome assembly. Bowtie version1.1.1 was used with parameter \u2013e 200; UMI quantification was used with parameter \u2013u 2 counts were ignored from UMIs split between more than 2 genes. genomic DNA GESTALT and scGESTALT libraries were processed using a custom pipeline available at https://github.com/shendurelab/Cas9FateMapping Genome build: GRCz10 CSV files for transcriptome data were generated using the inDrops pipeline. Each column in the CSV files contains a cell identifier and each row contains expression values for genes. Txt files for genomic DNA GESTALT libraries *allReadCounts contain lineage barcode sequences HMID column for each cell and their proportion in the sequenced libraries. scGESTALT data *GestMaster.txt contains the inDrops cell identifiers CellBarcode and BarcodeKey that were used to match barcodes to transcriptomes. They also contain lineage barcode sequences HMID column for each cell with a corresponding inDrops single cell gene expression profile  as well as the the t SNE cluster membership number ClusterIdent column Txt files ending in *stats.txt contain information about the each individually captured UMI or cell per sample. The barcode sequence aligned to a reference unedited sequence mergedRead column  mutations at each target site target[X] columns and the edited sequences at each target site sequence[X] columns were used for downstream analysis. inDropsExpMatrix noQ txt file is the gene expression matrix for the full dataset. Columns are individual cells from different batches of whole brains f1   f2   f3   f4   f5   f6  or brain regions fore   mid   hind . fall.inDrops.Robj is the processed Seurat R object  which can be loaded into R and explored.", "zebrafish brain", null, "Single cell suspensions were processed through inDrops to generate single cell cDNA libraries. cDNAs were in vitro transcribed and fragmented. The three prime fragments were reverse transcribed and prepared for sequencing Libraries were prepared as described in Zilionis et al.  2017  Nature Protocols PMID = 27929523", null, "tissue:brain|developmental stage:23 25dpf", "GSM2813946", "GSM2813946: DEW120 f3; Danio rerio; RNA Seq", "GSM2813946", null, "1", "Single cell suspensions were processed through inDrops to generate single cell cDNA libraries. cDNAs were in vitro transcribed and fragmented. The three prime fragments were reverse transcribed and prepared for sequencing Libraries were prepared as described in Zilionis et al.  2017  Nature Protocols PMID = 27929523", "GEO Accession:GSM2813946", "RNA-Seq", "TRANSCRIPTOMIC", "cDNA", "SINGLE", "ILLUMINA", "NextSeq 500", null, "SRP120009", null, "loader:fastq load.py|options:  appendBCtoName", "DEW120_Lane2.sorted.fastq.gz", "fastq", 1155369050.0, 19589150.0, "GSM2813946 r2", "0:58.98", "A:322620492;C:223054841;G:232826921;T:376859892;N:6904", 58, null, null, null, 322620492, 223054841, 232826921, 376859892, 6904, "SRX3287376", "SRS2596850", "SRA619743", "GEO", "Harvard University", 1, 0.90311, null, 0.23416, null, 0.78589, null, 0.55648, null, 61, null, "B", null, "usable mapping rate", "illumina", "nextseq", "unknown", "cdna_unspecified", "unknown", "sc", "single_cell_droplet", "indrops", null, "United States", "2017-10-16", "Larval", "Larval", "Brain", "Nervous System"], [43921, "SRR6176670", "SRX3287375", "SRS2596849", "SRP120009", "PRJNA414416", "Simultaneous single cell profiling of lineages and cell types in the vertebrate brain", "GSE105010", "Other", "The lineage relationships among the hundreds of cell types generated during development are difficult to reconstruct. A recent method  GESTALT  used CRISPR\u2013Cas9 barcode editing for large scale lineage tracing  but was restricted to early development and did not identify cell types. Here we present scGESTALT  which combines the lineage recording capabilities of GESTALT with cell type identification by single cell RNA sequencing. The method relies on an inducible system that enables barcodes to be edited at multiple time points  capturing lineage information from later stages of development. Sequencing of 60 000 transcriptomes from the juvenile zebrafish brain identified >100 cell types and marker genes. Using these data  we generate lineage trees with hundreds of branches that help uncover restrictions at the level of cell types  brain regions  and gene expression cascades during differentiation. scGESTALT can be applied to other multicellular organisms to simultaneously characterize molecular identities and lineage histories of thousands of cells during development and disease. Overall design: inDrops libraries of single cell transcriptomes and scGESTALT barcodes  and genomic DNA GESTALT libraries", null, "pubmed:29608178", null, "DEW119 f3", "GSM2813945", null, "source name:zebrafish brain|tissue:brain|developmental stage:23 25dpf", "DEW119 f3", "Single cell RNA Sequencing data FASTQ files were processed using the inDrops.py bioinformatics pipeline available at https://github.com/indrops/indrops. Transcriptome libraries were mapped to a zebrafish reference built from a custom GTF file and the zebrafish GRCz10 release 86 genome assembly. Bowtie version1.1.1 was used with parameter \u2013e 200; UMI quantification was used with parameter \u2013u 2 counts were ignored from UMIs split between more than 2 genes. genomic DNA GESTALT and scGESTALT libraries were processed using a custom pipeline available at https://github.com/shendurelab/Cas9FateMapping Genome build: GRCz10 CSV files for transcriptome data were generated using the inDrops pipeline. Each column in the CSV files contains a cell identifier and each row contains expression values for genes. Txt files for genomic DNA GESTALT libraries *allReadCounts contain lineage barcode sequences HMID column for each cell and their proportion in the sequenced libraries. scGESTALT data *GestMaster.txt contains the inDrops cell identifiers CellBarcode and BarcodeKey that were used to match barcodes to transcriptomes. They also contain lineage barcode sequences HMID column for each cell with a corresponding inDrops single cell gene expression profile  as well as the the t SNE cluster membership number ClusterIdent column Txt files ending in *stats.txt contain information about the each individually captured UMI or cell per sample. The barcode sequence aligned to a reference unedited sequence mergedRead column  mutations at each target site target[X] columns and the edited sequences at each target site sequence[X] columns were used for downstream analysis. inDropsExpMatrix noQ txt file is the gene expression matrix for the full dataset. Columns are individual cells from different batches of whole brains f1   f2   f3   f4   f5   f6  or brain regions fore   mid   hind . fall.inDrops.Robj is the processed Seurat R object  which can be loaded into R and explored.", "zebrafish brain", null, "Single cell suspensions were processed through inDrops to generate single cell cDNA libraries. cDNAs were in vitro transcribed and fragmented. The three prime fragments were reverse transcribed and prepared for sequencing Libraries were prepared as described in Zilionis et al.  2017  Nature Protocols PMID = 27929523", null, "tissue:brain|developmental stage:23 25dpf", "GSM2813945", "GSM2813945: DEW119 f3; Danio rerio; RNA Seq", "GSM2813945", null, "1", "Single cell suspensions were processed through inDrops to generate single cell cDNA libraries. cDNAs were in vitro transcribed and fragmented. The three prime fragments were reverse transcribed and prepared for sequencing Libraries were prepared as described in Zilionis et al.  2017  Nature Protocols PMID = 27929523", "GEO Accession:GSM2813945", "RNA-Seq", "TRANSCRIPTOMIC", "cDNA", "SINGLE", "ILLUMINA", "NextSeq 500", null, "SRP120009", null, "loader:fastq load.py|options:  appendBCtoName", "DEW119_Lane1.sorted.fastq.gz", "fastq", 1651297917.0, 27865122.0, "GSM2813945 r1", "0:59.26", "A:461526493;C:319892429;G:328940324;T:540930270;N:8401", 59, null, null, null, 461526493, 319892429, 328940324, 540930270, 8401, "SRX3287375", "SRS2596849", "SRA619743", "GEO", "Harvard University", 1, 0.90335, null, 0.2353, null, 0.78356, null, 0.55771, null, 61, null, "B", null, "usable mapping rate", "illumina", "nextseq", "unknown", "cdna_unspecified", "unknown", "sc", "single_cell_droplet", "indrops", null, "United States", "2017-10-16", "Larval", "Larval", "Brain", "Nervous System"], [43922, "SRR6176671", "SRX3287375", "SRS2596849", "SRP120009", "PRJNA414416", "Simultaneous single cell profiling of lineages and cell types in the vertebrate brain", "GSE105010", "Other", "The lineage relationships among the hundreds of cell types generated during development are difficult to reconstruct. A recent method  GESTALT  used CRISPR\u2013Cas9 barcode editing for large scale lineage tracing  but was restricted to early development and did not identify cell types. Here we present scGESTALT  which combines the lineage recording capabilities of GESTALT with cell type identification by single cell RNA sequencing. The method relies on an inducible system that enables barcodes to be edited at multiple time points  capturing lineage information from later stages of development. Sequencing of 60 000 transcriptomes from the juvenile zebrafish brain identified >100 cell types and marker genes. Using these data  we generate lineage trees with hundreds of branches that help uncover restrictions at the level of cell types  brain regions  and gene expression cascades during differentiation. scGESTALT can be applied to other multicellular organisms to simultaneously characterize molecular identities and lineage histories of thousands of cells during development and disease. Overall design: inDrops libraries of single cell transcriptomes and scGESTALT barcodes  and genomic DNA GESTALT libraries", null, "pubmed:29608178", null, "DEW119 f3", "GSM2813945", null, "source name:zebrafish brain|tissue:brain|developmental stage:23 25dpf", "DEW119 f3", "Single cell RNA Sequencing data FASTQ files were processed using the inDrops.py bioinformatics pipeline available at https://github.com/indrops/indrops. Transcriptome libraries were mapped to a zebrafish reference built from a custom GTF file and the zebrafish GRCz10 release 86 genome assembly. Bowtie version1.1.1 was used with parameter \u2013e 200; UMI quantification was used with parameter \u2013u 2 counts were ignored from UMIs split between more than 2 genes. genomic DNA GESTALT and scGESTALT libraries were processed using a custom pipeline available at https://github.com/shendurelab/Cas9FateMapping Genome build: GRCz10 CSV files for transcriptome data were generated using the inDrops pipeline. Each column in the CSV files contains a cell identifier and each row contains expression values for genes. Txt files for genomic DNA GESTALT libraries *allReadCounts contain lineage barcode sequences HMID column for each cell and their proportion in the sequenced libraries. scGESTALT data *GestMaster.txt contains the inDrops cell identifiers CellBarcode and BarcodeKey that were used to match barcodes to transcriptomes. They also contain lineage barcode sequences HMID column for each cell with a corresponding inDrops single cell gene expression profile  as well as the the t SNE cluster membership number ClusterIdent column Txt files ending in *stats.txt contain information about the each individually captured UMI or cell per sample. The barcode sequence aligned to a reference unedited sequence mergedRead column  mutations at each target site target[X] columns and the edited sequences at each target site sequence[X] columns were used for downstream analysis. inDropsExpMatrix noQ txt file is the gene expression matrix for the full dataset. Columns are individual cells from different batches of whole brains f1   f2   f3   f4   f5   f6  or brain regions fore   mid   hind . fall.inDrops.Robj is the processed Seurat R object  which can be loaded into R and explored.", "zebrafish brain", null, "Single cell suspensions were processed through inDrops to generate single cell cDNA libraries. cDNAs were in vitro transcribed and fragmented. The three prime fragments were reverse transcribed and prepared for sequencing Libraries were prepared as described in Zilionis et al.  2017  Nature Protocols PMID = 27929523", null, "tissue:brain|developmental stage:23 25dpf", "GSM2813945", "GSM2813945: DEW119 f3; Danio rerio; RNA Seq", "GSM2813945", null, "1", "Single cell suspensions were processed through inDrops to generate single cell cDNA libraries. cDNAs were in vitro transcribed and fragmented. The three prime fragments were reverse transcribed and prepared for sequencing Libraries were prepared as described in Zilionis et al.  2017  Nature Protocols PMID = 27929523", "GEO Accession:GSM2813945", "RNA-Seq", "TRANSCRIPTOMIC", "cDNA", "SINGLE", "ILLUMINA", "NextSeq 500", null, "SRP120009", null, "loader:fastq load.py|options:  appendBCtoName", "DEW119_Lane2.sorted.fastq.gz", "fastq", 1154728354.0, 19605904.0, "GSM2813945 r2", "0:58.90", "A:322008085;C:223457278;G:230921827;T:378334350;N:6814", 58, null, null, null, 322008085, 223457278, 230921827, 378334350, 6814, "SRX3287375", "SRS2596849", "SRA619743", "GEO", "Harvard University", 1, 0.9009, null, 0.23779, null, 0.78675, null, 0.55362, null, 61, null, "B", null, "usable mapping rate", "illumina", "nextseq", "unknown", "cdna_unspecified", "unknown", "sc", "single_cell_droplet", "indrops", null, "United States", "2017-10-16", "Larval", "Larval", "Brain", "Nervous System"], [43923, "SRR6176668", "SRX3287374", "SRS2596848", "SRP120009", "PRJNA414416", "Simultaneous single cell profiling of lineages and cell types in the vertebrate brain", "GSE105010", "Other", "The lineage relationships among the hundreds of cell types generated during development are difficult to reconstruct. A recent method  GESTALT  used CRISPR\u2013Cas9 barcode editing for large scale lineage tracing  but was restricted to early development and did not identify cell types. Here we present scGESTALT  which combines the lineage recording capabilities of GESTALT with cell type identification by single cell RNA sequencing. The method relies on an inducible system that enables barcodes to be edited at multiple time points  capturing lineage information from later stages of development. Sequencing of 60 000 transcriptomes from the juvenile zebrafish brain identified >100 cell types and marker genes. Using these data  we generate lineage trees with hundreds of branches that help uncover restrictions at the level of cell types  brain regions  and gene expression cascades during differentiation. scGESTALT can be applied to other multicellular organisms to simultaneously characterize molecular identities and lineage histories of thousands of cells during development and disease. Overall design: inDrops libraries of single cell transcriptomes and scGESTALT barcodes  and genomic DNA GESTALT libraries", null, "pubmed:29608178", null, "DEW118 f3", "GSM2813944", null, "source name:zebrafish brain|tissue:brain|developmental stage:23 25dpf", "DEW118 f3", "Single cell RNA Sequencing data FASTQ files were processed using the inDrops.py bioinformatics pipeline available at https://github.com/indrops/indrops. Transcriptome libraries were mapped to a zebrafish reference built from a custom GTF file and the zebrafish GRCz10 release 86 genome assembly. Bowtie version1.1.1 was used with parameter \u2013e 200; UMI quantification was used with parameter \u2013u 2 counts were ignored from UMIs split between more than 2 genes. genomic DNA GESTALT and scGESTALT libraries were processed using a custom pipeline available at https://github.com/shendurelab/Cas9FateMapping Genome build: GRCz10 CSV files for transcriptome data were generated using the inDrops pipeline. Each column in the CSV files contains a cell identifier and each row contains expression values for genes. Txt files for genomic DNA GESTALT libraries *allReadCounts contain lineage barcode sequences HMID column for each cell and their proportion in the sequenced libraries. scGESTALT data *GestMaster.txt contains the inDrops cell identifiers CellBarcode and BarcodeKey that were used to match barcodes to transcriptomes. They also contain lineage barcode sequences HMID column for each cell with a corresponding inDrops single cell gene expression profile  as well as the the t SNE cluster membership number ClusterIdent column Txt files ending in *stats.txt contain information about the each individually captured UMI or cell per sample. The barcode sequence aligned to a reference unedited sequence mergedRead column  mutations at each target site target[X] columns and the edited sequences at each target site sequence[X] columns were used for downstream analysis. inDropsExpMatrix noQ txt file is the gene expression matrix for the full dataset. Columns are individual cells from different batches of whole brains f1   f2   f3   f4   f5   f6  or brain regions fore   mid   hind . fall.inDrops.Robj is the processed Seurat R object  which can be loaded into R and explored.", "zebrafish brain", null, "Single cell suspensions were processed through inDrops to generate single cell cDNA libraries. cDNAs were in vitro transcribed and fragmented. The three prime fragments were reverse transcribed and prepared for sequencing Libraries were prepared as described in Zilionis et al.  2017  Nature Protocols PMID = 27929523", null, "tissue:brain|developmental stage:23 25dpf", "GSM2813944", "GSM2813944: DEW118 f3; Danio rerio; RNA Seq", "GSM2813944", null, "1", "Single cell suspensions were processed through inDrops to generate single cell cDNA libraries. cDNAs were in vitro transcribed and fragmented. The three prime fragments were reverse transcribed and prepared for sequencing Libraries were prepared as described in Zilionis et al.  2017  Nature Protocols PMID = 27929523", "GEO Accession:GSM2813944", "RNA-Seq", "TRANSCRIPTOMIC", "cDNA", "SINGLE", "ILLUMINA", "NextSeq 500", null, "SRP120009", null, "loader:fastq load.py|options:  appendBCtoName", "DEW118_Lane1.sorted.fastq.gz", "fastq", 1415243706.0, 23892816.0, "GSM2813944 r1", "0:59.23", "A:396812504;C:275820359;G:281479588;T:461124060;N:7195", 59, null, null, null, 396812504, 275820359, 281479588, 461124060, 7195, "SRX3287374", "SRS2596848", "SRA619743", "GEO", "Harvard University", 1, 0.90294, null, 0.23426, null, 0.78595, null, 0.52576, null, 61, null, "B", null, "usable mapping rate", "illumina", "nextseq", "unknown", "cdna_unspecified", "unknown", "sc", "single_cell_droplet", "indrops", null, "United States", "2017-10-16", "Larval", "Larval", "Brain", "Nervous System"], [43924, "SRR6176669", "SRX3287374", "SRS2596848", "SRP120009", "PRJNA414416", "Simultaneous single cell profiling of lineages and cell types in the vertebrate brain", "GSE105010", "Other", "The lineage relationships among the hundreds of cell types generated during development are difficult to reconstruct. A recent method  GESTALT  used CRISPR\u2013Cas9 barcode editing for large scale lineage tracing  but was restricted to early development and did not identify cell types. Here we present scGESTALT  which combines the lineage recording capabilities of GESTALT with cell type identification by single cell RNA sequencing. The method relies on an inducible system that enables barcodes to be edited at multiple time points  capturing lineage information from later stages of development. Sequencing of 60 000 transcriptomes from the juvenile zebrafish brain identified >100 cell types and marker genes. Using these data  we generate lineage trees with hundreds of branches that help uncover restrictions at the level of cell types  brain regions  and gene expression cascades during differentiation. scGESTALT can be applied to other multicellular organisms to simultaneously characterize molecular identities and lineage histories of thousands of cells during development and disease. Overall design: inDrops libraries of single cell transcriptomes and scGESTALT barcodes  and genomic DNA GESTALT libraries", null, "pubmed:29608178", null, "DEW118 f3", "GSM2813944", null, "source name:zebrafish brain|tissue:brain|developmental stage:23 25dpf", "DEW118 f3", "Single cell RNA Sequencing data FASTQ files were processed using the inDrops.py bioinformatics pipeline available at https://github.com/indrops/indrops. Transcriptome libraries were mapped to a zebrafish reference built from a custom GTF file and the zebrafish GRCz10 release 86 genome assembly. Bowtie version1.1.1 was used with parameter \u2013e 200; UMI quantification was used with parameter \u2013u 2 counts were ignored from UMIs split between more than 2 genes. genomic DNA GESTALT and scGESTALT libraries were processed using a custom pipeline available at https://github.com/shendurelab/Cas9FateMapping Genome build: GRCz10 CSV files for transcriptome data were generated using the inDrops pipeline. Each column in the CSV files contains a cell identifier and each row contains expression values for genes. Txt files for genomic DNA GESTALT libraries *allReadCounts contain lineage barcode sequences HMID column for each cell and their proportion in the sequenced libraries. scGESTALT data *GestMaster.txt contains the inDrops cell identifiers CellBarcode and BarcodeKey that were used to match barcodes to transcriptomes. They also contain lineage barcode sequences HMID column for each cell with a corresponding inDrops single cell gene expression profile  as well as the the t SNE cluster membership number ClusterIdent column Txt files ending in *stats.txt contain information about the each individually captured UMI or cell per sample. The barcode sequence aligned to a reference unedited sequence mergedRead column  mutations at each target site target[X] columns and the edited sequences at each target site sequence[X] columns were used for downstream analysis. inDropsExpMatrix noQ txt file is the gene expression matrix for the full dataset. Columns are individual cells from different batches of whole brains f1   f2   f3   f4   f5   f6  or brain regions fore   mid   hind . fall.inDrops.Robj is the processed Seurat R object  which can be loaded into R and explored.", "zebrafish brain", null, "Single cell suspensions were processed through inDrops to generate single cell cDNA libraries. cDNAs were in vitro transcribed and fragmented. The three prime fragments were reverse transcribed and prepared for sequencing Libraries were prepared as described in Zilionis et al.  2017  Nature Protocols PMID = 27929523", null, "tissue:brain|developmental stage:23 25dpf", "GSM2813944", "GSM2813944: DEW118 f3; Danio rerio; RNA Seq", "GSM2813944", null, "1", "Single cell suspensions were processed through inDrops to generate single cell cDNA libraries. cDNAs were in vitro transcribed and fragmented. The three prime fragments were reverse transcribed and prepared for sequencing Libraries were prepared as described in Zilionis et al.  2017  Nature Protocols PMID = 27929523", "GEO Accession:GSM2813944", "RNA-Seq", "TRANSCRIPTOMIC", "cDNA", "SINGLE", "ILLUMINA", "NextSeq 500", null, "SRP120009", null, "loader:fastq load.py|options:  appendBCtoName", "DEW118_Lane2.sorted.fastq.gz", "fastq", 984518204.0, 16715299.0, "GSM2813944 r2", "0:58.90", "A:275603270;C:191766995;G:196356019;T:320786079;N:5841", 58, null, null, null, 275603270, 191766995, 196356019, 320786079, 5841, "SRX3287374", "SRS2596848", "SRA619743", "GEO", "Harvard University", 1, 0.902, null, 0.23477, null, 0.78739, null, 0.5585, null, 61, null, "B", null, "usable mapping rate", "illumina", "nextseq", "unknown", "cdna_unspecified", "unknown", "sc", "single_cell_droplet", "indrops", null, "United States", "2017-10-16", "Larval", "Larval", "Brain", "Nervous System"], [43925, "SRR6176666", "SRX3287373", "SRS2596847", "SRP120009", "PRJNA414416", "Simultaneous single cell profiling of lineages and cell types in the vertebrate brain", "GSE105010", "Other", "The lineage relationships among the hundreds of cell types generated during development are difficult to reconstruct. A recent method  GESTALT  used CRISPR\u2013Cas9 barcode editing for large scale lineage tracing  but was restricted to early development and did not identify cell types. Here we present scGESTALT  which combines the lineage recording capabilities of GESTALT with cell type identification by single cell RNA sequencing. The method relies on an inducible system that enables barcodes to be edited at multiple time points  capturing lineage information from later stages of development. Sequencing of 60 000 transcriptomes from the juvenile zebrafish brain identified >100 cell types and marker genes. Using these data  we generate lineage trees with hundreds of branches that help uncover restrictions at the level of cell types  brain regions  and gene expression cascades during differentiation. scGESTALT can be applied to other multicellular organisms to simultaneously characterize molecular identities and lineage histories of thousands of cells during development and disease. Overall design: inDrops libraries of single cell transcriptomes and scGESTALT barcodes  and genomic DNA GESTALT libraries", null, "pubmed:29608178", null, "DEW117 f3", "GSM2813943", null, "source name:zebrafish brain|tissue:brain|developmental stage:23 25dpf", "DEW117 f3", "Single cell RNA Sequencing data FASTQ files were processed using the inDrops.py bioinformatics pipeline available at https://github.com/indrops/indrops. Transcriptome libraries were mapped to a zebrafish reference built from a custom GTF file and the zebrafish GRCz10 release 86 genome assembly. Bowtie version1.1.1 was used with parameter \u2013e 200; UMI quantification was used with parameter \u2013u 2 counts were ignored from UMIs split between more than 2 genes. genomic DNA GESTALT and scGESTALT libraries were processed using a custom pipeline available at https://github.com/shendurelab/Cas9FateMapping Genome build: GRCz10 CSV files for transcriptome data were generated using the inDrops pipeline. Each column in the CSV files contains a cell identifier and each row contains expression values for genes. Txt files for genomic DNA GESTALT libraries *allReadCounts contain lineage barcode sequences HMID column for each cell and their proportion in the sequenced libraries. scGESTALT data *GestMaster.txt contains the inDrops cell identifiers CellBarcode and BarcodeKey that were used to match barcodes to transcriptomes. They also contain lineage barcode sequences HMID column for each cell with a corresponding inDrops single cell gene expression profile  as well as the the t SNE cluster membership number ClusterIdent column Txt files ending in *stats.txt contain information about the each individually captured UMI or cell per sample. The barcode sequence aligned to a reference unedited sequence mergedRead column  mutations at each target site target[X] columns and the edited sequences at each target site sequence[X] columns were used for downstream analysis. inDropsExpMatrix noQ txt file is the gene expression matrix for the full dataset. Columns are individual cells from different batches of whole brains f1   f2   f3   f4   f5   f6  or brain regions fore   mid   hind . fall.inDrops.Robj is the processed Seurat R object  which can be loaded into R and explored.", "zebrafish brain", null, "Single cell suspensions were processed through inDrops to generate single cell cDNA libraries. cDNAs were in vitro transcribed and fragmented. The three prime fragments were reverse transcribed and prepared for sequencing Libraries were prepared as described in Zilionis et al.  2017  Nature Protocols PMID = 27929523", null, "tissue:brain|developmental stage:23 25dpf", "GSM2813943", "GSM2813943: DEW117 f3; Danio rerio; RNA Seq", "GSM2813943", null, "1", "Single cell suspensions were processed through inDrops to generate single cell cDNA libraries. cDNAs were in vitro transcribed and fragmented. The three prime fragments were reverse transcribed and prepared for sequencing Libraries were prepared as described in Zilionis et al.  2017  Nature Protocols PMID = 27929523", "GEO Accession:GSM2813943", "RNA-Seq", "TRANSCRIPTOMIC", "cDNA", "SINGLE", "ILLUMINA", "NextSeq 500", null, "SRP120009", null, "loader:fastq load.py|options:  appendBCtoName", "DEW117_Lane1.sorted.fastq.gz", "fastq", 1428952772.0, 24097871.0, "GSM2813943 r1", "0:59.30", "A:400414379;C:277758163;G:284791203;T:465981663;N:7364", 59, null, null, null, 400414379, 277758163, 284791203, 465981663, 7364, "SRX3287373", "SRS2596847", "SRA619743", "GEO", "Harvard University", 1, 0.90144, null, 0.23429, null, 0.78551, null, 0.53009, null, 60, null, "B", null, "usable mapping rate", "illumina", "nextseq", "unknown", "cdna_unspecified", "unknown", "sc", "single_cell_droplet", "indrops", null, "United States", "2017-10-16", "Larval", "Larval", "Brain", "Nervous System"], [43926, "SRR6176667", "SRX3287373", "SRS2596847", "SRP120009", "PRJNA414416", "Simultaneous single cell profiling of lineages and cell types in the vertebrate brain", "GSE105010", "Other", "The lineage relationships among the hundreds of cell types generated during development are difficult to reconstruct. A recent method  GESTALT  used CRISPR\u2013Cas9 barcode editing for large scale lineage tracing  but was restricted to early development and did not identify cell types. Here we present scGESTALT  which combines the lineage recording capabilities of GESTALT with cell type identification by single cell RNA sequencing. The method relies on an inducible system that enables barcodes to be edited at multiple time points  capturing lineage information from later stages of development. Sequencing of 60 000 transcriptomes from the juvenile zebrafish brain identified >100 cell types and marker genes. Using these data  we generate lineage trees with hundreds of branches that help uncover restrictions at the level of cell types  brain regions  and gene expression cascades during differentiation. scGESTALT can be applied to other multicellular organisms to simultaneously characterize molecular identities and lineage histories of thousands of cells during development and disease. Overall design: inDrops libraries of single cell transcriptomes and scGESTALT barcodes  and genomic DNA GESTALT libraries", null, "pubmed:29608178", null, "DEW117 f3", "GSM2813943", null, "source name:zebrafish brain|tissue:brain|developmental stage:23 25dpf", "DEW117 f3", "Single cell RNA Sequencing data FASTQ files were processed using the inDrops.py bioinformatics pipeline available at https://github.com/indrops/indrops. Transcriptome libraries were mapped to a zebrafish reference built from a custom GTF file and the zebrafish GRCz10 release 86 genome assembly. Bowtie version1.1.1 was used with parameter \u2013e 200; UMI quantification was used with parameter \u2013u 2 counts were ignored from UMIs split between more than 2 genes. genomic DNA GESTALT and scGESTALT libraries were processed using a custom pipeline available at https://github.com/shendurelab/Cas9FateMapping Genome build: GRCz10 CSV files for transcriptome data were generated using the inDrops pipeline. Each column in the CSV files contains a cell identifier and each row contains expression values for genes. Txt files for genomic DNA GESTALT libraries *allReadCounts contain lineage barcode sequences HMID column for each cell and their proportion in the sequenced libraries. scGESTALT data *GestMaster.txt contains the inDrops cell identifiers CellBarcode and BarcodeKey that were used to match barcodes to transcriptomes. They also contain lineage barcode sequences HMID column for each cell with a corresponding inDrops single cell gene expression profile  as well as the the t SNE cluster membership number ClusterIdent column Txt files ending in *stats.txt contain information about the each individually captured UMI or cell per sample. The barcode sequence aligned to a reference unedited sequence mergedRead column  mutations at each target site target[X] columns and the edited sequences at each target site sequence[X] columns were used for downstream analysis. inDropsExpMatrix noQ txt file is the gene expression matrix for the full dataset. Columns are individual cells from different batches of whole brains f1   f2   f3   f4   f5   f6  or brain regions fore   mid   hind . fall.inDrops.Robj is the processed Seurat R object  which can be loaded into R and explored.", "zebrafish brain", null, "Single cell suspensions were processed through inDrops to generate single cell cDNA libraries. cDNAs were in vitro transcribed and fragmented. The three prime fragments were reverse transcribed and prepared for sequencing Libraries were prepared as described in Zilionis et al.  2017  Nature Protocols PMID = 27929523", null, "tissue:brain|developmental stage:23 25dpf", "GSM2813943", "GSM2813943: DEW117 f3; Danio rerio; RNA Seq", "GSM2813943", null, "1", "Single cell suspensions were processed through inDrops to generate single cell cDNA libraries. cDNAs were in vitro transcribed and fragmented. The three prime fragments were reverse transcribed and prepared for sequencing Libraries were prepared as described in Zilionis et al.  2017  Nature Protocols PMID = 27929523", "GEO Accession:GSM2813943", "RNA-Seq", "TRANSCRIPTOMIC", "cDNA", "SINGLE", "ILLUMINA", "NextSeq 500", null, "SRP120009", null, "loader:fastq load.py|options:  appendBCtoName", "DEW117_Lane2.sorted.fastq.gz", "fastq", 1011980420.0, 17162523.0, "GSM2813943 r2", "0:58.96", "A:282793765;C:196692351;G:202514378;T:329973834;N:6092", 58, null, null, null, 282793765, 196692351, 202514378, 329973834, 6092, "SRX3287373", "SRS2596847", "SRA619743", "GEO", "Harvard University", 1, 0.90206, null, 0.23615, null, 0.78882, null, 0.55621, null, 61, null, "B", null, "usable mapping rate", "illumina", "nextseq", "unknown", "cdna_unspecified", "unknown", "sc", "single_cell_droplet", "indrops", null, "United States", "2017-10-16", "Larval", "Larval", "Brain", "Nervous System"], [43927, "SRR6176664", "SRX3287372", "SRS2596846", "SRP120009", "PRJNA414416", "Simultaneous single cell profiling of lineages and cell types in the vertebrate brain", "GSE105010", "Other", "The lineage relationships among the hundreds of cell types generated during development are difficult to reconstruct. A recent method  GESTALT  used CRISPR\u2013Cas9 barcode editing for large scale lineage tracing  but was restricted to early development and did not identify cell types. Here we present scGESTALT  which combines the lineage recording capabilities of GESTALT with cell type identification by single cell RNA sequencing. The method relies on an inducible system that enables barcodes to be edited at multiple time points  capturing lineage information from later stages of development. Sequencing of 60 000 transcriptomes from the juvenile zebrafish brain identified >100 cell types and marker genes. Using these data  we generate lineage trees with hundreds of branches that help uncover restrictions at the level of cell types  brain regions  and gene expression cascades during differentiation. scGESTALT can be applied to other multicellular organisms to simultaneously characterize molecular identities and lineage histories of thousands of cells during development and disease. Overall design: inDrops libraries of single cell transcriptomes and scGESTALT barcodes  and genomic DNA GESTALT libraries", null, "pubmed:29608178", null, "DEW116 f3", "GSM2813942", null, "source name:zebrafish brain|tissue:brain|developmental stage:23 25dpf", "DEW116 f3", "Single cell RNA Sequencing data FASTQ files were processed using the inDrops.py bioinformatics pipeline available at https://github.com/indrops/indrops. Transcriptome libraries were mapped to a zebrafish reference built from a custom GTF file and the zebrafish GRCz10 release 86 genome assembly. Bowtie version1.1.1 was used with parameter \u2013e 200; UMI quantification was used with parameter \u2013u 2 counts were ignored from UMIs split between more than 2 genes. genomic DNA GESTALT and scGESTALT libraries were processed using a custom pipeline available at https://github.com/shendurelab/Cas9FateMapping Genome build: GRCz10 CSV files for transcriptome data were generated using the inDrops pipeline. Each column in the CSV files contains a cell identifier and each row contains expression values for genes. Txt files for genomic DNA GESTALT libraries *allReadCounts contain lineage barcode sequences HMID column for each cell and their proportion in the sequenced libraries. scGESTALT data *GestMaster.txt contains the inDrops cell identifiers CellBarcode and BarcodeKey that were used to match barcodes to transcriptomes. They also contain lineage barcode sequences HMID column for each cell with a corresponding inDrops single cell gene expression profile  as well as the the t SNE cluster membership number ClusterIdent column Txt files ending in *stats.txt contain information about the each individually captured UMI or cell per sample. The barcode sequence aligned to a reference unedited sequence mergedRead column  mutations at each target site target[X] columns and the edited sequences at each target site sequence[X] columns were used for downstream analysis. inDropsExpMatrix noQ txt file is the gene expression matrix for the full dataset. Columns are individual cells from different batches of whole brains f1   f2   f3   f4   f5   f6  or brain regions fore   mid   hind . fall.inDrops.Robj is the processed Seurat R object  which can be loaded into R and explored.", "zebrafish brain", null, "Single cell suspensions were processed through inDrops to generate single cell cDNA libraries. cDNAs were in vitro transcribed and fragmented. The three prime fragments were reverse transcribed and prepared for sequencing Libraries were prepared as described in Zilionis et al.  2017  Nature Protocols PMID = 27929523", null, "tissue:brain|developmental stage:23 25dpf", "GSM2813942", "GSM2813942: DEW116 f3; Danio rerio; RNA Seq", "GSM2813942", null, "1", "Single cell suspensions were processed through inDrops to generate single cell cDNA libraries. cDNAs were in vitro transcribed and fragmented. The three prime fragments were reverse transcribed and prepared for sequencing Libraries were prepared as described in Zilionis et al.  2017  Nature Protocols PMID = 27929523", "GEO Accession:GSM2813942", "RNA-Seq", "TRANSCRIPTOMIC", "cDNA", "SINGLE", "ILLUMINA", "NextSeq 500", null, "SRP120009", null, "loader:fastq load.py|options:  appendBCtoName", "DEW116_Lane1.sorted.fastq.gz", "fastq", 2012245019.0, 33924773.0, "GSM2813942 r1", "0:59.31", "A:560262631;C:390864845;G:401224604;T:659882586;N:10353", 59, null, null, null, 560262631, 390864845, 401224604, 659882586, 10353, "SRX3287372", "SRS2596846", "SRA619743", "GEO", "Harvard University", 1, 0.90562, null, 0.21992, null, 0.78693, null, 0.5534, null, 59, null, "B", null, "usable mapping rate", "illumina", "nextseq", "unknown", "cdna_unspecified", "unknown", "sc", "single_cell_droplet", "indrops", null, "United States", "2017-10-16", "Larval", "Larval", "Brain", "Nervous System"], [43928, "SRR6176665", "SRX3287372", "SRS2596846", "SRP120009", "PRJNA414416", "Simultaneous single cell profiling of lineages and cell types in the vertebrate brain", "GSE105010", "Other", "The lineage relationships among the hundreds of cell types generated during development are difficult to reconstruct. A recent method  GESTALT  used CRISPR\u2013Cas9 barcode editing for large scale lineage tracing  but was restricted to early development and did not identify cell types. Here we present scGESTALT  which combines the lineage recording capabilities of GESTALT with cell type identification by single cell RNA sequencing. The method relies on an inducible system that enables barcodes to be edited at multiple time points  capturing lineage information from later stages of development. Sequencing of 60 000 transcriptomes from the juvenile zebrafish brain identified >100 cell types and marker genes. Using these data  we generate lineage trees with hundreds of branches that help uncover restrictions at the level of cell types  brain regions  and gene expression cascades during differentiation. scGESTALT can be applied to other multicellular organisms to simultaneously characterize molecular identities and lineage histories of thousands of cells during development and disease. Overall design: inDrops libraries of single cell transcriptomes and scGESTALT barcodes  and genomic DNA GESTALT libraries", null, "pubmed:29608178", null, "DEW116 f3", "GSM2813942", null, "source name:zebrafish brain|tissue:brain|developmental stage:23 25dpf", "DEW116 f3", "Single cell RNA Sequencing data FASTQ files were processed using the inDrops.py bioinformatics pipeline available at https://github.com/indrops/indrops. Transcriptome libraries were mapped to a zebrafish reference built from a custom GTF file and the zebrafish GRCz10 release 86 genome assembly. Bowtie version1.1.1 was used with parameter \u2013e 200; UMI quantification was used with parameter \u2013u 2 counts were ignored from UMIs split between more than 2 genes. genomic DNA GESTALT and scGESTALT libraries were processed using a custom pipeline available at https://github.com/shendurelab/Cas9FateMapping Genome build: GRCz10 CSV files for transcriptome data were generated using the inDrops pipeline. Each column in the CSV files contains a cell identifier and each row contains expression values for genes. Txt files for genomic DNA GESTALT libraries *allReadCounts contain lineage barcode sequences HMID column for each cell and their proportion in the sequenced libraries. scGESTALT data *GestMaster.txt contains the inDrops cell identifiers CellBarcode and BarcodeKey that were used to match barcodes to transcriptomes. They also contain lineage barcode sequences HMID column for each cell with a corresponding inDrops single cell gene expression profile  as well as the the t SNE cluster membership number ClusterIdent column Txt files ending in *stats.txt contain information about the each individually captured UMI or cell per sample. The barcode sequence aligned to a reference unedited sequence mergedRead column  mutations at each target site target[X] columns and the edited sequences at each target site sequence[X] columns were used for downstream analysis. inDropsExpMatrix noQ txt file is the gene expression matrix for the full dataset. Columns are individual cells from different batches of whole brains f1   f2   f3   f4   f5   f6  or brain regions fore   mid   hind . fall.inDrops.Robj is the processed Seurat R object  which can be loaded into R and explored.", "zebrafish brain", null, "Single cell suspensions were processed through inDrops to generate single cell cDNA libraries. cDNAs were in vitro transcribed and fragmented. The three prime fragments were reverse transcribed and prepared for sequencing Libraries were prepared as described in Zilionis et al.  2017  Nature Protocols PMID = 27929523", null, "tissue:brain|developmental stage:23 25dpf", "GSM2813942", "GSM2813942: DEW116 f3; Danio rerio; RNA Seq", "GSM2813942", null, "1", "Single cell suspensions were processed through inDrops to generate single cell cDNA libraries. cDNAs were in vitro transcribed and fragmented. The three prime fragments were reverse transcribed and prepared for sequencing Libraries were prepared as described in Zilionis et al.  2017  Nature Protocols PMID = 27929523", "GEO Accession:GSM2813942", "RNA-Seq", "TRANSCRIPTOMIC", "cDNA", "SINGLE", "ILLUMINA", "NextSeq 500", null, "SRP120009", null, "loader:fastq load.py|options:  appendBCtoName", "DEW116_Lane2.sorted.fastq.gz", "fastq", 1423765067.0, 24137401.0, "GSM2813942 r2", "0:58.99", "A:395355082;C:276477238;G:285074262;T:466849922;N:8563", 58, null, null, null, 395355082, 276477238, 285074262, 466849922, 8563, "SRX3287372", "SRS2596846", "SRA619743", "GEO", "Harvard University", 1, 0.90416, null, 0.22121, null, 0.78675, null, 0.55396, null, 61, null, "B", null, "usable mapping rate", "illumina", "nextseq", "unknown", "cdna_unspecified", "unknown", "sc", "single_cell_droplet", "indrops", null, "United States", "2017-10-16", "Larval", "Larval", "Brain", "Nervous System"]], "truncated": false, "filtered_table_rows_count": 308, "expanded_columns": [], "expandable_columns": [], "columns": ["rowid", "run.accession", "experiment.accession", "sample.accession", "study.accession", "bioproject", "study.title", "study.alias", "study.type", "study.abstract", "study.attributes", "study.PMIDs", "sample.description", "sample.title", "sample.alias", "sample.centername", "sample.attributes", "GEOsample.title", "GEOsample.dataprocessing", "GEOsample.source", "GEOsample.treatmentprotocol", "GEOsample.extractprotocol", "GEOsample.growthprotocol", "GEOsample.characteristics", "GEOsample.accession", "experiment.title", "experiment.alias", "experiment.library_name", "experiment.design_description", "experiment.library_construction_protocol", "experiment.attributes", "experiment.library_strategy", "experiment.library_source", "experiment.library_selection", "experiment.library_layout", "experiment.platform", "experiment.instrument_model", "experiment.spot_descriptor", "experiment.study_ref", "run.title", "run.attributes", "run.filename", "run.semantic_name", "run.total_bases", "run.total_spots", "run.alias", "run.read_lengths", "run.base_counts", "run.r1_length", "run.r2_length", "run.r3_length", "run.r4_length", "run.Acount", "run.Ccount", "run.Gcount", "run.Tcount", "run.Ncount", "run.experiment", "run.pool_member", "submission.accession", "submission.srasource", "submission.bioprojectsource", "seqdetective.n_mates", "seqdetective.mapping_rate.mate1", "seqdetective.mapping_rate.mate2", "seqdetective.nofeature_rate.mate1", "seqdetective.nofeature_rate.mate2", "seqdetective.sparsity.mate1", "seqdetective.sparsity.mate2", "seqdetective.pos_strand_rate.mate1", "seqdetective.pos_strand_rate.mate2", "seqdetective.readlen.mate1", "seqdetective.readlen.mate2", "seqdetective.judgement.mate1", "seqdetective.judgement.mate2", "seqdetective.judgement.reason", "platform_family", "instrument_generation", "read_bias", "selection_class", "prep_kit", "sc_or_bulk", "tech_class", "technology", "tech_variant", 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[experiment.platform], [experiment.instrument_model], [experiment.spot_descriptor], [experiment.study_ref], [run.title], [run.attributes], [run.filename], [run.semantic_name], [run.total_bases], [run.total_spots], [run.alias], [run.read_lengths], [run.base_counts], [run.r1_length], [run.r2_length], [run.r3_length], [run.r4_length], [run.Acount], [run.Ccount], [run.Gcount], [run.Tcount], [run.Ncount], [run.experiment], [run.pool_member], [submission.accession], [submission.srasource], [submission.bioprojectsource], [seqdetective.n_mates], [seqdetective.mapping_rate.mate1], [seqdetective.mapping_rate.mate2], [seqdetective.nofeature_rate.mate1], [seqdetective.nofeature_rate.mate2], [seqdetective.sparsity.mate1], [seqdetective.sparsity.mate2], [seqdetective.pos_strand_rate.mate1], [seqdetective.pos_strand_rate.mate2], [seqdetective.readlen.mate1], [seqdetective.readlen.mate2], [seqdetective.judgement.mate1], [seqdetective.judgement.mate2], [seqdetective.judgement.reason], platform_family, 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