metadata: run_metadata: 36 rows where experiment.library_selection = "other", experiment.library_source = "TRANSCRIPTOMIC" and technology = "10x"

36 rows where experiment.library_selection = "other", experiment.library_source = "TRANSCRIPTOMIC" and technology = "10x"

descending

Link	rowid ▼	run.accession	experiment.accession	sample.accession	study.accession	bioproject	study.title	study.alias	study.type	study.abstract	study.attributes	study.PMIDs	sample.title	sample.alias	sample.attributes	GEOsample.title	GEOsample.dataprocessing	GEOsample.source	GEOsample.treatmentprotocol	GEOsample.extractprotocol	GEOsample.growthprotocol	GEOsample.characteristics	GEOsample.accession	experiment.title	experiment.alias	experiment.library_name	experiment.design_description	experiment.library_construction_protocol	experiment.attributes	experiment.library_strategy	experiment.library_source	experiment.library_selection	experiment.library_layout	experiment.platform	experiment.instrument_model	experiment.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.r2_length	run.r3_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
28725	28725	SRR26623262	SRX22323921	SRS19374450	SRP469552	PRJNA1034159	Dissecting the spatiotemporal diversity of adult neural stem cells	GSE246714	Other	Adult stem cells are important for tissue turnover and regeneration. However in most adult systems it remains elusive how stem cells assume different functional states and support spatially patterned tissue architecture. Here we dissected the diversity of neural stem cells in the adult zebrafish brain an organ that is characterized by pronounced zonation and high regenerative capacity. We combined single cell transcriptomics of dissected brain regions with massively parallel lineage tracing and in vivo RNA metabolic labeling to analyze regulation of neural stem cells in space and time. We detected a large diversity of neural stem cells with some subtypes being restricted to a single brain region while others were found globally across the brain. Global stem cell states are linked to neurogenic differentiation with different states being involved in proliferative and non proliferative differentiation. Our work reveals principles of adult stem cell organization and establishes a resource for functional manipulation of neural stem cell subtypes. Overall design: Single cell RNA seq of adult zebrafish brain performed using 10x Genomics Gene Expression protocols to explore transcriptomic diversity of adult cell types. The samples were prepared either from whole brain FACS sorted cells GFP positive cells from gfap:GFP line or a dissected single brain region as indicated in the sample title. CRISPR based lineage tracing was performed for a subset of samples to dissect developmental lineage relationships between cells. ScSLAM seq was performed in combination with Notch pathway inhibition to assess response of stem cells to perturbation.		pubmed:38365956	Lineage tracing rep1 cirbpb scars	GSM7875190	source name:adult brain\|tissue:adult brain\|cell type:mixed tissue dissociation\|genotype:Tg[ubi:zebrabow M]\|geo loc name:missing\|collection date:missing	Lineage tracing rep1 cirbpb scars	The transcriptome libraries were demultiplexed and mapped with CellRanger 6.1.1. In order to reduce batch effect the gene expression matrices were processed with the SoupX tool Young & Behjati 2020 for removing ambient RNA contamination. A subset of libraries was also mapped with Velocyto v0.17.17 La Manno et al 2018 to create count matrices for unspliced and spliced molecules. The targeted lineage tracing libraries were aligned using bwa mem3 v.0.7.12 to individual references of endogenous genes used in the lineage tracing experiment actb1 actb2 cfl1 cirbpb rpl39 and ube2e1. The scars were filtered with a custom pipeline to eliminate sequences that originate from PCR or sequencing errors. For custom code and further downstream analysis see: https://github.com/nimitic/radial glia. Assembly: GRCz11 Supplementary files format and content: Tab separated barcode files and matrix files along with the tab separated gene file supplementary file are the output of CellRanger mapping further processed by the SoupX package for ambient RNA removal. Supplementary files format and content: Loom files are the output of velocyto mapping distinguishing spliced and unspliced mRNAs. Supplementary files format and content: filtered scars.csv files contain the output of a custom scar filtering pipeline.	adult brain		Adult zebrafish brain tissue was extracted optionally dissected to select a specific region of interest telencephalon diencephalon mesencephalon or rhombencephalon and then dissociated with a trypsin protocol. Briefly the samples were incubated in 750 µl of HBSS glucose solution and dissocated with 15 µl of 0.05% trypsin EDTA Gibco for 30 minutes at 37°C with intermittent mixing. The disociation was stopped by addition of 750 µl of a BSA EBSS HEPES solution. Further sample handling steps were carried out on ice and for centrifugation at 4°C. The sample was filtered through a 100 µm filter washed with cold HBSS and resuspended in 100 200 µl HBSS with 0.05% BSA then filtered through a 35 µm filter and loaded on the 10x Chromium Controller. For the scSLAM seq samples a papain dissociation protocol Worthington kit according to manufecturer's instructions was used followed by an adapted scSLAM seq protocol for labeling nascent transcripts see sample specific entry. The standard transcriptome libraries were constructed according to the manufacturer's protocol 10X Genomics. For targeted libraries of scars used for lineage tracing the cDNA obtained during the 10x protocol was used to set up individual PCR reactions for each target specifically amplifying the sgRNA target site. The libraries for targeted PCR were constructed with primers compatible with the 10X Genomics kit so they could be sequenced on the same platform.		tissue:adult brain\|cell type:mixed tissue dissociation\|genotype:Tg[ubi:zebrabow M]	GSM7875190	GSM7875190: Lineage tracing rep1 cirbpb scars; Danio rerio; OTHER	GSM7875190 r1	GSM7875190	1	Adult zebrafish brain tissue was extracted optionally dissected to select a specific region of interest telencephalon diencephalon mesencephalon or rhombencephalon and then dissociated with a trypsin protocol. Briefly the samples were incubated in 750 µl of HBSS glucose solution and dissocated with 15 µl of 0.05% trypsin EDTA Gibco for 30 minutes at 37°C with intermittent mixing. The disociation was stopped by addition of 750 µl of a BSA EBSS HEPES solution. Further sample handling steps were carried out on ice and for centrifugation at 4°C. The sample was filtered through a 100 µm filter washed with cold HBSS and resuspended in 100 200 µl HBSS with 0.05% BSA then filtered through a 35 µm filter and loaded on the 10x Chromium Controller. For the scSLAM seq samples a papain dissociation protocol Worthington kit according to manufecturer's instructions was used followed by an adapted scSLAM seq protocol for labeling nascent transcripts see sample specific entry. The standard transcriptome libraries were constructed according to the manufacturer's protocol 10X Genomics. For targeted libraries of scars used for lineage tracing the cDNA obtained during the 10x protocol was used to set up individual PCR reactions for each target specifically amplifying the sgRNA target site. The libraries for targeted PCR were constructed with primers compatible with the 10X Genomics kit so they could be sequenced on the same platform.		OTHER	TRANSCRIPTOMIC	other	PAIRED	ILLUMINA	NextSeq 500	SRP469552	loader:fastq load.py	lin1_cirbpb_scar_R1.fastq.gz lin1_cirbpb_scar_R2.fastq.gz	fastq fastq	157576336.0	949552.0	GSM7875190 r1											SRX22323921	SRS19374450	SRA1743007	Junker Lab, Berlin Institute for Medical Systems Biology, Max-Delbrück-Center for Molecular Medicine	Junker Lab, Berlin Institute for Medical Systems Biology, Max-Delbrück-Center for Molecular Medicine	2	0.00141	0.78069	0.00053	0.00877	0.99857	0.97822	0.33536	0.05199	28	120	T	B	sc-like readlen	illumina	nextseq	unknown	other	unknown	sc	single_cell_droplet	10x	Germany	2023-10-31	Adult	Adult	Brain	Nervous System
28726	28726	SRR26623263	SRX22323920	SRS19374449	SRP469552	PRJNA1034159	Dissecting the spatiotemporal diversity of adult neural stem cells	GSE246714	Other	Adult stem cells are important for tissue turnover and regeneration. However in most adult systems it remains elusive how stem cells assume different functional states and support spatially patterned tissue architecture. Here we dissected the diversity of neural stem cells in the adult zebrafish brain an organ that is characterized by pronounced zonation and high regenerative capacity. We combined single cell transcriptomics of dissected brain regions with massively parallel lineage tracing and in vivo RNA metabolic labeling to analyze regulation of neural stem cells in space and time. We detected a large diversity of neural stem cells with some subtypes being restricted to a single brain region while others were found globally across the brain. Global stem cell states are linked to neurogenic differentiation with different states being involved in proliferative and non proliferative differentiation. Our work reveals principles of adult stem cell organization and establishes a resource for functional manipulation of neural stem cell subtypes. Overall design: Single cell RNA seq of adult zebrafish brain performed using 10x Genomics Gene Expression protocols to explore transcriptomic diversity of adult cell types. The samples were prepared either from whole brain FACS sorted cells GFP positive cells from gfap:GFP line or a dissected single brain region as indicated in the sample title. CRISPR based lineage tracing was performed for a subset of samples to dissect developmental lineage relationships between cells. ScSLAM seq was performed in combination with Notch pathway inhibition to assess response of stem cells to perturbation.		pubmed:38365956	Lineage tracing rep1 cfl1 scars	GSM7875189	source name:adult brain\|tissue:adult brain\|cell type:mixed tissue dissociation\|genotype:Tg[ubi:zebrabow M]\|geo loc name:missing\|collection date:missing	Lineage tracing rep1 cfl1 scars	The transcriptome libraries were demultiplexed and mapped with CellRanger 6.1.1. In order to reduce batch effect the gene expression matrices were processed with the SoupX tool Young & Behjati 2020 for removing ambient RNA contamination. A subset of libraries was also mapped with Velocyto v0.17.17 La Manno et al 2018 to create count matrices for unspliced and spliced molecules. The targeted lineage tracing libraries were aligned using bwa mem3 v.0.7.12 to individual references of endogenous genes used in the lineage tracing experiment actb1 actb2 cfl1 cirbpb rpl39 and ube2e1. The scars were filtered with a custom pipeline to eliminate sequences that originate from PCR or sequencing errors. For custom code and further downstream analysis see: https://github.com/nimitic/radial glia. Assembly: GRCz11 Supplementary files format and content: Tab separated barcode files and matrix files along with the tab separated gene file supplementary file are the output of CellRanger mapping further processed by the SoupX package for ambient RNA removal. Supplementary files format and content: Loom files are the output of velocyto mapping distinguishing spliced and unspliced mRNAs. Supplementary files format and content: filtered scars.csv files contain the output of a custom scar filtering pipeline.	adult brain		Adult zebrafish brain tissue was extracted optionally dissected to select a specific region of interest telencephalon diencephalon mesencephalon or rhombencephalon and then dissociated with a trypsin protocol. Briefly the samples were incubated in 750 µl of HBSS glucose solution and dissocated with 15 µl of 0.05% trypsin EDTA Gibco for 30 minutes at 37°C with intermittent mixing. The disociation was stopped by addition of 750 µl of a BSA EBSS HEPES solution. Further sample handling steps were carried out on ice and for centrifugation at 4°C. The sample was filtered through a 100 µm filter washed with cold HBSS and resuspended in 100 200 µl HBSS with 0.05% BSA then filtered through a 35 µm filter and loaded on the 10x Chromium Controller. For the scSLAM seq samples a papain dissociation protocol Worthington kit according to manufecturer's instructions was used followed by an adapted scSLAM seq protocol for labeling nascent transcripts see sample specific entry. The standard transcriptome libraries were constructed according to the manufacturer's protocol 10X Genomics. For targeted libraries of scars used for lineage tracing the cDNA obtained during the 10x protocol was used to set up individual PCR reactions for each target specifically amplifying the sgRNA target site. The libraries for targeted PCR were constructed with primers compatible with the 10X Genomics kit so they could be sequenced on the same platform.		tissue:adult brain\|cell type:mixed tissue dissociation\|genotype:Tg[ubi:zebrabow M]	GSM7875189	GSM7875189: Lineage tracing rep1 cfl1 scars; Danio rerio; OTHER	GSM7875189 r1	GSM7875189	1	Adult zebrafish brain tissue was extracted optionally dissected to select a specific region of interest telencephalon diencephalon mesencephalon or rhombencephalon and then dissociated with a trypsin protocol. Briefly the samples were incubated in 750 µl of HBSS glucose solution and dissocated with 15 µl of 0.05% trypsin EDTA Gibco for 30 minutes at 37°C with intermittent mixing. The disociation was stopped by addition of 750 µl of a BSA EBSS HEPES solution. Further sample handling steps were carried out on ice and for centrifugation at 4°C. The sample was filtered through a 100 µm filter washed with cold HBSS and resuspended in 100 200 µl HBSS with 0.05% BSA then filtered through a 35 µm filter and loaded on the 10x Chromium Controller. For the scSLAM seq samples a papain dissociation protocol Worthington kit according to manufecturer's instructions was used followed by an adapted scSLAM seq protocol for labeling nascent transcripts see sample specific entry. The standard transcriptome libraries were constructed according to the manufacturer's protocol 10X Genomics. For targeted libraries of scars used for lineage tracing the cDNA obtained during the 10x protocol was used to set up individual PCR reactions for each target specifically amplifying the sgRNA target site. The libraries for targeted PCR were constructed with primers compatible with the 10X Genomics kit so they could be sequenced on the same platform.		OTHER	TRANSCRIPTOMIC	other	PAIRED	ILLUMINA	NextSeq 500	SRP469552	loader:fastq load.py	lin1_cfl1_scar_R2.fastq.gz lin1_cfl1_scar_R1.fastq.gz	fastq fastq	381453696.0	2184402.0	GSM7875189 r1											SRX22323920	SRS19374449	SRA1743007	Junker Lab, Berlin Institute for Medical Systems Biology, Max-Delbrück-Center for Molecular Medicine	Junker Lab, Berlin Institute for Medical Systems Biology, Max-Delbrück-Center for Molecular Medicine	2	0.0002	0.91626	0.00014	0.00132	0.99987	0.99726	0.16666	0.56363	28	120	T	B	sc-like readlen	illumina	nextseq	unknown	other	unknown	sc	single_cell_droplet	10x	Germany	2023-10-31	Adult	Adult	Brain	Nervous System
28727	28727	SRR26623264	SRX22323919	SRS19374448	SRP469552	PRJNA1034159	Dissecting the spatiotemporal diversity of adult neural stem cells	GSE246714	Other	Adult stem cells are important for tissue turnover and regeneration. However in most adult systems it remains elusive how stem cells assume different functional states and support spatially patterned tissue architecture. Here we dissected the diversity of neural stem cells in the adult zebrafish brain an organ that is characterized by pronounced zonation and high regenerative capacity. We combined single cell transcriptomics of dissected brain regions with massively parallel lineage tracing and in vivo RNA metabolic labeling to analyze regulation of neural stem cells in space and time. We detected a large diversity of neural stem cells with some subtypes being restricted to a single brain region while others were found globally across the brain. Global stem cell states are linked to neurogenic differentiation with different states being involved in proliferative and non proliferative differentiation. Our work reveals principles of adult stem cell organization and establishes a resource for functional manipulation of neural stem cell subtypes. Overall design: Single cell RNA seq of adult zebrafish brain performed using 10x Genomics Gene Expression protocols to explore transcriptomic diversity of adult cell types. The samples were prepared either from whole brain FACS sorted cells GFP positive cells from gfap:GFP line or a dissected single brain region as indicated in the sample title. CRISPR based lineage tracing was performed for a subset of samples to dissect developmental lineage relationships between cells. ScSLAM seq was performed in combination with Notch pathway inhibition to assess response of stem cells to perturbation.		pubmed:38365956	Lineage tracing rep1 actb2 scars	GSM7875188	source name:adult brain\|tissue:adult brain\|cell type:mixed tissue dissociation\|genotype:Tg[ubi:zebrabow M]\|geo loc name:missing\|collection date:missing	Lineage tracing rep1 actb2 scars	The transcriptome libraries were demultiplexed and mapped with CellRanger 6.1.1. In order to reduce batch effect the gene expression matrices were processed with the SoupX tool Young & Behjati 2020 for removing ambient RNA contamination. A subset of libraries was also mapped with Velocyto v0.17.17 La Manno et al 2018 to create count matrices for unspliced and spliced molecules. The targeted lineage tracing libraries were aligned using bwa mem3 v.0.7.12 to individual references of endogenous genes used in the lineage tracing experiment actb1 actb2 cfl1 cirbpb rpl39 and ube2e1. The scars were filtered with a custom pipeline to eliminate sequences that originate from PCR or sequencing errors. For custom code and further downstream analysis see: https://github.com/nimitic/radial glia. Assembly: GRCz11 Supplementary files format and content: Tab separated barcode files and matrix files along with the tab separated gene file supplementary file are the output of CellRanger mapping further processed by the SoupX package for ambient RNA removal. Supplementary files format and content: Loom files are the output of velocyto mapping distinguishing spliced and unspliced mRNAs. Supplementary files format and content: filtered scars.csv files contain the output of a custom scar filtering pipeline.	adult brain		Adult zebrafish brain tissue was extracted optionally dissected to select a specific region of interest telencephalon diencephalon mesencephalon or rhombencephalon and then dissociated with a trypsin protocol. Briefly the samples were incubated in 750 µl of HBSS glucose solution and dissocated with 15 µl of 0.05% trypsin EDTA Gibco for 30 minutes at 37°C with intermittent mixing. The disociation was stopped by addition of 750 µl of a BSA EBSS HEPES solution. Further sample handling steps were carried out on ice and for centrifugation at 4°C. The sample was filtered through a 100 µm filter washed with cold HBSS and resuspended in 100 200 µl HBSS with 0.05% BSA then filtered through a 35 µm filter and loaded on the 10x Chromium Controller. For the scSLAM seq samples a papain dissociation protocol Worthington kit according to manufecturer's instructions was used followed by an adapted scSLAM seq protocol for labeling nascent transcripts see sample specific entry. The standard transcriptome libraries were constructed according to the manufacturer's protocol 10X Genomics. For targeted libraries of scars used for lineage tracing the cDNA obtained during the 10x protocol was used to set up individual PCR reactions for each target specifically amplifying the sgRNA target site. The libraries for targeted PCR were constructed with primers compatible with the 10X Genomics kit so they could be sequenced on the same platform.		tissue:adult brain\|cell type:mixed tissue dissociation\|genotype:Tg[ubi:zebrabow M]	GSM7875188	GSM7875188: Lineage tracing rep1 actb2 scars; Danio rerio; OTHER	GSM7875188 r1	GSM7875188	1	Adult zebrafish brain tissue was extracted optionally dissected to select a specific region of interest telencephalon diencephalon mesencephalon or rhombencephalon and then dissociated with a trypsin protocol. Briefly the samples were incubated in 750 µl of HBSS glucose solution and dissocated with 15 µl of 0.05% trypsin EDTA Gibco for 30 minutes at 37°C with intermittent mixing. The disociation was stopped by addition of 750 µl of a BSA EBSS HEPES solution. Further sample handling steps were carried out on ice and for centrifugation at 4°C. The sample was filtered through a 100 µm filter washed with cold HBSS and resuspended in 100 200 µl HBSS with 0.05% BSA then filtered through a 35 µm filter and loaded on the 10x Chromium Controller. For the scSLAM seq samples a papain dissociation protocol Worthington kit according to manufecturer's instructions was used followed by an adapted scSLAM seq protocol for labeling nascent transcripts see sample specific entry. The standard transcriptome libraries were constructed according to the manufacturer's protocol 10X Genomics. For targeted libraries of scars used for lineage tracing the cDNA obtained during the 10x protocol was used to set up individual PCR reactions for each target specifically amplifying the sgRNA target site. The libraries for targeted PCR were constructed with primers compatible with the 10X Genomics kit so they could be sequenced on the same platform.		OTHER	TRANSCRIPTOMIC	other	PAIRED	ILLUMINA	NextSeq 500	SRP469552	loader:fastq load.py	lin1_actb2_scar_R1.fastq.gz lin1_actb2_scar_R2.fastq.gz	fastq fastq	491682118.0	2851156.0	GSM7875188 r1											SRX22323919	SRS19374448	SRA1743007	Junker Lab, Berlin Institute for Medical Systems Biology, Max-Delbrück-Center for Molecular Medicine	Junker Lab, Berlin Institute for Medical Systems Biology, Max-Delbrück-Center for Molecular Medicine	2	0.00051	0.41901	0.00032	0.0029	0.99945	0.99182	0.57575	0.007	28	120	T	B	sc-like readlen	illumina	nextseq	unknown	other	unknown	sc	single_cell_droplet	10x	Germany	2023-10-31	Adult	Adult	Brain	Nervous System
28728	28728	SRR26623265	SRX22323918	SRS19374446	SRP469552	PRJNA1034159	Dissecting the spatiotemporal diversity of adult neural stem cells	GSE246714	Other	Adult stem cells are important for tissue turnover and regeneration. However in most adult systems it remains elusive how stem cells assume different functional states and support spatially patterned tissue architecture. Here we dissected the diversity of neural stem cells in the adult zebrafish brain an organ that is characterized by pronounced zonation and high regenerative capacity. We combined single cell transcriptomics of dissected brain regions with massively parallel lineage tracing and in vivo RNA metabolic labeling to analyze regulation of neural stem cells in space and time. We detected a large diversity of neural stem cells with some subtypes being restricted to a single brain region while others were found globally across the brain. Global stem cell states are linked to neurogenic differentiation with different states being involved in proliferative and non proliferative differentiation. Our work reveals principles of adult stem cell organization and establishes a resource for functional manipulation of neural stem cell subtypes. Overall design: Single cell RNA seq of adult zebrafish brain performed using 10x Genomics Gene Expression protocols to explore transcriptomic diversity of adult cell types. The samples were prepared either from whole brain FACS sorted cells GFP positive cells from gfap:GFP line or a dissected single brain region as indicated in the sample title. CRISPR based lineage tracing was performed for a subset of samples to dissect developmental lineage relationships between cells. ScSLAM seq was performed in combination with Notch pathway inhibition to assess response of stem cells to perturbation.		pubmed:38365956	Lineage tracing rep1 actb1 scars	GSM7875187	source name:adult brain\|tissue:adult brain\|cell type:mixed tissue dissociation\|genotype:Tg[ubi:zebrabow M]\|geo loc name:missing\|collection date:missing	Lineage tracing rep1 actb1 scars	The transcriptome libraries were demultiplexed and mapped with CellRanger 6.1.1. In order to reduce batch effect the gene expression matrices were processed with the SoupX tool Young & Behjati 2020 for removing ambient RNA contamination. A subset of libraries was also mapped with Velocyto v0.17.17 La Manno et al 2018 to create count matrices for unspliced and spliced molecules. The targeted lineage tracing libraries were aligned using bwa mem3 v.0.7.12 to individual references of endogenous genes used in the lineage tracing experiment actb1 actb2 cfl1 cirbpb rpl39 and ube2e1. The scars were filtered with a custom pipeline to eliminate sequences that originate from PCR or sequencing errors. For custom code and further downstream analysis see: https://github.com/nimitic/radial glia. Assembly: GRCz11 Supplementary files format and content: Tab separated barcode files and matrix files along with the tab separated gene file supplementary file are the output of CellRanger mapping further processed by the SoupX package for ambient RNA removal. Supplementary files format and content: Loom files are the output of velocyto mapping distinguishing spliced and unspliced mRNAs. Supplementary files format and content: filtered scars.csv files contain the output of a custom scar filtering pipeline.	adult brain		Adult zebrafish brain tissue was extracted optionally dissected to select a specific region of interest telencephalon diencephalon mesencephalon or rhombencephalon and then dissociated with a trypsin protocol. Briefly the samples were incubated in 750 µl of HBSS glucose solution and dissocated with 15 µl of 0.05% trypsin EDTA Gibco for 30 minutes at 37°C with intermittent mixing. The disociation was stopped by addition of 750 µl of a BSA EBSS HEPES solution. Further sample handling steps were carried out on ice and for centrifugation at 4°C. The sample was filtered through a 100 µm filter washed with cold HBSS and resuspended in 100 200 µl HBSS with 0.05% BSA then filtered through a 35 µm filter and loaded on the 10x Chromium Controller. For the scSLAM seq samples a papain dissociation protocol Worthington kit according to manufecturer's instructions was used followed by an adapted scSLAM seq protocol for labeling nascent transcripts see sample specific entry. The standard transcriptome libraries were constructed according to the manufacturer's protocol 10X Genomics. For targeted libraries of scars used for lineage tracing the cDNA obtained during the 10x protocol was used to set up individual PCR reactions for each target specifically amplifying the sgRNA target site. The libraries for targeted PCR were constructed with primers compatible with the 10X Genomics kit so they could be sequenced on the same platform.		tissue:adult brain\|cell type:mixed tissue dissociation\|genotype:Tg[ubi:zebrabow M]	GSM7875187	GSM7875187: Lineage tracing rep1 actb1 scars; Danio rerio; OTHER	GSM7875187 r1	GSM7875187	1	Adult zebrafish brain tissue was extracted optionally dissected to select a specific region of interest telencephalon diencephalon mesencephalon or rhombencephalon and then dissociated with a trypsin protocol. Briefly the samples were incubated in 750 µl of HBSS glucose solution and dissocated with 15 µl of 0.05% trypsin EDTA Gibco for 30 minutes at 37°C with intermittent mixing. The disociation was stopped by addition of 750 µl of a BSA EBSS HEPES solution. Further sample handling steps were carried out on ice and for centrifugation at 4°C. The sample was filtered through a 100 µm filter washed with cold HBSS and resuspended in 100 200 µl HBSS with 0.05% BSA then filtered through a 35 µm filter and loaded on the 10x Chromium Controller. For the scSLAM seq samples a papain dissociation protocol Worthington kit according to manufecturer's instructions was used followed by an adapted scSLAM seq protocol for labeling nascent transcripts see sample specific entry. The standard transcriptome libraries were constructed according to the manufacturer's protocol 10X Genomics. For targeted libraries of scars used for lineage tracing the cDNA obtained during the 10x protocol was used to set up individual PCR reactions for each target specifically amplifying the sgRNA target site. The libraries for targeted PCR were constructed with primers compatible with the 10X Genomics kit so they could be sequenced on the same platform.		OTHER	TRANSCRIPTOMIC	other	PAIRED	ILLUMINA	NextSeq 500	SRP469552	loader:fastq load.py	lin1_actb1_scar_R1.fastq.gz lin1_actb1_scar_R2.fastq.gz	fastq fastq	390405832.0	2283319.0	GSM7875187 r1											SRX22323918	SRS19374446	SRA1743007	Junker Lab, Berlin Institute for Medical Systems Biology, Max-Delbrück-Center for Molecular Medicine	Junker Lab, Berlin Institute for Medical Systems Biology, Max-Delbrück-Center for Molecular Medicine	2	0.00038	0.73548	0.00017	0.00057	0.99963	0.99571	0.29729	0.00243	28	120	T	B	sc-like readlen	illumina	nextseq	unknown	other	unknown	sc	single_cell_droplet	10x	Germany	2023-10-31	Adult	Adult	Brain	Nervous System
28729	28729	SRR26623266	SRX22323917	SRS19374447	SRP469552	PRJNA1034159	Dissecting the spatiotemporal diversity of adult neural stem cells	GSE246714	Other	Adult stem cells are important for tissue turnover and regeneration. However in most adult systems it remains elusive how stem cells assume different functional states and support spatially patterned tissue architecture. Here we dissected the diversity of neural stem cells in the adult zebrafish brain an organ that is characterized by pronounced zonation and high regenerative capacity. We combined single cell transcriptomics of dissected brain regions with massively parallel lineage tracing and in vivo RNA metabolic labeling to analyze regulation of neural stem cells in space and time. We detected a large diversity of neural stem cells with some subtypes being restricted to a single brain region while others were found globally across the brain. Global stem cell states are linked to neurogenic differentiation with different states being involved in proliferative and non proliferative differentiation. Our work reveals principles of adult stem cell organization and establishes a resource for functional manipulation of neural stem cell subtypes. Overall design: Single cell RNA seq of adult zebrafish brain performed using 10x Genomics Gene Expression protocols to explore transcriptomic diversity of adult cell types. The samples were prepared either from whole brain FACS sorted cells GFP positive cells from gfap:GFP line or a dissected single brain region as indicated in the sample title. CRISPR based lineage tracing was performed for a subset of samples to dissect developmental lineage relationships between cells. ScSLAM seq was performed in combination with Notch pathway inhibition to assess response of stem cells to perturbation.		pubmed:38365956	Brain 23 telencephalon Notch inhibition scSLAMseq	GSM7875186	source name:adult brain\|tissue:adult brain\|tissue region:telencephalon\|cell type:mixed tissue dissociation\|genotype:wildtype\|treatment:Notch inhibition DAPT\|geo loc name:missing\|collection date:missing	Brain 23 telencephalon Notch inhibition scSLAMseq	The transcriptome libraries were demultiplexed and mapped with CellRanger 6.1.1. In order to reduce batch effect the gene expression matrices were processed with the SoupX tool Young & Behjati 2020 for removing ambient RNA contamination. A subset of libraries was also mapped with Velocyto v0.17.17 La Manno et al 2018 to create count matrices for unspliced and spliced molecules. The targeted lineage tracing libraries were aligned using bwa mem3 v.0.7.12 to individual references of endogenous genes used in the lineage tracing experiment actb1 actb2 cfl1 cirbpb rpl39 and ube2e1. The scars were filtered with a custom pipeline to eliminate sequences that originate from PCR or sequencing errors. For custom code and further downstream analysis see: https://github.com/nimitic/radial glia. Assembly: GRCz11 Supplementary files format and content: Tab separated barcode files and matrix files along with the tab separated gene file supplementary file are the output of CellRanger mapping further processed by the SoupX package for ambient RNA removal. Supplementary files format and content: Loom files are the output of velocyto mapping distinguishing spliced and unspliced mRNAs. Supplementary files format and content: filtered scars.csv files contain the output of a custom scar filtering pipeline. Library strategy: scSLAM seq	adult brain	Notch inhibiton: zebrafish were incubated in a water bath containing system water with 50 µM DAPT Gamma Secretase Inhibitor Sigma Aldrich for 48 hours.	The samples were prepared according to a scSLAM seq protocol adapted from Neuschulz et al 2023. Briefly: in order to label nascent transcripts 200 mM 4sU was delivered to fish brains by intraventricular injection 6 hours prior to sample collection. The brains were then collected and a single cell suspension was prepared by papain dissociation. The resulting cell suspension was fixed in 80% methanol and a conversion of 4sU using iodoactamide adding 111 µl 100 mM IAA to 800 µl fixed sample was done overnight. The following day the reaction was quanched with a quenching buffer containing 100 mM DTT post which the sample was washed with a wash buffer and filtered though a 35 µm filter. The sample was then loaded on a 10X Chromium Controller and processed according to the standard scRNA seq protocol. Adult zebrafish brain tissue was extracted optionally dissected to select a specific region of interest telencephalon diencephalon mesencephalon or rhombencephalon and then dissociated with a trypsin protocol. Briefly the samples were incubated in 750 µl of HBSS glucose solution and dissocated with 15 µl of 0.05% trypsin EDTA Gibco for 30 minutes at 37°C with intermittent mixing. The disociation was stopped by addition of 750 µl of a BSA EBSS HEPES solution. Further sample handling steps were carried out on ice and for centrifugation at 4°C. The sample was filtered through a 100 µm filter washed with cold HBSS and resuspended in 100 200 µl HBSS with 0.05% BSA then filtered through a 35 µm filter and loaded on the 10x Chromium Controller. For the scSLAM seq samples a papain dissociation protocol Worthington kit according to manufecturer's instructions was used followed by an adapted scSLAM seq protocol for labeling nascent transcripts see sample specific entry. The standard transcriptome libraries were constructed according to the manufacturer's protocol 10X Genomics. For targeted libraries of scars used for lineage tracing the cDNA obtained during the 10x protocol was used to set up individual PCR reactions …		tissue:adult brain\|tissue region:telencephalon\|cell type:mixed tissue dissociation\|genotype:wildtype\|treatment:Notch inhibition DAPT	GSM7875186	GSM7875186: Brain 23 telencephalon Notch inhibition scSLAMseq; Danio rerio; OTHER	GSM7875186 r1	GSM7875186	1	The samples were prepared according to a scSLAM seq protocol adapted from Neuschulz et al 2023. Briefly: in order to label nascent transcripts 200 mM 4sU was delivered to fish brains by intraventricular injection 6 hours prior to sample collection. The brains were then collected and a single cell suspension was prepared by papain dissociation. The resulting cell suspension was fixed in 80% methanol and a conversion of 4sU using iodoactamide adding 111 µl 100 mM IAA to 800 µl fixed sample was done overnight. The following day the reaction was quanched with a quenching buffer containing 100 mM DTT post which the sample was washed with a wash buffer and filtered though a 35 µm filter. The sample was then loaded on a 10X Chromium Controller and processed according to the standard scRNA seq protocol. Adult zebrafish brain tissue was extracted optionally dissected to select a specific region of interest telencephalon diencephalon mesencephalon or rhombencephalon and then dissociated with a trypsin protocol. Briefly the samples were incubated in 750 µl of HBSS glucose solution and dissocated with 15 µl of 0.05% trypsin EDTA Gibco for 30 minutes at 37°C with intermittent mixing. The disociation was stopped by addition of 750 µl of a BSA EBSS HEPES solution. Further sample handling steps were carried out on ice and for centrifugation at 4°C. The sample was filtered through a 100 µm filter washed with cold HBSS and resuspended in 100 200 µl HBSS with 0.05% BSA then filtered through a 35 µm filter and loaded on the 10x Chromium Controller. For the scSLAM seq samples a papain dissociation protocol Worthington kit according to manufecturer's instructions was used followed by an adapted scSLAM seq protocol for labeling nascent transcripts see sample specific entry. The standard transcriptome libraries were constructed according to the manufacturer's protocol 10X Genomics. For targeted libraries of scars used for lineage tracing the cDNA obtained during the 10x protocol was used to set up individual PCR reactions …		OTHER	TRANSCRIPTOMIC	other	PAIRED	ILLUMINA	Illumina NovaSeq 6000	SRP469552	loader:fastq load.py	b23_ni_tre_R1.fastq.gz b23_ni_tre_R2.fastq.gz	fastq fastq	62392458570.0	271271559.0	GSM7875186 r1	0:28 1:202	A:18682905645;C:13783082970;G:14751373116;T:15161083446;N:14013393	28	202		18682905645	13783082970	14751373116	15161083446	14013393	SRX22323917	SRS19374447	SRA1743007	Junker Lab, Berlin Institute for Medical Systems Biology, Max-Delbrück-Center for Molecular Medicine	Junker Lab, Berlin Institute for Medical Systems Biology, Max-Delbrück-Center for Molecular Medicine	2	0.01404	0.82351	0.00466	0.1026	0.99129	0.86774	0.36033	0.67345	28	202	T	B	sc-like readlen	illumina	novaseq_era	unknown	other	unknown	sc	single_cell_droplet	10x	Germany	2023-10-31	Adult	Adult	Brain	Nervous System
28730	28730	SRR26623267	SRX22323916	SRS19374445	SRP469552	PRJNA1034159	Dissecting the spatiotemporal diversity of adult neural stem cells	GSE246714	Other	Adult stem cells are important for tissue turnover and regeneration. However in most adult systems it remains elusive how stem cells assume different functional states and support spatially patterned tissue architecture. Here we dissected the diversity of neural stem cells in the adult zebrafish brain an organ that is characterized by pronounced zonation and high regenerative capacity. We combined single cell transcriptomics of dissected brain regions with massively parallel lineage tracing and in vivo RNA metabolic labeling to analyze regulation of neural stem cells in space and time. We detected a large diversity of neural stem cells with some subtypes being restricted to a single brain region while others were found globally across the brain. Global stem cell states are linked to neurogenic differentiation with different states being involved in proliferative and non proliferative differentiation. Our work reveals principles of adult stem cell organization and establishes a resource for functional manipulation of neural stem cell subtypes. Overall design: Single cell RNA seq of adult zebrafish brain performed using 10x Genomics Gene Expression protocols to explore transcriptomic diversity of adult cell types. The samples were prepared either from whole brain FACS sorted cells GFP positive cells from gfap:GFP line or a dissected single brain region as indicated in the sample title. CRISPR based lineage tracing was performed for a subset of samples to dissect developmental lineage relationships between cells. ScSLAM seq was performed in combination with Notch pathway inhibition to assess response of stem cells to perturbation.		pubmed:38365956	Brain 22 telencephalon control scSLAMseq	GSM7875185	source name:adult brain\|tissue:adult brain\|tissue region:telencephalon\|cell type:mixed tissue dissociation\|genotype:wildtype\|treatment:Control DMSO\|geo loc name:missing\|collection date:missing	Brain 22 telencephalon control scSLAMseq	The transcriptome libraries were demultiplexed and mapped with CellRanger 6.1.1. In order to reduce batch effect the gene expression matrices were processed with the SoupX tool Young & Behjati 2020 for removing ambient RNA contamination. A subset of libraries was also mapped with Velocyto v0.17.17 La Manno et al 2018 to create count matrices for unspliced and spliced molecules. The targeted lineage tracing libraries were aligned using bwa mem3 v.0.7.12 to individual references of endogenous genes used in the lineage tracing experiment actb1 actb2 cfl1 cirbpb rpl39 and ube2e1. The scars were filtered with a custom pipeline to eliminate sequences that originate from PCR or sequencing errors. For custom code and further downstream analysis see: https://github.com/nimitic/radial glia. Assembly: GRCz11 Supplementary files format and content: Tab separated barcode files and matrix files along with the tab separated gene file supplementary file are the output of CellRanger mapping further processed by the SoupX package for ambient RNA removal. Supplementary files format and content: Loom files are the output of velocyto mapping distinguishing spliced and unspliced mRNAs. Supplementary files format and content: filtered scars.csv files contain the output of a custom scar filtering pipeline. Library strategy: scSLAM seq	adult brain	Control for Notch inhibition: zebrafish were incubated in a water bath containing system water with 1:200 diluted DMSO for 48 hours.	The samples were prepared according to a scSLAM seq protocol adapted from Neuschulz et al 2023. Briefly: in order to label nascent transcripts 200 mM 4sU was delivered to fish brains by intraventricular injection 6 hours prior to sample collection. The brains were then collected and a single cell suspension was prepared by papain dissociation. The resulting cell suspension was fixed in 80% methanol and a conversion of 4sU using iodoactamide adding 111 µl 100 mM IAA to 800 µl fixed sample was done overnight. The following day the reaction was quanched with a quenching buffer containing 100 mM DTT post which the sample was washed with a wash buffer and filtered though a 35 µm filter. The sample was then loaded on a 10X Chromium Controller and processed according to the standard scRNA seq protocol. Adult zebrafish brain tissue was extracted optionally dissected to select a specific region of interest telencephalon diencephalon mesencephalon or rhombencephalon and then dissociated with a trypsin protocol. Briefly the samples were incubated in 750 µl of HBSS glucose solution and dissocated with 15 µl of 0.05% trypsin EDTA Gibco for 30 minutes at 37°C with intermittent mixing. The disociation was stopped by addition of 750 µl of a BSA EBSS HEPES solution. Further sample handling steps were carried out on ice and for centrifugation at 4°C. The sample was filtered through a 100 µm filter washed with cold HBSS and resuspended in 100 200 µl HBSS with 0.05% BSA then filtered through a 35 µm filter and loaded on the 10x Chromium Controller. For the scSLAM seq samples a papain dissociation protocol Worthington kit according to manufecturer's instructions was used followed by an adapted scSLAM seq protocol for labeling nascent transcripts see sample specific entry. The standard transcriptome libraries were constructed according to the manufacturer's protocol 10X Genomics. For targeted libraries of scars used for lineage tracing the cDNA obtained during the 10x protocol was used to set up individual PCR reactions …		tissue:adult brain\|tissue region:telencephalon\|cell type:mixed tissue dissociation\|genotype:wildtype\|treatment:Control DMSO	GSM7875185	GSM7875185: Brain 22 telencephalon control scSLAMseq; Danio rerio; OTHER	GSM7875185 r1	GSM7875185	1	The samples were prepared according to a scSLAM seq protocol adapted from Neuschulz et al 2023. Briefly: in order to label nascent transcripts 200 mM 4sU was delivered to fish brains by intraventricular injection 6 hours prior to sample collection. The brains were then collected and a single cell suspension was prepared by papain dissociation. The resulting cell suspension was fixed in 80% methanol and a conversion of 4sU using iodoactamide adding 111 µl 100 mM IAA to 800 µl fixed sample was done overnight. The following day the reaction was quanched with a quenching buffer containing 100 mM DTT post which the sample was washed with a wash buffer and filtered though a 35 µm filter. The sample was then loaded on a 10X Chromium Controller and processed according to the standard scRNA seq protocol. Adult zebrafish brain tissue was extracted optionally dissected to select a specific region of interest telencephalon diencephalon mesencephalon or rhombencephalon and then dissociated with a trypsin protocol. Briefly the samples were incubated in 750 µl of HBSS glucose solution and dissocated with 15 µl of 0.05% trypsin EDTA Gibco for 30 minutes at 37°C with intermittent mixing. The disociation was stopped by addition of 750 µl of a BSA EBSS HEPES solution. Further sample handling steps were carried out on ice and for centrifugation at 4°C. The sample was filtered through a 100 µm filter washed with cold HBSS and resuspended in 100 200 µl HBSS with 0.05% BSA then filtered through a 35 µm filter and loaded on the 10x Chromium Controller. For the scSLAM seq samples a papain dissociation protocol Worthington kit according to manufecturer's instructions was used followed by an adapted scSLAM seq protocol for labeling nascent transcripts see sample specific entry. The standard transcriptome libraries were constructed according to the manufacturer's protocol 10X Genomics. For targeted libraries of scars used for lineage tracing the cDNA obtained during the 10x protocol was used to set up individual PCR reactions …		OTHER	TRANSCRIPTOMIC	other	PAIRED	ILLUMINA	Illumina NovaSeq 6000	SRP469552	loader:fastq load.py	b22_ni_con_R1.fastq.gz b22_ni_con_R2.fastq.gz	fastq fastq	59793114490.0	259970063.0	GSM7875185 r1	0:28 1:202	A:18600519530;C:12714987967;G:14147370465;T:14316863915;N:13372613	28	202		18600519530	12714987967	14147370465	14316863915	13372613	SRX22323916	SRS19374445	SRA1743007	Junker Lab, Berlin Institute for Medical Systems Biology, Max-Delbrück-Center for Molecular Medicine	Junker Lab, Berlin Institute for Medical Systems Biology, Max-Delbrück-Center for Molecular Medicine	2	0.01402	0.79448	0.00498	0.11405	0.99074	0.8518	0.39874	0.67082	28	202	T	B	sc-like readlen	illumina	novaseq_era	unknown	other	unknown	sc	single_cell_droplet	10x	Germany	2023-10-31	Adult	Adult	Brain	Nervous System
28754	28754	SRR26623291	SRX22323897	SRS19374426	SRP469552	PRJNA1034159	Dissecting the spatiotemporal diversity of adult neural stem cells	GSE246714	Other	Adult stem cells are important for tissue turnover and regeneration. However in most adult systems it remains elusive how stem cells assume different functional states and support spatially patterned tissue architecture. Here we dissected the diversity of neural stem cells in the adult zebrafish brain an organ that is characterized by pronounced zonation and high regenerative capacity. We combined single cell transcriptomics of dissected brain regions with massively parallel lineage tracing and in vivo RNA metabolic labeling to analyze regulation of neural stem cells in space and time. We detected a large diversity of neural stem cells with some subtypes being restricted to a single brain region while others were found globally across the brain. Global stem cell states are linked to neurogenic differentiation with different states being involved in proliferative and non proliferative differentiation. Our work reveals principles of adult stem cell organization and establishes a resource for functional manipulation of neural stem cell subtypes. Overall design: Single cell RNA seq of adult zebrafish brain performed using 10x Genomics Gene Expression protocols to explore transcriptomic diversity of adult cell types. The samples were prepared either from whole brain FACS sorted cells GFP positive cells from gfap:GFP line or a dissected single brain region as indicated in the sample title. CRISPR based lineage tracing was performed for a subset of samples to dissect developmental lineage relationships between cells. ScSLAM seq was performed in combination with Notch pathway inhibition to assess response of stem cells to perturbation.		pubmed:38365956	Lineage tracing rep2 ube2e1 scars	GSM7875196	source name:adult brain\|tissue:adult brain\|cell type:mixed tissue dissociation\|genotype:Tg[ubi:zebrabow M]\|geo loc name:missing\|collection date:missing	Lineage tracing rep2 ube2e1 scars	The transcriptome libraries were demultiplexed and mapped with CellRanger 6.1.1. In order to reduce batch effect the gene expression matrices were processed with the SoupX tool Young & Behjati 2020 for removing ambient RNA contamination. A subset of libraries was also mapped with Velocyto v0.17.17 La Manno et al 2018 to create count matrices for unspliced and spliced molecules. The targeted lineage tracing libraries were aligned using bwa mem3 v.0.7.12 to individual references of endogenous genes used in the lineage tracing experiment actb1 actb2 cfl1 cirbpb rpl39 and ube2e1. The scars were filtered with a custom pipeline to eliminate sequences that originate from PCR or sequencing errors. For custom code and further downstream analysis see: https://github.com/nimitic/radial glia. Assembly: GRCz11 Supplementary files format and content: Tab separated barcode files and matrix files along with the tab separated gene file supplementary file are the output of CellRanger mapping further processed by the SoupX package for ambient RNA removal. Supplementary files format and content: Loom files are the output of velocyto mapping distinguishing spliced and unspliced mRNAs. Supplementary files format and content: filtered scars.csv files contain the output of a custom scar filtering pipeline.	adult brain		Adult zebrafish brain tissue was extracted optionally dissected to select a specific region of interest telencephalon diencephalon mesencephalon or rhombencephalon and then dissociated with a trypsin protocol. Briefly the samples were incubated in 750 µl of HBSS glucose solution and dissocated with 15 µl of 0.05% trypsin EDTA Gibco for 30 minutes at 37°C with intermittent mixing. The disociation was stopped by addition of 750 µl of a BSA EBSS HEPES solution. Further sample handling steps were carried out on ice and for centrifugation at 4°C. The sample was filtered through a 100 µm filter washed with cold HBSS and resuspended in 100 200 µl HBSS with 0.05% BSA then filtered through a 35 µm filter and loaded on the 10x Chromium Controller. For the scSLAM seq samples a papain dissociation protocol Worthington kit according to manufecturer's instructions was used followed by an adapted scSLAM seq protocol for labeling nascent transcripts see sample specific entry. The standard transcriptome libraries were constructed according to the manufacturer's protocol 10X Genomics. For targeted libraries of scars used for lineage tracing the cDNA obtained during the 10x protocol was used to set up individual PCR reactions for each target specifically amplifying the sgRNA target site. The libraries for targeted PCR were constructed with primers compatible with the 10X Genomics kit so they could be sequenced on the same platform.		tissue:adult brain\|cell type:mixed tissue dissociation\|genotype:Tg[ubi:zebrabow M]	GSM7875196	GSM7875196: Lineage tracing rep2 ube2e1 scars; Danio rerio; OTHER	GSM7875196 r1	GSM7875196	1	Adult zebrafish brain tissue was extracted optionally dissected to select a specific region of interest telencephalon diencephalon mesencephalon or rhombencephalon and then dissociated with a trypsin protocol. Briefly the samples were incubated in 750 µl of HBSS glucose solution and dissocated with 15 µl of 0.05% trypsin EDTA Gibco for 30 minutes at 37°C with intermittent mixing. The disociation was stopped by addition of 750 µl of a BSA EBSS HEPES solution. Further sample handling steps were carried out on ice and for centrifugation at 4°C. The sample was filtered through a 100 µm filter washed with cold HBSS and resuspended in 100 200 µl HBSS with 0.05% BSA then filtered through a 35 µm filter and loaded on the 10x Chromium Controller. For the scSLAM seq samples a papain dissociation protocol Worthington kit according to manufecturer's instructions was used followed by an adapted scSLAM seq protocol for labeling nascent transcripts see sample specific entry. The standard transcriptome libraries were constructed according to the manufacturer's protocol 10X Genomics. For targeted libraries of scars used for lineage tracing the cDNA obtained during the 10x protocol was used to set up individual PCR reactions for each target specifically amplifying the sgRNA target site. The libraries for targeted PCR were constructed with primers compatible with the 10X Genomics kit so they could be sequenced on the same platform.		OTHER	TRANSCRIPTOMIC	other	PAIRED	ILLUMINA	Illumina NovaSeq 6000	SRP469552	loader:fastq load.py	lin2_ube2e1_scar_R1.fastq.gz lin2_ube2e1_scar_R2.fastq.gz	fastq fastq	213092161.0	1190459.0	GSM7875196 r1											SRX22323897	SRS19374426	SRA1743007	Junker Lab, Berlin Institute for Medical Systems Biology, Max-Delbrück-Center for Molecular Medicine	Junker Lab, Berlin Institute for Medical Systems Biology, Max-Delbrück-Center for Molecular Medicine	2	0.00752	0.90776	0.00286	0.01739	0.99346	0.95444	0.46002	0.96817	28	151	T	B	sc-like readlen	illumina	novaseq_era	unknown	other	unknown	sc	single_cell_droplet	10x	Germany	2023-10-31	Adult	Adult	Brain	Nervous System
28755	28755	SRR26623292	SRX22323896	SRS19374425	SRP469552	PRJNA1034159	Dissecting the spatiotemporal diversity of adult neural stem cells	GSE246714	Other	Adult stem cells are important for tissue turnover and regeneration. However in most adult systems it remains elusive how stem cells assume different functional states and support spatially patterned tissue architecture. Here we dissected the diversity of neural stem cells in the adult zebrafish brain an organ that is characterized by pronounced zonation and high regenerative capacity. We combined single cell transcriptomics of dissected brain regions with massively parallel lineage tracing and in vivo RNA metabolic labeling to analyze regulation of neural stem cells in space and time. We detected a large diversity of neural stem cells with some subtypes being restricted to a single brain region while others were found globally across the brain. Global stem cell states are linked to neurogenic differentiation with different states being involved in proliferative and non proliferative differentiation. Our work reveals principles of adult stem cell organization and establishes a resource for functional manipulation of neural stem cell subtypes. Overall design: Single cell RNA seq of adult zebrafish brain performed using 10x Genomics Gene Expression protocols to explore transcriptomic diversity of adult cell types. The samples were prepared either from whole brain FACS sorted cells GFP positive cells from gfap:GFP line or a dissected single brain region as indicated in the sample title. CRISPR based lineage tracing was performed for a subset of samples to dissect developmental lineage relationships between cells. ScSLAM seq was performed in combination with Notch pathway inhibition to assess response of stem cells to perturbation.		pubmed:38365956	Lineage tracing rep2 rpl39 scars	GSM7875195	source name:adult brain\|tissue:adult brain\|cell type:mixed tissue dissociation\|genotype:Tg[ubi:zebrabow M]\|geo loc name:missing\|collection date:missing	Lineage tracing rep2 rpl39 scars	The transcriptome libraries were demultiplexed and mapped with CellRanger 6.1.1. In order to reduce batch effect the gene expression matrices were processed with the SoupX tool Young & Behjati 2020 for removing ambient RNA contamination. A subset of libraries was also mapped with Velocyto v0.17.17 La Manno et al 2018 to create count matrices for unspliced and spliced molecules. The targeted lineage tracing libraries were aligned using bwa mem3 v.0.7.12 to individual references of endogenous genes used in the lineage tracing experiment actb1 actb2 cfl1 cirbpb rpl39 and ube2e1. The scars were filtered with a custom pipeline to eliminate sequences that originate from PCR or sequencing errors. For custom code and further downstream analysis see: https://github.com/nimitic/radial glia. Assembly: GRCz11 Supplementary files format and content: Tab separated barcode files and matrix files along with the tab separated gene file supplementary file are the output of CellRanger mapping further processed by the SoupX package for ambient RNA removal. Supplementary files format and content: Loom files are the output of velocyto mapping distinguishing spliced and unspliced mRNAs. Supplementary files format and content: filtered scars.csv files contain the output of a custom scar filtering pipeline.	adult brain		Adult zebrafish brain tissue was extracted optionally dissected to select a specific region of interest telencephalon diencephalon mesencephalon or rhombencephalon and then dissociated with a trypsin protocol. Briefly the samples were incubated in 750 µl of HBSS glucose solution and dissocated with 15 µl of 0.05% trypsin EDTA Gibco for 30 minutes at 37°C with intermittent mixing. The disociation was stopped by addition of 750 µl of a BSA EBSS HEPES solution. Further sample handling steps were carried out on ice and for centrifugation at 4°C. The sample was filtered through a 100 µm filter washed with cold HBSS and resuspended in 100 200 µl HBSS with 0.05% BSA then filtered through a 35 µm filter and loaded on the 10x Chromium Controller. For the scSLAM seq samples a papain dissociation protocol Worthington kit according to manufecturer's instructions was used followed by an adapted scSLAM seq protocol for labeling nascent transcripts see sample specific entry. The standard transcriptome libraries were constructed according to the manufacturer's protocol 10X Genomics. For targeted libraries of scars used for lineage tracing the cDNA obtained during the 10x protocol was used to set up individual PCR reactions for each target specifically amplifying the sgRNA target site. The libraries for targeted PCR were constructed with primers compatible with the 10X Genomics kit so they could be sequenced on the same platform.		tissue:adult brain\|cell type:mixed tissue dissociation\|genotype:Tg[ubi:zebrabow M]	GSM7875195	GSM7875195: Lineage tracing rep2 rpl39 scars; Danio rerio; OTHER	GSM7875195 r1	GSM7875195	1	Adult zebrafish brain tissue was extracted optionally dissected to select a specific region of interest telencephalon diencephalon mesencephalon or rhombencephalon and then dissociated with a trypsin protocol. Briefly the samples were incubated in 750 µl of HBSS glucose solution and dissocated with 15 µl of 0.05% trypsin EDTA Gibco for 30 minutes at 37°C with intermittent mixing. The disociation was stopped by addition of 750 µl of a BSA EBSS HEPES solution. Further sample handling steps were carried out on ice and for centrifugation at 4°C. The sample was filtered through a 100 µm filter washed with cold HBSS and resuspended in 100 200 µl HBSS with 0.05% BSA then filtered through a 35 µm filter and loaded on the 10x Chromium Controller. For the scSLAM seq samples a papain dissociation protocol Worthington kit according to manufecturer's instructions was used followed by an adapted scSLAM seq protocol for labeling nascent transcripts see sample specific entry. The standard transcriptome libraries were constructed according to the manufacturer's protocol 10X Genomics. For targeted libraries of scars used for lineage tracing the cDNA obtained during the 10x protocol was used to set up individual PCR reactions for each target specifically amplifying the sgRNA target site. The libraries for targeted PCR were constructed with primers compatible with the 10X Genomics kit so they could be sequenced on the same platform.		OTHER	TRANSCRIPTOMIC	other	PAIRED	ILLUMINA	Illumina NovaSeq 6000	SRP469552	loader:fastq load.py	lin2_rpl39_scar_R2.fastq.gz lin2_rpl39_scar_R1.fastq.gz	fastq fastq	1023403502.0	5717338.0	GSM7875195 r1											SRX22323896	SRS19374425	SRA1743007	Junker Lab, Berlin Institute for Medical Systems Biology, Max-Delbrück-Center for Molecular Medicine	Junker Lab, Berlin Institute for Medical Systems Biology, Max-Delbrück-Center for Molecular Medicine	2	0.00086	0.83744	0.0003	0.00337	0.99845	0.97057	0.26605	0.40241	28	151	T	B	sc-like readlen	illumina	novaseq_era	unknown	other	unknown	sc	single_cell_droplet	10x	Germany	2023-10-31	Adult	Adult	Brain	Nervous System
28756	28756	SRR26623293	SRX22323895	SRS19374424	SRP469552	PRJNA1034159	Dissecting the spatiotemporal diversity of adult neural stem cells	GSE246714	Other	Adult stem cells are important for tissue turnover and regeneration. However in most adult systems it remains elusive how stem cells assume different functional states and support spatially patterned tissue architecture. Here we dissected the diversity of neural stem cells in the adult zebrafish brain an organ that is characterized by pronounced zonation and high regenerative capacity. We combined single cell transcriptomics of dissected brain regions with massively parallel lineage tracing and in vivo RNA metabolic labeling to analyze regulation of neural stem cells in space and time. We detected a large diversity of neural stem cells with some subtypes being restricted to a single brain region while others were found globally across the brain. Global stem cell states are linked to neurogenic differentiation with different states being involved in proliferative and non proliferative differentiation. Our work reveals principles of adult stem cell organization and establishes a resource for functional manipulation of neural stem cell subtypes. Overall design: Single cell RNA seq of adult zebrafish brain performed using 10x Genomics Gene Expression protocols to explore transcriptomic diversity of adult cell types. The samples were prepared either from whole brain FACS sorted cells GFP positive cells from gfap:GFP line or a dissected single brain region as indicated in the sample title. CRISPR based lineage tracing was performed for a subset of samples to dissect developmental lineage relationships between cells. ScSLAM seq was performed in combination with Notch pathway inhibition to assess response of stem cells to perturbation.		pubmed:38365956	Lineage tracing rep2 cirbpb scars	GSM7875194	source name:adult brain\|tissue:adult brain\|cell type:mixed tissue dissociation\|genotype:Tg[ubi:zebrabow M]\|geo loc name:missing\|collection date:missing	Lineage tracing rep2 cirbpb scars	The transcriptome libraries were demultiplexed and mapped with CellRanger 6.1.1. In order to reduce batch effect the gene expression matrices were processed with the SoupX tool Young & Behjati 2020 for removing ambient RNA contamination. A subset of libraries was also mapped with Velocyto v0.17.17 La Manno et al 2018 to create count matrices for unspliced and spliced molecules. The targeted lineage tracing libraries were aligned using bwa mem3 v.0.7.12 to individual references of endogenous genes used in the lineage tracing experiment actb1 actb2 cfl1 cirbpb rpl39 and ube2e1. The scars were filtered with a custom pipeline to eliminate sequences that originate from PCR or sequencing errors. For custom code and further downstream analysis see: https://github.com/nimitic/radial glia. Assembly: GRCz11 Supplementary files format and content: Tab separated barcode files and matrix files along with the tab separated gene file supplementary file are the output of CellRanger mapping further processed by the SoupX package for ambient RNA removal. Supplementary files format and content: Loom files are the output of velocyto mapping distinguishing spliced and unspliced mRNAs. Supplementary files format and content: filtered scars.csv files contain the output of a custom scar filtering pipeline.	adult brain		Adult zebrafish brain tissue was extracted optionally dissected to select a specific region of interest telencephalon diencephalon mesencephalon or rhombencephalon and then dissociated with a trypsin protocol. Briefly the samples were incubated in 750 µl of HBSS glucose solution and dissocated with 15 µl of 0.05% trypsin EDTA Gibco for 30 minutes at 37°C with intermittent mixing. The disociation was stopped by addition of 750 µl of a BSA EBSS HEPES solution. Further sample handling steps were carried out on ice and for centrifugation at 4°C. The sample was filtered through a 100 µm filter washed with cold HBSS and resuspended in 100 200 µl HBSS with 0.05% BSA then filtered through a 35 µm filter and loaded on the 10x Chromium Controller. For the scSLAM seq samples a papain dissociation protocol Worthington kit according to manufecturer's instructions was used followed by an adapted scSLAM seq protocol for labeling nascent transcripts see sample specific entry. The standard transcriptome libraries were constructed according to the manufacturer's protocol 10X Genomics. For targeted libraries of scars used for lineage tracing the cDNA obtained during the 10x protocol was used to set up individual PCR reactions for each target specifically amplifying the sgRNA target site. The libraries for targeted PCR were constructed with primers compatible with the 10X Genomics kit so they could be sequenced on the same platform.		tissue:adult brain\|cell type:mixed tissue dissociation\|genotype:Tg[ubi:zebrabow M]	GSM7875194	GSM7875194: Lineage tracing rep2 cirbpb scars; Danio rerio; OTHER	GSM7875194 r1	GSM7875194	1	Adult zebrafish brain tissue was extracted optionally dissected to select a specific region of interest telencephalon diencephalon mesencephalon or rhombencephalon and then dissociated with a trypsin protocol. Briefly the samples were incubated in 750 µl of HBSS glucose solution and dissocated with 15 µl of 0.05% trypsin EDTA Gibco for 30 minutes at 37°C with intermittent mixing. The disociation was stopped by addition of 750 µl of a BSA EBSS HEPES solution. Further sample handling steps were carried out on ice and for centrifugation at 4°C. The sample was filtered through a 100 µm filter washed with cold HBSS and resuspended in 100 200 µl HBSS with 0.05% BSA then filtered through a 35 µm filter and loaded on the 10x Chromium Controller. For the scSLAM seq samples a papain dissociation protocol Worthington kit according to manufecturer's instructions was used followed by an adapted scSLAM seq protocol for labeling nascent transcripts see sample specific entry. The standard transcriptome libraries were constructed according to the manufacturer's protocol 10X Genomics. For targeted libraries of scars used for lineage tracing the cDNA obtained during the 10x protocol was used to set up individual PCR reactions for each target specifically amplifying the sgRNA target site. The libraries for targeted PCR were constructed with primers compatible with the 10X Genomics kit so they could be sequenced on the same platform.		OTHER	TRANSCRIPTOMIC	other	PAIRED	ILLUMINA	Illumina NovaSeq 6000	SRP469552	loader:fastq load.py	lin2_cirbpb_scar_R1.fastq.gz lin2_cirbpb_scar_R2.fastq.gz	fastq fastq	276120388.0	1542572.0	GSM7875194 r1											SRX22323895	SRS19374424	SRA1743007	Junker Lab, Berlin Institute for Medical Systems Biology, Max-Delbrück-Center for Molecular Medicine	Junker Lab, Berlin Institute for Medical Systems Biology, Max-Delbrück-Center for Molecular Medicine	2	0.00206	0.80328	0.00078	0.00662	0.9975	0.98039	0.55421	0.0258	28	151	T	B	sc-like readlen	illumina	novaseq_era	unknown	other	unknown	sc	single_cell_droplet	10x	Germany	2023-10-31	Adult	Adult	Brain	Nervous System
28757	28757	SRR26623294	SRX22323894	SRS19374423	SRP469552	PRJNA1034159	Dissecting the spatiotemporal diversity of adult neural stem cells	GSE246714	Other	Adult stem cells are important for tissue turnover and regeneration. However in most adult systems it remains elusive how stem cells assume different functional states and support spatially patterned tissue architecture. Here we dissected the diversity of neural stem cells in the adult zebrafish brain an organ that is characterized by pronounced zonation and high regenerative capacity. We combined single cell transcriptomics of dissected brain regions with massively parallel lineage tracing and in vivo RNA metabolic labeling to analyze regulation of neural stem cells in space and time. We detected a large diversity of neural stem cells with some subtypes being restricted to a single brain region while others were found globally across the brain. Global stem cell states are linked to neurogenic differentiation with different states being involved in proliferative and non proliferative differentiation. Our work reveals principles of adult stem cell organization and establishes a resource for functional manipulation of neural stem cell subtypes. Overall design: Single cell RNA seq of adult zebrafish brain performed using 10x Genomics Gene Expression protocols to explore transcriptomic diversity of adult cell types. The samples were prepared either from whole brain FACS sorted cells GFP positive cells from gfap:GFP line or a dissected single brain region as indicated in the sample title. CRISPR based lineage tracing was performed for a subset of samples to dissect developmental lineage relationships between cells. ScSLAM seq was performed in combination with Notch pathway inhibition to assess response of stem cells to perturbation.		pubmed:38365956	Lineage tracing rep2 cfl1 scars	GSM7875193	source name:adult brain\|tissue:adult brain\|cell type:mixed tissue dissociation\|genotype:Tg[ubi:zebrabow M]\|geo loc name:missing\|collection date:missing	Lineage tracing rep2 cfl1 scars	The transcriptome libraries were demultiplexed and mapped with CellRanger 6.1.1. In order to reduce batch effect the gene expression matrices were processed with the SoupX tool Young & Behjati 2020 for removing ambient RNA contamination. A subset of libraries was also mapped with Velocyto v0.17.17 La Manno et al 2018 to create count matrices for unspliced and spliced molecules. The targeted lineage tracing libraries were aligned using bwa mem3 v.0.7.12 to individual references of endogenous genes used in the lineage tracing experiment actb1 actb2 cfl1 cirbpb rpl39 and ube2e1. The scars were filtered with a custom pipeline to eliminate sequences that originate from PCR or sequencing errors. For custom code and further downstream analysis see: https://github.com/nimitic/radial glia. Assembly: GRCz11 Supplementary files format and content: Tab separated barcode files and matrix files along with the tab separated gene file supplementary file are the output of CellRanger mapping further processed by the SoupX package for ambient RNA removal. Supplementary files format and content: Loom files are the output of velocyto mapping distinguishing spliced and unspliced mRNAs. Supplementary files format and content: filtered scars.csv files contain the output of a custom scar filtering pipeline.	adult brain		Adult zebrafish brain tissue was extracted optionally dissected to select a specific region of interest telencephalon diencephalon mesencephalon or rhombencephalon and then dissociated with a trypsin protocol. Briefly the samples were incubated in 750 µl of HBSS glucose solution and dissocated with 15 µl of 0.05% trypsin EDTA Gibco for 30 minutes at 37°C with intermittent mixing. The disociation was stopped by addition of 750 µl of a BSA EBSS HEPES solution. Further sample handling steps were carried out on ice and for centrifugation at 4°C. The sample was filtered through a 100 µm filter washed with cold HBSS and resuspended in 100 200 µl HBSS with 0.05% BSA then filtered through a 35 µm filter and loaded on the 10x Chromium Controller. For the scSLAM seq samples a papain dissociation protocol Worthington kit according to manufecturer's instructions was used followed by an adapted scSLAM seq protocol for labeling nascent transcripts see sample specific entry. The standard transcriptome libraries were constructed according to the manufacturer's protocol 10X Genomics. For targeted libraries of scars used for lineage tracing the cDNA obtained during the 10x protocol was used to set up individual PCR reactions for each target specifically amplifying the sgRNA target site. The libraries for targeted PCR were constructed with primers compatible with the 10X Genomics kit so they could be sequenced on the same platform.		tissue:adult brain\|cell type:mixed tissue dissociation\|genotype:Tg[ubi:zebrabow M]	GSM7875193	GSM7875193: Lineage tracing rep2 cfl1 scars; Danio rerio; OTHER	GSM7875193 r1	GSM7875193	1	Adult zebrafish brain tissue was extracted optionally dissected to select a specific region of interest telencephalon diencephalon mesencephalon or rhombencephalon and then dissociated with a trypsin protocol. Briefly the samples were incubated in 750 µl of HBSS glucose solution and dissocated with 15 µl of 0.05% trypsin EDTA Gibco for 30 minutes at 37°C with intermittent mixing. The disociation was stopped by addition of 750 µl of a BSA EBSS HEPES solution. Further sample handling steps were carried out on ice and for centrifugation at 4°C. The sample was filtered through a 100 µm filter washed with cold HBSS and resuspended in 100 200 µl HBSS with 0.05% BSA then filtered through a 35 µm filter and loaded on the 10x Chromium Controller. For the scSLAM seq samples a papain dissociation protocol Worthington kit according to manufecturer's instructions was used followed by an adapted scSLAM seq protocol for labeling nascent transcripts see sample specific entry. The standard transcriptome libraries were constructed according to the manufacturer's protocol 10X Genomics. For targeted libraries of scars used for lineage tracing the cDNA obtained during the 10x protocol was used to set up individual PCR reactions for each target specifically amplifying the sgRNA target site. The libraries for targeted PCR were constructed with primers compatible with the 10X Genomics kit so they could be sequenced on the same platform.		OTHER	TRANSCRIPTOMIC	other	PAIRED	ILLUMINA	Illumina NovaSeq 6000	SRP469552	loader:fastq load.py	lin2_cfl1_scar_R1.fastq.gz lin2_cfl1_scar_R2.fastq.gz	fastq fastq	791318009.0	4420771.0	GSM7875193 r1											SRX22323894	SRS19374423	SRA1743007	Junker Lab, Berlin Institute for Medical Systems Biology, Max-Delbrück-Center for Molecular Medicine	Junker Lab, Berlin Institute for Medical Systems Biology, Max-Delbrück-Center for Molecular Medicine	2	0.00067	0.46079	0.00055	0.16353	0.99965	0.99332	0.52631	0.44939	28	151	T	B	sc-like readlen	illumina	novaseq_era	unknown	other	unknown	sc	single_cell_droplet	10x	Germany	2023-10-31	Adult	Adult	Brain	Nervous System
28758	28758	SRR26623295	SRX22323893	SRS19374421	SRP469552	PRJNA1034159	Dissecting the spatiotemporal diversity of adult neural stem cells	GSE246714	Other	Adult stem cells are important for tissue turnover and regeneration. However in most adult systems it remains elusive how stem cells assume different functional states and support spatially patterned tissue architecture. Here we dissected the diversity of neural stem cells in the adult zebrafish brain an organ that is characterized by pronounced zonation and high regenerative capacity. We combined single cell transcriptomics of dissected brain regions with massively parallel lineage tracing and in vivo RNA metabolic labeling to analyze regulation of neural stem cells in space and time. We detected a large diversity of neural stem cells with some subtypes being restricted to a single brain region while others were found globally across the brain. Global stem cell states are linked to neurogenic differentiation with different states being involved in proliferative and non proliferative differentiation. Our work reveals principles of adult stem cell organization and establishes a resource for functional manipulation of neural stem cell subtypes. Overall design: Single cell RNA seq of adult zebrafish brain performed using 10x Genomics Gene Expression protocols to explore transcriptomic diversity of adult cell types. The samples were prepared either from whole brain FACS sorted cells GFP positive cells from gfap:GFP line or a dissected single brain region as indicated in the sample title. CRISPR based lineage tracing was performed for a subset of samples to dissect developmental lineage relationships between cells. ScSLAM seq was performed in combination with Notch pathway inhibition to assess response of stem cells to perturbation.		pubmed:38365956	Lineage tracing rep1 ube2e1 scars	GSM7875192	source name:adult brain\|tissue:adult brain\|cell type:mixed tissue dissociation\|genotype:Tg[ubi:zebrabow M]\|geo loc name:missing\|collection date:missing	Lineage tracing rep1 ube2e1 scars	The transcriptome libraries were demultiplexed and mapped with CellRanger 6.1.1. In order to reduce batch effect the gene expression matrices were processed with the SoupX tool Young & Behjati 2020 for removing ambient RNA contamination. A subset of libraries was also mapped with Velocyto v0.17.17 La Manno et al 2018 to create count matrices for unspliced and spliced molecules. The targeted lineage tracing libraries were aligned using bwa mem3 v.0.7.12 to individual references of endogenous genes used in the lineage tracing experiment actb1 actb2 cfl1 cirbpb rpl39 and ube2e1. The scars were filtered with a custom pipeline to eliminate sequences that originate from PCR or sequencing errors. For custom code and further downstream analysis see: https://github.com/nimitic/radial glia. Assembly: GRCz11 Supplementary files format and content: Tab separated barcode files and matrix files along with the tab separated gene file supplementary file are the output of CellRanger mapping further processed by the SoupX package for ambient RNA removal. Supplementary files format and content: Loom files are the output of velocyto mapping distinguishing spliced and unspliced mRNAs. Supplementary files format and content: filtered scars.csv files contain the output of a custom scar filtering pipeline.	adult brain		Adult zebrafish brain tissue was extracted optionally dissected to select a specific region of interest telencephalon diencephalon mesencephalon or rhombencephalon and then dissociated with a trypsin protocol. Briefly the samples were incubated in 750 µl of HBSS glucose solution and dissocated with 15 µl of 0.05% trypsin EDTA Gibco for 30 minutes at 37°C with intermittent mixing. The disociation was stopped by addition of 750 µl of a BSA EBSS HEPES solution. Further sample handling steps were carried out on ice and for centrifugation at 4°C. The sample was filtered through a 100 µm filter washed with cold HBSS and resuspended in 100 200 µl HBSS with 0.05% BSA then filtered through a 35 µm filter and loaded on the 10x Chromium Controller. For the scSLAM seq samples a papain dissociation protocol Worthington kit according to manufecturer's instructions was used followed by an adapted scSLAM seq protocol for labeling nascent transcripts see sample specific entry. The standard transcriptome libraries were constructed according to the manufacturer's protocol 10X Genomics. For targeted libraries of scars used for lineage tracing the cDNA obtained during the 10x protocol was used to set up individual PCR reactions for each target specifically amplifying the sgRNA target site. The libraries for targeted PCR were constructed with primers compatible with the 10X Genomics kit so they could be sequenced on the same platform.		tissue:adult brain\|cell type:mixed tissue dissociation\|genotype:Tg[ubi:zebrabow M]	GSM7875192	GSM7875192: Lineage tracing rep1 ube2e1 scars; Danio rerio; OTHER	GSM7875192 r1	GSM7875192	1	Adult zebrafish brain tissue was extracted optionally dissected to select a specific region of interest telencephalon diencephalon mesencephalon or rhombencephalon and then dissociated with a trypsin protocol. Briefly the samples were incubated in 750 µl of HBSS glucose solution and dissocated with 15 µl of 0.05% trypsin EDTA Gibco for 30 minutes at 37°C with intermittent mixing. The disociation was stopped by addition of 750 µl of a BSA EBSS HEPES solution. Further sample handling steps were carried out on ice and for centrifugation at 4°C. The sample was filtered through a 100 µm filter washed with cold HBSS and resuspended in 100 200 µl HBSS with 0.05% BSA then filtered through a 35 µm filter and loaded on the 10x Chromium Controller. For the scSLAM seq samples a papain dissociation protocol Worthington kit according to manufecturer's instructions was used followed by an adapted scSLAM seq protocol for labeling nascent transcripts see sample specific entry. The standard transcriptome libraries were constructed according to the manufacturer's protocol 10X Genomics. For targeted libraries of scars used for lineage tracing the cDNA obtained during the 10x protocol was used to set up individual PCR reactions for each target specifically amplifying the sgRNA target site. The libraries for targeted PCR were constructed with primers compatible with the 10X Genomics kit so they could be sequenced on the same platform.		OTHER	TRANSCRIPTOMIC	other	PAIRED	ILLUMINA	NextSeq 500	SRP469552	loader:fastq load.py	lin1_ube2e1_scar_R1.fastq.gz lin1_ube2e1_scar_R2.fastq.gz	fastq fastq	99407618.0	601781.0	GSM7875192 r1											SRX22323893	SRS19374421	SRA1743007	Junker Lab, Berlin Institute for Medical Systems Biology, Max-Delbrück-Center for Molecular Medicine	Junker Lab, Berlin Institute for Medical Systems Biology, Max-Delbrück-Center for Molecular Medicine	2	0.00657	0.84532	0.00205	0.03049	0.99537	0.95286	0.43306	0.19203	28	150	T	B	sc-like readlen	illumina	nextseq	unknown	other	unknown	sc	single_cell_droplet	10x	Germany	2023-10-31	Adult	Adult	Brain	Nervous System
28759	28759	SRR26623296	SRX22323892	SRS19374422	SRP469552	PRJNA1034159	Dissecting the spatiotemporal diversity of adult neural stem cells	GSE246714	Other	Adult stem cells are important for tissue turnover and regeneration. However in most adult systems it remains elusive how stem cells assume different functional states and support spatially patterned tissue architecture. Here we dissected the diversity of neural stem cells in the adult zebrafish brain an organ that is characterized by pronounced zonation and high regenerative capacity. We combined single cell transcriptomics of dissected brain regions with massively parallel lineage tracing and in vivo RNA metabolic labeling to analyze regulation of neural stem cells in space and time. We detected a large diversity of neural stem cells with some subtypes being restricted to a single brain region while others were found globally across the brain. Global stem cell states are linked to neurogenic differentiation with different states being involved in proliferative and non proliferative differentiation. Our work reveals principles of adult stem cell organization and establishes a resource for functional manipulation of neural stem cell subtypes. Overall design: Single cell RNA seq of adult zebrafish brain performed using 10x Genomics Gene Expression protocols to explore transcriptomic diversity of adult cell types. The samples were prepared either from whole brain FACS sorted cells GFP positive cells from gfap:GFP line or a dissected single brain region as indicated in the sample title. CRISPR based lineage tracing was performed for a subset of samples to dissect developmental lineage relationships between cells. ScSLAM seq was performed in combination with Notch pathway inhibition to assess response of stem cells to perturbation.		pubmed:38365956	Lineage tracing rep1 rpl39 scars	GSM7875191	source name:adult brain\|tissue:adult brain\|cell type:mixed tissue dissociation\|genotype:Tg[ubi:zebrabow M]\|geo loc name:missing\|collection date:missing	Lineage tracing rep1 rpl39 scars	The transcriptome libraries were demultiplexed and mapped with CellRanger 6.1.1. In order to reduce batch effect the gene expression matrices were processed with the SoupX tool Young & Behjati 2020 for removing ambient RNA contamination. A subset of libraries was also mapped with Velocyto v0.17.17 La Manno et al 2018 to create count matrices for unspliced and spliced molecules. The targeted lineage tracing libraries were aligned using bwa mem3 v.0.7.12 to individual references of endogenous genes used in the lineage tracing experiment actb1 actb2 cfl1 cirbpb rpl39 and ube2e1. The scars were filtered with a custom pipeline to eliminate sequences that originate from PCR or sequencing errors. For custom code and further downstream analysis see: https://github.com/nimitic/radial glia. Assembly: GRCz11 Supplementary files format and content: Tab separated barcode files and matrix files along with the tab separated gene file supplementary file are the output of CellRanger mapping further processed by the SoupX package for ambient RNA removal. Supplementary files format and content: Loom files are the output of velocyto mapping distinguishing spliced and unspliced mRNAs. Supplementary files format and content: filtered scars.csv files contain the output of a custom scar filtering pipeline.	adult brain		Adult zebrafish brain tissue was extracted optionally dissected to select a specific region of interest telencephalon diencephalon mesencephalon or rhombencephalon and then dissociated with a trypsin protocol. Briefly the samples were incubated in 750 µl of HBSS glucose solution and dissocated with 15 µl of 0.05% trypsin EDTA Gibco for 30 minutes at 37°C with intermittent mixing. The disociation was stopped by addition of 750 µl of a BSA EBSS HEPES solution. Further sample handling steps were carried out on ice and for centrifugation at 4°C. The sample was filtered through a 100 µm filter washed with cold HBSS and resuspended in 100 200 µl HBSS with 0.05% BSA then filtered through a 35 µm filter and loaded on the 10x Chromium Controller. For the scSLAM seq samples a papain dissociation protocol Worthington kit according to manufecturer's instructions was used followed by an adapted scSLAM seq protocol for labeling nascent transcripts see sample specific entry. The standard transcriptome libraries were constructed according to the manufacturer's protocol 10X Genomics. For targeted libraries of scars used for lineage tracing the cDNA obtained during the 10x protocol was used to set up individual PCR reactions for each target specifically amplifying the sgRNA target site. The libraries for targeted PCR were constructed with primers compatible with the 10X Genomics kit so they could be sequenced on the same platform.		tissue:adult brain\|cell type:mixed tissue dissociation\|genotype:Tg[ubi:zebrabow M]	GSM7875191	GSM7875191: Lineage tracing rep1 rpl39 scars; Danio rerio; OTHER	GSM7875191 r1	GSM7875191	1	Adult zebrafish brain tissue was extracted optionally dissected to select a specific region of interest telencephalon diencephalon mesencephalon or rhombencephalon and then dissociated with a trypsin protocol. Briefly the samples were incubated in 750 µl of HBSS glucose solution and dissocated with 15 µl of 0.05% trypsin EDTA Gibco for 30 minutes at 37°C with intermittent mixing. The disociation was stopped by addition of 750 µl of a BSA EBSS HEPES solution. Further sample handling steps were carried out on ice and for centrifugation at 4°C. The sample was filtered through a 100 µm filter washed with cold HBSS and resuspended in 100 200 µl HBSS with 0.05% BSA then filtered through a 35 µm filter and loaded on the 10x Chromium Controller. For the scSLAM seq samples a papain dissociation protocol Worthington kit according to manufecturer's instructions was used followed by an adapted scSLAM seq protocol for labeling nascent transcripts see sample specific entry. The standard transcriptome libraries were constructed according to the manufacturer's protocol 10X Genomics. For targeted libraries of scars used for lineage tracing the cDNA obtained during the 10x protocol was used to set up individual PCR reactions for each target specifically amplifying the sgRNA target site. The libraries for targeted PCR were constructed with primers compatible with the 10X Genomics kit so they could be sequenced on the same platform.		OTHER	TRANSCRIPTOMIC	other	PAIRED	ILLUMINA	NextSeq 500	SRP469552	loader:fastq load.py	lin1_rpl39_scar_R1.fastq.gz lin1_rpl39_scar_R2.fastq.gz	fastq fastq	401898718.0	2276836.0	GSM7875191 r1											SRX22323892	SRS19374422	SRA1743007	Junker Lab, Berlin Institute for Medical Systems Biology, Max-Delbrück-Center for Molecular Medicine	Junker Lab, Berlin Institute for Medical Systems Biology, Max-Delbrück-Center for Molecular Medicine	2	0.00037	0.2642	0.00015	0.00086	0.99922	0.99026	0.23076	0.42455	28	120	T	B	sc-like readlen	illumina	nextseq	unknown	other	unknown	sc	single_cell_droplet	10x	Germany	2023-10-31	Adult	Adult	Brain	Nervous System
43983	43983	SRR6811832	SRX3768872	SRS3023386	SRP121343	PRJNA415636	Simultaneous lineage tracing and cell type identification using CRISPR/Cas9 induced genetic scars	GSE106121	Other	A key goal of developmental biology is to understand how a single cell transforms into a full grown organism consisting of many different cell types. Single cell RNA sequencing scRNA seq has become a widely used method due to its ability to identify all cell types in a tissue or organ in a systematic manner. However a major challenge is to organize the resulting taxonomy of cell types into lineage trees revealing the developmental origin of cells. Here we present a strategy for simultaneous lineage tracing and transcriptome profiling in thousands of single cells. By combining scRNA seq with computational analysis of lineage barcodes generated by genome editing of transgenic reporter genes we reconstruct developmental lineage trees in zebrafish larvae and adult fish. In future analyses LINNAEUS LINeage tracing by Nuclease Activated Editing of Ubiquitous Sequences can be used as a systematic approach for identifying the lineage origin of novel cell types or of known cell types under different conditions. Overall design: Combining scRNA seq with computational analysis of lineage barcodes generated by genome editing of transgenic reporter genes.		pubmed:29644996	Larva F1 2 scar	GSM3032175	source name:Full organism\|strain/background:Zebrabow M\|tissue:Whole body\|developmental stage:Larva	Larva F1 2 scar	Library strategy: Targeted amplification Scar reads have the same structure as transcript reads: they consist of a barcode a UMI and a scar. The scar sequences were aligned using bwa mem6 to a reference of RFP. We defined a cell as a barcode with at least 500 reads. We removed reads that were unmapped had an incorrect barcode or did not start with the exact PCR primer we used. We truncated all scar sequences to 75 nucleotides and filtered out shorter sequences. To correct for sequencing errors we implemented several rounds of scar filtering Supplementary Fig. 2 in publication. We started by counting the number of times each molecule was sequenced. Sequencing errors will typically have fewer reads than the actual scars they originate from. As a first filtering step we therefore removed all molecules only seen once to reduce the complexity in the dataset for consecutive filtering steps. In the second filtering step we aimed to remove easily recognizable sequencing errors. To this end we consecutively considered scar sequences that have the same cellular barcode and UMI UMIs that have the same cellular barcode and scar sequence and cellular barcodes that have the same UMI and scar sequence. In each step we kept only the molecule with the highest number of reads. The rationale behind this is that it is very improbable to have two valid scar sequences in the same cell with the same UMI or to have a scar sequence with the same UMI appear in two different cells. The observation of two different UMIs for the same scar in the same cell is much more likely and corresponds to detection of multiple transcripts from the same locus but information about scar expression levels was not required in our downstream analysis. In the third filtering step we specifically targeted sequencing errors within each cell. We compared the scar sequences found within a cell to each other. We filtered out sequences that had a Hamming distance of 2 or less to another scar sequence in the same cell that occurred in at least eight tim…	Full organism		Single cell dissociation. 10X Genomics Chromium		strain/background:Zebrabow M\|tissue:Whole body\|developmental stage:Larva	GSM3032175	GSM3032175: Larva F1 2 scar; Danio rerio; OTHER	GSM3032175		1	Single cell dissociation. 10X Genomics Chromium	GEO Accession:GSM3032175	OTHER	TRANSCRIPTOMIC	other	PAIRED	ILLUMINA	Illumina HiSeq 2500	SRP121343		F1_2_scar_R1.fastq.gz F1_2_scar_R2.fastq.gz	fastq fastq	9301928572.0	75015553.0	GSM3032175 r1	0:26 1:98	A:2682085389;C:3008788535;G:2093169318;T:1516220703;N:1664627	26	98		2682085389	3008788535	2093169318	1516220703	1664627	SRX3768872	SRS3023386	SRA623333	GEO	Max Delbrück Center	2	0.0001	0.00173	4e-05	8e-05	0.99983	0.99667	0.33333	0.531	26	98	T	T	mates < 9% mapping rate	illumina	hiseq_era	unknown	other	unknown	sc	single_cell_droplet	10x	Germany	2018-03-06	Larval	Larval	Trunk	Surface Structure
43984	43984	SRR6811831	SRX3768871	SRS3023388	SRP121343	PRJNA415636	Simultaneous lineage tracing and cell type identification using CRISPR/Cas9 induced genetic scars	GSE106121	Other	A key goal of developmental biology is to understand how a single cell transforms into a full grown organism consisting of many different cell types. Single cell RNA sequencing scRNA seq has become a widely used method due to its ability to identify all cell types in a tissue or organ in a systematic manner. However a major challenge is to organize the resulting taxonomy of cell types into lineage trees revealing the developmental origin of cells. Here we present a strategy for simultaneous lineage tracing and transcriptome profiling in thousands of single cells. By combining scRNA seq with computational analysis of lineage barcodes generated by genome editing of transgenic reporter genes we reconstruct developmental lineage trees in zebrafish larvae and adult fish. In future analyses LINNAEUS LINeage tracing by Nuclease Activated Editing of Ubiquitous Sequences can be used as a systematic approach for identifying the lineage origin of novel cell types or of known cell types under different conditions. Overall design: Combining scRNA seq with computational analysis of lineage barcodes generated by genome editing of transgenic reporter genes.		pubmed:29644996	Larva F1 1 scar	GSM3032174	source name:Full organism\|strain/background:Zebrabow M\|tissue:Whole body\|developmental stage:Larva	Larva F1 1 scar	Library strategy: Targeted amplification Scar reads have the same structure as transcript reads: they consist of a barcode a UMI and a scar. The scar sequences were aligned using bwa mem6 to a reference of RFP. We defined a cell as a barcode with at least 500 reads. We removed reads that were unmapped had an incorrect barcode or did not start with the exact PCR primer we used. We truncated all scar sequences to 75 nucleotides and filtered out shorter sequences. To correct for sequencing errors we implemented several rounds of scar filtering Supplementary Fig. 2 in publication. We started by counting the number of times each molecule was sequenced. Sequencing errors will typically have fewer reads than the actual scars they originate from. As a first filtering step we therefore removed all molecules only seen once to reduce the complexity in the dataset for consecutive filtering steps. In the second filtering step we aimed to remove easily recognizable sequencing errors. To this end we consecutively considered scar sequences that have the same cellular barcode and UMI UMIs that have the same cellular barcode and scar sequence and cellular barcodes that have the same UMI and scar sequence. In each step we kept only the molecule with the highest number of reads. The rationale behind this is that it is very improbable to have two valid scar sequences in the same cell with the same UMI or to have a scar sequence with the same UMI appear in two different cells. The observation of two different UMIs for the same scar in the same cell is much more likely and corresponds to detection of multiple transcripts from the same locus but information about scar expression levels was not required in our downstream analysis. In the third filtering step we specifically targeted sequencing errors within each cell. We compared the scar sequences found within a cell to each other. We filtered out sequences that had a Hamming distance of 2 or less to another scar sequence in the same cell that occurred in at least eight tim…	Full organism		Single cell dissociation. 10X Genomics Chromium		strain/background:Zebrabow M\|tissue:Whole body\|developmental stage:Larva	GSM3032174	GSM3032174: Larva F1 1 scar; Danio rerio; OTHER	GSM3032174		1	Single cell dissociation. 10X Genomics Chromium	GEO Accession:GSM3032174	OTHER	TRANSCRIPTOMIC	other	PAIRED	ILLUMINA	Illumina HiSeq 2500	SRP121343		F1_1_scar_R1.fastq.gz F1_1_scar_R2.fastq.gz	fastq fastq	7772713576.0	62683174.0	GSM3032174 r1	0:26 1:98	A:2222016153;C:2555237987;G:1722216404;T:1271855931;N:1387101	26	98		2222016153	2555237987	1722216404	1271855931	1387101	SRX3768871	SRS3023388	SRA623333	GEO	Max Delbrück Center	2	0.00014	0.00171	8e-05	0.00012	0.99985	0.99642	0.44444	0.45454	26	98	T	T	mates < 9% mapping rate	illumina	hiseq_era	unknown	other	unknown	sc	single_cell_droplet	10x	Germany	2018-03-06	Larval	Larval	Trunk	Surface Structure
43987	43987	SRR6811828	SRX3768868	SRS3023414	SRP121343	PRJNA415636	Simultaneous lineage tracing and cell type identification using CRISPR/Cas9 induced genetic scars	GSE106121	Other	A key goal of developmental biology is to understand how a single cell transforms into a full grown organism consisting of many different cell types. Single cell RNA sequencing scRNA seq has become a widely used method due to its ability to identify all cell types in a tissue or organ in a systematic manner. However a major challenge is to organize the resulting taxonomy of cell types into lineage trees revealing the developmental origin of cells. Here we present a strategy for simultaneous lineage tracing and transcriptome profiling in thousands of single cells. By combining scRNA seq with computational analysis of lineage barcodes generated by genome editing of transgenic reporter genes we reconstruct developmental lineage trees in zebrafish larvae and adult fish. In future analyses LINNAEUS LINeage tracing by Nuclease Activated Editing of Ubiquitous Sequences can be used as a systematic approach for identifying the lineage origin of novel cell types or of known cell types under different conditions. Overall design: Combining scRNA seq with computational analysis of lineage barcodes generated by genome editing of transgenic reporter genes.		pubmed:29644996	Pancreas 3 exo scar	GSM3032171	source name:Pancreas except primary islet liver\|strain/background:Zebrabow M\|tissue:Pancreas except primary islet liver\|developmental stage:Adult	Pancreas 3 exo scar	Library strategy: Targeted amplification Scar reads have the same structure as transcript reads: they consist of a barcode a UMI and a scar. The scar sequences were aligned using bwa mem6 to a reference of RFP. We defined a cell as a barcode with at least 500 reads. We removed reads that were unmapped had an incorrect barcode or did not start with the exact PCR primer we used. We truncated all scar sequences to 75 nucleotides and filtered out shorter sequences. To correct for sequencing errors we implemented several rounds of scar filtering Supplementary Fig. 2 in publication. We started by counting the number of times each molecule was sequenced. Sequencing errors will typically have fewer reads than the actual scars they originate from. As a first filtering step we therefore removed all molecules only seen once to reduce the complexity in the dataset for consecutive filtering steps. In the second filtering step we aimed to remove easily recognizable sequencing errors. To this end we consecutively considered scar sequences that have the same cellular barcode and UMI UMIs that have the same cellular barcode and scar sequence and cellular barcodes that have the same UMI and scar sequence. In each step we kept only the molecule with the highest number of reads. The rationale behind this is that it is very improbable to have two valid scar sequences in the same cell with the same UMI or to have a scar sequence with the same UMI appear in two different cells. The observation of two different UMIs for the same scar in the same cell is much more likely and corresponds to detection of multiple transcripts from the same locus but information about scar expression levels was not required in our downstream analysis. In the third filtering step we specifically targeted sequencing errors within each cell. We compared the scar sequences found within a cell to each other. We filtered out sequences that had a Hamming distance of 2 or less to another scar sequence in the same cell that occurred in at least eight tim…	Pancreas except primary islet liver		Single cell dissociation. 10X Genomics Chromium		strain/background:Zebrabow M\|tissue:Pancreas except primary islet liver\|developmental stage:Adult	GSM3032171	GSM3032171: Pancreas 3 exo scar; Danio rerio; OTHER	GSM3032171		1	Single cell dissociation. 10X Genomics Chromium	GEO Accession:GSM3032171	OTHER	TRANSCRIPTOMIC	other	PAIRED	ILLUMINA	NextSeq 500	SRP121343		P7exo_scar_R1.fastq.gz P7exo_scar_R2.fastq.gz	fastq fastq	4291410352.0	34608148.0	GSM3032171 r1	0:26 1:98	A:1266869162;C:1345773385;G:950601988;T:726078376;N:2087441	26	98		1266869162	1345773385	950601988	726078376	2087441	SRX3768868	SRS3023414	SRA623333	GEO	Max Delbrück Center	2	0.00014	0.00241	0.00012	0.00024	0.99995	0.99691	0.0	0.65306	26	98	T	T	mates < 9% mapping rate	illumina	nextseq	unknown	other	unknown	sc	single_cell_droplet	10x	Germany	2018-03-06	Adult	Adult	Multi-tissue	Multi-system
43988	43988	SRR6811827	SRX3768867	SRS3023384	SRP121343	PRJNA415636	Simultaneous lineage tracing and cell type identification using CRISPR/Cas9 induced genetic scars	GSE106121	Other	A key goal of developmental biology is to understand how a single cell transforms into a full grown organism consisting of many different cell types. Single cell RNA sequencing scRNA seq has become a widely used method due to its ability to identify all cell types in a tissue or organ in a systematic manner. However a major challenge is to organize the resulting taxonomy of cell types into lineage trees revealing the developmental origin of cells. Here we present a strategy for simultaneous lineage tracing and transcriptome profiling in thousands of single cells. By combining scRNA seq with computational analysis of lineage barcodes generated by genome editing of transgenic reporter genes we reconstruct developmental lineage trees in zebrafish larvae and adult fish. In future analyses LINNAEUS LINeage tracing by Nuclease Activated Editing of Ubiquitous Sequences can be used as a systematic approach for identifying the lineage origin of novel cell types or of known cell types under different conditions. Overall design: Combining scRNA seq with computational analysis of lineage barcodes generated by genome editing of transgenic reporter genes.		pubmed:29644996	Pancreas 3 endo scar	GSM3032170	source name:Primary pancreatic islet\|strain/background:Zebrabow M\|tissue:Primary pancreatic islet\|developmental stage:Adult	Pancreas 3 endo scar	Library strategy: Targeted amplification Scar reads have the same structure as transcript reads: they consist of a barcode a UMI and a scar. The scar sequences were aligned using bwa mem6 to a reference of RFP. We defined a cell as a barcode with at least 500 reads. We removed reads that were unmapped had an incorrect barcode or did not start with the exact PCR primer we used. We truncated all scar sequences to 75 nucleotides and filtered out shorter sequences. To correct for sequencing errors we implemented several rounds of scar filtering Supplementary Fig. 2 in publication. We started by counting the number of times each molecule was sequenced. Sequencing errors will typically have fewer reads than the actual scars they originate from. As a first filtering step we therefore removed all molecules only seen once to reduce the complexity in the dataset for consecutive filtering steps. In the second filtering step we aimed to remove easily recognizable sequencing errors. To this end we consecutively considered scar sequences that have the same cellular barcode and UMI UMIs that have the same cellular barcode and scar sequence and cellular barcodes that have the same UMI and scar sequence. In each step we kept only the molecule with the highest number of reads. The rationale behind this is that it is very improbable to have two valid scar sequences in the same cell with the same UMI or to have a scar sequence with the same UMI appear in two different cells. The observation of two different UMIs for the same scar in the same cell is much more likely and corresponds to detection of multiple transcripts from the same locus but information about scar expression levels was not required in our downstream analysis. In the third filtering step we specifically targeted sequencing errors within each cell. We compared the scar sequences found within a cell to each other. We filtered out sequences that had a Hamming distance of 2 or less to another scar sequence in the same cell that occurred in at least eight tim…	Primary pancreatic islet		Single cell dissociation. 10X Genomics Chromium		strain/background:Zebrabow M\|tissue:Primary pancreatic islet\|developmental stage:Adult	GSM3032170	GSM3032170: Pancreas 3 endo scar; Danio rerio; OTHER	GSM3032170		1	Single cell dissociation. 10X Genomics Chromium	GEO Accession:GSM3032170	OTHER	TRANSCRIPTOMIC	other	PAIRED	ILLUMINA	NextSeq 500	SRP121343		P7endo_scar_R1.fastq.gz P7endo_scar_R2.fastq.gz	fastq fastq	2537781396.0	20465979.0	GSM3032170 r1	0:26 1:98	A:741745261;C:801599713;G:561531527;T:431666561;N:1238334	26	98		741745261	801599713	561531527	431666561	1238334	SRX3768867	SRS3023384	SRA623333	GEO	Max Delbrück Center	2	0.00014	0.00289	0.00012	0.00028	0.99995	0.99602	1.0	0.68478	26	98	T	T	mates < 9% mapping rate	illumina	nextseq	unknown	other	unknown	sc	single_cell_droplet	10x	Germany	2018-03-06	Adult	Adult	Pancreas	Endocrine System
43989	43989	SRR6811826	SRX3768866	SRS3023382	SRP121343	PRJNA415636	Simultaneous lineage tracing and cell type identification using CRISPR/Cas9 induced genetic scars	GSE106121	Other	A key goal of developmental biology is to understand how a single cell transforms into a full grown organism consisting of many different cell types. Single cell RNA sequencing scRNA seq has become a widely used method due to its ability to identify all cell types in a tissue or organ in a systematic manner. However a major challenge is to organize the resulting taxonomy of cell types into lineage trees revealing the developmental origin of cells. Here we present a strategy for simultaneous lineage tracing and transcriptome profiling in thousands of single cells. By combining scRNA seq with computational analysis of lineage barcodes generated by genome editing of transgenic reporter genes we reconstruct developmental lineage trees in zebrafish larvae and adult fish. In future analyses LINNAEUS LINeage tracing by Nuclease Activated Editing of Ubiquitous Sequences can be used as a systematic approach for identifying the lineage origin of novel cell types or of known cell types under different conditions. Overall design: Combining scRNA seq with computational analysis of lineage barcodes generated by genome editing of transgenic reporter genes.		pubmed:29644996	Heart 3 scar	GSM3032169	source name:Heart and blood\|strain/background:Zebrabow M\|tissue:Heart and blood\|developmental stage:Adult	Heart 3 scar	Library strategy: Targeted amplification Scar reads have the same structure as transcript reads: they consist of a barcode a UMI and a scar. The scar sequences were aligned using bwa mem6 to a reference of RFP. We defined a cell as a barcode with at least 500 reads. We removed reads that were unmapped had an incorrect barcode or did not start with the exact PCR primer we used. We truncated all scar sequences to 75 nucleotides and filtered out shorter sequences. To correct for sequencing errors we implemented several rounds of scar filtering Supplementary Fig. 2 in publication. We started by counting the number of times each molecule was sequenced. Sequencing errors will typically have fewer reads than the actual scars they originate from. As a first filtering step we therefore removed all molecules only seen once to reduce the complexity in the dataset for consecutive filtering steps. In the second filtering step we aimed to remove easily recognizable sequencing errors. To this end we consecutively considered scar sequences that have the same cellular barcode and UMI UMIs that have the same cellular barcode and scar sequence and cellular barcodes that have the same UMI and scar sequence. In each step we kept only the molecule with the highest number of reads. The rationale behind this is that it is very improbable to have two valid scar sequences in the same cell with the same UMI or to have a scar sequence with the same UMI appear in two different cells. The observation of two different UMIs for the same scar in the same cell is much more likely and corresponds to detection of multiple transcripts from the same locus but information about scar expression levels was not required in our downstream analysis. In the third filtering step we specifically targeted sequencing errors within each cell. We compared the scar sequences found within a cell to each other. We filtered out sequences that had a Hamming distance of 2 or less to another scar sequence in the same cell that occurred in at least eight tim…	Heart and blood		Single cell dissociation. 10X Genomics Chromium		strain/background:Zebrabow M\|tissue:Heart and blood\|developmental stage:Adult	GSM3032169	GSM3032169: Heart 3 scar; Danio rerio; OTHER	GSM3032169		1	Single cell dissociation. 10X Genomics Chromium	GEO Accession:GSM3032169	OTHER	TRANSCRIPTOMIC	other	PAIRED	ILLUMINA	NextSeq 500	SRP121343		H7_scar_R1.fastq.gz H7_scar_R2.fastq.gz	fastq fastq	3999397296.0	32253204.0	GSM3032169 r1	0:26 1:98	A:1170970784;C:1255843391;G:881154893;T:689473188;N:1955040	26	98		1170970784	1255843391	881154893	689473188	1955040	SRX3768866	SRS3023382	SRA623333	GEO	Max Delbrück Center	2	0.00018	0.00228	0.00015	0.00019	0.99991	0.99659	0.8	0.67235	26	98	T	T	mates < 9% mapping rate	illumina	nextseq	unknown	other	unknown	sc	single_cell_droplet	10x	Germany	2018-03-06	Adult	Adult	Multi-tissue	Multi-system
43990	43990	SRR6811825	SRX3768865	SRS3023383	SRP121343	PRJNA415636	Simultaneous lineage tracing and cell type identification using CRISPR/Cas9 induced genetic scars	GSE106121	Other	A key goal of developmental biology is to understand how a single cell transforms into a full grown organism consisting of many different cell types. Single cell RNA sequencing scRNA seq has become a widely used method due to its ability to identify all cell types in a tissue or organ in a systematic manner. However a major challenge is to organize the resulting taxonomy of cell types into lineage trees revealing the developmental origin of cells. Here we present a strategy for simultaneous lineage tracing and transcriptome profiling in thousands of single cells. By combining scRNA seq with computational analysis of lineage barcodes generated by genome editing of transgenic reporter genes we reconstruct developmental lineage trees in zebrafish larvae and adult fish. In future analyses LINNAEUS LINeage tracing by Nuclease Activated Editing of Ubiquitous Sequences can be used as a systematic approach for identifying the lineage origin of novel cell types or of known cell types under different conditions. Overall design: Combining scRNA seq with computational analysis of lineage barcodes generated by genome editing of transgenic reporter genes.		pubmed:29644996	Brain 3 scar	GSM3032168	source name:Brain\|strain/background:Zebrabow M\|tissue:Brain\|developmental stage:Adult	Brain 3 scar	Library strategy: Targeted amplification Scar reads have the same structure as transcript reads: they consist of a barcode a UMI and a scar. The scar sequences were aligned using bwa mem6 to a reference of RFP. We defined a cell as a barcode with at least 500 reads. We removed reads that were unmapped had an incorrect barcode or did not start with the exact PCR primer we used. We truncated all scar sequences to 75 nucleotides and filtered out shorter sequences. To correct for sequencing errors we implemented several rounds of scar filtering Supplementary Fig. 2 in publication. We started by counting the number of times each molecule was sequenced. Sequencing errors will typically have fewer reads than the actual scars they originate from. As a first filtering step we therefore removed all molecules only seen once to reduce the complexity in the dataset for consecutive filtering steps. In the second filtering step we aimed to remove easily recognizable sequencing errors. To this end we consecutively considered scar sequences that have the same cellular barcode and UMI UMIs that have the same cellular barcode and scar sequence and cellular barcodes that have the same UMI and scar sequence. In each step we kept only the molecule with the highest number of reads. The rationale behind this is that it is very improbable to have two valid scar sequences in the same cell with the same UMI or to have a scar sequence with the same UMI appear in two different cells. The observation of two different UMIs for the same scar in the same cell is much more likely and corresponds to detection of multiple transcripts from the same locus but information about scar expression levels was not required in our downstream analysis. In the third filtering step we specifically targeted sequencing errors within each cell. We compared the scar sequences found within a cell to each other. We filtered out sequences that had a Hamming distance of 2 or less to another scar sequence in the same cell that occurred in at least eight tim…	Brain		Single cell dissociation. 10X Genomics Chromium		strain/background:Zebrabow M\|tissue:Brain\|developmental stage:Adult	GSM3032168	GSM3032168: Brain 3 scar; Danio rerio; OTHER	GSM3032168		1	Single cell dissociation. 10X Genomics Chromium	GEO Accession:GSM3032168	OTHER	TRANSCRIPTOMIC	other	PAIRED	ILLUMINA	NextSeq 500	SRP121343		B7_scar_R2.fastq.gz B7_scar_R1.fastq.gz	fastq fastq	1359725968.0	10965532.0	GSM3032168 r1	0:26 1:98	A:397239266;C:429529094;G:288126336;T:244165622;N:665650	26	98		397239266	429529094	288126336	244165622	665650	SRX3768865	SRS3023383	SRA623333	GEO	Max Delbrück Center	2	0.00032	0.00295	0.00026	0.0003	0.99991	0.99642	0.3	0.64327	26	98	T	T	mates < 9% mapping rate	illumina	nextseq	unknown	other	unknown	sc	single_cell_droplet	10x	Germany	2018-03-06	Adult	Adult	Brain	Nervous System
43991	43991	SRR6811824	SRX3768864	SRS3023381	SRP121343	PRJNA415636	Simultaneous lineage tracing and cell type identification using CRISPR/Cas9 induced genetic scars	GSE106121	Other	A key goal of developmental biology is to understand how a single cell transforms into a full grown organism consisting of many different cell types. Single cell RNA sequencing scRNA seq has become a widely used method due to its ability to identify all cell types in a tissue or organ in a systematic manner. However a major challenge is to organize the resulting taxonomy of cell types into lineage trees revealing the developmental origin of cells. Here we present a strategy for simultaneous lineage tracing and transcriptome profiling in thousands of single cells. By combining scRNA seq with computational analysis of lineage barcodes generated by genome editing of transgenic reporter genes we reconstruct developmental lineage trees in zebrafish larvae and adult fish. In future analyses LINNAEUS LINeage tracing by Nuclease Activated Editing of Ubiquitous Sequences can be used as a systematic approach for identifying the lineage origin of novel cell types or of known cell types under different conditions. Overall design: Combining scRNA seq with computational analysis of lineage barcodes generated by genome editing of transgenic reporter genes.		pubmed:29644996	Larva 5 scar	GSM3032167	source name:Full organism\|strain/background:Zebrabow M\|tissue:Whole body\|developmental stage:Larva	Larva 5 scar	Library strategy: Targeted amplification Scar reads have the same structure as transcript reads: they consist of a barcode a UMI and a scar. The scar sequences were aligned using bwa mem6 to a reference of RFP. We defined a cell as a barcode with at least 500 reads. We removed reads that were unmapped had an incorrect barcode or did not start with the exact PCR primer we used. We truncated all scar sequences to 75 nucleotides and filtered out shorter sequences. To correct for sequencing errors we implemented several rounds of scar filtering Supplementary Fig. 2 in publication. We started by counting the number of times each molecule was sequenced. Sequencing errors will typically have fewer reads than the actual scars they originate from. As a first filtering step we therefore removed all molecules only seen once to reduce the complexity in the dataset for consecutive filtering steps. In the second filtering step we aimed to remove easily recognizable sequencing errors. To this end we consecutively considered scar sequences that have the same cellular barcode and UMI UMIs that have the same cellular barcode and scar sequence and cellular barcodes that have the same UMI and scar sequence. In each step we kept only the molecule with the highest number of reads. The rationale behind this is that it is very improbable to have two valid scar sequences in the same cell with the same UMI or to have a scar sequence with the same UMI appear in two different cells. The observation of two different UMIs for the same scar in the same cell is much more likely and corresponds to detection of multiple transcripts from the same locus but information about scar expression levels was not required in our downstream analysis. In the third filtering step we specifically targeted sequencing errors within each cell. We compared the scar sequences found within a cell to each other. We filtered out sequences that had a Hamming distance of 2 or less to another scar sequence in the same cell that occurred in at least eight tim…	Full organism		Single cell dissociation. 10X Genomics Chromium		strain/background:Zebrabow M\|tissue:Whole body\|developmental stage:Larva	GSM3032167	GSM3032167: Larva 5 scar; Danio rerio; OTHER	GSM3032167		1	Single cell dissociation. 10X Genomics Chromium	GEO Accession:GSM3032167	OTHER	TRANSCRIPTOMIC	other	PAIRED	ILLUMINA	Illumina HiSeq 2500	SRP121343		Z5_scar_R1.fastq.gz Z5_scar_R2.fastq.gz	fastq fastq	7672645700.0	61876175.0	GSM3032167 r1	0:26 1:98	A:2173948352;C:2439410929;G:1714624642;T:1343296377;N:1365400	26	98		2173948352	2439410929	1714624642	1343296377	1365400	SRX3768864	SRS3023381	SRA623333	GEO	Max Delbrück Center	2	0.00013	0.00164	9e-05	8e-05	0.99989	0.99669	0.83333	0.4918	26	98	T	T	mates < 9% mapping rate	illumina	hiseq_era	unknown	other	unknown	sc	single_cell_droplet	10x	Germany	2018-03-06	Larval	Larval	Trunk	Surface Structure
43992	43992	SRR6811823	SRX3768863	SRS3023380	SRP121343	PRJNA415636	Simultaneous lineage tracing and cell type identification using CRISPR/Cas9 induced genetic scars	GSE106121	Other	A key goal of developmental biology is to understand how a single cell transforms into a full grown organism consisting of many different cell types. Single cell RNA sequencing scRNA seq has become a widely used method due to its ability to identify all cell types in a tissue or organ in a systematic manner. However a major challenge is to organize the resulting taxonomy of cell types into lineage trees revealing the developmental origin of cells. Here we present a strategy for simultaneous lineage tracing and transcriptome profiling in thousands of single cells. By combining scRNA seq with computational analysis of lineage barcodes generated by genome editing of transgenic reporter genes we reconstruct developmental lineage trees in zebrafish larvae and adult fish. In future analyses LINNAEUS LINeage tracing by Nuclease Activated Editing of Ubiquitous Sequences can be used as a systematic approach for identifying the lineage origin of novel cell types or of known cell types under different conditions. Overall design: Combining scRNA seq with computational analysis of lineage barcodes generated by genome editing of transgenic reporter genes.		pubmed:29644996	Larva 4 scar	GSM3032166	source name:Full organism\|strain/background:Zebrabow M\|tissue:Whole body\|developmental stage:Larva	Larva 4 scar	Library strategy: Targeted amplification Scar reads have the same structure as transcript reads: they consist of a barcode a UMI and a scar. The scar sequences were aligned using bwa mem6 to a reference of RFP. We defined a cell as a barcode with at least 500 reads. We removed reads that were unmapped had an incorrect barcode or did not start with the exact PCR primer we used. We truncated all scar sequences to 75 nucleotides and filtered out shorter sequences. To correct for sequencing errors we implemented several rounds of scar filtering Supplementary Fig. 2 in publication. We started by counting the number of times each molecule was sequenced. Sequencing errors will typically have fewer reads than the actual scars they originate from. As a first filtering step we therefore removed all molecules only seen once to reduce the complexity in the dataset for consecutive filtering steps. In the second filtering step we aimed to remove easily recognizable sequencing errors. To this end we consecutively considered scar sequences that have the same cellular barcode and UMI UMIs that have the same cellular barcode and scar sequence and cellular barcodes that have the same UMI and scar sequence. In each step we kept only the molecule with the highest number of reads. The rationale behind this is that it is very improbable to have two valid scar sequences in the same cell with the same UMI or to have a scar sequence with the same UMI appear in two different cells. The observation of two different UMIs for the same scar in the same cell is much more likely and corresponds to detection of multiple transcripts from the same locus but information about scar expression levels was not required in our downstream analysis. In the third filtering step we specifically targeted sequencing errors within each cell. We compared the scar sequences found within a cell to each other. We filtered out sequences that had a Hamming distance of 2 or less to another scar sequence in the same cell that occurred in at least eight tim…	Full organism		Single cell dissociation. 10X Genomics Chromium		strain/background:Zebrabow M\|tissue:Whole body\|developmental stage:Larva	GSM3032166	GSM3032166: Larva 4 scar; Danio rerio; OTHER	GSM3032166		1	Single cell dissociation. 10X Genomics Chromium	GEO Accession:GSM3032166	OTHER	TRANSCRIPTOMIC	other	PAIRED	ILLUMINA	Illumina HiSeq 2500	SRP121343		Z4_scar_R1.fastq.gz Z4_scar_R2.fastq.gz	fastq fastq	7236183804.0	58356321.0	GSM3032166 r1	0:26 1:98	A:2080254513;C:2296727471;G:1625115068;T:1232786300;N:1300452	26	98		2080254513	2296727471	1625115068	1232786300	1300452	SRX3768863	SRS3023380	SRA623333	GEO	Max Delbrück Center	2	0.00014	0.0016	0.00011	7e-05	0.99993	0.99634	0.33333	0.46694	26	98	T	T	mates < 9% mapping rate	illumina	hiseq_era	unknown	other	unknown	sc	single_cell_droplet	10x	Germany	2018-03-06	Larval	Larval	Trunk	Surface Structure
44008	44008	SRR6211484	SRX3320759	SRS2626332	SRP121343	PRJNA415636	Simultaneous lineage tracing and cell type identification using CRISPR/Cas9 induced genetic scars	GSE106121	Other	A key goal of developmental biology is to understand how a single cell transforms into a full grown organism consisting of many different cell types. Single cell RNA sequencing scRNA seq has become a widely used method due to its ability to identify all cell types in a tissue or organ in a systematic manner. However a major challenge is to organize the resulting taxonomy of cell types into lineage trees revealing the developmental origin of cells. Here we present a strategy for simultaneous lineage tracing and transcriptome profiling in thousands of single cells. By combining scRNA seq with computational analysis of lineage barcodes generated by genome editing of transgenic reporter genes we reconstruct developmental lineage trees in zebrafish larvae and adult fish. In future analyses LINNAEUS LINeage tracing by Nuclease Activated Editing of Ubiquitous Sequences can be used as a systematic approach for identifying the lineage origin of novel cell types or of known cell types under different conditions. Overall design: Combining scRNA seq with computational analysis of lineage barcodes generated by genome editing of transgenic reporter genes.		pubmed:29644996	Heart 2 scar	GSM2830055	source name:Heart and blood\|strain/background:Zebrabow M\|tissue:Heart and blood\|developmental stage:Adult	Heart 2 scar	Library strategy: Targeted amplification Scar reads have the same structure as transcript reads: they consist of a barcode a UMI and a scar. The scar sequences were aligned using bwa mem6 to a reference of RFP. We defined a cell as a barcode with at least 500 reads. We removed reads that were unmapped had an incorrect barcode or did not start with the exact PCR primer we used. We truncated all scar sequences to 75 nucleotides and filtered out shorter sequences. To correct for sequencing errors we implemented several rounds of scar filtering Supplementary Fig. 2 in publication. We started by counting the number of times each molecule was sequenced. Sequencing errors will typically have fewer reads than the actual scars they originate from. As a first filtering step we therefore removed all molecules only seen once to reduce the complexity in the dataset for consecutive filtering steps. In the second filtering step we aimed to remove easily recognizable sequencing errors. To this end we consecutively considered scar sequences that have the same cellular barcode and UMI UMIs that have the same cellular barcode and scar sequence and cellular barcodes that have the same UMI and scar sequence. In each step we kept only the molecule with the highest number of reads. The rationale behind this is that it is very improbable to have two valid scar sequences in the same cell with the same UMI or to have a scar sequence with the same UMI appear in two different cells. The observation of two different UMIs for the same scar in the same cell is much more likely and corresponds to detection of multiple transcripts from the same locus but information about scar expression levels was not required in our downstream analysis. In the third filtering step we specifically targeted sequencing errors within each cell. We compared the scar sequences found within a cell to each other. We filtered out sequences that had a Hamming distance of 2 or less to another scar sequence in the same cell that occurred in at least eight tim…	Heart and blood		Single cell dissociation. 10X Genomics Chromium		strain/background:Zebrabow M\|tissue:Heart and blood\|developmental stage:Adult	GSM2830055	GSM2830055: Heart 2 scar; Danio rerio; OTHER	GSM2830055		1	Single cell dissociation. 10X Genomics Chromium	GEO Accession:GSM2830055	OTHER	TRANSCRIPTOMIC	other	PAIRED	ILLUMINA	NextSeq 500	SRP121343		H6_scar_R1.fastq.gz H6_scar_R2.fastq.gz	fastq fastq	2079107172.0	15065994.0	GSM2830055 r1	0:28 1:110	A:580640871;C:641740953;G:501505035;T:355092832;N:127481	28	110		580640871	641740953	501505035	355092832	127481	SRX3320759	SRS2626332	SRA623333	GEO	Max Delbrück Center	2	5e-05	0.00253	4e-05	0.00023	1.0	0.99778		0.58419	28	110	T	T	mates < 9% mapping rate	illumina	nextseq	unknown	other	unknown	sc	single_cell_droplet	10x	Germany	2017-10-24	Adult	Adult	Multi-tissue	Multi-system
44009	44009	SRR6211483	SRX3320758	SRS2626348	SRP121343	PRJNA415636	Simultaneous lineage tracing and cell type identification using CRISPR/Cas9 induced genetic scars	GSE106121	Other	A key goal of developmental biology is to understand how a single cell transforms into a full grown organism consisting of many different cell types. Single cell RNA sequencing scRNA seq has become a widely used method due to its ability to identify all cell types in a tissue or organ in a systematic manner. However a major challenge is to organize the resulting taxonomy of cell types into lineage trees revealing the developmental origin of cells. Here we present a strategy for simultaneous lineage tracing and transcriptome profiling in thousands of single cells. By combining scRNA seq with computational analysis of lineage barcodes generated by genome editing of transgenic reporter genes we reconstruct developmental lineage trees in zebrafish larvae and adult fish. In future analyses LINNAEUS LINeage tracing by Nuclease Activated Editing of Ubiquitous Sequences can be used as a systematic approach for identifying the lineage origin of novel cell types or of known cell types under different conditions. Overall design: Combining scRNA seq with computational analysis of lineage barcodes generated by genome editing of transgenic reporter genes.		pubmed:29644996	Pancreas 2 scar	GSM2830054	source name:Pancreas and liver\|strain/background:Zebrabow M\|tissue:Pancreas and liver\|developmental stage:Adult	Pancreas 2 scar	Library strategy: Targeted amplification Scar reads have the same structure as transcript reads: they consist of a barcode a UMI and a scar. The scar sequences were aligned using bwa mem6 to a reference of RFP. We defined a cell as a barcode with at least 500 reads. We removed reads that were unmapped had an incorrect barcode or did not start with the exact PCR primer we used. We truncated all scar sequences to 75 nucleotides and filtered out shorter sequences. To correct for sequencing errors we implemented several rounds of scar filtering Supplementary Fig. 2 in publication. We started by counting the number of times each molecule was sequenced. Sequencing errors will typically have fewer reads than the actual scars they originate from. As a first filtering step we therefore removed all molecules only seen once to reduce the complexity in the dataset for consecutive filtering steps. In the second filtering step we aimed to remove easily recognizable sequencing errors. To this end we consecutively considered scar sequences that have the same cellular barcode and UMI UMIs that have the same cellular barcode and scar sequence and cellular barcodes that have the same UMI and scar sequence. In each step we kept only the molecule with the highest number of reads. The rationale behind this is that it is very improbable to have two valid scar sequences in the same cell with the same UMI or to have a scar sequence with the same UMI appear in two different cells. The observation of two different UMIs for the same scar in the same cell is much more likely and corresponds to detection of multiple transcripts from the same locus but information about scar expression levels was not required in our downstream analysis. In the third filtering step we specifically targeted sequencing errors within each cell. We compared the scar sequences found within a cell to each other. We filtered out sequences that had a Hamming distance of 2 or less to another scar sequence in the same cell that occurred in at least eight tim…	Pancreas and liver		Single cell dissociation. 10X Genomics Chromium		strain/background:Zebrabow M\|tissue:Pancreas and liver\|developmental stage:Adult	GSM2830054	GSM2830054: Pancreas 2 scar; Danio rerio; OTHER	GSM2830054		1	Single cell dissociation. 10X Genomics Chromium	GEO Accession:GSM2830054	OTHER	TRANSCRIPTOMIC	other	PAIRED	ILLUMINA	NextSeq 500	SRP121343		P6_scar_R1.fastq.gz P6_scar_R2.fastq.gz	fastq fastq	1026715860.0	7439970.0	GSM2830054 r1	0:28 1:110	A:287816843;C:320879355;G:243598630;T:174359452;N:61580	28	110		287816843	320879355	243598630	174359452	61580	SRX3320758	SRS2626348	SRA623333	GEO	Max Delbrück Center	2	0.00029	0.00123	0.00028	6e-05	1.0	0.99914		0.25714	28	110	T	T	mates < 9% mapping rate	illumina	nextseq	unknown	other	unknown	sc	single_cell_droplet	10x	Germany	2017-10-24	Adult	Adult	Multi-tissue	Multi-system
44010	44010	SRR6211482	SRX3320757	SRS2626331	SRP121343	PRJNA415636	Simultaneous lineage tracing and cell type identification using CRISPR/Cas9 induced genetic scars	GSE106121	Other	A key goal of developmental biology is to understand how a single cell transforms into a full grown organism consisting of many different cell types. Single cell RNA sequencing scRNA seq has become a widely used method due to its ability to identify all cell types in a tissue or organ in a systematic manner. However a major challenge is to organize the resulting taxonomy of cell types into lineage trees revealing the developmental origin of cells. Here we present a strategy for simultaneous lineage tracing and transcriptome profiling in thousands of single cells. By combining scRNA seq with computational analysis of lineage barcodes generated by genome editing of transgenic reporter genes we reconstruct developmental lineage trees in zebrafish larvae and adult fish. In future analyses LINNAEUS LINeage tracing by Nuclease Activated Editing of Ubiquitous Sequences can be used as a systematic approach for identifying the lineage origin of novel cell types or of known cell types under different conditions. Overall design: Combining scRNA seq with computational analysis of lineage barcodes generated by genome editing of transgenic reporter genes.		pubmed:29644996	Heart 1 scar	GSM2830053	source name:Heart and blood\|strain/background:Zebrabow M\|tissue:Heart and blood\|developmental stage:Adult	Heart 1 scar	Library strategy: Targeted amplification Scar reads have the same structure as transcript reads: they consist of a barcode a UMI and a scar. The scar sequences were aligned using bwa mem6 to a reference of RFP. We defined a cell as a barcode with at least 500 reads. We removed reads that were unmapped had an incorrect barcode or did not start with the exact PCR primer we used. We truncated all scar sequences to 75 nucleotides and filtered out shorter sequences. To correct for sequencing errors we implemented several rounds of scar filtering Supplementary Fig. 2 in publication. We started by counting the number of times each molecule was sequenced. Sequencing errors will typically have fewer reads than the actual scars they originate from. As a first filtering step we therefore removed all molecules only seen once to reduce the complexity in the dataset for consecutive filtering steps. In the second filtering step we aimed to remove easily recognizable sequencing errors. To this end we consecutively considered scar sequences that have the same cellular barcode and UMI UMIs that have the same cellular barcode and scar sequence and cellular barcodes that have the same UMI and scar sequence. In each step we kept only the molecule with the highest number of reads. The rationale behind this is that it is very improbable to have two valid scar sequences in the same cell with the same UMI or to have a scar sequence with the same UMI appear in two different cells. The observation of two different UMIs for the same scar in the same cell is much more likely and corresponds to detection of multiple transcripts from the same locus but information about scar expression levels was not required in our downstream analysis. In the third filtering step we specifically targeted sequencing errors within each cell. We compared the scar sequences found within a cell to each other. We filtered out sequences that had a Hamming distance of 2 or less to another scar sequence in the same cell that occurred in at least eight tim…	Heart and blood		Single cell dissociation. 10X Genomics Chromium		strain/background:Zebrabow M\|tissue:Heart and blood\|developmental stage:Adult	GSM2830053	GSM2830053: Heart 1 scar; Danio rerio; OTHER	GSM2830053		1	Single cell dissociation. 10X Genomics Chromium	GEO Accession:GSM2830053	OTHER	TRANSCRIPTOMIC	other	PAIRED	ILLUMINA	NextSeq 500	SRP121343		H5_scar_R1.fastq.gz H5_scar_R2.fastq.gz	fastq fastq	1287546624.0	10218624.0	GSM2830053 r1	0:26 1:100	A:365272675;C:413037677;G:296495693;T:212570153;N:170426	26	100		365272675	413037677	296495693	212570153	170426	SRX3320757	SRS2626331	SRA623333	GEO	Max Delbrück Center	2	0.00023	0.00044	0.00022	3e-05	1.0	0.99955		0.32653	26	100	T	T	mates < 9% mapping rate	illumina	nextseq	unknown	other	unknown	sc	single_cell_droplet	10x	Germany	2017-10-24	Adult	Adult	Multi-tissue	Multi-system
44011	44011	SRR6211481	SRX3320756	SRS2626330	SRP121343	PRJNA415636	Simultaneous lineage tracing and cell type identification using CRISPR/Cas9 induced genetic scars	GSE106121	Other	A key goal of developmental biology is to understand how a single cell transforms into a full grown organism consisting of many different cell types. Single cell RNA sequencing scRNA seq has become a widely used method due to its ability to identify all cell types in a tissue or organ in a systematic manner. However a major challenge is to organize the resulting taxonomy of cell types into lineage trees revealing the developmental origin of cells. Here we present a strategy for simultaneous lineage tracing and transcriptome profiling in thousands of single cells. By combining scRNA seq with computational analysis of lineage barcodes generated by genome editing of transgenic reporter genes we reconstruct developmental lineage trees in zebrafish larvae and adult fish. In future analyses LINNAEUS LINeage tracing by Nuclease Activated Editing of Ubiquitous Sequences can be used as a systematic approach for identifying the lineage origin of novel cell types or of known cell types under different conditions. Overall design: Combining scRNA seq with computational analysis of lineage barcodes generated by genome editing of transgenic reporter genes.		pubmed:29644996	Brain 1 scar	GSM2830052	source name:Brain\|strain/background:Zebrabow M\|tissue:Brain\|developmental stage:Adult	Brain 1 scar	Library strategy: Targeted amplification Scar reads have the same structure as transcript reads: they consist of a barcode a UMI and a scar. The scar sequences were aligned using bwa mem6 to a reference of RFP. We defined a cell as a barcode with at least 500 reads. We removed reads that were unmapped had an incorrect barcode or did not start with the exact PCR primer we used. We truncated all scar sequences to 75 nucleotides and filtered out shorter sequences. To correct for sequencing errors we implemented several rounds of scar filtering Supplementary Fig. 2 in publication. We started by counting the number of times each molecule was sequenced. Sequencing errors will typically have fewer reads than the actual scars they originate from. As a first filtering step we therefore removed all molecules only seen once to reduce the complexity in the dataset for consecutive filtering steps. In the second filtering step we aimed to remove easily recognizable sequencing errors. To this end we consecutively considered scar sequences that have the same cellular barcode and UMI UMIs that have the same cellular barcode and scar sequence and cellular barcodes that have the same UMI and scar sequence. In each step we kept only the molecule with the highest number of reads. The rationale behind this is that it is very improbable to have two valid scar sequences in the same cell with the same UMI or to have a scar sequence with the same UMI appear in two different cells. The observation of two different UMIs for the same scar in the same cell is much more likely and corresponds to detection of multiple transcripts from the same locus but information about scar expression levels was not required in our downstream analysis. In the third filtering step we specifically targeted sequencing errors within each cell. We compared the scar sequences found within a cell to each other. We filtered out sequences that had a Hamming distance of 2 or less to another scar sequence in the same cell that occurred in at least eight tim…	Brain		Single cell dissociation. 10X Genomics Chromium		strain/background:Zebrabow M\|tissue:Brain\|developmental stage:Adult	GSM2830052	GSM2830052: Brain 1 scar; Danio rerio; OTHER	GSM2830052		1	Single cell dissociation. 10X Genomics Chromium	GEO Accession:GSM2830052	OTHER	TRANSCRIPTOMIC	other	PAIRED	ILLUMINA	NextSeq 500	SRP121343		B5_scar_R1.fastq.gz B5_scar_R2.fastq.gz	fastq fastq	438883452.0	3483202.0	GSM2830052 r1	0:26 1:100	A:125829044;C:139532010;G:100613706;T:72846911;N:61781	26	100		125829044	139532010	100613706	72846911	61781	SRX3320756	SRS2626330	SRA623333	GEO	Max Delbrück Center	2	0.00072	0.0013	0.00069	0.00011	0.99991	0.99878	0.75	0.36879	26	100	T	T	mates < 9% mapping rate	illumina	nextseq	unknown	other	unknown	sc	single_cell_droplet	10x	Germany	2017-10-24	Adult	Adult	Brain	Nervous System
44012	44012	SRR6211480	SRX3320755	SRS2626329	SRP121343	PRJNA415636	Simultaneous lineage tracing and cell type identification using CRISPR/Cas9 induced genetic scars	GSE106121	Other	A key goal of developmental biology is to understand how a single cell transforms into a full grown organism consisting of many different cell types. Single cell RNA sequencing scRNA seq has become a widely used method due to its ability to identify all cell types in a tissue or organ in a systematic manner. However a major challenge is to organize the resulting taxonomy of cell types into lineage trees revealing the developmental origin of cells. Here we present a strategy for simultaneous lineage tracing and transcriptome profiling in thousands of single cells. By combining scRNA seq with computational analysis of lineage barcodes generated by genome editing of transgenic reporter genes we reconstruct developmental lineage trees in zebrafish larvae and adult fish. In future analyses LINNAEUS LINeage tracing by Nuclease Activated Editing of Ubiquitous Sequences can be used as a systematic approach for identifying the lineage origin of novel cell types or of known cell types under different conditions. Overall design: Combining scRNA seq with computational analysis of lineage barcodes generated by genome editing of transgenic reporter genes.		pubmed:29644996	Pancreas 1 scar	GSM2830051	source name:Pancreas and liver\|strain/background:Zebrabow M\|tissue:Pancreas and liver\|developmental stage:Adult	Pancreas 1 scar	Library strategy: Targeted amplification Scar reads have the same structure as transcript reads: they consist of a barcode a UMI and a scar. The scar sequences were aligned using bwa mem6 to a reference of RFP. We defined a cell as a barcode with at least 500 reads. We removed reads that were unmapped had an incorrect barcode or did not start with the exact PCR primer we used. We truncated all scar sequences to 75 nucleotides and filtered out shorter sequences. To correct for sequencing errors we implemented several rounds of scar filtering Supplementary Fig. 2 in publication. We started by counting the number of times each molecule was sequenced. Sequencing errors will typically have fewer reads than the actual scars they originate from. As a first filtering step we therefore removed all molecules only seen once to reduce the complexity in the dataset for consecutive filtering steps. In the second filtering step we aimed to remove easily recognizable sequencing errors. To this end we consecutively considered scar sequences that have the same cellular barcode and UMI UMIs that have the same cellular barcode and scar sequence and cellular barcodes that have the same UMI and scar sequence. In each step we kept only the molecule with the highest number of reads. The rationale behind this is that it is very improbable to have two valid scar sequences in the same cell with the same UMI or to have a scar sequence with the same UMI appear in two different cells. The observation of two different UMIs for the same scar in the same cell is much more likely and corresponds to detection of multiple transcripts from the same locus but information about scar expression levels was not required in our downstream analysis. In the third filtering step we specifically targeted sequencing errors within each cell. We compared the scar sequences found within a cell to each other. We filtered out sequences that had a Hamming distance of 2 or less to another scar sequence in the same cell that occurred in at least eight tim…	Pancreas and liver		Single cell dissociation. 10X Genomics Chromium		strain/background:Zebrabow M\|tissue:Pancreas and liver\|developmental stage:Adult	GSM2830051	GSM2830051: Pancreas 1 scar; Danio rerio; OTHER	GSM2830051		1	Single cell dissociation. 10X Genomics Chromium	GEO Accession:GSM2830051	OTHER	TRANSCRIPTOMIC	other	PAIRED	ILLUMINA	NextSeq 500	SRP121343		P5_scar_R1.fastq.gz P5_scar_R2.fastq.gz	fastq fastq	1192602600.0	9465100.0	GSM2830051 r1	0:26 1:100	A:346841760;C:383266222;G:268916933;T:193424756;N:152929	26	100		346841760	383266222	268916933	193424756	152929	SRX3320755	SRS2626329	SRA623333	GEO	Max Delbrück Center	2	0.00021	0.00091	0.00014	2e-05	0.99997	0.99892	0.0	0.23931	26	100	T	T	mates < 9% mapping rate	illumina	nextseq	unknown	other	unknown	sc	single_cell_droplet	10x	Germany	2017-10-24	Adult	Adult	Multi-tissue	Multi-system
44013	44013	SRR6211477	SRX3320753	SRS2626327	SRP121343	PRJNA415636	Simultaneous lineage tracing and cell type identification using CRISPR/Cas9 induced genetic scars	GSE106121	Other	A key goal of developmental biology is to understand how a single cell transforms into a full grown organism consisting of many different cell types. Single cell RNA sequencing scRNA seq has become a widely used method due to its ability to identify all cell types in a tissue or organ in a systematic manner. However a major challenge is to organize the resulting taxonomy of cell types into lineage trees revealing the developmental origin of cells. Here we present a strategy for simultaneous lineage tracing and transcriptome profiling in thousands of single cells. By combining scRNA seq with computational analysis of lineage barcodes generated by genome editing of transgenic reporter genes we reconstruct developmental lineage trees in zebrafish larvae and adult fish. In future analyses LINNAEUS LINeage tracing by Nuclease Activated Editing of Ubiquitous Sequences can be used as a systematic approach for identifying the lineage origin of novel cell types or of known cell types under different conditions. Overall design: Combining scRNA seq with computational analysis of lineage barcodes generated by genome editing of transgenic reporter genes.		pubmed:29644996	Larva 1 scar	GSM2830049	source name:Full organism\|strain/background:Zebrabow M\|tissue:Whole body\|developmental stage:Larva	Larva 1 scar	Library strategy: Targeted amplification Scar reads have the same structure as transcript reads: they consist of a barcode a UMI and a scar. The scar sequences were aligned using bwa mem6 to a reference of RFP. We defined a cell as a barcode with at least 500 reads. We removed reads that were unmapped had an incorrect barcode or did not start with the exact PCR primer we used. We truncated all scar sequences to 75 nucleotides and filtered out shorter sequences. To correct for sequencing errors we implemented several rounds of scar filtering Supplementary Fig. 2 in publication. We started by counting the number of times each molecule was sequenced. Sequencing errors will typically have fewer reads than the actual scars they originate from. As a first filtering step we therefore removed all molecules only seen once to reduce the complexity in the dataset for consecutive filtering steps. In the second filtering step we aimed to remove easily recognizable sequencing errors. To this end we consecutively considered scar sequences that have the same cellular barcode and UMI UMIs that have the same cellular barcode and scar sequence and cellular barcodes that have the same UMI and scar sequence. In each step we kept only the molecule with the highest number of reads. The rationale behind this is that it is very improbable to have two valid scar sequences in the same cell with the same UMI or to have a scar sequence with the same UMI appear in two different cells. The observation of two different UMIs for the same scar in the same cell is much more likely and corresponds to detection of multiple transcripts from the same locus but information about scar expression levels was not required in our downstream analysis. In the third filtering step we specifically targeted sequencing errors within each cell. We compared the scar sequences found within a cell to each other. We filtered out sequences that had a Hamming distance of 2 or less to another scar sequence in the same cell that occurred in at least eight tim…	Full organism		Single cell dissociation. 10X Genomics Chromium		strain/background:Zebrabow M\|tissue:Whole body\|developmental stage:Larva	GSM2830049	GSM2830049: Larva 1 scar; Danio rerio; OTHER	GSM2830049		1	Single cell dissociation. 10X Genomics Chromium	GEO Accession:GSM2830049	OTHER	TRANSCRIPTOMIC	other	PAIRED	ILLUMINA	Illumina HiSeq 2500	SRP121343		Z2_1_scar_R1.fastq.gz Z2_1_scar_R2.fastq.gz Z2_1_scar_R3.fastq.gz	fastq fastq fastq	1358915680.0	8493223.0	GSM2830049 r1	0:130 1:14 2:16	A:286107087;C:390283378;G:268551180;T:159171790;N:5555	130	14	16	286107087	390283378	268551180	159171790	5555	SRX3320753	SRS2626327	SRA623333	GEO	Max Delbrück Center	1	0.00515		8e-05		0.99959		0.24561		130		T		under 1.2% mapping rate	illumina	hiseq_era	unknown	other	unknown	sc	single_cell_droplet	10x	Germany	2017-10-24	Larval	Larval	Trunk	Surface Structure
44014	44014	SRR6211478	SRX3320753	SRS2626327	SRP121343	PRJNA415636	Simultaneous lineage tracing and cell type identification using CRISPR/Cas9 induced genetic scars	GSE106121	Other	A key goal of developmental biology is to understand how a single cell transforms into a full grown organism consisting of many different cell types. Single cell RNA sequencing scRNA seq has become a widely used method due to its ability to identify all cell types in a tissue or organ in a systematic manner. However a major challenge is to organize the resulting taxonomy of cell types into lineage trees revealing the developmental origin of cells. Here we present a strategy for simultaneous lineage tracing and transcriptome profiling in thousands of single cells. By combining scRNA seq with computational analysis of lineage barcodes generated by genome editing of transgenic reporter genes we reconstruct developmental lineage trees in zebrafish larvae and adult fish. In future analyses LINNAEUS LINeage tracing by Nuclease Activated Editing of Ubiquitous Sequences can be used as a systematic approach for identifying the lineage origin of novel cell types or of known cell types under different conditions. Overall design: Combining scRNA seq with computational analysis of lineage barcodes generated by genome editing of transgenic reporter genes.		pubmed:29644996	Larva 1 scar	GSM2830049	source name:Full organism\|strain/background:Zebrabow M\|tissue:Whole body\|developmental stage:Larva	Larva 1 scar	Library strategy: Targeted amplification Scar reads have the same structure as transcript reads: they consist of a barcode a UMI and a scar. The scar sequences were aligned using bwa mem6 to a reference of RFP. We defined a cell as a barcode with at least 500 reads. We removed reads that were unmapped had an incorrect barcode or did not start with the exact PCR primer we used. We truncated all scar sequences to 75 nucleotides and filtered out shorter sequences. To correct for sequencing errors we implemented several rounds of scar filtering Supplementary Fig. 2 in publication. We started by counting the number of times each molecule was sequenced. Sequencing errors will typically have fewer reads than the actual scars they originate from. As a first filtering step we therefore removed all molecules only seen once to reduce the complexity in the dataset for consecutive filtering steps. In the second filtering step we aimed to remove easily recognizable sequencing errors. To this end we consecutively considered scar sequences that have the same cellular barcode and UMI UMIs that have the same cellular barcode and scar sequence and cellular barcodes that have the same UMI and scar sequence. In each step we kept only the molecule with the highest number of reads. The rationale behind this is that it is very improbable to have two valid scar sequences in the same cell with the same UMI or to have a scar sequence with the same UMI appear in two different cells. The observation of two different UMIs for the same scar in the same cell is much more likely and corresponds to detection of multiple transcripts from the same locus but information about scar expression levels was not required in our downstream analysis. In the third filtering step we specifically targeted sequencing errors within each cell. We compared the scar sequences found within a cell to each other. We filtered out sequences that had a Hamming distance of 2 or less to another scar sequence in the same cell that occurred in at least eight tim…	Full organism		Single cell dissociation. 10X Genomics Chromium		strain/background:Zebrabow M\|tissue:Whole body\|developmental stage:Larva	GSM2830049	GSM2830049: Larva 1 scar; Danio rerio; OTHER	GSM2830049		1	Single cell dissociation. 10X Genomics Chromium	GEO Accession:GSM2830049	OTHER	TRANSCRIPTOMIC	other	PAIRED	ILLUMINA	Illumina HiSeq 2500	SRP121343		Z2_2_scar_R1.fastq.gz Z2_2_scar_R2.fastq.gz Z2_2_scar_R3.fastq.gz	fastq fastq fastq	1103410080.0	6896313.0	GSM2830049 r2	0:130 1:14 2:16	A:232542409;C:315925727;G:218437491;T:129610895;N:4168	130	14	16	232542409	315925727	218437491	129610895	4168	SRX3320753	SRS2626327	SRA623333	GEO	Max Delbrück Center	1	0.00786		0.0		0.99922		0.31764		130		T		under 1.2% mapping rate	illumina	hiseq_era	unknown	other	unknown	sc	single_cell_droplet	10x	Germany	2017-10-24	Larval	Larval	Trunk	Surface Structure
66757	66757	SRR23717090	SRX19578259	SRS16961241	SRP342737	PRJNA773778	Activation of lineage competence in hemogenic endothelium precedes the formation of hematopoietic stem cell heterogeneity [Zebrafish.STRT seq]	GSE186425	Other	Using a combination of single cell multi omics lineage tracing and functional assays we show that embryonic HSPCs are originated from heterogeneous hemogenic endothelial cells HECs during zebrafish embryogenesis. Overall design: Hematopoietic stem and progenitor cells HSPCs are considered as a heterogeneous population but where and how HSPC heterogeneity occurs remain unclear. Here we performed scRNA seq and scATAC seq with zebrafish 36 hpf VDA derived kdrl+runx1 kdrl+runx1+ and kdrl runx1+ cells. To determine the transcriptional signatures of spi2+ lineages in zebrafish we performed STRT seq with spi2: Gal4;UAS:GFP+ kdrl:mCherry+ HECs and spi2: Gal4;UAS:GFP+ kdrl:mCherry hematopoietic cells at 36 hpf. To investigate the underlying molecular mechanism upon spi2 deficiency we performed scRNA seq with the sorted ECs kdrl+runx1 HECs kdrl+runx1+ and hematopoietic cells kdrl runx1+ from spi2 morphants at 36 hpf. To determine whether spi2 can directly modulate transcriptional programs in EC/HEC we examined genome wide spi2 binding by cut tag assay in fli1a flag spi2 EGFP+ cells sorted from trunk region of Tg fli1a flag spi2 GFP embryos at 36 hpf.	parent bioproject:PRJNA773771	pubmed:37016019	DP1 36hpf	GSM7083138	source name:Zebrafish trunk region\|tissue:Zebrafish trunk region 36hpf\|cells:single spi2: Gal4;UAS:GFP+ kdrl:mCherry+ HECs and spi2: Gal4;UAS:GFP+ kdrl:mCherry hematopoietic cells\|Stage:36 hpf loc name:missing\|collection date:missing	DP1 36hpf	For scRNA seq and scATAC seq based on 10x Genomics，raw data files were processed by Cell Ranger software suite with default mapping parameters using the GRCz11 reference genome. For STRT seq raw reads were first de multiplexed by barcode sequences in reads 2 to yield separate read files for individual cells then the transcripts sequences of each cell in reads 1 were separated based on corresponding reads 2. Simultaneously UMI sequences in reads 2 were integrated into reads 1. The template switching oligo TSO sequence polyA sequence and the low quality reads N > 10% in reads 1 were subsequently removed by Python scripts and Trimmomatic version 0.36. Next the clean reads were aligned to the zebrafish genome GRCz11 from Ensembl using HISAT2 version 2.1.0 with known gene annotation. Only protein coding genes were retained and the abundance of each gene were estimated by counting the reads that duplicated UMIs have been excluded. For cut&tag reads were aligned to GRCz11 by Bowtie2. Only uniquely mapped reads with mapping quality score ≥ 30 were kept using Samtools software. post merging replicates MACS2 was used for the peak calling. Assembly: GRCz11 Library strategy: STRT seq	Zebrafish trunk region		For 10x Genomics based scRNA seq and scATAC seq in zebrafish 40 000 mCherry+ GFP cells 40 000 mCherry+ GFP+ cells and 30 000 mCherry GFP+ cells were sorted from Tg kdrl:mCherry/runx1:enGFP at 36 hpf. For STRT seq in zebrafish single spi2: Gal4;UAS:GFP+ kdrl:mCherry+ HECs and spi2: Gal4;UAS:GFP+ kdrl:mCherry hematopoietic cells were sorted from the trunk region of Tg spi2: Gal4;UAS:GFP/ kdrl:mCherry at 36 hpf. For scRNA seq of spi2 morphants at 36 hpf in zebrafish ECs kdrl+runx1 HECs kdrl+runx1+ and hematopoietic cells kdrl runx1+ were sorted. For bulk CUT&TAG in zebrafish fli1a flag spi2 EGFP+ cells were sorted from the trunk region of Tg fli1a flag spi2 GFP embryos at 36 hpf. For 10x Genomics based scRNA seq and scATAC seq in zebrafish we loaded 20 000 cells for further 10x Genomics based scRNA seq and 90 000 cells for further 10x Genomics based scATAC seq. For scRNA seq libraries were prepared using Single Cell 3’ Library and Gel Bead Kit V3.1. Sequencing was performed on an Illumina Novaseq6000 platform to generate 150 bp paired end reads. For ATAC seq nuclei were isolated and washed according to the methods supplied by 10x Genomics. Libraries were prepared using the Chromium Chip E Single Cell Kit and Chromium Single Cell ATAC Library & Gel Bead Kit and further sequenced on an Illumina Novaseq6000 platform to generate 50 bp paired end reads. For 10x Genomics based scRNA seq in mice libraries were prepared using Single Cell 3’ Library and Gel Bead Kit V3.1. Sequencing was performed on an Illumina Novaseq6000 platform to generate 150 bp paired end reads. For STRT seq in zebrafish the end repair and dA tailing of the DNA fragments and ligation of the adaptors to the DNA fragments were performed according to the KAPA Hyper Prep Kits with PCR Library Amplification/Illumina series. post the adaptor ligation step the final PCR was performed. The libraries were sequenced on an Illumina Novaseq6000 platform to generate 150 bp paired end reads. For bulk CUT&TAG libraries were prepared according to Hyp…		tissue:Zebrafish trunk region 36hpf\|cells:single spi2: Gal4;UAS:GFP+ kdrl:mCherry+ HECs and spi2: Gal4;UAS:GFP+ kdrl:mCherry hematopoietic cells\|Stage:36 hpf	GSM7083138	GSM7083138: DP1 36hpf; Danio rerio; OTHER	GSM7083138 r1	GSM7083138	1	For 10x Genomics based scRNA seq and scATAC seq in zebrafish 40 000 mCherry+ GFP cells 40 000 mCherry+ GFP+ cells and 30 000 mCherry GFP+ cells were sorted from Tg kdrl:mCherry/runx1:enGFP at 36 hpf. For STRT seq in zebrafish single spi2: Gal4;UAS:GFP+ kdrl:mCherry+ HECs and spi2: Gal4;UAS:GFP+ kdrl:mCherry hematopoietic cells were sorted from the trunk region of Tg spi2: Gal4;UAS:GFP/ kdrl:mCherry at 36 hpf. For scRNA seq of spi2 morphants at 36 hpf in zebrafish ECs kdrl+runx1 HECs kdrl+runx1+ and hematopoietic cells kdrl runx1+ were sorted. For bulk CUT&TAG in zebrafish fli1a flag spi2 EGFP+ cells were sorted from the trunk region of Tg fli1a flag spi2 GFP embryos at 36 hpf. For 10x Genomics based scRNA seq and scATAC seq in zebrafish we loaded 20 000 cells for further 10x Genomics based scRNA seq and 90 000 cells for further 10x Genomics based scATAC seq. For scRNA seq libraries were prepared using Single Cell three prime Library and Gel Bead Kit V3.1. Sequencing was performed on an Illumina Novaseq6000 platform to generate 150 bp paired end reads. For ATAC seq nuclei were isolated and washed according to the methods supplied by 10x Genomics. Libraries were prepared using the Chromium Chip E Single Cell Kit and Chromium Single Cell ATAC Library & Gel Bead Kit and further sequenced on an Illumina Novaseq6000 platform to generate 50 bp paired end reads. For 10x Genomics based scRNA seq in mice libraries were prepared using Single Cell three prime Library and Gel Bead Kit V3.1. Sequencing was performed on an Illumina Novaseq6000 platform to generate 150 bp paired end reads. For STRT seq in zebrafish the end repair and dA tailing of the DNA fragments and ligation of the adaptors to the DNA fragments were performed according to the KAPA Hyper Prep Kits with PCR Library Amplification/Illumina series. post the adaptor ligation step the final PCR was performed. The libraries were sequenced on an Illumina Novaseq6000 platform to generate 150 bp paired end reads. For bulk CUT&TAG libraries were prepare…		OTHER	TRANSCRIPTOMIC	other	PAIRED	ILLUMINA	Illumina NovaSeq 6000	SRP342737		36hpf-DP1_FKDL202627688-1a_1.raw.fq.gz 36hpf-DP1_FKDL202627688-1a_2.raw.fq.gz	fastq fastq	36641999100.0	122139997.0	GSM7083138 r1	0:150 1:150	A:11840633171;C:5285812067;G:6638992204;T:12876249105;N:312553	150	150		11840633171	5285812067	6638992204	12876249105	312553	SRX19578259	SRS16961241	SRA1600575	Group of Hematopoiesis and Cardiovascular Development, INSTITUTE OF ZOOLOGY, CHINESE ACADEMY OF SCIENCES	Group of Hematopoiesis and Cardiovascular Development, INSTITUTE OF ZOOLOGY, CHINESE ACADEMY OF SCIENCES	2	0.8912	0.01571	0.09673	0.00653	0.86145	0.99849	0.61834	0.72033	150	150	B	T	mate2 technical by mapping diff	illumina	novaseq_era	unknown	poly_a	unknown	sc	single_cell_droplet	10x	China	2023-03-06	Pharyngula	Embryo	Trunk	Surface Structure
66758	66758	SRR23717091	SRX19578258	SRS16961240	SRP342737	PRJNA773778	Activation of lineage competence in hemogenic endothelium precedes the formation of hematopoietic stem cell heterogeneity [Zebrafish.STRT seq]	GSE186425	Other	Using a combination of single cell multi omics lineage tracing and functional assays we show that embryonic HSPCs are originated from heterogeneous hemogenic endothelial cells HECs during zebrafish embryogenesis. Overall design: Hematopoietic stem and progenitor cells HSPCs are considered as a heterogeneous population but where and how HSPC heterogeneity occurs remain unclear. Here we performed scRNA seq and scATAC seq with zebrafish 36 hpf VDA derived kdrl+runx1 kdrl+runx1+ and kdrl runx1+ cells. To determine the transcriptional signatures of spi2+ lineages in zebrafish we performed STRT seq with spi2: Gal4;UAS:GFP+ kdrl:mCherry+ HECs and spi2: Gal4;UAS:GFP+ kdrl:mCherry hematopoietic cells at 36 hpf. To investigate the underlying molecular mechanism upon spi2 deficiency we performed scRNA seq with the sorted ECs kdrl+runx1 HECs kdrl+runx1+ and hematopoietic cells kdrl runx1+ from spi2 morphants at 36 hpf. To determine whether spi2 can directly modulate transcriptional programs in EC/HEC we examined genome wide spi2 binding by cut tag assay in fli1a flag spi2 EGFP+ cells sorted from trunk region of Tg fli1a flag spi2 GFP embryos at 36 hpf.	parent bioproject:PRJNA773771	pubmed:37016019	DP2 36hpf	GSM7083139	source name:Zebrafish trunk region\|tissue:Zebrafish trunk region 36hpf\|cells:single spi2: Gal4;UAS:GFP+ kdrl:mCherry+ HECs and spi2: Gal4;UAS:GFP+ kdrl:mCherry hematopoietic cells\|Stage:36 hpf loc name:missing\|collection date:missing	DP2 36hpf	For scRNA seq and scATAC seq based on 10x Genomics，raw data files were processed by Cell Ranger software suite with default mapping parameters using the GRCz11 reference genome. For STRT seq raw reads were first de multiplexed by barcode sequences in reads 2 to yield separate read files for individual cells then the transcripts sequences of each cell in reads 1 were separated based on corresponding reads 2. Simultaneously UMI sequences in reads 2 were integrated into reads 1. The template switching oligo TSO sequence polyA sequence and the low quality reads N > 10% in reads 1 were subsequently removed by Python scripts and Trimmomatic version 0.36. Next the clean reads were aligned to the zebrafish genome GRCz11 from Ensembl using HISAT2 version 2.1.0 with known gene annotation. Only protein coding genes were retained and the abundance of each gene were estimated by counting the reads that duplicated UMIs have been excluded. For cut&tag reads were aligned to GRCz11 by Bowtie2. Only uniquely mapped reads with mapping quality score ≥ 30 were kept using Samtools software. post merging replicates MACS2 was used for the peak calling. Assembly: GRCz11 Library strategy: STRT seq	Zebrafish trunk region		For 10x Genomics based scRNA seq and scATAC seq in zebrafish 40 000 mCherry+ GFP cells 40 000 mCherry+ GFP+ cells and 30 000 mCherry GFP+ cells were sorted from Tg kdrl:mCherry/runx1:enGFP at 36 hpf. For STRT seq in zebrafish single spi2: Gal4;UAS:GFP+ kdrl:mCherry+ HECs and spi2: Gal4;UAS:GFP+ kdrl:mCherry hematopoietic cells were sorted from the trunk region of Tg spi2: Gal4;UAS:GFP/ kdrl:mCherry at 36 hpf. For scRNA seq of spi2 morphants at 36 hpf in zebrafish ECs kdrl+runx1 HECs kdrl+runx1+ and hematopoietic cells kdrl runx1+ were sorted. For bulk CUT&TAG in zebrafish fli1a flag spi2 EGFP+ cells were sorted from the trunk region of Tg fli1a flag spi2 GFP embryos at 36 hpf. For 10x Genomics based scRNA seq and scATAC seq in zebrafish we loaded 20 000 cells for further 10x Genomics based scRNA seq and 90 000 cells for further 10x Genomics based scATAC seq. For scRNA seq libraries were prepared using Single Cell 3’ Library and Gel Bead Kit V3.1. Sequencing was performed on an Illumina Novaseq6000 platform to generate 150 bp paired end reads. For ATAC seq nuclei were isolated and washed according to the methods supplied by 10x Genomics. Libraries were prepared using the Chromium Chip E Single Cell Kit and Chromium Single Cell ATAC Library & Gel Bead Kit and further sequenced on an Illumina Novaseq6000 platform to generate 50 bp paired end reads. For 10x Genomics based scRNA seq in mice libraries were prepared using Single Cell 3’ Library and Gel Bead Kit V3.1. Sequencing was performed on an Illumina Novaseq6000 platform to generate 150 bp paired end reads. For STRT seq in zebrafish the end repair and dA tailing of the DNA fragments and ligation of the adaptors to the DNA fragments were performed according to the KAPA Hyper Prep Kits with PCR Library Amplification/Illumina series. post the adaptor ligation step the final PCR was performed. The libraries were sequenced on an Illumina Novaseq6000 platform to generate 150 bp paired end reads. For bulk CUT&TAG libraries were prepared according to Hyp…		tissue:Zebrafish trunk region 36hpf\|cells:single spi2: Gal4;UAS:GFP+ kdrl:mCherry+ HECs and spi2: Gal4;UAS:GFP+ kdrl:mCherry hematopoietic cells\|Stage:36 hpf	GSM7083139	GSM7083139: DP2 36hpf; Danio rerio; OTHER	GSM7083139 r1	GSM7083139	1	For 10x Genomics based scRNA seq and scATAC seq in zebrafish 40 000 mCherry+ GFP cells 40 000 mCherry+ GFP+ cells and 30 000 mCherry GFP+ cells were sorted from Tg kdrl:mCherry/runx1:enGFP at 36 hpf. For STRT seq in zebrafish single spi2: Gal4;UAS:GFP+ kdrl:mCherry+ HECs and spi2: Gal4;UAS:GFP+ kdrl:mCherry hematopoietic cells were sorted from the trunk region of Tg spi2: Gal4;UAS:GFP/ kdrl:mCherry at 36 hpf. For scRNA seq of spi2 morphants at 36 hpf in zebrafish ECs kdrl+runx1 HECs kdrl+runx1+ and hematopoietic cells kdrl runx1+ were sorted. For bulk CUT&TAG in zebrafish fli1a flag spi2 EGFP+ cells were sorted from the trunk region of Tg fli1a flag spi2 GFP embryos at 36 hpf. For 10x Genomics based scRNA seq and scATAC seq in zebrafish we loaded 20 000 cells for further 10x Genomics based scRNA seq and 90 000 cells for further 10x Genomics based scATAC seq. For scRNA seq libraries were prepared using Single Cell three prime Library and Gel Bead Kit V3.1. Sequencing was performed on an Illumina Novaseq6000 platform to generate 150 bp paired end reads. For ATAC seq nuclei were isolated and washed according to the methods supplied by 10x Genomics. Libraries were prepared using the Chromium Chip E Single Cell Kit and Chromium Single Cell ATAC Library & Gel Bead Kit and further sequenced on an Illumina Novaseq6000 platform to generate 50 bp paired end reads. For 10x Genomics based scRNA seq in mice libraries were prepared using Single Cell three prime Library and Gel Bead Kit V3.1. Sequencing was performed on an Illumina Novaseq6000 platform to generate 150 bp paired end reads. For STRT seq in zebrafish the end repair and dA tailing of the DNA fragments and ligation of the adaptors to the DNA fragments were performed according to the KAPA Hyper Prep Kits with PCR Library Amplification/Illumina series. post the adaptor ligation step the final PCR was performed. The libraries were sequenced on an Illumina Novaseq6000 platform to generate 150 bp paired end reads. For bulk CUT&TAG libraries were prepare…		OTHER	TRANSCRIPTOMIC	other	PAIRED	ILLUMINA	Illumina NovaSeq 6000	SRP342737		36hpf-DP2_FKDL202627692-1a_1.raw.fq.gz 36hpf-DP2_FKDL202627692-1a_2.raw.fq.gz	fastq fastq	45339599100.0	151131997.0	GSM7083139 r1	0:150 1:150	A:14092451998;C:7125629842;G:9985822921;T:14135305628;N:388711	150	150		14092451998	7125629842	9985822921	14135305628	388711	SRX19578258	SRS16961240	SRA1600575	Group of Hematopoiesis and Cardiovascular Development, INSTITUTE OF ZOOLOGY, CHINESE ACADEMY OF SCIENCES	Group of Hematopoiesis and Cardiovascular Development, INSTITUTE OF ZOOLOGY, CHINESE ACADEMY OF SCIENCES	2	0.79702	0.00323	0.08737	0.00105	0.88663	0.99882	0.62618	0.6	150	150	B	T	mate2 technical by mapping diff	illumina	novaseq_era	unknown	poly_a	unknown	sc	single_cell_droplet	10x	China	2023-03-06	Pharyngula	Embryo	Trunk	Surface Structure
66759	66759	SRR23717092	SRX19578257	SRS16961239	SRP342737	PRJNA773778	Activation of lineage competence in hemogenic endothelium precedes the formation of hematopoietic stem cell heterogeneity [Zebrafish.STRT seq]	GSE186425	Other	Using a combination of single cell multi omics lineage tracing and functional assays we show that embryonic HSPCs are originated from heterogeneous hemogenic endothelial cells HECs during zebrafish embryogenesis. Overall design: Hematopoietic stem and progenitor cells HSPCs are considered as a heterogeneous population but where and how HSPC heterogeneity occurs remain unclear. Here we performed scRNA seq and scATAC seq with zebrafish 36 hpf VDA derived kdrl+runx1 kdrl+runx1+ and kdrl runx1+ cells. To determine the transcriptional signatures of spi2+ lineages in zebrafish we performed STRT seq with spi2: Gal4;UAS:GFP+ kdrl:mCherry+ HECs and spi2: Gal4;UAS:GFP+ kdrl:mCherry hematopoietic cells at 36 hpf. To investigate the underlying molecular mechanism upon spi2 deficiency we performed scRNA seq with the sorted ECs kdrl+runx1 HECs kdrl+runx1+ and hematopoietic cells kdrl runx1+ from spi2 morphants at 36 hpf. To determine whether spi2 can directly modulate transcriptional programs in EC/HEC we examined genome wide spi2 binding by cut tag assay in fli1a flag spi2 EGFP+ cells sorted from trunk region of Tg fli1a flag spi2 GFP embryos at 36 hpf.	parent bioproject:PRJNA773771	pubmed:37016019	SP 36hpf	GSM7083140	source name:Zebrafish trunk region\|tissue:Zebrafish trunk region 36hpf\|cells:single spi2: Gal4;UAS:GFP+ kdrl:mCherry+ HECs and spi2: Gal4;UAS:GFP+ kdrl:mCherry hematopoietic cells\|Stage:36 hpf loc name:missing\|collection date:missing	SP 36hpf	For scRNA seq and scATAC seq based on 10x Genomics，raw data files were processed by Cell Ranger software suite with default mapping parameters using the GRCz11 reference genome. For STRT seq raw reads were first de multiplexed by barcode sequences in reads 2 to yield separate read files for individual cells then the transcripts sequences of each cell in reads 1 were separated based on corresponding reads 2. Simultaneously UMI sequences in reads 2 were integrated into reads 1. The template switching oligo TSO sequence polyA sequence and the low quality reads N > 10% in reads 1 were subsequently removed by Python scripts and Trimmomatic version 0.36. Next the clean reads were aligned to the zebrafish genome GRCz11 from Ensembl using HISAT2 version 2.1.0 with known gene annotation. Only protein coding genes were retained and the abundance of each gene were estimated by counting the reads that duplicated UMIs have been excluded. For cut&tag reads were aligned to GRCz11 by Bowtie2. Only uniquely mapped reads with mapping quality score ≥ 30 were kept using Samtools software. post merging replicates MACS2 was used for the peak calling. Assembly: GRCz11 Library strategy: STRT seq	Zebrafish trunk region		For 10x Genomics based scRNA seq and scATAC seq in zebrafish 40 000 mCherry+ GFP cells 40 000 mCherry+ GFP+ cells and 30 000 mCherry GFP+ cells were sorted from Tg kdrl:mCherry/runx1:enGFP at 36 hpf. For STRT seq in zebrafish single spi2: Gal4;UAS:GFP+ kdrl:mCherry+ HECs and spi2: Gal4;UAS:GFP+ kdrl:mCherry hematopoietic cells were sorted from the trunk region of Tg spi2: Gal4;UAS:GFP/ kdrl:mCherry at 36 hpf. For scRNA seq of spi2 morphants at 36 hpf in zebrafish ECs kdrl+runx1 HECs kdrl+runx1+ and hematopoietic cells kdrl runx1+ were sorted. For bulk CUT&TAG in zebrafish fli1a flag spi2 EGFP+ cells were sorted from the trunk region of Tg fli1a flag spi2 GFP embryos at 36 hpf. For 10x Genomics based scRNA seq and scATAC seq in zebrafish we loaded 20 000 cells for further 10x Genomics based scRNA seq and 90 000 cells for further 10x Genomics based scATAC seq. For scRNA seq libraries were prepared using Single Cell 3’ Library and Gel Bead Kit V3.1. Sequencing was performed on an Illumina Novaseq6000 platform to generate 150 bp paired end reads. For ATAC seq nuclei were isolated and washed according to the methods supplied by 10x Genomics. Libraries were prepared using the Chromium Chip E Single Cell Kit and Chromium Single Cell ATAC Library & Gel Bead Kit and further sequenced on an Illumina Novaseq6000 platform to generate 50 bp paired end reads. For 10x Genomics based scRNA seq in mice libraries were prepared using Single Cell 3’ Library and Gel Bead Kit V3.1. Sequencing was performed on an Illumina Novaseq6000 platform to generate 150 bp paired end reads. For STRT seq in zebrafish the end repair and dA tailing of the DNA fragments and ligation of the adaptors to the DNA fragments were performed according to the KAPA Hyper Prep Kits with PCR Library Amplification/Illumina series. post the adaptor ligation step the final PCR was performed. The libraries were sequenced on an Illumina Novaseq6000 platform to generate 150 bp paired end reads. For bulk CUT&TAG libraries were prepared according to Hyp…		tissue:Zebrafish trunk region 36hpf\|cells:single spi2: Gal4;UAS:GFP+ kdrl:mCherry+ HECs and spi2: Gal4;UAS:GFP+ kdrl:mCherry hematopoietic cells\|Stage:36 hpf	GSM7083140	GSM7083140: SP 36hpf; Danio rerio; OTHER	GSM7083140 r1	GSM7083140	1	For 10x Genomics based scRNA seq and scATAC seq in zebrafish 40 000 mCherry+ GFP cells 40 000 mCherry+ GFP+ cells and 30 000 mCherry GFP+ cells were sorted from Tg kdrl:mCherry/runx1:enGFP at 36 hpf. For STRT seq in zebrafish single spi2: Gal4;UAS:GFP+ kdrl:mCherry+ HECs and spi2: Gal4;UAS:GFP+ kdrl:mCherry hematopoietic cells were sorted from the trunk region of Tg spi2: Gal4;UAS:GFP/ kdrl:mCherry at 36 hpf. For scRNA seq of spi2 morphants at 36 hpf in zebrafish ECs kdrl+runx1 HECs kdrl+runx1+ and hematopoietic cells kdrl runx1+ were sorted. For bulk CUT&TAG in zebrafish fli1a flag spi2 EGFP+ cells were sorted from the trunk region of Tg fli1a flag spi2 GFP embryos at 36 hpf. For 10x Genomics based scRNA seq and scATAC seq in zebrafish we loaded 20 000 cells for further 10x Genomics based scRNA seq and 90 000 cells for further 10x Genomics based scATAC seq. For scRNA seq libraries were prepared using Single Cell three prime Library and Gel Bead Kit V3.1. Sequencing was performed on an Illumina Novaseq6000 platform to generate 150 bp paired end reads. For ATAC seq nuclei were isolated and washed according to the methods supplied by 10x Genomics. Libraries were prepared using the Chromium Chip E Single Cell Kit and Chromium Single Cell ATAC Library & Gel Bead Kit and further sequenced on an Illumina Novaseq6000 platform to generate 50 bp paired end reads. For 10x Genomics based scRNA seq in mice libraries were prepared using Single Cell three prime Library and Gel Bead Kit V3.1. Sequencing was performed on an Illumina Novaseq6000 platform to generate 150 bp paired end reads. For STRT seq in zebrafish the end repair and dA tailing of the DNA fragments and ligation of the adaptors to the DNA fragments were performed according to the KAPA Hyper Prep Kits with PCR Library Amplification/Illumina series. post the adaptor ligation step the final PCR was performed. The libraries were sequenced on an Illumina Novaseq6000 platform to generate 150 bp paired end reads. For bulk CUT&TAG libraries were prepare…		OTHER	TRANSCRIPTOMIC	other	PAIRED	ILLUMINA	Illumina NovaSeq 6000	SRP342737		36hpf-SP_FKDL202627693-1a_1.raw.fq.gz 36hpf-SP_FKDL202627693-1a_2.raw.fq.gz	fastq fastq	44757872400.0	149192908.0	GSM7083140 r1	0:150 1:150	A:13194662551;C:6817138207;G:9879151250;T:14866177703;N:742689	150	150		13194662551	6817138207	9879151250	14866177703	742689	SRX19578257	SRS16961239	SRA1600575	Group of Hematopoiesis and Cardiovascular Development, INSTITUTE OF ZOOLOGY, CHINESE ACADEMY OF SCIENCES	Group of Hematopoiesis and Cardiovascular Development, INSTITUTE OF ZOOLOGY, CHINESE ACADEMY OF SCIENCES	2	0.7854	0.00221	0.062	0.00084	0.90995	0.99904	0.43661	0.67741	150	150	B	T	mate2 technical by mapping diff	illumina	novaseq_era	unknown	poly_a	unknown	sc	single_cell_droplet	10x	China	2023-03-06	Pharyngula	Embryo	Trunk	Surface Structure
66926	66926	SRR16832335	SRX13025545	SRS10963312	SRP344879	PRJNA778402	Atlas of lymphangiogenesis [scATAC Seq]	GSE188340	Other	During development lymphatic vasculature forms as a second and distinct network that derives from embryonic blood vessels. Transdifferentiation of venous endothelial cells into specified lymphatic endothelial cells LECs is the first step in this process. Transdifferentiation and specification of LEC fate requires Prox1 but how Prox1 regulates transdifferentiation and differentiation is not fully understood. We present a single cell transcriptomic atlas of lymphangiogenesis spanning four key developmental stages that reveals new markers and functional regulators of lymphatic development. We extend this to comprehensively profile single cell transcriptomic and chromatin changes controlled by Prox1 using zygotic prox1a mutants which form lymphatics that then dedifferentiate. Combining this with single cell analysis of Prox1 null double prox1a/prox1b maternal zygotic mutants we reveal in depth the role of Prox1 in control of LEC fate specification and differentiation. This resource reveals dual and progressive functions for Prox1 blocking blood vascular and hematopoietic fate while simultaneously up regulating a small number of early acting genes that include tspan18a/b and lgals3a/b which are essential for lymphangiogenesis. This embryonic developmental resource will serve as a baseline to better understand both developmental and pathological lymphangiogenesis in the future. [Citations in sample metadata correspond to reference numbers in the associated publication.] Overall design: Single cell ATAC profiles of endothelial cells from developing zebrafish at 4dpf in Zprox / mutant and matched WT siblings. All code associated with this data and publication are publicly available under an open source software license at: https://atlassian.petermac.org.au/bitbucket/users/tyrone.chen/repos/hogan lab/browse/2022 Grimm Mason et al PROX1 NatCellBiol/	parent bioproject:PRJNA778399	pubmed:36912146	20158: Zprox1ab 4dpf scATAC seq	GSM5677991	source name:embryo\|tissue:embryo\|transgenic:Tgfli1a:negfp;Tg 5.2lyve1b:dsRed\|facs:nEGFP and dsRed2 double positive\|Stage:4dpf\|genotype:Zprox1ab mutant\|platform:10X Chromium scATAC\|facility:Peter Mac Molecular Genomics\|cellranger atac pipeline:2.0.0\|r version:4.0.5\|internal id:20158\|gap study version:ATAC 1.1	20158: Zprox1ab 4dpf scATAC seq	Where necessary fastq files were made using Cell Ranger [14] version 3.1.0 or 3.0.2 mkfastq. Sequencing QC was assessed using FastQC 0.11.6 and MultiQC viewer for aggregated reports. Cell Ranger count and aggr were used to generate aggregated count files mapped to GRCz11 Ensembl 101 without xxx normalisation. Doublets were identified from the filtered aggregated count files using Scrublet [15] in Python version 3.6 and filtered from subsequent analyses. For the MZprox1 / mutant and Zprox1 / mutant datasets filtered aggregated count files were processed sc transform normalised filtered and clustered louvain using Seurat version 2.0 [16] and 3.0 [17] respectively for R statistical software version 4.0.2. QC was evaluated before and post normalisation using plot functions in Seurat and scater 1.20.1 [18] and all thresholds and settings are described in scripting. Cluster solutions were evaluated using ClusTree [19]. Datasets used in the atlas of lymphangiogenesis were processed filtered merged and log normalised using Seurat version 3.0 [17] with QC and settings as above. Merged data was clustered and normalised using CSS simspec [20] and clustering and cluster evaluation performed on this object only as described above. For all scRNA seq datasets cluster phenotype was determined using the expression of key markers Supplementary Table with the aid of CellXGene visualisation software [21]. All downstream analysis and plotting were performed using Seurat version 3.0 [17] using default settings. For velocity analysis loom files containing RNA velocity information were first generated from the 10X data associated with each sample using velocyto.R 0.6. Relevant sample subsets were then combined with loompy 3.0.6. From pre computed Seurat UMAPs in scRNA seq processing and analysis cell barcode and associated metadata were obtained and combined with the loom files. With the combined velocity scores and cell metadata velocity maps were plotted with velocyto.R 0.6 and overlaid on the UMAPs. Genome build: …	embryo	All injections were performed as previously described [10]. CRISPR genome editing for tspan18a/b was performed as previously described [11] and all genotyping confirmed using PCR [12]. slc7a7aBAC:slc7a7a Citrineuom10 and fabp11aBAC:fabp11a Citrineuom10 recombineering was performed as previously described.	Single cell suspensions were sorted by FACS and prepared for nuclei isolation as previously described by 10x Genomics Demonstrated Protocol for Single Cell ATAC Sequencing CG000169 Rev D. Cell suspensions were pelleted 300 x g for 5 minutes and rinsed with PBS + 0.04% BSA. Cells were resuspended in 95uL of freshly prepared lysis buffer 10mM Tris HCl pH 7.4 10 mM NaCl 3 mM MgCl2 0.1% Tween 20 0.1% NP40 Substitute 0.01% Digitonin and 1% BSA and incubated on ice for 1 minute. 100uL of chilled wash buffer 10 mM Tris HCl pH 7.4 10 mM NaCl 3 mM MgCl2 0.1% Tween 20 1% BSA was used to neutralise the reaction before the nuclei were pelleted 500 x g for 5 minutes and resuspended again in 7uL of 1x Nuclei Buffer 10X Genomics Cat# PN 2000153/2000207. Presence of healthy and intact nuclei was assessed by visual inspection on a brightfield microscope using Trypan Blue staining Thermo Fisher Cat# T10282 and Countess Cell Counting Chamber Slides Thermo Fisher Cat# C10228. Single nuclei suspensions were resuspended at approximately 5000 nuclei per L before undergoing tagmentation for 60min at 37C. post tagmentation nuclei were partitioned and barcoded using the 10X Genomics Chromium Controller 10X Genomics and the Single Cell ATAC Reagent Kit V1.1; 10X Genomics; PN 1000176 . Tagmented nuclei were loaded onto the Chromium Single Cell Chip H 10X Genomics; PN 1000162 GEM generation barcoding and library construction was performed according to the 10X Genomics Chromium User Guide. The resulting single cell ATAC libraries contained unique sample indices for each sample. The libraries were quantified on the Agilent BioAnalyzer 2100 using the High Sensitivity DNA Kit Agilent 5067 4626 and pooled in equimolar ratios. Sequencing was performed on an Illumina NextSeq 500 using a 150 cycle High Output Kit as follows: 50bp Read1 8bp i7 index 16bp i5 index 50bp Read2 achieving a read depth of 25 000 read pairs per nucleus.	Zebrafish work was conducted in compliance with animal ethics committees at University of Queensland and Peter MacCallum Cancer Centre. The uq52bh dut mutant was isolated in a previously described genetic screen [7]. The genetic mapping approach was performed as previously described [8 9].	tissue:embryo\|transgenic:Tgfli1a:negfp;Tg 5.2lyve1b:dsRed\|facs:nEGFP and dsRed2 double positive\|Stage:4dpf\|genotype:Zprox1ab mutant\|platform:10X Chromium scATAC\|version:ATAC 1.1\|facility:Peter Mac Molecular Genomics\|cellranger atac pipeline:2.0.0\|r version:4.0.5\|internal id:20158	GSM5677991	GSM5677991: 20158: Zprox1ab 4dpf scATAC seq; Danio rerio; ATAC seq	GSM5677991 r1	GSM5677991	1	Single cell suspensions were sorted by FACS and prepared for nuclei isolation as previously described by 10x Genomics Demonstrated Protocol for Single Cell ATAC Sequencing CG000169 Rev D. Cell suspensions were pelleted 300 x g for 5 minutes and rinsed with PBS + 0.04% BSA. Cells were resuspended in 95uL of freshly prepared lysis buffer 10mM Tris HCl pH 7.4 10 mM NaCl 3 mM MgCl2 0.1% Tween 20 0.1% NP40 Substitute 0.01% Digitonin and 1% BSA and incubated on ice for 1 minute. 100uL of chilled wash buffer 10 mM Tris HCl pH 7.4 10 mM NaCl 3 mM MgCl2 0.1% Tween 20 1% BSA was used to neutralise the reaction before the nuclei were pelleted 500 x g for 5 minutes and resuspended again in 7uL of 1x Nuclei Buffer 10X Genomics Cat# PN 2000153/2000207. Presence of healthy and intact nuclei was assessed by visual inspection on a brightfield microscope using Trypan Blue staining Thermo Fisher Cat# T10282 and Countess Cell Counting Chamber Slides Thermo Fisher Cat# C10228. Single nuclei suspensions were resuspended at approximately 5000 nuclei per L before undergoing tagmentation for 60min at 37C. post tagmentation nuclei were partitioned and barcoded using the 10X Genomics Chromium Controller 10X Genomics and the Single Cell ATAC Reagent Kit V1.1; 10X Genomics; PN 1000176 . Tagmented nuclei were loaded onto the Chromium Single Cell Chip H 10X Genomics; PN 1000162 GEM generation barcoding and library construction was performed according to the 10X Genomics Chromium User Guide. The resulting single cell ATAC libraries contained unique sample indices for each sample. The libraries were quantified on the Agilent BioAnalyzer 2100 using the High Sensitivity DNA Kit Agilent 5067 4626 and pooled in equimolar ratios. Sequencing was performed on an Illumina NextSeq 500 using a 150 cycle High Output Kit as follows: 50bp Read1 8bp i7 index 16bp i5 index 50bp Read2 achieving a read depth of 25 000 read pairs per nucleus.		ATAC-seq	TRANSCRIPTOMIC	other	PAIRED	ILLUMINA	NextSeq 500	SRP344879	loader:fastq load.py\|options: readTypes=BTB read1PairFiles=20158 Prox Mut S7 R1 001.fastq.gz read2PairFiles=20158 Prox Mut S7 R2 001.fastq.gz read3PairFiles=20158 Prox Mut S7 R3 001.fastq.gz	20158_Prox-Mut_S7_R1_001.fastq.gz 20158_Prox-Mut_S7_R2_001.fastq.gz 20158_Prox-Mut_S7_R3_001.fastq.gz	fastq fastq fastq	10416417336.0	65926692.0	GSM5677991 r1	0:71 1:16 2:71	A:2269946152;C:2457596815;G:2197719841;T:2435900098;N:427358	71	16	71	2269946152	2457596815	2197719841	2435900098	427358	SRX13025545	SRS10963312	SRA1327178	Computational Biology Lab, School of Biological Sciences, Monash University	Computational Biology Lab, School of Biological Sciences, Monash University	2	0.39271	0.39125	0.30282	0.30048	0.79829	0.79803	0.52845	0.52503	71	71	B	B	biological fallback assumption	illumina	nextseq	unknown	other	unknown	sc	single_cell_droplet	10x	Australia	2021-11-06	Larval	Larval	Embryo Imprecise	All anatomical structures
66927	66927	SRR16832336	SRX13025544	SRS10963311	SRP344879	PRJNA778402	Atlas of lymphangiogenesis [scATAC Seq]	GSE188340	Other	During development lymphatic vasculature forms as a second and distinct network that derives from embryonic blood vessels. Transdifferentiation of venous endothelial cells into specified lymphatic endothelial cells LECs is the first step in this process. Transdifferentiation and specification of LEC fate requires Prox1 but how Prox1 regulates transdifferentiation and differentiation is not fully understood. We present a single cell transcriptomic atlas of lymphangiogenesis spanning four key developmental stages that reveals new markers and functional regulators of lymphatic development. We extend this to comprehensively profile single cell transcriptomic and chromatin changes controlled by Prox1 using zygotic prox1a mutants which form lymphatics that then dedifferentiate. Combining this with single cell analysis of Prox1 null double prox1a/prox1b maternal zygotic mutants we reveal in depth the role of Prox1 in control of LEC fate specification and differentiation. This resource reveals dual and progressive functions for Prox1 blocking blood vascular and hematopoietic fate while simultaneously up regulating a small number of early acting genes that include tspan18a/b and lgals3a/b which are essential for lymphangiogenesis. This embryonic developmental resource will serve as a baseline to better understand both developmental and pathological lymphangiogenesis in the future. [Citations in sample metadata correspond to reference numbers in the associated publication.] Overall design: Single cell ATAC profiles of endothelial cells from developing zebrafish at 4dpf in Zprox / mutant and matched WT siblings. All code associated with this data and publication are publicly available under an open source software license at: https://atlassian.petermac.org.au/bitbucket/users/tyrone.chen/repos/hogan lab/browse/2022 Grimm Mason et al PROX1 NatCellBiol/	parent bioproject:PRJNA778399	pubmed:36912146	20157: WT 4dpf scATAC seq	GSM5677990	source name:embryo\|tissue:embryo\|transgenic:Tgfli1a:negfp;Tg 5.2lyve1b:dsRed\|facs:nEGFP and dsRed2 double positive\|Stage:4dpf\|genotype:WT\|platform:10X Chromium scATAC\|facility:Peter Mac Molecular Genomics\|cellranger atac pipeline:2.0.0\|r version:4.0.5\|internal id:20157\|gap study version:ATAC 1.1	20157: WT 4dpf scATAC seq	Where necessary fastq files were made using Cell Ranger [14] version 3.1.0 or 3.0.2 mkfastq. Sequencing QC was assessed using FastQC 0.11.6 and MultiQC viewer for aggregated reports. Cell Ranger count and aggr were used to generate aggregated count files mapped to GRCz11 Ensembl 101 without xxx normalisation. Doublets were identified from the filtered aggregated count files using Scrublet [15] in Python version 3.6 and filtered from subsequent analyses. For the MZprox1 / mutant and Zprox1 / mutant datasets filtered aggregated count files were processed sc transform normalised filtered and clustered louvain using Seurat version 2.0 [16] and 3.0 [17] respectively for R statistical software version 4.0.2. QC was evaluated before and post normalisation using plot functions in Seurat and scater 1.20.1 [18] and all thresholds and settings are described in scripting. Cluster solutions were evaluated using ClusTree [19]. Datasets used in the atlas of lymphangiogenesis were processed filtered merged and log normalised using Seurat version 3.0 [17] with QC and settings as above. Merged data was clustered and normalised using CSS simspec [20] and clustering and cluster evaluation performed on this object only as described above. For all scRNA seq datasets cluster phenotype was determined using the expression of key markers Supplementary Table with the aid of CellXGene visualisation software [21]. All downstream analysis and plotting were performed using Seurat version 3.0 [17] using default settings. For velocity analysis loom files containing RNA velocity information were first generated from the 10X data associated with each sample using velocyto.R 0.6. Relevant sample subsets were then combined with loompy 3.0.6. From pre computed Seurat UMAPs in scRNA seq processing and analysis cell barcode and associated metadata were obtained and combined with the loom files. With the combined velocity scores and cell metadata velocity maps were plotted with velocyto.R 0.6 and overlaid on the UMAPs. Genome build: …	embryo	All injections were performed as previously described [10]. CRISPR genome editing for tspan18a/b was performed as previously described [11] and all genotyping confirmed using PCR [12]. slc7a7aBAC:slc7a7a Citrineuom10 and fabp11aBAC:fabp11a Citrineuom10 recombineering was performed as previously described.	Single cell suspensions were sorted by FACS and prepared for nuclei isolation as previously described by 10x Genomics Demonstrated Protocol for Single Cell ATAC Sequencing CG000169 Rev D. Cell suspensions were pelleted 300 x g for 5 minutes and rinsed with PBS + 0.04% BSA. Cells were resuspended in 95uL of freshly prepared lysis buffer 10mM Tris HCl pH 7.4 10 mM NaCl 3 mM MgCl2 0.1% Tween 20 0.1% NP40 Substitute 0.01% Digitonin and 1% BSA and incubated on ice for 1 minute. 100uL of chilled wash buffer 10 mM Tris HCl pH 7.4 10 mM NaCl 3 mM MgCl2 0.1% Tween 20 1% BSA was used to neutralise the reaction before the nuclei were pelleted 500 x g for 5 minutes and resuspended again in 7uL of 1x Nuclei Buffer 10X Genomics Cat# PN 2000153/2000207. Presence of healthy and intact nuclei was assessed by visual inspection on a brightfield microscope using Trypan Blue staining Thermo Fisher Cat# T10282 and Countess Cell Counting Chamber Slides Thermo Fisher Cat# C10228. Single nuclei suspensions were resuspended at approximately 5000 nuclei per L before undergoing tagmentation for 60min at 37C. post tagmentation nuclei were partitioned and barcoded using the 10X Genomics Chromium Controller 10X Genomics and the Single Cell ATAC Reagent Kit V1.1; 10X Genomics; PN 1000176 . Tagmented nuclei were loaded onto the Chromium Single Cell Chip H 10X Genomics; PN 1000162 GEM generation barcoding and library construction was performed according to the 10X Genomics Chromium User Guide. The resulting single cell ATAC libraries contained unique sample indices for each sample. The libraries were quantified on the Agilent BioAnalyzer 2100 using the High Sensitivity DNA Kit Agilent 5067 4626 and pooled in equimolar ratios. Sequencing was performed on an Illumina NextSeq 500 using a 150 cycle High Output Kit as follows: 50bp Read1 8bp i7 index 16bp i5 index 50bp Read2 achieving a read depth of 25 000 read pairs per nucleus.	Zebrafish work was conducted in compliance with animal ethics committees at University of Queensland and Peter MacCallum Cancer Centre. The uq52bh dut mutant was isolated in a previously described genetic screen [7]. The genetic mapping approach was performed as previously described [8 9].	tissue:embryo\|transgenic:Tgfli1a:negfp;Tg 5.2lyve1b:dsRed\|facs:nEGFP and dsRed2 double positive\|Stage:4dpf\|genotype:WT\|platform:10X Chromium scATAC\|version:ATAC 1.1\|facility:Peter Mac Molecular Genomics\|cellranger atac pipeline:2.0.0\|r version:4.0.5\|internal id:20157	GSM5677990	GSM5677990: 20157: WT 4dpf scATAC seq; Danio rerio; ATAC seq	GSM5677990 r1	GSM5677990	1	Single cell suspensions were sorted by FACS and prepared for nuclei isolation as previously described by 10x Genomics Demonstrated Protocol for Single Cell ATAC Sequencing CG000169 Rev D. Cell suspensions were pelleted 300 x g for 5 minutes and rinsed with PBS + 0.04% BSA. Cells were resuspended in 95uL of freshly prepared lysis buffer 10mM Tris HCl pH 7.4 10 mM NaCl 3 mM MgCl2 0.1% Tween 20 0.1% NP40 Substitute 0.01% Digitonin and 1% BSA and incubated on ice for 1 minute. 100uL of chilled wash buffer 10 mM Tris HCl pH 7.4 10 mM NaCl 3 mM MgCl2 0.1% Tween 20 1% BSA was used to neutralise the reaction before the nuclei were pelleted 500 x g for 5 minutes and resuspended again in 7uL of 1x Nuclei Buffer 10X Genomics Cat# PN 2000153/2000207. Presence of healthy and intact nuclei was assessed by visual inspection on a brightfield microscope using Trypan Blue staining Thermo Fisher Cat# T10282 and Countess Cell Counting Chamber Slides Thermo Fisher Cat# C10228. Single nuclei suspensions were resuspended at approximately 5000 nuclei per L before undergoing tagmentation for 60min at 37C. post tagmentation nuclei were partitioned and barcoded using the 10X Genomics Chromium Controller 10X Genomics and the Single Cell ATAC Reagent Kit V1.1; 10X Genomics; PN 1000176 . Tagmented nuclei were loaded onto the Chromium Single Cell Chip H 10X Genomics; PN 1000162 GEM generation barcoding and library construction was performed according to the 10X Genomics Chromium User Guide. The resulting single cell ATAC libraries contained unique sample indices for each sample. The libraries were quantified on the Agilent BioAnalyzer 2100 using the High Sensitivity DNA Kit Agilent 5067 4626 and pooled in equimolar ratios. Sequencing was performed on an Illumina NextSeq 500 using a 150 cycle High Output Kit as follows: 50bp Read1 8bp i7 index 16bp i5 index 50bp Read2 achieving a read depth of 25 000 read pairs per nucleus.		ATAC-seq	TRANSCRIPTOMIC	other	PAIRED	ILLUMINA	NextSeq 500	SRP344879	loader:fastq load.py\|options: readTypes=BTB	20157_Wild-Type_S6_R1_001.fastq.gz 20157_Wild-Type_S6_R2_001.fastq.gz 20157_Wild-Type_S6_R3_001.fastq.gz	fastq fastq fastq	9771147706.0	61842707.0	GSM5677990 r1	0:71 1:16 2:71	A:2361071727;C:2051725100;G:1909318228;T:2459149367;N:399972	71	16	71	2361071727	2051725100	1909318228	2459149367	399972	SRX13025544	SRS10963311	SRA1327178	Computational Biology Lab, School of Biological Sciences, Monash University	Computational Biology Lab, School of Biological Sciences, Monash University	2	0.83384	0.83194	0.67607	0.67264	0.68014	0.67992	0.50904	0.50945	71	71	B	B	biological fallback assumption	illumina	nextseq	unknown	other	unknown	sc	single_cell_droplet	10x	Australia	2021-11-06	Larval	Larval	Embryo Imprecise	All anatomical structures
72561	72561	SRR22722190	SRX18683720	SRS16126888	SRP412911	PRJNA911847	Transgenic IDH2 R172K and IDH2 R140Q zebrafish models recapitulated features of human acute myeloid leukaemia	PRJNA911847	Other	Isocitrate dehydrogenase 2 IDH2 mutations occur in more than 15% of cytogenetically normal acute myeloid leukemia CN AML but comparative studies of their roles in leukemogenesis have been scarce. We generated zebrafish models of IDH2R172K and IDH2R140Q AML and reported their pathologic functional and transcriptomic features and therapeutic responses to target therapies. Transgenic embryos co expressing FLT3ITD and IDH2 mutations showed accentuation of myelopoiesis. As these embryos were raised to maturity full blown leukemia ensued with multi lineage dysplasia increase in myeloblasts and marrow cellularity and splenomegaly. The leukemia cells were transplantable into primary and secondary recipients and resulted in more aggressive disease. TgRunx1:FLT3ITDIDH2R172K but not TgRunx1:FLT3ITDIDH2R140Q zebrafish showed increase in T cell development at embryonic and adult stage. Single cell transcriptomic analysis revealed increased myeloid skewing differentiation blockade and enrichment of leukemia associated gene signatures in both zebrafish models. TgRunx1:FLT3ITDIDH2R172K but not TgRunx1:FLT3ITDIDH2R140Q zebrafish showed increase in interferon signals at adult stage. Leukemic phenotypes in both zebrafish could be ameliorated by quizartinib and enasidenib. In conclusion the zebrafish models of IDH2 mutated AML recapitulated the morphologic clinical functional and transcriptomic characteristics of human diseases and provided the prototype for developing zebrafish leukemia models of other genotypes that would become a platform for high throughput drug screening				Transgenic FLT3 ITD IDH2 R140Q	strain:TU\|isolate:not applicable\|breed:not applicable\|cultivar:not applicable\|ecotype:not applicable\|age:8 month\|dev stage:8 month\|sex:pooled male and female\|tissue:kidney marrow\|genotype:Transgenic Runx1:FLT3ITDIDH2R140Q\|BioSampleModel:Model organism or animal									Single cell RNA seq of Danio rerio: Transgenic FLT3 ITD IDH2 R140Q	ITD140Q	ITD140Q	Single cell RNA seq library.Single viable KM cells were collected from the transgenic double mutant n=3; pooled and WT n=3; pooled fish at 7 mpf into 0.9X PBS with 5% FBS. Their viability was examined by 0.4% Trypan blue staining under microscopy. The single cell library was constructed using the ChromiumTM Controller and ChromiumTM Next GEM Single Cell 3 Kit v3.1 10x Genomics Pleasanton CA. Complementary DNA cDNA was synthesized from the fragmentated RNAs using N6 random primers followed by end repair and ligation to BGISEQ sequencer compatible adapters. Quality control of the final library was performed by checking the distribution of the fragments size using the Agilent 2100 bioanalyzer and quantification was performed by real time quantitative PCR using TaqMan probes. The final products were sequenced using the DNBSEQTM platform BGI HK China.			OTHER	TRANSCRIPTOMIC	other	PAIRED	DNBSEQ	DNBSEQ-G400	SRP412911	loader:fastq load.py	ITD172KA_S1_L001_I1_001.fastq.gz ITD172KA_S1_L001_R1_001.fastq.gz ITD172KA_S1_L001_R2_001.fastq.gz	fastq fastq fastq	52149830422.0	410628586.0	ITD172KA S1 L001 I1 001.fastq.gz	0:8 1:28 2:91	A:10282953217;C:8459559201;G:8851285287;T:9772343870;N:1059751	8	28	91	10282953217	8459559201	8851285287	9772343870	1059751	SRX18683720	SRS16126888	SRA1558824	The University of Hong Kong\|Department of Medicine	The University of Hong Kong	1	0.88465		0.13112		0.81789		0.55842		91		B		usable mapping rate	bgi	bgi	unknown	random_priming	unknown	sc	single_cell_droplet	10x	China	2022-12-14	Adult	Adult	Kidney	Renal System
72562	72562	SRR22722191	SRX18683719	SRS16126887	SRP412911	PRJNA911847	Transgenic IDH2 R172K and IDH2 R140Q zebrafish models recapitulated features of human acute myeloid leukaemia	PRJNA911847	Other	Isocitrate dehydrogenase 2 IDH2 mutations occur in more than 15% of cytogenetically normal acute myeloid leukemia CN AML but comparative studies of their roles in leukemogenesis have been scarce. We generated zebrafish models of IDH2R172K and IDH2R140Q AML and reported their pathologic functional and transcriptomic features and therapeutic responses to target therapies. Transgenic embryos co expressing FLT3ITD and IDH2 mutations showed accentuation of myelopoiesis. As these embryos were raised to maturity full blown leukemia ensued with multi lineage dysplasia increase in myeloblasts and marrow cellularity and splenomegaly. The leukemia cells were transplantable into primary and secondary recipients and resulted in more aggressive disease. TgRunx1:FLT3ITDIDH2R172K but not TgRunx1:FLT3ITDIDH2R140Q zebrafish showed increase in T cell development at embryonic and adult stage. Single cell transcriptomic analysis revealed increased myeloid skewing differentiation blockade and enrichment of leukemia associated gene signatures in both zebrafish models. TgRunx1:FLT3ITDIDH2R172K but not TgRunx1:FLT3ITDIDH2R140Q zebrafish showed increase in interferon signals at adult stage. Leukemic phenotypes in both zebrafish could be ameliorated by quizartinib and enasidenib. In conclusion the zebrafish models of IDH2 mutated AML recapitulated the morphologic clinical functional and transcriptomic characteristics of human diseases and provided the prototype for developing zebrafish leukemia models of other genotypes that would become a platform for high throughput drug screening				Transgenic FLT3 ITD IDH2 R172K	strain:TU\|isolate:not applicable\|breed:not applicable\|cultivar:not applicable\|ecotype:not applicable\|age:8 month\|dev stage:8 month\|sex:pooled male and female\|tissue:kidney marrow\|genotype:Transgenic Runx1:FLT3ITDIDH2R172K\|BioSampleModel:Model organism or animal									Single cell RNA seq of Danio rerio: Transgenic FLT3 ITD IDH2 R172K	ITD172K	ITD172K	Single cell RNA seq library.Single viable KM cells were collected from the transgenic double mutant n=3; pooled and WT n=3; pooled fish at 7 mpf into 0.9X PBS with 5% FBS. Their viability was examined by 0.4% Trypan blue staining under microscopy. The single cell library was constructed using the ChromiumTM Controller and ChromiumTM Next GEM Single Cell 3 Kit v3.1 10x Genomics Pleasanton CA. Complementary DNA cDNA was synthesized from the fragmentated RNAs using N6 random primers followed by end repair and ligation to BGISEQ sequencer compatible adapters. Quality control of the final library was performed by checking the distribution of the fragments size using the Agilent 2100 bioanalyzer and quantification was performed by real time quantitative PCR using TaqMan probes. The final products were sequenced using the DNBSEQTM platform BGI HK China.			OTHER	TRANSCRIPTOMIC	other	PAIRED	DNBSEQ	DNBSEQ-G400	SRP412911	loader:fastq load.py	ITD140QA_S1_L004_I1_001.fastq.gz ITD140QA_S1_L004_R1_001.fastq.gz ITD140QA_S1_L004_R2_001.fastq.gz	fastq fastq fastq	28595025233.0	225157679.0	ITD140QA S1 L004 I1 001.fastq.gz	0:8 1:28 2:91	A:5628987577;C:4565848641;G:4959209904;T:5302394659;N:32908008	8	28	91	5628987577	4565848641	4959209904	5302394659	32908008	SRX18683719	SRS16126887	SRA1558824	The University of Hong Kong\|Department of Medicine	The University of Hong Kong	1	0.81404		0.11315		0.83327		0.56222		91		B		usable mapping rate	bgi	bgi	unknown	random_priming	unknown	sc	single_cell_droplet	10x	China	2022-12-13	Adult	Adult	Kidney	Renal System
72563	72563	SRR22722192	SRX18683718	SRS16126886	SRP412911	PRJNA911847	Transgenic IDH2 R172K and IDH2 R140Q zebrafish models recapitulated features of human acute myeloid leukaemia	PRJNA911847	Other	Isocitrate dehydrogenase 2 IDH2 mutations occur in more than 15% of cytogenetically normal acute myeloid leukemia CN AML but comparative studies of their roles in leukemogenesis have been scarce. We generated zebrafish models of IDH2R172K and IDH2R140Q AML and reported their pathologic functional and transcriptomic features and therapeutic responses to target therapies. Transgenic embryos co expressing FLT3ITD and IDH2 mutations showed accentuation of myelopoiesis. As these embryos were raised to maturity full blown leukemia ensued with multi lineage dysplasia increase in myeloblasts and marrow cellularity and splenomegaly. The leukemia cells were transplantable into primary and secondary recipients and resulted in more aggressive disease. TgRunx1:FLT3ITDIDH2R172K but not TgRunx1:FLT3ITDIDH2R140Q zebrafish showed increase in T cell development at embryonic and adult stage. Single cell transcriptomic analysis revealed increased myeloid skewing differentiation blockade and enrichment of leukemia associated gene signatures in both zebrafish models. TgRunx1:FLT3ITDIDH2R172K but not TgRunx1:FLT3ITDIDH2R140Q zebrafish showed increase in interferon signals at adult stage. Leukemic phenotypes in both zebrafish could be ameliorated by quizartinib and enasidenib. In conclusion the zebrafish models of IDH2 mutated AML recapitulated the morphologic clinical functional and transcriptomic characteristics of human diseases and provided the prototype for developing zebrafish leukemia models of other genotypes that would become a platform for high throughput drug screening				Wild type	strain:TU\|isolate:not applicable\|breed:not applicable\|cultivar:not applicable\|ecotype:not applicable\|age:8 month\|dev stage:8 month\|sex:pooled male and female\|tissue:kidney marrow\|genotype:Wild type\|BioSampleModel:Model organism or animal									Single cell RNA seq of Danio rerio: Wild type	Control	Control	Single cell RNA seq library.Single viable KM cells were collected from the transgenic double mutant n=3; pooled and WT n=3; pooled fish at 7 mpf into 0.9X PBS with 5% FBS. Their viability was examined by 0.4% Trypan blue staining under microscopy. The single cell library was constructed using the ChromiumTM Controller and ChromiumTM Next GEM Single Cell 3 Kit v3.1 10x Genomics Pleasanton CA. Complementary DNA cDNA was synthesized from the fragmentated RNAs using N6 random primers followed by end repair and ligation to BGISEQ sequencer compatible adapters. Quality control of the final library was performed by checking the distribution of the fragments size using the Agilent 2100 bioanalyzer and quantification was performed by real time quantitative PCR using TaqMan probes. The final products were sequenced using the DNBSEQTM platform BGI HK China.			OTHER	TRANSCRIPTOMIC	other	PAIRED	DNBSEQ	DNBSEQ-G400	SRP412911	loader:fastq load.py	ControlA_S1_L003_R2_001.fastq.gz ControlA_S1_L003_R1_001.fastq.gz ControlA_S1_L003_I1_001.fastq.gz	fastq fastq fastq	37892727491.0	298367933.0	ControlA S1 L003 I1 001.fastq.gz	0:8 1:28 2:91	A:7386602298;C:6174680460;G:6579652828;T:6955791213;N:54755104	8	28	91	7386602298	6174680460	6579652828	6955791213	54755104	SRX18683718	SRS16126886	SRA1558824	The University of Hong Kong\|Department of Medicine	The University of Hong Kong	1	0.81686		0.11049		0.84139		0.46084		91		B		usable mapping rate	bgi	bgi	unknown	random_priming	unknown	sc	single_cell_droplet	10x	China	2022-12-14	Adult	Adult	Kidney	Renal System
72833	72833	SRR23210472	SRX19158411	SRS16570832	SRP418915	PRJNA927004	Differential Nodal level promotes mesendoderm cell fate segregation mediated by chromatin organization	GSE223636	Other	Purpose: To investigate the mechanism of prechordal plate and anterior endoderm separation . Methods: Nodal injected explants injected with 10pg ndr2 mRNA constructed from lft1 mutants and ndr1 morphants were harvested at xxxhpf. Libraries were prepared using Chromium Controller and Chromium Single Cell three primeLibrary & Gel Bead Kit v3 10x Genomics PN 1000075 according to the manufacturer's protocol for 10000 cells recovery. For single cell multiomics zebrafish embryos at 6 hpf were harvested. Libraries were prepared using Chromium Next GEM Single Cell Multiome ATAC + Gene Expression Reagent Bundle 10x Genomics 4 rxns PN 1000285 according to the manufacturer's protocol for 10000 cells recovery. Results: A total of 10 614 single cell transcriptomes and 4 335 multiomics were collected post stringent quality control measures. Conclusions: A slight bias in Nodal signaling promotes a differential chromatin structure between prechordal plate and endoderm which drives a differential expression of those key regulators such as gsc and ripply1 in these two cell lineages and further regulates mesendoderm cell fate separation. Overall design: zebrafish Nodal explants constructed from lft1 mutants and ndr1 morphants were harvested at 6hpf for scRNA seq. Zebrafish embryos were harvested at 6hpf for single cell multiomics.			zebrafish embryo multiomics expression	GSM6969677	source name:zebrafish cells\|strain:AB\|tissue:embryonic cells\|age:6hpf	zebrafish embryo multiomics expression	Illumina sequencing reads were aligned to the zebrafish mRNA reference genome GRCz11 using the 10x Genomics CellRanger pipeline version 6.1.2 and cellranger arc version 2.0.0 with default parameters. Assembly: GRCz11 Supplementary files format and content: tar archivr or gzip compressed files included filtered gene bc matrices and ATAC fragments post running CellRanger or cellranger arc pipeline. Library strategy: scMultiome GEX	zebrafish cells	10pg of ndr2 mRNA was injected to one cell of embryonic animal pole at xxx cell stage. All embryos were incubated in 0.3x Danieau buffer until 1k stage then were transferred to Dulbecco's Modified Eagle Medium. Animal pole explants corresponding roughly to half of the blastula were incubated to 6hpf corresponding to embryonic developmental stage.	Libraries were prepared using Chromium Controller and Chromium Single Cell 3’Library & Gel Bead Kit v3 10x Genomics PN 1000075 and Chromium Next GEM Single Cell Multiome ATAC + Gene Expression Reagent Bundle 10x Genomics 4 rxns PN 1000285 according to the manufacturer’s protocol for 10000 cells recovery.	Explants were cultured in a Petri dish coated with 1.5% agarose filled with Dulbecco's Modified Eagle Medium	strain:AB\|tissue:embryonic cells\|age:6hpf	GSM6969677	GSM6969677: zebrafish embryo multiomics expression; Danio rerio; OTHER	GSM6969677 r1	GSM6969677	1	Libraries were prepared using Chromium Controller and Chromium Single Cell three primeLibrary & Gel Bead Kit v3 10x Genomics PN 1000075 and Chromium Next GEM Single Cell Multiome ATAC + Gene Expression Reagent Bundle 10x Genomics 4 rxns PN 1000285 according to the manufacturer's protocol for 10000 cells recovery.		OTHER	TRANSCRIPTOMIC	other	PAIRED	ILLUMINA	Illumina NovaSeq 6000	SRP418915	loader:fastq load.py	6-M-GEX-merge_S1_L001_R1_001.fastq.gz 6-M-GEX-merge_S1_L001_R2_001.fastq.gz	fastq fastq	67059456600.0	223531522.0	GSM6969677 r1	0:150 1:150	A:20681044309;C:12370348813;G:11664316954;T:22341885756;N:1860768	150	150		20681044309	12370348813	11664316954	22341885756	1860768	SRX19158411	SRS16570832	SRA1581154	Institute of genetics, Zhejiang University	Institute of genetics, Zhejiang University	2	0.45314	0.8745	0.16489	0.2261	0.99072	0.82641	0.79221	0.75799	150	150	T	B	mate1 technical by mapping diff	illumina	novaseq_era	unknown	other	unknown	sc	single_cell_droplet	10x	China	2023-01-24	Gastrula	Embryo	Embryo Imprecise	All anatomical structures

Advanced export

JSON shape: default, array, newline-delimited

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");;