metadata: run_metadata: 9 rows where experiment.library_selection = "other", technology = "10x" and tissue

9 rows where experiment.library_selection = "other", technology = "10x" and tissue_curation = "Trunk"

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.extractprotocol	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.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
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
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
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

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