metadata: run_metadata: 141 rows where technology = "indrops" and tissue

141 rows where technology = "indrops" and tissue_curation = "Brain"

descending

Link	rowid ▼	run.accession	experiment.accession	sample.accession	study.accession	bioproject	study.title	study.alias	study.type	study.abstract	study.PMIDs	sample.title	sample.alias	sample.attributes	GEOsample.title	GEOsample.dataprocessing	GEOsample.source	GEOsample.extractprotocol	GEOsample.characteristics	GEOsample.accession	experiment.title	experiment.alias	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.study_ref	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.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	submission.bioprojectsource.country	earliest_date	devstage_curation	devstage_curation_coarse	tissue_curation	tissue_curation_coarse
43842	43842	SRR6176750	SRX3287416	SRS2596889	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–Cas9 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	pubmed:29608178	ZF3 scGSTLT	GSM2813986	source name:zebrafish brain\|tissue:brain\|developmental stage:23 25dpf	ZF3 scGSTLT	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 –e 200; UMI quantification was used with parameter –u 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	Single cell suspensions were processed through inDrops to generate single cell cDNA libraries. cDNAs were in vitro transcribed reverse transcribed and prepared for sequencing Gestalt barcode was PCR amplified by two step PCR. Sample indices and flow cell adaptors were then added by PCR.	tissue:brain\|developmental stage:23 25dpf	GSM2813986	GSM2813986: ZF3 scGSTLT; Danio rerio; OTHER	GSM2813986	1	Single cell suspensions were processed through inDrops to generate single cell cDNA libraries. cDNAs were in vitro transcribed reverse transcribed and prepared for sequencing Gestalt barcode was PCR amplified by two step PCR. Sample indices and flow cell adaptors were then added by PCR.	GEO Accession:GSM2813986	OTHER	TRANSCRIPTOMIC	other	SINGLE	ILLUMINA	Illumina MiSeq	SRP120009	loader:fastq load.py\|options: appendBCtoName	F6_UMI.merged.fq.gz	fastq	786192951.0	2901081.0	GSM2813986 r1	0:271	A:215364033;C:176865797;G:224444690;T:169518396;N:35	271	215364033	176865797	224444690	169518396	35	SRX3287416	SRS2596889	SRA619743	GEO	Harvard University	1	0.00904		0.0		0.99997		0.0		271		T		under 1.2% mapping rate	illumina	miseq	unknown	other	unknown	sc	single_cell_droplet	indrops	United States	2017-10-16	Larval	Larval	Brain	Nervous System
43843	43843	SRR6176749	SRX3287415	SRS2596888	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–Cas9 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	pubmed:29608178	ZF2 scGSTLT	GSM2813985	source name:zebrafish brain\|tissue:brain\|developmental stage:23 25dpf	ZF2 scGSTLT	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 –e 200; UMI quantification was used with parameter –u 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	Single cell suspensions were processed through inDrops to generate single cell cDNA libraries. cDNAs were in vitro transcribed reverse transcribed and prepared for sequencing Gestalt barcode was PCR amplified by two step PCR. Sample indices and flow cell adaptors were then added by PCR.	tissue:brain\|developmental stage:23 25dpf	GSM2813985	GSM2813985: ZF2 scGSTLT; Danio rerio; OTHER	GSM2813985	1	Single cell suspensions were processed through inDrops to generate single cell cDNA libraries. cDNAs were in vitro transcribed reverse transcribed and prepared for sequencing Gestalt barcode was PCR amplified by two step PCR. Sample indices and flow cell adaptors were then added by PCR.	GEO Accession:GSM2813985	OTHER	TRANSCRIPTOMIC	other	SINGLE	ILLUMINA	Illumina MiSeq	SRP120009	loader:fastq load.py\|options: appendBCtoName	F5_UMI.merged.fq.gz	fastq	106145626.0	391949.0	GSM2813985 r1	0:270.81	A:29282656;C:22944139;G:29276360;T:24642464;N:7	270	29282656	22944139	29276360	24642464	7	SRX3287415	SRS2596888	SRA619743	GEO	Harvard University	1	2e-05		0.0		0.99995		0.0		270		T		under 1.2% mapping rate	illumina	miseq	unknown	other	unknown	sc	single_cell_droplet	indrops	United States	2017-10-16	Larval	Larval	Brain	Nervous System
43844	43844	SRR6176748	SRX3287414	SRS2596887	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–Cas9 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	pubmed:29608178	ZF1 scGSTLT	GSM2813984	source name:zebrafish brain\|tissue:brain\|developmental stage:23 25dpf	ZF1 scGSTLT	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 –e 200; UMI quantification was used with parameter –u 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	Single cell suspensions were processed through inDrops to generate single cell cDNA libraries. cDNAs were in vitro transcribed reverse transcribed and prepared for sequencing Gestalt barcode was PCR amplified by two step PCR. Sample indices and flow cell adaptors were then added by PCR.	tissue:brain\|developmental stage:23 25dpf	GSM2813984	GSM2813984: ZF1 scGSTLT; Danio rerio; OTHER	GSM2813984	1	Single cell suspensions were processed through inDrops to generate single cell cDNA libraries. cDNAs were in vitro transcribed reverse transcribed and prepared for sequencing Gestalt barcode was PCR amplified by two step PCR. Sample indices and flow cell adaptors were then added by PCR.	GEO Accession:GSM2813984	OTHER	TRANSCRIPTOMIC	other	SINGLE	ILLUMINA	Illumina MiSeq	SRP120009	loader:fastq load.py\|options: appendBCtoName	F3_UMI.merged.fq.gz	fastq	817055786.0	3014966.0	GSM2813984 r1	0:271	A:235846657;C:170311176;G:222336783;T:188561170;N:0	271	235846657	170311176	222336783	188561170	0	SRX3287414	SRS2596887	SRA619743	GEO	Harvard University	1	4e-05		0.0		0.99995		0.4		271		T		under 1.2% mapping rate	illumina	miseq	unknown	other	unknown	sc	single_cell_droplet	indrops	United States	2017-10-16	Larval	Larval	Brain	Nervous System
43845	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–Cas9 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	pubmed:29608178	DEW157 mid	GSM2813983	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 –e 200; UMI quantification was used with parameter –u 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	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	tissue:brain\|developmental stage:23 25dpf	GSM2813983	GSM2813983: DEW157 mid; Danio rerio; RNA Seq	GSM2813983	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	SRP120009	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	974428815	692114455	739762074	1137804675	16780	SRX3287413	SRS2596886	SRA619743	GEO	Harvard University	1	0.89639		0.20572		0.77784		0.5325		61		B		usable mapping rate	illumina	nextseq	unknown	cdna_unspecified	unknown	sc	single_cell_droplet	indrops	United States	2017-10-16	Larval	Larval	Brain	Nervous System
43846	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–Cas9 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	pubmed:29608178	DEW157 mid	GSM2813983	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 –e 200; UMI quantification was used with parameter –u 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	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	tissue:brain\|developmental stage:23 25dpf	GSM2813983	GSM2813983: DEW157 mid; Danio rerio; RNA Seq	GSM2813983	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	SRP120009	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	645085555	458868888	493735966	754390615	6435	SRX3287413	SRS2596886	SRA619743	GEO	Harvard University	1	0.89716		0.2057		0.7782		0.5213		61		B		usable mapping rate	illumina	nextseq	unknown	cdna_unspecified	unknown	sc	single_cell_droplet	indrops	United States	2017-10-16	Larval	Larval	Brain	Nervous System
43847	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–Cas9 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	pubmed:29608178	DEW156 mid	GSM2813982	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 –e 200; UMI quantification was used with parameter –u 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	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	tissue:brain\|developmental stage:23 25dpf	GSM2813982	GSM2813982: DEW156 mid; Danio rerio; RNA Seq	GSM2813982	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	SRP120009	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	607624943	431025702	463801442	711931460	10694	SRX3287412	SRS2596885	SRA619743	GEO	Harvard University	1	0.89922		0.20797		0.77626		0.52363		61		B		usable mapping rate	illumina	nextseq	unknown	cdna_unspecified	unknown	sc	single_cell_droplet	indrops	United States	2017-10-16	Larval	Larval	Brain	Nervous System
43848	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–Cas9 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	pubmed:29608178	DEW156 mid	GSM2813982	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 –e 200; UMI quantification was used with parameter –u 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	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	tissue:brain\|developmental stage:23 25dpf	GSM2813982	GSM2813982: DEW156 mid; Danio rerio; RNA Seq	GSM2813982	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	SRP120009	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	396430204	281605460	305048301	465226358	4110	SRX3287412	SRS2596885	SRA619743	GEO	Harvard University	1	0.90054		0.21045		0.77508		0.51559		61		B		usable mapping rate	illumina	nextseq	unknown	cdna_unspecified	unknown	sc	single_cell_droplet	indrops	United States	2017-10-16	Larval	Larval	Brain	Nervous System
43849	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–Cas9 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	pubmed:29608178	DEW155 mid	GSM2813981	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 –e 200; UMI quantification was used with parameter –u 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	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	tissue:brain\|developmental stage:23 25dpf	GSM2813981	GSM2813981: DEW155 mid; Danio rerio; RNA Seq	GSM2813981	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	SRP120009	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	382023525	266729277	288053480	442115668	5926	SRX3287411	SRS2596902	SRA619743	GEO	Harvard University	1	0.89076		0.20521		0.78281		0.53104		61		B		usable mapping rate	illumina	nextseq	unknown	cdna_unspecified	unknown	sc	single_cell_droplet	indrops	United States	2017-10-16	Larval	Larval	Brain	Nervous System
43850	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–Cas9 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	pubmed:29608178	DEW155 mid	GSM2813981	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 –e 200; UMI quantification was used with parameter –u 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	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	tissue:brain\|developmental stage:23 25dpf	GSM2813981	GSM2813981: DEW155 mid; Danio rerio; RNA Seq	GSM2813981	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	SRP120009	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	270631925	189594639	205361829	313174989	2516	SRX3287411	SRS2596902	SRA619743	GEO	Harvard University	1	0.89018		0.20474		0.78198		0.5019		46		B		usable mapping rate	illumina	nextseq	unknown	cdna_unspecified	unknown	sc	single_cell_droplet	indrops	United States	2017-10-16	Larval	Larval	Brain	Nervous System
43851	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–Cas9 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	pubmed:29608178	DEW154 hind	GSM2813980	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 –e 200; UMI quantification was used with parameter –u 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	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	tissue:brain\|developmental stage:23 25dpf	GSM2813980	GSM2813980: DEW154 hind; Danio rerio; RNA Seq	GSM2813980	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	SRP120009	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	54153798	35006191	40959293	61621602	742	SRX3287410	SRS2596884	SRA619743	GEO	Harvard University	1	0.86682		0.2127		0.79695		0.52028		61		B		usable mapping rate	illumina	nextseq	unknown	cdna_unspecified	unknown	sc	single_cell_droplet	indrops	United States	2017-10-16	Larval	Larval	Brain	Nervous System
43852	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–Cas9 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	pubmed:29608178	DEW154 hind	GSM2813980	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 –e 200; UMI quantification was used with parameter –u 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	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	tissue:brain\|developmental stage:23 25dpf	GSM2813980	GSM2813980: DEW154 hind; Danio rerio; RNA Seq	GSM2813980	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	SRP120009	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	49679029	31732468	36877474	56139060	360	SRX3287410	SRS2596884	SRA619743	GEO	Harvard University	1	0.85562		0.21627		0.79839		0.52737		61		B		usable mapping rate	illumina	nextseq	unknown	cdna_unspecified	unknown	sc	single_cell_droplet	indrops	United States	2017-10-16	Larval	Larval	Brain	Nervous System
43853	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–Cas9 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	pubmed:29608178	DEW153 hind	GSM2813979	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 –e 200; UMI quantification was used with parameter –u 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	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	tissue:brain\|developmental stage:23 25dpf	GSM2813979	GSM2813979: DEW153 hind; Danio rerio; RNA Seq	GSM2813979	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	SRP120009	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	607831752	435134646	462618869	700932389	10515	SRX3287409	SRS2596883	SRA619743	GEO	Harvard University	1	0.89527		0.20046		0.78259		0.50876		60		B		usable mapping rate	illumina	nextseq	unknown	cdna_unspecified	unknown	sc	single_cell_droplet	indrops	United States	2017-10-16	Larval	Larval	Brain	Nervous System
43854	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–Cas9 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	pubmed:29608178	DEW153 hind	GSM2813979	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 –e 200; UMI quantification was used with parameter –u 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	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	tissue:brain\|developmental stage:23 25dpf	GSM2813979	GSM2813979: DEW153 hind; Danio rerio; RNA Seq	GSM2813979	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	SRP120009	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	398797215	285454365	305822323	460699598	4247	SRX3287409	SRS2596883	SRA619743	GEO	Harvard University	1	0.89512		0.20047		0.78222		0.53369		60		B		usable mapping rate	illumina	nextseq	unknown	cdna_unspecified	unknown	sc	single_cell_droplet	indrops	United States	2017-10-16	Larval	Larval	Brain	Nervous System
43855	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–Cas9 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	pubmed:29608178	DEW152 hind	GSM2813978	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 –e 200; UMI quantification was used with parameter –u 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	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	tissue:brain\|developmental stage:23 25dpf	GSM2813978	GSM2813978: DEW152 hind; Danio rerio; RNA Seq	GSM2813978	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	SRP120009	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	552258277	393949059	416248897	643531466	9567	SRX3287408	SRS2596882	SRA619743	GEO	Harvard University	1	0.89818		0.20396		0.78001		0.5176		61		B		usable mapping rate	illumina	nextseq	unknown	cdna_unspecified	unknown	sc	single_cell_droplet	indrops	United States	2017-10-16	Larval	Larval	Brain	Nervous System
43856	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–Cas9 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	pubmed:29608178	DEW152 hind	GSM2813978	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 –e 200; UMI quantification was used with parameter –u 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	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	tissue:brain\|developmental stage:23 25dpf	GSM2813978	GSM2813978: DEW152 hind; Danio rerio; RNA Seq	GSM2813978	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	SRP120009	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	358953147	256408571	272527726	418731243	3686	SRX3287408	SRS2596882	SRA619743	GEO	Harvard University	1	0.89769		0.20479		0.78204		0.51518		61		B		usable mapping rate	illumina	nextseq	unknown	cdna_unspecified	unknown	sc	single_cell_droplet	indrops	United States	2017-10-16	Larval	Larval	Brain	Nervous System
43857	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–Cas9 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	pubmed:29608178	DEW151 fore	GSM2813977	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 –e 200; UMI quantification was used with parameter –u 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	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	tissue:brain\|developmental stage:23 25dpf	GSM2813977	GSM2813977: DEW151 fore; Danio rerio; RNA Seq	GSM2813977	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	SRP120009	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	494848942	336194488	366298578	584233757	8278	SRX3287407	SRS2596880	SRA619743	GEO	Harvard University	1	0.88247		0.27771		0.78311		0.52819		61		B		usable mapping rate	illumina	nextseq	unknown	cdna_unspecified	unknown	sc	single_cell_droplet	indrops	United States	2017-10-16	Larval	Larval	Brain	Nervous System
43858	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–Cas9 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	pubmed:29608178	DEW151 fore	GSM2813977	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 –e 200; UMI quantification was used with parameter –u 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	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	tissue:brain\|developmental stage:23 25dpf	GSM2813977	GSM2813977: DEW151 fore; Danio rerio; RNA Seq	GSM2813977	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	SRP120009	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	320886308	218034938	239168032	379434373	3097	SRX3287407	SRS2596880	SRA619743	GEO	Harvard University	1	0.88331		0.27805		0.78599		0.52469		60		B		usable mapping rate	illumina	nextseq	unknown	cdna_unspecified	unknown	sc	single_cell_droplet	indrops	United States	2017-10-16	Larval	Larval	Brain	Nervous System
43859	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–Cas9 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	pubmed:29608178	DEW150 fore	GSM2813976	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 –e 200; UMI quantification was used with parameter –u 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	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	tissue:brain\|developmental stage:23 25dpf	GSM2813976	GSM2813976: DEW150 fore; Danio rerio; RNA Seq	GSM2813976	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	SRP120009	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	563752104	383031134	429113539	656370389	9446	SRX3287406	SRS2596881	SRA619743	GEO	Harvard University	1	0.8788		0.27566		0.78995		0.521		61		B		usable mapping rate	illumina	nextseq	unknown	cdna_unspecified	unknown	sc	single_cell_droplet	indrops	United States	2017-10-16	Larval	Larval	Brain	Nervous System
43860	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–Cas9 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	pubmed:29608178	DEW150 fore	GSM2813976	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 –e 200; UMI quantification was used with parameter –u 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	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	tissue:brain\|developmental stage:23 25dpf	GSM2813976	GSM2813976: DEW150 fore; Danio rerio; RNA Seq	GSM2813976	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	SRP120009	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	364153526	247636153	279420005	424635138	3771	SRX3287406	SRS2596881	SRA619743	GEO	Harvard University	1	0.87913		0.27404		0.78666		0.52726		61		B		usable mapping rate	illumina	nextseq	unknown	cdna_unspecified	unknown	sc	single_cell_droplet	indrops	United States	2017-10-16	Larval	Larval	Brain	Nervous System
43861	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–Cas9 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	pubmed:29608178	DEW149 fore	GSM2813975	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 –e 200; UMI quantification was used with parameter –u 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	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	tissue:brain\|developmental stage:23 25dpf	GSM2813975	GSM2813975: DEW149 fore; Danio rerio; RNA Seq	GSM2813975	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	SRP120009	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	567524269	385217324	426974572	661258382	9587	SRX3287405	SRS2596879	SRA619743	GEO	Harvard University	1	0.8811		0.27464		0.78654		0.52635		61		B		usable mapping rate	illumina	nextseq	unknown	cdna_unspecified	unknown	sc	single_cell_droplet	indrops	United States	2017-10-16	Larval	Larval	Brain	Nervous System
43862	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–Cas9 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	pubmed:29608178	DEW149 fore	GSM2813975	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 –e 200; UMI quantification was used with parameter –u 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	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	tissue:brain\|developmental stage:23 25dpf	GSM2813975	GSM2813975: DEW149 fore; Danio rerio; RNA Seq	GSM2813975	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	SRP120009	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	367652083	249666726	278649220	428897643	3760	SRX3287405	SRS2596879	SRA619743	GEO	Harvard University	1	0.88017		0.27604		0.78658		0.53022		60		B		usable mapping rate	illumina	nextseq	unknown	cdna_unspecified	unknown	sc	single_cell_droplet	indrops	United States	2017-10-16	Larval	Larval	Brain	Nervous System
43863	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–Cas9 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	pubmed:29608178	DEW148 f6	GSM2813974	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 –e 200; UMI quantification was used with parameter –u 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	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	tissue:brain\|developmental stage:23 25dpf	GSM2813974	GSM2813974: DEW148 f6; Danio rerio; RNA Seq	GSM2813974	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	SRP120009	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	492007833	343654151	370938043	577142407	3731	SRX3287404	SRS2596878	SRA619743	GEO	Harvard University	1	0.86946		0.22807		0.79462		0.51961		61		B		usable mapping rate	illumina	nextseq	unknown	cdna_unspecified	unknown	sc	single_cell_droplet	indrops	United States	2017-10-16	Larval	Larval	Brain	Nervous System
43864	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–Cas9 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	pubmed:29608178	DEW148 f6	GSM2813974	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 –e 200; UMI quantification was used with parameter –u 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	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	tissue:brain\|developmental stage:23 25dpf	GSM2813974	GSM2813974: DEW148 f6; Danio rerio; RNA Seq	GSM2813974	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	SRP120009	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	644394074	446370620	489186161	742017510	7728	SRX3287404	SRS2596878	SRA619743	GEO	Harvard University	1	0.8902		0.22908		0.78689		0.52036		61		B		usable mapping rate	illumina	nextseq	unknown	cdna_unspecified	unknown	sc	single_cell_droplet	indrops	United States	2017-10-16	Larval	Larval	Brain	Nervous System
43865	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–Cas9 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	pubmed:29608178	DEW147 f6	GSM2813973	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 –e 200; UMI quantification was used with parameter –u 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	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	tissue:brain\|developmental stage:23 25dpf	GSM2813973	GSM2813973: DEW147 f6; Danio rerio; RNA Seq	GSM2813973	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	SRP120009	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	514475433	356385455	385168377	605118287	4174	SRX3287403	SRS2596877	SRA619743	GEO	Harvard University	1	0.86477		0.23182		0.79454		0.51967		41		B		usable mapping rate	illumina	nextseq	unknown	cdna_unspecified	unknown	sc	single_cell_droplet	indrops	United States	2017-10-16	Larval	Larval	Brain	Nervous System
43866	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–Cas9 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	pubmed:29608178	DEW147 f6	GSM2813973	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 –e 200; UMI quantification was used with parameter –u 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	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	tissue:brain\|developmental stage:23 25dpf	GSM2813973	GSM2813973: DEW147 f6; Danio rerio; RNA Seq	GSM2813973	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	SRP120009	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	662878220	455374350	499509285	766312566	8009	SRX3287403	SRS2596877	SRA619743	GEO	Harvard University	1	0.88512		0.23293		0.7878		0.51475		61		B		usable mapping rate	illumina	nextseq	unknown	cdna_unspecified	unknown	sc	single_cell_droplet	indrops	United States	2017-10-16	Larval	Larval	Brain	Nervous System
43867	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–Cas9 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	pubmed:29608178	DEW146 f6	GSM2813972	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 –e 200; UMI quantification was used with parameter –u 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	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	tissue:brain\|developmental stage:23 25dpf	GSM2813972	GSM2813972: DEW146 f6; Danio rerio; RNA Seq	GSM2813972	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	SRP120009	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	518564123	360410554	392630549	607497754	3785	SRX3287402	SRS2596876	SRA619743	GEO	Harvard University	1	0.868		0.22669		0.79506		0.50915		55		B		usable mapping rate	illumina	nextseq	unknown	cdna_unspecified	unknown	sc	single_cell_droplet	indrops	United States	2017-10-16	Larval	Larval	Brain	Nervous System
43868	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–Cas9 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	pubmed:29608178	DEW146 f6	GSM2813972	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 –e 200; UMI quantification was used with parameter –u 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	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	tissue:brain\|developmental stage:23 25dpf	GSM2813972	GSM2813972: DEW146 f6; Danio rerio; RNA Seq	GSM2813972	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	SRP120009	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	694138768	479060448	529998089	797829519	8183	SRX3287402	SRS2596876	SRA619743	GEO	Harvard University	1	0.8925		0.2282		0.78739		0.49912		45		B		usable mapping rate	illumina	nextseq	unknown	cdna_unspecified	unknown	sc	single_cell_droplet	indrops	United States	2017-10-16	Larval	Larval	Brain	Nervous System
43869	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–Cas9 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	pubmed:29608178	DEW145 f6	GSM2813971	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 –e 200; UMI quantification was used with parameter –u 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	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	tissue:brain\|developmental stage:23 25dpf	GSM2813971	GSM2813971: DEW145 f6; Danio rerio; RNA Seq	GSM2813971	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	SRP120009	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	532782579	372215991	405559545	625595853	4238	SRX3287401	SRS2596875	SRA619743	GEO	Harvard University	1	0.87324		0.22959		0.79399		0.50829		61		B		usable mapping rate	illumina	nextseq	unknown	cdna_unspecified	unknown	sc	single_cell_droplet	indrops	United States	2017-10-16	Larval	Larval	Brain	Nervous System
43870	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–Cas9 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	pubmed:29608178	DEW145 f6	GSM2813971	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 –e 200; UMI quantification was used with parameter –u 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	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	tissue:brain\|developmental stage:23 25dpf	GSM2813971	GSM2813971: DEW145 f6; Danio rerio; RNA Seq	GSM2813971	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	SRP120009	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	694942009	481699208	532793934	801914020	8382	SRX3287401	SRS2596875	SRA619743	GEO	Harvard University	1	0.89493		0.23125		0.78837		0.52183		61		B		usable mapping rate	illumina	nextseq	unknown	cdna_unspecified	unknown	sc	single_cell_droplet	indrops	United States	2017-10-16	Larval	Larval	Brain	Nervous System
43871	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–Cas9 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	pubmed:29608178	DEW144 f6	GSM2813970	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 –e 200; UMI quantification was used with parameter –u 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	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	tissue:brain\|developmental stage:23 25dpf	GSM2813970	GSM2813970: DEW144 f6; Danio rerio; RNA Seq	GSM2813970	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	SRP120009	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	549834507	383404208	413619246	647965404	4320	SRX3287400	SRS2596874	SRA619743	GEO	Harvard University	1	0.87127		0.23087		0.79235		0.51641		38		B		usable mapping rate	illumina	nextseq	unknown	cdna_unspecified	unknown	sc	single_cell_droplet	indrops	United States	2017-10-16	Larval	Larval	Brain	Nervous System
43872	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–Cas9 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	pubmed:29608178	DEW144 f6	GSM2813970	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 –e 200; UMI quantification was used with parameter –u 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	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	tissue:brain\|developmental stage:23 25dpf	GSM2813970	GSM2813970: DEW144 f6; Danio rerio; RNA Seq	GSM2813970	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	SRP120009	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	714324521	494519068	541009379	826557189	8738	SRX3287400	SRS2596874	SRA619743	GEO	Harvard University	1	0.89423		0.23084		0.78644		0.50488		61		B		usable mapping rate	illumina	nextseq	unknown	cdna_unspecified	unknown	sc	single_cell_droplet	indrops	United States	2017-10-16	Larval	Larval	Brain	Nervous System
43873	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–Cas9 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	pubmed:29608178	DEW143 f6	GSM2813969	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 –e 200; UMI quantification was used with parameter –u 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	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	tissue:brain\|developmental stage:23 25dpf	GSM2813969	GSM2813969: DEW143 f6; Danio rerio; RNA Seq	GSM2813969	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	SRP120009	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	510615110	356714130	381836358	604740730	3780	SRX3287399	SRS2596873	SRA619743	GEO	Harvard University	1	0.87029		0.22942		0.7948		0.52291		61		B		usable mapping rate	illumina	nextseq	unknown	cdna_unspecified	unknown	sc	single_cell_droplet	indrops	United States	2017-10-16	Larval	Larval	Brain	Nervous System
43874	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–Cas9 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	pubmed:29608178	DEW143 f6	GSM2813969	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 –e 200; UMI quantification was used with parameter –u 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	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	tissue:brain\|developmental stage:23 25dpf	GSM2813969	GSM2813969: DEW143 f6; Danio rerio; RNA Seq	GSM2813969	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	SRP120009	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	678158197	470881649	512160662	788663100	8141	SRX3287399	SRS2596873	SRA619743	GEO	Harvard University	1	0.8929		0.22935		0.78772		0.50436		61		B		usable mapping rate	illumina	nextseq	unknown	cdna_unspecified	unknown	sc	single_cell_droplet	indrops	United States	2017-10-16	Larval	Larval	Brain	Nervous System
43875	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–Cas9 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	pubmed:29608178	DEW142 f6	GSM2813968	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 –e 200; UMI quantification was used with parameter –u 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	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	tissue:brain\|developmental stage:23 25dpf	GSM2813968	GSM2813968: DEW142 f6; Danio rerio; RNA Seq	GSM2813968	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	SRP120009	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	488608268	339649107	361663399	572705563	3524	SRX3287398	SRS2596872	SRA619743	GEO	Harvard University	1	0.86716		0.22745		0.79847		0.52104		57		B		usable mapping rate	illumina	nextseq	unknown	cdna_unspecified	unknown	sc	single_cell_droplet	indrops	United States	2017-10-16	Larval	Larval	Brain	Nervous System
43876	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–Cas9 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	pubmed:29608178	DEW142 f6	GSM2813968	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 –e 200; UMI quantification was used with parameter –u 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	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	tissue:brain\|developmental stage:23 25dpf	GSM2813968	GSM2813968: DEW142 f6; Danio rerio; RNA Seq	GSM2813968	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	SRP120009	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	654034260	451467879	488256208	753098041	7868	SRX3287398	SRS2596872	SRA619743	GEO	Harvard University	1	0.88889		0.22997		0.79241		0.51721		61		B		usable mapping rate	illumina	nextseq	unknown	cdna_unspecified	unknown	sc	single_cell_droplet	indrops	United States	2017-10-16	Larval	Larval	Brain	Nervous System
43877	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–Cas9 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	pubmed:29608178	DEW141 f5	GSM2813967	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 –e 200; UMI quantification was used with parameter –u 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	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	tissue:brain\|developmental stage:23 25dpf	GSM2813967	GSM2813967: DEW141 f5; Danio rerio; RNA Seq	GSM2813967	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	SRP120009	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	778811258	528303302	570412822	919568474	6721	SRX3287397	SRS2596871	SRA619743	GEO	Harvard University	1	0.89023		0.23795		0.782		0.50945		61		B		usable mapping rate	illumina	nextseq	unknown	cdna_unspecified	unknown	sc	single_cell_droplet	indrops	United States	2017-10-16	Larval	Larval	Brain	Nervous System
43878	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–Cas9 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	pubmed:29608178	DEW141 f5	GSM2813967	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 –e 200; UMI quantification was used with parameter –u 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	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	tissue:brain\|developmental stage:23 25dpf	GSM2813967	GSM2813967: DEW141 f5; Danio rerio; RNA Seq	GSM2813967	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	SRP120009	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	997648014	674533198	734148175	1171466042	12675	SRX3287397	SRS2596871	SRA619743	GEO	Harvard University	1	0.89059		0.23311		0.78025		0.4954		60		B		usable mapping rate	illumina	nextseq	unknown	cdna_unspecified	unknown	sc	single_cell_droplet	indrops	United States	2017-10-16	Larval	Larval	Brain	Nervous System
43879	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–Cas9 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	pubmed:29608178	DEW140 f5	GSM2813966	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 –e 200; UMI quantification was used with parameter –u 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	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	tissue:brain\|developmental stage:23 25dpf	GSM2813966	GSM2813966: DEW140 f5; Danio rerio; RNA Seq	GSM2813966	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	SRP120009	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	812726490	548270217	594090392	962652776	6917	SRX3287396	SRS2596870	SRA619743	GEO	Harvard University	1	0.89062		0.23582		0.78403		0.5019		61		B		usable mapping rate	illumina	nextseq	unknown	cdna_unspecified	unknown	sc	single_cell_droplet	indrops	United States	2017-10-16	Larval	Larval	Brain	Nervous System
43880	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–Cas9 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	pubmed:29608178	DEW140 f5	GSM2813966	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 –e 200; UMI quantification was used with parameter –u 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	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	tissue:brain\|developmental stage:23 25dpf	GSM2813966	GSM2813966: DEW140 f5; Danio rerio; RNA Seq	GSM2813966	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	SRP120009	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	1037269475	697925461	761607666	1221586424	13155	SRX3287396	SRS2596870	SRA619743	GEO	Harvard University	1	0.89177		0.23224		0.78216		0.51101		61		B		usable mapping rate	illumina	nextseq	unknown	cdna_unspecified	unknown	sc	single_cell_droplet	indrops	United States	2017-10-16	Larval	Larval	Brain	Nervous System
43881	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–Cas9 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	pubmed:29608178	DEW139 f5	GSM2813965	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 –e 200; UMI quantification was used with parameter –u 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	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	tissue:brain\|developmental stage:23 25dpf	GSM2813965	GSM2813965: DEW139 f5; Danio rerio; RNA Seq	GSM2813965	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	SRP120009	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	805337279	546305611	596251042	957149051	6801	SRX3287395	SRS2596869	SRA619743	GEO	Harvard University	1	0.89282		0.23416		0.78281		0.50737		61		B		usable mapping rate	illumina	nextseq	unknown	cdna_unspecified	unknown	sc	single_cell_droplet	indrops	United States	2017-10-16	Larval	Larval	Brain	Nervous System
43882	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–Cas9 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	pubmed:29608178	DEW139 f5	GSM2813965	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 –e 200; UMI quantification was used with parameter –u 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	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	tissue:brain\|developmental stage:23 25dpf	GSM2813965	GSM2813965: DEW139 f5; Danio rerio; RNA Seq	GSM2813965	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	SRP120009	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	1037172797	701422244	771367652	1225456437	13151	SRX3287395	SRS2596869	SRA619743	GEO	Harvard University	1	0.89351		0.23166		0.78259		0.50069		33		B		usable mapping rate	illumina	nextseq	unknown	cdna_unspecified	unknown	sc	single_cell_droplet	indrops	United States	2017-10-16	Larval	Larval	Brain	Nervous System
43883	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–Cas9 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	pubmed:29608178	DEW138 f5	GSM2813964	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 –e 200; UMI quantification was used with parameter –u 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	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	tissue:brain\|developmental stage:23 25dpf	GSM2813964	GSM2813964: DEW138 f5; Danio rerio; RNA Seq	GSM2813964	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	SRP120009	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	742846652	514118077	553047856	882827383	6459	SRX3287394	SRS2596868	SRA619743	GEO	Harvard University	1	0.89246		0.23163		0.78228		0.51357		61		B		usable mapping rate	illumina	nextseq	unknown	cdna_unspecified	unknown	sc	single_cell_droplet	indrops	United States	2017-10-16	Larval	Larval	Brain	Nervous System
43884	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–Cas9 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	pubmed:29608178	DEW138 f5	GSM2813964	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 –e 200; UMI quantification was used with parameter –u 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	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	tissue:brain\|developmental stage:23 25dpf	GSM2813964	GSM2813964: DEW138 f5; Danio rerio; RNA Seq	GSM2813964	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	SRP120009	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	956805112	660226337	715337930	1130599701	12327	SRX3287394	SRS2596868	SRA619743	GEO	Harvard University	1	0.89374		0.22971		0.78214		0.51135		60		B		usable mapping rate	illumina	nextseq	unknown	cdna_unspecified	unknown	sc	single_cell_droplet	indrops	United States	2017-10-16	Larval	Larval	Brain	Nervous System
43885	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–Cas9 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	pubmed:29608178	DEW137 f5	GSM2813963	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 –e 200; UMI quantification was used with parameter –u 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	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	tissue:brain\|developmental stage:23 25dpf	GSM2813963	GSM2813963: DEW137 f5; Danio rerio; RNA Seq	GSM2813963	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	SRP120009	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	899541071	615022999	668853277	1066951383	7736	SRX3287393	SRS2596867	SRA619743	GEO	Harvard University	1	0.8922		0.22976		0.7823		0.49602		61		B		usable mapping rate	illumina	nextseq	unknown	cdna_unspecified	unknown	sc	single_cell_droplet	indrops	United States	2017-10-16	Larval	Larval	Brain	Nervous System
43886	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–Cas9 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	pubmed:29608178	DEW137 f5	GSM2813963	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 –e 200; UMI quantification was used with parameter –u 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	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	tissue:brain\|developmental stage:23 25dpf	GSM2813963	GSM2813963: DEW137 f5; Danio rerio; RNA Seq	GSM2813963	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	SRP120009	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	1149440282	783582822	858546692	1355437992	14262	SRX3287393	SRS2596867	SRA619743	GEO	Harvard University	1	0.89401		0.2275		0.78046		0.49962		61		B		usable mapping rate	illumina	nextseq	unknown	cdna_unspecified	unknown	sc	single_cell_droplet	indrops	United States	2017-10-16	Larval	Larval	Brain	Nervous System
43887	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–Cas9 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	pubmed:29608178	DEW136 f5	GSM2813962	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 –e 200; UMI quantification was used with parameter –u 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	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	tissue:brain\|developmental stage:23 25dpf	GSM2813962	GSM2813962: DEW136 f5; Danio rerio; RNA Seq	GSM2813962	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	SRP120009	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	742027813	505890516	549783741	883331533	6307	SRX3287392	SRS2596866	SRA619743	GEO	Harvard University	1	0.89063		0.23313		0.78466		0.50689		16		B		usable mapping rate	illumina	nextseq	unknown	cdna_unspecified	unknown	sc	single_cell_droplet	indrops	United States	2017-10-16	Larval	Larval	Brain	Nervous System
43888	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–Cas9 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	pubmed:29608178	DEW136 f5	GSM2813962	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 –e 200; UMI quantification was used with parameter –u 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	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	tissue:brain\|developmental stage:23 25dpf	GSM2813962	GSM2813962: DEW136 f5; Danio rerio; RNA Seq	GSM2813962	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	SRP120009	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	956852778	650048613	712593784	1132601522	11986	SRX3287392	SRS2596866	SRA619743	GEO	Harvard University	1	0.89419		0.23225		0.77981		0.49604		55		B		usable mapping rate	illumina	nextseq	unknown	cdna_unspecified	unknown	sc	single_cell_droplet	indrops	United States	2017-10-16	Larval	Larval	Brain	Nervous System
43889	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–Cas9 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	pubmed:29608178	DEW135 f5	GSM2813961	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 –e 200; UMI quantification was used with parameter –u 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	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	tissue:brain\|developmental stage:23 25dpf	GSM2813961	GSM2813961: DEW135 f5; Danio rerio; RNA Seq	GSM2813961	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	SRP120009	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	709801542	479379107	526974755	841352077	6075	SRX3287391	SRS2596865	SRA619743	GEO	Harvard University	1	0.88899		0.23784		0.78348		0.50227		61		B		usable mapping rate	illumina	nextseq	unknown	cdna_unspecified	unknown	sc	single_cell_droplet	indrops	United States	2017-10-16	Larval	Larval	Brain	Nervous System
43890	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–Cas9 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	pubmed:29608178	DEW135 f5	GSM2813961	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 –e 200; UMI quantification was used with parameter –u 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	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	tissue:brain\|developmental stage:23 25dpf	GSM2813961	GSM2813961: DEW135 f5; Danio rerio; RNA Seq	GSM2813961	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	SRP120009	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	914468451	615561913	682649039	1078015822	11605	SRX3287391	SRS2596865	SRA619743	GEO	Harvard University	1	0.89195		0.23748		0.78025		0.48835		61		B		usable mapping rate	illumina	nextseq	unknown	cdna_unspecified	unknown	sc	single_cell_droplet	indrops	United States	2017-10-16	Larval	Larval	Brain	Nervous System
43891	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–Cas9 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	pubmed:29608178	DEW134 f4	GSM2813960	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 –e 200; UMI quantification was used with parameter –u 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	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	tissue:brain\|developmental stage:23 25dpf	GSM2813960	GSM2813960: DEW134 f4; Danio rerio; RNA Seq	GSM2813960	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	SRP120009	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	581873993	401061575	406205462	643409873	3720	SRX3287390	SRS2596864	SRA619743	GEO	Harvard University	1	0.89159		0.24077		0.79052		0.59755		61		B		usable mapping rate	illumina	nextseq	unknown	cdna_unspecified	unknown	sc	single_cell_droplet	indrops	United States	2017-10-16	Larval	Larval	Brain	Nervous System
43892	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–Cas9 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	pubmed:29608178	DEW134 f4	GSM2813960	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 –e 200; UMI quantification was used with parameter –u 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	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	tissue:brain\|developmental stage:23 25dpf	GSM2813960	GSM2813960: DEW134 f4; Danio rerio; RNA Seq	GSM2813960	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	SRP120009	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	658586250	454192659	461779091	728613596	8428	SRX3287390	SRS2596864	SRA619743	GEO	Harvard University	1	0.89876		0.24328		0.79233		0.5978		61		B		usable mapping rate	illumina	nextseq	unknown	cdna_unspecified	unknown	sc	single_cell_droplet	indrops	United States	2017-10-16	Larval	Larval	Brain	Nervous System
43893	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–Cas9 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	pubmed:29608178	DEW133 f4	GSM2813959	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 –e 200; UMI quantification was used with parameter –u 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	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	tissue:brain\|developmental stage:23 25dpf	GSM2813959	GSM2813959: DEW133 f4; Danio rerio; RNA Seq	GSM2813959	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	SRP120009	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	422761649	294239860	298842946	464414920	2786	SRX3287389	SRS2596863	SRA619743	GEO	Harvard University	1	0.89359		0.23759		0.79162		0.5992		61		B		usable mapping rate	illumina	nextseq	unknown	cdna_unspecified	unknown	sc	single_cell_droplet	indrops	United States	2017-10-16	Larval	Larval	Brain	Nervous System
43894	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–Cas9 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	pubmed:29608178	DEW133 f4	GSM2813959	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 –e 200; UMI quantification was used with parameter –u 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	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	tissue:brain\|developmental stage:23 25dpf	GSM2813959	GSM2813959: DEW133 f4; Danio rerio; RNA Seq	GSM2813959	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	SRP120009	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	472962184	329028443	335649862	519891706	6378	SRX3287389	SRS2596863	SRA619743	GEO	Harvard University	1	0.89934		0.23901		0.79287		0.59481		61		B		usable mapping rate	illumina	nextseq	unknown	cdna_unspecified	unknown	sc	single_cell_droplet	indrops	United States	2017-10-16	Larval	Larval	Brain	Nervous System
43895	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–Cas9 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	pubmed:29608178	DEW132 f4	GSM2813958	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 –e 200; UMI quantification was used with parameter –u 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	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	tissue:brain\|developmental stage:23 25dpf	GSM2813958	GSM2813958: DEW132 f4; Danio rerio; RNA Seq	GSM2813958	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	SRP120009	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	925435996	642008408	651239986	1023778585	6043	SRX3287388	SRS2596862	SRA619743	GEO	Harvard University	1	0.89535		0.23494		0.78995		0.57979		61		B		usable mapping rate	illumina	nextseq	unknown	cdna_unspecified	unknown	sc	single_cell_droplet	indrops	United States	2017-10-16	Larval	Larval	Brain	Nervous System
43896	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–Cas9 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	pubmed:29608178	DEW132 f4	GSM2813958	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 –e 200; UMI quantification was used with parameter –u 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	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	tissue:brain\|developmental stage:23 25dpf	GSM2813958	GSM2813958: DEW132 f4; Danio rerio; RNA Seq	GSM2813958	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	SRP120009	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	1044846621	725295067	738582256	1156757558	13565	SRX3287388	SRS2596862	SRA619743	GEO	Harvard University	1	0.89933		0.23593		0.78794		0.59183		61		B		usable mapping rate	illumina	nextseq	unknown	cdna_unspecified	unknown	sc	single_cell_droplet	indrops	United States	2017-10-16	Larval	Larval	Brain	Nervous System
43897	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–Cas9 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	pubmed:29608178	DEW131 f4	GSM2813957	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 –e 200; UMI quantification was used with parameter –u 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	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	tissue:brain\|developmental stage:23 25dpf	GSM2813957	GSM2813957: DEW131 f4; Danio rerio; RNA Seq	GSM2813957	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	SRP120009	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	409174223	280349860	288337035	451459797	2473	SRX3287387	SRS2596861	SRA619743	GEO	Harvard University	1	0.89191		0.24025		0.78806		0.59636		61		B		usable mapping rate	illumina	nextseq	unknown	cdna_unspecified	unknown	sc	single_cell_droplet	indrops	United States	2017-10-16	Larval	Larval	Brain	Nervous System
43898	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–Cas9 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	pubmed:29608178	DEW131 f4	GSM2813957	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 –e 200; UMI quantification was used with parameter –u 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	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	tissue:brain\|developmental stage:23 25dpf	GSM2813957	GSM2813957: DEW131 f4; Danio rerio; RNA Seq	GSM2813957	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	SRP120009	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	382643211	258086557	269989349	422608190	4455	SRX3287387	SRS2596861	SRA619743	GEO	Harvard University	1	0.89439		0.24514		0.78918		0.59083		61		B		usable mapping rate	illumina	nextseq	unknown	cdna_unspecified	unknown	sc	single_cell_droplet	indrops	United States	2017-10-16	Larval	Larval	Brain	Nervous System
43899	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–Cas9 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	pubmed:29608178	DEW130 f4	GSM2813956	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 –e 200; UMI quantification was used with parameter –u 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	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	tissue:brain\|developmental stage:23 25dpf	GSM2813956	GSM2813956: DEW130 f4; Danio rerio; RNA Seq	GSM2813956	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	SRP120009	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	232201519	150952153	168770024	250400724	945	SRX3287386	SRS2596860	SRA619743	GEO	Harvard University	1	0.87297		0.2473		0.79494		0.56771		61		B		usable mapping rate	illumina	nextseq	unknown	cdna_unspecified	unknown	sc	single_cell_droplet	indrops	United States	2017-10-16	Larval	Larval	Brain	Nervous System
43900	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–Cas9 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	pubmed:29608178	DEW130 f4	GSM2813956	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 –e 200; UMI quantification was used with parameter –u 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	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	tissue:brain\|developmental stage:23 25dpf	GSM2813956	GSM2813956: DEW130 f4; Danio rerio; RNA Seq	GSM2813956	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	SRP120009	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	63499390	38493171	47755138	67847146	588	SRX3287386	SRS2596860	SRA619743	GEO	Harvard University	1	0.8761		0.25865		0.79575		0.55816		61		B		usable mapping rate	illumina	nextseq	unknown	cdna_unspecified	unknown	sc	single_cell_droplet	indrops	United States	2017-10-16	Larval	Larval	Brain	Nervous System
43901	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–Cas9 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	pubmed:29608178	DEW129 f4	GSM2813955	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 –e 200; UMI quantification was used with parameter –u 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	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	tissue:brain\|developmental stage:23 25dpf	GSM2813955	GSM2813955: DEW129 f4; Danio rerio; RNA Seq	GSM2813955	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	SRP120009	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	459523325	318570707	331034880	499391844	2891	SRX3287385	SRS2596859	SRA619743	GEO	Harvard University	1	0.89225		0.23911		0.79444		0.59646		60		B		usable mapping rate	illumina	nextseq	unknown	cdna_unspecified	unknown	sc	single_cell_droplet	indrops	United States	2017-10-16	Larval	Larval	Brain	Nervous System
43902	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–Cas9 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	pubmed:29608178	DEW129 f4	GSM2813955	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 –e 200; UMI quantification was used with parameter –u 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	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	tissue:brain\|developmental stage:23 25dpf	GSM2813955	GSM2813955: DEW129 f4; Danio rerio; RNA Seq	GSM2813955	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	SRP120009	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	518689812	359842055	374765615	563659156	6994	SRX3287385	SRS2596859	SRA619743	GEO	Harvard University	1	0.89748		0.24157		0.79632		0.59646		60		B		usable mapping rate	illumina	nextseq	unknown	cdna_unspecified	unknown	sc	single_cell_droplet	indrops	United States	2017-10-16	Larval	Larval	Brain	Nervous System
43903	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–Cas9 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	pubmed:29608178	DEW128 f4	GSM2813954	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 –e 200; UMI quantification was used with parameter –u 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	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	tissue:brain\|developmental stage:23 25dpf	GSM2813954	GSM2813954: DEW128 f4; Danio rerio; RNA Seq	GSM2813954	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	SRP120009	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	433396829	297731146	312171109	486178018	2807	SRX3287384	SRS2596858	SRA619743	GEO	Harvard University	1	0.8923		0.24087		0.79048		0.56588		61		B		usable mapping rate	illumina	nextseq	unknown	cdna_unspecified	unknown	sc	single_cell_droplet	indrops	United States	2017-10-16	Larval	Larval	Brain	Nervous System
43904	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–Cas9 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	pubmed:29608178	DEW128 f4	GSM2813954	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 –e 200; UMI quantification was used with parameter –u 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	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	tissue:brain\|developmental stage:23 25dpf	GSM2813954	GSM2813954: DEW128 f4; Danio rerio; RNA Seq	GSM2813954	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	SRP120009	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	487868018	335153495	352552183	547251517	6489	SRX3287384	SRS2596858	SRA619743	GEO	Harvard University	1	0.89701		0.24322		0.78912		0.58095		61		B		usable mapping rate	illumina	nextseq	unknown	cdna_unspecified	unknown	sc	single_cell_droplet	indrops	United States	2017-10-16	Larval	Larval	Brain	Nervous System
43905	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–Cas9 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	pubmed:29608178	DEW127 f4	GSM2813953	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 –e 200; UMI quantification was used with parameter –u 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	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	tissue:brain\|developmental stage:23 25dpf	GSM2813953	GSM2813953: DEW127 f4; Danio rerio; RNA Seq	GSM2813953	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	SRP120009	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	365922709	250482345	264361533	408667760	2297	SRX3287383	SRS2596857	SRA619743	GEO	Harvard University	1	0.88934		0.24432		0.79312		0.57617		61		B		usable mapping rate	illumina	nextseq	unknown	cdna_unspecified	unknown	sc	single_cell_droplet	indrops	United States	2017-10-16	Larval	Larval	Brain	Nervous System
43906	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–Cas9 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	pubmed:29608178	DEW127 f4	GSM2813953	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 –e 200; UMI quantification was used with parameter –u 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	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	tissue:brain\|developmental stage:23 25dpf	GSM2813953	GSM2813953: DEW127 f4; Danio rerio; RNA Seq	GSM2813953	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	SRP120009	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	411699390	281768037	298187448	459726810	5303	SRX3287383	SRS2596857	SRA619743	GEO	Harvard University	1	0.89514		0.24484		0.792		0.58002		60		B		usable mapping rate	illumina	nextseq	unknown	cdna_unspecified	unknown	sc	single_cell_droplet	indrops	United States	2017-10-16	Larval	Larval	Brain	Nervous System
43907	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–Cas9 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	pubmed:29608178	DEW126 f4	GSM2813952	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 –e 200; UMI quantification was used with parameter –u 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	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	tissue:brain\|developmental stage:23 25dpf	GSM2813952	GSM2813952: DEW126 f4; Danio rerio; RNA Seq	GSM2813952	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	SRP120009	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	491786960	337694973	345595607	557148186	3182	SRX3287382	SRS2596856	SRA619743	GEO	Harvard University	1	0.89381		0.23387		0.78845		0.58208		59		B		usable mapping rate	illumina	nextseq	unknown	cdna_unspecified	unknown	sc	single_cell_droplet	indrops	United States	2017-10-16	Larval	Larval	Brain	Nervous System
43908	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–Cas9 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	pubmed:29608178	DEW126 f4	GSM2813952	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 –e 200; UMI quantification was used with parameter –u 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	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	tissue:brain\|developmental stage:23 25dpf	GSM2813952	GSM2813952: DEW126 f4; Danio rerio; RNA Seq	GSM2813952	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	SRP120009	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	555108782	381036712	391375322	629446011	7287	SRX3287382	SRS2596856	SRA619743	GEO	Harvard University	1	0.89893		0.23409		0.78784		0.57241		60		B		usable mapping rate	illumina	nextseq	unknown	cdna_unspecified	unknown	sc	single_cell_droplet	indrops	United States	2017-10-16	Larval	Larval	Brain	Nervous System
43909	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–Cas9 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	pubmed:29608178	DEW125 f4	GSM2813951	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 –e 200; UMI quantification was used with parameter –u 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	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	tissue:brain\|developmental stage:23 25dpf	GSM2813951	GSM2813951: DEW125 f4; Danio rerio; RNA Seq	GSM2813951	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	SRP120009	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	584737414	400546905	408496810	658351017	3824	SRX3287381	SRS2596855	SRA619743	GEO	Harvard University	1	0.89325		0.2332		0.78752		0.59224		61		B		usable mapping rate	illumina	nextseq	unknown	cdna_unspecified	unknown	sc	single_cell_droplet	indrops	United States	2017-10-16	Larval	Larval	Brain	Nervous System
43910	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–Cas9 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	pubmed:29608178	DEW125 f4	GSM2813951	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 –e 200; UMI quantification was used with parameter –u 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	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	tissue:brain\|developmental stage:23 25dpf	GSM2813951	GSM2813951: DEW125 f4; Danio rerio; RNA Seq	GSM2813951	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	SRP120009	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	659601063	451827371	462587835	743268509	8756	SRX3287381	SRS2596855	SRA619743	GEO	Harvard University	1	0.89719		0.23578		0.78622		0.58561		61		B		usable mapping rate	illumina	nextseq	unknown	cdna_unspecified	unknown	sc	single_cell_droplet	indrops	United States	2017-10-16	Larval	Larval	Brain	Nervous System
43911	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–Cas9 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	pubmed:29608178	DEW124 f4	GSM2813950	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 –e 200; UMI quantification was used with parameter –u 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	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	tissue:brain\|developmental stage:23 25dpf	GSM2813950	GSM2813950: DEW124 f4; Danio rerio; RNA Seq	GSM2813950	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	SRP120009	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	505313337	347024008	355651877	571089434	3271	SRX3287380	SRS2596854	SRA619743	GEO	Harvard University	1	0.89472		0.23434		0.78904		0.55858		61		B		usable mapping rate	illumina	nextseq	unknown	cdna_unspecified	unknown	sc	single_cell_droplet	indrops	United States	2017-10-16	Larval	Larval	Brain	Nervous System
43912	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–Cas9 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	pubmed:29608178	DEW124 f4	GSM2813950	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 –e 200; UMI quantification was used with parameter –u 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	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	tissue:brain\|developmental stage:23 25dpf	GSM2813950	GSM2813950: DEW124 f4; Danio rerio; RNA Seq	GSM2813950	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	SRP120009	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	569364072	391252006	402616764	644211365	7566	SRX3287380	SRS2596854	SRA619743	GEO	Harvard University	1	0.89984		0.23537		0.78737		0.58513		61		B		usable mapping rate	illumina	nextseq	unknown	cdna_unspecified	unknown	sc	single_cell_droplet	indrops	United States	2017-10-16	Larval	Larval	Brain	Nervous System
43913	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–Cas9 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	pubmed:29608178	DEW123 f4	GSM2813949	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 –e 200; UMI quantification was used with parameter –u 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	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	tissue:brain\|developmental stage:23 25dpf	GSM2813949	GSM2813949: DEW123 f4; Danio rerio; RNA Seq	GSM2813949	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	SRP120009	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	590232722	406414232	415274944	669658374	3718	SRX3287379	SRS2596853	SRA619743	GEO	Harvard University	1	0.89541		0.23231		0.78705		0.58604		61		B		usable mapping rate	illumina	nextseq	unknown	cdna_unspecified	unknown	sc	single_cell_droplet	indrops	United States	2017-10-16	Larval	Larval	Brain	Nervous System
43914	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–Cas9 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	pubmed:29608178	DEW123 f4	GSM2813949	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 –e 200; UMI quantification was used with parameter –u 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	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	tissue:brain\|developmental stage:23 25dpf	GSM2813949	GSM2813949: DEW123 f4; Danio rerio; RNA Seq	GSM2813949	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	SRP120009	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	664664451	457682624	469409290	754906648	8812	SRX3287379	SRS2596853	SRA619743	GEO	Harvard University	1	0.90006		0.23442		0.78464		0.5855		61		B		usable mapping rate	illumina	nextseq	unknown	cdna_unspecified	unknown	sc	single_cell_droplet	indrops	United States	2017-10-16	Larval	Larval	Brain	Nervous System
43915	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–Cas9 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	pubmed:29608178	DEW122 f3	GSM2813948	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 –e 200; UMI quantification was used with parameter –u 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	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	tissue:brain\|developmental stage:23 25dpf	GSM2813948	GSM2813948: DEW122 f3; Danio rerio; RNA Seq	GSM2813948	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	SRP120009	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	437298698	301001398	315561940	508707589	7902	SRX3287378	SRS2596852	SRA619743	GEO	Harvard University	1	0.90139		0.23545		0.78468		0.55319		61		B		usable mapping rate	illumina	nextseq	unknown	cdna_unspecified	unknown	sc	single_cell_droplet	indrops	United States	2017-10-16	Larval	Larval	Brain	Nervous System
43916	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–Cas9 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	pubmed:29608178	DEW122 f3	GSM2813948	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 –e 200; UMI quantification was used with parameter –u 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	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	tissue:brain\|developmental stage:23 25dpf	GSM2813948	GSM2813948: DEW122 f3; Danio rerio; RNA Seq	GSM2813948	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	SRP120009	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	303730851	209482142	220498036	354466632	6611	SRX3287378	SRS2596852	SRA619743	GEO	Harvard University	1	0.90138		0.23552		0.78563		0.55162		61		B		usable mapping rate	illumina	nextseq	unknown	cdna_unspecified	unknown	sc	single_cell_droplet	indrops	United States	2017-10-16	Larval	Larval	Brain	Nervous System
43917	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–Cas9 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	pubmed:29608178	DEW121 f3	GSM2813947	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 –e 200; UMI quantification was used with parameter –u 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	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	tissue:brain\|developmental stage:23 25dpf	GSM2813947	GSM2813947: DEW121 f3; Danio rerio; RNA Seq	GSM2813947	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	SRP120009	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	526886888	368493310	381057562	612844022	9739	SRX3287377	SRS2596851	SRA619743	GEO	Harvard University	1	0.90379		0.22768		0.78622		0.56039		61		B		usable mapping rate	illumina	nextseq	unknown	cdna_unspecified	unknown	sc	single_cell_droplet	indrops	United States	2017-10-16	Larval	Larval	Brain	Nervous System
43918	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–Cas9 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	pubmed:29608178	DEW121 f3	GSM2813947	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 –e 200; UMI quantification was used with parameter –u 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	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	tissue:brain\|developmental stage:23 25dpf	GSM2813947	GSM2813947: DEW121 f3; Danio rerio; RNA Seq	GSM2813947	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	SRP120009	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	376141462	263880457	273962700	438594952	8349	SRX3287377	SRS2596851	SRA619743	GEO	Harvard University	1	0.90519		0.22909		0.78614		0.55263		61		B		usable mapping rate	illumina	nextseq	unknown	cdna_unspecified	unknown	sc	single_cell_droplet	indrops	United States	2017-10-16	Larval	Larval	Brain	Nervous System
43919	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–Cas9 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	pubmed:29608178	DEW120 f3	GSM2813946	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 –e 200; UMI quantification was used with parameter –u 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	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	tissue:brain\|developmental stage:23 25dpf	GSM2813946	GSM2813946: DEW120 f3; Danio rerio; RNA Seq	GSM2813946	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	SRP120009	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	457777447	315526292	328034539	533465836	8546	SRX3287376	SRS2596850	SRA619743	GEO	Harvard University	1	0.90285		0.23468		0.7833		0.53639		61		B		usable mapping rate	illumina	nextseq	unknown	cdna_unspecified	unknown	sc	single_cell_droplet	indrops	United States	2017-10-16	Larval	Larval	Brain	Nervous System
43920	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–Cas9 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	pubmed:29608178	DEW120 f3	GSM2813946	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 –e 200; UMI quantification was used with parameter –u 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	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	tissue:brain\|developmental stage:23 25dpf	GSM2813946	GSM2813946: DEW120 f3; Danio rerio; RNA Seq	GSM2813946	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	SRP120009	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	322620492	223054841	232826921	376859892	6904	SRX3287376	SRS2596850	SRA619743	GEO	Harvard University	1	0.90311		0.23416		0.78589		0.55648		61		B		usable mapping rate	illumina	nextseq	unknown	cdna_unspecified	unknown	sc	single_cell_droplet	indrops	United States	2017-10-16	Larval	Larval	Brain	Nervous System
43921	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–Cas9 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	pubmed:29608178	DEW119 f3	GSM2813945	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 –e 200; UMI quantification was used with parameter –u 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	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	tissue:brain\|developmental stage:23 25dpf	GSM2813945	GSM2813945: DEW119 f3; Danio rerio; RNA Seq	GSM2813945	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	SRP120009	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	461526493	319892429	328940324	540930270	8401	SRX3287375	SRS2596849	SRA619743	GEO	Harvard University	1	0.90335		0.2353		0.78356		0.55771		61		B		usable mapping rate	illumina	nextseq	unknown	cdna_unspecified	unknown	sc	single_cell_droplet	indrops	United States	2017-10-16	Larval	Larval	Brain	Nervous System
43922	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–Cas9 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	pubmed:29608178	DEW119 f3	GSM2813945	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 –e 200; UMI quantification was used with parameter –u 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	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	tissue:brain\|developmental stage:23 25dpf	GSM2813945	GSM2813945: DEW119 f3; Danio rerio; RNA Seq	GSM2813945	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	SRP120009	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	322008085	223457278	230921827	378334350	6814	SRX3287375	SRS2596849	SRA619743	GEO	Harvard University	1	0.9009		0.23779		0.78675		0.55362		61		B		usable mapping rate	illumina	nextseq	unknown	cdna_unspecified	unknown	sc	single_cell_droplet	indrops	United States	2017-10-16	Larval	Larval	Brain	Nervous System
43923	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–Cas9 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	pubmed:29608178	DEW118 f3	GSM2813944	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 –e 200; UMI quantification was used with parameter –u 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	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	tissue:brain\|developmental stage:23 25dpf	GSM2813944	GSM2813944: DEW118 f3; Danio rerio; RNA Seq	GSM2813944	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	SRP120009	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	396812504	275820359	281479588	461124060	7195	SRX3287374	SRS2596848	SRA619743	GEO	Harvard University	1	0.90294		0.23426		0.78595		0.52576		61		B		usable mapping rate	illumina	nextseq	unknown	cdna_unspecified	unknown	sc	single_cell_droplet	indrops	United States	2017-10-16	Larval	Larval	Brain	Nervous System
43924	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–Cas9 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	pubmed:29608178	DEW118 f3	GSM2813944	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 –e 200; UMI quantification was used with parameter –u 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	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	tissue:brain\|developmental stage:23 25dpf	GSM2813944	GSM2813944: DEW118 f3; Danio rerio; RNA Seq	GSM2813944	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	SRP120009	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	275603270	191766995	196356019	320786079	5841	SRX3287374	SRS2596848	SRA619743	GEO	Harvard University	1	0.902		0.23477		0.78739		0.5585		61		B		usable mapping rate	illumina	nextseq	unknown	cdna_unspecified	unknown	sc	single_cell_droplet	indrops	United States	2017-10-16	Larval	Larval	Brain	Nervous System
43925	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–Cas9 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	pubmed:29608178	DEW117 f3	GSM2813943	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 –e 200; UMI quantification was used with parameter –u 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	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	tissue:brain\|developmental stage:23 25dpf	GSM2813943	GSM2813943: DEW117 f3; Danio rerio; RNA Seq	GSM2813943	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	SRP120009	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	400414379	277758163	284791203	465981663	7364	SRX3287373	SRS2596847	SRA619743	GEO	Harvard University	1	0.90144		0.23429		0.78551		0.53009		60		B		usable mapping rate	illumina	nextseq	unknown	cdna_unspecified	unknown	sc	single_cell_droplet	indrops	United States	2017-10-16	Larval	Larval	Brain	Nervous System
43926	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–Cas9 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	pubmed:29608178	DEW117 f3	GSM2813943	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 –e 200; UMI quantification was used with parameter –u 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	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	tissue:brain\|developmental stage:23 25dpf	GSM2813943	GSM2813943: DEW117 f3; Danio rerio; RNA Seq	GSM2813943	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	SRP120009	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	282793765	196692351	202514378	329973834	6092	SRX3287373	SRS2596847	SRA619743	GEO	Harvard University	1	0.90206		0.23615		0.78882		0.55621		61		B		usable mapping rate	illumina	nextseq	unknown	cdna_unspecified	unknown	sc	single_cell_droplet	indrops	United States	2017-10-16	Larval	Larval	Brain	Nervous System
43927	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–Cas9 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	pubmed:29608178	DEW116 f3	GSM2813942	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 –e 200; UMI quantification was used with parameter –u 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	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	tissue:brain\|developmental stage:23 25dpf	GSM2813942	GSM2813942: DEW116 f3; Danio rerio; RNA Seq	GSM2813942	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	SRP120009	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	560262631	390864845	401224604	659882586	10353	SRX3287372	SRS2596846	SRA619743	GEO	Harvard University	1	0.90562		0.21992		0.78693		0.5534		59		B		usable mapping rate	illumina	nextseq	unknown	cdna_unspecified	unknown	sc	single_cell_droplet	indrops	United States	2017-10-16	Larval	Larval	Brain	Nervous System
43928	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–Cas9 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	pubmed:29608178	DEW116 f3	GSM2813942	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 –e 200; UMI quantification was used with parameter –u 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	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	tissue:brain\|developmental stage:23 25dpf	GSM2813942	GSM2813942: DEW116 f3; Danio rerio; RNA Seq	GSM2813942	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	SRP120009	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	395355082	276477238	285074262	466849922	8563	SRX3287372	SRS2596846	SRA619743	GEO	Harvard University	1	0.90416		0.22121		0.78675		0.55396		61		B		usable mapping rate	illumina	nextseq	unknown	cdna_unspecified	unknown	sc	single_cell_droplet	indrops	United States	2017-10-16	Larval	Larval	Brain	Nervous System
43929	43929	SRR6176662	SRX3287371	SRS2596845	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–Cas9 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	pubmed:29608178	DEW115 f3	GSM2813941	source name:zebrafish brain\|tissue:brain\|developmental stage:23 25dpf	DEW115 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 –e 200; UMI quantification was used with parameter –u 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	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	tissue:brain\|developmental stage:23 25dpf	GSM2813941	GSM2813941: DEW115 f3; Danio rerio; RNA Seq	GSM2813941	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:GSM2813941	RNA-Seq	TRANSCRIPTOMIC	cDNA	SINGLE	ILLUMINA	NextSeq 500	SRP120009	loader:fastq load.py\|options: appendBCtoName	DEW115_Lane1.sorted.fastq.gz	fastq	1717624979.0	28952740.0	GSM2813941 r1	0:59.33	A:478636885;C:333886152;G:340549498;T:564543456;N:8988	59	478636885	333886152	340549498	564543456	8988	SRX3287371	SRS2596845	SRA619743	GEO	Harvard University	1	0.90693		0.22334		0.78413		0.52539		61		B		usable mapping rate	illumina	nextseq	unknown	cdna_unspecified	unknown	sc	single_cell_droplet	indrops	United States	2017-10-16	Larval	Larval	Brain	Nervous System
43930	43930	SRR6176663	SRX3287371	SRS2596845	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–Cas9 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	pubmed:29608178	DEW115 f3	GSM2813941	source name:zebrafish brain\|tissue:brain\|developmental stage:23 25dpf	DEW115 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 –e 200; UMI quantification was used with parameter –u 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	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	tissue:brain\|developmental stage:23 25dpf	GSM2813941	GSM2813941: DEW115 f3; Danio rerio; RNA Seq	GSM2813941	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:GSM2813941	RNA-Seq	TRANSCRIPTOMIC	cDNA	SINGLE	ILLUMINA	NextSeq 500	SRP120009	loader:fastq load.py\|options: appendBCtoName	DEW115_Lane2.sorted.fastq.gz	fastq	1219363584.0	20663045.0	GSM2813941 r2	0:59.01	A:338605262;C:237038312;G:242941921;T:400770619;N:7470	59	338605262	237038312	242941921	400770619	7470	SRX3287371	SRS2596845	SRA619743	GEO	Harvard University	1	0.90596		0.22301		0.78551		0.55808		61		B		usable mapping rate	illumina	nextseq	unknown	cdna_unspecified	unknown	sc	single_cell_droplet	indrops	United States	2017-10-16	Larval	Larval	Brain	Nervous System
43931	43931	SRR6176660	SRX3287370	SRS2596844	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–Cas9 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	pubmed:29608178	DEW114 f3	GSM2813940	source name:zebrafish brain\|tissue:brain\|developmental stage:23 25dpf	DEW114 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 –e 200; UMI quantification was used with parameter –u 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	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	tissue:brain\|developmental stage:23 25dpf	GSM2813940	GSM2813940: DEW114 f3; Danio rerio; RNA Seq	GSM2813940	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:GSM2813940	RNA-Seq	TRANSCRIPTOMIC	cDNA	SINGLE	ILLUMINA	NextSeq 500	SRP120009	loader:fastq load.py\|options: appendBCtoName	DEW114_Lane1.sorted.fastq.gz	fastq	1549123868.0	26108472.0	GSM2813940 r1	0:59.33	A:430749955;C:300872192;G:309906524;T:507586964;N:8233	59	430749955	300872192	309906524	507586964	8233	SRX3287370	SRS2596844	SRA619743	GEO	Harvard University	1	0.90579		0.22372		0.78579		0.55541		61		B		usable mapping rate	illumina	nextseq	unknown	cdna_unspecified	unknown	sc	single_cell_droplet	indrops	United States	2017-10-16	Larval	Larval	Brain	Nervous System
43932	43932	SRR6176661	SRX3287370	SRS2596844	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–Cas9 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	pubmed:29608178	DEW114 f3	GSM2813940	source name:zebrafish brain\|tissue:brain\|developmental stage:23 25dpf	DEW114 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 –e 200; UMI quantification was used with parameter –u 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	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	tissue:brain\|developmental stage:23 25dpf	GSM2813940	GSM2813940: DEW114 f3; Danio rerio; RNA Seq	GSM2813940	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:GSM2813940	RNA-Seq	TRANSCRIPTOMIC	cDNA	SINGLE	ILLUMINA	NextSeq 500	SRP120009	loader:fastq load.py\|options: appendBCtoName	DEW114_Lane2.sorted.fastq.gz	fastq	1101127221.0	18660492.0	GSM2813940 r2	0:59.01	A:305331353;C:213952288;G:221171510;T:360665299;N:6771	59	305331353	213952288	221171510	360665299	6771	SRX3287370	SRS2596844	SRA619743	GEO	Harvard University	1	0.90488		0.22206		0.78464		0.54904		60		B		usable mapping rate	illumina	nextseq	unknown	cdna_unspecified	unknown	sc	single_cell_droplet	indrops	United States	2017-10-16	Larval	Larval	Brain	Nervous System
43933	43933	SRR6176658	SRX3287369	SRS2596843	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–Cas9 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	pubmed:29608178	DEW113 f3	GSM2813939	source name:zebrafish brain\|tissue:brain\|developmental stage:23 25dpf	DEW113 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 –e 200; UMI quantification was used with parameter –u 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	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	tissue:brain\|developmental stage:23 25dpf	GSM2813939	GSM2813939: DEW113 f3; Danio rerio; RNA Seq	GSM2813939	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:GSM2813939	RNA-Seq	TRANSCRIPTOMIC	cDNA	SINGLE	ILLUMINA	NextSeq 500	SRP120009	loader:fastq load.py\|options: appendBCtoName	DEW113_Lane1.sorted.fastq.gz	fastq	1675874174.0	28272098.0	GSM2813939 r1	0:59.28	A:467036447;C:327861754;G:332191673;T:548775559;N:8741	59	467036447	327861754	332191673	548775559	8741	SRX3287369	SRS2596843	SRA619743	GEO	Harvard University	1	0.9056		0.22368		0.78687		0.55635		61		B		usable mapping rate	illumina	nextseq	unknown	cdna_unspecified	unknown	sc	single_cell_droplet	indrops	United States	2017-10-16	Larval	Larval	Brain	Nervous System
43934	43934	SRR6176659	SRX3287369	SRS2596843	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–Cas9 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	pubmed:29608178	DEW113 f3	GSM2813939	source name:zebrafish brain\|tissue:brain\|developmental stage:23 25dpf	DEW113 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 –e 200; UMI quantification was used with parameter –u 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	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	tissue:brain\|developmental stage:23 25dpf	GSM2813939	GSM2813939: DEW113 f3; Danio rerio; RNA Seq	GSM2813939	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:GSM2813939	RNA-Seq	TRANSCRIPTOMIC	cDNA	SINGLE	ILLUMINA	NextSeq 500	SRP120009	loader:fastq load.py\|options: appendBCtoName	DEW113_Lane2.sorted.fastq.gz	fastq	1195816427.0	20294413.0	GSM2813939 r2	0:58.92	A:332326182;C:233846546;G:238054655;T:391581847;N:7197	58	332326182	233846546	238054655	391581847	7197	SRX3287369	SRS2596843	SRA619743	GEO	Harvard University	1	0.90561		0.22638		0.79019		0.55492		17		B		usable mapping rate	illumina	nextseq	unknown	cdna_unspecified	unknown	sc	single_cell_droplet	indrops	United States	2017-10-16	Larval	Larval	Brain	Nervous System
43935	43935	SRR6176656	SRX3287368	SRS2596842	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–Cas9 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	pubmed:29608178	DEW112 f3	GSM2813938	source name:zebrafish brain\|tissue:brain\|developmental stage:23 25dpf	DEW112 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 –e 200; UMI quantification was used with parameter –u 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	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	tissue:brain\|developmental stage:23 25dpf	GSM2813938	GSM2813938: DEW112 f3; Danio rerio; RNA Seq	GSM2813938	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:GSM2813938	RNA-Seq	TRANSCRIPTOMIC	cDNA	SINGLE	ILLUMINA	NextSeq 500	SRP120009	loader:fastq load.py\|options: appendBCtoName	DEW112_Lane1.sorted.fastq.gz	fastq	1639316237.0	27632907.0	GSM2813938 r1	0:59.32	A:456011495;C:320039859;G:326641916;T:536614375;N:8592	59	456011495	320039859	326641916	536614375	8592	SRX3287368	SRS2596842	SRA619743	GEO	Harvard University	1	0.90753		0.22305		0.78683		0.53922		61		B		usable mapping rate	illumina	nextseq	unknown	cdna_unspecified	unknown	sc	single_cell_droplet	indrops	United States	2017-10-16	Larval	Larval	Brain	Nervous System
43936	43936	SRR6176657	SRX3287368	SRS2596842	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–Cas9 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	pubmed:29608178	DEW112 f3	GSM2813938	source name:zebrafish brain\|tissue:brain\|developmental stage:23 25dpf	DEW112 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 –e 200; UMI quantification was used with parameter –u 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	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	tissue:brain\|developmental stage:23 25dpf	GSM2813938	GSM2813938: DEW112 f3; Danio rerio; RNA Seq	GSM2813938	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:GSM2813938	RNA-Seq	TRANSCRIPTOMIC	cDNA	SINGLE	ILLUMINA	NextSeq 500	SRP120009	loader:fastq load.py\|options: appendBCtoName	DEW112_Lane2.sorted.fastq.gz	fastq	1157204768.0	19611901.0	GSM2813938 r2	0:59.01	A:321050401;C:225993253;G:231573930;T:378580037;N:7147	59	321050401	225993253	231573930	378580037	7147	SRX3287368	SRS2596842	SRA619743	GEO	Harvard University	1	0.90611		0.22337		0.78709		0.56089		61		B		usable mapping rate	illumina	nextseq	unknown	cdna_unspecified	unknown	sc	single_cell_droplet	indrops	United States	2017-10-16	Larval	Larval	Brain	Nervous System
43937	43937	SRR6176654	SRX3287367	SRS2596841	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–Cas9 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	pubmed:29608178	DEW111 f3	GSM2813937	source name:zebrafish brain\|tissue:brain\|developmental stage:23 25dpf	DEW111 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 –e 200; UMI quantification was used with parameter –u 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	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	tissue:brain\|developmental stage:23 25dpf	GSM2813937	GSM2813937: DEW111 f3; Danio rerio; RNA Seq	GSM2813937	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:GSM2813937	RNA-Seq	TRANSCRIPTOMIC	cDNA	SINGLE	ILLUMINA	NextSeq 500	SRP120009	loader:fastq load.py\|options: appendBCtoName	DEW111_Lane1.sorted.fastq.gz	fastq	1650256459.0	27821349.0	GSM2813937 r1	0:59.32	A:460087799;C:320609407;G:328249759;T:541300806;N:8688	59	460087799	320609407	328249759	541300806	8688	SRX3287367	SRS2596841	SRA619743	GEO	Harvard University	1	0.90326		0.22991		0.78587		0.53703		26		B		usable mapping rate	illumina	nextseq	unknown	cdna_unspecified	unknown	sc	single_cell_droplet	indrops	United States	2017-10-16	Larval	Larval	Brain	Nervous System
43938	43938	SRR6176655	SRX3287367	SRS2596841	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–Cas9 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	pubmed:29608178	DEW111 f3	GSM2813937	source name:zebrafish brain\|tissue:brain\|developmental stage:23 25dpf	DEW111 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 –e 200; UMI quantification was used with parameter –u 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	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	tissue:brain\|developmental stage:23 25dpf	GSM2813937	GSM2813937: DEW111 f3; Danio rerio; RNA Seq	GSM2813937	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:GSM2813937	RNA-Seq	TRANSCRIPTOMIC	cDNA	SINGLE	ILLUMINA	NextSeq 500	SRP120009	loader:fastq load.py\|options: appendBCtoName	DEW111_Lane2.sorted.fastq.gz	fastq	1173991671.0	19902418.0	GSM2813937 r2	0:58.99	A:326374255;C:228102980;G:234509805;T:384997536;N:7095	58	326374255	228102980	234509805	384997536	7095	SRX3287367	SRS2596841	SRA619743	GEO	Harvard University	1	0.90285		0.22962		0.78845		0.54578		60		B		usable mapping rate	illumina	nextseq	unknown	cdna_unspecified	unknown	sc	single_cell_droplet	indrops	United States	2017-10-16	Larval	Larval	Brain	Nervous System
43939	43939	SRR6176649	SRX3287366	SRS2596840	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–Cas9 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	pubmed:29608178	DEW067 f2	GSM2813936	source name:zebrafish brain\|tissue:brain\|developmental stage:23 25dpf	DEW067 f2	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 –e 200; UMI quantification was used with parameter –u 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	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	tissue:brain\|developmental stage:23 25dpf	GSM2813936	GSM2813936: DEW067 f2; Danio rerio; RNA Seq	GSM2813936	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:GSM2813936	RNA-Seq	TRANSCRIPTOMIC	cDNA	PAIRED	ILLUMINA	NextSeq 500	SRP120009	loader:fastq load.py\|options: appendBCtoName	FC1_DEW-067_S10.R1.fastq.gz FC1_DEW-067_S10.R2.fastq.gz	fastq fastq	2551096506.0	30568258.0	GSM2813936 r1		A:557861215;C:486414729;G:614056373;T:892106619;N:657570		557861215	486414729	614056373	892106619	657570	SRX3287366	SRS2596840	SRA619743	GEO	Harvard University	2	0.74967	0.00628	0.22106	0.006	0.79527	0.99945	0.55023	0.46875	34	49	B	T	mate2 technical by mapping diff	illumina	nextseq	unknown	cdna_unspecified	unknown	sc	single_cell_droplet	indrops	United States	2017-10-16	Larval	Larval	Brain	Nervous System
43940	43940	SRR6176650	SRX3287366	SRS2596840	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–Cas9 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	pubmed:29608178	DEW067 f2	GSM2813936	source name:zebrafish brain\|tissue:brain\|developmental stage:23 25dpf	DEW067 f2	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 –e 200; UMI quantification was used with parameter –u 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	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	tissue:brain\|developmental stage:23 25dpf	GSM2813936	GSM2813936: DEW067 f2; Danio rerio; RNA Seq	GSM2813936	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:GSM2813936	RNA-Seq	TRANSCRIPTOMIC	cDNA	PAIRED	ILLUMINA	NextSeq 500	SRP120009	loader:fastq load.py\|options: appendBCtoName	FC2_DEW-067_S10.R1.fastq.gz FC2_DEW-067_S10.R2.fastq.gz	fastq fastq	1155281120.0	13849592.0	GSM2813936 r2		A:233187378;C:202743748;G:364218355;T:354503618;N:628021		233187378	202743748	364218355	354503618	628021	SRX3287366	SRS2596840	SRA619743	GEO	Harvard University	2	0.75419	0.02424	0.22148	0.02238	0.79415	0.99943	0.55145	0.9	34	50	B	T	mate2 technical by mapping diff	illumina	nextseq	unknown	cdna_unspecified	unknown	sc	single_cell_droplet	indrops	United States	2017-10-16	Larval	Larval	Brain	Nervous System
43941	43941	SRR6176651	SRX3287366	SRS2596840	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–Cas9 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	pubmed:29608178	DEW067 f2	GSM2813936	source name:zebrafish brain\|tissue:brain\|developmental stage:23 25dpf	DEW067 f2	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 –e 200; UMI quantification was used with parameter –u 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	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	tissue:brain\|developmental stage:23 25dpf	GSM2813936	GSM2813936: DEW067 f2; Danio rerio; RNA Seq	GSM2813936	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:GSM2813936	RNA-Seq	TRANSCRIPTOMIC	cDNA	PAIRED	ILLUMINA	NextSeq 500	SRP120009	loader:fastq load.py\|options: appendBCtoName	FC3_DEW-067_S10.R1.fastq.gz FC3_DEW-067_S10.R2.fastq.gz	fastq fastq	2630905456.0	31531644.0	GSM2813936 r3		A:571354506;C:501012015;G:621212959;T:936052979;N:1272997		571354506	501012015	621212959	936052979	1272997	SRX3287366	SRS2596840	SRA619743	GEO	Harvard University	2	0.75366	0.0038	0.22017	0.00351	0.79265	0.99931	0.5547	0.63414	33	50	B	T	mate2 technical by mapping diff	illumina	nextseq	unknown	cdna_unspecified	unknown	sc	single_cell_droplet	indrops	United States	2017-10-16	Larval	Larval	Brain	Nervous System

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CREATE TABLE run_metadata("run.accession" VARCHAR, "experiment.accession" VARCHAR, "sample.accession" VARCHAR, "study.accession" VARCHAR, bioproject VARCHAR, "study.title" VARCHAR, "study.alias" VARCHAR, "study.type" VARCHAR, "study.abstract" VARCHAR, "study.attributes" VARCHAR, "study.PMIDs" VARCHAR, "sample.description" VARCHAR, "sample.title" VARCHAR, "sample.alias" VARCHAR, "sample.centername" VARCHAR, "sample.attributes" VARCHAR, "GEOsample.title" VARCHAR, "GEOsample.dataprocessing" VARCHAR, "GEOsample.source" VARCHAR, "GEOsample.treatmentprotocol" VARCHAR, "GEOsample.extractprotocol" VARCHAR, "GEOsample.growthprotocol" VARCHAR, "GEOsample.characteristics" VARCHAR, "GEOsample.accession" VARCHAR, "experiment.title" VARCHAR, "experiment.alias" VARCHAR, "experiment.library_name" VARCHAR, "experiment.design_description" VARCHAR, "experiment.library_construction_protocol" VARCHAR, "experiment.attributes" VARCHAR, "experiment.library_strategy" VARCHAR, "experiment.library_source" VARCHAR, "experiment.library_selection" VARCHAR, "experiment.library_layout" VARCHAR, "experiment.platform" VARCHAR, "experiment.instrument_model" VARCHAR, "experiment.spot_descriptor" VARCHAR, "experiment.study_ref" VARCHAR, "run.title" VARCHAR, "run.attributes" VARCHAR, "run.filename" VARCHAR, "run.semantic_name" VARCHAR, "run.total_bases" DOUBLE, "run.total_spots" DOUBLE, "run.alias" VARCHAR, "run.read_lengths" VARCHAR, "run.base_counts" VARCHAR, "run.r1_length" BIGINT, "run.r2_length" BIGINT, "run.r3_length" BIGINT, "run.r4_length" BIGINT, "run.Acount" BIGINT, "run.Ccount" BIGINT, "run.Gcount" BIGINT, "run.Tcount" BIGINT, "run.Ncount" BIGINT, "run.experiment" VARCHAR, "run.pool_member" VARCHAR, "submission.accession" VARCHAR, "submission.srasource" VARCHAR, "submission.bioprojectsource" VARCHAR, "seqdetective.n_mates" BIGINT, "seqdetective.mapping_rate.mate1" DOUBLE, "seqdetective.mapping_rate.mate2" DOUBLE, "seqdetective.nofeature_rate.mate1" DOUBLE, "seqdetective.nofeature_rate.mate2" DOUBLE, "seqdetective.sparsity.mate1" DOUBLE, "seqdetective.sparsity.mate2" DOUBLE, "seqdetective.pos_strand_rate.mate1" DOUBLE, "seqdetective.pos_strand_rate.mate2" DOUBLE, "seqdetective.readlen.mate1" BIGINT, "seqdetective.readlen.mate2" BIGINT, "seqdetective.judgement.mate1" VARCHAR, "seqdetective.judgement.mate2" VARCHAR, "seqdetective.judgement.reason" VARCHAR, platform_family VARCHAR, instrument_generation VARCHAR, read_bias VARCHAR, selection_class VARCHAR, prep_kit VARCHAR, sc_or_bulk VARCHAR, tech_class VARCHAR, technology VARCHAR, tech_variant VARCHAR, "submission.bioprojectsource.country" VARCHAR, earliest_date DATE, devstage_curation VARCHAR, devstage_curation_coarse VARCHAR, tissue_curation VARCHAR, tissue_curation_coarse VARCHAR);;
CREATE INDEX idx_run_bioproject ON run_metadata(bioproject);;
CREATE INDEX idx_run_run_accession ON run_metadata("run.accession");;