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109 rows where experiment.library_layout = "PAIRED", technology = "celseq" and tissue_curation = "Blood"

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devstage_curation_coarse 2

devstage_curation 2

tissue_curation_coarse 1

tissue_curation 1

  • Blood · 109 ✖

technology 1

  • celseq · 109 ✖

experiment.platform 1

experiment.library_strategy 1

experiment.library_source 1

experiment.library_selection 1

experiment.library_layout 1

  • PAIRED · 109 ✖
Link rowid ▼ run.accession experiment.accession sample.accession study.accession bioproject study.title study.alias study.type study.abstract study.attributes study.PMIDs sample.description sample.title sample.alias sample.centername sample.attributes GEOsample.title GEOsample.dataprocessing GEOsample.source GEOsample.treatmentprotocol GEOsample.extractprotocol GEOsample.growthprotocol GEOsample.characteristics GEOsample.accession experiment.title experiment.alias experiment.library_name experiment.design_description experiment.library_construction_protocol experiment.attributes experiment.library_strategy experiment.library_source experiment.library_selection experiment.library_layout experiment.platform experiment.instrument_model experiment.spot_descriptor experiment.study_ref run.title run.attributes run.filename run.semantic_name run.total_bases run.total_spots run.alias run.read_lengths run.base_counts run.r1_length run.r2_length run.r3_length run.r4_length run.Acount run.Ccount run.Gcount run.Tcount run.Ncount run.experiment run.pool_member submission.accession submission.srasource submission.bioprojectsource seqdetective.n_mates seqdetective.mapping_rate.mate1 seqdetective.mapping_rate.mate2 seqdetective.nofeature_rate.mate1 seqdetective.nofeature_rate.mate2 seqdetective.sparsity.mate1 seqdetective.sparsity.mate2 seqdetective.pos_strand_rate.mate1 seqdetective.pos_strand_rate.mate2 seqdetective.readlen.mate1 seqdetective.readlen.mate2 seqdetective.judgement.mate1 seqdetective.judgement.mate2 seqdetective.judgement.reason platform_family instrument_generation read_bias selection_class prep_kit sc_or_bulk tech_class technology tech_variant submission.bioprojectsource.country earliest_date devstage_curation devstage_curation_coarse tissue_curation tissue_curation_coarse
47869 47869 SRR6908743 SRX3856808 SRS3100400 SRP136633 PRJNA446034 Cell type purification by single cell transcriptome trained sorting GSE112438 Other Traditional cell type enrichment using fluorescence activated cell sorting FACS relies on methods that specifically label the cell type of interest. Here we propose GateID a computational method that combines single cell transcriptomics for unbiased cell type identification with FACS index sorting to purify cell types of choice. We validate GateID by purifying various cell types from the zebrafish kidney marrow and the human pancreas without xxx to specific antibodies or transgenes. Overall design: Single cell mRNA sequencing data of unenriched and enriched cells from zebrafish whole kidney marrow and human pancreas using GateID. For each cell we provide libraries with transcriptome information. An unenriched sample is transcriptome data from a live single cell FACS sort while an enriched library has transcriptome data for GateID enriched single cells for a given cell type.   pubmed:31585086   WKM9 eosinophils GSM3070146   tissue:WKM9 eosinophils|FISHid:WKM9|cell type:whole kidney marrow single cells|cell subtype:eosinophils WKM9 eosinophils In transcriptome libraries first read contains UMI 6 first nucleotides and cell barcode from 7 to 14 nucleotides and second reads contains biological information. Second reads with a valid cell barcode in corresponding first read are mapped to the reference transcriptome. Multimappers are not considered as valid. The cell specific barcode file is added as a part of processed data files. Genome build: All transcriptome libraries were mapped to Danio rerio assembly Zv9 ensemble 74 extended with ERCC92 Supplementary files format and content: Tabular separated files with transcript count rows per cell columns. Cells in each file are labeled according to barcode ID. The cell specific barcode file is provided in the celseq2 bc.csv.gz 1st column represents index of the cell specific barcode and the second column is the barcode WKM9 eosinophils   post organ isolation live single cells are sorted into 384 well plates containing mineral oil uniquely barcoded cell specific primers for mRNA detection Spike in controls and and RNAse inhibitor. cellseq2 Illumina TruSeq adapaters   FISHid:WKM9|cell type:whole kidney marrow single cells|cell subtype:eosinophils GSM3070146 GSM3070146: WKM9 eosinophils; Danio rerio; RNA Seq GSM3070146   1 post organ isolation live single cells are sorted into 384 well plates containing mineral oil uniquely barcoded cell specific primers for mRNA detection Spike in controls and and RNAse inhibitor. cellseq2 Illumina TruSeq adapaters GEO Accession:GSM3070146 RNA-Seq TRANSCRIPTOMIC cDNA PAIRED ILLUMINA NextSeq 500   SRP136633     WKM9_eosinophils_L001_R1_001.fastq.gz WKM9_eosinophils_L001_R2_001.fastq.gz fastq fastq 1378279915.0 9133302.0 GSM3070146 r1 0:75.43 1:75.48 A:398409215;C:214050926;G:243007260;T:522806293;N:6221 75 75     398409215 214050926 243007260 522806293 6221 SRX3856808 SRS3100400 SRA675960 GEO Hubrecht Institute 2 0.32967 0.72245 0.27664 0.49284 0.96217 0.88688 0.44743 0.5676 76 73 T B mate1 technical by mapping diff illumina nextseq unknown random_priming trueseq sc single_cell_plate celseq   Netherlands 2018-03-28 Undetermined Undetermined Blood Hematopoietic System
47870 47870 SRR6908744 SRX3856808 SRS3100400 SRP136633 PRJNA446034 Cell type purification by single cell transcriptome trained sorting GSE112438 Other Traditional cell type enrichment using fluorescence activated cell sorting FACS relies on methods that specifically label the cell type of interest. Here we propose GateID a computational method that combines single cell transcriptomics for unbiased cell type identification with FACS index sorting to purify cell types of choice. We validate GateID by purifying various cell types from the zebrafish kidney marrow and the human pancreas without xxx to specific antibodies or transgenes. Overall design: Single cell mRNA sequencing data of unenriched and enriched cells from zebrafish whole kidney marrow and human pancreas using GateID. For each cell we provide libraries with transcriptome information. An unenriched sample is transcriptome data from a live single cell FACS sort while an enriched library has transcriptome data for GateID enriched single cells for a given cell type.   pubmed:31585086   WKM9 eosinophils GSM3070146   tissue:WKM9 eosinophils|FISHid:WKM9|cell type:whole kidney marrow single cells|cell subtype:eosinophils WKM9 eosinophils In transcriptome libraries first read contains UMI 6 first nucleotides and cell barcode from 7 to 14 nucleotides and second reads contains biological information. Second reads with a valid cell barcode in corresponding first read are mapped to the reference transcriptome. Multimappers are not considered as valid. The cell specific barcode file is added as a part of processed data files. Genome build: All transcriptome libraries were mapped to Danio rerio assembly Zv9 ensemble 74 extended with ERCC92 Supplementary files format and content: Tabular separated files with transcript count rows per cell columns. Cells in each file are labeled according to barcode ID. The cell specific barcode file is provided in the celseq2 bc.csv.gz 1st column represents index of the cell specific barcode and the second column is the barcode WKM9 eosinophils   post organ isolation live single cells are sorted into 384 well plates containing mineral oil uniquely barcoded cell specific primers for mRNA detection Spike in controls and and RNAse inhibitor. cellseq2 Illumina TruSeq adapaters   FISHid:WKM9|cell type:whole kidney marrow single cells|cell subtype:eosinophils GSM3070146 GSM3070146: WKM9 eosinophils; Danio rerio; RNA Seq GSM3070146   1 post organ isolation live single cells are sorted into 384 well plates containing mineral oil uniquely barcoded cell specific primers for mRNA detection Spike in controls and and RNAse inhibitor. cellseq2 Illumina TruSeq adapaters GEO Accession:GSM3070146 RNA-Seq TRANSCRIPTOMIC cDNA PAIRED ILLUMINA NextSeq 500   SRP136633     WKM9_eosinophils_L002_R1_001.fastq.gz WKM9_eosinophils_L002_R2_001.fastq.gz fastq fastq 1347027331.0 8926023.0 GSM3070146 r2 0:75.43 1:75.48 A:386373331;C:208319487;G:242442973;T:509887930;N:3610 75 75     386373331 208319487 242442973 509887930 3610 SRX3856808 SRS3100400 SRA675960 GEO Hubrecht Institute 2 0.32008 0.72405 0.26859 0.48848 0.96512 0.88791 0.46692 0.55937 76 76 T B mate1 technical by mapping diff illumina nextseq unknown random_priming trueseq sc single_cell_plate celseq   Netherlands 2018-03-28 Undetermined Undetermined Blood Hematopoietic System
47871 47871 SRR6908745 SRX3856808 SRS3100400 SRP136633 PRJNA446034 Cell type purification by single cell transcriptome trained sorting GSE112438 Other Traditional cell type enrichment using fluorescence activated cell sorting FACS relies on methods that specifically label the cell type of interest. Here we propose GateID a computational method that combines single cell transcriptomics for unbiased cell type identification with FACS index sorting to purify cell types of choice. We validate GateID by purifying various cell types from the zebrafish kidney marrow and the human pancreas without xxx to specific antibodies or transgenes. Overall design: Single cell mRNA sequencing data of unenriched and enriched cells from zebrafish whole kidney marrow and human pancreas using GateID. For each cell we provide libraries with transcriptome information. An unenriched sample is transcriptome data from a live single cell FACS sort while an enriched library has transcriptome data for GateID enriched single cells for a given cell type.   pubmed:31585086   WKM9 eosinophils GSM3070146   tissue:WKM9 eosinophils|FISHid:WKM9|cell type:whole kidney marrow single cells|cell subtype:eosinophils WKM9 eosinophils In transcriptome libraries first read contains UMI 6 first nucleotides and cell barcode from 7 to 14 nucleotides and second reads contains biological information. Second reads with a valid cell barcode in corresponding first read are mapped to the reference transcriptome. Multimappers are not considered as valid. The cell specific barcode file is added as a part of processed data files. Genome build: All transcriptome libraries were mapped to Danio rerio assembly Zv9 ensemble 74 extended with ERCC92 Supplementary files format and content: Tabular separated files with transcript count rows per cell columns. Cells in each file are labeled according to barcode ID. The cell specific barcode file is provided in the celseq2 bc.csv.gz 1st column represents index of the cell specific barcode and the second column is the barcode WKM9 eosinophils   post organ isolation live single cells are sorted into 384 well plates containing mineral oil uniquely barcoded cell specific primers for mRNA detection Spike in controls and and RNAse inhibitor. cellseq2 Illumina TruSeq adapaters   FISHid:WKM9|cell type:whole kidney marrow single cells|cell subtype:eosinophils GSM3070146 GSM3070146: WKM9 eosinophils; Danio rerio; RNA Seq GSM3070146   1 post organ isolation live single cells are sorted into 384 well plates containing mineral oil uniquely barcoded cell specific primers for mRNA detection Spike in controls and and RNAse inhibitor. cellseq2 Illumina TruSeq adapaters GEO Accession:GSM3070146 RNA-Seq TRANSCRIPTOMIC cDNA PAIRED ILLUMINA NextSeq 500   SRP136633     WKM9_eosinophils_L003_R1_001.fastq.gz WKM9_eosinophils_L003_R2_001.fastq.gz fastq fastq 1241011911.0 8223327.0 GSM3070146 r3 0:75.44 1:75.48 A:356514159;C:192418351;G:221034567;T:471023588;N:21246 75 75     356514159 192418351 221034567 471023588 21246 SRX3856808 SRS3100400 SRA675960 GEO Hubrecht Institute 2 0.31504 0.71934 0.26517 0.48722 0.96932 0.89499 0.47715 0.56814 76 75 T B mate1 technical by mapping diff illumina nextseq unknown random_priming trueseq sc single_cell_plate celseq   Netherlands 2018-03-28 Undetermined Undetermined Blood Hematopoietic System
47872 47872 SRR6908746 SRX3856808 SRS3100400 SRP136633 PRJNA446034 Cell type purification by single cell transcriptome trained sorting GSE112438 Other Traditional cell type enrichment using fluorescence activated cell sorting FACS relies on methods that specifically label the cell type of interest. Here we propose GateID a computational method that combines single cell transcriptomics for unbiased cell type identification with FACS index sorting to purify cell types of choice. We validate GateID by purifying various cell types from the zebrafish kidney marrow and the human pancreas without xxx to specific antibodies or transgenes. Overall design: Single cell mRNA sequencing data of unenriched and enriched cells from zebrafish whole kidney marrow and human pancreas using GateID. For each cell we provide libraries with transcriptome information. An unenriched sample is transcriptome data from a live single cell FACS sort while an enriched library has transcriptome data for GateID enriched single cells for a given cell type.   pubmed:31585086   WKM9 eosinophils GSM3070146   tissue:WKM9 eosinophils|FISHid:WKM9|cell type:whole kidney marrow single cells|cell subtype:eosinophils WKM9 eosinophils In transcriptome libraries first read contains UMI 6 first nucleotides and cell barcode from 7 to 14 nucleotides and second reads contains biological information. Second reads with a valid cell barcode in corresponding first read are mapped to the reference transcriptome. Multimappers are not considered as valid. The cell specific barcode file is added as a part of processed data files. Genome build: All transcriptome libraries were mapped to Danio rerio assembly Zv9 ensemble 74 extended with ERCC92 Supplementary files format and content: Tabular separated files with transcript count rows per cell columns. Cells in each file are labeled according to barcode ID. The cell specific barcode file is provided in the celseq2 bc.csv.gz 1st column represents index of the cell specific barcode and the second column is the barcode WKM9 eosinophils   post organ isolation live single cells are sorted into 384 well plates containing mineral oil uniquely barcoded cell specific primers for mRNA detection Spike in controls and and RNAse inhibitor. cellseq2 Illumina TruSeq adapaters   FISHid:WKM9|cell type:whole kidney marrow single cells|cell subtype:eosinophils GSM3070146 GSM3070146: WKM9 eosinophils; Danio rerio; RNA Seq GSM3070146   1 post organ isolation live single cells are sorted into 384 well plates containing mineral oil uniquely barcoded cell specific primers for mRNA detection Spike in controls and and RNAse inhibitor. cellseq2 Illumina TruSeq adapaters GEO Accession:GSM3070146 RNA-Seq TRANSCRIPTOMIC cDNA PAIRED ILLUMINA NextSeq 500   SRP136633     WKM9_eosinophils_L004_R1_001.fastq.gz WKM9_eosinophils_L004_R2_001.fastq.gz fastq fastq 1199788386.0 7950347.0 GSM3070146 r4 0:75.44 1:75.47 A:343966384;C:184985849;G:216629920;T:454187010;N:19223 75 75     343966384 184985849 216629920 454187010 19223 SRX3856808 SRS3100400 SRA675960 GEO Hubrecht Institute 2 0.32136 0.71802 0.27107 0.48741 0.97098 0.90057 0.4616 0.56009 76 76 T B mate1 technical by mapping diff illumina nextseq unknown random_priming trueseq sc single_cell_plate celseq   Netherlands 2018-03-28 Undetermined Undetermined Blood Hematopoietic System
47917 47917 SRR6908693 SRX3856796 SRS3100388 SRP136633 PRJNA446034 Cell type purification by single cell transcriptome trained sorting GSE112438 Other Traditional cell type enrichment using fluorescence activated cell sorting FACS relies on methods that specifically label the cell type of interest. Here we propose GateID a computational method that combines single cell transcriptomics for unbiased cell type identification with FACS index sorting to purify cell types of choice. We validate GateID by purifying various cell types from the zebrafish kidney marrow and the human pancreas without xxx to specific antibodies or transgenes. Overall design: Single cell mRNA sequencing data of unenriched and enriched cells from zebrafish whole kidney marrow and human pancreas using GateID. For each cell we provide libraries with transcriptome information. An unenriched sample is transcriptome data from a live single cell FACS sort while an enriched library has transcriptome data for GateID enriched single cells for a given cell type.   pubmed:31585086   WKM4 eosinophils GSM3070134   tissue:WKM4 eosinophils|FISHid:WKM4|cell type:whole kidney marrow single cells|cell subtype:eosinophils WKM4 eosinophils In transcriptome libraries first read contains UMI 6 first nucleotides and cell barcode from 7 to 14 nucleotides and second reads contains biological information. Second reads with a valid cell barcode in corresponding first read are mapped to the reference transcriptome. Multimappers are not considered as valid. The cell specific barcode file is added as a part of processed data files. Genome build: All transcriptome libraries were mapped to Danio rerio assembly Zv9 ensemble 74 extended with ERCC92 Supplementary files format and content: Tabular separated files with transcript count rows per cell columns. Cells in each file are labeled according to barcode ID. The cell specific barcode file is provided in the celseq2 bc.csv.gz 1st column represents index of the cell specific barcode and the second column is the barcode WKM4 eosinophils   post organ isolation live single cells are sorted into 384 well plates containing mineral oil uniquely barcoded cell specific primers for mRNA detection Spike in controls and and RNAse inhibitor. cellseq2 Illumina TruSeq adapaters   FISHid:WKM4|cell type:whole kidney marrow single cells|cell subtype:eosinophils GSM3070134 GSM3070134: WKM4 eosinophils; Danio rerio; RNA Seq GSM3070134   1 post organ isolation live single cells are sorted into 384 well plates containing mineral oil uniquely barcoded cell specific primers for mRNA detection Spike in controls and and RNAse inhibitor. cellseq2 Illumina TruSeq adapaters GEO Accession:GSM3070134 RNA-Seq TRANSCRIPTOMIC cDNA PAIRED ILLUMINA NextSeq 500   SRP136633     WKM4_eosinophils_L001_R1_001.fastq.gz WKM4_eosinophils_L001_R2_001.fastq.gz fastq fastq 1044383799.0 6918578.0 GSM3070134 r1 0:75.49 1:75.46 A:293266328;C:159927306;G:179144897;T:412003143;N:42125 75 75     293266328 159927306 179144897 412003143 42125 SRX3856796 SRS3100388 SRA675960 GEO Hubrecht Institute 2 0.38953 0.76677 0.33438 0.55076 0.94775 0.83664 0.47684 0.51667 75 75 T B mate1 technical by mapping diff illumina nextseq unknown random_priming trueseq sc single_cell_plate celseq   Netherlands 2018-03-28 Undetermined Undetermined Blood Hematopoietic System
47918 47918 SRR6908694 SRX3856796 SRS3100388 SRP136633 PRJNA446034 Cell type purification by single cell transcriptome trained sorting GSE112438 Other Traditional cell type enrichment using fluorescence activated cell sorting FACS relies on methods that specifically label the cell type of interest. Here we propose GateID a computational method that combines single cell transcriptomics for unbiased cell type identification with FACS index sorting to purify cell types of choice. We validate GateID by purifying various cell types from the zebrafish kidney marrow and the human pancreas without xxx to specific antibodies or transgenes. Overall design: Single cell mRNA sequencing data of unenriched and enriched cells from zebrafish whole kidney marrow and human pancreas using GateID. For each cell we provide libraries with transcriptome information. An unenriched sample is transcriptome data from a live single cell FACS sort while an enriched library has transcriptome data for GateID enriched single cells for a given cell type.   pubmed:31585086   WKM4 eosinophils GSM3070134   tissue:WKM4 eosinophils|FISHid:WKM4|cell type:whole kidney marrow single cells|cell subtype:eosinophils WKM4 eosinophils In transcriptome libraries first read contains UMI 6 first nucleotides and cell barcode from 7 to 14 nucleotides and second reads contains biological information. Second reads with a valid cell barcode in corresponding first read are mapped to the reference transcriptome. Multimappers are not considered as valid. The cell specific barcode file is added as a part of processed data files. Genome build: All transcriptome libraries were mapped to Danio rerio assembly Zv9 ensemble 74 extended with ERCC92 Supplementary files format and content: Tabular separated files with transcript count rows per cell columns. Cells in each file are labeled according to barcode ID. The cell specific barcode file is provided in the celseq2 bc.csv.gz 1st column represents index of the cell specific barcode and the second column is the barcode WKM4 eosinophils   post organ isolation live single cells are sorted into 384 well plates containing mineral oil uniquely barcoded cell specific primers for mRNA detection Spike in controls and and RNAse inhibitor. cellseq2 Illumina TruSeq adapaters   FISHid:WKM4|cell type:whole kidney marrow single cells|cell subtype:eosinophils GSM3070134 GSM3070134: WKM4 eosinophils; Danio rerio; RNA Seq GSM3070134   1 post organ isolation live single cells are sorted into 384 well plates containing mineral oil uniquely barcoded cell specific primers for mRNA detection Spike in controls and and RNAse inhibitor. cellseq2 Illumina TruSeq adapaters GEO Accession:GSM3070134 RNA-Seq TRANSCRIPTOMIC cDNA PAIRED ILLUMINA NextSeq 500   SRP136633     WKM4_eosinophils_L002_R1_001.fastq.gz WKM4_eosinophils_L002_R2_001.fastq.gz fastq fastq 1065518294.0 7059081.0 GSM3070134 r2 0:75.49 1:75.45 A:299578147;C:162208243;G:185866185;T:417812882;N:52837 75 75     299578147 162208243 185866185 417812882 52837 SRX3856796 SRS3100388 SRA675960 GEO Hubrecht Institute 2 0.36825 0.75817 0.3148 0.55624 0.95061 0.84794 0.47713 0.52081 76 75 T B mate1 technical by mapping diff illumina nextseq unknown random_priming trueseq sc single_cell_plate celseq   Netherlands 2018-03-28 Undetermined Undetermined Blood Hematopoietic System
47919 47919 SRR6908695 SRX3856796 SRS3100388 SRP136633 PRJNA446034 Cell type purification by single cell transcriptome trained sorting GSE112438 Other Traditional cell type enrichment using fluorescence activated cell sorting FACS relies on methods that specifically label the cell type of interest. Here we propose GateID a computational method that combines single cell transcriptomics for unbiased cell type identification with FACS index sorting to purify cell types of choice. We validate GateID by purifying various cell types from the zebrafish kidney marrow and the human pancreas without xxx to specific antibodies or transgenes. Overall design: Single cell mRNA sequencing data of unenriched and enriched cells from zebrafish whole kidney marrow and human pancreas using GateID. For each cell we provide libraries with transcriptome information. An unenriched sample is transcriptome data from a live single cell FACS sort while an enriched library has transcriptome data for GateID enriched single cells for a given cell type.   pubmed:31585086   WKM4 eosinophils GSM3070134   tissue:WKM4 eosinophils|FISHid:WKM4|cell type:whole kidney marrow single cells|cell subtype:eosinophils WKM4 eosinophils In transcriptome libraries first read contains UMI 6 first nucleotides and cell barcode from 7 to 14 nucleotides and second reads contains biological information. Second reads with a valid cell barcode in corresponding first read are mapped to the reference transcriptome. Multimappers are not considered as valid. The cell specific barcode file is added as a part of processed data files. Genome build: All transcriptome libraries were mapped to Danio rerio assembly Zv9 ensemble 74 extended with ERCC92 Supplementary files format and content: Tabular separated files with transcript count rows per cell columns. Cells in each file are labeled according to barcode ID. The cell specific barcode file is provided in the celseq2 bc.csv.gz 1st column represents index of the cell specific barcode and the second column is the barcode WKM4 eosinophils   post organ isolation live single cells are sorted into 384 well plates containing mineral oil uniquely barcoded cell specific primers for mRNA detection Spike in controls and and RNAse inhibitor. cellseq2 Illumina TruSeq adapaters   FISHid:WKM4|cell type:whole kidney marrow single cells|cell subtype:eosinophils GSM3070134 GSM3070134: WKM4 eosinophils; Danio rerio; RNA Seq GSM3070134   1 post organ isolation live single cells are sorted into 384 well plates containing mineral oil uniquely barcoded cell specific primers for mRNA detection Spike in controls and and RNAse inhibitor. cellseq2 Illumina TruSeq adapaters GEO Accession:GSM3070134 RNA-Seq TRANSCRIPTOMIC cDNA PAIRED ILLUMINA NextSeq 500   SRP136633     WKM4_eosinophils_L003_R1_001.fastq.gz WKM4_eosinophils_L003_R2_001.fastq.gz fastq fastq 1020526841.0 6760661.0 GSM3070134 r3 0:75.50 1:75.45 A:287644655;C:155831653;G:174910820;T:402133481;N:6232 75 75     287644655 155831653 174910820 402133481 6232 SRX3856796 SRS3100388 SRA675960 GEO Hubrecht Institute 2 0.38266 0.76495 0.32861 0.55487 0.94909 0.84502 0.47881 0.50844 76 75 T B mate1 technical by mapping diff illumina nextseq unknown random_priming trueseq sc single_cell_plate celseq   Netherlands 2018-03-28 Undetermined Undetermined Blood Hematopoietic System
47920 47920 SRR6908696 SRX3856796 SRS3100388 SRP136633 PRJNA446034 Cell type purification by single cell transcriptome trained sorting GSE112438 Other Traditional cell type enrichment using fluorescence activated cell sorting FACS relies on methods that specifically label the cell type of interest. Here we propose GateID a computational method that combines single cell transcriptomics for unbiased cell type identification with FACS index sorting to purify cell types of choice. We validate GateID by purifying various cell types from the zebrafish kidney marrow and the human pancreas without xxx to specific antibodies or transgenes. Overall design: Single cell mRNA sequencing data of unenriched and enriched cells from zebrafish whole kidney marrow and human pancreas using GateID. For each cell we provide libraries with transcriptome information. An unenriched sample is transcriptome data from a live single cell FACS sort while an enriched library has transcriptome data for GateID enriched single cells for a given cell type.   pubmed:31585086   WKM4 eosinophils GSM3070134   tissue:WKM4 eosinophils|FISHid:WKM4|cell type:whole kidney marrow single cells|cell subtype:eosinophils WKM4 eosinophils In transcriptome libraries first read contains UMI 6 first nucleotides and cell barcode from 7 to 14 nucleotides and second reads contains biological information. Second reads with a valid cell barcode in corresponding first read are mapped to the reference transcriptome. Multimappers are not considered as valid. The cell specific barcode file is added as a part of processed data files. Genome build: All transcriptome libraries were mapped to Danio rerio assembly Zv9 ensemble 74 extended with ERCC92 Supplementary files format and content: Tabular separated files with transcript count rows per cell columns. Cells in each file are labeled according to barcode ID. The cell specific barcode file is provided in the celseq2 bc.csv.gz 1st column represents index of the cell specific barcode and the second column is the barcode WKM4 eosinophils   post organ isolation live single cells are sorted into 384 well plates containing mineral oil uniquely barcoded cell specific primers for mRNA detection Spike in controls and and RNAse inhibitor. cellseq2 Illumina TruSeq adapaters   FISHid:WKM4|cell type:whole kidney marrow single cells|cell subtype:eosinophils GSM3070134 GSM3070134: WKM4 eosinophils; Danio rerio; RNA Seq GSM3070134   1 post organ isolation live single cells are sorted into 384 well plates containing mineral oil uniquely barcoded cell specific primers for mRNA detection Spike in controls and and RNAse inhibitor. cellseq2 Illumina TruSeq adapaters GEO Accession:GSM3070134 RNA-Seq TRANSCRIPTOMIC cDNA PAIRED ILLUMINA NextSeq 500   SRP136633     WKM4_eosinophils_L004_R2_001.fastq.gz WKM4_eosinophils_L004_R1_001.fastq.gz fastq fastq 1036249497.0 6864317.0 GSM3070134 r4 0:75.50 1:75.46 A:290241796;C:157806083;G:180760457;T:407436042;N:5119 75 75     290241796 157806083 180760457 407436042 5119 SRX3856796 SRS3100388 SRA675960 GEO Hubrecht Institute 2 0.38456 0.76284 0.32829 0.54843 0.9497 0.84668 0.48302 0.51789 76 75 T B mate1 technical by mapping diff illumina nextseq unknown random_priming trueseq sc single_cell_plate celseq   Netherlands 2018-03-28 Undetermined Undetermined Blood Hematopoietic System
47925 47925 SRR6908685 SRX3856794 SRS3100386 SRP136633 PRJNA446034 Cell type purification by single cell transcriptome trained sorting GSE112438 Other Traditional cell type enrichment using fluorescence activated cell sorting FACS relies on methods that specifically label the cell type of interest. Here we propose GateID a computational method that combines single cell transcriptomics for unbiased cell type identification with FACS index sorting to purify cell types of choice. We validate GateID by purifying various cell types from the zebrafish kidney marrow and the human pancreas without xxx to specific antibodies or transgenes. Overall design: Single cell mRNA sequencing data of unenriched and enriched cells from zebrafish whole kidney marrow and human pancreas using GateID. For each cell we provide libraries with transcriptome information. An unenriched sample is transcriptome data from a live single cell FACS sort while an enriched library has transcriptome data for GateID enriched single cells for a given cell type.   pubmed:31585086   WKM3 eosinophils GSM3070132   tissue:WKM3 eosinophils|FISHid:WKM3|cell type:whole kidney marrow single cells|cell subtype:eosinophils WKM3 eosinophils In transcriptome libraries first read contains UMI 6 first nucleotides and cell barcode from 7 to 14 nucleotides and second reads contains biological information. Second reads with a valid cell barcode in corresponding first read are mapped to the reference transcriptome. Multimappers are not considered as valid. The cell specific barcode file is added as a part of processed data files. Genome build: All transcriptome libraries were mapped to Danio rerio assembly Zv9 ensemble 74 extended with ERCC92 Supplementary files format and content: Tabular separated files with transcript count rows per cell columns. Cells in each file are labeled according to barcode ID. The cell specific barcode file is provided in the celseq2 bc.csv.gz 1st column represents index of the cell specific barcode and the second column is the barcode WKM3 eosinophils   post organ isolation live single cells are sorted into 384 well plates containing mineral oil uniquely barcoded cell specific primers for mRNA detection Spike in controls and and RNAse inhibitor. cellseq2 Illumina TruSeq adapaters   FISHid:WKM3|cell type:whole kidney marrow single cells|cell subtype:eosinophils GSM3070132 GSM3070132: WKM3 eosinophils; Danio rerio; RNA Seq GSM3070132   1 post organ isolation live single cells are sorted into 384 well plates containing mineral oil uniquely barcoded cell specific primers for mRNA detection Spike in controls and and RNAse inhibitor. cellseq2 Illumina TruSeq adapaters GEO Accession:GSM3070132 RNA-Seq TRANSCRIPTOMIC cDNA PAIRED ILLUMINA NextSeq 500   SRP136633     WKM3_eosinophils_L001_R1_001.fastq.gz WKM3_eosinophils_L001_R2_001.fastq.gz fastq fastq 1288630538.0 8538558.0 GSM3070132 r1 0:75.44 1:75.48 A:384649233;C:189860338;G:186795029;T:527108032;N:217906 75 75     384649233 189860338 186795029 527108032 217906 SRX3856794 SRS3100386 SRA675960 GEO Hubrecht Institute 2 0.38213 0.80648 0.32 0.39443 0.96749 0.92997 0.49763 0.55351 75 75 T B mate1 technical by mapping diff illumina nextseq unknown random_priming trueseq sc single_cell_plate celseq   Netherlands 2018-03-28 Undetermined Undetermined Blood Hematopoietic System
47926 47926 SRR6908686 SRX3856794 SRS3100386 SRP136633 PRJNA446034 Cell type purification by single cell transcriptome trained sorting GSE112438 Other Traditional cell type enrichment using fluorescence activated cell sorting FACS relies on methods that specifically label the cell type of interest. Here we propose GateID a computational method that combines single cell transcriptomics for unbiased cell type identification with FACS index sorting to purify cell types of choice. We validate GateID by purifying various cell types from the zebrafish kidney marrow and the human pancreas without xxx to specific antibodies or transgenes. Overall design: Single cell mRNA sequencing data of unenriched and enriched cells from zebrafish whole kidney marrow and human pancreas using GateID. For each cell we provide libraries with transcriptome information. An unenriched sample is transcriptome data from a live single cell FACS sort while an enriched library has transcriptome data for GateID enriched single cells for a given cell type.   pubmed:31585086   WKM3 eosinophils GSM3070132   tissue:WKM3 eosinophils|FISHid:WKM3|cell type:whole kidney marrow single cells|cell subtype:eosinophils WKM3 eosinophils In transcriptome libraries first read contains UMI 6 first nucleotides and cell barcode from 7 to 14 nucleotides and second reads contains biological information. Second reads with a valid cell barcode in corresponding first read are mapped to the reference transcriptome. Multimappers are not considered as valid. The cell specific barcode file is added as a part of processed data files. Genome build: All transcriptome libraries were mapped to Danio rerio assembly Zv9 ensemble 74 extended with ERCC92 Supplementary files format and content: Tabular separated files with transcript count rows per cell columns. Cells in each file are labeled according to barcode ID. The cell specific barcode file is provided in the celseq2 bc.csv.gz 1st column represents index of the cell specific barcode and the second column is the barcode WKM3 eosinophils   post organ isolation live single cells are sorted into 384 well plates containing mineral oil uniquely barcoded cell specific primers for mRNA detection Spike in controls and and RNAse inhibitor. cellseq2 Illumina TruSeq adapaters   FISHid:WKM3|cell type:whole kidney marrow single cells|cell subtype:eosinophils GSM3070132 GSM3070132: WKM3 eosinophils; Danio rerio; RNA Seq GSM3070132   1 post organ isolation live single cells are sorted into 384 well plates containing mineral oil uniquely barcoded cell specific primers for mRNA detection Spike in controls and and RNAse inhibitor. cellseq2 Illumina TruSeq adapaters GEO Accession:GSM3070132 RNA-Seq TRANSCRIPTOMIC cDNA PAIRED ILLUMINA NextSeq 500   SRP136633     WKM3_eosinophils_L002_R1_001.fastq.gz WKM3_eosinophils_L002_R2_001.fastq.gz fastq fastq 1176806695.0 7797829.0 GSM3070132 r2 0:75.44 1:75.48 A:349406324;C:172751257;G:173925861;T:480558924;N:164329 75 75     349406324 172751257 173925861 480558924 164329 SRX3856794 SRS3100386 SRA675960 GEO Hubrecht Institute 2 0.3792 0.81293 0.31911 0.39312 0.96834 0.93176 0.52878 0.54431 75 76 T B mate1 technical by mapping diff illumina nextseq unknown random_priming trueseq sc single_cell_plate celseq   Netherlands 2018-03-28 Undetermined Undetermined Blood Hematopoietic System
47927 47927 SRR6908687 SRX3856794 SRS3100386 SRP136633 PRJNA446034 Cell type purification by single cell transcriptome trained sorting GSE112438 Other Traditional cell type enrichment using fluorescence activated cell sorting FACS relies on methods that specifically label the cell type of interest. Here we propose GateID a computational method that combines single cell transcriptomics for unbiased cell type identification with FACS index sorting to purify cell types of choice. We validate GateID by purifying various cell types from the zebrafish kidney marrow and the human pancreas without xxx to specific antibodies or transgenes. Overall design: Single cell mRNA sequencing data of unenriched and enriched cells from zebrafish whole kidney marrow and human pancreas using GateID. For each cell we provide libraries with transcriptome information. An unenriched sample is transcriptome data from a live single cell FACS sort while an enriched library has transcriptome data for GateID enriched single cells for a given cell type.   pubmed:31585086   WKM3 eosinophils GSM3070132   tissue:WKM3 eosinophils|FISHid:WKM3|cell type:whole kidney marrow single cells|cell subtype:eosinophils WKM3 eosinophils In transcriptome libraries first read contains UMI 6 first nucleotides and cell barcode from 7 to 14 nucleotides and second reads contains biological information. Second reads with a valid cell barcode in corresponding first read are mapped to the reference transcriptome. Multimappers are not considered as valid. The cell specific barcode file is added as a part of processed data files. Genome build: All transcriptome libraries were mapped to Danio rerio assembly Zv9 ensemble 74 extended with ERCC92 Supplementary files format and content: Tabular separated files with transcript count rows per cell columns. Cells in each file are labeled according to barcode ID. The cell specific barcode file is provided in the celseq2 bc.csv.gz 1st column represents index of the cell specific barcode and the second column is the barcode WKM3 eosinophils   post organ isolation live single cells are sorted into 384 well plates containing mineral oil uniquely barcoded cell specific primers for mRNA detection Spike in controls and and RNAse inhibitor. cellseq2 Illumina TruSeq adapaters   FISHid:WKM3|cell type:whole kidney marrow single cells|cell subtype:eosinophils GSM3070132 GSM3070132: WKM3 eosinophils; Danio rerio; RNA Seq GSM3070132   1 post organ isolation live single cells are sorted into 384 well plates containing mineral oil uniquely barcoded cell specific primers for mRNA detection Spike in controls and and RNAse inhibitor. cellseq2 Illumina TruSeq adapaters GEO Accession:GSM3070132 RNA-Seq TRANSCRIPTOMIC cDNA PAIRED ILLUMINA NextSeq 500   SRP136633     WKM3_eosinophils_L003_R1_001.fastq.gz WKM3_eosinophils_L003_R2_001.fastq.gz fastq fastq 1341470236.0 8888024.0 GSM3070132 r3 0:75.45 1:75.48 A:395746232;C:196893713;G:196590339;T:552105050;N:134902 75 75     395746232 196893713 196590339 552105050 134902 SRX3856794 SRS3100386 SRA675960 GEO Hubrecht Institute 2 0.38231 0.81774 0.3205 0.39891 0.97279 0.92817 0.47656 0.53862 75 74 T B mate1 technical by mapping diff illumina nextseq unknown random_priming trueseq sc single_cell_plate celseq   Netherlands 2018-03-28 Undetermined Undetermined Blood Hematopoietic System
47928 47928 SRR6908688 SRX3856794 SRS3100386 SRP136633 PRJNA446034 Cell type purification by single cell transcriptome trained sorting GSE112438 Other Traditional cell type enrichment using fluorescence activated cell sorting FACS relies on methods that specifically label the cell type of interest. Here we propose GateID a computational method that combines single cell transcriptomics for unbiased cell type identification with FACS index sorting to purify cell types of choice. We validate GateID by purifying various cell types from the zebrafish kidney marrow and the human pancreas without xxx to specific antibodies or transgenes. Overall design: Single cell mRNA sequencing data of unenriched and enriched cells from zebrafish whole kidney marrow and human pancreas using GateID. For each cell we provide libraries with transcriptome information. An unenriched sample is transcriptome data from a live single cell FACS sort while an enriched library has transcriptome data for GateID enriched single cells for a given cell type.   pubmed:31585086   WKM3 eosinophils GSM3070132   tissue:WKM3 eosinophils|FISHid:WKM3|cell type:whole kidney marrow single cells|cell subtype:eosinophils WKM3 eosinophils In transcriptome libraries first read contains UMI 6 first nucleotides and cell barcode from 7 to 14 nucleotides and second reads contains biological information. Second reads with a valid cell barcode in corresponding first read are mapped to the reference transcriptome. Multimappers are not considered as valid. The cell specific barcode file is added as a part of processed data files. Genome build: All transcriptome libraries were mapped to Danio rerio assembly Zv9 ensemble 74 extended with ERCC92 Supplementary files format and content: Tabular separated files with transcript count rows per cell columns. Cells in each file are labeled according to barcode ID. The cell specific barcode file is provided in the celseq2 bc.csv.gz 1st column represents index of the cell specific barcode and the second column is the barcode WKM3 eosinophils   post organ isolation live single cells are sorted into 384 well plates containing mineral oil uniquely barcoded cell specific primers for mRNA detection Spike in controls and and RNAse inhibitor. cellseq2 Illumina TruSeq adapaters   FISHid:WKM3|cell type:whole kidney marrow single cells|cell subtype:eosinophils GSM3070132 GSM3070132: WKM3 eosinophils; Danio rerio; RNA Seq GSM3070132   1 post organ isolation live single cells are sorted into 384 well plates containing mineral oil uniquely barcoded cell specific primers for mRNA detection Spike in controls and and RNAse inhibitor. cellseq2 Illumina TruSeq adapaters GEO Accession:GSM3070132 RNA-Seq TRANSCRIPTOMIC cDNA PAIRED ILLUMINA NextSeq 500   SRP136633     WKM3_eosinophils_L004_R1_001.fastq.gz WKM3_eosinophils_L004_R2_001.fastq.gz fastq fastq 1280409546.0 8484656.0 GSM3070132 r4 0:75.44 1:75.47 A:378449241;C:186686350;G:189951619;T:525218711;N:103625 75 75     378449241 186686350 189951619 525218711 103625 SRX3856794 SRS3100386 SRA675960 GEO Hubrecht Institute 2 0.36531 0.80793 0.30661 0.38147 0.97492 0.94401 0.47223 0.55437 76 76 T B mate1 technical by mapping diff illumina nextseq unknown random_priming trueseq sc single_cell_plate celseq   Netherlands 2018-03-28 Undetermined Undetermined Blood Hematopoietic System
47929 47929 SRR6908681 SRX3856793 SRS3100385 SRP136633 PRJNA446034 Cell type purification by single cell transcriptome trained sorting GSE112438 Other Traditional cell type enrichment using fluorescence activated cell sorting FACS relies on methods that specifically label the cell type of interest. Here we propose GateID a computational method that combines single cell transcriptomics for unbiased cell type identification with FACS index sorting to purify cell types of choice. We validate GateID by purifying various cell types from the zebrafish kidney marrow and the human pancreas without xxx to specific antibodies or transgenes. Overall design: Single cell mRNA sequencing data of unenriched and enriched cells from zebrafish whole kidney marrow and human pancreas using GateID. For each cell we provide libraries with transcriptome information. An unenriched sample is transcriptome data from a live single cell FACS sort while an enriched library has transcriptome data for GateID enriched single cells for a given cell type.   pubmed:31585086   WKM3 classicalgate eosinophils GSM3070131   tissue:WKM3 classicalgate eosinophils|FISHid:WKM3|cell type:whole kidney marrow single cells|cell subtype:classicalgate eosinophils WKM3 classicalgate eosinophils In transcriptome libraries first read contains UMI 6 first nucleotides and cell barcode from 7 to 14 nucleotides and second reads contains biological information. Second reads with a valid cell barcode in corresponding first read are mapped to the reference transcriptome. Multimappers are not considered as valid. The cell specific barcode file is added as a part of processed data files. Genome build: All transcriptome libraries were mapped to Danio rerio assembly Zv9 ensemble 74 extended with ERCC92 Supplementary files format and content: Tabular separated files with transcript count rows per cell columns. Cells in each file are labeled according to barcode ID. The cell specific barcode file is provided in the celseq2 bc.csv.gz 1st column represents index of the cell specific barcode and the second column is the barcode WKM3 classicalgate eosinophils   post organ isolation live single cells are sorted into 384 well plates containing mineral oil uniquely barcoded cell specific primers for mRNA detection Spike in controls and and RNAse inhibitor. cellseq2 Illumina TruSeq adapaters   FISHid:WKM3|cell type:whole kidney marrow single cells|cell subtype:classicalgate eosinophils GSM3070131 GSM3070131: WKM3 classicalgate eosinophils; Danio rerio; RNA Seq GSM3070131   1 post organ isolation live single cells are sorted into 384 well plates containing mineral oil uniquely barcoded cell specific primers for mRNA detection Spike in controls and and RNAse inhibitor. cellseq2 Illumina TruSeq adapaters GEO Accession:GSM3070131 RNA-Seq TRANSCRIPTOMIC cDNA PAIRED ILLUMINA NextSeq 500   SRP136633     WKM3_classicalgate-eosinophils_L001_R1_001.fastq.gz WKM3_classicalgate-eosinophils_L001_R2_001.fastq.gz fastq fastq 1462702668.0 9694435.0 GSM3070131 r1 0:75.42 1:75.46 A:445709472;C:205316750;G:205641600;T:605784979;N:249867 75 75     445709472 205316750 205641600 605784979 249867 SRX3856793 SRS3100385 SRA675960 GEO Hubrecht Institute 2 0.39724 0.82292 0.32577 0.36062 0.9713 0.9301 0.46129 0.54716 75 75 T B mate1 technical by mapping diff illumina nextseq unknown random_priming trueseq sc single_cell_plate celseq   Netherlands 2018-03-28 Undetermined Undetermined Blood Hematopoietic System
47930 47930 SRR6908682 SRX3856793 SRS3100385 SRP136633 PRJNA446034 Cell type purification by single cell transcriptome trained sorting GSE112438 Other Traditional cell type enrichment using fluorescence activated cell sorting FACS relies on methods that specifically label the cell type of interest. Here we propose GateID a computational method that combines single cell transcriptomics for unbiased cell type identification with FACS index sorting to purify cell types of choice. We validate GateID by purifying various cell types from the zebrafish kidney marrow and the human pancreas without xxx to specific antibodies or transgenes. Overall design: Single cell mRNA sequencing data of unenriched and enriched cells from zebrafish whole kidney marrow and human pancreas using GateID. For each cell we provide libraries with transcriptome information. An unenriched sample is transcriptome data from a live single cell FACS sort while an enriched library has transcriptome data for GateID enriched single cells for a given cell type.   pubmed:31585086   WKM3 classicalgate eosinophils GSM3070131   tissue:WKM3 classicalgate eosinophils|FISHid:WKM3|cell type:whole kidney marrow single cells|cell subtype:classicalgate eosinophils WKM3 classicalgate eosinophils In transcriptome libraries first read contains UMI 6 first nucleotides and cell barcode from 7 to 14 nucleotides and second reads contains biological information. Second reads with a valid cell barcode in corresponding first read are mapped to the reference transcriptome. Multimappers are not considered as valid. The cell specific barcode file is added as a part of processed data files. Genome build: All transcriptome libraries were mapped to Danio rerio assembly Zv9 ensemble 74 extended with ERCC92 Supplementary files format and content: Tabular separated files with transcript count rows per cell columns. Cells in each file are labeled according to barcode ID. The cell specific barcode file is provided in the celseq2 bc.csv.gz 1st column represents index of the cell specific barcode and the second column is the barcode WKM3 classicalgate eosinophils   post organ isolation live single cells are sorted into 384 well plates containing mineral oil uniquely barcoded cell specific primers for mRNA detection Spike in controls and and RNAse inhibitor. cellseq2 Illumina TruSeq adapaters   FISHid:WKM3|cell type:whole kidney marrow single cells|cell subtype:classicalgate eosinophils GSM3070131 GSM3070131: WKM3 classicalgate eosinophils; Danio rerio; RNA Seq GSM3070131   1 post organ isolation live single cells are sorted into 384 well plates containing mineral oil uniquely barcoded cell specific primers for mRNA detection Spike in controls and and RNAse inhibitor. cellseq2 Illumina TruSeq adapaters GEO Accession:GSM3070131 RNA-Seq TRANSCRIPTOMIC cDNA PAIRED ILLUMINA NextSeq 500   SRP136633     WKM3_classicalgate-eosinophils_L002_R2_001.fastq.gz WKM3_classicalgate-eosinophils_L002_R1_001.fastq.gz fastq fastq 1331127146.0 8822708.0 GSM3070131 r2 0:75.42 1:75.45 A:403738285;C:185977311;G:190160316;T:551055048;N:196186 75 75     403738285 185977311 190160316 551055048 196186 SRX3856793 SRS3100385 SRA675960 GEO Hubrecht Institute 2 0.39004 0.81794 0.32291 0.35472 0.97228 0.93626 0.48683 0.54607 76 75 T B mate1 technical by mapping diff illumina nextseq unknown random_priming trueseq sc single_cell_plate celseq   Netherlands 2018-03-28 Undetermined Undetermined Blood Hematopoietic System
47931 47931 SRR6908683 SRX3856793 SRS3100385 SRP136633 PRJNA446034 Cell type purification by single cell transcriptome trained sorting GSE112438 Other Traditional cell type enrichment using fluorescence activated cell sorting FACS relies on methods that specifically label the cell type of interest. Here we propose GateID a computational method that combines single cell transcriptomics for unbiased cell type identification with FACS index sorting to purify cell types of choice. We validate GateID by purifying various cell types from the zebrafish kidney marrow and the human pancreas without xxx to specific antibodies or transgenes. Overall design: Single cell mRNA sequencing data of unenriched and enriched cells from zebrafish whole kidney marrow and human pancreas using GateID. For each cell we provide libraries with transcriptome information. An unenriched sample is transcriptome data from a live single cell FACS sort while an enriched library has transcriptome data for GateID enriched single cells for a given cell type.   pubmed:31585086   WKM3 classicalgate eosinophils GSM3070131   tissue:WKM3 classicalgate eosinophils|FISHid:WKM3|cell type:whole kidney marrow single cells|cell subtype:classicalgate eosinophils WKM3 classicalgate eosinophils In transcriptome libraries first read contains UMI 6 first nucleotides and cell barcode from 7 to 14 nucleotides and second reads contains biological information. Second reads with a valid cell barcode in corresponding first read are mapped to the reference transcriptome. Multimappers are not considered as valid. The cell specific barcode file is added as a part of processed data files. Genome build: All transcriptome libraries were mapped to Danio rerio assembly Zv9 ensemble 74 extended with ERCC92 Supplementary files format and content: Tabular separated files with transcript count rows per cell columns. Cells in each file are labeled according to barcode ID. The cell specific barcode file is provided in the celseq2 bc.csv.gz 1st column represents index of the cell specific barcode and the second column is the barcode WKM3 classicalgate eosinophils   post organ isolation live single cells are sorted into 384 well plates containing mineral oil uniquely barcoded cell specific primers for mRNA detection Spike in controls and and RNAse inhibitor. cellseq2 Illumina TruSeq adapaters   FISHid:WKM3|cell type:whole kidney marrow single cells|cell subtype:classicalgate eosinophils GSM3070131 GSM3070131: WKM3 classicalgate eosinophils; Danio rerio; RNA Seq GSM3070131   1 post organ isolation live single cells are sorted into 384 well plates containing mineral oil uniquely barcoded cell specific primers for mRNA detection Spike in controls and and RNAse inhibitor. cellseq2 Illumina TruSeq adapaters GEO Accession:GSM3070131 RNA-Seq TRANSCRIPTOMIC cDNA PAIRED ILLUMINA NextSeq 500   SRP136633     WKM3_classicalgate-eosinophils_L003_R2_001.fastq.gz WKM3_classicalgate-eosinophils_L003_R1_001.fastq.gz fastq fastq 1530092022.0 10140266.0 GSM3070131 r3 0:75.43 1:75.46 A:460384140;C:214158501;G:217473146;T:637914633;N:161602 75 75     460384140 214158501 217473146 637914633 161602 SRX3856793 SRS3100385 SRA675960 GEO Hubrecht Institute 2 0.39487 0.82643 0.32278 0.35403 0.97555 0.92989 0.49836 0.55498 75 75 T B mate1 technical by mapping diff illumina nextseq unknown random_priming trueseq sc single_cell_plate celseq   Netherlands 2018-03-28 Undetermined Undetermined Blood Hematopoietic System
47932 47932 SRR6908684 SRX3856793 SRS3100385 SRP136633 PRJNA446034 Cell type purification by single cell transcriptome trained sorting GSE112438 Other Traditional cell type enrichment using fluorescence activated cell sorting FACS relies on methods that specifically label the cell type of interest. Here we propose GateID a computational method that combines single cell transcriptomics for unbiased cell type identification with FACS index sorting to purify cell types of choice. We validate GateID by purifying various cell types from the zebrafish kidney marrow and the human pancreas without xxx to specific antibodies or transgenes. Overall design: Single cell mRNA sequencing data of unenriched and enriched cells from zebrafish whole kidney marrow and human pancreas using GateID. For each cell we provide libraries with transcriptome information. An unenriched sample is transcriptome data from a live single cell FACS sort while an enriched library has transcriptome data for GateID enriched single cells for a given cell type.   pubmed:31585086   WKM3 classicalgate eosinophils GSM3070131   tissue:WKM3 classicalgate eosinophils|FISHid:WKM3|cell type:whole kidney marrow single cells|cell subtype:classicalgate eosinophils WKM3 classicalgate eosinophils In transcriptome libraries first read contains UMI 6 first nucleotides and cell barcode from 7 to 14 nucleotides and second reads contains biological information. Second reads with a valid cell barcode in corresponding first read are mapped to the reference transcriptome. Multimappers are not considered as valid. The cell specific barcode file is added as a part of processed data files. Genome build: All transcriptome libraries were mapped to Danio rerio assembly Zv9 ensemble 74 extended with ERCC92 Supplementary files format and content: Tabular separated files with transcript count rows per cell columns. Cells in each file are labeled according to barcode ID. The cell specific barcode file is provided in the celseq2 bc.csv.gz 1st column represents index of the cell specific barcode and the second column is the barcode WKM3 classicalgate eosinophils   post organ isolation live single cells are sorted into 384 well plates containing mineral oil uniquely barcoded cell specific primers for mRNA detection Spike in controls and and RNAse inhibitor. cellseq2 Illumina TruSeq adapaters   FISHid:WKM3|cell type:whole kidney marrow single cells|cell subtype:classicalgate eosinophils GSM3070131 GSM3070131: WKM3 classicalgate eosinophils; Danio rerio; RNA Seq GSM3070131   1 post organ isolation live single cells are sorted into 384 well plates containing mineral oil uniquely barcoded cell specific primers for mRNA detection Spike in controls and and RNAse inhibitor. cellseq2 Illumina TruSeq adapaters GEO Accession:GSM3070131 RNA-Seq TRANSCRIPTOMIC cDNA PAIRED ILLUMINA NextSeq 500   SRP136633     WKM3_classicalgate-eosinophils_L004_R1_001.fastq.gz WKM3_classicalgate-eosinophils_L004_R2_001.fastq.gz fastq fastq 1434145773.0 9505626.0 GSM3070131 r4 0:75.43 1:75.45 A:431807112;C:200115802;G:205473991;T:596630601;N:118267 75 75     431807112 200115802 205473991 596630601 118267 SRX3856793 SRS3100385 SRA675960 GEO Hubrecht Institute 2 0.38268 0.82073 0.31346 0.34043 0.97678 0.94172 0.45791 0.5661 75 75 T B mate1 technical by mapping diff illumina nextseq unknown random_priming trueseq sc single_cell_plate celseq   Netherlands 2018-03-28 Undetermined Undetermined Blood Hematopoietic System
47937 47937 SRR6908673 SRX3856790 SRS3100384 SRP136633 PRJNA446034 Cell type purification by single cell transcriptome trained sorting GSE112438 Other Traditional cell type enrichment using fluorescence activated cell sorting FACS relies on methods that specifically label the cell type of interest. Here we propose GateID a computational method that combines single cell transcriptomics for unbiased cell type identification with FACS index sorting to purify cell types of choice. We validate GateID by purifying various cell types from the zebrafish kidney marrow and the human pancreas without xxx to specific antibodies or transgenes. Overall design: Single cell mRNA sequencing data of unenriched and enriched cells from zebrafish whole kidney marrow and human pancreas using GateID. For each cell we provide libraries with transcriptome information. An unenriched sample is transcriptome data from a live single cell FACS sort while an enriched library has transcriptome data for GateID enriched single cells for a given cell type.   pubmed:31585086   WKM2 eosinophils GSM3070129   tissue:WKM2 eosinophils|FISHid:WKM2|cell type:whole kidney marrow single cells|cell subtype:eosinophils WKM2 eosinophils In transcriptome libraries first read contains UMI 6 first nucleotides and cell barcode from 7 to 14 nucleotides and second reads contains biological information. Second reads with a valid cell barcode in corresponding first read are mapped to the reference transcriptome. Multimappers are not considered as valid. The cell specific barcode file is added as a part of processed data files. Genome build: All transcriptome libraries were mapped to Danio rerio assembly Zv9 ensemble 74 extended with ERCC92 Supplementary files format and content: Tabular separated files with transcript count rows per cell columns. Cells in each file are labeled according to barcode ID. The cell specific barcode file is provided in the celseq2 bc.csv.gz 1st column represents index of the cell specific barcode and the second column is the barcode WKM2 eosinophils   post organ isolation live single cells are sorted into 384 well plates containing mineral oil uniquely barcoded cell specific primers for mRNA detection Spike in controls and and RNAse inhibitor. cellseq2 Illumina TruSeq adapaters   FISHid:WKM2|cell type:whole kidney marrow single cells|cell subtype:eosinophils GSM3070129 GSM3070129: WKM2 eosinophils; Danio rerio; RNA Seq GSM3070129   1 post organ isolation live single cells are sorted into 384 well plates containing mineral oil uniquely barcoded cell specific primers for mRNA detection Spike in controls and and RNAse inhibitor. cellseq2 Illumina TruSeq adapaters GEO Accession:GSM3070129 RNA-Seq TRANSCRIPTOMIC cDNA PAIRED ILLUMINA NextSeq 500   SRP136633     WKM2_eosinophils_L001_R1_001.fastq.gz WKM2_eosinophils_L001_R2_001.fastq.gz fastq fastq 1444176720.0 9566997.0 GSM3070129 r1 0:75.46 1:75.50 A:432764637;C:224282941;G:222436448;T:564444426;N:248268 75 75     432764637 224282941 222436448 564444426 248268 SRX3856790 SRS3100384 SRA675960 GEO Hubrecht Institute 2 0.37168 0.75194 0.32262 0.52753 0.95915 0.94073 0.51621 0.55453 76 76 T B mate1 technical by mapping diff illumina nextseq unknown random_priming trueseq sc single_cell_plate celseq   Netherlands 2018-03-28 Undetermined Undetermined Blood Hematopoietic System
47938 47938 SRR6908674 SRX3856790 SRS3100384 SRP136633 PRJNA446034 Cell type purification by single cell transcriptome trained sorting GSE112438 Other Traditional cell type enrichment using fluorescence activated cell sorting FACS relies on methods that specifically label the cell type of interest. Here we propose GateID a computational method that combines single cell transcriptomics for unbiased cell type identification with FACS index sorting to purify cell types of choice. We validate GateID by purifying various cell types from the zebrafish kidney marrow and the human pancreas without xxx to specific antibodies or transgenes. Overall design: Single cell mRNA sequencing data of unenriched and enriched cells from zebrafish whole kidney marrow and human pancreas using GateID. For each cell we provide libraries with transcriptome information. An unenriched sample is transcriptome data from a live single cell FACS sort while an enriched library has transcriptome data for GateID enriched single cells for a given cell type.   pubmed:31585086   WKM2 eosinophils GSM3070129   tissue:WKM2 eosinophils|FISHid:WKM2|cell type:whole kidney marrow single cells|cell subtype:eosinophils WKM2 eosinophils In transcriptome libraries first read contains UMI 6 first nucleotides and cell barcode from 7 to 14 nucleotides and second reads contains biological information. Second reads with a valid cell barcode in corresponding first read are mapped to the reference transcriptome. Multimappers are not considered as valid. The cell specific barcode file is added as a part of processed data files. Genome build: All transcriptome libraries were mapped to Danio rerio assembly Zv9 ensemble 74 extended with ERCC92 Supplementary files format and content: Tabular separated files with transcript count rows per cell columns. Cells in each file are labeled according to barcode ID. The cell specific barcode file is provided in the celseq2 bc.csv.gz 1st column represents index of the cell specific barcode and the second column is the barcode WKM2 eosinophils   post organ isolation live single cells are sorted into 384 well plates containing mineral oil uniquely barcoded cell specific primers for mRNA detection Spike in controls and and RNAse inhibitor. cellseq2 Illumina TruSeq adapaters   FISHid:WKM2|cell type:whole kidney marrow single cells|cell subtype:eosinophils GSM3070129 GSM3070129: WKM2 eosinophils; Danio rerio; RNA Seq GSM3070129   1 post organ isolation live single cells are sorted into 384 well plates containing mineral oil uniquely barcoded cell specific primers for mRNA detection Spike in controls and and RNAse inhibitor. cellseq2 Illumina TruSeq adapaters GEO Accession:GSM3070129 RNA-Seq TRANSCRIPTOMIC cDNA PAIRED ILLUMINA NextSeq 500   SRP136633     WKM2_eosinophils_L002_R1_001.fastq.gz WKM2_eosinophils_L002_R2_001.fastq.gz fastq fastq 1320784826.0 8749678.0 GSM3070129 r2 0:75.45 1:75.50 A:392835077;C:204358703;G:208201633;T:515202839;N:186574 75 75     392835077 204358703 208201633 515202839 186574 SRX3856790 SRS3100384 SRA675960 GEO Hubrecht Institute 2 0.36921 0.73955 0.32271 0.52168 0.96047 0.94643 0.49733 0.53116 76 76 T B mate1 technical by mapping diff illumina nextseq unknown random_priming trueseq sc single_cell_plate celseq   Netherlands 2018-03-28 Undetermined Undetermined Blood Hematopoietic System
47939 47939 SRR6908675 SRX3856790 SRS3100384 SRP136633 PRJNA446034 Cell type purification by single cell transcriptome trained sorting GSE112438 Other Traditional cell type enrichment using fluorescence activated cell sorting FACS relies on methods that specifically label the cell type of interest. Here we propose GateID a computational method that combines single cell transcriptomics for unbiased cell type identification with FACS index sorting to purify cell types of choice. We validate GateID by purifying various cell types from the zebrafish kidney marrow and the human pancreas without xxx to specific antibodies or transgenes. Overall design: Single cell mRNA sequencing data of unenriched and enriched cells from zebrafish whole kidney marrow and human pancreas using GateID. For each cell we provide libraries with transcriptome information. An unenriched sample is transcriptome data from a live single cell FACS sort while an enriched library has transcriptome data for GateID enriched single cells for a given cell type.   pubmed:31585086   WKM2 eosinophils GSM3070129   tissue:WKM2 eosinophils|FISHid:WKM2|cell type:whole kidney marrow single cells|cell subtype:eosinophils WKM2 eosinophils In transcriptome libraries first read contains UMI 6 first nucleotides and cell barcode from 7 to 14 nucleotides and second reads contains biological information. Second reads with a valid cell barcode in corresponding first read are mapped to the reference transcriptome. Multimappers are not considered as valid. The cell specific barcode file is added as a part of processed data files. Genome build: All transcriptome libraries were mapped to Danio rerio assembly Zv9 ensemble 74 extended with ERCC92 Supplementary files format and content: Tabular separated files with transcript count rows per cell columns. Cells in each file are labeled according to barcode ID. The cell specific barcode file is provided in the celseq2 bc.csv.gz 1st column represents index of the cell specific barcode and the second column is the barcode WKM2 eosinophils   post organ isolation live single cells are sorted into 384 well plates containing mineral oil uniquely barcoded cell specific primers for mRNA detection Spike in controls and and RNAse inhibitor. cellseq2 Illumina TruSeq adapaters   FISHid:WKM2|cell type:whole kidney marrow single cells|cell subtype:eosinophils GSM3070129 GSM3070129: WKM2 eosinophils; Danio rerio; RNA Seq GSM3070129   1 post organ isolation live single cells are sorted into 384 well plates containing mineral oil uniquely barcoded cell specific primers for mRNA detection Spike in controls and and RNAse inhibitor. cellseq2 Illumina TruSeq adapaters GEO Accession:GSM3070129 RNA-Seq TRANSCRIPTOMIC cDNA PAIRED ILLUMINA NextSeq 500   SRP136633     WKM2_eosinophils_L003_R2_001.fastq.gz WKM2_eosinophils_L003_R1_001.fastq.gz fastq fastq 1500143635.0 9937133.0 GSM3070129 r3 0:75.46 1:75.50 A:445152171;C:231621688;G:233127317;T:590090278;N:152181 75 75     445152171 231621688 233127317 590090278 152181 SRX3856790 SRS3100384 SRA675960 GEO Hubrecht Institute 2 0.36533 0.75138 0.31769 0.52682 0.96731 0.94209 0.50569 0.54346 76 75 T B mate1 technical by mapping diff illumina nextseq unknown random_priming trueseq sc single_cell_plate celseq   Netherlands 2018-03-28 Undetermined Undetermined Blood Hematopoietic System
47940 47940 SRR6908676 SRX3856790 SRS3100384 SRP136633 PRJNA446034 Cell type purification by single cell transcriptome trained sorting GSE112438 Other Traditional cell type enrichment using fluorescence activated cell sorting FACS relies on methods that specifically label the cell type of interest. Here we propose GateID a computational method that combines single cell transcriptomics for unbiased cell type identification with FACS index sorting to purify cell types of choice. We validate GateID by purifying various cell types from the zebrafish kidney marrow and the human pancreas without xxx to specific antibodies or transgenes. Overall design: Single cell mRNA sequencing data of unenriched and enriched cells from zebrafish whole kidney marrow and human pancreas using GateID. For each cell we provide libraries with transcriptome information. An unenriched sample is transcriptome data from a live single cell FACS sort while an enriched library has transcriptome data for GateID enriched single cells for a given cell type.   pubmed:31585086   WKM2 eosinophils GSM3070129   tissue:WKM2 eosinophils|FISHid:WKM2|cell type:whole kidney marrow single cells|cell subtype:eosinophils WKM2 eosinophils In transcriptome libraries first read contains UMI 6 first nucleotides and cell barcode from 7 to 14 nucleotides and second reads contains biological information. Second reads with a valid cell barcode in corresponding first read are mapped to the reference transcriptome. Multimappers are not considered as valid. The cell specific barcode file is added as a part of processed data files. Genome build: All transcriptome libraries were mapped to Danio rerio assembly Zv9 ensemble 74 extended with ERCC92 Supplementary files format and content: Tabular separated files with transcript count rows per cell columns. Cells in each file are labeled according to barcode ID. The cell specific barcode file is provided in the celseq2 bc.csv.gz 1st column represents index of the cell specific barcode and the second column is the barcode WKM2 eosinophils   post organ isolation live single cells are sorted into 384 well plates containing mineral oil uniquely barcoded cell specific primers for mRNA detection Spike in controls and and RNAse inhibitor. cellseq2 Illumina TruSeq adapaters   FISHid:WKM2|cell type:whole kidney marrow single cells|cell subtype:eosinophils GSM3070129 GSM3070129: WKM2 eosinophils; Danio rerio; RNA Seq GSM3070129   1 post organ isolation live single cells are sorted into 384 well plates containing mineral oil uniquely barcoded cell specific primers for mRNA detection Spike in controls and and RNAse inhibitor. cellseq2 Illumina TruSeq adapaters GEO Accession:GSM3070129 RNA-Seq TRANSCRIPTOMIC cDNA PAIRED ILLUMINA NextSeq 500   SRP136633     WKM2_eosinophils_L004_R1_001.fastq.gz WKM2_eosinophils_L004_R2_001.fastq.gz fastq fastq 1428668507.0 9464755.0 GSM3070129 r4 0:75.46 1:75.49 A:423108347;C:219338398;G:226517917;T:559587502;N:116343 75 75     423108347 219338398 226517917 559587502 116343 SRX3856790 SRS3100384 SRA675960 GEO Hubrecht Institute 2 0.35962 0.7405 0.31464 0.51805 0.96962 0.95747 0.48862 0.53364 76 76 T B mate1 technical by mapping diff illumina nextseq unknown random_priming trueseq sc single_cell_plate celseq   Netherlands 2018-03-28 Undetermined Undetermined Blood Hematopoietic System
47941 47941 SRR6908669 SRX3856789 SRS3100381 SRP136633 PRJNA446034 Cell type purification by single cell transcriptome trained sorting GSE112438 Other Traditional cell type enrichment using fluorescence activated cell sorting FACS relies on methods that specifically label the cell type of interest. Here we propose GateID a computational method that combines single cell transcriptomics for unbiased cell type identification with FACS index sorting to purify cell types of choice. We validate GateID by purifying various cell types from the zebrafish kidney marrow and the human pancreas without xxx to specific antibodies or transgenes. Overall design: Single cell mRNA sequencing data of unenriched and enriched cells from zebrafish whole kidney marrow and human pancreas using GateID. For each cell we provide libraries with transcriptome information. An unenriched sample is transcriptome data from a live single cell FACS sort while an enriched library has transcriptome data for GateID enriched single cells for a given cell type.   pubmed:31585086   WKM2 classicalgate eosinophils GSM3070128   tissue:WKM2 classicalgate eosinophils|FISHid:WKM2|cell type:whole kidney marrow single cells|cell subtype:classicalgate eosinophils WKM2 classicalgate eosinophils In transcriptome libraries first read contains UMI 6 first nucleotides and cell barcode from 7 to 14 nucleotides and second reads contains biological information. Second reads with a valid cell barcode in corresponding first read are mapped to the reference transcriptome. Multimappers are not considered as valid. The cell specific barcode file is added as a part of processed data files. Genome build: All transcriptome libraries were mapped to Danio rerio assembly Zv9 ensemble 74 extended with ERCC92 Supplementary files format and content: Tabular separated files with transcript count rows per cell columns. Cells in each file are labeled according to barcode ID. The cell specific barcode file is provided in the celseq2 bc.csv.gz 1st column represents index of the cell specific barcode and the second column is the barcode WKM2 classicalgate eosinophils   post organ isolation live single cells are sorted into 384 well plates containing mineral oil uniquely barcoded cell specific primers for mRNA detection Spike in controls and and RNAse inhibitor. cellseq2 Illumina TruSeq adapaters   FISHid:WKM2|cell type:whole kidney marrow single cells|cell subtype:classicalgate eosinophils GSM3070128 GSM3070128: WKM2 classicalgate eosinophils; Danio rerio; RNA Seq GSM3070128   1 post organ isolation live single cells are sorted into 384 well plates containing mineral oil uniquely barcoded cell specific primers for mRNA detection Spike in controls and and RNAse inhibitor. cellseq2 Illumina TruSeq adapaters GEO Accession:GSM3070128 RNA-Seq TRANSCRIPTOMIC cDNA PAIRED ILLUMINA NextSeq 500   SRP136633     WKM2_classicalgate-eosinophils_L001_R2_001.fastq.gz WKM2_classicalgate-eosinophils_L001_R1_001.fastq.gz fastq fastq 880478792.0 5834816.0 GSM3070128 r1 0:75.43 1:75.47 A:267072322;C:127440559;G:126304290;T:359512123;N:149498 75 75     267072322 127440559 126304290 359512123 149498 SRX3856789 SRS3100381 SRA675960 GEO Hubrecht Institute 2 0.3788 0.812 0.28918 0.38972 0.96595 0.92904 0.5376 0.55651 75 75 T B mate1 technical by mapping diff illumina nextseq unknown random_priming trueseq sc single_cell_plate celseq   Netherlands 2018-03-28 Undetermined Undetermined Blood Hematopoietic System
47942 47942 SRR6908670 SRX3856789 SRS3100381 SRP136633 PRJNA446034 Cell type purification by single cell transcriptome trained sorting GSE112438 Other Traditional cell type enrichment using fluorescence activated cell sorting FACS relies on methods that specifically label the cell type of interest. Here we propose GateID a computational method that combines single cell transcriptomics for unbiased cell type identification with FACS index sorting to purify cell types of choice. We validate GateID by purifying various cell types from the zebrafish kidney marrow and the human pancreas without xxx to specific antibodies or transgenes. Overall design: Single cell mRNA sequencing data of unenriched and enriched cells from zebrafish whole kidney marrow and human pancreas using GateID. For each cell we provide libraries with transcriptome information. An unenriched sample is transcriptome data from a live single cell FACS sort while an enriched library has transcriptome data for GateID enriched single cells for a given cell type.   pubmed:31585086   WKM2 classicalgate eosinophils GSM3070128   tissue:WKM2 classicalgate eosinophils|FISHid:WKM2|cell type:whole kidney marrow single cells|cell subtype:classicalgate eosinophils WKM2 classicalgate eosinophils In transcriptome libraries first read contains UMI 6 first nucleotides and cell barcode from 7 to 14 nucleotides and second reads contains biological information. Second reads with a valid cell barcode in corresponding first read are mapped to the reference transcriptome. Multimappers are not considered as valid. The cell specific barcode file is added as a part of processed data files. Genome build: All transcriptome libraries were mapped to Danio rerio assembly Zv9 ensemble 74 extended with ERCC92 Supplementary files format and content: Tabular separated files with transcript count rows per cell columns. Cells in each file are labeled according to barcode ID. The cell specific barcode file is provided in the celseq2 bc.csv.gz 1st column represents index of the cell specific barcode and the second column is the barcode WKM2 classicalgate eosinophils   post organ isolation live single cells are sorted into 384 well plates containing mineral oil uniquely barcoded cell specific primers for mRNA detection Spike in controls and and RNAse inhibitor. cellseq2 Illumina TruSeq adapaters   FISHid:WKM2|cell type:whole kidney marrow single cells|cell subtype:classicalgate eosinophils GSM3070128 GSM3070128: WKM2 classicalgate eosinophils; Danio rerio; RNA Seq GSM3070128   1 post organ isolation live single cells are sorted into 384 well plates containing mineral oil uniquely barcoded cell specific primers for mRNA detection Spike in controls and and RNAse inhibitor. cellseq2 Illumina TruSeq adapaters GEO Accession:GSM3070128 RNA-Seq TRANSCRIPTOMIC cDNA PAIRED ILLUMINA NextSeq 500   SRP136633     WKM2_classicalgate-eosinophils_L002_R1_001.fastq.gz WKM2_classicalgate-eosinophils_L002_R2_001.fastq.gz fastq fastq 806110283.0 5342262.0 GSM3070128 r2 0:75.42 1:75.47 A:243426891;C:116100169;G:117840212;T:328625621;N:117390 75 75     243426891 116100169 117840212 328625621 117390 SRX3856789 SRS3100381 SRA675960 GEO Hubrecht Institute 2 0.38141 0.80952 0.29221 0.37995 0.96546 0.93381 0.54149 0.55378 75 76 T B mate1 technical by mapping diff illumina nextseq unknown random_priming trueseq sc single_cell_plate celseq   Netherlands 2018-03-28 Undetermined Undetermined Blood Hematopoietic System
47943 47943 SRR6908671 SRX3856789 SRS3100381 SRP136633 PRJNA446034 Cell type purification by single cell transcriptome trained sorting GSE112438 Other Traditional cell type enrichment using fluorescence activated cell sorting FACS relies on methods that specifically label the cell type of interest. Here we propose GateID a computational method that combines single cell transcriptomics for unbiased cell type identification with FACS index sorting to purify cell types of choice. We validate GateID by purifying various cell types from the zebrafish kidney marrow and the human pancreas without xxx to specific antibodies or transgenes. Overall design: Single cell mRNA sequencing data of unenriched and enriched cells from zebrafish whole kidney marrow and human pancreas using GateID. For each cell we provide libraries with transcriptome information. An unenriched sample is transcriptome data from a live single cell FACS sort while an enriched library has transcriptome data for GateID enriched single cells for a given cell type.   pubmed:31585086   WKM2 classicalgate eosinophils GSM3070128   tissue:WKM2 classicalgate eosinophils|FISHid:WKM2|cell type:whole kidney marrow single cells|cell subtype:classicalgate eosinophils WKM2 classicalgate eosinophils In transcriptome libraries first read contains UMI 6 first nucleotides and cell barcode from 7 to 14 nucleotides and second reads contains biological information. Second reads with a valid cell barcode in corresponding first read are mapped to the reference transcriptome. Multimappers are not considered as valid. The cell specific barcode file is added as a part of processed data files. Genome build: All transcriptome libraries were mapped to Danio rerio assembly Zv9 ensemble 74 extended with ERCC92 Supplementary files format and content: Tabular separated files with transcript count rows per cell columns. Cells in each file are labeled according to barcode ID. The cell specific barcode file is provided in the celseq2 bc.csv.gz 1st column represents index of the cell specific barcode and the second column is the barcode WKM2 classicalgate eosinophils   post organ isolation live single cells are sorted into 384 well plates containing mineral oil uniquely barcoded cell specific primers for mRNA detection Spike in controls and and RNAse inhibitor. cellseq2 Illumina TruSeq adapaters   FISHid:WKM2|cell type:whole kidney marrow single cells|cell subtype:classicalgate eosinophils GSM3070128 GSM3070128: WKM2 classicalgate eosinophils; Danio rerio; RNA Seq GSM3070128   1 post organ isolation live single cells are sorted into 384 well plates containing mineral oil uniquely barcoded cell specific primers for mRNA detection Spike in controls and and RNAse inhibitor. cellseq2 Illumina TruSeq adapaters GEO Accession:GSM3070128 RNA-Seq TRANSCRIPTOMIC cDNA PAIRED ILLUMINA NextSeq 500   SRP136633     WKM2_classicalgate-eosinophils_L003_R1_001.fastq.gz WKM2_classicalgate-eosinophils_L003_R2_001.fastq.gz fastq fastq 924045470.0 6123105.0 GSM3070128 r3 0:75.44 1:75.47 A:276824263;C:133108077;G:134012420;T:380008370;N:92340 75 75     276824263 133108077 134012420 380008370 92340 SRX3856789 SRS3100381 SRA675960 GEO Hubrecht Institute 2 0.3819 0.81801 0.29161 0.38224 0.97124 0.92809 0.53982 0.54629 76 76 T B mate1 technical by mapping diff illumina nextseq unknown random_priming trueseq sc single_cell_plate celseq   Netherlands 2018-03-28 Undetermined Undetermined Blood Hematopoietic System
47944 47944 SRR6908672 SRX3856789 SRS3100381 SRP136633 PRJNA446034 Cell type purification by single cell transcriptome trained sorting GSE112438 Other Traditional cell type enrichment using fluorescence activated cell sorting FACS relies on methods that specifically label the cell type of interest. Here we propose GateID a computational method that combines single cell transcriptomics for unbiased cell type identification with FACS index sorting to purify cell types of choice. We validate GateID by purifying various cell types from the zebrafish kidney marrow and the human pancreas without xxx to specific antibodies or transgenes. Overall design: Single cell mRNA sequencing data of unenriched and enriched cells from zebrafish whole kidney marrow and human pancreas using GateID. For each cell we provide libraries with transcriptome information. An unenriched sample is transcriptome data from a live single cell FACS sort while an enriched library has transcriptome data for GateID enriched single cells for a given cell type.   pubmed:31585086   WKM2 classicalgate eosinophils GSM3070128   tissue:WKM2 classicalgate eosinophils|FISHid:WKM2|cell type:whole kidney marrow single cells|cell subtype:classicalgate eosinophils WKM2 classicalgate eosinophils In transcriptome libraries first read contains UMI 6 first nucleotides and cell barcode from 7 to 14 nucleotides and second reads contains biological information. Second reads with a valid cell barcode in corresponding first read are mapped to the reference transcriptome. Multimappers are not considered as valid. The cell specific barcode file is added as a part of processed data files. Genome build: All transcriptome libraries were mapped to Danio rerio assembly Zv9 ensemble 74 extended with ERCC92 Supplementary files format and content: Tabular separated files with transcript count rows per cell columns. Cells in each file are labeled according to barcode ID. The cell specific barcode file is provided in the celseq2 bc.csv.gz 1st column represents index of the cell specific barcode and the second column is the barcode WKM2 classicalgate eosinophils   post organ isolation live single cells are sorted into 384 well plates containing mineral oil uniquely barcoded cell specific primers for mRNA detection Spike in controls and and RNAse inhibitor. cellseq2 Illumina TruSeq adapaters   FISHid:WKM2|cell type:whole kidney marrow single cells|cell subtype:classicalgate eosinophils GSM3070128 GSM3070128: WKM2 classicalgate eosinophils; Danio rerio; RNA Seq GSM3070128   1 post organ isolation live single cells are sorted into 384 well plates containing mineral oil uniquely barcoded cell specific primers for mRNA detection Spike in controls and and RNAse inhibitor. cellseq2 Illumina TruSeq adapaters GEO Accession:GSM3070128 RNA-Seq TRANSCRIPTOMIC cDNA PAIRED ILLUMINA NextSeq 500   SRP136633     WKM2_classicalgate-eosinophils_L004_R1_001.fastq.gz WKM2_classicalgate-eosinophils_L004_R2_001.fastq.gz fastq fastq 867397884.0 5748519.0 GSM3070128 r4 0:75.43 1:75.46 A:260294749;C:124376674;G:127341359;T:355314902;N:70200 75 75     260294749 124376674 127341359 355314902 70200 SRX3856789 SRS3100381 SRA675960 GEO Hubrecht Institute 2 0.37148 0.80708 0.28632 0.3846 0.97299 0.94474 0.5375 0.54419 76 76 T B mate1 technical by mapping diff illumina nextseq unknown random_priming trueseq sc single_cell_plate celseq   Netherlands 2018-03-28 Undetermined Undetermined Blood Hematopoietic System
62567 62567 SRR13238450 SRX9670474 SRS7869376 SRP297552 PRJNA684129 Skeletal muscle injury responsive macrophage subsets in larval zebrafish GSE162979 Transcriptome Analysis Single cell RNA sequencing scRNA seq was applied to identify and characterise macrophage subsets that responded to larval zebrafish muscle injury. The Tgmpeg1:mCherry transgenic zebrafish line was utilised to isolate mCherry expressing macrophages by FACS. Following needle stab muscle injury of a 4 dpf dpf larvae the wound site was dissected out at 1 2 and 3 xxx post injury dpi for macrophage isolation. Macrophages isolated from 4 dpf uninjured larvae were also included. This analysis led to the identification of 8 discrete clusters of macrophages one of which corresponded to uninjured macrophages. The 7 wound present macrophage subsets highlighted greater macrophage heterogeneity than previously described in an in vivo skeletal muscle injury context. Overall design: Total of 4 different time points were assayed including a 4 dpf uninjured control and 3 time points during injury resolution 1 3 dpi.   pubmed:33568815   PN8 4dpf uninjured mpeg1 mcherry R2 GSM4969715   tissue:mpeg1 expressing macrophages|development stage:4 dpf|strain:TL|transgenic line:Tgmpeg1:mCherry|cell type:macrophages|treatment:uninjured|xxx post injury:n/a PN8 4dpf uninjured mpeg1 mcherry R2 Paired reads were mapped against the zebrafish reference assembly version 9 Zv9 using bwa with a transcriptome dataset with improved 3’ UTR annotations to increase the mapability of transcripts. Read 1 was used for assigning reads to correct cells and libraries R1: 6 nt UMI + 8 nt cell barcode while read 2 was mapped to gene models. The scripts to generate the count files can be found here https://github.com/vertesy/TheCorvinas/tree/master/Python/MapAndGo2 with the readme files found here https://github.com/vertesy/TheCorvinas/blob/master/Python/MapAndGo/Readme MapAndGo.md. Read counts were first corrected for UMI barcode by removing duplicate reads that had an identical combination of library cellular and molecular barcodes that were mapped to the same gene. Transcript counts were then adjusted to the expected number of molecules based on counts 4096 possible UMI’s and Poissonian counting statistics. Genome build: Zv9 Supplementary files format and content: Each sample's cell transcript counts matrices are gzipped TSV files. The sequencing facility did not provide cell barcode sequences for each cell ID. mpeg1 expressing macrophages 4dpf larvae were anaesthetized in 0.01% tricaine in Ringer’s solution. Mechanical injures were targeted to the dorsal myotome above the cloaca when the larvae is oriented dorsal to the top anterior to the left. The myotome was subjected to a single 30 gauge needle puncture that generates an extensive injury with many damaged muscle fibres. Muscle injury region was dissected out and tissue dissociated into a single cell suspension. Whole larva were utilised for uninjured time point. Cells were sorted using a FACS Aria II BD biosciences. Live individual macrophages based on mCherry fluorescence DAPI exclusion and forward and side scatter properties were sorted into pre prepared 384 well plates containing 100 200 nl of CEL seq primers dNTPs and synthetic mRNA Spike Ins contained in 5 μl of Vapor Lock Qiagen. Immediately following sorting plates were spun down and frozen at 80°C until sequencing. Single cell RNA sequencing libraries were prepared using the SORT seq platform. Here the Cel Seq2 protocol is followed with the aid of robotic liquid handlers. This protocol results in each cell being barcoded and generating single cell transcriptomes of all isolated macrophages. All procedures involving animals at the Hubrecht Institute were approved by the local animal experiments committees and performed in compliance with animal welfare laws guidelines and policies according to national and European law. Staging and husbandry were performed using standard established and approved protocols. All embryos were maintained in Ringer’s solution at 28.5°C and treated with 0.003% 1 phenyl 2 thiourea PTU Sigma Aldrich from 8 hpf. development stage:4 dpf|strain:TL|transgenic line:Tgmpeg1:mCherry|cell type:macrophages|treatment:uninjured|xxx post injury:n/a GSM4969715 GSM4969715: PN8 4dpf uninjured mpeg1 mcherry R2; Danio rerio; RNA Seq GSM4969715   1 Muscle injury region was dissected out and tissue dissociated into a single cell suspension. Whole larva were utilised for uninjured time point. Cells were sorted using a FACS Aria II BD biosciences. Live individual macrophages based on mCherry fluorescence DAPI exclusion and forward and side scatter properties were sorted into pre prepared 384 well plates containing 100 200 nl of CEL seq primers dNTPs and synthetic mRNA Spike Ins contained in 5 μl of Vapor Lock Qiagen. Immediately following sorting plates were spun down and frozen at 80°C until sequencing. Single cell RNA sequencing libraries were prepared using the SORT seq platform. Here the Cel Seq2 protocol is followed with the aid of robotic liquid handlers. This protocol results in each cell being barcoded and generating single cell transcriptomes of all isolated macrophages. GEO Accession:GSM4969715 RNA-Seq TRANSCRIPTOMIC cDNA PAIRED ILLUMINA NextSeq 500   SRP297552     PN8_R1_cat.fastq.gz PN8_R2_cat.fastq.gz fastq fastq 12597661755.0 83472446.0 GSM4969715 r1 0:75.48 1:75.44 A:3580107664;C:1794661271;G:2126218655;T:5095225054;N:1449111 75 75     3580107664 1794661271 2126218655 5095225054 1449111 SRX9670474 SRS7869376 SRA1169931 GEO The University of Melbourne 2 0.24621 0.75686 0.17058 0.19967 0.99198 0.83075 0.4368 0.53942 75 75 T B mate1 technical by mapping diff illumina nextseq unknown random_priming unknown sc single_cell_plate celseq   Australia 2020-12-10 Larval Larval Blood Hematopoietic System
62568 62568 SRR13238446 SRX9670473 SRS7869375 SRP297552 PRJNA684129 Skeletal muscle injury responsive macrophage subsets in larval zebrafish GSE162979 Transcriptome Analysis Single cell RNA sequencing scRNA seq was applied to identify and characterise macrophage subsets that responded to larval zebrafish muscle injury. The Tgmpeg1:mCherry transgenic zebrafish line was utilised to isolate mCherry expressing macrophages by FACS. Following needle stab muscle injury of a 4 dpf dpf larvae the wound site was dissected out at 1 2 and 3 xxx post injury dpi for macrophage isolation. Macrophages isolated from 4 dpf uninjured larvae were also included. This analysis led to the identification of 8 discrete clusters of macrophages one of which corresponded to uninjured macrophages. The 7 wound present macrophage subsets highlighted greater macrophage heterogeneity than previously described in an in vivo skeletal muscle injury context. Overall design: Total of 4 different time points were assayed including a 4 dpf uninjured control and 3 time points during injury resolution 1 3 dpi.   pubmed:33568815   PN7 3dpi mpeg1 mcherry R2 GSM4969714   tissue:mpeg1 expressing macrophages|development stage:7 dpf|strain:TL|transgenic line:Tgmpeg1:mCherry|cell type:macrophages|treatment:injured muscle|xxx post injury:3 PN7 3dpi mpeg1 mcherry R2 Paired reads were mapped against the zebrafish reference assembly version 9 Zv9 using bwa with a transcriptome dataset with improved 3’ UTR annotations to increase the mapability of transcripts. Read 1 was used for assigning reads to correct cells and libraries R1: 6 nt UMI + 8 nt cell barcode while read 2 was mapped to gene models. The scripts to generate the count files can be found here https://github.com/vertesy/TheCorvinas/tree/master/Python/MapAndGo2 with the readme files found here https://github.com/vertesy/TheCorvinas/blob/master/Python/MapAndGo/Readme MapAndGo.md. Read counts were first corrected for UMI barcode by removing duplicate reads that had an identical combination of library cellular and molecular barcodes that were mapped to the same gene. Transcript counts were then adjusted to the expected number of molecules based on counts 4096 possible UMI’s and Poissonian counting statistics. Genome build: Zv9 Supplementary files format and content: Each sample's cell transcript counts matrices are gzipped TSV files. The sequencing facility did not provide cell barcode sequences for each cell ID. mpeg1 expressing macrophages 4dpf larvae were anaesthetized in 0.01% tricaine in Ringer’s solution. Mechanical injures were targeted to the dorsal myotome above the cloaca when the larvae is oriented dorsal to the top anterior to the left. The myotome was subjected to a single 30 gauge needle puncture that generates an extensive injury with many damaged muscle fibres. Muscle injury region was dissected out and tissue dissociated into a single cell suspension. Whole larva were utilised for uninjured time point. Cells were sorted using a FACS Aria II BD biosciences. Live individual macrophages based on mCherry fluorescence DAPI exclusion and forward and side scatter properties were sorted into pre prepared 384 well plates containing 100 200 nl of CEL seq primers dNTPs and synthetic mRNA Spike Ins contained in 5 μl of Vapor Lock Qiagen. Immediately following sorting plates were spun down and frozen at 80°C until sequencing. Single cell RNA sequencing libraries were prepared using the SORT seq platform. Here the Cel Seq2 protocol is followed with the aid of robotic liquid handlers. This protocol results in each cell being barcoded and generating single cell transcriptomes of all isolated macrophages. All procedures involving animals at the Hubrecht Institute were approved by the local animal experiments committees and performed in compliance with animal welfare laws guidelines and policies according to national and European law. Staging and husbandry were performed using standard established and approved protocols. All embryos were maintained in Ringer’s solution at 28.5°C and treated with 0.003% 1 phenyl 2 thiourea PTU Sigma Aldrich from 8 hpf. development stage:7 dpf|strain:TL|transgenic line:Tgmpeg1:mCherry|cell type:macrophages|treatment:injured muscle|xxx post injury:3 GSM4969714 GSM4969714: PN7 3dpi mpeg1 mcherry R2; Danio rerio; RNA Seq GSM4969714   1 Muscle injury region was dissected out and tissue dissociated into a single cell suspension. Whole larva were utilised for uninjured time point. Cells were sorted using a FACS Aria II BD biosciences. Live individual macrophages based on mCherry fluorescence DAPI exclusion and forward and side scatter properties were sorted into pre prepared 384 well plates containing 100 200 nl of CEL seq primers dNTPs and synthetic mRNA Spike Ins contained in 5 μl of Vapor Lock Qiagen. Immediately following sorting plates were spun down and frozen at 80°C until sequencing. Single cell RNA sequencing libraries were prepared using the SORT seq platform. Here the Cel Seq2 protocol is followed with the aid of robotic liquid handlers. This protocol results in each cell being barcoded and generating single cell transcriptomes of all isolated macrophages. GEO Accession:GSM4969714 RNA-Seq TRANSCRIPTOMIC cDNA PAIRED ILLUMINA NextSeq 500   SRP297552     PN7_L001_R1_001.fastq.gz PN7_L001_R2_001.fastq.gz fastq fastq 6533788395.0 43286860.0 GSM4969714 r1 0:75.49 1:75.45 A:1875974034;C:979457427;G:1120952137;T:2556380216;N:1024581 75 75     1875974034 979457427 1120952137 2556380216 1024581 SRX9670473 SRS7869375 SRA1169931 GEO The University of Melbourne 2 0.18309 0.71892 0.15278 0.43253 0.99178 0.84682 0.46869 0.53212 75 75 T B mate1 technical by mapping diff illumina nextseq unknown random_priming unknown sc single_cell_plate celseq   Australia 2020-12-10 Larval Larval Blood Hematopoietic System
62569 62569 SRR13238447 SRX9670473 SRS7869375 SRP297552 PRJNA684129 Skeletal muscle injury responsive macrophage subsets in larval zebrafish GSE162979 Transcriptome Analysis Single cell RNA sequencing scRNA seq was applied to identify and characterise macrophage subsets that responded to larval zebrafish muscle injury. The Tgmpeg1:mCherry transgenic zebrafish line was utilised to isolate mCherry expressing macrophages by FACS. Following needle stab muscle injury of a 4 dpf dpf larvae the wound site was dissected out at 1 2 and 3 xxx post injury dpi for macrophage isolation. Macrophages isolated from 4 dpf uninjured larvae were also included. This analysis led to the identification of 8 discrete clusters of macrophages one of which corresponded to uninjured macrophages. The 7 wound present macrophage subsets highlighted greater macrophage heterogeneity than previously described in an in vivo skeletal muscle injury context. Overall design: Total of 4 different time points were assayed including a 4 dpf uninjured control and 3 time points during injury resolution 1 3 dpi.   pubmed:33568815   PN7 3dpi mpeg1 mcherry R2 GSM4969714   tissue:mpeg1 expressing macrophages|development stage:7 dpf|strain:TL|transgenic line:Tgmpeg1:mCherry|cell type:macrophages|treatment:injured muscle|xxx post injury:3 PN7 3dpi mpeg1 mcherry R2 Paired reads were mapped against the zebrafish reference assembly version 9 Zv9 using bwa with a transcriptome dataset with improved 3’ UTR annotations to increase the mapability of transcripts. Read 1 was used for assigning reads to correct cells and libraries R1: 6 nt UMI + 8 nt cell barcode while read 2 was mapped to gene models. The scripts to generate the count files can be found here https://github.com/vertesy/TheCorvinas/tree/master/Python/MapAndGo2 with the readme files found here https://github.com/vertesy/TheCorvinas/blob/master/Python/MapAndGo/Readme MapAndGo.md. Read counts were first corrected for UMI barcode by removing duplicate reads that had an identical combination of library cellular and molecular barcodes that were mapped to the same gene. Transcript counts were then adjusted to the expected number of molecules based on counts 4096 possible UMI’s and Poissonian counting statistics. Genome build: Zv9 Supplementary files format and content: Each sample's cell transcript counts matrices are gzipped TSV files. The sequencing facility did not provide cell barcode sequences for each cell ID. mpeg1 expressing macrophages 4dpf larvae were anaesthetized in 0.01% tricaine in Ringer’s solution. Mechanical injures were targeted to the dorsal myotome above the cloaca when the larvae is oriented dorsal to the top anterior to the left. The myotome was subjected to a single 30 gauge needle puncture that generates an extensive injury with many damaged muscle fibres. Muscle injury region was dissected out and tissue dissociated into a single cell suspension. Whole larva were utilised for uninjured time point. Cells were sorted using a FACS Aria II BD biosciences. Live individual macrophages based on mCherry fluorescence DAPI exclusion and forward and side scatter properties were sorted into pre prepared 384 well plates containing 100 200 nl of CEL seq primers dNTPs and synthetic mRNA Spike Ins contained in 5 μl of Vapor Lock Qiagen. Immediately following sorting plates were spun down and frozen at 80°C until sequencing. Single cell RNA sequencing libraries were prepared using the SORT seq platform. Here the Cel Seq2 protocol is followed with the aid of robotic liquid handlers. This protocol results in each cell being barcoded and generating single cell transcriptomes of all isolated macrophages. All procedures involving animals at the Hubrecht Institute were approved by the local animal experiments committees and performed in compliance with animal welfare laws guidelines and policies according to national and European law. Staging and husbandry were performed using standard established and approved protocols. All embryos were maintained in Ringer’s solution at 28.5°C and treated with 0.003% 1 phenyl 2 thiourea PTU Sigma Aldrich from 8 hpf. development stage:7 dpf|strain:TL|transgenic line:Tgmpeg1:mCherry|cell type:macrophages|treatment:injured muscle|xxx post injury:3 GSM4969714 GSM4969714: PN7 3dpi mpeg1 mcherry R2; Danio rerio; RNA Seq GSM4969714   1 Muscle injury region was dissected out and tissue dissociated into a single cell suspension. Whole larva were utilised for uninjured time point. Cells were sorted using a FACS Aria II BD biosciences. Live individual macrophages based on mCherry fluorescence DAPI exclusion and forward and side scatter properties were sorted into pre prepared 384 well plates containing 100 200 nl of CEL seq primers dNTPs and synthetic mRNA Spike Ins contained in 5 μl of Vapor Lock Qiagen. Immediately following sorting plates were spun down and frozen at 80°C until sequencing. Single cell RNA sequencing libraries were prepared using the SORT seq platform. Here the Cel Seq2 protocol is followed with the aid of robotic liquid handlers. This protocol results in each cell being barcoded and generating single cell transcriptomes of all isolated macrophages. GEO Accession:GSM4969714 RNA-Seq TRANSCRIPTOMIC cDNA PAIRED ILLUMINA NextSeq 500   SRP297552     PN7_L002_R1_001.fastq.gz PN7_L002_R2_001.fastq.gz fastq fastq 6528184943.0 43245004.0 GSM4969714 r2 0:75.50 1:75.46 A:1862291360;C:977585295;G:1137753556;T:2549421996;N:1132736 75 75     1862291360 977585295 1137753556 2549421996 1132736 SRX9670473 SRS7869375 SRA1169931 GEO The University of Melbourne 2 0.19676 0.71816 0.16378 0.43047 0.98938 0.84559 0.4879 0.53494 76 76 T B mate1 technical by mapping diff illumina nextseq unknown random_priming unknown sc single_cell_plate celseq   Australia 2020-12-10 Larval Larval Blood Hematopoietic System
62570 62570 SRR13238448 SRX9670473 SRS7869375 SRP297552 PRJNA684129 Skeletal muscle injury responsive macrophage subsets in larval zebrafish GSE162979 Transcriptome Analysis Single cell RNA sequencing scRNA seq was applied to identify and characterise macrophage subsets that responded to larval zebrafish muscle injury. The Tgmpeg1:mCherry transgenic zebrafish line was utilised to isolate mCherry expressing macrophages by FACS. Following needle stab muscle injury of a 4 dpf dpf larvae the wound site was dissected out at 1 2 and 3 xxx post injury dpi for macrophage isolation. Macrophages isolated from 4 dpf uninjured larvae were also included. This analysis led to the identification of 8 discrete clusters of macrophages one of which corresponded to uninjured macrophages. The 7 wound present macrophage subsets highlighted greater macrophage heterogeneity than previously described in an in vivo skeletal muscle injury context. Overall design: Total of 4 different time points were assayed including a 4 dpf uninjured control and 3 time points during injury resolution 1 3 dpi.   pubmed:33568815   PN7 3dpi mpeg1 mcherry R2 GSM4969714   tissue:mpeg1 expressing macrophages|development stage:7 dpf|strain:TL|transgenic line:Tgmpeg1:mCherry|cell type:macrophages|treatment:injured muscle|xxx post injury:3 PN7 3dpi mpeg1 mcherry R2 Paired reads were mapped against the zebrafish reference assembly version 9 Zv9 using bwa with a transcriptome dataset with improved 3’ UTR annotations to increase the mapability of transcripts. Read 1 was used for assigning reads to correct cells and libraries R1: 6 nt UMI + 8 nt cell barcode while read 2 was mapped to gene models. The scripts to generate the count files can be found here https://github.com/vertesy/TheCorvinas/tree/master/Python/MapAndGo2 with the readme files found here https://github.com/vertesy/TheCorvinas/blob/master/Python/MapAndGo/Readme MapAndGo.md. Read counts were first corrected for UMI barcode by removing duplicate reads that had an identical combination of library cellular and molecular barcodes that were mapped to the same gene. Transcript counts were then adjusted to the expected number of molecules based on counts 4096 possible UMI’s and Poissonian counting statistics. Genome build: Zv9 Supplementary files format and content: Each sample's cell transcript counts matrices are gzipped TSV files. The sequencing facility did not provide cell barcode sequences for each cell ID. mpeg1 expressing macrophages 4dpf larvae were anaesthetized in 0.01% tricaine in Ringer’s solution. Mechanical injures were targeted to the dorsal myotome above the cloaca when the larvae is oriented dorsal to the top anterior to the left. The myotome was subjected to a single 30 gauge needle puncture that generates an extensive injury with many damaged muscle fibres. Muscle injury region was dissected out and tissue dissociated into a single cell suspension. Whole larva were utilised for uninjured time point. Cells were sorted using a FACS Aria II BD biosciences. Live individual macrophages based on mCherry fluorescence DAPI exclusion and forward and side scatter properties were sorted into pre prepared 384 well plates containing 100 200 nl of CEL seq primers dNTPs and synthetic mRNA Spike Ins contained in 5 μl of Vapor Lock Qiagen. Immediately following sorting plates were spun down and frozen at 80°C until sequencing. Single cell RNA sequencing libraries were prepared using the SORT seq platform. Here the Cel Seq2 protocol is followed with the aid of robotic liquid handlers. This protocol results in each cell being barcoded and generating single cell transcriptomes of all isolated macrophages. All procedures involving animals at the Hubrecht Institute were approved by the local animal experiments committees and performed in compliance with animal welfare laws guidelines and policies according to national and European law. Staging and husbandry were performed using standard established and approved protocols. All embryos were maintained in Ringer’s solution at 28.5°C and treated with 0.003% 1 phenyl 2 thiourea PTU Sigma Aldrich from 8 hpf. development stage:7 dpf|strain:TL|transgenic line:Tgmpeg1:mCherry|cell type:macrophages|treatment:injured muscle|xxx post injury:3 GSM4969714 GSM4969714: PN7 3dpi mpeg1 mcherry R2; Danio rerio; RNA Seq GSM4969714   1 Muscle injury region was dissected out and tissue dissociated into a single cell suspension. Whole larva were utilised for uninjured time point. Cells were sorted using a FACS Aria II BD biosciences. Live individual macrophages based on mCherry fluorescence DAPI exclusion and forward and side scatter properties were sorted into pre prepared 384 well plates containing 100 200 nl of CEL seq primers dNTPs and synthetic mRNA Spike Ins contained in 5 μl of Vapor Lock Qiagen. Immediately following sorting plates were spun down and frozen at 80°C until sequencing. Single cell RNA sequencing libraries were prepared using the SORT seq platform. Here the Cel Seq2 protocol is followed with the aid of robotic liquid handlers. This protocol results in each cell being barcoded and generating single cell transcriptomes of all isolated macrophages. GEO Accession:GSM4969714 RNA-Seq TRANSCRIPTOMIC cDNA PAIRED ILLUMINA NextSeq 500   SRP297552     PN7_L003_R1_001.fastq.gz PN7_L003_R2_001.fastq.gz fastq fastq 6312954721.0 41820466.0 GSM4969714 r3 0:75.49 1:75.46 A:1801851943;C:946692024;G:1087319440;T:2476657942;N:433372 75 75     1801851943 946692024 1087319440 2476657942 433372 SRX9670473 SRS7869375 SRA1169931 GEO The University of Melbourne 2 0.19676 0.71745 0.16319 0.42904 0.99295 0.84822 0.47188 0.52687 76 73 T B mate1 technical by mapping diff illumina nextseq unknown random_priming unknown sc single_cell_plate celseq   Australia 2020-12-10 Larval Larval Blood Hematopoietic System
62571 62571 SRR13238449 SRX9670473 SRS7869375 SRP297552 PRJNA684129 Skeletal muscle injury responsive macrophage subsets in larval zebrafish GSE162979 Transcriptome Analysis Single cell RNA sequencing scRNA seq was applied to identify and characterise macrophage subsets that responded to larval zebrafish muscle injury. The Tgmpeg1:mCherry transgenic zebrafish line was utilised to isolate mCherry expressing macrophages by FACS. Following needle stab muscle injury of a 4 dpf dpf larvae the wound site was dissected out at 1 2 and 3 xxx post injury dpi for macrophage isolation. Macrophages isolated from 4 dpf uninjured larvae were also included. This analysis led to the identification of 8 discrete clusters of macrophages one of which corresponded to uninjured macrophages. The 7 wound present macrophage subsets highlighted greater macrophage heterogeneity than previously described in an in vivo skeletal muscle injury context. Overall design: Total of 4 different time points were assayed including a 4 dpf uninjured control and 3 time points during injury resolution 1 3 dpi.   pubmed:33568815   PN7 3dpi mpeg1 mcherry R2 GSM4969714   tissue:mpeg1 expressing macrophages|development stage:7 dpf|strain:TL|transgenic line:Tgmpeg1:mCherry|cell type:macrophages|treatment:injured muscle|xxx post injury:3 PN7 3dpi mpeg1 mcherry R2 Paired reads were mapped against the zebrafish reference assembly version 9 Zv9 using bwa with a transcriptome dataset with improved 3’ UTR annotations to increase the mapability of transcripts. Read 1 was used for assigning reads to correct cells and libraries R1: 6 nt UMI + 8 nt cell barcode while read 2 was mapped to gene models. The scripts to generate the count files can be found here https://github.com/vertesy/TheCorvinas/tree/master/Python/MapAndGo2 with the readme files found here https://github.com/vertesy/TheCorvinas/blob/master/Python/MapAndGo/Readme MapAndGo.md. Read counts were first corrected for UMI barcode by removing duplicate reads that had an identical combination of library cellular and molecular barcodes that were mapped to the same gene. Transcript counts were then adjusted to the expected number of molecules based on counts 4096 possible UMI’s and Poissonian counting statistics. Genome build: Zv9 Supplementary files format and content: Each sample's cell transcript counts matrices are gzipped TSV files. The sequencing facility did not provide cell barcode sequences for each cell ID. mpeg1 expressing macrophages 4dpf larvae were anaesthetized in 0.01% tricaine in Ringer’s solution. Mechanical injures were targeted to the dorsal myotome above the cloaca when the larvae is oriented dorsal to the top anterior to the left. The myotome was subjected to a single 30 gauge needle puncture that generates an extensive injury with many damaged muscle fibres. Muscle injury region was dissected out and tissue dissociated into a single cell suspension. Whole larva were utilised for uninjured time point. Cells were sorted using a FACS Aria II BD biosciences. Live individual macrophages based on mCherry fluorescence DAPI exclusion and forward and side scatter properties were sorted into pre prepared 384 well plates containing 100 200 nl of CEL seq primers dNTPs and synthetic mRNA Spike Ins contained in 5 μl of Vapor Lock Qiagen. Immediately following sorting plates were spun down and frozen at 80°C until sequencing. Single cell RNA sequencing libraries were prepared using the SORT seq platform. Here the Cel Seq2 protocol is followed with the aid of robotic liquid handlers. This protocol results in each cell being barcoded and generating single cell transcriptomes of all isolated macrophages. All procedures involving animals at the Hubrecht Institute were approved by the local animal experiments committees and performed in compliance with animal welfare laws guidelines and policies according to national and European law. Staging and husbandry were performed using standard established and approved protocols. All embryos were maintained in Ringer’s solution at 28.5°C and treated with 0.003% 1 phenyl 2 thiourea PTU Sigma Aldrich from 8 hpf. development stage:7 dpf|strain:TL|transgenic line:Tgmpeg1:mCherry|cell type:macrophages|treatment:injured muscle|xxx post injury:3 GSM4969714 GSM4969714: PN7 3dpi mpeg1 mcherry R2; Danio rerio; RNA Seq GSM4969714   1 Muscle injury region was dissected out and tissue dissociated into a single cell suspension. Whole larva were utilised for uninjured time point. Cells were sorted using a FACS Aria II BD biosciences. Live individual macrophages based on mCherry fluorescence DAPI exclusion and forward and side scatter properties were sorted into pre prepared 384 well plates containing 100 200 nl of CEL seq primers dNTPs and synthetic mRNA Spike Ins contained in 5 μl of Vapor Lock Qiagen. Immediately following sorting plates were spun down and frozen at 80°C until sequencing. Single cell RNA sequencing libraries were prepared using the SORT seq platform. Here the Cel Seq2 protocol is followed with the aid of robotic liquid handlers. This protocol results in each cell being barcoded and generating single cell transcriptomes of all isolated macrophages. GEO Accession:GSM4969714 RNA-Seq TRANSCRIPTOMIC cDNA PAIRED ILLUMINA NextSeq 500   SRP297552     PN7_L004_R1_001.fastq.gz PN7_L004_R2_001.fastq.gz fastq fastq 6302598305.0 41751846.0 GSM4969714 r4 0:75.49 1:75.46 A:1794933968;C:941128970;G:1101536340;T:2464462858;N:536169 75 75     1794933968 941128970 1101536340 2464462858 536169 SRX9670473 SRS7869375 SRA1169931 GEO The University of Melbourne 2 0.20948 0.71708 0.17441 0.42976 0.99137 0.84729 0.45996 0.53153 74 75 T B mate1 technical by mapping diff illumina nextseq unknown random_priming unknown sc single_cell_plate celseq   Australia 2020-12-10 Larval Larval Blood Hematopoietic System
62572 62572 SRR13238442 SRX9670472 SRS7869374 SRP297552 PRJNA684129 Skeletal muscle injury responsive macrophage subsets in larval zebrafish GSE162979 Transcriptome Analysis Single cell RNA sequencing scRNA seq was applied to identify and characterise macrophage subsets that responded to larval zebrafish muscle injury. The Tgmpeg1:mCherry transgenic zebrafish line was utilised to isolate mCherry expressing macrophages by FACS. Following needle stab muscle injury of a 4 dpf dpf larvae the wound site was dissected out at 1 2 and 3 xxx post injury dpi for macrophage isolation. Macrophages isolated from 4 dpf uninjured larvae were also included. This analysis led to the identification of 8 discrete clusters of macrophages one of which corresponded to uninjured macrophages. The 7 wound present macrophage subsets highlighted greater macrophage heterogeneity than previously described in an in vivo skeletal muscle injury context. Overall design: Total of 4 different time points were assayed including a 4 dpf uninjured control and 3 time points during injury resolution 1 3 dpi.   pubmed:33568815   PN5 1dpi mpeg1 mcherry R2 GSM4969713   tissue:mpeg1 expressing macrophages|development stage:5 dpf|strain:TL|transgenic line:Tgmpeg1:mCherry|cell type:macrophages|treatment:injured muscle|xxx post injury:1 PN5 1dpi mpeg1 mcherry R2 Paired reads were mapped against the zebrafish reference assembly version 9 Zv9 using bwa with a transcriptome dataset with improved 3’ UTR annotations to increase the mapability of transcripts. Read 1 was used for assigning reads to correct cells and libraries R1: 6 nt UMI + 8 nt cell barcode while read 2 was mapped to gene models. The scripts to generate the count files can be found here https://github.com/vertesy/TheCorvinas/tree/master/Python/MapAndGo2 with the readme files found here https://github.com/vertesy/TheCorvinas/blob/master/Python/MapAndGo/Readme MapAndGo.md. Read counts were first corrected for UMI barcode by removing duplicate reads that had an identical combination of library cellular and molecular barcodes that were mapped to the same gene. Transcript counts were then adjusted to the expected number of molecules based on counts 4096 possible UMI’s and Poissonian counting statistics. Genome build: Zv9 Supplementary files format and content: Each sample's cell transcript counts matrices are gzipped TSV files. The sequencing facility did not provide cell barcode sequences for each cell ID. mpeg1 expressing macrophages 4dpf larvae were anaesthetized in 0.01% tricaine in Ringer’s solution. Mechanical injures were targeted to the dorsal myotome above the cloaca when the larvae is oriented dorsal to the top anterior to the left. The myotome was subjected to a single 30 gauge needle puncture that generates an extensive injury with many damaged muscle fibres. Muscle injury region was dissected out and tissue dissociated into a single cell suspension. Whole larva were utilised for uninjured time point. Cells were sorted using a FACS Aria II BD biosciences. Live individual macrophages based on mCherry fluorescence DAPI exclusion and forward and side scatter properties were sorted into pre prepared 384 well plates containing 100 200 nl of CEL seq primers dNTPs and synthetic mRNA Spike Ins contained in 5 μl of Vapor Lock Qiagen. Immediately following sorting plates were spun down and frozen at 80°C until sequencing. Single cell RNA sequencing libraries were prepared using the SORT seq platform. Here the Cel Seq2 protocol is followed with the aid of robotic liquid handlers. This protocol results in each cell being barcoded and generating single cell transcriptomes of all isolated macrophages. All procedures involving animals at the Hubrecht Institute were approved by the local animal experiments committees and performed in compliance with animal welfare laws guidelines and policies according to national and European law. Staging and husbandry were performed using standard established and approved protocols. All embryos were maintained in Ringer’s solution at 28.5°C and treated with 0.003% 1 phenyl 2 thiourea PTU Sigma Aldrich from 8 hpf. development stage:5 dpf|strain:TL|transgenic line:Tgmpeg1:mCherry|cell type:macrophages|treatment:injured muscle|xxx post injury:1 GSM4969713 GSM4969713: PN5 1dpi mpeg1 mcherry R2; Danio rerio; RNA Seq GSM4969713   1 Muscle injury region was dissected out and tissue dissociated into a single cell suspension. Whole larva were utilised for uninjured time point. Cells were sorted using a FACS Aria II BD biosciences. Live individual macrophages based on mCherry fluorescence DAPI exclusion and forward and side scatter properties were sorted into pre prepared 384 well plates containing 100 200 nl of CEL seq primers dNTPs and synthetic mRNA Spike Ins contained in 5 μl of Vapor Lock Qiagen. Immediately following sorting plates were spun down and frozen at 80°C until sequencing. Single cell RNA sequencing libraries were prepared using the SORT seq platform. Here the Cel Seq2 protocol is followed with the aid of robotic liquid handlers. This protocol results in each cell being barcoded and generating single cell transcriptomes of all isolated macrophages. GEO Accession:GSM4969713 RNA-Seq TRANSCRIPTOMIC cDNA PAIRED ILLUMINA NextSeq 500   SRP297552     PN5_L001_R1_001.fastq.gz PN5_L001_R2_001.fastq.gz fastq fastq 2804071552.0 18580299.0 GSM4969713 r1 0:75.46 1:75.46 A:780538559;C:397784495;G:463784341;T:1161529955;N:434202 75 75     780538559 397784495 463784341 1161529955 434202 SRX9670472 SRS7869374 SRA1169931 GEO The University of Melbourne 2 0.15961 0.76669 0.0903 0.2248 0.9903 0.85979 0.49059 0.54085 76 76 T B mate1 technical by mapping diff illumina nextseq unknown random_priming unknown sc single_cell_plate celseq   Australia 2020-12-10 Larval Larval Blood Hematopoietic System
62573 62573 SRR13238443 SRX9670472 SRS7869374 SRP297552 PRJNA684129 Skeletal muscle injury responsive macrophage subsets in larval zebrafish GSE162979 Transcriptome Analysis Single cell RNA sequencing scRNA seq was applied to identify and characterise macrophage subsets that responded to larval zebrafish muscle injury. The Tgmpeg1:mCherry transgenic zebrafish line was utilised to isolate mCherry expressing macrophages by FACS. Following needle stab muscle injury of a 4 dpf dpf larvae the wound site was dissected out at 1 2 and 3 xxx post injury dpi for macrophage isolation. Macrophages isolated from 4 dpf uninjured larvae were also included. This analysis led to the identification of 8 discrete clusters of macrophages one of which corresponded to uninjured macrophages. The 7 wound present macrophage subsets highlighted greater macrophage heterogeneity than previously described in an in vivo skeletal muscle injury context. Overall design: Total of 4 different time points were assayed including a 4 dpf uninjured control and 3 time points during injury resolution 1 3 dpi.   pubmed:33568815   PN5 1dpi mpeg1 mcherry R2 GSM4969713   tissue:mpeg1 expressing macrophages|development stage:5 dpf|strain:TL|transgenic line:Tgmpeg1:mCherry|cell type:macrophages|treatment:injured muscle|xxx post injury:1 PN5 1dpi mpeg1 mcherry R2 Paired reads were mapped against the zebrafish reference assembly version 9 Zv9 using bwa with a transcriptome dataset with improved 3’ UTR annotations to increase the mapability of transcripts. Read 1 was used for assigning reads to correct cells and libraries R1: 6 nt UMI + 8 nt cell barcode while read 2 was mapped to gene models. The scripts to generate the count files can be found here https://github.com/vertesy/TheCorvinas/tree/master/Python/MapAndGo2 with the readme files found here https://github.com/vertesy/TheCorvinas/blob/master/Python/MapAndGo/Readme MapAndGo.md. Read counts were first corrected for UMI barcode by removing duplicate reads that had an identical combination of library cellular and molecular barcodes that were mapped to the same gene. Transcript counts were then adjusted to the expected number of molecules based on counts 4096 possible UMI’s and Poissonian counting statistics. Genome build: Zv9 Supplementary files format and content: Each sample's cell transcript counts matrices are gzipped TSV files. The sequencing facility did not provide cell barcode sequences for each cell ID. mpeg1 expressing macrophages 4dpf larvae were anaesthetized in 0.01% tricaine in Ringer’s solution. Mechanical injures were targeted to the dorsal myotome above the cloaca when the larvae is oriented dorsal to the top anterior to the left. The myotome was subjected to a single 30 gauge needle puncture that generates an extensive injury with many damaged muscle fibres. Muscle injury region was dissected out and tissue dissociated into a single cell suspension. Whole larva were utilised for uninjured time point. Cells were sorted using a FACS Aria II BD biosciences. Live individual macrophages based on mCherry fluorescence DAPI exclusion and forward and side scatter properties were sorted into pre prepared 384 well plates containing 100 200 nl of CEL seq primers dNTPs and synthetic mRNA Spike Ins contained in 5 μl of Vapor Lock Qiagen. Immediately following sorting plates were spun down and frozen at 80°C until sequencing. Single cell RNA sequencing libraries were prepared using the SORT seq platform. Here the Cel Seq2 protocol is followed with the aid of robotic liquid handlers. This protocol results in each cell being barcoded and generating single cell transcriptomes of all isolated macrophages. All procedures involving animals at the Hubrecht Institute were approved by the local animal experiments committees and performed in compliance with animal welfare laws guidelines and policies according to national and European law. Staging and husbandry were performed using standard established and approved protocols. All embryos were maintained in Ringer’s solution at 28.5°C and treated with 0.003% 1 phenyl 2 thiourea PTU Sigma Aldrich from 8 hpf. development stage:5 dpf|strain:TL|transgenic line:Tgmpeg1:mCherry|cell type:macrophages|treatment:injured muscle|xxx post injury:1 GSM4969713 GSM4969713: PN5 1dpi mpeg1 mcherry R2; Danio rerio; RNA Seq GSM4969713   1 Muscle injury region was dissected out and tissue dissociated into a single cell suspension. Whole larva were utilised for uninjured time point. Cells were sorted using a FACS Aria II BD biosciences. Live individual macrophages based on mCherry fluorescence DAPI exclusion and forward and side scatter properties were sorted into pre prepared 384 well plates containing 100 200 nl of CEL seq primers dNTPs and synthetic mRNA Spike Ins contained in 5 μl of Vapor Lock Qiagen. Immediately following sorting plates were spun down and frozen at 80°C until sequencing. Single cell RNA sequencing libraries were prepared using the SORT seq platform. Here the Cel Seq2 protocol is followed with the aid of robotic liquid handlers. This protocol results in each cell being barcoded and generating single cell transcriptomes of all isolated macrophages. GEO Accession:GSM4969713 RNA-Seq TRANSCRIPTOMIC cDNA PAIRED ILLUMINA NextSeq 500   SRP297552     PN5_L002_R1_001.fastq.gz PN5_L002_R2_001.fastq.gz fastq fastq 2817528079.0 18667012.0 GSM4969713 r2 0:75.47 1:75.46 A:777879955;C:399565496;G:475237777;T:1164353068;N:491783 75 75     777879955 399565496 475237777 1164353068 491783 SRX9670472 SRS7869374 SRA1169931 GEO The University of Melbourne 2 0.16708 0.7689 0.09701 0.22575 0.98963 0.86087 0.49215 0.54034 76 76 T B mate1 technical by mapping diff illumina nextseq unknown random_priming unknown sc single_cell_plate celseq   Australia 2020-12-10 Larval Larval Blood Hematopoietic System
62574 62574 SRR13238444 SRX9670472 SRS7869374 SRP297552 PRJNA684129 Skeletal muscle injury responsive macrophage subsets in larval zebrafish GSE162979 Transcriptome Analysis Single cell RNA sequencing scRNA seq was applied to identify and characterise macrophage subsets that responded to larval zebrafish muscle injury. The Tgmpeg1:mCherry transgenic zebrafish line was utilised to isolate mCherry expressing macrophages by FACS. Following needle stab muscle injury of a 4 dpf dpf larvae the wound site was dissected out at 1 2 and 3 xxx post injury dpi for macrophage isolation. Macrophages isolated from 4 dpf uninjured larvae were also included. This analysis led to the identification of 8 discrete clusters of macrophages one of which corresponded to uninjured macrophages. The 7 wound present macrophage subsets highlighted greater macrophage heterogeneity than previously described in an in vivo skeletal muscle injury context. Overall design: Total of 4 different time points were assayed including a 4 dpf uninjured control and 3 time points during injury resolution 1 3 dpi.   pubmed:33568815   PN5 1dpi mpeg1 mcherry R2 GSM4969713   tissue:mpeg1 expressing macrophages|development stage:5 dpf|strain:TL|transgenic line:Tgmpeg1:mCherry|cell type:macrophages|treatment:injured muscle|xxx post injury:1 PN5 1dpi mpeg1 mcherry R2 Paired reads were mapped against the zebrafish reference assembly version 9 Zv9 using bwa with a transcriptome dataset with improved 3’ UTR annotations to increase the mapability of transcripts. Read 1 was used for assigning reads to correct cells and libraries R1: 6 nt UMI + 8 nt cell barcode while read 2 was mapped to gene models. The scripts to generate the count files can be found here https://github.com/vertesy/TheCorvinas/tree/master/Python/MapAndGo2 with the readme files found here https://github.com/vertesy/TheCorvinas/blob/master/Python/MapAndGo/Readme MapAndGo.md. Read counts were first corrected for UMI barcode by removing duplicate reads that had an identical combination of library cellular and molecular barcodes that were mapped to the same gene. Transcript counts were then adjusted to the expected number of molecules based on counts 4096 possible UMI’s and Poissonian counting statistics. Genome build: Zv9 Supplementary files format and content: Each sample's cell transcript counts matrices are gzipped TSV files. The sequencing facility did not provide cell barcode sequences for each cell ID. mpeg1 expressing macrophages 4dpf larvae were anaesthetized in 0.01% tricaine in Ringer’s solution. Mechanical injures were targeted to the dorsal myotome above the cloaca when the larvae is oriented dorsal to the top anterior to the left. The myotome was subjected to a single 30 gauge needle puncture that generates an extensive injury with many damaged muscle fibres. Muscle injury region was dissected out and tissue dissociated into a single cell suspension. Whole larva were utilised for uninjured time point. Cells were sorted using a FACS Aria II BD biosciences. Live individual macrophages based on mCherry fluorescence DAPI exclusion and forward and side scatter properties were sorted into pre prepared 384 well plates containing 100 200 nl of CEL seq primers dNTPs and synthetic mRNA Spike Ins contained in 5 μl of Vapor Lock Qiagen. Immediately following sorting plates were spun down and frozen at 80°C until sequencing. Single cell RNA sequencing libraries were prepared using the SORT seq platform. Here the Cel Seq2 protocol is followed with the aid of robotic liquid handlers. This protocol results in each cell being barcoded and generating single cell transcriptomes of all isolated macrophages. All procedures involving animals at the Hubrecht Institute were approved by the local animal experiments committees and performed in compliance with animal welfare laws guidelines and policies according to national and European law. Staging and husbandry were performed using standard established and approved protocols. All embryos were maintained in Ringer’s solution at 28.5°C and treated with 0.003% 1 phenyl 2 thiourea PTU Sigma Aldrich from 8 hpf. development stage:5 dpf|strain:TL|transgenic line:Tgmpeg1:mCherry|cell type:macrophages|treatment:injured muscle|xxx post injury:1 GSM4969713 GSM4969713: PN5 1dpi mpeg1 mcherry R2; Danio rerio; RNA Seq GSM4969713   1 Muscle injury region was dissected out and tissue dissociated into a single cell suspension. Whole larva were utilised for uninjured time point. Cells were sorted using a FACS Aria II BD biosciences. Live individual macrophages based on mCherry fluorescence DAPI exclusion and forward and side scatter properties were sorted into pre prepared 384 well plates containing 100 200 nl of CEL seq primers dNTPs and synthetic mRNA Spike Ins contained in 5 μl of Vapor Lock Qiagen. Immediately following sorting plates were spun down and frozen at 80°C until sequencing. Single cell RNA sequencing libraries were prepared using the SORT seq platform. Here the Cel Seq2 protocol is followed with the aid of robotic liquid handlers. This protocol results in each cell being barcoded and generating single cell transcriptomes of all isolated macrophages. GEO Accession:GSM4969713 RNA-Seq TRANSCRIPTOMIC cDNA PAIRED ILLUMINA NextSeq 500   SRP297552     PN5_L003_R1_001.fastq.gz PN5_L003_R2_001.fastq.gz fastq fastq 2709779117.0 17953752.0 GSM4969713 r3 0:75.47 1:75.46 A:747919461;C:385238024;G:450429962;T:1126007119;N:184551 75 75     747919461 385238024 450429962 1126007119 184551 SRX9670472 SRS7869374 SRA1169931 GEO The University of Melbourne 2 0.16104 0.7674 0.09406 0.22591 0.99235 0.86005 0.52979 0.51658 76 76 T B mate1 technical by mapping diff illumina nextseq unknown random_priming unknown sc single_cell_plate celseq   Australia 2020-12-10 Larval Larval Blood Hematopoietic System
62575 62575 SRR13238445 SRX9670472 SRS7869374 SRP297552 PRJNA684129 Skeletal muscle injury responsive macrophage subsets in larval zebrafish GSE162979 Transcriptome Analysis Single cell RNA sequencing scRNA seq was applied to identify and characterise macrophage subsets that responded to larval zebrafish muscle injury. The Tgmpeg1:mCherry transgenic zebrafish line was utilised to isolate mCherry expressing macrophages by FACS. Following needle stab muscle injury of a 4 dpf dpf larvae the wound site was dissected out at 1 2 and 3 xxx post injury dpi for macrophage isolation. Macrophages isolated from 4 dpf uninjured larvae were also included. This analysis led to the identification of 8 discrete clusters of macrophages one of which corresponded to uninjured macrophages. The 7 wound present macrophage subsets highlighted greater macrophage heterogeneity than previously described in an in vivo skeletal muscle injury context. Overall design: Total of 4 different time points were assayed including a 4 dpf uninjured control and 3 time points during injury resolution 1 3 dpi.   pubmed:33568815   PN5 1dpi mpeg1 mcherry R2 GSM4969713   tissue:mpeg1 expressing macrophages|development stage:5 dpf|strain:TL|transgenic line:Tgmpeg1:mCherry|cell type:macrophages|treatment:injured muscle|xxx post injury:1 PN5 1dpi mpeg1 mcherry R2 Paired reads were mapped against the zebrafish reference assembly version 9 Zv9 using bwa with a transcriptome dataset with improved 3’ UTR annotations to increase the mapability of transcripts. Read 1 was used for assigning reads to correct cells and libraries R1: 6 nt UMI + 8 nt cell barcode while read 2 was mapped to gene models. The scripts to generate the count files can be found here https://github.com/vertesy/TheCorvinas/tree/master/Python/MapAndGo2 with the readme files found here https://github.com/vertesy/TheCorvinas/blob/master/Python/MapAndGo/Readme MapAndGo.md. Read counts were first corrected for UMI barcode by removing duplicate reads that had an identical combination of library cellular and molecular barcodes that were mapped to the same gene. Transcript counts were then adjusted to the expected number of molecules based on counts 4096 possible UMI’s and Poissonian counting statistics. Genome build: Zv9 Supplementary files format and content: Each sample's cell transcript counts matrices are gzipped TSV files. The sequencing facility did not provide cell barcode sequences for each cell ID. mpeg1 expressing macrophages 4dpf larvae were anaesthetized in 0.01% tricaine in Ringer’s solution. Mechanical injures were targeted to the dorsal myotome above the cloaca when the larvae is oriented dorsal to the top anterior to the left. The myotome was subjected to a single 30 gauge needle puncture that generates an extensive injury with many damaged muscle fibres. Muscle injury region was dissected out and tissue dissociated into a single cell suspension. Whole larva were utilised for uninjured time point. Cells were sorted using a FACS Aria II BD biosciences. Live individual macrophages based on mCherry fluorescence DAPI exclusion and forward and side scatter properties were sorted into pre prepared 384 well plates containing 100 200 nl of CEL seq primers dNTPs and synthetic mRNA Spike Ins contained in 5 μl of Vapor Lock Qiagen. Immediately following sorting plates were spun down and frozen at 80°C until sequencing. Single cell RNA sequencing libraries were prepared using the SORT seq platform. Here the Cel Seq2 protocol is followed with the aid of robotic liquid handlers. This protocol results in each cell being barcoded and generating single cell transcriptomes of all isolated macrophages. All procedures involving animals at the Hubrecht Institute were approved by the local animal experiments committees and performed in compliance with animal welfare laws guidelines and policies according to national and European law. Staging and husbandry were performed using standard established and approved protocols. All embryos were maintained in Ringer’s solution at 28.5°C and treated with 0.003% 1 phenyl 2 thiourea PTU Sigma Aldrich from 8 hpf. development stage:5 dpf|strain:TL|transgenic line:Tgmpeg1:mCherry|cell type:macrophages|treatment:injured muscle|xxx post injury:1 GSM4969713 GSM4969713: PN5 1dpi mpeg1 mcherry R2; Danio rerio; RNA Seq GSM4969713   1 Muscle injury region was dissected out and tissue dissociated into a single cell suspension. Whole larva were utilised for uninjured time point. Cells were sorted using a FACS Aria II BD biosciences. Live individual macrophages based on mCherry fluorescence DAPI exclusion and forward and side scatter properties were sorted into pre prepared 384 well plates containing 100 200 nl of CEL seq primers dNTPs and synthetic mRNA Spike Ins contained in 5 μl of Vapor Lock Qiagen. Immediately following sorting plates were spun down and frozen at 80°C until sequencing. Single cell RNA sequencing libraries were prepared using the SORT seq platform. Here the Cel Seq2 protocol is followed with the aid of robotic liquid handlers. This protocol results in each cell being barcoded and generating single cell transcriptomes of all isolated macrophages. GEO Accession:GSM4969713 RNA-Seq TRANSCRIPTOMIC cDNA PAIRED ILLUMINA NextSeq 500   SRP297552     PN5_L004_R1_001.fastq.gz PN5_L004_R2_001.fastq.gz fastq fastq 2712637937.0 17972560.0 GSM4969713 r4 0:75.47 1:75.47 A:746503332;C:383902822;G:458791789;T:1123213585;N:226409 75 75     746503332 383902822 458791789 1123213585 226409 SRX9670472 SRS7869374 SRA1169931 GEO The University of Melbourne 2 0.16976 0.76668 0.09686 0.22439 0.99054 0.86052 0.47154 0.53536 76 76 T B mate1 technical by mapping diff illumina nextseq unknown random_priming unknown sc single_cell_plate celseq   Australia 2020-12-10 Larval Larval Blood Hematopoietic System
62576 62576 SRR13238438 SRX9670471 SRS7869373 SRP297552 PRJNA684129 Skeletal muscle injury responsive macrophage subsets in larval zebrafish GSE162979 Transcriptome Analysis Single cell RNA sequencing scRNA seq was applied to identify and characterise macrophage subsets that responded to larval zebrafish muscle injury. The Tgmpeg1:mCherry transgenic zebrafish line was utilised to isolate mCherry expressing macrophages by FACS. Following needle stab muscle injury of a 4 dpf dpf larvae the wound site was dissected out at 1 2 and 3 xxx post injury dpi for macrophage isolation. Macrophages isolated from 4 dpf uninjured larvae were also included. This analysis led to the identification of 8 discrete clusters of macrophages one of which corresponded to uninjured macrophages. The 7 wound present macrophage subsets highlighted greater macrophage heterogeneity than previously described in an in vivo skeletal muscle injury context. Overall design: Total of 4 different time points were assayed including a 4 dpf uninjured control and 3 time points during injury resolution 1 3 dpi.   pubmed:33568815   PN4 3dpi mpeg1 mcherry R1 GSM4969712   tissue:mpeg1 expressing macrophages|development stage:7 dpf|strain:TL|transgenic line:Tgmpeg1:mCherry|cell type:macrophages|treatment:injured muscle|xxx post injury:3 PN4 3dpi mpeg1 mcherry R1 Paired reads were mapped against the zebrafish reference assembly version 9 Zv9 using bwa with a transcriptome dataset with improved 3’ UTR annotations to increase the mapability of transcripts. Read 1 was used for assigning reads to correct cells and libraries R1: 6 nt UMI + 8 nt cell barcode while read 2 was mapped to gene models. The scripts to generate the count files can be found here https://github.com/vertesy/TheCorvinas/tree/master/Python/MapAndGo2 with the readme files found here https://github.com/vertesy/TheCorvinas/blob/master/Python/MapAndGo/Readme MapAndGo.md. Read counts were first corrected for UMI barcode by removing duplicate reads that had an identical combination of library cellular and molecular barcodes that were mapped to the same gene. Transcript counts were then adjusted to the expected number of molecules based on counts 4096 possible UMI’s and Poissonian counting statistics. Genome build: Zv9 Supplementary files format and content: Each sample's cell transcript counts matrices are gzipped TSV files. The sequencing facility did not provide cell barcode sequences for each cell ID. mpeg1 expressing macrophages 4dpf larvae were anaesthetized in 0.01% tricaine in Ringer’s solution. Mechanical injures were targeted to the dorsal myotome above the cloaca when the larvae is oriented dorsal to the top anterior to the left. The myotome was subjected to a single 30 gauge needle puncture that generates an extensive injury with many damaged muscle fibres. Muscle injury region was dissected out and tissue dissociated into a single cell suspension. Whole larva were utilised for uninjured time point. Cells were sorted using a FACS Aria II BD biosciences. Live individual macrophages based on mCherry fluorescence DAPI exclusion and forward and side scatter properties were sorted into pre prepared 384 well plates containing 100 200 nl of CEL seq primers dNTPs and synthetic mRNA Spike Ins contained in 5 μl of Vapor Lock Qiagen. Immediately following sorting plates were spun down and frozen at 80°C until sequencing. Single cell RNA sequencing libraries were prepared using the SORT seq platform. Here the Cel Seq2 protocol is followed with the aid of robotic liquid handlers. This protocol results in each cell being barcoded and generating single cell transcriptomes of all isolated macrophages. All procedures involving animals at the Hubrecht Institute were approved by the local animal experiments committees and performed in compliance with animal welfare laws guidelines and policies according to national and European law. Staging and husbandry were performed using standard established and approved protocols. All embryos were maintained in Ringer’s solution at 28.5°C and treated with 0.003% 1 phenyl 2 thiourea PTU Sigma Aldrich from 8 hpf. development stage:7 dpf|strain:TL|transgenic line:Tgmpeg1:mCherry|cell type:macrophages|treatment:injured muscle|xxx post injury:3 GSM4969712 GSM4969712: PN4 3dpi mpeg1 mcherry R1; Danio rerio; RNA Seq GSM4969712   1 Muscle injury region was dissected out and tissue dissociated into a single cell suspension. Whole larva were utilised for uninjured time point. Cells were sorted using a FACS Aria II BD biosciences. Live individual macrophages based on mCherry fluorescence DAPI exclusion and forward and side scatter properties were sorted into pre prepared 384 well plates containing 100 200 nl of CEL seq primers dNTPs and synthetic mRNA Spike Ins contained in 5 μl of Vapor Lock Qiagen. Immediately following sorting plates were spun down and frozen at 80°C until sequencing. Single cell RNA sequencing libraries were prepared using the SORT seq platform. Here the Cel Seq2 protocol is followed with the aid of robotic liquid handlers. This protocol results in each cell being barcoded and generating single cell transcriptomes of all isolated macrophages. GEO Accession:GSM4969712 RNA-Seq TRANSCRIPTOMIC cDNA PAIRED ILLUMINA NextSeq 500   SRP297552     PN4_L001_R1_001.fastq.gz PN4_L001_R2_001.fastq.gz fastq fastq 1188135797.0 13884556.0 GSM4969712 r1 0:26 1:59.57 A:315958963;C:231575226;G:212680418;T:392625872;N:35295318 26 59     315958963 231575226 212680418 392625872 35295318 SRX9670471 SRS7869373 SRA1169931 GEO The University of Melbourne 2 0.09037 0.64724 0.08438 0.52624 0.99046 0.94712 0.45562 0.48233 26 59 T B sc-like readlen illumina nextseq unknown random_priming unknown sc single_cell_plate celseq   Australia 2020-12-10 Larval Larval Blood Hematopoietic System
62577 62577 SRR13238439 SRX9670471 SRS7869373 SRP297552 PRJNA684129 Skeletal muscle injury responsive macrophage subsets in larval zebrafish GSE162979 Transcriptome Analysis Single cell RNA sequencing scRNA seq was applied to identify and characterise macrophage subsets that responded to larval zebrafish muscle injury. The Tgmpeg1:mCherry transgenic zebrafish line was utilised to isolate mCherry expressing macrophages by FACS. Following needle stab muscle injury of a 4 dpf dpf larvae the wound site was dissected out at 1 2 and 3 xxx post injury dpi for macrophage isolation. Macrophages isolated from 4 dpf uninjured larvae were also included. This analysis led to the identification of 8 discrete clusters of macrophages one of which corresponded to uninjured macrophages. The 7 wound present macrophage subsets highlighted greater macrophage heterogeneity than previously described in an in vivo skeletal muscle injury context. Overall design: Total of 4 different time points were assayed including a 4 dpf uninjured control and 3 time points during injury resolution 1 3 dpi.   pubmed:33568815   PN4 3dpi mpeg1 mcherry R1 GSM4969712   tissue:mpeg1 expressing macrophages|development stage:7 dpf|strain:TL|transgenic line:Tgmpeg1:mCherry|cell type:macrophages|treatment:injured muscle|xxx post injury:3 PN4 3dpi mpeg1 mcherry R1 Paired reads were mapped against the zebrafish reference assembly version 9 Zv9 using bwa with a transcriptome dataset with improved 3’ UTR annotations to increase the mapability of transcripts. Read 1 was used for assigning reads to correct cells and libraries R1: 6 nt UMI + 8 nt cell barcode while read 2 was mapped to gene models. The scripts to generate the count files can be found here https://github.com/vertesy/TheCorvinas/tree/master/Python/MapAndGo2 with the readme files found here https://github.com/vertesy/TheCorvinas/blob/master/Python/MapAndGo/Readme MapAndGo.md. Read counts were first corrected for UMI barcode by removing duplicate reads that had an identical combination of library cellular and molecular barcodes that were mapped to the same gene. Transcript counts were then adjusted to the expected number of molecules based on counts 4096 possible UMI’s and Poissonian counting statistics. Genome build: Zv9 Supplementary files format and content: Each sample's cell transcript counts matrices are gzipped TSV files. The sequencing facility did not provide cell barcode sequences for each cell ID. mpeg1 expressing macrophages 4dpf larvae were anaesthetized in 0.01% tricaine in Ringer’s solution. Mechanical injures were targeted to the dorsal myotome above the cloaca when the larvae is oriented dorsal to the top anterior to the left. The myotome was subjected to a single 30 gauge needle puncture that generates an extensive injury with many damaged muscle fibres. Muscle injury region was dissected out and tissue dissociated into a single cell suspension. Whole larva were utilised for uninjured time point. Cells were sorted using a FACS Aria II BD biosciences. Live individual macrophages based on mCherry fluorescence DAPI exclusion and forward and side scatter properties were sorted into pre prepared 384 well plates containing 100 200 nl of CEL seq primers dNTPs and synthetic mRNA Spike Ins contained in 5 μl of Vapor Lock Qiagen. Immediately following sorting plates were spun down and frozen at 80°C until sequencing. Single cell RNA sequencing libraries were prepared using the SORT seq platform. Here the Cel Seq2 protocol is followed with the aid of robotic liquid handlers. This protocol results in each cell being barcoded and generating single cell transcriptomes of all isolated macrophages. All procedures involving animals at the Hubrecht Institute were approved by the local animal experiments committees and performed in compliance with animal welfare laws guidelines and policies according to national and European law. Staging and husbandry were performed using standard established and approved protocols. All embryos were maintained in Ringer’s solution at 28.5°C and treated with 0.003% 1 phenyl 2 thiourea PTU Sigma Aldrich from 8 hpf. development stage:7 dpf|strain:TL|transgenic line:Tgmpeg1:mCherry|cell type:macrophages|treatment:injured muscle|xxx post injury:3 GSM4969712 GSM4969712: PN4 3dpi mpeg1 mcherry R1; Danio rerio; RNA Seq GSM4969712   1 Muscle injury region was dissected out and tissue dissociated into a single cell suspension. Whole larva were utilised for uninjured time point. Cells were sorted using a FACS Aria II BD biosciences. Live individual macrophages based on mCherry fluorescence DAPI exclusion and forward and side scatter properties were sorted into pre prepared 384 well plates containing 100 200 nl of CEL seq primers dNTPs and synthetic mRNA Spike Ins contained in 5 μl of Vapor Lock Qiagen. Immediately following sorting plates were spun down and frozen at 80°C until sequencing. Single cell RNA sequencing libraries were prepared using the SORT seq platform. Here the Cel Seq2 protocol is followed with the aid of robotic liquid handlers. This protocol results in each cell being barcoded and generating single cell transcriptomes of all isolated macrophages. GEO Accession:GSM4969712 RNA-Seq TRANSCRIPTOMIC cDNA PAIRED ILLUMINA NextSeq 500   SRP297552     PN4_L002_R1_001.fastq.gz PN4_L002_R2_001.fastq.gz fastq fastq 1144513224.0 13373871.0 GSM4969712 r2 0:26 1:59.58 A:301945874;C:221045328;G:212388479;T:374986241;N:34147302 26 59     301945874 221045328 212388479 374986241 34147302 SRX9670471 SRS7869373 SRA1169931 GEO The University of Melbourne 2 0.08887 0.6406 0.08286 0.52058 0.9907 0.94903 0.44936 0.4831 26 57 T B sc-like readlen illumina nextseq unknown random_priming unknown sc single_cell_plate celseq   Australia 2020-12-10 Larval Larval Blood Hematopoietic System
62578 62578 SRR13238440 SRX9670471 SRS7869373 SRP297552 PRJNA684129 Skeletal muscle injury responsive macrophage subsets in larval zebrafish GSE162979 Transcriptome Analysis Single cell RNA sequencing scRNA seq was applied to identify and characterise macrophage subsets that responded to larval zebrafish muscle injury. The Tgmpeg1:mCherry transgenic zebrafish line was utilised to isolate mCherry expressing macrophages by FACS. Following needle stab muscle injury of a 4 dpf dpf larvae the wound site was dissected out at 1 2 and 3 xxx post injury dpi for macrophage isolation. Macrophages isolated from 4 dpf uninjured larvae were also included. This analysis led to the identification of 8 discrete clusters of macrophages one of which corresponded to uninjured macrophages. The 7 wound present macrophage subsets highlighted greater macrophage heterogeneity than previously described in an in vivo skeletal muscle injury context. Overall design: Total of 4 different time points were assayed including a 4 dpf uninjured control and 3 time points during injury resolution 1 3 dpi.   pubmed:33568815   PN4 3dpi mpeg1 mcherry R1 GSM4969712   tissue:mpeg1 expressing macrophages|development stage:7 dpf|strain:TL|transgenic line:Tgmpeg1:mCherry|cell type:macrophages|treatment:injured muscle|xxx post injury:3 PN4 3dpi mpeg1 mcherry R1 Paired reads were mapped against the zebrafish reference assembly version 9 Zv9 using bwa with a transcriptome dataset with improved 3’ UTR annotations to increase the mapability of transcripts. Read 1 was used for assigning reads to correct cells and libraries R1: 6 nt UMI + 8 nt cell barcode while read 2 was mapped to gene models. The scripts to generate the count files can be found here https://github.com/vertesy/TheCorvinas/tree/master/Python/MapAndGo2 with the readme files found here https://github.com/vertesy/TheCorvinas/blob/master/Python/MapAndGo/Readme MapAndGo.md. Read counts were first corrected for UMI barcode by removing duplicate reads that had an identical combination of library cellular and molecular barcodes that were mapped to the same gene. Transcript counts were then adjusted to the expected number of molecules based on counts 4096 possible UMI’s and Poissonian counting statistics. Genome build: Zv9 Supplementary files format and content: Each sample's cell transcript counts matrices are gzipped TSV files. The sequencing facility did not provide cell barcode sequences for each cell ID. mpeg1 expressing macrophages 4dpf larvae were anaesthetized in 0.01% tricaine in Ringer’s solution. Mechanical injures were targeted to the dorsal myotome above the cloaca when the larvae is oriented dorsal to the top anterior to the left. The myotome was subjected to a single 30 gauge needle puncture that generates an extensive injury with many damaged muscle fibres. Muscle injury region was dissected out and tissue dissociated into a single cell suspension. Whole larva were utilised for uninjured time point. Cells were sorted using a FACS Aria II BD biosciences. Live individual macrophages based on mCherry fluorescence DAPI exclusion and forward and side scatter properties were sorted into pre prepared 384 well plates containing 100 200 nl of CEL seq primers dNTPs and synthetic mRNA Spike Ins contained in 5 μl of Vapor Lock Qiagen. Immediately following sorting plates were spun down and frozen at 80°C until sequencing. Single cell RNA sequencing libraries were prepared using the SORT seq platform. Here the Cel Seq2 protocol is followed with the aid of robotic liquid handlers. This protocol results in each cell being barcoded and generating single cell transcriptomes of all isolated macrophages. All procedures involving animals at the Hubrecht Institute were approved by the local animal experiments committees and performed in compliance with animal welfare laws guidelines and policies according to national and European law. Staging and husbandry were performed using standard established and approved protocols. All embryos were maintained in Ringer’s solution at 28.5°C and treated with 0.003% 1 phenyl 2 thiourea PTU Sigma Aldrich from 8 hpf. development stage:7 dpf|strain:TL|transgenic line:Tgmpeg1:mCherry|cell type:macrophages|treatment:injured muscle|xxx post injury:3 GSM4969712 GSM4969712: PN4 3dpi mpeg1 mcherry R1; Danio rerio; RNA Seq GSM4969712   1 Muscle injury region was dissected out and tissue dissociated into a single cell suspension. Whole larva were utilised for uninjured time point. Cells were sorted using a FACS Aria II BD biosciences. Live individual macrophages based on mCherry fluorescence DAPI exclusion and forward and side scatter properties were sorted into pre prepared 384 well plates containing 100 200 nl of CEL seq primers dNTPs and synthetic mRNA Spike Ins contained in 5 μl of Vapor Lock Qiagen. Immediately following sorting plates were spun down and frozen at 80°C until sequencing. Single cell RNA sequencing libraries were prepared using the SORT seq platform. Here the Cel Seq2 protocol is followed with the aid of robotic liquid handlers. This protocol results in each cell being barcoded and generating single cell transcriptomes of all isolated macrophages. GEO Accession:GSM4969712 RNA-Seq TRANSCRIPTOMIC cDNA PAIRED ILLUMINA NextSeq 500   SRP297552     PN4_L003_R1_001.fastq.gz PN4_L003_R2_001.fastq.gz fastq fastq 1173422430.0 13712474.0 GSM4969712 r3 0:26 1:59.57 A:312225339;C:228926320;G:209623486;T:387242633;N:35404652 26 59     312225339 228926320 209623486 387242633 35404652 SRX9670471 SRS7869373 SRA1169931 GEO The University of Melbourne 2 0.08927 0.64246 0.08325 0.52118 0.99076 0.94805 0.44477 0.48419 26 60 T B sc-like readlen illumina nextseq unknown random_priming unknown sc single_cell_plate celseq   Australia 2020-12-10 Larval Larval Blood Hematopoietic System
62579 62579 SRR13238441 SRX9670471 SRS7869373 SRP297552 PRJNA684129 Skeletal muscle injury responsive macrophage subsets in larval zebrafish GSE162979 Transcriptome Analysis Single cell RNA sequencing scRNA seq was applied to identify and characterise macrophage subsets that responded to larval zebrafish muscle injury. The Tgmpeg1:mCherry transgenic zebrafish line was utilised to isolate mCherry expressing macrophages by FACS. Following needle stab muscle injury of a 4 dpf dpf larvae the wound site was dissected out at 1 2 and 3 xxx post injury dpi for macrophage isolation. Macrophages isolated from 4 dpf uninjured larvae were also included. This analysis led to the identification of 8 discrete clusters of macrophages one of which corresponded to uninjured macrophages. The 7 wound present macrophage subsets highlighted greater macrophage heterogeneity than previously described in an in vivo skeletal muscle injury context. Overall design: Total of 4 different time points were assayed including a 4 dpf uninjured control and 3 time points during injury resolution 1 3 dpi.   pubmed:33568815   PN4 3dpi mpeg1 mcherry R1 GSM4969712   tissue:mpeg1 expressing macrophages|development stage:7 dpf|strain:TL|transgenic line:Tgmpeg1:mCherry|cell type:macrophages|treatment:injured muscle|xxx post injury:3 PN4 3dpi mpeg1 mcherry R1 Paired reads were mapped against the zebrafish reference assembly version 9 Zv9 using bwa with a transcriptome dataset with improved 3’ UTR annotations to increase the mapability of transcripts. Read 1 was used for assigning reads to correct cells and libraries R1: 6 nt UMI + 8 nt cell barcode while read 2 was mapped to gene models. The scripts to generate the count files can be found here https://github.com/vertesy/TheCorvinas/tree/master/Python/MapAndGo2 with the readme files found here https://github.com/vertesy/TheCorvinas/blob/master/Python/MapAndGo/Readme MapAndGo.md. Read counts were first corrected for UMI barcode by removing duplicate reads that had an identical combination of library cellular and molecular barcodes that were mapped to the same gene. Transcript counts were then adjusted to the expected number of molecules based on counts 4096 possible UMI’s and Poissonian counting statistics. Genome build: Zv9 Supplementary files format and content: Each sample's cell transcript counts matrices are gzipped TSV files. The sequencing facility did not provide cell barcode sequences for each cell ID. mpeg1 expressing macrophages 4dpf larvae were anaesthetized in 0.01% tricaine in Ringer’s solution. Mechanical injures were targeted to the dorsal myotome above the cloaca when the larvae is oriented dorsal to the top anterior to the left. The myotome was subjected to a single 30 gauge needle puncture that generates an extensive injury with many damaged muscle fibres. Muscle injury region was dissected out and tissue dissociated into a single cell suspension. Whole larva were utilised for uninjured time point. Cells were sorted using a FACS Aria II BD biosciences. Live individual macrophages based on mCherry fluorescence DAPI exclusion and forward and side scatter properties were sorted into pre prepared 384 well plates containing 100 200 nl of CEL seq primers dNTPs and synthetic mRNA Spike Ins contained in 5 μl of Vapor Lock Qiagen. Immediately following sorting plates were spun down and frozen at 80°C until sequencing. Single cell RNA sequencing libraries were prepared using the SORT seq platform. Here the Cel Seq2 protocol is followed with the aid of robotic liquid handlers. This protocol results in each cell being barcoded and generating single cell transcriptomes of all isolated macrophages. All procedures involving animals at the Hubrecht Institute were approved by the local animal experiments committees and performed in compliance with animal welfare laws guidelines and policies according to national and European law. Staging and husbandry were performed using standard established and approved protocols. All embryos were maintained in Ringer’s solution at 28.5°C and treated with 0.003% 1 phenyl 2 thiourea PTU Sigma Aldrich from 8 hpf. development stage:7 dpf|strain:TL|transgenic line:Tgmpeg1:mCherry|cell type:macrophages|treatment:injured muscle|xxx post injury:3 GSM4969712 GSM4969712: PN4 3dpi mpeg1 mcherry R1; Danio rerio; RNA Seq GSM4969712   1 Muscle injury region was dissected out and tissue dissociated into a single cell suspension. Whole larva were utilised for uninjured time point. Cells were sorted using a FACS Aria II BD biosciences. Live individual macrophages based on mCherry fluorescence DAPI exclusion and forward and side scatter properties were sorted into pre prepared 384 well plates containing 100 200 nl of CEL seq primers dNTPs and synthetic mRNA Spike Ins contained in 5 μl of Vapor Lock Qiagen. Immediately following sorting plates were spun down and frozen at 80°C until sequencing. Single cell RNA sequencing libraries were prepared using the SORT seq platform. Here the Cel Seq2 protocol is followed with the aid of robotic liquid handlers. This protocol results in each cell being barcoded and generating single cell transcriptomes of all isolated macrophages. GEO Accession:GSM4969712 RNA-Seq TRANSCRIPTOMIC cDNA PAIRED ILLUMINA NextSeq 500   SRP297552     PN4_L004_R1_001.fastq.gz PN4_L004_R2_001.fastq.gz fastq fastq 1147622587.0 13410181.0 GSM4969712 r4 0:26 1:59.58 A:303224338;C:221709862;G:212063267;T:376057151;N:34567969 26 59     303224338 221709862 212063267 376057151 34567969 SRX9670471 SRS7869373 SRA1169931 GEO The University of Melbourne 2 0.08878 0.63849 0.08271 0.51688 0.99066 0.9486 0.43689 0.47686 26 60 T B sc-like readlen illumina nextseq unknown random_priming unknown sc single_cell_plate celseq   Australia 2020-12-10 Larval Larval Blood Hematopoietic System
62580 62580 SRR13238434 SRX9670470 SRS7869372 SRP297552 PRJNA684129 Skeletal muscle injury responsive macrophage subsets in larval zebrafish GSE162979 Transcriptome Analysis Single cell RNA sequencing scRNA seq was applied to identify and characterise macrophage subsets that responded to larval zebrafish muscle injury. The Tgmpeg1:mCherry transgenic zebrafish line was utilised to isolate mCherry expressing macrophages by FACS. Following needle stab muscle injury of a 4 dpf dpf larvae the wound site was dissected out at 1 2 and 3 xxx post injury dpi for macrophage isolation. Macrophages isolated from 4 dpf uninjured larvae were also included. This analysis led to the identification of 8 discrete clusters of macrophages one of which corresponded to uninjured macrophages. The 7 wound present macrophage subsets highlighted greater macrophage heterogeneity than previously described in an in vivo skeletal muscle injury context. Overall design: Total of 4 different time points were assayed including a 4 dpf uninjured control and 3 time points during injury resolution 1 3 dpi.   pubmed:33568815   PN3 2dpi mpeg1 mcherry R1 GSM4969711   tissue:mpeg1 expressing macrophages|development stage:6 dpf|strain:TL|transgenic line:Tgmpeg1:mCherry|cell type:macrophages|treatment:injured muscle|xxx post injury:2 PN3 2dpi mpeg1 mcherry R1 Paired reads were mapped against the zebrafish reference assembly version 9 Zv9 using bwa with a transcriptome dataset with improved 3’ UTR annotations to increase the mapability of transcripts. Read 1 was used for assigning reads to correct cells and libraries R1: 6 nt UMI + 8 nt cell barcode while read 2 was mapped to gene models. The scripts to generate the count files can be found here https://github.com/vertesy/TheCorvinas/tree/master/Python/MapAndGo2 with the readme files found here https://github.com/vertesy/TheCorvinas/blob/master/Python/MapAndGo/Readme MapAndGo.md. Read counts were first corrected for UMI barcode by removing duplicate reads that had an identical combination of library cellular and molecular barcodes that were mapped to the same gene. Transcript counts were then adjusted to the expected number of molecules based on counts 4096 possible UMI’s and Poissonian counting statistics. Genome build: Zv9 Supplementary files format and content: Each sample's cell transcript counts matrices are gzipped TSV files. The sequencing facility did not provide cell barcode sequences for each cell ID. mpeg1 expressing macrophages 4dpf larvae were anaesthetized in 0.01% tricaine in Ringer’s solution. Mechanical injures were targeted to the dorsal myotome above the cloaca when the larvae is oriented dorsal to the top anterior to the left. The myotome was subjected to a single 30 gauge needle puncture that generates an extensive injury with many damaged muscle fibres. Muscle injury region was dissected out and tissue dissociated into a single cell suspension. Whole larva were utilised for uninjured time point. Cells were sorted using a FACS Aria II BD biosciences. Live individual macrophages based on mCherry fluorescence DAPI exclusion and forward and side scatter properties were sorted into pre prepared 384 well plates containing 100 200 nl of CEL seq primers dNTPs and synthetic mRNA Spike Ins contained in 5 μl of Vapor Lock Qiagen. Immediately following sorting plates were spun down and frozen at 80°C until sequencing. Single cell RNA sequencing libraries were prepared using the SORT seq platform. Here the Cel Seq2 protocol is followed with the aid of robotic liquid handlers. This protocol results in each cell being barcoded and generating single cell transcriptomes of all isolated macrophages. All procedures involving animals at the Hubrecht Institute were approved by the local animal experiments committees and performed in compliance with animal welfare laws guidelines and policies according to national and European law. Staging and husbandry were performed using standard established and approved protocols. All embryos were maintained in Ringer’s solution at 28.5°C and treated with 0.003% 1 phenyl 2 thiourea PTU Sigma Aldrich from 8 hpf. development stage:6 dpf|strain:TL|transgenic line:Tgmpeg1:mCherry|cell type:macrophages|treatment:injured muscle|xxx post injury:2 GSM4969711 GSM4969711: PN3 2dpi mpeg1 mcherry R1; Danio rerio; RNA Seq GSM4969711   1 Muscle injury region was dissected out and tissue dissociated into a single cell suspension. Whole larva were utilised for uninjured time point. Cells were sorted using a FACS Aria II BD biosciences. Live individual macrophages based on mCherry fluorescence DAPI exclusion and forward and side scatter properties were sorted into pre prepared 384 well plates containing 100 200 nl of CEL seq primers dNTPs and synthetic mRNA Spike Ins contained in 5 μl of Vapor Lock Qiagen. Immediately following sorting plates were spun down and frozen at 80°C until sequencing. Single cell RNA sequencing libraries were prepared using the SORT seq platform. Here the Cel Seq2 protocol is followed with the aid of robotic liquid handlers. This protocol results in each cell being barcoded and generating single cell transcriptomes of all isolated macrophages. GEO Accession:GSM4969711 RNA-Seq TRANSCRIPTOMIC cDNA PAIRED ILLUMINA NextSeq 500   SRP297552     PN3_L001_R1_001.fastq.gz PN3_L001_R2_001.fastq.gz fastq fastq 3045940998.0 20182475.0 GSM4969711 r1 0:75.46 1:75.46 A:861591362;C:472200330;G:548416371;T:1163271404;N:461531 75 75     861591362 472200330 548416371 1163271404 461531 SRX9670470 SRS7869372 SRA1169931 GEO The University of Melbourne 2 0.22646 0.71156 0.13481 0.24351 0.98184 0.85178 0.4659 0.56773 76 75 T B mate1 technical by mapping diff illumina nextseq unknown random_priming unknown sc single_cell_plate celseq   Australia 2020-12-10 Larval Larval Blood Hematopoietic System
62581 62581 SRR13238435 SRX9670470 SRS7869372 SRP297552 PRJNA684129 Skeletal muscle injury responsive macrophage subsets in larval zebrafish GSE162979 Transcriptome Analysis Single cell RNA sequencing scRNA seq was applied to identify and characterise macrophage subsets that responded to larval zebrafish muscle injury. The Tgmpeg1:mCherry transgenic zebrafish line was utilised to isolate mCherry expressing macrophages by FACS. Following needle stab muscle injury of a 4 dpf dpf larvae the wound site was dissected out at 1 2 and 3 xxx post injury dpi for macrophage isolation. Macrophages isolated from 4 dpf uninjured larvae were also included. This analysis led to the identification of 8 discrete clusters of macrophages one of which corresponded to uninjured macrophages. The 7 wound present macrophage subsets highlighted greater macrophage heterogeneity than previously described in an in vivo skeletal muscle injury context. Overall design: Total of 4 different time points were assayed including a 4 dpf uninjured control and 3 time points during injury resolution 1 3 dpi.   pubmed:33568815   PN3 2dpi mpeg1 mcherry R1 GSM4969711   tissue:mpeg1 expressing macrophages|development stage:6 dpf|strain:TL|transgenic line:Tgmpeg1:mCherry|cell type:macrophages|treatment:injured muscle|xxx post injury:2 PN3 2dpi mpeg1 mcherry R1 Paired reads were mapped against the zebrafish reference assembly version 9 Zv9 using bwa with a transcriptome dataset with improved 3’ UTR annotations to increase the mapability of transcripts. Read 1 was used for assigning reads to correct cells and libraries R1: 6 nt UMI + 8 nt cell barcode while read 2 was mapped to gene models. The scripts to generate the count files can be found here https://github.com/vertesy/TheCorvinas/tree/master/Python/MapAndGo2 with the readme files found here https://github.com/vertesy/TheCorvinas/blob/master/Python/MapAndGo/Readme MapAndGo.md. Read counts were first corrected for UMI barcode by removing duplicate reads that had an identical combination of library cellular and molecular barcodes that were mapped to the same gene. Transcript counts were then adjusted to the expected number of molecules based on counts 4096 possible UMI’s and Poissonian counting statistics. Genome build: Zv9 Supplementary files format and content: Each sample's cell transcript counts matrices are gzipped TSV files. The sequencing facility did not provide cell barcode sequences for each cell ID. mpeg1 expressing macrophages 4dpf larvae were anaesthetized in 0.01% tricaine in Ringer’s solution. Mechanical injures were targeted to the dorsal myotome above the cloaca when the larvae is oriented dorsal to the top anterior to the left. The myotome was subjected to a single 30 gauge needle puncture that generates an extensive injury with many damaged muscle fibres. Muscle injury region was dissected out and tissue dissociated into a single cell suspension. Whole larva were utilised for uninjured time point. Cells were sorted using a FACS Aria II BD biosciences. Live individual macrophages based on mCherry fluorescence DAPI exclusion and forward and side scatter properties were sorted into pre prepared 384 well plates containing 100 200 nl of CEL seq primers dNTPs and synthetic mRNA Spike Ins contained in 5 μl of Vapor Lock Qiagen. Immediately following sorting plates were spun down and frozen at 80°C until sequencing. Single cell RNA sequencing libraries were prepared using the SORT seq platform. Here the Cel Seq2 protocol is followed with the aid of robotic liquid handlers. This protocol results in each cell being barcoded and generating single cell transcriptomes of all isolated macrophages. All procedures involving animals at the Hubrecht Institute were approved by the local animal experiments committees and performed in compliance with animal welfare laws guidelines and policies according to national and European law. Staging and husbandry were performed using standard established and approved protocols. All embryos were maintained in Ringer’s solution at 28.5°C and treated with 0.003% 1 phenyl 2 thiourea PTU Sigma Aldrich from 8 hpf. development stage:6 dpf|strain:TL|transgenic line:Tgmpeg1:mCherry|cell type:macrophages|treatment:injured muscle|xxx post injury:2 GSM4969711 GSM4969711: PN3 2dpi mpeg1 mcherry R1; Danio rerio; RNA Seq GSM4969711   1 Muscle injury region was dissected out and tissue dissociated into a single cell suspension. Whole larva were utilised for uninjured time point. Cells were sorted using a FACS Aria II BD biosciences. Live individual macrophages based on mCherry fluorescence DAPI exclusion and forward and side scatter properties were sorted into pre prepared 384 well plates containing 100 200 nl of CEL seq primers dNTPs and synthetic mRNA Spike Ins contained in 5 μl of Vapor Lock Qiagen. Immediately following sorting plates were spun down and frozen at 80°C until sequencing. Single cell RNA sequencing libraries were prepared using the SORT seq platform. Here the Cel Seq2 protocol is followed with the aid of robotic liquid handlers. This protocol results in each cell being barcoded and generating single cell transcriptomes of all isolated macrophages. GEO Accession:GSM4969711 RNA-Seq TRANSCRIPTOMIC cDNA PAIRED ILLUMINA NextSeq 500   SRP297552     PN3_L002_R1_001.fastq.gz PN3_L002_R2_001.fastq.gz fastq fastq 3058134810.0 20261124.0 GSM4969711 r2 0:75.47 1:75.47 A:859026588;C:473360269;G:558456557;T:1166762411;N:528985 75 75     859026588 473360269 558456557 1166762411 528985 SRX9670470 SRS7869372 SRA1169931 GEO The University of Melbourne 2 0.23705 0.71418 0.143 0.24407 0.9779 0.85224 0.49099 0.56732 76 76 T B mate1 technical by mapping diff illumina nextseq unknown random_priming unknown sc single_cell_plate celseq   Australia 2020-12-10 Larval Larval Blood Hematopoietic System
62582 62582 SRR13238436 SRX9670470 SRS7869372 SRP297552 PRJNA684129 Skeletal muscle injury responsive macrophage subsets in larval zebrafish GSE162979 Transcriptome Analysis Single cell RNA sequencing scRNA seq was applied to identify and characterise macrophage subsets that responded to larval zebrafish muscle injury. The Tgmpeg1:mCherry transgenic zebrafish line was utilised to isolate mCherry expressing macrophages by FACS. Following needle stab muscle injury of a 4 dpf dpf larvae the wound site was dissected out at 1 2 and 3 xxx post injury dpi for macrophage isolation. Macrophages isolated from 4 dpf uninjured larvae were also included. This analysis led to the identification of 8 discrete clusters of macrophages one of which corresponded to uninjured macrophages. The 7 wound present macrophage subsets highlighted greater macrophage heterogeneity than previously described in an in vivo skeletal muscle injury context. Overall design: Total of 4 different time points were assayed including a 4 dpf uninjured control and 3 time points during injury resolution 1 3 dpi.   pubmed:33568815   PN3 2dpi mpeg1 mcherry R1 GSM4969711   tissue:mpeg1 expressing macrophages|development stage:6 dpf|strain:TL|transgenic line:Tgmpeg1:mCherry|cell type:macrophages|treatment:injured muscle|xxx post injury:2 PN3 2dpi mpeg1 mcherry R1 Paired reads were mapped against the zebrafish reference assembly version 9 Zv9 using bwa with a transcriptome dataset with improved 3’ UTR annotations to increase the mapability of transcripts. Read 1 was used for assigning reads to correct cells and libraries R1: 6 nt UMI + 8 nt cell barcode while read 2 was mapped to gene models. The scripts to generate the count files can be found here https://github.com/vertesy/TheCorvinas/tree/master/Python/MapAndGo2 with the readme files found here https://github.com/vertesy/TheCorvinas/blob/master/Python/MapAndGo/Readme MapAndGo.md. Read counts were first corrected for UMI barcode by removing duplicate reads that had an identical combination of library cellular and molecular barcodes that were mapped to the same gene. Transcript counts were then adjusted to the expected number of molecules based on counts 4096 possible UMI’s and Poissonian counting statistics. Genome build: Zv9 Supplementary files format and content: Each sample's cell transcript counts matrices are gzipped TSV files. The sequencing facility did not provide cell barcode sequences for each cell ID. mpeg1 expressing macrophages 4dpf larvae were anaesthetized in 0.01% tricaine in Ringer’s solution. Mechanical injures were targeted to the dorsal myotome above the cloaca when the larvae is oriented dorsal to the top anterior to the left. The myotome was subjected to a single 30 gauge needle puncture that generates an extensive injury with many damaged muscle fibres. Muscle injury region was dissected out and tissue dissociated into a single cell suspension. Whole larva were utilised for uninjured time point. Cells were sorted using a FACS Aria II BD biosciences. Live individual macrophages based on mCherry fluorescence DAPI exclusion and forward and side scatter properties were sorted into pre prepared 384 well plates containing 100 200 nl of CEL seq primers dNTPs and synthetic mRNA Spike Ins contained in 5 μl of Vapor Lock Qiagen. Immediately following sorting plates were spun down and frozen at 80°C until sequencing. Single cell RNA sequencing libraries were prepared using the SORT seq platform. Here the Cel Seq2 protocol is followed with the aid of robotic liquid handlers. This protocol results in each cell being barcoded and generating single cell transcriptomes of all isolated macrophages. All procedures involving animals at the Hubrecht Institute were approved by the local animal experiments committees and performed in compliance with animal welfare laws guidelines and policies according to national and European law. Staging and husbandry were performed using standard established and approved protocols. All embryos were maintained in Ringer’s solution at 28.5°C and treated with 0.003% 1 phenyl 2 thiourea PTU Sigma Aldrich from 8 hpf. development stage:6 dpf|strain:TL|transgenic line:Tgmpeg1:mCherry|cell type:macrophages|treatment:injured muscle|xxx post injury:2 GSM4969711 GSM4969711: PN3 2dpi mpeg1 mcherry R1; Danio rerio; RNA Seq GSM4969711   1 Muscle injury region was dissected out and tissue dissociated into a single cell suspension. Whole larva were utilised for uninjured time point. Cells were sorted using a FACS Aria II BD biosciences. Live individual macrophages based on mCherry fluorescence DAPI exclusion and forward and side scatter properties were sorted into pre prepared 384 well plates containing 100 200 nl of CEL seq primers dNTPs and synthetic mRNA Spike Ins contained in 5 μl of Vapor Lock Qiagen. Immediately following sorting plates were spun down and frozen at 80°C until sequencing. Single cell RNA sequencing libraries were prepared using the SORT seq platform. Here the Cel Seq2 protocol is followed with the aid of robotic liquid handlers. This protocol results in each cell being barcoded and generating single cell transcriptomes of all isolated macrophages. GEO Accession:GSM4969711 RNA-Seq TRANSCRIPTOMIC cDNA PAIRED ILLUMINA NextSeq 500   SRP297552     PN3_L003_R1_001.fastq.gz PN3_L003_R2_001.fastq.gz fastq fastq 2937750186.0 19464119.0 GSM4969711 r3 0:75.46 1:75.47 A:826799903;C:453565630;G:532874749;T:1124319105;N:190799 75 75     826799903 453565630 532874749 1124319105 190799 SRX9670470 SRS7869372 SRA1169931 GEO The University of Melbourne 2 0.22661 0.71083 0.13025 0.24386 0.98388 0.85214 0.48448 0.56575 76 76 T B mate1 technical by mapping diff illumina nextseq unknown random_priming unknown sc single_cell_plate celseq   Australia 2020-12-10 Larval Larval Blood Hematopoietic System
62583 62583 SRR13238437 SRX9670470 SRS7869372 SRP297552 PRJNA684129 Skeletal muscle injury responsive macrophage subsets in larval zebrafish GSE162979 Transcriptome Analysis Single cell RNA sequencing scRNA seq was applied to identify and characterise macrophage subsets that responded to larval zebrafish muscle injury. The Tgmpeg1:mCherry transgenic zebrafish line was utilised to isolate mCherry expressing macrophages by FACS. Following needle stab muscle injury of a 4 dpf dpf larvae the wound site was dissected out at 1 2 and 3 xxx post injury dpi for macrophage isolation. Macrophages isolated from 4 dpf uninjured larvae were also included. This analysis led to the identification of 8 discrete clusters of macrophages one of which corresponded to uninjured macrophages. The 7 wound present macrophage subsets highlighted greater macrophage heterogeneity than previously described in an in vivo skeletal muscle injury context. Overall design: Total of 4 different time points were assayed including a 4 dpf uninjured control and 3 time points during injury resolution 1 3 dpi.   pubmed:33568815   PN3 2dpi mpeg1 mcherry R1 GSM4969711   tissue:mpeg1 expressing macrophages|development stage:6 dpf|strain:TL|transgenic line:Tgmpeg1:mCherry|cell type:macrophages|treatment:injured muscle|xxx post injury:2 PN3 2dpi mpeg1 mcherry R1 Paired reads were mapped against the zebrafish reference assembly version 9 Zv9 using bwa with a transcriptome dataset with improved 3’ UTR annotations to increase the mapability of transcripts. Read 1 was used for assigning reads to correct cells and libraries R1: 6 nt UMI + 8 nt cell barcode while read 2 was mapped to gene models. The scripts to generate the count files can be found here https://github.com/vertesy/TheCorvinas/tree/master/Python/MapAndGo2 with the readme files found here https://github.com/vertesy/TheCorvinas/blob/master/Python/MapAndGo/Readme MapAndGo.md. Read counts were first corrected for UMI barcode by removing duplicate reads that had an identical combination of library cellular and molecular barcodes that were mapped to the same gene. Transcript counts were then adjusted to the expected number of molecules based on counts 4096 possible UMI’s and Poissonian counting statistics. Genome build: Zv9 Supplementary files format and content: Each sample's cell transcript counts matrices are gzipped TSV files. The sequencing facility did not provide cell barcode sequences for each cell ID. mpeg1 expressing macrophages 4dpf larvae were anaesthetized in 0.01% tricaine in Ringer’s solution. Mechanical injures were targeted to the dorsal myotome above the cloaca when the larvae is oriented dorsal to the top anterior to the left. The myotome was subjected to a single 30 gauge needle puncture that generates an extensive injury with many damaged muscle fibres. Muscle injury region was dissected out and tissue dissociated into a single cell suspension. Whole larva were utilised for uninjured time point. Cells were sorted using a FACS Aria II BD biosciences. Live individual macrophages based on mCherry fluorescence DAPI exclusion and forward and side scatter properties were sorted into pre prepared 384 well plates containing 100 200 nl of CEL seq primers dNTPs and synthetic mRNA Spike Ins contained in 5 μl of Vapor Lock Qiagen. Immediately following sorting plates were spun down and frozen at 80°C until sequencing. Single cell RNA sequencing libraries were prepared using the SORT seq platform. Here the Cel Seq2 protocol is followed with the aid of robotic liquid handlers. This protocol results in each cell being barcoded and generating single cell transcriptomes of all isolated macrophages. All procedures involving animals at the Hubrecht Institute were approved by the local animal experiments committees and performed in compliance with animal welfare laws guidelines and policies according to national and European law. Staging and husbandry were performed using standard established and approved protocols. All embryos were maintained in Ringer’s solution at 28.5°C and treated with 0.003% 1 phenyl 2 thiourea PTU Sigma Aldrich from 8 hpf. development stage:6 dpf|strain:TL|transgenic line:Tgmpeg1:mCherry|cell type:macrophages|treatment:injured muscle|xxx post injury:2 GSM4969711 GSM4969711: PN3 2dpi mpeg1 mcherry R1; Danio rerio; RNA Seq GSM4969711   1 Muscle injury region was dissected out and tissue dissociated into a single cell suspension. Whole larva were utilised for uninjured time point. Cells were sorted using a FACS Aria II BD biosciences. Live individual macrophages based on mCherry fluorescence DAPI exclusion and forward and side scatter properties were sorted into pre prepared 384 well plates containing 100 200 nl of CEL seq primers dNTPs and synthetic mRNA Spike Ins contained in 5 μl of Vapor Lock Qiagen. Immediately following sorting plates were spun down and frozen at 80°C until sequencing. Single cell RNA sequencing libraries were prepared using the SORT seq platform. Here the Cel Seq2 protocol is followed with the aid of robotic liquid handlers. This protocol results in each cell being barcoded and generating single cell transcriptomes of all isolated macrophages. GEO Accession:GSM4969711 RNA-Seq TRANSCRIPTOMIC cDNA PAIRED ILLUMINA NextSeq 500   SRP297552     PN3_L004_R1_001.fastq.gz PN3_L004_R2_001.fastq.gz fastq fastq 2947338266.0 19527523.0 GSM4969711 r4 0:75.46 1:75.47 A:826921712;C:453009451;G:543071709;T:1124092188;N:243206 75 75     826921712 453009451 543071709 1124092188 243206 SRX9670470 SRS7869372 SRA1169931 GEO The University of Melbourne 2 0.23443 0.71193 0.13859 0.24106 0.98202 0.85202 0.44751 0.55877 76 75 T B mate1 technical by mapping diff illumina nextseq unknown random_priming unknown sc single_cell_plate celseq   Australia 2020-12-10 Larval Larval Blood Hematopoietic System
62584 62584 SRR13238430 SRX9670469 SRS7869371 SRP297552 PRJNA684129 Skeletal muscle injury responsive macrophage subsets in larval zebrafish GSE162979 Transcriptome Analysis Single cell RNA sequencing scRNA seq was applied to identify and characterise macrophage subsets that responded to larval zebrafish muscle injury. The Tgmpeg1:mCherry transgenic zebrafish line was utilised to isolate mCherry expressing macrophages by FACS. Following needle stab muscle injury of a 4 dpf dpf larvae the wound site was dissected out at 1 2 and 3 xxx post injury dpi for macrophage isolation. Macrophages isolated from 4 dpf uninjured larvae were also included. This analysis led to the identification of 8 discrete clusters of macrophages one of which corresponded to uninjured macrophages. The 7 wound present macrophage subsets highlighted greater macrophage heterogeneity than previously described in an in vivo skeletal muscle injury context. Overall design: Total of 4 different time points were assayed including a 4 dpf uninjured control and 3 time points during injury resolution 1 3 dpi.   pubmed:33568815   PN2 1dpi mpeg1 mcherry R1 GSM4969710   tissue:mpeg1 expressing macrophages|development stage:5 dpf|strain:TL|transgenic line:Tgmpeg1:mCherry|cell type:macrophages|treatment:injured muscle|xxx post injury:1 PN2 1dpi mpeg1 mcherry R1 Paired reads were mapped against the zebrafish reference assembly version 9 Zv9 using bwa with a transcriptome dataset with improved 3’ UTR annotations to increase the mapability of transcripts. Read 1 was used for assigning reads to correct cells and libraries R1: 6 nt UMI + 8 nt cell barcode while read 2 was mapped to gene models. The scripts to generate the count files can be found here https://github.com/vertesy/TheCorvinas/tree/master/Python/MapAndGo2 with the readme files found here https://github.com/vertesy/TheCorvinas/blob/master/Python/MapAndGo/Readme MapAndGo.md. Read counts were first corrected for UMI barcode by removing duplicate reads that had an identical combination of library cellular and molecular barcodes that were mapped to the same gene. Transcript counts were then adjusted to the expected number of molecules based on counts 4096 possible UMI’s and Poissonian counting statistics. Genome build: Zv9 Supplementary files format and content: Each sample's cell transcript counts matrices are gzipped TSV files. The sequencing facility did not provide cell barcode sequences for each cell ID. mpeg1 expressing macrophages 4dpf larvae were anaesthetized in 0.01% tricaine in Ringer’s solution. Mechanical injures were targeted to the dorsal myotome above the cloaca when the larvae is oriented dorsal to the top anterior to the left. The myotome was subjected to a single 30 gauge needle puncture that generates an extensive injury with many damaged muscle fibres. Muscle injury region was dissected out and tissue dissociated into a single cell suspension. Whole larva were utilised for uninjured time point. Cells were sorted using a FACS Aria II BD biosciences. Live individual macrophages based on mCherry fluorescence DAPI exclusion and forward and side scatter properties were sorted into pre prepared 384 well plates containing 100 200 nl of CEL seq primers dNTPs and synthetic mRNA Spike Ins contained in 5 μl of Vapor Lock Qiagen. Immediately following sorting plates were spun down and frozen at 80°C until sequencing. Single cell RNA sequencing libraries were prepared using the SORT seq platform. Here the Cel Seq2 protocol is followed with the aid of robotic liquid handlers. This protocol results in each cell being barcoded and generating single cell transcriptomes of all isolated macrophages. All procedures involving animals at the Hubrecht Institute were approved by the local animal experiments committees and performed in compliance with animal welfare laws guidelines and policies according to national and European law. Staging and husbandry were performed using standard established and approved protocols. All embryos were maintained in Ringer’s solution at 28.5°C and treated with 0.003% 1 phenyl 2 thiourea PTU Sigma Aldrich from 8 hpf. development stage:5 dpf|strain:TL|transgenic line:Tgmpeg1:mCherry|cell type:macrophages|treatment:injured muscle|xxx post injury:1 GSM4969710 GSM4969710: PN2 1dpi mpeg1 mcherry R1; Danio rerio; RNA Seq GSM4969710   1 Muscle injury region was dissected out and tissue dissociated into a single cell suspension. Whole larva were utilised for uninjured time point. Cells were sorted using a FACS Aria II BD biosciences. Live individual macrophages based on mCherry fluorescence DAPI exclusion and forward and side scatter properties were sorted into pre prepared 384 well plates containing 100 200 nl of CEL seq primers dNTPs and synthetic mRNA Spike Ins contained in 5 μl of Vapor Lock Qiagen. Immediately following sorting plates were spun down and frozen at 80°C until sequencing. Single cell RNA sequencing libraries were prepared using the SORT seq platform. Here the Cel Seq2 protocol is followed with the aid of robotic liquid handlers. This protocol results in each cell being barcoded and generating single cell transcriptomes of all isolated macrophages. GEO Accession:GSM4969710 RNA-Seq TRANSCRIPTOMIC cDNA PAIRED ILLUMINA NextSeq 500   SRP297552     PN2_L001_R1_001.fastq.gz PN2_L001_R2_001.fastq.gz fastq fastq 1737466730.0 20301987.0 GSM4969710 r1 0:26 1:59.58 A:422780101;C:341883160;G:323452885;T:619204653;N:30145931 26 59     422780101 341883160 323452885 619204653 30145931 SRX9670469 SRS7869371 SRA1169931 GEO The University of Melbourne 2 0.01965 0.91425 0.01776 0.64626 0.9948 0.9124 0.50147 0.52977 26 60 T B sc-like readlen illumina nextseq unknown random_priming unknown sc single_cell_plate celseq   Australia 2020-12-10 Larval Larval Blood Hematopoietic System
62585 62585 SRR13238431 SRX9670469 SRS7869371 SRP297552 PRJNA684129 Skeletal muscle injury responsive macrophage subsets in larval zebrafish GSE162979 Transcriptome Analysis Single cell RNA sequencing scRNA seq was applied to identify and characterise macrophage subsets that responded to larval zebrafish muscle injury. The Tgmpeg1:mCherry transgenic zebrafish line was utilised to isolate mCherry expressing macrophages by FACS. Following needle stab muscle injury of a 4 dpf dpf larvae the wound site was dissected out at 1 2 and 3 xxx post injury dpi for macrophage isolation. Macrophages isolated from 4 dpf uninjured larvae were also included. This analysis led to the identification of 8 discrete clusters of macrophages one of which corresponded to uninjured macrophages. The 7 wound present macrophage subsets highlighted greater macrophage heterogeneity than previously described in an in vivo skeletal muscle injury context. Overall design: Total of 4 different time points were assayed including a 4 dpf uninjured control and 3 time points during injury resolution 1 3 dpi.   pubmed:33568815   PN2 1dpi mpeg1 mcherry R1 GSM4969710   tissue:mpeg1 expressing macrophages|development stage:5 dpf|strain:TL|transgenic line:Tgmpeg1:mCherry|cell type:macrophages|treatment:injured muscle|xxx post injury:1 PN2 1dpi mpeg1 mcherry R1 Paired reads were mapped against the zebrafish reference assembly version 9 Zv9 using bwa with a transcriptome dataset with improved 3’ UTR annotations to increase the mapability of transcripts. Read 1 was used for assigning reads to correct cells and libraries R1: 6 nt UMI + 8 nt cell barcode while read 2 was mapped to gene models. The scripts to generate the count files can be found here https://github.com/vertesy/TheCorvinas/tree/master/Python/MapAndGo2 with the readme files found here https://github.com/vertesy/TheCorvinas/blob/master/Python/MapAndGo/Readme MapAndGo.md. Read counts were first corrected for UMI barcode by removing duplicate reads that had an identical combination of library cellular and molecular barcodes that were mapped to the same gene. Transcript counts were then adjusted to the expected number of molecules based on counts 4096 possible UMI’s and Poissonian counting statistics. Genome build: Zv9 Supplementary files format and content: Each sample's cell transcript counts matrices are gzipped TSV files. The sequencing facility did not provide cell barcode sequences for each cell ID. mpeg1 expressing macrophages 4dpf larvae were anaesthetized in 0.01% tricaine in Ringer’s solution. Mechanical injures were targeted to the dorsal myotome above the cloaca when the larvae is oriented dorsal to the top anterior to the left. The myotome was subjected to a single 30 gauge needle puncture that generates an extensive injury with many damaged muscle fibres. Muscle injury region was dissected out and tissue dissociated into a single cell suspension. Whole larva were utilised for uninjured time point. Cells were sorted using a FACS Aria II BD biosciences. Live individual macrophages based on mCherry fluorescence DAPI exclusion and forward and side scatter properties were sorted into pre prepared 384 well plates containing 100 200 nl of CEL seq primers dNTPs and synthetic mRNA Spike Ins contained in 5 μl of Vapor Lock Qiagen. Immediately following sorting plates were spun down and frozen at 80°C until sequencing. Single cell RNA sequencing libraries were prepared using the SORT seq platform. Here the Cel Seq2 protocol is followed with the aid of robotic liquid handlers. This protocol results in each cell being barcoded and generating single cell transcriptomes of all isolated macrophages. All procedures involving animals at the Hubrecht Institute were approved by the local animal experiments committees and performed in compliance with animal welfare laws guidelines and policies according to national and European law. Staging and husbandry were performed using standard established and approved protocols. All embryos were maintained in Ringer’s solution at 28.5°C and treated with 0.003% 1 phenyl 2 thiourea PTU Sigma Aldrich from 8 hpf. development stage:5 dpf|strain:TL|transgenic line:Tgmpeg1:mCherry|cell type:macrophages|treatment:injured muscle|xxx post injury:1 GSM4969710 GSM4969710: PN2 1dpi mpeg1 mcherry R1; Danio rerio; RNA Seq GSM4969710   1 Muscle injury region was dissected out and tissue dissociated into a single cell suspension. Whole larva were utilised for uninjured time point. Cells were sorted using a FACS Aria II BD biosciences. Live individual macrophages based on mCherry fluorescence DAPI exclusion and forward and side scatter properties were sorted into pre prepared 384 well plates containing 100 200 nl of CEL seq primers dNTPs and synthetic mRNA Spike Ins contained in 5 μl of Vapor Lock Qiagen. Immediately following sorting plates were spun down and frozen at 80°C until sequencing. Single cell RNA sequencing libraries were prepared using the SORT seq platform. Here the Cel Seq2 protocol is followed with the aid of robotic liquid handlers. This protocol results in each cell being barcoded and generating single cell transcriptomes of all isolated macrophages. GEO Accession:GSM4969710 RNA-Seq TRANSCRIPTOMIC cDNA PAIRED ILLUMINA NextSeq 500   SRP297552     PN2_L002_R1_001.fastq.gz PN2_L002_R2_001.fastq.gz fastq fastq 1683950505.0 19675642.0 GSM4969710 r2 0:26 1:59.59 A:406617443;C:329099407;G:322324098;T:596755074;N:29154483 26 59     406617443 329099407 322324098 596755074 29154483 SRX9670469 SRS7869371 SRA1169931 GEO The University of Melbourne 2 0.0185 0.9085 0.01676 0.64398 0.99502 0.91364 0.50788 0.53846 26 60 T B sc-like readlen illumina nextseq unknown random_priming unknown sc single_cell_plate celseq   Australia 2020-12-10 Larval Larval Blood Hematopoietic System
62586 62586 SRR13238432 SRX9670469 SRS7869371 SRP297552 PRJNA684129 Skeletal muscle injury responsive macrophage subsets in larval zebrafish GSE162979 Transcriptome Analysis Single cell RNA sequencing scRNA seq was applied to identify and characterise macrophage subsets that responded to larval zebrafish muscle injury. The Tgmpeg1:mCherry transgenic zebrafish line was utilised to isolate mCherry expressing macrophages by FACS. Following needle stab muscle injury of a 4 dpf dpf larvae the wound site was dissected out at 1 2 and 3 xxx post injury dpi for macrophage isolation. Macrophages isolated from 4 dpf uninjured larvae were also included. This analysis led to the identification of 8 discrete clusters of macrophages one of which corresponded to uninjured macrophages. The 7 wound present macrophage subsets highlighted greater macrophage heterogeneity than previously described in an in vivo skeletal muscle injury context. Overall design: Total of 4 different time points were assayed including a 4 dpf uninjured control and 3 time points during injury resolution 1 3 dpi.   pubmed:33568815   PN2 1dpi mpeg1 mcherry R1 GSM4969710   tissue:mpeg1 expressing macrophages|development stage:5 dpf|strain:TL|transgenic line:Tgmpeg1:mCherry|cell type:macrophages|treatment:injured muscle|xxx post injury:1 PN2 1dpi mpeg1 mcherry R1 Paired reads were mapped against the zebrafish reference assembly version 9 Zv9 using bwa with a transcriptome dataset with improved 3’ UTR annotations to increase the mapability of transcripts. Read 1 was used for assigning reads to correct cells and libraries R1: 6 nt UMI + 8 nt cell barcode while read 2 was mapped to gene models. The scripts to generate the count files can be found here https://github.com/vertesy/TheCorvinas/tree/master/Python/MapAndGo2 with the readme files found here https://github.com/vertesy/TheCorvinas/blob/master/Python/MapAndGo/Readme MapAndGo.md. Read counts were first corrected for UMI barcode by removing duplicate reads that had an identical combination of library cellular and molecular barcodes that were mapped to the same gene. Transcript counts were then adjusted to the expected number of molecules based on counts 4096 possible UMI’s and Poissonian counting statistics. Genome build: Zv9 Supplementary files format and content: Each sample's cell transcript counts matrices are gzipped TSV files. The sequencing facility did not provide cell barcode sequences for each cell ID. mpeg1 expressing macrophages 4dpf larvae were anaesthetized in 0.01% tricaine in Ringer’s solution. Mechanical injures were targeted to the dorsal myotome above the cloaca when the larvae is oriented dorsal to the top anterior to the left. The myotome was subjected to a single 30 gauge needle puncture that generates an extensive injury with many damaged muscle fibres. Muscle injury region was dissected out and tissue dissociated into a single cell suspension. Whole larva were utilised for uninjured time point. Cells were sorted using a FACS Aria II BD biosciences. Live individual macrophages based on mCherry fluorescence DAPI exclusion and forward and side scatter properties were sorted into pre prepared 384 well plates containing 100 200 nl of CEL seq primers dNTPs and synthetic mRNA Spike Ins contained in 5 μl of Vapor Lock Qiagen. Immediately following sorting plates were spun down and frozen at 80°C until sequencing. Single cell RNA sequencing libraries were prepared using the SORT seq platform. Here the Cel Seq2 protocol is followed with the aid of robotic liquid handlers. This protocol results in each cell being barcoded and generating single cell transcriptomes of all isolated macrophages. All procedures involving animals at the Hubrecht Institute were approved by the local animal experiments committees and performed in compliance with animal welfare laws guidelines and policies according to national and European law. Staging and husbandry were performed using standard established and approved protocols. All embryos were maintained in Ringer’s solution at 28.5°C and treated with 0.003% 1 phenyl 2 thiourea PTU Sigma Aldrich from 8 hpf. development stage:5 dpf|strain:TL|transgenic line:Tgmpeg1:mCherry|cell type:macrophages|treatment:injured muscle|xxx post injury:1 GSM4969710 GSM4969710: PN2 1dpi mpeg1 mcherry R1; Danio rerio; RNA Seq GSM4969710   1 Muscle injury region was dissected out and tissue dissociated into a single cell suspension. Whole larva were utilised for uninjured time point. Cells were sorted using a FACS Aria II BD biosciences. Live individual macrophages based on mCherry fluorescence DAPI exclusion and forward and side scatter properties were sorted into pre prepared 384 well plates containing 100 200 nl of CEL seq primers dNTPs and synthetic mRNA Spike Ins contained in 5 μl of Vapor Lock Qiagen. Immediately following sorting plates were spun down and frozen at 80°C until sequencing. Single cell RNA sequencing libraries were prepared using the SORT seq platform. Here the Cel Seq2 protocol is followed with the aid of robotic liquid handlers. This protocol results in each cell being barcoded and generating single cell transcriptomes of all isolated macrophages. GEO Accession:GSM4969710 RNA-Seq TRANSCRIPTOMIC cDNA PAIRED ILLUMINA NextSeq 500   SRP297552     PN2_L003_R1_001.fastq.gz PN2_L003_R2_001.fastq.gz fastq fastq 1715335402.0 20043345.0 GSM4969710 r3 0:26 1:59.58 A:417485809;C:337845935;G:318495667;T:611426599;N:30081392 26 59     417485809 337845935 318495667 611426599 30081392 SRX9670469 SRS7869371 SRA1169931 GEO The University of Melbourne 2 0.01941 0.91463 0.01744 0.64848 0.9946 0.91313 0.47058 0.53898 26 60 T B sc-like readlen illumina nextseq unknown random_priming unknown sc single_cell_plate celseq   Australia 2020-12-10 Larval Larval Blood Hematopoietic System
62587 62587 SRR13238433 SRX9670469 SRS7869371 SRP297552 PRJNA684129 Skeletal muscle injury responsive macrophage subsets in larval zebrafish GSE162979 Transcriptome Analysis Single cell RNA sequencing scRNA seq was applied to identify and characterise macrophage subsets that responded to larval zebrafish muscle injury. The Tgmpeg1:mCherry transgenic zebrafish line was utilised to isolate mCherry expressing macrophages by FACS. Following needle stab muscle injury of a 4 dpf dpf larvae the wound site was dissected out at 1 2 and 3 xxx post injury dpi for macrophage isolation. Macrophages isolated from 4 dpf uninjured larvae were also included. This analysis led to the identification of 8 discrete clusters of macrophages one of which corresponded to uninjured macrophages. The 7 wound present macrophage subsets highlighted greater macrophage heterogeneity than previously described in an in vivo skeletal muscle injury context. Overall design: Total of 4 different time points were assayed including a 4 dpf uninjured control and 3 time points during injury resolution 1 3 dpi.   pubmed:33568815   PN2 1dpi mpeg1 mcherry R1 GSM4969710   tissue:mpeg1 expressing macrophages|development stage:5 dpf|strain:TL|transgenic line:Tgmpeg1:mCherry|cell type:macrophages|treatment:injured muscle|xxx post injury:1 PN2 1dpi mpeg1 mcherry R1 Paired reads were mapped against the zebrafish reference assembly version 9 Zv9 using bwa with a transcriptome dataset with improved 3’ UTR annotations to increase the mapability of transcripts. Read 1 was used for assigning reads to correct cells and libraries R1: 6 nt UMI + 8 nt cell barcode while read 2 was mapped to gene models. The scripts to generate the count files can be found here https://github.com/vertesy/TheCorvinas/tree/master/Python/MapAndGo2 with the readme files found here https://github.com/vertesy/TheCorvinas/blob/master/Python/MapAndGo/Readme MapAndGo.md. Read counts were first corrected for UMI barcode by removing duplicate reads that had an identical combination of library cellular and molecular barcodes that were mapped to the same gene. Transcript counts were then adjusted to the expected number of molecules based on counts 4096 possible UMI’s and Poissonian counting statistics. Genome build: Zv9 Supplementary files format and content: Each sample's cell transcript counts matrices are gzipped TSV files. The sequencing facility did not provide cell barcode sequences for each cell ID. mpeg1 expressing macrophages 4dpf larvae were anaesthetized in 0.01% tricaine in Ringer’s solution. Mechanical injures were targeted to the dorsal myotome above the cloaca when the larvae is oriented dorsal to the top anterior to the left. The myotome was subjected to a single 30 gauge needle puncture that generates an extensive injury with many damaged muscle fibres. Muscle injury region was dissected out and tissue dissociated into a single cell suspension. Whole larva were utilised for uninjured time point. Cells were sorted using a FACS Aria II BD biosciences. Live individual macrophages based on mCherry fluorescence DAPI exclusion and forward and side scatter properties were sorted into pre prepared 384 well plates containing 100 200 nl of CEL seq primers dNTPs and synthetic mRNA Spike Ins contained in 5 μl of Vapor Lock Qiagen. Immediately following sorting plates were spun down and frozen at 80°C until sequencing. Single cell RNA sequencing libraries were prepared using the SORT seq platform. Here the Cel Seq2 protocol is followed with the aid of robotic liquid handlers. This protocol results in each cell being barcoded and generating single cell transcriptomes of all isolated macrophages. All procedures involving animals at the Hubrecht Institute were approved by the local animal experiments committees and performed in compliance with animal welfare laws guidelines and policies according to national and European law. Staging and husbandry were performed using standard established and approved protocols. All embryos were maintained in Ringer’s solution at 28.5°C and treated with 0.003% 1 phenyl 2 thiourea PTU Sigma Aldrich from 8 hpf. development stage:5 dpf|strain:TL|transgenic line:Tgmpeg1:mCherry|cell type:macrophages|treatment:injured muscle|xxx post injury:1 GSM4969710 GSM4969710: PN2 1dpi mpeg1 mcherry R1; Danio rerio; RNA Seq GSM4969710   1 Muscle injury region was dissected out and tissue dissociated into a single cell suspension. Whole larva were utilised for uninjured time point. Cells were sorted using a FACS Aria II BD biosciences. Live individual macrophages based on mCherry fluorescence DAPI exclusion and forward and side scatter properties were sorted into pre prepared 384 well plates containing 100 200 nl of CEL seq primers dNTPs and synthetic mRNA Spike Ins contained in 5 μl of Vapor Lock Qiagen. Immediately following sorting plates were spun down and frozen at 80°C until sequencing. Single cell RNA sequencing libraries were prepared using the SORT seq platform. Here the Cel Seq2 protocol is followed with the aid of robotic liquid handlers. This protocol results in each cell being barcoded and generating single cell transcriptomes of all isolated macrophages. GEO Accession:GSM4969710 RNA-Seq TRANSCRIPTOMIC cDNA PAIRED ILLUMINA NextSeq 500   SRP297552     PN2_L004_R1_001.fastq.gz PN2_L004_R2_001.fastq.gz fastq fastq 1687227882.0 19713999.0 GSM4969710 r4 0:26 1:59.59 A:408029041;C:330002530;G:321413836;T:598268431;N:29514044 26 59     408029041 330002530 321413836 598268431 29514044 SRX9670469 SRS7869371 SRA1169931 GEO The University of Melbourne 2 0.01885 0.90779 0.01704 0.64337 0.99492 0.91534 0.51524 0.54086 26 59 T B sc-like readlen illumina nextseq unknown random_priming unknown sc single_cell_plate celseq   Australia 2020-12-10 Larval Larval Blood Hematopoietic System
63272 63272 SRR13724975 SRX10113008 SRS8268904 SRP306756 PRJNA702191 Phosphatidylinositol 3 kinase signaling controls survival and stemness of hematopoietic stem and progenitor cells GSE166900 Transcriptome Analysis Loss of Pten and inhibition of PI3K induced apoptosis of hematopoietic stem/progenitor cells upon endothelial to hematopoietic transition and surviving hematopoietic stem/progenitor cells committed to all blood lineages but displayed reduced stemness Overall design: AGMs 36hpf or CHTs 5dpf were dissected and dissociated using FACS sorting HSPCs were isolated and single cell RNA seq was performed.   pubmed:33714985   SBF 23 GSM5087787   tissue:Caudal Hematopoietic Tissue|developmental stage:5dpf SBF 23 Well numbers X001 X384 in *.TranscriptCounts.tsv refer to well numbers of the 384 well plate in the following order: A1 to A24 B1 to B24 until P24. The right mate of each read pair was mapped to the ensemble of all gene loci. Reads mapping to multiple loci were discarded. During sequencing Read 1 was assigned 26/75* base pairs and was used for identification of the Illumina library barcode cel barcode and UMI. R2 was assigned 60/75* base pairs and used to map to the reference transcriptome of Zv9 with BWA Anders and Huber 2010. Data was demultiplexed as described in Grün et al. 2014. Mapping and generation of count tables was automated using the MapAndGo script1. https://github.com/anna alemany/transcriptomics/tree/master/mapandgo Tabular separated file indicating number of transcripts per section obtained as previously described Grün Dominic Lennart Kester and Alexander Van Oudenaarden. "Validation of noise models for single cell transcriptomics." Nature methods 11.6 2014: 637. We refer to transcripts as unique molecules based on UMI correction. *coutc* files contain number of reads *coutb* files contain number of observed UMI and *coutt* files contain number of unique UMI corrected transcripts per gene per cell The left read contains the barcode information: the first eight bases correspond to a cell specific barcode* followed by a 6bp unique molecular identifier UMI. The remainder of the left read contains a polyT stretch followed by a number <40 of transcript derived bases. Read 1 was not used for quantification. cell specific barcodes are specified in the file “cel seq2 barcodes.csv” Genome build: zV9 Danio Rerio Caudal Hematopoietic Tissue   Cells were sorted into 384 well plates. All wells underwent Cel Seq2 protocol RNA libraries were prepared for sequencing using standard Illumina protocols   developmental stage:5dpf|sort day see plate layout:13|genotype or treatment see plate layout:LY294002 treated GSM5087787 GSM5087787: SBF 23; Danio rerio; RNA Seq GSM5087787   1 Cells were sorted into 384 well plates. All wells underwent Cel Seq2 protocol RNA libraries were prepared for sequencing using standard Illumina protocols GEO Accession:GSM5087787 RNA-Seq TRANSCRIPTOMIC cDNA PAIRED ILLUMINA NextSeq 500   SRP306756     HUB-SF-023_HM5N7BGXC_S5_L001_R1_001.fastq.gz HUB-SF-023_HM5N7BGXC_S5_L001_R2_001.fastq.gz fastq fastq 497476718.0 5784613.0 GSM5087787 r1 0:26 1:60 A:119810734;C:98970597;G:101956236;T:176037139;N:702012 26 60     119810734 98970597 101956236 176037139 702012 SRX10113008 SRS8268904 SRA1196923 GEO Hubrecht Institute 2 0.09006 0.8442 0.08375 0.19753 0.99253 0.84589 0.68372 0.57253 26 60 T B sc-like readlen illumina nextseq unknown cdna_unspecified unknown sc single_cell_plate celseq   Netherlands 2021-02-16 Larval Larval Blood Hematopoietic System
63273 63273 SRR13724976 SRX10113008 SRS8268904 SRP306756 PRJNA702191 Phosphatidylinositol 3 kinase signaling controls survival and stemness of hematopoietic stem and progenitor cells GSE166900 Transcriptome Analysis Loss of Pten and inhibition of PI3K induced apoptosis of hematopoietic stem/progenitor cells upon endothelial to hematopoietic transition and surviving hematopoietic stem/progenitor cells committed to all blood lineages but displayed reduced stemness Overall design: AGMs 36hpf or CHTs 5dpf were dissected and dissociated using FACS sorting HSPCs were isolated and single cell RNA seq was performed.   pubmed:33714985   SBF 23 GSM5087787   tissue:Caudal Hematopoietic Tissue|developmental stage:5dpf SBF 23 Well numbers X001 X384 in *.TranscriptCounts.tsv refer to well numbers of the 384 well plate in the following order: A1 to A24 B1 to B24 until P24. The right mate of each read pair was mapped to the ensemble of all gene loci. Reads mapping to multiple loci were discarded. During sequencing Read 1 was assigned 26/75* base pairs and was used for identification of the Illumina library barcode cel barcode and UMI. R2 was assigned 60/75* base pairs and used to map to the reference transcriptome of Zv9 with BWA Anders and Huber 2010. Data was demultiplexed as described in Grün et al. 2014. Mapping and generation of count tables was automated using the MapAndGo script1. https://github.com/anna alemany/transcriptomics/tree/master/mapandgo Tabular separated file indicating number of transcripts per section obtained as previously described Grün Dominic Lennart Kester and Alexander Van Oudenaarden. "Validation of noise models for single cell transcriptomics." Nature methods 11.6 2014: 637. We refer to transcripts as unique molecules based on UMI correction. *coutc* files contain number of reads *coutb* files contain number of observed UMI and *coutt* files contain number of unique UMI corrected transcripts per gene per cell The left read contains the barcode information: the first eight bases correspond to a cell specific barcode* followed by a 6bp unique molecular identifier UMI. The remainder of the left read contains a polyT stretch followed by a number <40 of transcript derived bases. Read 1 was not used for quantification. cell specific barcodes are specified in the file “cel seq2 barcodes.csv” Genome build: zV9 Danio Rerio Caudal Hematopoietic Tissue   Cells were sorted into 384 well plates. All wells underwent Cel Seq2 protocol RNA libraries were prepared for sequencing using standard Illumina protocols   developmental stage:5dpf|sort day see plate layout:13|genotype or treatment see plate layout:LY294002 treated GSM5087787 GSM5087787: SBF 23; Danio rerio; RNA Seq GSM5087787   1 Cells were sorted into 384 well plates. All wells underwent Cel Seq2 protocol RNA libraries were prepared for sequencing using standard Illumina protocols GEO Accession:GSM5087787 RNA-Seq TRANSCRIPTOMIC cDNA PAIRED ILLUMINA NextSeq 500   SRP306756     HUB-SF-023_HM5N7BGXC_S5_L002_R1_001.fastq.gz HUB-SF-023_HM5N7BGXC_S5_L002_R2_001.fastq.gz fastq fastq 485306600.0 5643100.0 GSM5087787 r2 0:26 1:60 A:115996506;C:95759572;G:102194588;T:170746790;N:609144 26 60     115996506 95759572 102194588 170746790 609144 SRX10113008 SRS8268904 SRA1196923 GEO Hubrecht Institute 2 0.09067 0.83684 0.08385 0.19289 0.9919 0.8477 0.67154 0.57449 26 60 T B sc-like readlen illumina nextseq unknown cdna_unspecified unknown sc single_cell_plate celseq   Netherlands 2021-02-16 Larval Larval Blood Hematopoietic System
63274 63274 SRR13724977 SRX10113008 SRS8268904 SRP306756 PRJNA702191 Phosphatidylinositol 3 kinase signaling controls survival and stemness of hematopoietic stem and progenitor cells GSE166900 Transcriptome Analysis Loss of Pten and inhibition of PI3K induced apoptosis of hematopoietic stem/progenitor cells upon endothelial to hematopoietic transition and surviving hematopoietic stem/progenitor cells committed to all blood lineages but displayed reduced stemness Overall design: AGMs 36hpf or CHTs 5dpf were dissected and dissociated using FACS sorting HSPCs were isolated and single cell RNA seq was performed.   pubmed:33714985   SBF 23 GSM5087787   tissue:Caudal Hematopoietic Tissue|developmental stage:5dpf SBF 23 Well numbers X001 X384 in *.TranscriptCounts.tsv refer to well numbers of the 384 well plate in the following order: A1 to A24 B1 to B24 until P24. The right mate of each read pair was mapped to the ensemble of all gene loci. Reads mapping to multiple loci were discarded. During sequencing Read 1 was assigned 26/75* base pairs and was used for identification of the Illumina library barcode cel barcode and UMI. R2 was assigned 60/75* base pairs and used to map to the reference transcriptome of Zv9 with BWA Anders and Huber 2010. Data was demultiplexed as described in Grün et al. 2014. Mapping and generation of count tables was automated using the MapAndGo script1. https://github.com/anna alemany/transcriptomics/tree/master/mapandgo Tabular separated file indicating number of transcripts per section obtained as previously described Grün Dominic Lennart Kester and Alexander Van Oudenaarden. "Validation of noise models for single cell transcriptomics." Nature methods 11.6 2014: 637. We refer to transcripts as unique molecules based on UMI correction. *coutc* files contain number of reads *coutb* files contain number of observed UMI and *coutt* files contain number of unique UMI corrected transcripts per gene per cell The left read contains the barcode information: the first eight bases correspond to a cell specific barcode* followed by a 6bp unique molecular identifier UMI. The remainder of the left read contains a polyT stretch followed by a number <40 of transcript derived bases. Read 1 was not used for quantification. cell specific barcodes are specified in the file “cel seq2 barcodes.csv” Genome build: zV9 Danio Rerio Caudal Hematopoietic Tissue   Cells were sorted into 384 well plates. All wells underwent Cel Seq2 protocol RNA libraries were prepared for sequencing using standard Illumina protocols   developmental stage:5dpf|sort day see plate layout:13|genotype or treatment see plate layout:LY294002 treated GSM5087787 GSM5087787: SBF 23; Danio rerio; RNA Seq GSM5087787   1 Cells were sorted into 384 well plates. All wells underwent Cel Seq2 protocol RNA libraries were prepared for sequencing using standard Illumina protocols GEO Accession:GSM5087787 RNA-Seq TRANSCRIPTOMIC cDNA PAIRED ILLUMINA NextSeq 500   SRP306756     HUB-SF-023_HM5N7BGXC_S5_L003_R1_001.fastq.gz HUB-SF-023_HM5N7BGXC_S5_L003_R2_001.fastq.gz fastq fastq 500039776.0 5814416.0 GSM5087787 r3 0:26 1:60 A:120392168;C:99507866;G:102641157;T:177069144;N:429441 26 60     120392168 99507866 102641157 177069144 429441 SRX10113008 SRS8268904 SRA1196923 GEO Hubrecht Institute 2 0.09148 0.84686 0.0847 0.19685 0.99141 0.84753 0.65939 0.57293 26 60 T B sc-like readlen illumina nextseq unknown cdna_unspecified unknown sc single_cell_plate celseq   Netherlands 2021-02-16 Larval Larval Blood Hematopoietic System
63275 63275 SRR13724978 SRX10113008 SRS8268904 SRP306756 PRJNA702191 Phosphatidylinositol 3 kinase signaling controls survival and stemness of hematopoietic stem and progenitor cells GSE166900 Transcriptome Analysis Loss of Pten and inhibition of PI3K induced apoptosis of hematopoietic stem/progenitor cells upon endothelial to hematopoietic transition and surviving hematopoietic stem/progenitor cells committed to all blood lineages but displayed reduced stemness Overall design: AGMs 36hpf or CHTs 5dpf were dissected and dissociated using FACS sorting HSPCs were isolated and single cell RNA seq was performed.   pubmed:33714985   SBF 23 GSM5087787   tissue:Caudal Hematopoietic Tissue|developmental stage:5dpf SBF 23 Well numbers X001 X384 in *.TranscriptCounts.tsv refer to well numbers of the 384 well plate in the following order: A1 to A24 B1 to B24 until P24. The right mate of each read pair was mapped to the ensemble of all gene loci. Reads mapping to multiple loci were discarded. During sequencing Read 1 was assigned 26/75* base pairs and was used for identification of the Illumina library barcode cel barcode and UMI. R2 was assigned 60/75* base pairs and used to map to the reference transcriptome of Zv9 with BWA Anders and Huber 2010. Data was demultiplexed as described in Grün et al. 2014. Mapping and generation of count tables was automated using the MapAndGo script1. https://github.com/anna alemany/transcriptomics/tree/master/mapandgo Tabular separated file indicating number of transcripts per section obtained as previously described Grün Dominic Lennart Kester and Alexander Van Oudenaarden. "Validation of noise models for single cell transcriptomics." Nature methods 11.6 2014: 637. We refer to transcripts as unique molecules based on UMI correction. *coutc* files contain number of reads *coutb* files contain number of observed UMI and *coutt* files contain number of unique UMI corrected transcripts per gene per cell The left read contains the barcode information: the first eight bases correspond to a cell specific barcode* followed by a 6bp unique molecular identifier UMI. The remainder of the left read contains a polyT stretch followed by a number <40 of transcript derived bases. Read 1 was not used for quantification. cell specific barcodes are specified in the file “cel seq2 barcodes.csv” Genome build: zV9 Danio Rerio Caudal Hematopoietic Tissue   Cells were sorted into 384 well plates. All wells underwent Cel Seq2 protocol RNA libraries were prepared for sequencing using standard Illumina protocols   developmental stage:5dpf|sort day see plate layout:13|genotype or treatment see plate layout:LY294002 treated GSM5087787 GSM5087787: SBF 23; Danio rerio; RNA Seq GSM5087787   1 Cells were sorted into 384 well plates. All wells underwent Cel Seq2 protocol RNA libraries were prepared for sequencing using standard Illumina protocols GEO Accession:GSM5087787 RNA-Seq TRANSCRIPTOMIC cDNA PAIRED ILLUMINA NextSeq 500   SRP306756     HUB-SF-023_HM5N7BGXC_S5_L004_R1_001.fastq.gz HUB-SF-023_HM5N7BGXC_S5_L004_R2_001.fastq.gz fastq fastq 492861184.0 5730944.0 GSM5087787 r4 0:26 1:60 A:117758138;C:97299695;G:103802938;T:173573965;N:426448 26 60     117758138 97299695 103802938 173573965 426448 SRX10113008 SRS8268904 SRA1196923 GEO Hubrecht Institute 2 0.09041 0.83933 0.0839 0.19275 0.99267 0.84723 0.63166 0.57545 26 60 T B sc-like readlen illumina nextseq unknown cdna_unspecified unknown sc single_cell_plate celseq   Netherlands 2021-02-16 Larval Larval Blood Hematopoietic System
63276 63276 SRR13724971 SRX10113007 SRS8268903 SRP306756 PRJNA702191 Phosphatidylinositol 3 kinase signaling controls survival and stemness of hematopoietic stem and progenitor cells GSE166900 Transcriptome Analysis Loss of Pten and inhibition of PI3K induced apoptosis of hematopoietic stem/progenitor cells upon endothelial to hematopoietic transition and surviving hematopoietic stem/progenitor cells committed to all blood lineages but displayed reduced stemness Overall design: AGMs 36hpf or CHTs 5dpf were dissected and dissociated using FACS sorting HSPCs were isolated and single cell RNA seq was performed.   pubmed:33714985   SBF 22 GSM5087786   tissue:Caudal Hematopoietic Tissue|developmental stage:5dpf SBF 22 Well numbers X001 X384 in *.TranscriptCounts.tsv refer to well numbers of the 384 well plate in the following order: A1 to A24 B1 to B24 until P24. The right mate of each read pair was mapped to the ensemble of all gene loci. Reads mapping to multiple loci were discarded. During sequencing Read 1 was assigned 26/75* base pairs and was used for identification of the Illumina library barcode cel barcode and UMI. R2 was assigned 60/75* base pairs and used to map to the reference transcriptome of Zv9 with BWA Anders and Huber 2010. Data was demultiplexed as described in Grün et al. 2014. Mapping and generation of count tables was automated using the MapAndGo script1. https://github.com/anna alemany/transcriptomics/tree/master/mapandgo Tabular separated file indicating number of transcripts per section obtained as previously described Grün Dominic Lennart Kester and Alexander Van Oudenaarden. "Validation of noise models for single cell transcriptomics." Nature methods 11.6 2014: 637. We refer to transcripts as unique molecules based on UMI correction. *coutc* files contain number of reads *coutb* files contain number of observed UMI and *coutt* files contain number of unique UMI corrected transcripts per gene per cell The left read contains the barcode information: the first eight bases correspond to a cell specific barcode* followed by a 6bp unique molecular identifier UMI. The remainder of the left read contains a polyT stretch followed by a number <40 of transcript derived bases. Read 1 was not used for quantification. cell specific barcodes are specified in the file “cel seq2 barcodes.csv” Genome build: zV9 Danio Rerio Caudal Hematopoietic Tissue   Cells were sorted into 384 well plates. All wells underwent Cel Seq2 protocol RNA libraries were prepared for sequencing using standard Illumina protocols   developmental stage:5dpf|sort day see plate layout:13|genotype or treatment see plate layout:LY294002 treated GSM5087786 GSM5087786: SBF 22; Danio rerio; RNA Seq GSM5087786   1 Cells were sorted into 384 well plates. All wells underwent Cel Seq2 protocol RNA libraries were prepared for sequencing using standard Illumina protocols GEO Accession:GSM5087786 RNA-Seq TRANSCRIPTOMIC cDNA PAIRED ILLUMINA NextSeq 500   SRP306756     HUB-SF-022_HM5N7BGXC_S4_L001_R1_001.fastq.gz HUB-SF-022_HM5N7BGXC_S4_L001_R2_001.fastq.gz fastq fastq 692152252.0 8048282.0 GSM5087786 r1 0:26 1:60 A:163624673;C:136146319;G:138410010;T:253002242;N:969008 26 60     163624673 136146319 138410010 253002242 969008 SRX10113007 SRS8268903 SRA1196923 GEO Hubrecht Institute 2 0.1078 0.88463 0.09968 0.20723 0.9903 0.82254 0.61429 0.54857 26 60 T B sc-like readlen illumina nextseq unknown cdna_unspecified unknown sc single_cell_plate celseq   Netherlands 2021-02-16 Larval Larval Blood Hematopoietic System
63277 63277 SRR13724972 SRX10113007 SRS8268903 SRP306756 PRJNA702191 Phosphatidylinositol 3 kinase signaling controls survival and stemness of hematopoietic stem and progenitor cells GSE166900 Transcriptome Analysis Loss of Pten and inhibition of PI3K induced apoptosis of hematopoietic stem/progenitor cells upon endothelial to hematopoietic transition and surviving hematopoietic stem/progenitor cells committed to all blood lineages but displayed reduced stemness Overall design: AGMs 36hpf or CHTs 5dpf were dissected and dissociated using FACS sorting HSPCs were isolated and single cell RNA seq was performed.   pubmed:33714985   SBF 22 GSM5087786   tissue:Caudal Hematopoietic Tissue|developmental stage:5dpf SBF 22 Well numbers X001 X384 in *.TranscriptCounts.tsv refer to well numbers of the 384 well plate in the following order: A1 to A24 B1 to B24 until P24. The right mate of each read pair was mapped to the ensemble of all gene loci. Reads mapping to multiple loci were discarded. During sequencing Read 1 was assigned 26/75* base pairs and was used for identification of the Illumina library barcode cel barcode and UMI. R2 was assigned 60/75* base pairs and used to map to the reference transcriptome of Zv9 with BWA Anders and Huber 2010. Data was demultiplexed as described in Grün et al. 2014. Mapping and generation of count tables was automated using the MapAndGo script1. https://github.com/anna alemany/transcriptomics/tree/master/mapandgo Tabular separated file indicating number of transcripts per section obtained as previously described Grün Dominic Lennart Kester and Alexander Van Oudenaarden. "Validation of noise models for single cell transcriptomics." Nature methods 11.6 2014: 637. We refer to transcripts as unique molecules based on UMI correction. *coutc* files contain number of reads *coutb* files contain number of observed UMI and *coutt* files contain number of unique UMI corrected transcripts per gene per cell The left read contains the barcode information: the first eight bases correspond to a cell specific barcode* followed by a 6bp unique molecular identifier UMI. The remainder of the left read contains a polyT stretch followed by a number <40 of transcript derived bases. Read 1 was not used for quantification. cell specific barcodes are specified in the file “cel seq2 barcodes.csv” Genome build: zV9 Danio Rerio Caudal Hematopoietic Tissue   Cells were sorted into 384 well plates. All wells underwent Cel Seq2 protocol RNA libraries were prepared for sequencing using standard Illumina protocols   developmental stage:5dpf|sort day see plate layout:13|genotype or treatment see plate layout:LY294002 treated GSM5087786 GSM5087786: SBF 22; Danio rerio; RNA Seq GSM5087786   1 Cells were sorted into 384 well plates. All wells underwent Cel Seq2 protocol RNA libraries were prepared for sequencing using standard Illumina protocols GEO Accession:GSM5087786 RNA-Seq TRANSCRIPTOMIC cDNA PAIRED ILLUMINA NextSeq 500   SRP306756     HUB-SF-022_HM5N7BGXC_S4_L002_R1_001.fastq.gz HUB-SF-022_HM5N7BGXC_S4_L002_R2_001.fastq.gz fastq fastq 671222346.0 7804911.0 GSM5087786 r2 0:26 1:60 A:157709930;C:131021057;G:137587478;T:244048639;N:855242 26 60     157709930 131021057 137587478 244048639 855242 SRX10113007 SRS8268903 SRA1196923 GEO Hubrecht Institute 2 0.10866 0.87439 0.10052 0.20141 0.9904 0.82546 0.61794 0.56015 26 60 T B sc-like readlen illumina nextseq unknown cdna_unspecified unknown sc single_cell_plate celseq   Netherlands 2021-02-16 Larval Larval Blood Hematopoietic System
63278 63278 SRR13724973 SRX10113007 SRS8268903 SRP306756 PRJNA702191 Phosphatidylinositol 3 kinase signaling controls survival and stemness of hematopoietic stem and progenitor cells GSE166900 Transcriptome Analysis Loss of Pten and inhibition of PI3K induced apoptosis of hematopoietic stem/progenitor cells upon endothelial to hematopoietic transition and surviving hematopoietic stem/progenitor cells committed to all blood lineages but displayed reduced stemness Overall design: AGMs 36hpf or CHTs 5dpf were dissected and dissociated using FACS sorting HSPCs were isolated and single cell RNA seq was performed.   pubmed:33714985   SBF 22 GSM5087786   tissue:Caudal Hematopoietic Tissue|developmental stage:5dpf SBF 22 Well numbers X001 X384 in *.TranscriptCounts.tsv refer to well numbers of the 384 well plate in the following order: A1 to A24 B1 to B24 until P24. The right mate of each read pair was mapped to the ensemble of all gene loci. Reads mapping to multiple loci were discarded. During sequencing Read 1 was assigned 26/75* base pairs and was used for identification of the Illumina library barcode cel barcode and UMI. R2 was assigned 60/75* base pairs and used to map to the reference transcriptome of Zv9 with BWA Anders and Huber 2010. Data was demultiplexed as described in Grün et al. 2014. Mapping and generation of count tables was automated using the MapAndGo script1. https://github.com/anna alemany/transcriptomics/tree/master/mapandgo Tabular separated file indicating number of transcripts per section obtained as previously described Grün Dominic Lennart Kester and Alexander Van Oudenaarden. "Validation of noise models for single cell transcriptomics." Nature methods 11.6 2014: 637. We refer to transcripts as unique molecules based on UMI correction. *coutc* files contain number of reads *coutb* files contain number of observed UMI and *coutt* files contain number of unique UMI corrected transcripts per gene per cell The left read contains the barcode information: the first eight bases correspond to a cell specific barcode* followed by a 6bp unique molecular identifier UMI. The remainder of the left read contains a polyT stretch followed by a number <40 of transcript derived bases. Read 1 was not used for quantification. cell specific barcodes are specified in the file “cel seq2 barcodes.csv” Genome build: zV9 Danio Rerio Caudal Hematopoietic Tissue   Cells were sorted into 384 well plates. All wells underwent Cel Seq2 protocol RNA libraries were prepared for sequencing using standard Illumina protocols   developmental stage:5dpf|sort day see plate layout:13|genotype or treatment see plate layout:LY294002 treated GSM5087786 GSM5087786: SBF 22; Danio rerio; RNA Seq GSM5087786   1 Cells were sorted into 384 well plates. All wells underwent Cel Seq2 protocol RNA libraries were prepared for sequencing using standard Illumina protocols GEO Accession:GSM5087786 RNA-Seq TRANSCRIPTOMIC cDNA PAIRED ILLUMINA NextSeq 500   SRP306756     HUB-SF-022_HM5N7BGXC_S4_L003_R1_001.fastq.gz HUB-SF-022_HM5N7BGXC_S4_L003_R2_001.fastq.gz fastq fastq 695196652.0 8083682.0 GSM5087786 r3 0:26 1:60 A:164311022;C:136759406;G:139356369;T:254166382;N:603473 26 60     164311022 136759406 139356369 254166382 603473 SRX10113007 SRS8268903 SRA1196923 GEO Hubrecht Institute 2 0.10819 0.8846 0.1002 0.20578 0.99066 0.82343 0.62859 0.57036 26 60 T B sc-like readlen illumina nextseq unknown cdna_unspecified unknown sc single_cell_plate celseq   Netherlands 2021-02-16 Larval Larval Blood Hematopoietic System
63279 63279 SRR13724974 SRX10113007 SRS8268903 SRP306756 PRJNA702191 Phosphatidylinositol 3 kinase signaling controls survival and stemness of hematopoietic stem and progenitor cells GSE166900 Transcriptome Analysis Loss of Pten and inhibition of PI3K induced apoptosis of hematopoietic stem/progenitor cells upon endothelial to hematopoietic transition and surviving hematopoietic stem/progenitor cells committed to all blood lineages but displayed reduced stemness Overall design: AGMs 36hpf or CHTs 5dpf were dissected and dissociated using FACS sorting HSPCs were isolated and single cell RNA seq was performed.   pubmed:33714985   SBF 22 GSM5087786   tissue:Caudal Hematopoietic Tissue|developmental stage:5dpf SBF 22 Well numbers X001 X384 in *.TranscriptCounts.tsv refer to well numbers of the 384 well plate in the following order: A1 to A24 B1 to B24 until P24. The right mate of each read pair was mapped to the ensemble of all gene loci. Reads mapping to multiple loci were discarded. During sequencing Read 1 was assigned 26/75* base pairs and was used for identification of the Illumina library barcode cel barcode and UMI. R2 was assigned 60/75* base pairs and used to map to the reference transcriptome of Zv9 with BWA Anders and Huber 2010. Data was demultiplexed as described in Grün et al. 2014. Mapping and generation of count tables was automated using the MapAndGo script1. https://github.com/anna alemany/transcriptomics/tree/master/mapandgo Tabular separated file indicating number of transcripts per section obtained as previously described Grün Dominic Lennart Kester and Alexander Van Oudenaarden. "Validation of noise models for single cell transcriptomics." Nature methods 11.6 2014: 637. We refer to transcripts as unique molecules based on UMI correction. *coutc* files contain number of reads *coutb* files contain number of observed UMI and *coutt* files contain number of unique UMI corrected transcripts per gene per cell The left read contains the barcode information: the first eight bases correspond to a cell specific barcode* followed by a 6bp unique molecular identifier UMI. The remainder of the left read contains a polyT stretch followed by a number <40 of transcript derived bases. Read 1 was not used for quantification. cell specific barcodes are specified in the file “cel seq2 barcodes.csv” Genome build: zV9 Danio Rerio Caudal Hematopoietic Tissue   Cells were sorted into 384 well plates. All wells underwent Cel Seq2 protocol RNA libraries were prepared for sequencing using standard Illumina protocols   developmental stage:5dpf|sort day see plate layout:13|genotype or treatment see plate layout:LY294002 treated GSM5087786 GSM5087786: SBF 22; Danio rerio; RNA Seq GSM5087786   1 Cells were sorted into 384 well plates. All wells underwent Cel Seq2 protocol RNA libraries were prepared for sequencing using standard Illumina protocols GEO Accession:GSM5087786 RNA-Seq TRANSCRIPTOMIC cDNA PAIRED ILLUMINA NextSeq 500   SRP306756     HUB-SF-022_HM5N7BGXC_S4_L004_R1_001.fastq.gz HUB-SF-022_HM5N7BGXC_S4_L004_R2_001.fastq.gz fastq fastq 682727942.0 7938697.0 GSM5087786 r4 0:26 1:60 A:160371802;C:133327309;G:140017340;T:248412347;N:599144 26 60     160371802 133327309 140017340 248412347 599144 SRX10113007 SRS8268903 SRA1196923 GEO Hubrecht Institute 2 0.10911 0.87796 0.10042 0.20317 0.99001 0.82499 0.60817 0.54631 26 60 T B sc-like readlen illumina nextseq unknown cdna_unspecified unknown sc single_cell_plate celseq   Netherlands 2021-02-16 Larval Larval Blood Hematopoietic System
63280 63280 SRR13724967 SRX10113006 SRS8268902 SRP306756 PRJNA702191 Phosphatidylinositol 3 kinase signaling controls survival and stemness of hematopoietic stem and progenitor cells GSE166900 Transcriptome Analysis Loss of Pten and inhibition of PI3K induced apoptosis of hematopoietic stem/progenitor cells upon endothelial to hematopoietic transition and surviving hematopoietic stem/progenitor cells committed to all blood lineages but displayed reduced stemness Overall design: AGMs 36hpf or CHTs 5dpf were dissected and dissociated using FACS sorting HSPCs were isolated and single cell RNA seq was performed.   pubmed:33714985   SBF 21 GSM5087785   tissue:Caudal Hematopoietic Tissue|developmental stage:5dpf SBF 21 Well numbers X001 X384 in *.TranscriptCounts.tsv refer to well numbers of the 384 well plate in the following order: A1 to A24 B1 to B24 until P24. The right mate of each read pair was mapped to the ensemble of all gene loci. Reads mapping to multiple loci were discarded. During sequencing Read 1 was assigned 26/75* base pairs and was used for identification of the Illumina library barcode cel barcode and UMI. R2 was assigned 60/75* base pairs and used to map to the reference transcriptome of Zv9 with BWA Anders and Huber 2010. Data was demultiplexed as described in Grün et al. 2014. Mapping and generation of count tables was automated using the MapAndGo script1. https://github.com/anna alemany/transcriptomics/tree/master/mapandgo Tabular separated file indicating number of transcripts per section obtained as previously described Grün Dominic Lennart Kester and Alexander Van Oudenaarden. "Validation of noise models for single cell transcriptomics." Nature methods 11.6 2014: 637. We refer to transcripts as unique molecules based on UMI correction. *coutc* files contain number of reads *coutb* files contain number of observed UMI and *coutt* files contain number of unique UMI corrected transcripts per gene per cell The left read contains the barcode information: the first eight bases correspond to a cell specific barcode* followed by a 6bp unique molecular identifier UMI. The remainder of the left read contains a polyT stretch followed by a number <40 of transcript derived bases. Read 1 was not used for quantification. cell specific barcodes are specified in the file “cel seq2 barcodes.csv” Genome build: zV9 Danio Rerio Caudal Hematopoietic Tissue   Cells were sorted into 384 well plates. All wells underwent Cel Seq2 protocol RNA libraries were prepared for sequencing using standard Illumina protocols   developmental stage:5dpf|sort day see plate layout:13|genotype or treatment see plate layout:wildtype GSM5087785 GSM5087785: SBF 21; Danio rerio; RNA Seq GSM5087785   1 Cells were sorted into 384 well plates. All wells underwent Cel Seq2 protocol RNA libraries were prepared for sequencing using standard Illumina protocols GEO Accession:GSM5087785 RNA-Seq TRANSCRIPTOMIC cDNA PAIRED ILLUMINA NextSeq 500   SRP306756     HUB-SF-021_HCYCGBGXC_S2_L001_R1_001.fastq.gz HUB-SF-021_HCYCGBGXC_S2_L001_R2_001.fastq.gz fastq fastq 658122482.0 7652587.0 GSM5087785 r1 0:26 1:60 A:151648153;C:131732368;G:132232707;T:242262395;N:246859 26 60     151648153 131732368 132232707 242262395 246859 SRX10113006 SRS8268902 SRA1196923 GEO Hubrecht Institute 2 0.10959 0.88687 0.10237 0.18233 0.9906 0.8327 0.63384 0.51974 26 60 T B sc-like readlen illumina nextseq unknown cdna_unspecified unknown sc single_cell_plate celseq   Netherlands 2021-02-16 Larval Larval Blood Hematopoietic System
63281 63281 SRR13724968 SRX10113006 SRS8268902 SRP306756 PRJNA702191 Phosphatidylinositol 3 kinase signaling controls survival and stemness of hematopoietic stem and progenitor cells GSE166900 Transcriptome Analysis Loss of Pten and inhibition of PI3K induced apoptosis of hematopoietic stem/progenitor cells upon endothelial to hematopoietic transition and surviving hematopoietic stem/progenitor cells committed to all blood lineages but displayed reduced stemness Overall design: AGMs 36hpf or CHTs 5dpf were dissected and dissociated using FACS sorting HSPCs were isolated and single cell RNA seq was performed.   pubmed:33714985   SBF 21 GSM5087785   tissue:Caudal Hematopoietic Tissue|developmental stage:5dpf SBF 21 Well numbers X001 X384 in *.TranscriptCounts.tsv refer to well numbers of the 384 well plate in the following order: A1 to A24 B1 to B24 until P24. The right mate of each read pair was mapped to the ensemble of all gene loci. Reads mapping to multiple loci were discarded. During sequencing Read 1 was assigned 26/75* base pairs and was used for identification of the Illumina library barcode cel barcode and UMI. R2 was assigned 60/75* base pairs and used to map to the reference transcriptome of Zv9 with BWA Anders and Huber 2010. Data was demultiplexed as described in Grün et al. 2014. Mapping and generation of count tables was automated using the MapAndGo script1. https://github.com/anna alemany/transcriptomics/tree/master/mapandgo Tabular separated file indicating number of transcripts per section obtained as previously described Grün Dominic Lennart Kester and Alexander Van Oudenaarden. "Validation of noise models for single cell transcriptomics." Nature methods 11.6 2014: 637. We refer to transcripts as unique molecules based on UMI correction. *coutc* files contain number of reads *coutb* files contain number of observed UMI and *coutt* files contain number of unique UMI corrected transcripts per gene per cell The left read contains the barcode information: the first eight bases correspond to a cell specific barcode* followed by a 6bp unique molecular identifier UMI. The remainder of the left read contains a polyT stretch followed by a number <40 of transcript derived bases. Read 1 was not used for quantification. cell specific barcodes are specified in the file “cel seq2 barcodes.csv” Genome build: zV9 Danio Rerio Caudal Hematopoietic Tissue   Cells were sorted into 384 well plates. All wells underwent Cel Seq2 protocol RNA libraries were prepared for sequencing using standard Illumina protocols   developmental stage:5dpf|sort day see plate layout:13|genotype or treatment see plate layout:wildtype GSM5087785 GSM5087785: SBF 21; Danio rerio; RNA Seq GSM5087785   1 Cells were sorted into 384 well plates. All wells underwent Cel Seq2 protocol RNA libraries were prepared for sequencing using standard Illumina protocols GEO Accession:GSM5087785 RNA-Seq TRANSCRIPTOMIC cDNA PAIRED ILLUMINA NextSeq 500   SRP306756     HUB-SF-021_HCYCGBGXC_S2_L002_R1_001.fastq.gz HUB-SF-021_HCYCGBGXC_S2_L002_R2_001.fastq.gz fastq fastq 634600880.0 7379080.0 GSM5087785 r2 0:26 1:60 A:145007716;C:126037130;G:131042698;T:232293498;N:219838 26 60     145007716 126037130 131042698 232293498 219838 SRX10113006 SRS8268902 SRA1196923 GEO Hubrecht Institute 2 0.10993 0.87545 0.10298 0.18121 0.99131 0.83266 0.62709 0.51781 26 60 T B sc-like readlen illumina nextseq unknown cdna_unspecified unknown sc single_cell_plate celseq   Netherlands 2021-02-16 Larval Larval Blood Hematopoietic System
63282 63282 SRR13724969 SRX10113006 SRS8268902 SRP306756 PRJNA702191 Phosphatidylinositol 3 kinase signaling controls survival and stemness of hematopoietic stem and progenitor cells GSE166900 Transcriptome Analysis Loss of Pten and inhibition of PI3K induced apoptosis of hematopoietic stem/progenitor cells upon endothelial to hematopoietic transition and surviving hematopoietic stem/progenitor cells committed to all blood lineages but displayed reduced stemness Overall design: AGMs 36hpf or CHTs 5dpf were dissected and dissociated using FACS sorting HSPCs were isolated and single cell RNA seq was performed.   pubmed:33714985   SBF 21 GSM5087785   tissue:Caudal Hematopoietic Tissue|developmental stage:5dpf SBF 21 Well numbers X001 X384 in *.TranscriptCounts.tsv refer to well numbers of the 384 well plate in the following order: A1 to A24 B1 to B24 until P24. The right mate of each read pair was mapped to the ensemble of all gene loci. Reads mapping to multiple loci were discarded. During sequencing Read 1 was assigned 26/75* base pairs and was used for identification of the Illumina library barcode cel barcode and UMI. R2 was assigned 60/75* base pairs and used to map to the reference transcriptome of Zv9 with BWA Anders and Huber 2010. Data was demultiplexed as described in Grün et al. 2014. Mapping and generation of count tables was automated using the MapAndGo script1. https://github.com/anna alemany/transcriptomics/tree/master/mapandgo Tabular separated file indicating number of transcripts per section obtained as previously described Grün Dominic Lennart Kester and Alexander Van Oudenaarden. "Validation of noise models for single cell transcriptomics." Nature methods 11.6 2014: 637. We refer to transcripts as unique molecules based on UMI correction. *coutc* files contain number of reads *coutb* files contain number of observed UMI and *coutt* files contain number of unique UMI corrected transcripts per gene per cell The left read contains the barcode information: the first eight bases correspond to a cell specific barcode* followed by a 6bp unique molecular identifier UMI. The remainder of the left read contains a polyT stretch followed by a number <40 of transcript derived bases. Read 1 was not used for quantification. cell specific barcodes are specified in the file “cel seq2 barcodes.csv” Genome build: zV9 Danio Rerio Caudal Hematopoietic Tissue   Cells were sorted into 384 well plates. All wells underwent Cel Seq2 protocol RNA libraries were prepared for sequencing using standard Illumina protocols   developmental stage:5dpf|sort day see plate layout:13|genotype or treatment see plate layout:wildtype GSM5087785 GSM5087785: SBF 21; Danio rerio; RNA Seq GSM5087785   1 Cells were sorted into 384 well plates. All wells underwent Cel Seq2 protocol RNA libraries were prepared for sequencing using standard Illumina protocols GEO Accession:GSM5087785 RNA-Seq TRANSCRIPTOMIC cDNA PAIRED ILLUMINA NextSeq 500   SRP306756     HUB-SF-021_HCYCGBGXC_S2_L003_R1_001.fastq.gz HUB-SF-021_HCYCGBGXC_S2_L003_R2_001.fastq.gz fastq fastq 662619594.0 7704879.0 GSM5087785 r3 0:26 1:60 A:152792611;C:132773186;G:132769428;T:244139865;N:144504 26 60     152792611 132773186 132769428 244139865 144504 SRX10113006 SRS8268902 SRA1196923 GEO Hubrecht Institute 2 0.11075 0.88934 0.10373 0.18458 0.99147 0.83309 0.63813 0.50617 26 60 T B sc-like readlen illumina nextseq unknown cdna_unspecified unknown sc single_cell_plate celseq   Netherlands 2021-02-16 Larval Larval Blood Hematopoietic System
63283 63283 SRR13724970 SRX10113006 SRS8268902 SRP306756 PRJNA702191 Phosphatidylinositol 3 kinase signaling controls survival and stemness of hematopoietic stem and progenitor cells GSE166900 Transcriptome Analysis Loss of Pten and inhibition of PI3K induced apoptosis of hematopoietic stem/progenitor cells upon endothelial to hematopoietic transition and surviving hematopoietic stem/progenitor cells committed to all blood lineages but displayed reduced stemness Overall design: AGMs 36hpf or CHTs 5dpf were dissected and dissociated using FACS sorting HSPCs were isolated and single cell RNA seq was performed.   pubmed:33714985   SBF 21 GSM5087785   tissue:Caudal Hematopoietic Tissue|developmental stage:5dpf SBF 21 Well numbers X001 X384 in *.TranscriptCounts.tsv refer to well numbers of the 384 well plate in the following order: A1 to A24 B1 to B24 until P24. The right mate of each read pair was mapped to the ensemble of all gene loci. Reads mapping to multiple loci were discarded. During sequencing Read 1 was assigned 26/75* base pairs and was used for identification of the Illumina library barcode cel barcode and UMI. R2 was assigned 60/75* base pairs and used to map to the reference transcriptome of Zv9 with BWA Anders and Huber 2010. Data was demultiplexed as described in Grün et al. 2014. Mapping and generation of count tables was automated using the MapAndGo script1. https://github.com/anna alemany/transcriptomics/tree/master/mapandgo Tabular separated file indicating number of transcripts per section obtained as previously described Grün Dominic Lennart Kester and Alexander Van Oudenaarden. "Validation of noise models for single cell transcriptomics." Nature methods 11.6 2014: 637. We refer to transcripts as unique molecules based on UMI correction. *coutc* files contain number of reads *coutb* files contain number of observed UMI and *coutt* files contain number of unique UMI corrected transcripts per gene per cell The left read contains the barcode information: the first eight bases correspond to a cell specific barcode* followed by a 6bp unique molecular identifier UMI. The remainder of the left read contains a polyT stretch followed by a number <40 of transcript derived bases. Read 1 was not used for quantification. cell specific barcodes are specified in the file “cel seq2 barcodes.csv” Genome build: zV9 Danio Rerio Caudal Hematopoietic Tissue   Cells were sorted into 384 well plates. All wells underwent Cel Seq2 protocol RNA libraries were prepared for sequencing using standard Illumina protocols   developmental stage:5dpf|sort day see plate layout:13|genotype or treatment see plate layout:wildtype GSM5087785 GSM5087785: SBF 21; Danio rerio; RNA Seq GSM5087785   1 Cells were sorted into 384 well plates. All wells underwent Cel Seq2 protocol RNA libraries were prepared for sequencing using standard Illumina protocols GEO Accession:GSM5087785 RNA-Seq TRANSCRIPTOMIC cDNA PAIRED ILLUMINA NextSeq 500   SRP306756     HUB-SF-021_HCYCGBGXC_S2_L004_R1_001.fastq.gz HUB-SF-021_HCYCGBGXC_S2_L004_R2_001.fastq.gz fastq fastq 649414380.0 7551330.0 GSM5087785 r4 0:26 1:60 A:148520869;C:129109435;G:133769528;T:237898818;N:115730 26 60     148520869 129109435 133769528 237898818 115730 SRX10113006 SRS8268902 SRA1196923 GEO Hubrecht Institute 2 0.11034 0.877 0.10316 0.18038 0.99113 0.83266 0.63882 0.50615 26 60 T B sc-like readlen illumina nextseq unknown cdna_unspecified unknown sc single_cell_plate celseq   Netherlands 2021-02-16 Larval Larval Blood Hematopoietic System
63284 63284 SRR13724963 SRX10113005 SRS8268901 SRP306756 PRJNA702191 Phosphatidylinositol 3 kinase signaling controls survival and stemness of hematopoietic stem and progenitor cells GSE166900 Transcriptome Analysis Loss of Pten and inhibition of PI3K induced apoptosis of hematopoietic stem/progenitor cells upon endothelial to hematopoietic transition and surviving hematopoietic stem/progenitor cells committed to all blood lineages but displayed reduced stemness Overall design: AGMs 36hpf or CHTs 5dpf were dissected and dissociated using FACS sorting HSPCs were isolated and single cell RNA seq was performed.   pubmed:33714985   SBF 20 GSM5087784   tissue:Caudal Hematopoietic Tissue|developmental stage:5dpf SBF 20 Well numbers X001 X384 in *.TranscriptCounts.tsv refer to well numbers of the 384 well plate in the following order: A1 to A24 B1 to B24 until P24. The right mate of each read pair was mapped to the ensemble of all gene loci. Reads mapping to multiple loci were discarded. During sequencing Read 1 was assigned 26/75* base pairs and was used for identification of the Illumina library barcode cel barcode and UMI. R2 was assigned 60/75* base pairs and used to map to the reference transcriptome of Zv9 with BWA Anders and Huber 2010. Data was demultiplexed as described in Grün et al. 2014. Mapping and generation of count tables was automated using the MapAndGo script1. https://github.com/anna alemany/transcriptomics/tree/master/mapandgo Tabular separated file indicating number of transcripts per section obtained as previously described Grün Dominic Lennart Kester and Alexander Van Oudenaarden. "Validation of noise models for single cell transcriptomics." Nature methods 11.6 2014: 637. We refer to transcripts as unique molecules based on UMI correction. *coutc* files contain number of reads *coutb* files contain number of observed UMI and *coutt* files contain number of unique UMI corrected transcripts per gene per cell The left read contains the barcode information: the first eight bases correspond to a cell specific barcode* followed by a 6bp unique molecular identifier UMI. The remainder of the left read contains a polyT stretch followed by a number <40 of transcript derived bases. Read 1 was not used for quantification. cell specific barcodes are specified in the file “cel seq2 barcodes.csv” Genome build: zV9 Danio Rerio Caudal Hematopoietic Tissue   Cells were sorted into 384 well plates. All wells underwent Cel Seq2 protocol RNA libraries were prepared for sequencing using standard Illumina protocols   developmental stage:5dpf|sort day see plate layout:13|genotype or treatment see plate layout:wildtype GSM5087784 GSM5087784: SBF 20; Danio rerio; RNA Seq GSM5087784   1 Cells were sorted into 384 well plates. All wells underwent Cel Seq2 protocol RNA libraries were prepared for sequencing using standard Illumina protocols GEO Accession:GSM5087784 RNA-Seq TRANSCRIPTOMIC cDNA PAIRED ILLUMINA NextSeq 500   SRP306756     HUB-SF-020_HCYCGBGXC_S1_L001_R1_001.fastq.gz HUB-SF-020_HCYCGBGXC_S1_L001_R2_001.fastq.gz fastq fastq 670472340.0 7796190.0 GSM5087784 r1 0:26 1:60 A:162379562;C:131115631;G:127994412;T:248733255;N:249480 26 60     162379562 131115631 127994412 248733255 249480 SRX10113005 SRS8268901 SRA1196923 GEO Hubrecht Institute 2 0.08755 0.71055 0.0825 0.15153 0.99358 0.83755 0.60375 0.5249 26 60 T B sc-like readlen illumina nextseq unknown cdna_unspecified unknown sc single_cell_plate celseq   Netherlands 2021-02-16 Larval Larval Blood Hematopoietic System
63285 63285 SRR13724964 SRX10113005 SRS8268901 SRP306756 PRJNA702191 Phosphatidylinositol 3 kinase signaling controls survival and stemness of hematopoietic stem and progenitor cells GSE166900 Transcriptome Analysis Loss of Pten and inhibition of PI3K induced apoptosis of hematopoietic stem/progenitor cells upon endothelial to hematopoietic transition and surviving hematopoietic stem/progenitor cells committed to all blood lineages but displayed reduced stemness Overall design: AGMs 36hpf or CHTs 5dpf were dissected and dissociated using FACS sorting HSPCs were isolated and single cell RNA seq was performed.   pubmed:33714985   SBF 20 GSM5087784   tissue:Caudal Hematopoietic Tissue|developmental stage:5dpf SBF 20 Well numbers X001 X384 in *.TranscriptCounts.tsv refer to well numbers of the 384 well plate in the following order: A1 to A24 B1 to B24 until P24. The right mate of each read pair was mapped to the ensemble of all gene loci. Reads mapping to multiple loci were discarded. During sequencing Read 1 was assigned 26/75* base pairs and was used for identification of the Illumina library barcode cel barcode and UMI. R2 was assigned 60/75* base pairs and used to map to the reference transcriptome of Zv9 with BWA Anders and Huber 2010. Data was demultiplexed as described in Grün et al. 2014. Mapping and generation of count tables was automated using the MapAndGo script1. https://github.com/anna alemany/transcriptomics/tree/master/mapandgo Tabular separated file indicating number of transcripts per section obtained as previously described Grün Dominic Lennart Kester and Alexander Van Oudenaarden. "Validation of noise models for single cell transcriptomics." Nature methods 11.6 2014: 637. We refer to transcripts as unique molecules based on UMI correction. *coutc* files contain number of reads *coutb* files contain number of observed UMI and *coutt* files contain number of unique UMI corrected transcripts per gene per cell The left read contains the barcode information: the first eight bases correspond to a cell specific barcode* followed by a 6bp unique molecular identifier UMI. The remainder of the left read contains a polyT stretch followed by a number <40 of transcript derived bases. Read 1 was not used for quantification. cell specific barcodes are specified in the file “cel seq2 barcodes.csv” Genome build: zV9 Danio Rerio Caudal Hematopoietic Tissue   Cells were sorted into 384 well plates. All wells underwent Cel Seq2 protocol RNA libraries were prepared for sequencing using standard Illumina protocols   developmental stage:5dpf|sort day see plate layout:13|genotype or treatment see plate layout:wildtype GSM5087784 GSM5087784: SBF 20; Danio rerio; RNA Seq GSM5087784   1 Cells were sorted into 384 well plates. All wells underwent Cel Seq2 protocol RNA libraries were prepared for sequencing using standard Illumina protocols GEO Accession:GSM5087784 RNA-Seq TRANSCRIPTOMIC cDNA PAIRED ILLUMINA NextSeq 500   SRP306756     HUB-SF-020_HCYCGBGXC_S1_L002_R1_001.fastq.gz HUB-SF-020_HCYCGBGXC_S1_L002_R2_001.fastq.gz fastq fastq 650513374.0 7564109.0 GSM5087784 r2 0:26 1:60 A:156597182;C:126392295;G:127063431;T:240228431;N:232035 26 60     156597182 126392295 127063431 240228431 232035 SRX10113005 SRS8268901 SRA1196923 GEO Hubrecht Institute 2 0.08613 0.70505 0.08121 0.15296 0.99375 0.84189 0.60729 0.53 26 60 T B sc-like readlen illumina nextseq unknown cdna_unspecified unknown sc single_cell_plate celseq   Netherlands 2021-02-16 Larval Larval Blood Hematopoietic System
63286 63286 SRR13724965 SRX10113005 SRS8268901 SRP306756 PRJNA702191 Phosphatidylinositol 3 kinase signaling controls survival and stemness of hematopoietic stem and progenitor cells GSE166900 Transcriptome Analysis Loss of Pten and inhibition of PI3K induced apoptosis of hematopoietic stem/progenitor cells upon endothelial to hematopoietic transition and surviving hematopoietic stem/progenitor cells committed to all blood lineages but displayed reduced stemness Overall design: AGMs 36hpf or CHTs 5dpf were dissected and dissociated using FACS sorting HSPCs were isolated and single cell RNA seq was performed.   pubmed:33714985   SBF 20 GSM5087784   tissue:Caudal Hematopoietic Tissue|developmental stage:5dpf SBF 20 Well numbers X001 X384 in *.TranscriptCounts.tsv refer to well numbers of the 384 well plate in the following order: A1 to A24 B1 to B24 until P24. The right mate of each read pair was mapped to the ensemble of all gene loci. Reads mapping to multiple loci were discarded. During sequencing Read 1 was assigned 26/75* base pairs and was used for identification of the Illumina library barcode cel barcode and UMI. R2 was assigned 60/75* base pairs and used to map to the reference transcriptome of Zv9 with BWA Anders and Huber 2010. Data was demultiplexed as described in Grün et al. 2014. Mapping and generation of count tables was automated using the MapAndGo script1. https://github.com/anna alemany/transcriptomics/tree/master/mapandgo Tabular separated file indicating number of transcripts per section obtained as previously described Grün Dominic Lennart Kester and Alexander Van Oudenaarden. "Validation of noise models for single cell transcriptomics." Nature methods 11.6 2014: 637. We refer to transcripts as unique molecules based on UMI correction. *coutc* files contain number of reads *coutb* files contain number of observed UMI and *coutt* files contain number of unique UMI corrected transcripts per gene per cell The left read contains the barcode information: the first eight bases correspond to a cell specific barcode* followed by a 6bp unique molecular identifier UMI. The remainder of the left read contains a polyT stretch followed by a number <40 of transcript derived bases. Read 1 was not used for quantification. cell specific barcodes are specified in the file “cel seq2 barcodes.csv” Genome build: zV9 Danio Rerio Caudal Hematopoietic Tissue   Cells were sorted into 384 well plates. All wells underwent Cel Seq2 protocol RNA libraries were prepared for sequencing using standard Illumina protocols   developmental stage:5dpf|sort day see plate layout:13|genotype or treatment see plate layout:wildtype GSM5087784 GSM5087784: SBF 20; Danio rerio; RNA Seq GSM5087784   1 Cells were sorted into 384 well plates. All wells underwent Cel Seq2 protocol RNA libraries were prepared for sequencing using standard Illumina protocols GEO Accession:GSM5087784 RNA-Seq TRANSCRIPTOMIC cDNA PAIRED ILLUMINA NextSeq 500   SRP306756     HUB-SF-020_HCYCGBGXC_S1_L003_R1_001.fastq.gz HUB-SF-020_HCYCGBGXC_S1_L003_R2_001.fastq.gz fastq fastq 675299090.0 7852315.0 GSM5087784 r3 0:26 1:60 A:163628742;C:132107557;G:128715474;T:250701110;N:146207 26 60     163628742 132107557 128715474 250701110 146207 SRX10113005 SRS8268901 SRA1196923 GEO Hubrecht Institute 2 0.08641 0.71045 0.08125 0.15261 0.99316 0.84007 0.57932 0.52835 26 60 T B sc-like readlen illumina nextseq unknown cdna_unspecified unknown sc single_cell_plate celseq   Netherlands 2021-02-16 Larval Larval Blood Hematopoietic System
63287 63287 SRR13724966 SRX10113005 SRS8268901 SRP306756 PRJNA702191 Phosphatidylinositol 3 kinase signaling controls survival and stemness of hematopoietic stem and progenitor cells GSE166900 Transcriptome Analysis Loss of Pten and inhibition of PI3K induced apoptosis of hematopoietic stem/progenitor cells upon endothelial to hematopoietic transition and surviving hematopoietic stem/progenitor cells committed to all blood lineages but displayed reduced stemness Overall design: AGMs 36hpf or CHTs 5dpf were dissected and dissociated using FACS sorting HSPCs were isolated and single cell RNA seq was performed.   pubmed:33714985   SBF 20 GSM5087784   tissue:Caudal Hematopoietic Tissue|developmental stage:5dpf SBF 20 Well numbers X001 X384 in *.TranscriptCounts.tsv refer to well numbers of the 384 well plate in the following order: A1 to A24 B1 to B24 until P24. The right mate of each read pair was mapped to the ensemble of all gene loci. Reads mapping to multiple loci were discarded. During sequencing Read 1 was assigned 26/75* base pairs and was used for identification of the Illumina library barcode cel barcode and UMI. R2 was assigned 60/75* base pairs and used to map to the reference transcriptome of Zv9 with BWA Anders and Huber 2010. Data was demultiplexed as described in Grün et al. 2014. Mapping and generation of count tables was automated using the MapAndGo script1. https://github.com/anna alemany/transcriptomics/tree/master/mapandgo Tabular separated file indicating number of transcripts per section obtained as previously described Grün Dominic Lennart Kester and Alexander Van Oudenaarden. "Validation of noise models for single cell transcriptomics." Nature methods 11.6 2014: 637. We refer to transcripts as unique molecules based on UMI correction. *coutc* files contain number of reads *coutb* files contain number of observed UMI and *coutt* files contain number of unique UMI corrected transcripts per gene per cell The left read contains the barcode information: the first eight bases correspond to a cell specific barcode* followed by a 6bp unique molecular identifier UMI. The remainder of the left read contains a polyT stretch followed by a number <40 of transcript derived bases. Read 1 was not used for quantification. cell specific barcodes are specified in the file “cel seq2 barcodes.csv” Genome build: zV9 Danio Rerio Caudal Hematopoietic Tissue   Cells were sorted into 384 well plates. All wells underwent Cel Seq2 protocol RNA libraries were prepared for sequencing using standard Illumina protocols   developmental stage:5dpf|sort day see plate layout:13|genotype or treatment see plate layout:wildtype GSM5087784 GSM5087784: SBF 20; Danio rerio; RNA Seq GSM5087784   1 Cells were sorted into 384 well plates. All wells underwent Cel Seq2 protocol RNA libraries were prepared for sequencing using standard Illumina protocols GEO Accession:GSM5087784 RNA-Seq TRANSCRIPTOMIC cDNA PAIRED ILLUMINA NextSeq 500   SRP306756     HUB-SF-020_HCYCGBGXC_S1_L004_R1_001.fastq.gz HUB-SF-020_HCYCGBGXC_S1_L004_R2_001.fastq.gz fastq fastq 664144890.0 7722615.0 GSM5087784 r4 0:26 1:60 A:160104966;C:129173502;G:129351342;T:245402375;N:112705 26 60     160104966 129173502 129351342 245402375 112705 SRX10113005 SRS8268901 SRA1196923 GEO Hubrecht Institute 2 0.08629 0.70513 0.08129 0.15138 0.99389 0.83962 0.57309 0.52054 26 60 T B sc-like readlen illumina nextseq unknown cdna_unspecified unknown sc single_cell_plate celseq   Netherlands 2021-02-16 Larval Larval Blood Hematopoietic System
63300 63300 SRR13724947 SRX10113001 SRS8268897 SRP306756 PRJNA702191 Phosphatidylinositol 3 kinase signaling controls survival and stemness of hematopoietic stem and progenitor cells GSE166900 Transcriptome Analysis Loss of Pten and inhibition of PI3K induced apoptosis of hematopoietic stem/progenitor cells upon endothelial to hematopoietic transition and surviving hematopoietic stem/progenitor cells committed to all blood lineages but displayed reduced stemness Overall design: AGMs 36hpf or CHTs 5dpf were dissected and dissociated using FACS sorting HSPCs were isolated and single cell RNA seq was performed.   pubmed:33714985   SBF 16 GSM5087780   tissue:Caudal Hematopoietic Tissue|developmental stage:5dpf SBF 16 Well numbers X001 X384 in *.TranscriptCounts.tsv refer to well numbers of the 384 well plate in the following order: A1 to A24 B1 to B24 until P24. The right mate of each read pair was mapped to the ensemble of all gene loci. Reads mapping to multiple loci were discarded. During sequencing Read 1 was assigned 26/75* base pairs and was used for identification of the Illumina library barcode cel barcode and UMI. R2 was assigned 60/75* base pairs and used to map to the reference transcriptome of Zv9 with BWA Anders and Huber 2010. Data was demultiplexed as described in Grün et al. 2014. Mapping and generation of count tables was automated using the MapAndGo script1. https://github.com/anna alemany/transcriptomics/tree/master/mapandgo Tabular separated file indicating number of transcripts per section obtained as previously described Grün Dominic Lennart Kester and Alexander Van Oudenaarden. "Validation of noise models for single cell transcriptomics." Nature methods 11.6 2014: 637. We refer to transcripts as unique molecules based on UMI correction. *coutc* files contain number of reads *coutb* files contain number of observed UMI and *coutt* files contain number of unique UMI corrected transcripts per gene per cell The left read contains the barcode information: the first eight bases correspond to a cell specific barcode* followed by a 6bp unique molecular identifier UMI. The remainder of the left read contains a polyT stretch followed by a number <40 of transcript derived bases. Read 1 was not used for quantification. cell specific barcodes are specified in the file “cel seq2 barcodes.csv” Genome build: zV9 Danio Rerio Caudal Hematopoietic Tissue   Cells were sorted into 384 well plates. All wells underwent Cel Seq2 protocol RNA libraries were prepared for sequencing using standard Illumina protocols   developmental stage:5dpf|sort day see plate layout:9|genotype or treatment see plate layout:pten mutant GSM5087780 GSM5087780: SBF 16; Danio rerio; RNA Seq GSM5087780   1 Cells were sorted into 384 well plates. All wells underwent Cel Seq2 protocol RNA libraries were prepared for sequencing using standard Illumina protocols GEO Accession:GSM5087780 RNA-Seq TRANSCRIPTOMIC cDNA PAIRED ILLUMINA NextSeq 500   SRP306756     HUB-SF-016_HF2K3BGXC_S4_L001_R1_001.fastq.gz HUB-SF-016_HF2K3BGXC_S4_L001_R2_001.fastq.gz fastq fastq 893323710.0 10387485.0 GSM5087780 r1 0:26 1:60 A:215688011;C:172149456;G:160430721;T:344813481;N:242041 26 60     215688011 172149456 160430721 344813481 242041 SRX10113001 SRS8268897 SRA1196923 GEO Hubrecht Institute 2 0.11394 0.8793 0.10621 0.22519 0.99137 0.79788 0.64872 0.49415 26 60 T B sc-like readlen illumina nextseq unknown cdna_unspecified unknown sc single_cell_plate celseq   Netherlands 2021-02-16 Larval Larval Blood Hematopoietic System
63301 63301 SRR13724948 SRX10113001 SRS8268897 SRP306756 PRJNA702191 Phosphatidylinositol 3 kinase signaling controls survival and stemness of hematopoietic stem and progenitor cells GSE166900 Transcriptome Analysis Loss of Pten and inhibition of PI3K induced apoptosis of hematopoietic stem/progenitor cells upon endothelial to hematopoietic transition and surviving hematopoietic stem/progenitor cells committed to all blood lineages but displayed reduced stemness Overall design: AGMs 36hpf or CHTs 5dpf were dissected and dissociated using FACS sorting HSPCs were isolated and single cell RNA seq was performed.   pubmed:33714985   SBF 16 GSM5087780   tissue:Caudal Hematopoietic Tissue|developmental stage:5dpf SBF 16 Well numbers X001 X384 in *.TranscriptCounts.tsv refer to well numbers of the 384 well plate in the following order: A1 to A24 B1 to B24 until P24. The right mate of each read pair was mapped to the ensemble of all gene loci. Reads mapping to multiple loci were discarded. During sequencing Read 1 was assigned 26/75* base pairs and was used for identification of the Illumina library barcode cel barcode and UMI. R2 was assigned 60/75* base pairs and used to map to the reference transcriptome of Zv9 with BWA Anders and Huber 2010. Data was demultiplexed as described in Grün et al. 2014. Mapping and generation of count tables was automated using the MapAndGo script1. https://github.com/anna alemany/transcriptomics/tree/master/mapandgo Tabular separated file indicating number of transcripts per section obtained as previously described Grün Dominic Lennart Kester and Alexander Van Oudenaarden. "Validation of noise models for single cell transcriptomics." Nature methods 11.6 2014: 637. We refer to transcripts as unique molecules based on UMI correction. *coutc* files contain number of reads *coutb* files contain number of observed UMI and *coutt* files contain number of unique UMI corrected transcripts per gene per cell The left read contains the barcode information: the first eight bases correspond to a cell specific barcode* followed by a 6bp unique molecular identifier UMI. The remainder of the left read contains a polyT stretch followed by a number <40 of transcript derived bases. Read 1 was not used for quantification. cell specific barcodes are specified in the file “cel seq2 barcodes.csv” Genome build: zV9 Danio Rerio Caudal Hematopoietic Tissue   Cells were sorted into 384 well plates. All wells underwent Cel Seq2 protocol RNA libraries were prepared for sequencing using standard Illumina protocols   developmental stage:5dpf|sort day see plate layout:9|genotype or treatment see plate layout:pten mutant GSM5087780 GSM5087780: SBF 16; Danio rerio; RNA Seq GSM5087780   1 Cells were sorted into 384 well plates. All wells underwent Cel Seq2 protocol RNA libraries were prepared for sequencing using standard Illumina protocols GEO Accession:GSM5087780 RNA-Seq TRANSCRIPTOMIC cDNA PAIRED ILLUMINA NextSeq 500   SRP306756     HUB-SF-016_HF2K3BGXC_S4_L002_R1_001.fastq.gz HUB-SF-016_HF2K3BGXC_S4_L002_R2_001.fastq.gz fastq fastq 881459494.0 10249529.0 GSM5087780 r2 0:26 1:60 A:211516598;C:168660798;G:162462426;T:338597998;N:221674 26 60     211516598 168660798 162462426 338597998 221674 SRX10113001 SRS8268897 SRA1196923 GEO Hubrecht Institute 2 0.11358 0.87585 0.10567 0.22576 0.99107 0.79961 0.6459 0.50316 26 60 T B sc-like readlen illumina nextseq unknown cdna_unspecified unknown sc single_cell_plate celseq   Netherlands 2021-02-16 Larval Larval Blood Hematopoietic System
63302 63302 SRR13724949 SRX10113001 SRS8268897 SRP306756 PRJNA702191 Phosphatidylinositol 3 kinase signaling controls survival and stemness of hematopoietic stem and progenitor cells GSE166900 Transcriptome Analysis Loss of Pten and inhibition of PI3K induced apoptosis of hematopoietic stem/progenitor cells upon endothelial to hematopoietic transition and surviving hematopoietic stem/progenitor cells committed to all blood lineages but displayed reduced stemness Overall design: AGMs 36hpf or CHTs 5dpf were dissected and dissociated using FACS sorting HSPCs were isolated and single cell RNA seq was performed.   pubmed:33714985   SBF 16 GSM5087780   tissue:Caudal Hematopoietic Tissue|developmental stage:5dpf SBF 16 Well numbers X001 X384 in *.TranscriptCounts.tsv refer to well numbers of the 384 well plate in the following order: A1 to A24 B1 to B24 until P24. The right mate of each read pair was mapped to the ensemble of all gene loci. Reads mapping to multiple loci were discarded. During sequencing Read 1 was assigned 26/75* base pairs and was used for identification of the Illumina library barcode cel barcode and UMI. R2 was assigned 60/75* base pairs and used to map to the reference transcriptome of Zv9 with BWA Anders and Huber 2010. Data was demultiplexed as described in Grün et al. 2014. Mapping and generation of count tables was automated using the MapAndGo script1. https://github.com/anna alemany/transcriptomics/tree/master/mapandgo Tabular separated file indicating number of transcripts per section obtained as previously described Grün Dominic Lennart Kester and Alexander Van Oudenaarden. "Validation of noise models for single cell transcriptomics." Nature methods 11.6 2014: 637. We refer to transcripts as unique molecules based on UMI correction. *coutc* files contain number of reads *coutb* files contain number of observed UMI and *coutt* files contain number of unique UMI corrected transcripts per gene per cell The left read contains the barcode information: the first eight bases correspond to a cell specific barcode* followed by a 6bp unique molecular identifier UMI. The remainder of the left read contains a polyT stretch followed by a number <40 of transcript derived bases. Read 1 was not used for quantification. cell specific barcodes are specified in the file “cel seq2 barcodes.csv” Genome build: zV9 Danio Rerio Caudal Hematopoietic Tissue   Cells were sorted into 384 well plates. All wells underwent Cel Seq2 protocol RNA libraries were prepared for sequencing using standard Illumina protocols   developmental stage:5dpf|sort day see plate layout:9|genotype or treatment see plate layout:pten mutant GSM5087780 GSM5087780: SBF 16; Danio rerio; RNA Seq GSM5087780   1 Cells were sorted into 384 well plates. All wells underwent Cel Seq2 protocol RNA libraries were prepared for sequencing using standard Illumina protocols GEO Accession:GSM5087780 RNA-Seq TRANSCRIPTOMIC cDNA PAIRED ILLUMINA NextSeq 500   SRP306756     HUB-SF-016_HF2K3BGXC_S4_L003_R1_001.fastq.gz HUB-SF-016_HF2K3BGXC_S4_L003_R2_001.fastq.gz fastq fastq 903230050.0 10502675.0 GSM5087780 r3 0:26 1:60 A:217946630;C:174137499;G:162397642;T:348582978;N:165301 26 60     217946630 174137499 162397642 348582978 165301 SRX10113001 SRS8268897 SRA1196923 GEO Hubrecht Institute 2 0.1146 0.87911 0.10656 0.22385 0.99135 0.79506 0.64195 0.48554 26 60 T B sc-like readlen illumina nextseq unknown cdna_unspecified unknown sc single_cell_plate celseq   Netherlands 2021-02-16 Larval Larval Blood Hematopoietic System
63303 63303 SRR13724950 SRX10113001 SRS8268897 SRP306756 PRJNA702191 Phosphatidylinositol 3 kinase signaling controls survival and stemness of hematopoietic stem and progenitor cells GSE166900 Transcriptome Analysis Loss of Pten and inhibition of PI3K induced apoptosis of hematopoietic stem/progenitor cells upon endothelial to hematopoietic transition and surviving hematopoietic stem/progenitor cells committed to all blood lineages but displayed reduced stemness Overall design: AGMs 36hpf or CHTs 5dpf were dissected and dissociated using FACS sorting HSPCs were isolated and single cell RNA seq was performed.   pubmed:33714985   SBF 16 GSM5087780   tissue:Caudal Hematopoietic Tissue|developmental stage:5dpf SBF 16 Well numbers X001 X384 in *.TranscriptCounts.tsv refer to well numbers of the 384 well plate in the following order: A1 to A24 B1 to B24 until P24. The right mate of each read pair was mapped to the ensemble of all gene loci. Reads mapping to multiple loci were discarded. During sequencing Read 1 was assigned 26/75* base pairs and was used for identification of the Illumina library barcode cel barcode and UMI. R2 was assigned 60/75* base pairs and used to map to the reference transcriptome of Zv9 with BWA Anders and Huber 2010. Data was demultiplexed as described in Grün et al. 2014. Mapping and generation of count tables was automated using the MapAndGo script1. https://github.com/anna alemany/transcriptomics/tree/master/mapandgo Tabular separated file indicating number of transcripts per section obtained as previously described Grün Dominic Lennart Kester and Alexander Van Oudenaarden. "Validation of noise models for single cell transcriptomics." Nature methods 11.6 2014: 637. We refer to transcripts as unique molecules based on UMI correction. *coutc* files contain number of reads *coutb* files contain number of observed UMI and *coutt* files contain number of unique UMI corrected transcripts per gene per cell The left read contains the barcode information: the first eight bases correspond to a cell specific barcode* followed by a 6bp unique molecular identifier UMI. The remainder of the left read contains a polyT stretch followed by a number <40 of transcript derived bases. Read 1 was not used for quantification. cell specific barcodes are specified in the file “cel seq2 barcodes.csv” Genome build: zV9 Danio Rerio Caudal Hematopoietic Tissue   Cells were sorted into 384 well plates. All wells underwent Cel Seq2 protocol RNA libraries were prepared for sequencing using standard Illumina protocols   developmental stage:5dpf|sort day see plate layout:9|genotype or treatment see plate layout:pten mutant GSM5087780 GSM5087780: SBF 16; Danio rerio; RNA Seq GSM5087780   1 Cells were sorted into 384 well plates. All wells underwent Cel Seq2 protocol RNA libraries were prepared for sequencing using standard Illumina protocols GEO Accession:GSM5087780 RNA-Seq TRANSCRIPTOMIC cDNA PAIRED ILLUMINA NextSeq 500   SRP306756     HUB-SF-016_HF2K3BGXC_S4_L004_R1_001.fastq.gz HUB-SF-016_HF2K3BGXC_S4_L004_R2_001.fastq.gz fastq fastq 888385074.0 10330059.0 GSM5087780 r4 0:26 1:60 A:213229735;C:170216776;G:163340402;T:341452439;N:145722 26 60     213229735 170216776 163340402 341452439 145722 SRX10113001 SRS8268897 SRA1196923 GEO Hubrecht Institute 2 0.11473 0.87581 0.10648 0.22398 0.99121 0.79868 0.66731 0.49086 26 60 T B sc-like readlen illumina nextseq unknown cdna_unspecified unknown sc single_cell_plate celseq   Netherlands 2021-02-16 Larval Larval Blood Hematopoietic System
63304 63304 SRR13724943 SRX10113000 SRS8268896 SRP306756 PRJNA702191 Phosphatidylinositol 3 kinase signaling controls survival and stemness of hematopoietic stem and progenitor cells GSE166900 Transcriptome Analysis Loss of Pten and inhibition of PI3K induced apoptosis of hematopoietic stem/progenitor cells upon endothelial to hematopoietic transition and surviving hematopoietic stem/progenitor cells committed to all blood lineages but displayed reduced stemness Overall design: AGMs 36hpf or CHTs 5dpf were dissected and dissociated using FACS sorting HSPCs were isolated and single cell RNA seq was performed.   pubmed:33714985   SBF 15 GSM5087779   tissue:Caudal Hematopoietic Tissue|developmental stage:5dpf SBF 15 Well numbers X001 X384 in *.TranscriptCounts.tsv refer to well numbers of the 384 well plate in the following order: A1 to A24 B1 to B24 until P24. The right mate of each read pair was mapped to the ensemble of all gene loci. Reads mapping to multiple loci were discarded. During sequencing Read 1 was assigned 26/75* base pairs and was used for identification of the Illumina library barcode cel barcode and UMI. R2 was assigned 60/75* base pairs and used to map to the reference transcriptome of Zv9 with BWA Anders and Huber 2010. Data was demultiplexed as described in Grün et al. 2014. Mapping and generation of count tables was automated using the MapAndGo script1. https://github.com/anna alemany/transcriptomics/tree/master/mapandgo Tabular separated file indicating number of transcripts per section obtained as previously described Grün Dominic Lennart Kester and Alexander Van Oudenaarden. "Validation of noise models for single cell transcriptomics." Nature methods 11.6 2014: 637. We refer to transcripts as unique molecules based on UMI correction. *coutc* files contain number of reads *coutb* files contain number of observed UMI and *coutt* files contain number of unique UMI corrected transcripts per gene per cell The left read contains the barcode information: the first eight bases correspond to a cell specific barcode* followed by a 6bp unique molecular identifier UMI. The remainder of the left read contains a polyT stretch followed by a number <40 of transcript derived bases. Read 1 was not used for quantification. cell specific barcodes are specified in the file “cel seq2 barcodes.csv” Genome build: zV9 Danio Rerio Caudal Hematopoietic Tissue   Cells were sorted into 384 well plates. All wells underwent Cel Seq2 protocol RNA libraries were prepared for sequencing using standard Illumina protocols   developmental stage:5dpf|sort day see plate layout:8 and 9|genotype or treatment see plate layout:pten sibling GSM5087779 GSM5087779: SBF 15; Danio rerio; RNA Seq GSM5087779   1 Cells were sorted into 384 well plates. All wells underwent Cel Seq2 protocol RNA libraries were prepared for sequencing using standard Illumina protocols GEO Accession:GSM5087779 RNA-Seq TRANSCRIPTOMIC cDNA PAIRED ILLUMINA NextSeq 500   SRP306756     HUB-SF-015_HF2K3BGXC_S3_L001_R1_001.fastq.gz HUB-SF-015_HF2K3BGXC_S3_L001_R2_001.fastq.gz fastq fastq 970642010.0 11286535.0 GSM5087779 r1 0:26 1:60 A:230129031;C:189649389;G:181291980;T:369305643;N:265967 26 60     230129031 189649389 181291980 369305643 265967 SRX10113000 SRS8268896 SRA1196923 GEO Hubrecht Institute 2 0.10865 0.90227 0.10078 0.22378 0.99044 0.79995 0.64439 0.5348 26 60 T B sc-like readlen illumina nextseq unknown cdna_unspecified unknown sc single_cell_plate celseq   Netherlands 2021-02-16 Larval Larval Blood Hematopoietic System
63305 63305 SRR13724944 SRX10113000 SRS8268896 SRP306756 PRJNA702191 Phosphatidylinositol 3 kinase signaling controls survival and stemness of hematopoietic stem and progenitor cells GSE166900 Transcriptome Analysis Loss of Pten and inhibition of PI3K induced apoptosis of hematopoietic stem/progenitor cells upon endothelial to hematopoietic transition and surviving hematopoietic stem/progenitor cells committed to all blood lineages but displayed reduced stemness Overall design: AGMs 36hpf or CHTs 5dpf were dissected and dissociated using FACS sorting HSPCs were isolated and single cell RNA seq was performed.   pubmed:33714985   SBF 15 GSM5087779   tissue:Caudal Hematopoietic Tissue|developmental stage:5dpf SBF 15 Well numbers X001 X384 in *.TranscriptCounts.tsv refer to well numbers of the 384 well plate in the following order: A1 to A24 B1 to B24 until P24. The right mate of each read pair was mapped to the ensemble of all gene loci. Reads mapping to multiple loci were discarded. During sequencing Read 1 was assigned 26/75* base pairs and was used for identification of the Illumina library barcode cel barcode and UMI. R2 was assigned 60/75* base pairs and used to map to the reference transcriptome of Zv9 with BWA Anders and Huber 2010. Data was demultiplexed as described in Grün et al. 2014. Mapping and generation of count tables was automated using the MapAndGo script1. https://github.com/anna alemany/transcriptomics/tree/master/mapandgo Tabular separated file indicating number of transcripts per section obtained as previously described Grün Dominic Lennart Kester and Alexander Van Oudenaarden. "Validation of noise models for single cell transcriptomics." Nature methods 11.6 2014: 637. We refer to transcripts as unique molecules based on UMI correction. *coutc* files contain number of reads *coutb* files contain number of observed UMI and *coutt* files contain number of unique UMI corrected transcripts per gene per cell The left read contains the barcode information: the first eight bases correspond to a cell specific barcode* followed by a 6bp unique molecular identifier UMI. The remainder of the left read contains a polyT stretch followed by a number <40 of transcript derived bases. Read 1 was not used for quantification. cell specific barcodes are specified in the file “cel seq2 barcodes.csv” Genome build: zV9 Danio Rerio Caudal Hematopoietic Tissue   Cells were sorted into 384 well plates. All wells underwent Cel Seq2 protocol RNA libraries were prepared for sequencing using standard Illumina protocols   developmental stage:5dpf|sort day see plate layout:8 and 9|genotype or treatment see plate layout:pten sibling GSM5087779 GSM5087779: SBF 15; Danio rerio; RNA Seq GSM5087779   1 Cells were sorted into 384 well plates. All wells underwent Cel Seq2 protocol RNA libraries were prepared for sequencing using standard Illumina protocols GEO Accession:GSM5087779 RNA-Seq TRANSCRIPTOMIC cDNA PAIRED ILLUMINA NextSeq 500   SRP306756     HUB-SF-015_HF2K3BGXC_S3_L002_R1_001.fastq.gz HUB-SF-015_HF2K3BGXC_S3_L002_R2_001.fastq.gz fastq fastq 961301980.0 11177930.0 GSM5087779 r2 0:26 1:60 A:226486582;C:186449767;G:184134038;T:363988209;N:243384 26 60     226486582 186449767 184134038 363988209 243384 SRX10113000 SRS8268896 SRA1196923 GEO Hubrecht Institute 2 0.10842 0.89459 0.10101 0.22365 0.99125 0.80247 0.66283 0.54558 26 60 T B sc-like readlen illumina nextseq unknown cdna_unspecified unknown sc single_cell_plate celseq   Netherlands 2021-02-16 Larval Larval Blood Hematopoietic System
63306 63306 SRR13724945 SRX10113000 SRS8268896 SRP306756 PRJNA702191 Phosphatidylinositol 3 kinase signaling controls survival and stemness of hematopoietic stem and progenitor cells GSE166900 Transcriptome Analysis Loss of Pten and inhibition of PI3K induced apoptosis of hematopoietic stem/progenitor cells upon endothelial to hematopoietic transition and surviving hematopoietic stem/progenitor cells committed to all blood lineages but displayed reduced stemness Overall design: AGMs 36hpf or CHTs 5dpf were dissected and dissociated using FACS sorting HSPCs were isolated and single cell RNA seq was performed.   pubmed:33714985   SBF 15 GSM5087779   tissue:Caudal Hematopoietic Tissue|developmental stage:5dpf SBF 15 Well numbers X001 X384 in *.TranscriptCounts.tsv refer to well numbers of the 384 well plate in the following order: A1 to A24 B1 to B24 until P24. The right mate of each read pair was mapped to the ensemble of all gene loci. Reads mapping to multiple loci were discarded. During sequencing Read 1 was assigned 26/75* base pairs and was used for identification of the Illumina library barcode cel barcode and UMI. R2 was assigned 60/75* base pairs and used to map to the reference transcriptome of Zv9 with BWA Anders and Huber 2010. Data was demultiplexed as described in Grün et al. 2014. Mapping and generation of count tables was automated using the MapAndGo script1. https://github.com/anna alemany/transcriptomics/tree/master/mapandgo Tabular separated file indicating number of transcripts per section obtained as previously described Grün Dominic Lennart Kester and Alexander Van Oudenaarden. "Validation of noise models for single cell transcriptomics." Nature methods 11.6 2014: 637. We refer to transcripts as unique molecules based on UMI correction. *coutc* files contain number of reads *coutb* files contain number of observed UMI and *coutt* files contain number of unique UMI corrected transcripts per gene per cell The left read contains the barcode information: the first eight bases correspond to a cell specific barcode* followed by a 6bp unique molecular identifier UMI. The remainder of the left read contains a polyT stretch followed by a number <40 of transcript derived bases. Read 1 was not used for quantification. cell specific barcodes are specified in the file “cel seq2 barcodes.csv” Genome build: zV9 Danio Rerio Caudal Hematopoietic Tissue   Cells were sorted into 384 well plates. All wells underwent Cel Seq2 protocol RNA libraries were prepared for sequencing using standard Illumina protocols   developmental stage:5dpf|sort day see plate layout:8 and 9|genotype or treatment see plate layout:pten sibling GSM5087779 GSM5087779: SBF 15; Danio rerio; RNA Seq GSM5087779   1 Cells were sorted into 384 well plates. All wells underwent Cel Seq2 protocol RNA libraries were prepared for sequencing using standard Illumina protocols GEO Accession:GSM5087779 RNA-Seq TRANSCRIPTOMIC cDNA PAIRED ILLUMINA NextSeq 500   SRP306756     HUB-SF-015_HF2K3BGXC_S3_L003_R1_001.fastq.gz HUB-SF-015_HF2K3BGXC_S3_L003_R2_001.fastq.gz fastq fastq 980250446.0 11398261.0 GSM5087779 r3 0:26 1:60 A:232141061;C:191560900;G:183395535;T:372971986;N:180964 26 60     232141061 191560900 183395535 372971986 180964 SRX10113000 SRS8268896 SRA1196923 GEO Hubrecht Institute 2 0.10739 0.899 0.0995 0.22483 0.99121 0.80178 0.59488 0.53306 26 60 T B sc-like readlen illumina nextseq unknown cdna_unspecified unknown sc single_cell_plate celseq   Netherlands 2021-02-16 Larval Larval Blood Hematopoietic System
63307 63307 SRR13724946 SRX10113000 SRS8268896 SRP306756 PRJNA702191 Phosphatidylinositol 3 kinase signaling controls survival and stemness of hematopoietic stem and progenitor cells GSE166900 Transcriptome Analysis Loss of Pten and inhibition of PI3K induced apoptosis of hematopoietic stem/progenitor cells upon endothelial to hematopoietic transition and surviving hematopoietic stem/progenitor cells committed to all blood lineages but displayed reduced stemness Overall design: AGMs 36hpf or CHTs 5dpf were dissected and dissociated using FACS sorting HSPCs were isolated and single cell RNA seq was performed.   pubmed:33714985   SBF 15 GSM5087779   tissue:Caudal Hematopoietic Tissue|developmental stage:5dpf SBF 15 Well numbers X001 X384 in *.TranscriptCounts.tsv refer to well numbers of the 384 well plate in the following order: A1 to A24 B1 to B24 until P24. The right mate of each read pair was mapped to the ensemble of all gene loci. Reads mapping to multiple loci were discarded. During sequencing Read 1 was assigned 26/75* base pairs and was used for identification of the Illumina library barcode cel barcode and UMI. R2 was assigned 60/75* base pairs and used to map to the reference transcriptome of Zv9 with BWA Anders and Huber 2010. Data was demultiplexed as described in Grün et al. 2014. Mapping and generation of count tables was automated using the MapAndGo script1. https://github.com/anna alemany/transcriptomics/tree/master/mapandgo Tabular separated file indicating number of transcripts per section obtained as previously described Grün Dominic Lennart Kester and Alexander Van Oudenaarden. "Validation of noise models for single cell transcriptomics." Nature methods 11.6 2014: 637. We refer to transcripts as unique molecules based on UMI correction. *coutc* files contain number of reads *coutb* files contain number of observed UMI and *coutt* files contain number of unique UMI corrected transcripts per gene per cell The left read contains the barcode information: the first eight bases correspond to a cell specific barcode* followed by a 6bp unique molecular identifier UMI. The remainder of the left read contains a polyT stretch followed by a number <40 of transcript derived bases. Read 1 was not used for quantification. cell specific barcodes are specified in the file “cel seq2 barcodes.csv” Genome build: zV9 Danio Rerio Caudal Hematopoietic Tissue   Cells were sorted into 384 well plates. All wells underwent Cel Seq2 protocol RNA libraries were prepared for sequencing using standard Illumina protocols   developmental stage:5dpf|sort day see plate layout:8 and 9|genotype or treatment see plate layout:pten sibling GSM5087779 GSM5087779: SBF 15; Danio rerio; RNA Seq GSM5087779   1 Cells were sorted into 384 well plates. All wells underwent Cel Seq2 protocol RNA libraries were prepared for sequencing using standard Illumina protocols GEO Accession:GSM5087779 RNA-Seq TRANSCRIPTOMIC cDNA PAIRED ILLUMINA NextSeq 500   SRP306756     HUB-SF-015_HF2K3BGXC_S3_L004_R1_001.fastq.gz HUB-SF-015_HF2K3BGXC_S3_L004_R2_001.fastq.gz fastq fastq 968248716.0 11258706.0 GSM5087779 r4 0:26 1:60 A:228063830;C:188015346;G:185144342;T:366859683;N:165515 26 60     228063830 188015346 185144342 366859683 165515 SRX10113000 SRS8268896 SRA1196923 GEO Hubrecht Institute 2 0.10816 0.89375 0.10038 0.22155 0.99082 0.80129 0.64329 0.54557 26 60 T B sc-like readlen illumina nextseq unknown cdna_unspecified unknown sc single_cell_plate celseq   Netherlands 2021-02-16 Larval Larval Blood Hematopoietic System
63308 63308 SRR13724939 SRX10112999 SRS8268895 SRP306756 PRJNA702191 Phosphatidylinositol 3 kinase signaling controls survival and stemness of hematopoietic stem and progenitor cells GSE166900 Transcriptome Analysis Loss of Pten and inhibition of PI3K induced apoptosis of hematopoietic stem/progenitor cells upon endothelial to hematopoietic transition and surviving hematopoietic stem/progenitor cells committed to all blood lineages but displayed reduced stemness Overall design: AGMs 36hpf or CHTs 5dpf were dissected and dissociated using FACS sorting HSPCs were isolated and single cell RNA seq was performed.   pubmed:33714985   SBF 14 GSM5087778   tissue:Caudal Hematopoietic Tissue|developmental stage:5dpf SBF 14 Well numbers X001 X384 in *.TranscriptCounts.tsv refer to well numbers of the 384 well plate in the following order: A1 to A24 B1 to B24 until P24. The right mate of each read pair was mapped to the ensemble of all gene loci. Reads mapping to multiple loci were discarded. During sequencing Read 1 was assigned 26/75* base pairs and was used for identification of the Illumina library barcode cel barcode and UMI. R2 was assigned 60/75* base pairs and used to map to the reference transcriptome of Zv9 with BWA Anders and Huber 2010. Data was demultiplexed as described in Grün et al. 2014. Mapping and generation of count tables was automated using the MapAndGo script1. https://github.com/anna alemany/transcriptomics/tree/master/mapandgo Tabular separated file indicating number of transcripts per section obtained as previously described Grün Dominic Lennart Kester and Alexander Van Oudenaarden. "Validation of noise models for single cell transcriptomics." Nature methods 11.6 2014: 637. We refer to transcripts as unique molecules based on UMI correction. *coutc* files contain number of reads *coutb* files contain number of observed UMI and *coutt* files contain number of unique UMI corrected transcripts per gene per cell The left read contains the barcode information: the first eight bases correspond to a cell specific barcode* followed by a 6bp unique molecular identifier UMI. The remainder of the left read contains a polyT stretch followed by a number <40 of transcript derived bases. Read 1 was not used for quantification. cell specific barcodes are specified in the file “cel seq2 barcodes.csv” Genome build: zV9 Danio Rerio Caudal Hematopoietic Tissue   Cells were sorted into 384 well plates. All wells underwent Cel Seq2 protocol RNA libraries were prepared for sequencing using standard Illumina protocols   developmental stage:5dpf|sort day see plate layout:8|genotype or treatment see plate layout:pten sibling GSM5087778 GSM5087778: SBF 14; Danio rerio; RNA Seq GSM5087778   1 Cells were sorted into 384 well plates. All wells underwent Cel Seq2 protocol RNA libraries were prepared for sequencing using standard Illumina protocols GEO Accession:GSM5087778 RNA-Seq TRANSCRIPTOMIC cDNA PAIRED ILLUMINA NextSeq 500   SRP306756     HUB-SF-014_HF2K3BGXC_S2_L001_R1_001.fastq.gz HUB-SF-014_HF2K3BGXC_S2_L001_R2_001.fastq.gz fastq fastq 976057946.0 11349511.0 GSM5087778 r1 0:26 1:60 A:237465510;C:185333631;G:178581338;T:374412410;N:265057 26 60     237465510 185333631 178581338 374412410 265057 SRX10112999 SRS8268895 SRA1196923 GEO Hubrecht Institute 2 0.11694 0.87501 0.10988 0.22762 0.99105 0.80466 0.6127 0.53801 26 60 T B sc-like readlen illumina nextseq unknown cdna_unspecified unknown sc single_cell_plate celseq   Netherlands 2021-02-16 Larval Larval Blood Hematopoietic System
63309 63309 SRR13724940 SRX10112999 SRS8268895 SRP306756 PRJNA702191 Phosphatidylinositol 3 kinase signaling controls survival and stemness of hematopoietic stem and progenitor cells GSE166900 Transcriptome Analysis Loss of Pten and inhibition of PI3K induced apoptosis of hematopoietic stem/progenitor cells upon endothelial to hematopoietic transition and surviving hematopoietic stem/progenitor cells committed to all blood lineages but displayed reduced stemness Overall design: AGMs 36hpf or CHTs 5dpf were dissected and dissociated using FACS sorting HSPCs were isolated and single cell RNA seq was performed.   pubmed:33714985   SBF 14 GSM5087778   tissue:Caudal Hematopoietic Tissue|developmental stage:5dpf SBF 14 Well numbers X001 X384 in *.TranscriptCounts.tsv refer to well numbers of the 384 well plate in the following order: A1 to A24 B1 to B24 until P24. The right mate of each read pair was mapped to the ensemble of all gene loci. Reads mapping to multiple loci were discarded. During sequencing Read 1 was assigned 26/75* base pairs and was used for identification of the Illumina library barcode cel barcode and UMI. R2 was assigned 60/75* base pairs and used to map to the reference transcriptome of Zv9 with BWA Anders and Huber 2010. Data was demultiplexed as described in Grün et al. 2014. Mapping and generation of count tables was automated using the MapAndGo script1. https://github.com/anna alemany/transcriptomics/tree/master/mapandgo Tabular separated file indicating number of transcripts per section obtained as previously described Grün Dominic Lennart Kester and Alexander Van Oudenaarden. "Validation of noise models for single cell transcriptomics." Nature methods 11.6 2014: 637. We refer to transcripts as unique molecules based on UMI correction. *coutc* files contain number of reads *coutb* files contain number of observed UMI and *coutt* files contain number of unique UMI corrected transcripts per gene per cell The left read contains the barcode information: the first eight bases correspond to a cell specific barcode* followed by a 6bp unique molecular identifier UMI. The remainder of the left read contains a polyT stretch followed by a number <40 of transcript derived bases. Read 1 was not used for quantification. cell specific barcodes are specified in the file “cel seq2 barcodes.csv” Genome build: zV9 Danio Rerio Caudal Hematopoietic Tissue   Cells were sorted into 384 well plates. All wells underwent Cel Seq2 protocol RNA libraries were prepared for sequencing using standard Illumina protocols   developmental stage:5dpf|sort day see plate layout:8|genotype or treatment see plate layout:pten sibling GSM5087778 GSM5087778: SBF 14; Danio rerio; RNA Seq GSM5087778   1 Cells were sorted into 384 well plates. All wells underwent Cel Seq2 protocol RNA libraries were prepared for sequencing using standard Illumina protocols GEO Accession:GSM5087778 RNA-Seq TRANSCRIPTOMIC cDNA PAIRED ILLUMINA NextSeq 500   SRP306756     HUB-SF-014_HF2K3BGXC_S2_L002_R1_001.fastq.gz HUB-SF-014_HF2K3BGXC_S2_L002_R2_001.fastq.gz fastq fastq 965409254.0 11225689.0 GSM5087778 r2 0:26 1:60 A:233470732;C:182177939;G:181007701;T:368507903;N:244979 26 60     233470732 182177939 181007701 368507903 244979 SRX10112999 SRS8268895 SRA1196923 GEO Hubrecht Institute 2 0.11556 0.87036 0.10865 0.22635 0.99149 0.80377 0.64351 0.54454 26 60 T B sc-like readlen illumina nextseq unknown cdna_unspecified unknown sc single_cell_plate celseq   Netherlands 2021-02-16 Larval Larval Blood Hematopoietic System
63310 63310 SRR13724941 SRX10112999 SRS8268895 SRP306756 PRJNA702191 Phosphatidylinositol 3 kinase signaling controls survival and stemness of hematopoietic stem and progenitor cells GSE166900 Transcriptome Analysis Loss of Pten and inhibition of PI3K induced apoptosis of hematopoietic stem/progenitor cells upon endothelial to hematopoietic transition and surviving hematopoietic stem/progenitor cells committed to all blood lineages but displayed reduced stemness Overall design: AGMs 36hpf or CHTs 5dpf were dissected and dissociated using FACS sorting HSPCs were isolated and single cell RNA seq was performed.   pubmed:33714985   SBF 14 GSM5087778   tissue:Caudal Hematopoietic Tissue|developmental stage:5dpf SBF 14 Well numbers X001 X384 in *.TranscriptCounts.tsv refer to well numbers of the 384 well plate in the following order: A1 to A24 B1 to B24 until P24. The right mate of each read pair was mapped to the ensemble of all gene loci. Reads mapping to multiple loci were discarded. During sequencing Read 1 was assigned 26/75* base pairs and was used for identification of the Illumina library barcode cel barcode and UMI. R2 was assigned 60/75* base pairs and used to map to the reference transcriptome of Zv9 with BWA Anders and Huber 2010. Data was demultiplexed as described in Grün et al. 2014. Mapping and generation of count tables was automated using the MapAndGo script1. https://github.com/anna alemany/transcriptomics/tree/master/mapandgo Tabular separated file indicating number of transcripts per section obtained as previously described Grün Dominic Lennart Kester and Alexander Van Oudenaarden. "Validation of noise models for single cell transcriptomics." Nature methods 11.6 2014: 637. We refer to transcripts as unique molecules based on UMI correction. *coutc* files contain number of reads *coutb* files contain number of observed UMI and *coutt* files contain number of unique UMI corrected transcripts per gene per cell The left read contains the barcode information: the first eight bases correspond to a cell specific barcode* followed by a 6bp unique molecular identifier UMI. The remainder of the left read contains a polyT stretch followed by a number <40 of transcript derived bases. Read 1 was not used for quantification. cell specific barcodes are specified in the file “cel seq2 barcodes.csv” Genome build: zV9 Danio Rerio Caudal Hematopoietic Tissue   Cells were sorted into 384 well plates. All wells underwent Cel Seq2 protocol RNA libraries were prepared for sequencing using standard Illumina protocols   developmental stage:5dpf|sort day see plate layout:8|genotype or treatment see plate layout:pten sibling GSM5087778 GSM5087778: SBF 14; Danio rerio; RNA Seq GSM5087778   1 Cells were sorted into 384 well plates. All wells underwent Cel Seq2 protocol RNA libraries were prepared for sequencing using standard Illumina protocols GEO Accession:GSM5087778 RNA-Seq TRANSCRIPTOMIC cDNA PAIRED ILLUMINA NextSeq 500   SRP306756     HUB-SF-014_HF2K3BGXC_S2_L003_R1_001.fastq.gz HUB-SF-014_HF2K3BGXC_S2_L003_R2_001.fastq.gz fastq fastq 985955772.0 11464602.0 GSM5087778 r3 0:26 1:60 A:239638838;C:187344246;G:180748672;T:378041406;N:182610 26 60     239638838 187344246 180748672 378041406 182610 SRX10112999 SRS8268895 SRA1196923 GEO Hubrecht Institute 2 0.11546 0.87433 0.10858 0.22786 0.99125 0.80391 0.631 0.54393 26 60 T B sc-like readlen illumina nextseq unknown cdna_unspecified unknown sc single_cell_plate celseq   Netherlands 2021-02-16 Larval Larval Blood Hematopoietic System
63311 63311 SRR13724942 SRX10112999 SRS8268895 SRP306756 PRJNA702191 Phosphatidylinositol 3 kinase signaling controls survival and stemness of hematopoietic stem and progenitor cells GSE166900 Transcriptome Analysis Loss of Pten and inhibition of PI3K induced apoptosis of hematopoietic stem/progenitor cells upon endothelial to hematopoietic transition and surviving hematopoietic stem/progenitor cells committed to all blood lineages but displayed reduced stemness Overall design: AGMs 36hpf or CHTs 5dpf were dissected and dissociated using FACS sorting HSPCs were isolated and single cell RNA seq was performed.   pubmed:33714985   SBF 14 GSM5087778   tissue:Caudal Hematopoietic Tissue|developmental stage:5dpf SBF 14 Well numbers X001 X384 in *.TranscriptCounts.tsv refer to well numbers of the 384 well plate in the following order: A1 to A24 B1 to B24 until P24. The right mate of each read pair was mapped to the ensemble of all gene loci. Reads mapping to multiple loci were discarded. During sequencing Read 1 was assigned 26/75* base pairs and was used for identification of the Illumina library barcode cel barcode and UMI. R2 was assigned 60/75* base pairs and used to map to the reference transcriptome of Zv9 with BWA Anders and Huber 2010. Data was demultiplexed as described in Grün et al. 2014. Mapping and generation of count tables was automated using the MapAndGo script1. https://github.com/anna alemany/transcriptomics/tree/master/mapandgo Tabular separated file indicating number of transcripts per section obtained as previously described Grün Dominic Lennart Kester and Alexander Van Oudenaarden. "Validation of noise models for single cell transcriptomics." Nature methods 11.6 2014: 637. We refer to transcripts as unique molecules based on UMI correction. *coutc* files contain number of reads *coutb* files contain number of observed UMI and *coutt* files contain number of unique UMI corrected transcripts per gene per cell The left read contains the barcode information: the first eight bases correspond to a cell specific barcode* followed by a 6bp unique molecular identifier UMI. The remainder of the left read contains a polyT stretch followed by a number <40 of transcript derived bases. Read 1 was not used for quantification. cell specific barcodes are specified in the file “cel seq2 barcodes.csv” Genome build: zV9 Danio Rerio Caudal Hematopoietic Tissue   Cells were sorted into 384 well plates. All wells underwent Cel Seq2 protocol RNA libraries were prepared for sequencing using standard Illumina protocols   developmental stage:5dpf|sort day see plate layout:8|genotype or treatment see plate layout:pten sibling GSM5087778 GSM5087778: SBF 14; Danio rerio; RNA Seq GSM5087778   1 Cells were sorted into 384 well plates. All wells underwent Cel Seq2 protocol RNA libraries were prepared for sequencing using standard Illumina protocols GEO Accession:GSM5087778 RNA-Seq TRANSCRIPTOMIC cDNA PAIRED ILLUMINA NextSeq 500   SRP306756     HUB-SF-014_HF2K3BGXC_S2_L004_R1_001.fastq.gz HUB-SF-014_HF2K3BGXC_S2_L004_R2_001.fastq.gz fastq fastq 974397286.0 11330201.0 GSM5087778 r4 0:26 1:60 A:235631286;C:184094472;G:182394896;T:372112389;N:164243 26 60     235631286 184094472 182394896 372112389 164243 SRX10112999 SRS8268895 SRA1196923 GEO Hubrecht Institute 2 0.11524 0.86944 0.10831 0.22553 0.99113 0.80235 0.62124 0.52989 26 60 T B sc-like readlen illumina nextseq unknown cdna_unspecified unknown sc single_cell_plate celseq   Netherlands 2021-02-16 Larval Larval Blood Hematopoietic System
63312 63312 SRR13724935 SRX10112998 SRS8268894 SRP306756 PRJNA702191 Phosphatidylinositol 3 kinase signaling controls survival and stemness of hematopoietic stem and progenitor cells GSE166900 Transcriptome Analysis Loss of Pten and inhibition of PI3K induced apoptosis of hematopoietic stem/progenitor cells upon endothelial to hematopoietic transition and surviving hematopoietic stem/progenitor cells committed to all blood lineages but displayed reduced stemness Overall design: AGMs 36hpf or CHTs 5dpf were dissected and dissociated using FACS sorting HSPCs were isolated and single cell RNA seq was performed.   pubmed:33714985   SBF 13 GSM5087777   tissue:Caudal Hematopoietic Tissue|developmental stage:5dpf SBF 13 Well numbers X001 X384 in *.TranscriptCounts.tsv refer to well numbers of the 384 well plate in the following order: A1 to A24 B1 to B24 until P24. The right mate of each read pair was mapped to the ensemble of all gene loci. Reads mapping to multiple loci were discarded. During sequencing Read 1 was assigned 26/75* base pairs and was used for identification of the Illumina library barcode cel barcode and UMI. R2 was assigned 60/75* base pairs and used to map to the reference transcriptome of Zv9 with BWA Anders and Huber 2010. Data was demultiplexed as described in Grün et al. 2014. Mapping and generation of count tables was automated using the MapAndGo script1. https://github.com/anna alemany/transcriptomics/tree/master/mapandgo Tabular separated file indicating number of transcripts per section obtained as previously described Grün Dominic Lennart Kester and Alexander Van Oudenaarden. "Validation of noise models for single cell transcriptomics." Nature methods 11.6 2014: 637. We refer to transcripts as unique molecules based on UMI correction. *coutc* files contain number of reads *coutb* files contain number of observed UMI and *coutt* files contain number of unique UMI corrected transcripts per gene per cell The left read contains the barcode information: the first eight bases correspond to a cell specific barcode* followed by a 6bp unique molecular identifier UMI. The remainder of the left read contains a polyT stretch followed by a number <40 of transcript derived bases. Read 1 was not used for quantification. cell specific barcodes are specified in the file “cel seq2 barcodes.csv” Genome build: zV9 Danio Rerio Caudal Hematopoietic Tissue   Cells were sorted into 384 well plates. All wells underwent Cel Seq2 protocol RNA libraries were prepared for sequencing using standard Illumina protocols   developmental stage:5dpf|sort day see plate layout:8 and 9|genotype or treatment see plate layout:pten mutant GSM5087777 GSM5087777: SBF 13; Danio rerio; RNA Seq GSM5087777   1 Cells were sorted into 384 well plates. All wells underwent Cel Seq2 protocol RNA libraries were prepared for sequencing using standard Illumina protocols GEO Accession:GSM5087777 RNA-Seq TRANSCRIPTOMIC cDNA PAIRED ILLUMINA NextSeq 500   SRP306756     HUB-SF-013_HF2K3BGXC_S1_L001_R1_001.fastq.gz HUB-SF-013_HF2K3BGXC_S1_L001_R2_001.fastq.gz fastq fastq 690673654.0 8031089.0 GSM5087777 r1 0:26 1:60 A:165347028;C:133794630;G:125515389;T:265833433;N:183174 26 60     165347028 133794630 125515389 265833433 183174 SRX10112998 SRS8268894 SRA1196923 GEO Hubrecht Institute 2 0.11353 0.8909 0.10742 0.23068 0.99277 0.79882 0.74535 0.51897 26 60 T B sc-like readlen illumina nextseq unknown cdna_unspecified unknown sc single_cell_plate celseq   Netherlands 2021-02-16 Larval Larval Blood Hematopoietic System
63313 63313 SRR13724936 SRX10112998 SRS8268894 SRP306756 PRJNA702191 Phosphatidylinositol 3 kinase signaling controls survival and stemness of hematopoietic stem and progenitor cells GSE166900 Transcriptome Analysis Loss of Pten and inhibition of PI3K induced apoptosis of hematopoietic stem/progenitor cells upon endothelial to hematopoietic transition and surviving hematopoietic stem/progenitor cells committed to all blood lineages but displayed reduced stemness Overall design: AGMs 36hpf or CHTs 5dpf were dissected and dissociated using FACS sorting HSPCs were isolated and single cell RNA seq was performed.   pubmed:33714985   SBF 13 GSM5087777   tissue:Caudal Hematopoietic Tissue|developmental stage:5dpf SBF 13 Well numbers X001 X384 in *.TranscriptCounts.tsv refer to well numbers of the 384 well plate in the following order: A1 to A24 B1 to B24 until P24. The right mate of each read pair was mapped to the ensemble of all gene loci. Reads mapping to multiple loci were discarded. During sequencing Read 1 was assigned 26/75* base pairs and was used for identification of the Illumina library barcode cel barcode and UMI. R2 was assigned 60/75* base pairs and used to map to the reference transcriptome of Zv9 with BWA Anders and Huber 2010. Data was demultiplexed as described in Grün et al. 2014. Mapping and generation of count tables was automated using the MapAndGo script1. https://github.com/anna alemany/transcriptomics/tree/master/mapandgo Tabular separated file indicating number of transcripts per section obtained as previously described Grün Dominic Lennart Kester and Alexander Van Oudenaarden. "Validation of noise models for single cell transcriptomics." Nature methods 11.6 2014: 637. We refer to transcripts as unique molecules based on UMI correction. *coutc* files contain number of reads *coutb* files contain number of observed UMI and *coutt* files contain number of unique UMI corrected transcripts per gene per cell The left read contains the barcode information: the first eight bases correspond to a cell specific barcode* followed by a 6bp unique molecular identifier UMI. The remainder of the left read contains a polyT stretch followed by a number <40 of transcript derived bases. Read 1 was not used for quantification. cell specific barcodes are specified in the file “cel seq2 barcodes.csv” Genome build: zV9 Danio Rerio Caudal Hematopoietic Tissue   Cells were sorted into 384 well plates. All wells underwent Cel Seq2 protocol RNA libraries were prepared for sequencing using standard Illumina protocols   developmental stage:5dpf|sort day see plate layout:8 and 9|genotype or treatment see plate layout:pten mutant GSM5087777 GSM5087777: SBF 13; Danio rerio; RNA Seq GSM5087777   1 Cells were sorted into 384 well plates. All wells underwent Cel Seq2 protocol RNA libraries were prepared for sequencing using standard Illumina protocols GEO Accession:GSM5087777 RNA-Seq TRANSCRIPTOMIC cDNA PAIRED ILLUMINA NextSeq 500   SRP306756     HUB-SF-013_HF2K3BGXC_S1_L002_R1_001.fastq.gz HUB-SF-013_HF2K3BGXC_S1_L002_R2_001.fastq.gz fastq fastq 683166886.0 7943801.0 GSM5087777 r2 0:26 1:60 A:162565401;C:131419325;G:127253506;T:261755096;N:173558 26 60     162565401 131419325 127253506 261755096 173558 SRX10112998 SRS8268894 SRA1196923 GEO Hubrecht Institute 2 0.11219 0.8861 0.10605 0.2309 0.99293 0.79772 0.71515 0.49234 26 60 T B sc-like readlen illumina nextseq unknown cdna_unspecified unknown sc single_cell_plate celseq   Netherlands 2021-02-16 Larval Larval Blood Hematopoietic System
63314 63314 SRR13724937 SRX10112998 SRS8268894 SRP306756 PRJNA702191 Phosphatidylinositol 3 kinase signaling controls survival and stemness of hematopoietic stem and progenitor cells GSE166900 Transcriptome Analysis Loss of Pten and inhibition of PI3K induced apoptosis of hematopoietic stem/progenitor cells upon endothelial to hematopoietic transition and surviving hematopoietic stem/progenitor cells committed to all blood lineages but displayed reduced stemness Overall design: AGMs 36hpf or CHTs 5dpf were dissected and dissociated using FACS sorting HSPCs were isolated and single cell RNA seq was performed.   pubmed:33714985   SBF 13 GSM5087777   tissue:Caudal Hematopoietic Tissue|developmental stage:5dpf SBF 13 Well numbers X001 X384 in *.TranscriptCounts.tsv refer to well numbers of the 384 well plate in the following order: A1 to A24 B1 to B24 until P24. The right mate of each read pair was mapped to the ensemble of all gene loci. Reads mapping to multiple loci were discarded. During sequencing Read 1 was assigned 26/75* base pairs and was used for identification of the Illumina library barcode cel barcode and UMI. R2 was assigned 60/75* base pairs and used to map to the reference transcriptome of Zv9 with BWA Anders and Huber 2010. Data was demultiplexed as described in Grün et al. 2014. Mapping and generation of count tables was automated using the MapAndGo script1. https://github.com/anna alemany/transcriptomics/tree/master/mapandgo Tabular separated file indicating number of transcripts per section obtained as previously described Grün Dominic Lennart Kester and Alexander Van Oudenaarden. "Validation of noise models for single cell transcriptomics." Nature methods 11.6 2014: 637. We refer to transcripts as unique molecules based on UMI correction. *coutc* files contain number of reads *coutb* files contain number of observed UMI and *coutt* files contain number of unique UMI corrected transcripts per gene per cell The left read contains the barcode information: the first eight bases correspond to a cell specific barcode* followed by a 6bp unique molecular identifier UMI. The remainder of the left read contains a polyT stretch followed by a number <40 of transcript derived bases. Read 1 was not used for quantification. cell specific barcodes are specified in the file “cel seq2 barcodes.csv” Genome build: zV9 Danio Rerio Caudal Hematopoietic Tissue   Cells were sorted into 384 well plates. All wells underwent Cel Seq2 protocol RNA libraries were prepared for sequencing using standard Illumina protocols   developmental stage:5dpf|sort day see plate layout:8 and 9|genotype or treatment see plate layout:pten mutant GSM5087777 GSM5087777: SBF 13; Danio rerio; RNA Seq GSM5087777   1 Cells were sorted into 384 well plates. All wells underwent Cel Seq2 protocol RNA libraries were prepared for sequencing using standard Illumina protocols GEO Accession:GSM5087777 RNA-Seq TRANSCRIPTOMIC cDNA PAIRED ILLUMINA NextSeq 500   SRP306756     HUB-SF-013_HF2K3BGXC_S1_L003_R1_001.fastq.gz HUB-SF-013_HF2K3BGXC_S1_L003_R2_001.fastq.gz fastq fastq 697771320.0 8113620.0 GSM5087777 r3 0:26 1:60 A:166913595;C:135202382;G:126993028;T:268530489;N:131826 26 60     166913595 135202382 126993028 268530489 131826 SRX10112998 SRS8268894 SRA1196923 GEO Hubrecht Institute 2 0.11411 0.88985 0.10845 0.23286 0.99389 0.79926 0.70458 0.51326 26 60 T B sc-like readlen illumina nextseq unknown cdna_unspecified unknown sc single_cell_plate celseq   Netherlands 2021-02-16 Larval Larval Blood Hematopoietic System
63315 63315 SRR13724938 SRX10112998 SRS8268894 SRP306756 PRJNA702191 Phosphatidylinositol 3 kinase signaling controls survival and stemness of hematopoietic stem and progenitor cells GSE166900 Transcriptome Analysis Loss of Pten and inhibition of PI3K induced apoptosis of hematopoietic stem/progenitor cells upon endothelial to hematopoietic transition and surviving hematopoietic stem/progenitor cells committed to all blood lineages but displayed reduced stemness Overall design: AGMs 36hpf or CHTs 5dpf were dissected and dissociated using FACS sorting HSPCs were isolated and single cell RNA seq was performed.   pubmed:33714985   SBF 13 GSM5087777   tissue:Caudal Hematopoietic Tissue|developmental stage:5dpf SBF 13 Well numbers X001 X384 in *.TranscriptCounts.tsv refer to well numbers of the 384 well plate in the following order: A1 to A24 B1 to B24 until P24. The right mate of each read pair was mapped to the ensemble of all gene loci. Reads mapping to multiple loci were discarded. During sequencing Read 1 was assigned 26/75* base pairs and was used for identification of the Illumina library barcode cel barcode and UMI. R2 was assigned 60/75* base pairs and used to map to the reference transcriptome of Zv9 with BWA Anders and Huber 2010. Data was demultiplexed as described in Grün et al. 2014. Mapping and generation of count tables was automated using the MapAndGo script1. https://github.com/anna alemany/transcriptomics/tree/master/mapandgo Tabular separated file indicating number of transcripts per section obtained as previously described Grün Dominic Lennart Kester and Alexander Van Oudenaarden. "Validation of noise models for single cell transcriptomics." Nature methods 11.6 2014: 637. We refer to transcripts as unique molecules based on UMI correction. *coutc* files contain number of reads *coutb* files contain number of observed UMI and *coutt* files contain number of unique UMI corrected transcripts per gene per cell The left read contains the barcode information: the first eight bases correspond to a cell specific barcode* followed by a 6bp unique molecular identifier UMI. The remainder of the left read contains a polyT stretch followed by a number <40 of transcript derived bases. Read 1 was not used for quantification. cell specific barcodes are specified in the file “cel seq2 barcodes.csv” Genome build: zV9 Danio Rerio Caudal Hematopoietic Tissue   Cells were sorted into 384 well plates. All wells underwent Cel Seq2 protocol RNA libraries were prepared for sequencing using standard Illumina protocols   developmental stage:5dpf|sort day see plate layout:8 and 9|genotype or treatment see plate layout:pten mutant GSM5087777 GSM5087777: SBF 13; Danio rerio; RNA Seq GSM5087777   1 Cells were sorted into 384 well plates. All wells underwent Cel Seq2 protocol RNA libraries were prepared for sequencing using standard Illumina protocols GEO Accession:GSM5087777 RNA-Seq TRANSCRIPTOMIC cDNA PAIRED ILLUMINA NextSeq 500   SRP306756     HUB-SF-013_HF2K3BGXC_S1_L004_R1_001.fastq.gz HUB-SF-013_HF2K3BGXC_S1_L004_R2_001.fastq.gz fastq fastq 688771248.0 8008968.0 GSM5087777 r4 0:26 1:60 A:163859028;C:132720871;G:128041363;T:264036472;N:113514 26 60     163859028 132720871 128041363 264036472 113514 SRX10112998 SRS8268894 SRA1196923 GEO Hubrecht Institute 2 0.11296 0.88749 0.10693 0.23017 0.99326 0.7974 0.7162 0.5208 26 60 T B sc-like readlen illumina nextseq unknown cdna_unspecified unknown sc single_cell_plate celseq   Netherlands 2021-02-16 Larval Larval Blood Hematopoietic System
63429 63429 SRR13796920 SRX10181147 SRS8330085 SRP308406 PRJNA705198 Inflammation potentiates JMML like blood defects in Shp2 mutant Noonan syndrome GSE167787 Transcriptome Analysis Hematopoietic stem and progenitor cells derived from a zebrafish model of Noonan syndrome carrying a patient associated Shp2 D61G mutation display an expansion of monocyte/macrophage progenitors with an inflammatory gene expression signature. Overall design: CHTs 5dpf were dissected and dissociated using FACS sorting HSPCs were isolated and single cell RNA seq was performed.   pubmed:35535491   SBF 12 GSM5112481   source name:CHT|tissue:Hematopoietic stem and progenitor cells|developmental stage:5dpf|sort day:1|genotype:Ptpn11a D61G wt SBF 12 Well numbers X001 X384 in *.TranscriptCounts.tsv refer to well numbers of the 384 well plate in the following order: A1 to A24 B1 to B24 until P24. The right mate of each read pair was mapped to the ensemble of all gene loci. Reads mapping to multiple loci were discarded. During sequencing Read 1 was assigned 26/75* base pairs and was used for identification of the Illumina library barcode cel barcode and UMI. R2 was assigned 60/75* base pairs and used to map to the reference transcriptome of Zv9 with BWA Anders and Huber 2010. Data was demultiplexed as described in Grün et al. 2014. Mapping and generation of count tables was automated using the MapAndGo script1. https://github.com/anna alemany/transcriptomics/tree/master/mapandgo Tabular separated file indicating number of transcripts per section obtained as previously described Grün Dominic Lennart Kester and Alexander Van Oudenaarden. "Validation of noise models for single cell transcriptomics." Nature methods 11.6 2014: 637. We refer to transcripts as unique molecules based on UMI correction. *coutc* files contain number of reads *coutb* files contain number of observed UMI and *coutt* files contain number of unique UMI corrected transcripts per gene per cell The left read contains the barcode information: the first eight bases correspond to a cell specific barcode* followed by a 6bp unique molecular identifier UMI. The remainder of the left read contains a polyT stretch followed by a number <40 of transcript derived bases. Read 1 was not used for quantification. Genome build: zV9 Danio Rerio Supplementary files format and content: cell specific barcodes are specified in the file “cel seq2 barcodes.csv” Supplementary files format and content: transcriptCounts.tsv CHT   Cells were sorted into 384 well plates. All wells underwent Cel Seq2 protocol RNA libraries were prepared for sequencing using standard Illumina protocols   tissue:Hematopoietic stem and progenitor cells|developmental stage:5dpf|sort day:1|genotype:Ptpn11a D61G wt GSM5112481 GSM5112481: SBF 12; Danio rerio; RNA Seq GSM5112481   1 Cells were sorted into 384 well plates. All wells underwent Cel Seq2 protocol RNA libraries were prepared for sequencing using standard Illumina protocols GEO Accession:GSM5112481 RNA-Seq TRANSCRIPTOMIC cDNA PAIRED ILLUMINA NextSeq 500   SRP308406     HUB-SF-012_HGTH5BGX9_S3_L001_R1_001.fastq.gz HUB-SF-012_HGTH5BGX9_S3_L001_R2_001.fastq.gz fastq fastq 1016566526.0 11820541.0 GSM5112481 r1 0:26 1:60 A:258517762;C:193293456;G:198337892;T:366347669;N:69747 26 60     258517762 193293456 198337892 366347669 69747 SRX10181147 SRS8330085 SRA1200709 GEO Hubrecht Institute 2 0.10087 0.87592 0.09049 0.30217 0.98437 0.87332 0.60839 0.53992 26 60 T B sc-like readlen illumina nextseq unknown cdna_unspecified unknown sc single_cell_plate celseq   Netherlands 2021-02-26 Larval Larval Blood Hematopoietic System
63430 63430 SRR13796921 SRX10181147 SRS8330085 SRP308406 PRJNA705198 Inflammation potentiates JMML like blood defects in Shp2 mutant Noonan syndrome GSE167787 Transcriptome Analysis Hematopoietic stem and progenitor cells derived from a zebrafish model of Noonan syndrome carrying a patient associated Shp2 D61G mutation display an expansion of monocyte/macrophage progenitors with an inflammatory gene expression signature. Overall design: CHTs 5dpf were dissected and dissociated using FACS sorting HSPCs were isolated and single cell RNA seq was performed.   pubmed:35535491   SBF 12 GSM5112481   source name:CHT|tissue:Hematopoietic stem and progenitor cells|developmental stage:5dpf|sort day:1|genotype:Ptpn11a D61G wt SBF 12 Well numbers X001 X384 in *.TranscriptCounts.tsv refer to well numbers of the 384 well plate in the following order: A1 to A24 B1 to B24 until P24. The right mate of each read pair was mapped to the ensemble of all gene loci. Reads mapping to multiple loci were discarded. During sequencing Read 1 was assigned 26/75* base pairs and was used for identification of the Illumina library barcode cel barcode and UMI. R2 was assigned 60/75* base pairs and used to map to the reference transcriptome of Zv9 with BWA Anders and Huber 2010. Data was demultiplexed as described in Grün et al. 2014. Mapping and generation of count tables was automated using the MapAndGo script1. https://github.com/anna alemany/transcriptomics/tree/master/mapandgo Tabular separated file indicating number of transcripts per section obtained as previously described Grün Dominic Lennart Kester and Alexander Van Oudenaarden. "Validation of noise models for single cell transcriptomics." Nature methods 11.6 2014: 637. We refer to transcripts as unique molecules based on UMI correction. *coutc* files contain number of reads *coutb* files contain number of observed UMI and *coutt* files contain number of unique UMI corrected transcripts per gene per cell The left read contains the barcode information: the first eight bases correspond to a cell specific barcode* followed by a 6bp unique molecular identifier UMI. The remainder of the left read contains a polyT stretch followed by a number <40 of transcript derived bases. Read 1 was not used for quantification. Genome build: zV9 Danio Rerio Supplementary files format and content: cell specific barcodes are specified in the file “cel seq2 barcodes.csv” Supplementary files format and content: transcriptCounts.tsv CHT   Cells were sorted into 384 well plates. All wells underwent Cel Seq2 protocol RNA libraries were prepared for sequencing using standard Illumina protocols   tissue:Hematopoietic stem and progenitor cells|developmental stage:5dpf|sort day:1|genotype:Ptpn11a D61G wt GSM5112481 GSM5112481: SBF 12; Danio rerio; RNA Seq GSM5112481   1 Cells were sorted into 384 well plates. All wells underwent Cel Seq2 protocol RNA libraries were prepared for sequencing using standard Illumina protocols GEO Accession:GSM5112481 RNA-Seq TRANSCRIPTOMIC cDNA PAIRED ILLUMINA NextSeq 500   SRP308406     HUB-SF-012_HGTH5BGX9_S3_L002_R1_001.fastq.gz HUB-SF-012_HGTH5BGX9_S3_L002_R2_001.fastq.gz fastq fastq 1146135158.0 13327153.0 GSM5112481 r2 0:26 1:60 A:292267930;C:216111016;G:226225839;T:411456512;N:73861 26 60     292267930 216111016 226225839 411456512 73861 SRX10181147 SRS8330085 SRA1200709 GEO Hubrecht Institute 2 0.09995 0.86944 0.09018 0.30092 0.98417 0.88718 0.66592 0.54858 26 60 T B sc-like readlen illumina nextseq unknown cdna_unspecified unknown sc single_cell_plate celseq   Netherlands 2021-02-26 Larval Larval Blood Hematopoietic System
63431 63431 SRR13796922 SRX10181147 SRS8330085 SRP308406 PRJNA705198 Inflammation potentiates JMML like blood defects in Shp2 mutant Noonan syndrome GSE167787 Transcriptome Analysis Hematopoietic stem and progenitor cells derived from a zebrafish model of Noonan syndrome carrying a patient associated Shp2 D61G mutation display an expansion of monocyte/macrophage progenitors with an inflammatory gene expression signature. Overall design: CHTs 5dpf were dissected and dissociated using FACS sorting HSPCs were isolated and single cell RNA seq was performed.   pubmed:35535491   SBF 12 GSM5112481   source name:CHT|tissue:Hematopoietic stem and progenitor cells|developmental stage:5dpf|sort day:1|genotype:Ptpn11a D61G wt SBF 12 Well numbers X001 X384 in *.TranscriptCounts.tsv refer to well numbers of the 384 well plate in the following order: A1 to A24 B1 to B24 until P24. The right mate of each read pair was mapped to the ensemble of all gene loci. Reads mapping to multiple loci were discarded. During sequencing Read 1 was assigned 26/75* base pairs and was used for identification of the Illumina library barcode cel barcode and UMI. R2 was assigned 60/75* base pairs and used to map to the reference transcriptome of Zv9 with BWA Anders and Huber 2010. Data was demultiplexed as described in Grün et al. 2014. Mapping and generation of count tables was automated using the MapAndGo script1. https://github.com/anna alemany/transcriptomics/tree/master/mapandgo Tabular separated file indicating number of transcripts per section obtained as previously described Grün Dominic Lennart Kester and Alexander Van Oudenaarden. "Validation of noise models for single cell transcriptomics." Nature methods 11.6 2014: 637. We refer to transcripts as unique molecules based on UMI correction. *coutc* files contain number of reads *coutb* files contain number of observed UMI and *coutt* files contain number of unique UMI corrected transcripts per gene per cell The left read contains the barcode information: the first eight bases correspond to a cell specific barcode* followed by a 6bp unique molecular identifier UMI. The remainder of the left read contains a polyT stretch followed by a number <40 of transcript derived bases. Read 1 was not used for quantification. Genome build: zV9 Danio Rerio Supplementary files format and content: cell specific barcodes are specified in the file “cel seq2 barcodes.csv” Supplementary files format and content: transcriptCounts.tsv CHT   Cells were sorted into 384 well plates. All wells underwent Cel Seq2 protocol RNA libraries were prepared for sequencing using standard Illumina protocols   tissue:Hematopoietic stem and progenitor cells|developmental stage:5dpf|sort day:1|genotype:Ptpn11a D61G wt GSM5112481 GSM5112481: SBF 12; Danio rerio; RNA Seq GSM5112481   1 Cells were sorted into 384 well plates. All wells underwent Cel Seq2 protocol RNA libraries were prepared for sequencing using standard Illumina protocols GEO Accession:GSM5112481 RNA-Seq TRANSCRIPTOMIC cDNA PAIRED ILLUMINA NextSeq 500   SRP308406     HUB-SF-012_HGTH5BGX9_S3_L003_R1_001.fastq.gz HUB-SF-012_HGTH5BGX9_S3_L003_R2_001.fastq.gz fastq fastq 963769406.0 11206621.0 GSM5112481 r3 0:26 1:60 A:245356294;C:183306950;G:187774778;T:347228429;N:102955 26 60     245356294 183306950 187774778 347228429 102955 SRX10181147 SRS8330085 SRA1200709 GEO Hubrecht Institute 2 0.10126 0.87396 0.09109 0.30243 0.98368 0.88073 0.65519 0.54199 26 60 T B sc-like readlen illumina nextseq unknown cdna_unspecified unknown sc single_cell_plate celseq   Netherlands 2021-02-26 Larval Larval Blood Hematopoietic System
63432 63432 SRR13796923 SRX10181147 SRS8330085 SRP308406 PRJNA705198 Inflammation potentiates JMML like blood defects in Shp2 mutant Noonan syndrome GSE167787 Transcriptome Analysis Hematopoietic stem and progenitor cells derived from a zebrafish model of Noonan syndrome carrying a patient associated Shp2 D61G mutation display an expansion of monocyte/macrophage progenitors with an inflammatory gene expression signature. Overall design: CHTs 5dpf were dissected and dissociated using FACS sorting HSPCs were isolated and single cell RNA seq was performed.   pubmed:35535491   SBF 12 GSM5112481   source name:CHT|tissue:Hematopoietic stem and progenitor cells|developmental stage:5dpf|sort day:1|genotype:Ptpn11a D61G wt SBF 12 Well numbers X001 X384 in *.TranscriptCounts.tsv refer to well numbers of the 384 well plate in the following order: A1 to A24 B1 to B24 until P24. The right mate of each read pair was mapped to the ensemble of all gene loci. Reads mapping to multiple loci were discarded. During sequencing Read 1 was assigned 26/75* base pairs and was used for identification of the Illumina library barcode cel barcode and UMI. R2 was assigned 60/75* base pairs and used to map to the reference transcriptome of Zv9 with BWA Anders and Huber 2010. Data was demultiplexed as described in Grün et al. 2014. Mapping and generation of count tables was automated using the MapAndGo script1. https://github.com/anna alemany/transcriptomics/tree/master/mapandgo Tabular separated file indicating number of transcripts per section obtained as previously described Grün Dominic Lennart Kester and Alexander Van Oudenaarden. "Validation of noise models for single cell transcriptomics." Nature methods 11.6 2014: 637. We refer to transcripts as unique molecules based on UMI correction. *coutc* files contain number of reads *coutb* files contain number of observed UMI and *coutt* files contain number of unique UMI corrected transcripts per gene per cell The left read contains the barcode information: the first eight bases correspond to a cell specific barcode* followed by a 6bp unique molecular identifier UMI. The remainder of the left read contains a polyT stretch followed by a number <40 of transcript derived bases. Read 1 was not used for quantification. Genome build: zV9 Danio Rerio Supplementary files format and content: cell specific barcodes are specified in the file “cel seq2 barcodes.csv” Supplementary files format and content: transcriptCounts.tsv CHT   Cells were sorted into 384 well plates. All wells underwent Cel Seq2 protocol RNA libraries were prepared for sequencing using standard Illumina protocols   tissue:Hematopoietic stem and progenitor cells|developmental stage:5dpf|sort day:1|genotype:Ptpn11a D61G wt GSM5112481 GSM5112481: SBF 12; Danio rerio; RNA Seq GSM5112481   1 Cells were sorted into 384 well plates. All wells underwent Cel Seq2 protocol RNA libraries were prepared for sequencing using standard Illumina protocols GEO Accession:GSM5112481 RNA-Seq TRANSCRIPTOMIC cDNA PAIRED ILLUMINA NextSeq 500   SRP308406     HUB-SF-012_HGTH5BGX9_S3_L004_R1_001.fastq.gz HUB-SF-012_HGTH5BGX9_S3_L004_R2_001.fastq.gz fastq fastq 1140514542.0 13261797.0 GSM5112481 r4 0:26 1:60 A:290520599;C:215252651;G:225061538;T:409541594;N:138160 26 60     290520599 215252651 225061538 409541594 138160 SRX10181147 SRS8330085 SRA1200709 GEO Hubrecht Institute 2 0.10102 0.86956 0.09125 0.30071 0.98451 0.88736 0.53521 0.53558 26 60 T B sc-like readlen illumina nextseq unknown cdna_unspecified unknown sc single_cell_plate celseq   Netherlands 2021-02-26 Larval Larval Blood Hematopoietic System
63433 63433 SRR13796916 SRX10181146 SRS8330086 SRP308406 PRJNA705198 Inflammation potentiates JMML like blood defects in Shp2 mutant Noonan syndrome GSE167787 Transcriptome Analysis Hematopoietic stem and progenitor cells derived from a zebrafish model of Noonan syndrome carrying a patient associated Shp2 D61G mutation display an expansion of monocyte/macrophage progenitors with an inflammatory gene expression signature. Overall design: CHTs 5dpf were dissected and dissociated using FACS sorting HSPCs were isolated and single cell RNA seq was performed.   pubmed:35535491   SBF 11 GSM5112480   source name:CHT|tissue:Hematopoietic stem and progenitor cells|developmental stage:5dpf|sort day:2|genotype:Ptpn11a D61G wt SBF 11 Well numbers X001 X384 in *.TranscriptCounts.tsv refer to well numbers of the 384 well plate in the following order: A1 to A24 B1 to B24 until P24. The right mate of each read pair was mapped to the ensemble of all gene loci. Reads mapping to multiple loci were discarded. During sequencing Read 1 was assigned 26/75* base pairs and was used for identification of the Illumina library barcode cel barcode and UMI. R2 was assigned 60/75* base pairs and used to map to the reference transcriptome of Zv9 with BWA Anders and Huber 2010. Data was demultiplexed as described in Grün et al. 2014. Mapping and generation of count tables was automated using the MapAndGo script1. https://github.com/anna alemany/transcriptomics/tree/master/mapandgo Tabular separated file indicating number of transcripts per section obtained as previously described Grün Dominic Lennart Kester and Alexander Van Oudenaarden. "Validation of noise models for single cell transcriptomics." Nature methods 11.6 2014: 637. We refer to transcripts as unique molecules based on UMI correction. *coutc* files contain number of reads *coutb* files contain number of observed UMI and *coutt* files contain number of unique UMI corrected transcripts per gene per cell The left read contains the barcode information: the first eight bases correspond to a cell specific barcode* followed by a 6bp unique molecular identifier UMI. The remainder of the left read contains a polyT stretch followed by a number <40 of transcript derived bases. Read 1 was not used for quantification. Genome build: zV9 Danio Rerio Supplementary files format and content: cell specific barcodes are specified in the file “cel seq2 barcodes.csv” Supplementary files format and content: transcriptCounts.tsv CHT   Cells were sorted into 384 well plates. All wells underwent Cel Seq2 protocol RNA libraries were prepared for sequencing using standard Illumina protocols   tissue:Hematopoietic stem and progenitor cells|developmental stage:5dpf|sort day:2|genotype:Ptpn11a D61G wt GSM5112480 GSM5112480: SBF 11; Danio rerio; RNA Seq GSM5112480   1 Cells were sorted into 384 well plates. All wells underwent Cel Seq2 protocol RNA libraries were prepared for sequencing using standard Illumina protocols GEO Accession:GSM5112480 RNA-Seq TRANSCRIPTOMIC cDNA PAIRED ILLUMINA NextSeq 500   SRP308406     HUB-SF-011_HC3G3BGX9_S3_L001_R1_001.fastq.gz HUB-SF-011_HC3G3BGX9_S3_L001_R2_001.fastq.gz fastq fastq 2027227948.0 23572418.0 GSM5112480 r1 0:26 1:60 A:487249504;C:382982297;G:406178334;T:748816347;N:2001466 26 60     487249504 382982297 406178334 748816347 2001466 SRX10181146 SRS8330086 SRA1200709 GEO Hubrecht Institute 2 0.11389 0.89804 0.10582 0.18502 0.98764 0.81511 0.62692 0.54589 26 60 T B sc-like readlen illumina nextseq unknown cdna_unspecified unknown sc single_cell_plate celseq   Netherlands 2021-02-26 Larval Larval Blood Hematopoietic System
63434 63434 SRR13796917 SRX10181146 SRS8330086 SRP308406 PRJNA705198 Inflammation potentiates JMML like blood defects in Shp2 mutant Noonan syndrome GSE167787 Transcriptome Analysis Hematopoietic stem and progenitor cells derived from a zebrafish model of Noonan syndrome carrying a patient associated Shp2 D61G mutation display an expansion of monocyte/macrophage progenitors with an inflammatory gene expression signature. Overall design: CHTs 5dpf were dissected and dissociated using FACS sorting HSPCs were isolated and single cell RNA seq was performed.   pubmed:35535491   SBF 11 GSM5112480   source name:CHT|tissue:Hematopoietic stem and progenitor cells|developmental stage:5dpf|sort day:2|genotype:Ptpn11a D61G wt SBF 11 Well numbers X001 X384 in *.TranscriptCounts.tsv refer to well numbers of the 384 well plate in the following order: A1 to A24 B1 to B24 until P24. The right mate of each read pair was mapped to the ensemble of all gene loci. Reads mapping to multiple loci were discarded. During sequencing Read 1 was assigned 26/75* base pairs and was used for identification of the Illumina library barcode cel barcode and UMI. R2 was assigned 60/75* base pairs and used to map to the reference transcriptome of Zv9 with BWA Anders and Huber 2010. Data was demultiplexed as described in Grün et al. 2014. Mapping and generation of count tables was automated using the MapAndGo script1. https://github.com/anna alemany/transcriptomics/tree/master/mapandgo Tabular separated file indicating number of transcripts per section obtained as previously described Grün Dominic Lennart Kester and Alexander Van Oudenaarden. "Validation of noise models for single cell transcriptomics." Nature methods 11.6 2014: 637. We refer to transcripts as unique molecules based on UMI correction. *coutc* files contain number of reads *coutb* files contain number of observed UMI and *coutt* files contain number of unique UMI corrected transcripts per gene per cell The left read contains the barcode information: the first eight bases correspond to a cell specific barcode* followed by a 6bp unique molecular identifier UMI. The remainder of the left read contains a polyT stretch followed by a number <40 of transcript derived bases. Read 1 was not used for quantification. Genome build: zV9 Danio Rerio Supplementary files format and content: cell specific barcodes are specified in the file “cel seq2 barcodes.csv” Supplementary files format and content: transcriptCounts.tsv CHT   Cells were sorted into 384 well plates. All wells underwent Cel Seq2 protocol RNA libraries were prepared for sequencing using standard Illumina protocols   tissue:Hematopoietic stem and progenitor cells|developmental stage:5dpf|sort day:2|genotype:Ptpn11a D61G wt GSM5112480 GSM5112480: SBF 11; Danio rerio; RNA Seq GSM5112480   1 Cells were sorted into 384 well plates. All wells underwent Cel Seq2 protocol RNA libraries were prepared for sequencing using standard Illumina protocols GEO Accession:GSM5112480 RNA-Seq TRANSCRIPTOMIC cDNA PAIRED ILLUMINA NextSeq 500   SRP308406     HUB-SF-011_HC3G3BGX9_S3_L002_R1_001.fastq.gz HUB-SF-011_HC3G3BGX9_S3_L002_R2_001.fastq.gz fastq fastq 1999932666.0 23255031.0 GSM5112480 r2 0:26 1:60 A:478003679;C:375161652;G:410447722;T:734665663;N:1653950 26 60     478003679 375161652 410447722 734665663 1653950 SRX10181146 SRS8330086 SRA1200709 GEO Hubrecht Institute 2 0.11419 0.89112 0.10658 0.18317 0.98821 0.82014 0.67778 0.54943 26 60 T B sc-like readlen illumina nextseq unknown cdna_unspecified unknown sc single_cell_plate celseq   Netherlands 2021-02-26 Larval Larval Blood Hematopoietic System
63435 63435 SRR13796918 SRX10181146 SRS8330086 SRP308406 PRJNA705198 Inflammation potentiates JMML like blood defects in Shp2 mutant Noonan syndrome GSE167787 Transcriptome Analysis Hematopoietic stem and progenitor cells derived from a zebrafish model of Noonan syndrome carrying a patient associated Shp2 D61G mutation display an expansion of monocyte/macrophage progenitors with an inflammatory gene expression signature. Overall design: CHTs 5dpf were dissected and dissociated using FACS sorting HSPCs were isolated and single cell RNA seq was performed.   pubmed:35535491   SBF 11 GSM5112480   source name:CHT|tissue:Hematopoietic stem and progenitor cells|developmental stage:5dpf|sort day:2|genotype:Ptpn11a D61G wt SBF 11 Well numbers X001 X384 in *.TranscriptCounts.tsv refer to well numbers of the 384 well plate in the following order: A1 to A24 B1 to B24 until P24. The right mate of each read pair was mapped to the ensemble of all gene loci. Reads mapping to multiple loci were discarded. During sequencing Read 1 was assigned 26/75* base pairs and was used for identification of the Illumina library barcode cel barcode and UMI. R2 was assigned 60/75* base pairs and used to map to the reference transcriptome of Zv9 with BWA Anders and Huber 2010. Data was demultiplexed as described in Grün et al. 2014. Mapping and generation of count tables was automated using the MapAndGo script1. https://github.com/anna alemany/transcriptomics/tree/master/mapandgo Tabular separated file indicating number of transcripts per section obtained as previously described Grün Dominic Lennart Kester and Alexander Van Oudenaarden. "Validation of noise models for single cell transcriptomics." Nature methods 11.6 2014: 637. We refer to transcripts as unique molecules based on UMI correction. *coutc* files contain number of reads *coutb* files contain number of observed UMI and *coutt* files contain number of unique UMI corrected transcripts per gene per cell The left read contains the barcode information: the first eight bases correspond to a cell specific barcode* followed by a 6bp unique molecular identifier UMI. The remainder of the left read contains a polyT stretch followed by a number <40 of transcript derived bases. Read 1 was not used for quantification. Genome build: zV9 Danio Rerio Supplementary files format and content: cell specific barcodes are specified in the file “cel seq2 barcodes.csv” Supplementary files format and content: transcriptCounts.tsv CHT   Cells were sorted into 384 well plates. All wells underwent Cel Seq2 protocol RNA libraries were prepared for sequencing using standard Illumina protocols   tissue:Hematopoietic stem and progenitor cells|developmental stage:5dpf|sort day:2|genotype:Ptpn11a D61G wt GSM5112480 GSM5112480: SBF 11; Danio rerio; RNA Seq GSM5112480   1 Cells were sorted into 384 well plates. All wells underwent Cel Seq2 protocol RNA libraries were prepared for sequencing using standard Illumina protocols GEO Accession:GSM5112480 RNA-Seq TRANSCRIPTOMIC cDNA PAIRED ILLUMINA NextSeq 500   SRP308406     HUB-SF-011_HC3G3BGX9_S3_L003_R1_001.fastq.gz HUB-SF-011_HC3G3BGX9_S3_L003_R2_001.fastq.gz fastq fastq 2039794182.0 23718537.0 GSM5112480 r3 0:26 1:60 A:490289218;C:385796618;G:409139531;T:753471927;N:1096888 26 60     490289218 385796618 409139531 753471927 1096888 SRX10181146 SRS8330086 SRA1200709 GEO Hubrecht Institute 2 0.11344 0.8978 0.10607 0.18636 0.98806 0.8172 0.67666 0.55221 26 60 T B sc-like readlen illumina nextseq unknown cdna_unspecified unknown sc single_cell_plate celseq   Netherlands 2021-02-26 Larval Larval Blood Hematopoietic System
63436 63436 SRR13796919 SRX10181146 SRS8330086 SRP308406 PRJNA705198 Inflammation potentiates JMML like blood defects in Shp2 mutant Noonan syndrome GSE167787 Transcriptome Analysis Hematopoietic stem and progenitor cells derived from a zebrafish model of Noonan syndrome carrying a patient associated Shp2 D61G mutation display an expansion of monocyte/macrophage progenitors with an inflammatory gene expression signature. Overall design: CHTs 5dpf were dissected and dissociated using FACS sorting HSPCs were isolated and single cell RNA seq was performed.   pubmed:35535491   SBF 11 GSM5112480   source name:CHT|tissue:Hematopoietic stem and progenitor cells|developmental stage:5dpf|sort day:2|genotype:Ptpn11a D61G wt SBF 11 Well numbers X001 X384 in *.TranscriptCounts.tsv refer to well numbers of the 384 well plate in the following order: A1 to A24 B1 to B24 until P24. The right mate of each read pair was mapped to the ensemble of all gene loci. Reads mapping to multiple loci were discarded. During sequencing Read 1 was assigned 26/75* base pairs and was used for identification of the Illumina library barcode cel barcode and UMI. R2 was assigned 60/75* base pairs and used to map to the reference transcriptome of Zv9 with BWA Anders and Huber 2010. Data was demultiplexed as described in Grün et al. 2014. Mapping and generation of count tables was automated using the MapAndGo script1. https://github.com/anna alemany/transcriptomics/tree/master/mapandgo Tabular separated file indicating number of transcripts per section obtained as previously described Grün Dominic Lennart Kester and Alexander Van Oudenaarden. "Validation of noise models for single cell transcriptomics." Nature methods 11.6 2014: 637. We refer to transcripts as unique molecules based on UMI correction. *coutc* files contain number of reads *coutb* files contain number of observed UMI and *coutt* files contain number of unique UMI corrected transcripts per gene per cell The left read contains the barcode information: the first eight bases correspond to a cell specific barcode* followed by a 6bp unique molecular identifier UMI. The remainder of the left read contains a polyT stretch followed by a number <40 of transcript derived bases. Read 1 was not used for quantification. Genome build: zV9 Danio Rerio Supplementary files format and content: cell specific barcodes are specified in the file “cel seq2 barcodes.csv” Supplementary files format and content: transcriptCounts.tsv CHT   Cells were sorted into 384 well plates. All wells underwent Cel Seq2 protocol RNA libraries were prepared for sequencing using standard Illumina protocols   tissue:Hematopoietic stem and progenitor cells|developmental stage:5dpf|sort day:2|genotype:Ptpn11a D61G wt GSM5112480 GSM5112480: SBF 11; Danio rerio; RNA Seq GSM5112480   1 Cells were sorted into 384 well plates. All wells underwent Cel Seq2 protocol RNA libraries were prepared for sequencing using standard Illumina protocols GEO Accession:GSM5112480 RNA-Seq TRANSCRIPTOMIC cDNA PAIRED ILLUMINA NextSeq 500   SRP308406     HUB-SF-011_HC3G3BGX9_S3_L004_R1_001.fastq.gz HUB-SF-011_HC3G3BGX9_S3_L004_R2_001.fastq.gz fastq fastq 2013817194.0 23416479.0 GSM5112480 r4 0:26 1:60 A:481151864;C:378494789;G:412660160;T:740589390;N:920991 26 60     481151864 378494789 412660160 740589390 920991 SRX10181146 SRS8330086 SRA1200709 GEO Hubrecht Institute 2 0.11292 0.89432 0.10554 0.18314 0.98845 0.81957 0.6891 0.53999 26 60 T B sc-like readlen illumina nextseq unknown cdna_unspecified unknown sc single_cell_plate celseq   Netherlands 2021-02-26 Larval Larval Blood Hematopoietic System
63437 63437 SRR13796912 SRX10181145 SRS8330084 SRP308406 PRJNA705198 Inflammation potentiates JMML like blood defects in Shp2 mutant Noonan syndrome GSE167787 Transcriptome Analysis Hematopoietic stem and progenitor cells derived from a zebrafish model of Noonan syndrome carrying a patient associated Shp2 D61G mutation display an expansion of monocyte/macrophage progenitors with an inflammatory gene expression signature. Overall design: CHTs 5dpf were dissected and dissociated using FACS sorting HSPCs were isolated and single cell RNA seq was performed.   pubmed:35535491   SBF 10 GSM5112479   source name:CHT|tissue:Hematopoietic stem and progenitor cells|developmental stage:5dpf|sort day:2|genotype:Ptpn11a D61G het SBF 10 Well numbers X001 X384 in *.TranscriptCounts.tsv refer to well numbers of the 384 well plate in the following order: A1 to A24 B1 to B24 until P24. The right mate of each read pair was mapped to the ensemble of all gene loci. Reads mapping to multiple loci were discarded. During sequencing Read 1 was assigned 26/75* base pairs and was used for identification of the Illumina library barcode cel barcode and UMI. R2 was assigned 60/75* base pairs and used to map to the reference transcriptome of Zv9 with BWA Anders and Huber 2010. Data was demultiplexed as described in Grün et al. 2014. Mapping and generation of count tables was automated using the MapAndGo script1. https://github.com/anna alemany/transcriptomics/tree/master/mapandgo Tabular separated file indicating number of transcripts per section obtained as previously described Grün Dominic Lennart Kester and Alexander Van Oudenaarden. "Validation of noise models for single cell transcriptomics." Nature methods 11.6 2014: 637. We refer to transcripts as unique molecules based on UMI correction. *coutc* files contain number of reads *coutb* files contain number of observed UMI and *coutt* files contain number of unique UMI corrected transcripts per gene per cell The left read contains the barcode information: the first eight bases correspond to a cell specific barcode* followed by a 6bp unique molecular identifier UMI. The remainder of the left read contains a polyT stretch followed by a number <40 of transcript derived bases. Read 1 was not used for quantification. Genome build: zV9 Danio Rerio Supplementary files format and content: cell specific barcodes are specified in the file “cel seq2 barcodes.csv” Supplementary files format and content: transcriptCounts.tsv CHT   Cells were sorted into 384 well plates. All wells underwent Cel Seq2 protocol RNA libraries were prepared for sequencing using standard Illumina protocols   tissue:Hematopoietic stem and progenitor cells|developmental stage:5dpf|sort day:2|genotype:Ptpn11a D61G het GSM5112479 GSM5112479: SBF 10; Danio rerio; RNA Seq GSM5112479   1 Cells were sorted into 384 well plates. All wells underwent Cel Seq2 protocol RNA libraries were prepared for sequencing using standard Illumina protocols GEO Accession:GSM5112479 RNA-Seq TRANSCRIPTOMIC cDNA PAIRED ILLUMINA NextSeq 500   SRP308406     HUB-SF-010_HC3G3BGX9_S2_L001_R1_001.fastq.gz HUB-SF-010_HC3G3BGX9_S2_L001_R2_001.fastq.gz fastq fastq 1610958450.0 18732075.0 GSM5112479 r1 0:26 1:60 A:389547193;C:302781707;G:326844536;T:590189547;N:1595467 26 60     389547193 302781707 326844536 590189547 1595467 SRX10181145 SRS8330084 SRA1200709 GEO Hubrecht Institute 2 0.10963 0.89017 0.10115 0.23685 0.98687 0.81146 0.63352 0.56378 26 60 T B sc-like readlen illumina nextseq unknown cdna_unspecified unknown sc single_cell_plate celseq   Netherlands 2021-02-26 Larval Larval Blood Hematopoietic System
63438 63438 SRR13796913 SRX10181145 SRS8330084 SRP308406 PRJNA705198 Inflammation potentiates JMML like blood defects in Shp2 mutant Noonan syndrome GSE167787 Transcriptome Analysis Hematopoietic stem and progenitor cells derived from a zebrafish model of Noonan syndrome carrying a patient associated Shp2 D61G mutation display an expansion of monocyte/macrophage progenitors with an inflammatory gene expression signature. Overall design: CHTs 5dpf were dissected and dissociated using FACS sorting HSPCs were isolated and single cell RNA seq was performed.   pubmed:35535491   SBF 10 GSM5112479   source name:CHT|tissue:Hematopoietic stem and progenitor cells|developmental stage:5dpf|sort day:2|genotype:Ptpn11a D61G het SBF 10 Well numbers X001 X384 in *.TranscriptCounts.tsv refer to well numbers of the 384 well plate in the following order: A1 to A24 B1 to B24 until P24. The right mate of each read pair was mapped to the ensemble of all gene loci. Reads mapping to multiple loci were discarded. During sequencing Read 1 was assigned 26/75* base pairs and was used for identification of the Illumina library barcode cel barcode and UMI. R2 was assigned 60/75* base pairs and used to map to the reference transcriptome of Zv9 with BWA Anders and Huber 2010. Data was demultiplexed as described in Grün et al. 2014. Mapping and generation of count tables was automated using the MapAndGo script1. https://github.com/anna alemany/transcriptomics/tree/master/mapandgo Tabular separated file indicating number of transcripts per section obtained as previously described Grün Dominic Lennart Kester and Alexander Van Oudenaarden. "Validation of noise models for single cell transcriptomics." Nature methods 11.6 2014: 637. We refer to transcripts as unique molecules based on UMI correction. *coutc* files contain number of reads *coutb* files contain number of observed UMI and *coutt* files contain number of unique UMI corrected transcripts per gene per cell The left read contains the barcode information: the first eight bases correspond to a cell specific barcode* followed by a 6bp unique molecular identifier UMI. The remainder of the left read contains a polyT stretch followed by a number <40 of transcript derived bases. Read 1 was not used for quantification. Genome build: zV9 Danio Rerio Supplementary files format and content: cell specific barcodes are specified in the file “cel seq2 barcodes.csv” Supplementary files format and content: transcriptCounts.tsv CHT   Cells were sorted into 384 well plates. All wells underwent Cel Seq2 protocol RNA libraries were prepared for sequencing using standard Illumina protocols   tissue:Hematopoietic stem and progenitor cells|developmental stage:5dpf|sort day:2|genotype:Ptpn11a D61G het GSM5112479 GSM5112479: SBF 10; Danio rerio; RNA Seq GSM5112479   1 Cells were sorted into 384 well plates. All wells underwent Cel Seq2 protocol RNA libraries were prepared for sequencing using standard Illumina protocols GEO Accession:GSM5112479 RNA-Seq TRANSCRIPTOMIC cDNA PAIRED ILLUMINA NextSeq 500   SRP308406     HUB-SF-010_HC3G3BGX9_S2_L002_R1_001.fastq.gz HUB-SF-010_HC3G3BGX9_S2_L002_R2_001.fastq.gz fastq fastq 1583386162.0 18411467.0 GSM5112479 r2 0:26 1:60 A:380386845;C:295140984;G:329894906;T:576664334;N:1299093 26 60     380386845 295140984 329894906 576664334 1299093 SRX10181145 SRS8330084 SRA1200709 GEO Hubrecht Institute 2 0.10962 0.88453 0.10118 0.23383 0.98664 0.81667 0.67591 0.55812 26 60 T B sc-like readlen illumina nextseq unknown cdna_unspecified unknown sc single_cell_plate celseq   Netherlands 2021-02-26 Larval Larval Blood Hematopoietic System
63439 63439 SRR13796914 SRX10181145 SRS8330084 SRP308406 PRJNA705198 Inflammation potentiates JMML like blood defects in Shp2 mutant Noonan syndrome GSE167787 Transcriptome Analysis Hematopoietic stem and progenitor cells derived from a zebrafish model of Noonan syndrome carrying a patient associated Shp2 D61G mutation display an expansion of monocyte/macrophage progenitors with an inflammatory gene expression signature. Overall design: CHTs 5dpf were dissected and dissociated using FACS sorting HSPCs were isolated and single cell RNA seq was performed.   pubmed:35535491   SBF 10 GSM5112479   source name:CHT|tissue:Hematopoietic stem and progenitor cells|developmental stage:5dpf|sort day:2|genotype:Ptpn11a D61G het SBF 10 Well numbers X001 X384 in *.TranscriptCounts.tsv refer to well numbers of the 384 well plate in the following order: A1 to A24 B1 to B24 until P24. The right mate of each read pair was mapped to the ensemble of all gene loci. Reads mapping to multiple loci were discarded. During sequencing Read 1 was assigned 26/75* base pairs and was used for identification of the Illumina library barcode cel barcode and UMI. R2 was assigned 60/75* base pairs and used to map to the reference transcriptome of Zv9 with BWA Anders and Huber 2010. Data was demultiplexed as described in Grün et al. 2014. Mapping and generation of count tables was automated using the MapAndGo script1. https://github.com/anna alemany/transcriptomics/tree/master/mapandgo Tabular separated file indicating number of transcripts per section obtained as previously described Grün Dominic Lennart Kester and Alexander Van Oudenaarden. "Validation of noise models for single cell transcriptomics." Nature methods 11.6 2014: 637. We refer to transcripts as unique molecules based on UMI correction. *coutc* files contain number of reads *coutb* files contain number of observed UMI and *coutt* files contain number of unique UMI corrected transcripts per gene per cell The left read contains the barcode information: the first eight bases correspond to a cell specific barcode* followed by a 6bp unique molecular identifier UMI. The remainder of the left read contains a polyT stretch followed by a number <40 of transcript derived bases. Read 1 was not used for quantification. Genome build: zV9 Danio Rerio Supplementary files format and content: cell specific barcodes are specified in the file “cel seq2 barcodes.csv” Supplementary files format and content: transcriptCounts.tsv CHT   Cells were sorted into 384 well plates. All wells underwent Cel Seq2 protocol RNA libraries were prepared for sequencing using standard Illumina protocols   tissue:Hematopoietic stem and progenitor cells|developmental stage:5dpf|sort day:2|genotype:Ptpn11a D61G het GSM5112479 GSM5112479: SBF 10; Danio rerio; RNA Seq GSM5112479   1 Cells were sorted into 384 well plates. All wells underwent Cel Seq2 protocol RNA libraries were prepared for sequencing using standard Illumina protocols GEO Accession:GSM5112479 RNA-Seq TRANSCRIPTOMIC cDNA PAIRED ILLUMINA NextSeq 500   SRP308406     HUB-SF-010_HC3G3BGX9_S2_L003_R1_001.fastq.gz HUB-SF-010_HC3G3BGX9_S2_L003_R2_001.fastq.gz fastq fastq 1619903654.0 18836089.0 GSM5112479 r3 0:26 1:60 A:391769647;C:304794878;G:328917945;T:593544281;N:876903 26 60     391769647 304794878 328917945 593544281 876903 SRX10181145 SRS8330084 SRA1200709 GEO Hubrecht Institute 2 0.10986 0.8905 0.10142 0.23934 0.98658 0.81203 0.65872 0.55794 26 60 T B sc-like readlen illumina nextseq unknown cdna_unspecified unknown sc single_cell_plate celseq   Netherlands 2021-02-26 Larval Larval Blood Hematopoietic System
63440 63440 SRR13796915 SRX10181145 SRS8330084 SRP308406 PRJNA705198 Inflammation potentiates JMML like blood defects in Shp2 mutant Noonan syndrome GSE167787 Transcriptome Analysis Hematopoietic stem and progenitor cells derived from a zebrafish model of Noonan syndrome carrying a patient associated Shp2 D61G mutation display an expansion of monocyte/macrophage progenitors with an inflammatory gene expression signature. Overall design: CHTs 5dpf were dissected and dissociated using FACS sorting HSPCs were isolated and single cell RNA seq was performed.   pubmed:35535491   SBF 10 GSM5112479   source name:CHT|tissue:Hematopoietic stem and progenitor cells|developmental stage:5dpf|sort day:2|genotype:Ptpn11a D61G het SBF 10 Well numbers X001 X384 in *.TranscriptCounts.tsv refer to well numbers of the 384 well plate in the following order: A1 to A24 B1 to B24 until P24. The right mate of each read pair was mapped to the ensemble of all gene loci. Reads mapping to multiple loci were discarded. During sequencing Read 1 was assigned 26/75* base pairs and was used for identification of the Illumina library barcode cel barcode and UMI. R2 was assigned 60/75* base pairs and used to map to the reference transcriptome of Zv9 with BWA Anders and Huber 2010. Data was demultiplexed as described in Grün et al. 2014. Mapping and generation of count tables was automated using the MapAndGo script1. https://github.com/anna alemany/transcriptomics/tree/master/mapandgo Tabular separated file indicating number of transcripts per section obtained as previously described Grün Dominic Lennart Kester and Alexander Van Oudenaarden. "Validation of noise models for single cell transcriptomics." Nature methods 11.6 2014: 637. We refer to transcripts as unique molecules based on UMI correction. *coutc* files contain number of reads *coutb* files contain number of observed UMI and *coutt* files contain number of unique UMI corrected transcripts per gene per cell The left read contains the barcode information: the first eight bases correspond to a cell specific barcode* followed by a 6bp unique molecular identifier UMI. The remainder of the left read contains a polyT stretch followed by a number <40 of transcript derived bases. Read 1 was not used for quantification. Genome build: zV9 Danio Rerio Supplementary files format and content: cell specific barcodes are specified in the file “cel seq2 barcodes.csv” Supplementary files format and content: transcriptCounts.tsv CHT   Cells were sorted into 384 well plates. All wells underwent Cel Seq2 protocol RNA libraries were prepared for sequencing using standard Illumina protocols   tissue:Hematopoietic stem and progenitor cells|developmental stage:5dpf|sort day:2|genotype:Ptpn11a D61G het GSM5112479 GSM5112479: SBF 10; Danio rerio; RNA Seq GSM5112479   1 Cells were sorted into 384 well plates. All wells underwent Cel Seq2 protocol RNA libraries were prepared for sequencing using standard Illumina protocols GEO Accession:GSM5112479 RNA-Seq TRANSCRIPTOMIC cDNA PAIRED ILLUMINA NextSeq 500   SRP308406     HUB-SF-010_HC3G3BGX9_S2_L004_R1_001.fastq.gz HUB-SF-010_HC3G3BGX9_S2_L004_R2_001.fastq.gz fastq fastq 1596085868.0 18559138.0 GSM5112479 r4 0:26 1:60 A:383421874;C:298147890;G:331867424;T:581903375;N:745305 26 60     383421874 298147890 331867424 581903375 745305 SRX10181145 SRS8330084 SRA1200709 GEO Hubrecht Institute 2 0.10906 0.88371 0.10063 0.23355 0.98648 0.81458 0.64123 0.5629 26 60 T B sc-like readlen illumina nextseq unknown cdna_unspecified unknown sc single_cell_plate celseq   Netherlands 2021-02-26 Larval Larval Blood Hematopoietic System
63441 63441 SRR13796908 SRX10181144 SRS8330083 SRP308406 PRJNA705198 Inflammation potentiates JMML like blood defects in Shp2 mutant Noonan syndrome GSE167787 Transcriptome Analysis Hematopoietic stem and progenitor cells derived from a zebrafish model of Noonan syndrome carrying a patient associated Shp2 D61G mutation display an expansion of monocyte/macrophage progenitors with an inflammatory gene expression signature. Overall design: CHTs 5dpf were dissected and dissociated using FACS sorting HSPCs were isolated and single cell RNA seq was performed.   pubmed:35535491   SBF 9 GSM5112478   source name:CHT|tissue:Hematopoietic stem and progenitor cells|developmental stage:5dpf|sort day:2|genotype:Ptpn11a D61G hom SBF 9 Well numbers X001 X384 in *.TranscriptCounts.tsv refer to well numbers of the 384 well plate in the following order: A1 to A24 B1 to B24 until P24. The right mate of each read pair was mapped to the ensemble of all gene loci. Reads mapping to multiple loci were discarded. During sequencing Read 1 was assigned 26/75* base pairs and was used for identification of the Illumina library barcode cel barcode and UMI. R2 was assigned 60/75* base pairs and used to map to the reference transcriptome of Zv9 with BWA Anders and Huber 2010. Data was demultiplexed as described in Grün et al. 2014. Mapping and generation of count tables was automated using the MapAndGo script1. https://github.com/anna alemany/transcriptomics/tree/master/mapandgo Tabular separated file indicating number of transcripts per section obtained as previously described Grün Dominic Lennart Kester and Alexander Van Oudenaarden. "Validation of noise models for single cell transcriptomics." Nature methods 11.6 2014: 637. We refer to transcripts as unique molecules based on UMI correction. *coutc* files contain number of reads *coutb* files contain number of observed UMI and *coutt* files contain number of unique UMI corrected transcripts per gene per cell The left read contains the barcode information: the first eight bases correspond to a cell specific barcode* followed by a 6bp unique molecular identifier UMI. The remainder of the left read contains a polyT stretch followed by a number <40 of transcript derived bases. Read 1 was not used for quantification. Genome build: zV9 Danio Rerio Supplementary files format and content: cell specific barcodes are specified in the file “cel seq2 barcodes.csv” Supplementary files format and content: transcriptCounts.tsv CHT   Cells were sorted into 384 well plates. All wells underwent Cel Seq2 protocol RNA libraries were prepared for sequencing using standard Illumina protocols   tissue:Hematopoietic stem and progenitor cells|developmental stage:5dpf|sort day:2|genotype:Ptpn11a D61G hom GSM5112478 GSM5112478: SBF 9; Danio rerio; RNA Seq GSM5112478   1 Cells were sorted into 384 well plates. All wells underwent Cel Seq2 protocol RNA libraries were prepared for sequencing using standard Illumina protocols GEO Accession:GSM5112478 RNA-Seq TRANSCRIPTOMIC cDNA PAIRED ILLUMINA NextSeq 500   SRP308406     HUB-SF-009_HC3G3BGX9_S1_L001_R1_001.fastq.gz HUB-SF-009_HC3G3BGX9_S1_L001_R2_001.fastq.gz fastq fastq 1855679620.0 21577670.0 GSM5112478 r1 0:26 1:60 A:458278515;C:342344685;G:364113864;T:689097098;N:1845458 26 60     458278515 342344685 364113864 689097098 1845458 SRX10181144 SRS8330083 SRA1200709 GEO Hubrecht Institute 2 0.10703 0.88445 0.09795 0.22234 0.98541 0.8131 0.63679 0.56953 26 60 T B sc-like readlen illumina nextseq unknown cdna_unspecified unknown sc single_cell_plate celseq   Netherlands 2021-02-26 Larval Larval Blood Hematopoietic System
63442 63442 SRR13796909 SRX10181144 SRS8330083 SRP308406 PRJNA705198 Inflammation potentiates JMML like blood defects in Shp2 mutant Noonan syndrome GSE167787 Transcriptome Analysis Hematopoietic stem and progenitor cells derived from a zebrafish model of Noonan syndrome carrying a patient associated Shp2 D61G mutation display an expansion of monocyte/macrophage progenitors with an inflammatory gene expression signature. Overall design: CHTs 5dpf were dissected and dissociated using FACS sorting HSPCs were isolated and single cell RNA seq was performed.   pubmed:35535491   SBF 9 GSM5112478   source name:CHT|tissue:Hematopoietic stem and progenitor cells|developmental stage:5dpf|sort day:2|genotype:Ptpn11a D61G hom SBF 9 Well numbers X001 X384 in *.TranscriptCounts.tsv refer to well numbers of the 384 well plate in the following order: A1 to A24 B1 to B24 until P24. The right mate of each read pair was mapped to the ensemble of all gene loci. Reads mapping to multiple loci were discarded. During sequencing Read 1 was assigned 26/75* base pairs and was used for identification of the Illumina library barcode cel barcode and UMI. R2 was assigned 60/75* base pairs and used to map to the reference transcriptome of Zv9 with BWA Anders and Huber 2010. Data was demultiplexed as described in Grün et al. 2014. Mapping and generation of count tables was automated using the MapAndGo script1. https://github.com/anna alemany/transcriptomics/tree/master/mapandgo Tabular separated file indicating number of transcripts per section obtained as previously described Grün Dominic Lennart Kester and Alexander Van Oudenaarden. "Validation of noise models for single cell transcriptomics." Nature methods 11.6 2014: 637. We refer to transcripts as unique molecules based on UMI correction. *coutc* files contain number of reads *coutb* files contain number of observed UMI and *coutt* files contain number of unique UMI corrected transcripts per gene per cell The left read contains the barcode information: the first eight bases correspond to a cell specific barcode* followed by a 6bp unique molecular identifier UMI. The remainder of the left read contains a polyT stretch followed by a number <40 of transcript derived bases. Read 1 was not used for quantification. Genome build: zV9 Danio Rerio Supplementary files format and content: cell specific barcodes are specified in the file “cel seq2 barcodes.csv” Supplementary files format and content: transcriptCounts.tsv CHT   Cells were sorted into 384 well plates. All wells underwent Cel Seq2 protocol RNA libraries were prepared for sequencing using standard Illumina protocols   tissue:Hematopoietic stem and progenitor cells|developmental stage:5dpf|sort day:2|genotype:Ptpn11a D61G hom GSM5112478 GSM5112478: SBF 9; Danio rerio; RNA Seq GSM5112478   1 Cells were sorted into 384 well plates. All wells underwent Cel Seq2 protocol RNA libraries were prepared for sequencing using standard Illumina protocols GEO Accession:GSM5112478 RNA-Seq TRANSCRIPTOMIC cDNA PAIRED ILLUMINA NextSeq 500   SRP308406     HUB-SF-009_HC3G3BGX9_S1_L002_R1_001.fastq.gz HUB-SF-009_HC3G3BGX9_S1_L002_R2_001.fastq.gz fastq fastq 1825706986.0 21229151.0 GSM5112478 r2 0:26 1:60 A:448022522;C:333977248;G:368097047;T:674103924;N:1506245 26 60     448022522 333977248 368097047 674103924 1506245 SRX10181144 SRS8330083 SRA1200709 GEO Hubrecht Institute 2 0.10438 0.87891 0.09574 0.21829 0.98634 0.81613 0.64544 0.57047 26 60 T B sc-like readlen illumina nextseq unknown cdna_unspecified unknown sc single_cell_plate celseq   Netherlands 2021-02-26 Larval Larval Blood Hematopoietic System
63443 63443 SRR13796910 SRX10181144 SRS8330083 SRP308406 PRJNA705198 Inflammation potentiates JMML like blood defects in Shp2 mutant Noonan syndrome GSE167787 Transcriptome Analysis Hematopoietic stem and progenitor cells derived from a zebrafish model of Noonan syndrome carrying a patient associated Shp2 D61G mutation display an expansion of monocyte/macrophage progenitors with an inflammatory gene expression signature. Overall design: CHTs 5dpf were dissected and dissociated using FACS sorting HSPCs were isolated and single cell RNA seq was performed.   pubmed:35535491   SBF 9 GSM5112478   source name:CHT|tissue:Hematopoietic stem and progenitor cells|developmental stage:5dpf|sort day:2|genotype:Ptpn11a D61G hom SBF 9 Well numbers X001 X384 in *.TranscriptCounts.tsv refer to well numbers of the 384 well plate in the following order: A1 to A24 B1 to B24 until P24. The right mate of each read pair was mapped to the ensemble of all gene loci. Reads mapping to multiple loci were discarded. During sequencing Read 1 was assigned 26/75* base pairs and was used for identification of the Illumina library barcode cel barcode and UMI. R2 was assigned 60/75* base pairs and used to map to the reference transcriptome of Zv9 with BWA Anders and Huber 2010. Data was demultiplexed as described in Grün et al. 2014. Mapping and generation of count tables was automated using the MapAndGo script1. https://github.com/anna alemany/transcriptomics/tree/master/mapandgo Tabular separated file indicating number of transcripts per section obtained as previously described Grün Dominic Lennart Kester and Alexander Van Oudenaarden. "Validation of noise models for single cell transcriptomics." Nature methods 11.6 2014: 637. We refer to transcripts as unique molecules based on UMI correction. *coutc* files contain number of reads *coutb* files contain number of observed UMI and *coutt* files contain number of unique UMI corrected transcripts per gene per cell The left read contains the barcode information: the first eight bases correspond to a cell specific barcode* followed by a 6bp unique molecular identifier UMI. The remainder of the left read contains a polyT stretch followed by a number <40 of transcript derived bases. Read 1 was not used for quantification. Genome build: zV9 Danio Rerio Supplementary files format and content: cell specific barcodes are specified in the file “cel seq2 barcodes.csv” Supplementary files format and content: transcriptCounts.tsv CHT   Cells were sorted into 384 well plates. All wells underwent Cel Seq2 protocol RNA libraries were prepared for sequencing using standard Illumina protocols   tissue:Hematopoietic stem and progenitor cells|developmental stage:5dpf|sort day:2|genotype:Ptpn11a D61G hom GSM5112478 GSM5112478: SBF 9; Danio rerio; RNA Seq GSM5112478   1 Cells were sorted into 384 well plates. All wells underwent Cel Seq2 protocol RNA libraries were prepared for sequencing using standard Illumina protocols GEO Accession:GSM5112478 RNA-Seq TRANSCRIPTOMIC cDNA PAIRED ILLUMINA NextSeq 500   SRP308406     HUB-SF-009_HC3G3BGX9_S1_L003_R1_001.fastq.gz HUB-SF-009_HC3G3BGX9_S1_L003_R2_001.fastq.gz fastq fastq 1864395892.0 21679022.0 GSM5112478 r3 0:26 1:60 A:460571042;C:344273745;G:366053633;T:692497127;N:1000345 26 60     460571042 344273745 366053633 692497127 1000345 SRX10181144 SRS8330083 SRA1200709 GEO Hubrecht Institute 2 0.10621 0.88439 0.09702 0.2214 0.98528 0.81464 0.58596 0.56967 26 60 T B sc-like readlen illumina nextseq unknown cdna_unspecified unknown sc single_cell_plate celseq   Netherlands 2021-02-26 Larval Larval Blood Hematopoietic System
63444 63444 SRR13796911 SRX10181144 SRS8330083 SRP308406 PRJNA705198 Inflammation potentiates JMML like blood defects in Shp2 mutant Noonan syndrome GSE167787 Transcriptome Analysis Hematopoietic stem and progenitor cells derived from a zebrafish model of Noonan syndrome carrying a patient associated Shp2 D61G mutation display an expansion of monocyte/macrophage progenitors with an inflammatory gene expression signature. Overall design: CHTs 5dpf were dissected and dissociated using FACS sorting HSPCs were isolated and single cell RNA seq was performed.   pubmed:35535491   SBF 9 GSM5112478   source name:CHT|tissue:Hematopoietic stem and progenitor cells|developmental stage:5dpf|sort day:2|genotype:Ptpn11a D61G hom SBF 9 Well numbers X001 X384 in *.TranscriptCounts.tsv refer to well numbers of the 384 well plate in the following order: A1 to A24 B1 to B24 until P24. The right mate of each read pair was mapped to the ensemble of all gene loci. Reads mapping to multiple loci were discarded. During sequencing Read 1 was assigned 26/75* base pairs and was used for identification of the Illumina library barcode cel barcode and UMI. R2 was assigned 60/75* base pairs and used to map to the reference transcriptome of Zv9 with BWA Anders and Huber 2010. Data was demultiplexed as described in Grün et al. 2014. Mapping and generation of count tables was automated using the MapAndGo script1. https://github.com/anna alemany/transcriptomics/tree/master/mapandgo Tabular separated file indicating number of transcripts per section obtained as previously described Grün Dominic Lennart Kester and Alexander Van Oudenaarden. "Validation of noise models for single cell transcriptomics." Nature methods 11.6 2014: 637. We refer to transcripts as unique molecules based on UMI correction. *coutc* files contain number of reads *coutb* files contain number of observed UMI and *coutt* files contain number of unique UMI corrected transcripts per gene per cell The left read contains the barcode information: the first eight bases correspond to a cell specific barcode* followed by a 6bp unique molecular identifier UMI. The remainder of the left read contains a polyT stretch followed by a number <40 of transcript derived bases. Read 1 was not used for quantification. Genome build: zV9 Danio Rerio Supplementary files format and content: cell specific barcodes are specified in the file “cel seq2 barcodes.csv” Supplementary files format and content: transcriptCounts.tsv CHT   Cells were sorted into 384 well plates. All wells underwent Cel Seq2 protocol RNA libraries were prepared for sequencing using standard Illumina protocols   tissue:Hematopoietic stem and progenitor cells|developmental stage:5dpf|sort day:2|genotype:Ptpn11a D61G hom GSM5112478 GSM5112478: SBF 9; Danio rerio; RNA Seq GSM5112478   1 Cells were sorted into 384 well plates. All wells underwent Cel Seq2 protocol RNA libraries were prepared for sequencing using standard Illumina protocols GEO Accession:GSM5112478 RNA-Seq TRANSCRIPTOMIC cDNA PAIRED ILLUMINA NextSeq 500   SRP308406     HUB-SF-009_HC3G3BGX9_S1_L004_R1_001.fastq.gz HUB-SF-009_HC3G3BGX9_S1_L004_R2_001.fastq.gz fastq fastq 1839478166.0 21389281.0 GSM5112478 r4 0:26 1:60 A:451446429;C:337220595;G:370055154;T:679895438;N:860550 26 60     451446429 337220595 370055154 679895438 860550 SRX10181144 SRS8330083 SRA1200709 GEO Hubrecht Institute 2 0.10617 0.88085 0.09686 0.21868 0.98498 0.81718 0.62816 0.5696 26 60 T B sc-like readlen illumina nextseq unknown cdna_unspecified unknown sc single_cell_plate celseq   Netherlands 2021-02-26 Larval Larval Blood Hematopoietic System
63445 63445 SRR13796904 SRX10181143 SRS8330081 SRP308406 PRJNA705198 Inflammation potentiates JMML like blood defects in Shp2 mutant Noonan syndrome GSE167787 Transcriptome Analysis Hematopoietic stem and progenitor cells derived from a zebrafish model of Noonan syndrome carrying a patient associated Shp2 D61G mutation display an expansion of monocyte/macrophage progenitors with an inflammatory gene expression signature. Overall design: CHTs 5dpf were dissected and dissociated using FACS sorting HSPCs were isolated and single cell RNA seq was performed.   pubmed:35535491   SBF 8 GSM5112477   source name:CHT|tissue:Hematopoietic stem and progenitor cells|developmental stage:5dpf|sort day:1|genotype:Ptpn11a D61G het SBF 8 Well numbers X001 X384 in *.TranscriptCounts.tsv refer to well numbers of the 384 well plate in the following order: A1 to A24 B1 to B24 until P24. The right mate of each read pair was mapped to the ensemble of all gene loci. Reads mapping to multiple loci were discarded. During sequencing Read 1 was assigned 26/75* base pairs and was used for identification of the Illumina library barcode cel barcode and UMI. R2 was assigned 60/75* base pairs and used to map to the reference transcriptome of Zv9 with BWA Anders and Huber 2010. Data was demultiplexed as described in Grün et al. 2014. Mapping and generation of count tables was automated using the MapAndGo script1. https://github.com/anna alemany/transcriptomics/tree/master/mapandgo Tabular separated file indicating number of transcripts per section obtained as previously described Grün Dominic Lennart Kester and Alexander Van Oudenaarden. "Validation of noise models for single cell transcriptomics." Nature methods 11.6 2014: 637. We refer to transcripts as unique molecules based on UMI correction. *coutc* files contain number of reads *coutb* files contain number of observed UMI and *coutt* files contain number of unique UMI corrected transcripts per gene per cell The left read contains the barcode information: the first eight bases correspond to a cell specific barcode* followed by a 6bp unique molecular identifier UMI. The remainder of the left read contains a polyT stretch followed by a number <40 of transcript derived bases. Read 1 was not used for quantification. Genome build: zV9 Danio Rerio Supplementary files format and content: cell specific barcodes are specified in the file “cel seq2 barcodes.csv” Supplementary files format and content: transcriptCounts.tsv CHT   Cells were sorted into 384 well plates. All wells underwent Cel Seq2 protocol RNA libraries were prepared for sequencing using standard Illumina protocols   tissue:Hematopoietic stem and progenitor cells|developmental stage:5dpf|sort day:1|genotype:Ptpn11a D61G het GSM5112477 GSM5112477: SBF 8; Danio rerio; RNA Seq GSM5112477   1 Cells were sorted into 384 well plates. All wells underwent Cel Seq2 protocol RNA libraries were prepared for sequencing using standard Illumina protocols GEO Accession:GSM5112477 RNA-Seq TRANSCRIPTOMIC cDNA PAIRED ILLUMINA NextSeq 500   SRP308406     HUB-SF-008_HGTH5BGX9_S2_L001_R1_001.fastq.gz HUB-SF-008_HGTH5BGX9_S2_L001_R2_001.fastq.gz fastq fastq 772020022.0 8976977.0 GSM5112477 r1 0:26 1:60 A:194575582;C:146362159;G:151020276;T:280006174;N:55831 26 60     194575582 146362159 151020276 280006174 55831 SRX10181143 SRS8330081 SRA1200709 GEO Hubrecht Institute 2 0.10904 0.87989 0.10063 0.30282 0.98813 0.87316 0.63273 0.561 26 60 T B sc-like readlen illumina nextseq unknown cdna_unspecified unknown sc single_cell_plate celseq   Netherlands 2021-02-26 Larval Larval Blood Hematopoietic System
63446 63446 SRR13796905 SRX10181143 SRS8330081 SRP308406 PRJNA705198 Inflammation potentiates JMML like blood defects in Shp2 mutant Noonan syndrome GSE167787 Transcriptome Analysis Hematopoietic stem and progenitor cells derived from a zebrafish model of Noonan syndrome carrying a patient associated Shp2 D61G mutation display an expansion of monocyte/macrophage progenitors with an inflammatory gene expression signature. Overall design: CHTs 5dpf were dissected and dissociated using FACS sorting HSPCs were isolated and single cell RNA seq was performed.   pubmed:35535491   SBF 8 GSM5112477   source name:CHT|tissue:Hematopoietic stem and progenitor cells|developmental stage:5dpf|sort day:1|genotype:Ptpn11a D61G het SBF 8 Well numbers X001 X384 in *.TranscriptCounts.tsv refer to well numbers of the 384 well plate in the following order: A1 to A24 B1 to B24 until P24. The right mate of each read pair was mapped to the ensemble of all gene loci. Reads mapping to multiple loci were discarded. During sequencing Read 1 was assigned 26/75* base pairs and was used for identification of the Illumina library barcode cel barcode and UMI. R2 was assigned 60/75* base pairs and used to map to the reference transcriptome of Zv9 with BWA Anders and Huber 2010. Data was demultiplexed as described in Grün et al. 2014. Mapping and generation of count tables was automated using the MapAndGo script1. https://github.com/anna alemany/transcriptomics/tree/master/mapandgo Tabular separated file indicating number of transcripts per section obtained as previously described Grün Dominic Lennart Kester and Alexander Van Oudenaarden. "Validation of noise models for single cell transcriptomics." Nature methods 11.6 2014: 637. We refer to transcripts as unique molecules based on UMI correction. *coutc* files contain number of reads *coutb* files contain number of observed UMI and *coutt* files contain number of unique UMI corrected transcripts per gene per cell The left read contains the barcode information: the first eight bases correspond to a cell specific barcode* followed by a 6bp unique molecular identifier UMI. The remainder of the left read contains a polyT stretch followed by a number <40 of transcript derived bases. Read 1 was not used for quantification. Genome build: zV9 Danio Rerio Supplementary files format and content: cell specific barcodes are specified in the file “cel seq2 barcodes.csv” Supplementary files format and content: transcriptCounts.tsv CHT   Cells were sorted into 384 well plates. All wells underwent Cel Seq2 protocol RNA libraries were prepared for sequencing using standard Illumina protocols   tissue:Hematopoietic stem and progenitor cells|developmental stage:5dpf|sort day:1|genotype:Ptpn11a D61G het GSM5112477 GSM5112477: SBF 8; Danio rerio; RNA Seq GSM5112477   1 Cells were sorted into 384 well plates. All wells underwent Cel Seq2 protocol RNA libraries were prepared for sequencing using standard Illumina protocols GEO Accession:GSM5112477 RNA-Seq TRANSCRIPTOMIC cDNA PAIRED ILLUMINA NextSeq 500   SRP308406     HUB-SF-008_HGTH5BGX9_S2_L002_R1_001.fastq.gz HUB-SF-008_HGTH5BGX9_S2_L002_R2_001.fastq.gz fastq fastq 886592834.0 10309219.0 GSM5112477 r2 0:26 1:60 A:223882690;C:166641295;G:175644772;T:320365577;N:58500 26 60     223882690 166641295 175644772 320365577 58500 SRX10181143 SRS8330081 SRA1200709 GEO Hubrecht Institute 2 0.10986 0.88074 0.10164 0.30327 0.98829 0.88371 0.59242 0.55532 26 60 T B sc-like readlen illumina nextseq unknown cdna_unspecified unknown sc single_cell_plate celseq   Netherlands 2021-02-26 Larval Larval Blood Hematopoietic System
63447 63447 SRR13796906 SRX10181143 SRS8330081 SRP308406 PRJNA705198 Inflammation potentiates JMML like blood defects in Shp2 mutant Noonan syndrome GSE167787 Transcriptome Analysis Hematopoietic stem and progenitor cells derived from a zebrafish model of Noonan syndrome carrying a patient associated Shp2 D61G mutation display an expansion of monocyte/macrophage progenitors with an inflammatory gene expression signature. Overall design: CHTs 5dpf were dissected and dissociated using FACS sorting HSPCs were isolated and single cell RNA seq was performed.   pubmed:35535491   SBF 8 GSM5112477   source name:CHT|tissue:Hematopoietic stem and progenitor cells|developmental stage:5dpf|sort day:1|genotype:Ptpn11a D61G het SBF 8 Well numbers X001 X384 in *.TranscriptCounts.tsv refer to well numbers of the 384 well plate in the following order: A1 to A24 B1 to B24 until P24. The right mate of each read pair was mapped to the ensemble of all gene loci. Reads mapping to multiple loci were discarded. During sequencing Read 1 was assigned 26/75* base pairs and was used for identification of the Illumina library barcode cel barcode and UMI. R2 was assigned 60/75* base pairs and used to map to the reference transcriptome of Zv9 with BWA Anders and Huber 2010. Data was demultiplexed as described in Grün et al. 2014. Mapping and generation of count tables was automated using the MapAndGo script1. https://github.com/anna alemany/transcriptomics/tree/master/mapandgo Tabular separated file indicating number of transcripts per section obtained as previously described Grün Dominic Lennart Kester and Alexander Van Oudenaarden. "Validation of noise models for single cell transcriptomics." Nature methods 11.6 2014: 637. We refer to transcripts as unique molecules based on UMI correction. *coutc* files contain number of reads *coutb* files contain number of observed UMI and *coutt* files contain number of unique UMI corrected transcripts per gene per cell The left read contains the barcode information: the first eight bases correspond to a cell specific barcode* followed by a 6bp unique molecular identifier UMI. The remainder of the left read contains a polyT stretch followed by a number <40 of transcript derived bases. Read 1 was not used for quantification. Genome build: zV9 Danio Rerio Supplementary files format and content: cell specific barcodes are specified in the file “cel seq2 barcodes.csv” Supplementary files format and content: transcriptCounts.tsv CHT   Cells were sorted into 384 well plates. All wells underwent Cel Seq2 protocol RNA libraries were prepared for sequencing using standard Illumina protocols   tissue:Hematopoietic stem and progenitor cells|developmental stage:5dpf|sort day:1|genotype:Ptpn11a D61G het GSM5112477 GSM5112477: SBF 8; Danio rerio; RNA Seq GSM5112477   1 Cells were sorted into 384 well plates. All wells underwent Cel Seq2 protocol RNA libraries were prepared for sequencing using standard Illumina protocols GEO Accession:GSM5112477 RNA-Seq TRANSCRIPTOMIC cDNA PAIRED ILLUMINA NextSeq 500   SRP308406     HUB-SF-008_HGTH5BGX9_S2_L003_R1_001.fastq.gz HUB-SF-008_HGTH5BGX9_S2_L003_R2_001.fastq.gz fastq fastq 734558078.0 8541373.0 GSM5112477 r3 0:26 1:60 A:185319149;C:139282626;G:143448338;T:266428561;N:79404 26 60     185319149 139282626 143448338 266428561 79404 SRX10181143 SRS8330081 SRA1200709 GEO Hubrecht Institute 2 0.10916 0.87916 0.10099 0.30197 0.98833 0.87923 0.58189 0.56714 26 60 T B sc-like readlen illumina nextseq unknown cdna_unspecified unknown sc single_cell_plate celseq   Netherlands 2021-02-26 Larval Larval Blood Hematopoietic System
63448 63448 SRR13796907 SRX10181143 SRS8330081 SRP308406 PRJNA705198 Inflammation potentiates JMML like blood defects in Shp2 mutant Noonan syndrome GSE167787 Transcriptome Analysis Hematopoietic stem and progenitor cells derived from a zebrafish model of Noonan syndrome carrying a patient associated Shp2 D61G mutation display an expansion of monocyte/macrophage progenitors with an inflammatory gene expression signature. Overall design: CHTs 5dpf were dissected and dissociated using FACS sorting HSPCs were isolated and single cell RNA seq was performed.   pubmed:35535491   SBF 8 GSM5112477   source name:CHT|tissue:Hematopoietic stem and progenitor cells|developmental stage:5dpf|sort day:1|genotype:Ptpn11a D61G het SBF 8 Well numbers X001 X384 in *.TranscriptCounts.tsv refer to well numbers of the 384 well plate in the following order: A1 to A24 B1 to B24 until P24. The right mate of each read pair was mapped to the ensemble of all gene loci. Reads mapping to multiple loci were discarded. During sequencing Read 1 was assigned 26/75* base pairs and was used for identification of the Illumina library barcode cel barcode and UMI. R2 was assigned 60/75* base pairs and used to map to the reference transcriptome of Zv9 with BWA Anders and Huber 2010. Data was demultiplexed as described in Grün et al. 2014. Mapping and generation of count tables was automated using the MapAndGo script1. https://github.com/anna alemany/transcriptomics/tree/master/mapandgo Tabular separated file indicating number of transcripts per section obtained as previously described Grün Dominic Lennart Kester and Alexander Van Oudenaarden. "Validation of noise models for single cell transcriptomics." Nature methods 11.6 2014: 637. We refer to transcripts as unique molecules based on UMI correction. *coutc* files contain number of reads *coutb* files contain number of observed UMI and *coutt* files contain number of unique UMI corrected transcripts per gene per cell The left read contains the barcode information: the first eight bases correspond to a cell specific barcode* followed by a 6bp unique molecular identifier UMI. The remainder of the left read contains a polyT stretch followed by a number <40 of transcript derived bases. Read 1 was not used for quantification. Genome build: zV9 Danio Rerio Supplementary files format and content: cell specific barcodes are specified in the file “cel seq2 barcodes.csv” Supplementary files format and content: transcriptCounts.tsv CHT   Cells were sorted into 384 well plates. All wells underwent Cel Seq2 protocol RNA libraries were prepared for sequencing using standard Illumina protocols   tissue:Hematopoietic stem and progenitor cells|developmental stage:5dpf|sort day:1|genotype:Ptpn11a D61G het GSM5112477 GSM5112477: SBF 8; Danio rerio; RNA Seq GSM5112477   1 Cells were sorted into 384 well plates. All wells underwent Cel Seq2 protocol RNA libraries were prepared for sequencing using standard Illumina protocols GEO Accession:GSM5112477 RNA-Seq TRANSCRIPTOMIC cDNA PAIRED ILLUMINA NextSeq 500   SRP308406     HUB-SF-008_HGTH5BGX9_S2_L004_R1_001.fastq.gz HUB-SF-008_HGTH5BGX9_S2_L004_R2_001.fastq.gz fastq fastq 882950304.0 10266864.0 GSM5112477 r4 0:26 1:60 A:222773331;C:166128102;G:174740389;T:319199710;N:108772 26 60     222773331 166128102 174740389 319199710 108772 SRX10181143 SRS8330081 SRA1200709 GEO Hubrecht Institute 2 0.1101 0.87613 0.10193 0.29947 0.98863 0.88503 0.61646 0.56483 26 60 T B sc-like readlen illumina nextseq unknown cdna_unspecified unknown sc single_cell_plate celseq   Netherlands 2021-02-26 Larval Larval Blood Hematopoietic System
63449 63449 SRR13796900 SRX10181142 SRS8330082 SRP308406 PRJNA705198 Inflammation potentiates JMML like blood defects in Shp2 mutant Noonan syndrome GSE167787 Transcriptome Analysis Hematopoietic stem and progenitor cells derived from a zebrafish model of Noonan syndrome carrying a patient associated Shp2 D61G mutation display an expansion of monocyte/macrophage progenitors with an inflammatory gene expression signature. Overall design: CHTs 5dpf were dissected and dissociated using FACS sorting HSPCs were isolated and single cell RNA seq was performed.   pubmed:35535491   SBF 7 GSM5112476   source name:CHT|tissue:Hematopoietic stem and progenitor cells|developmental stage:5dpf|sort day:1|genotype:Ptpn11a D61G hom SBF 7 Well numbers X001 X384 in *.TranscriptCounts.tsv refer to well numbers of the 384 well plate in the following order: A1 to A24 B1 to B24 until P24. The right mate of each read pair was mapped to the ensemble of all gene loci. Reads mapping to multiple loci were discarded. During sequencing Read 1 was assigned 26/75* base pairs and was used for identification of the Illumina library barcode cel barcode and UMI. R2 was assigned 60/75* base pairs and used to map to the reference transcriptome of Zv9 with BWA Anders and Huber 2010. Data was demultiplexed as described in Grün et al. 2014. Mapping and generation of count tables was automated using the MapAndGo script1. https://github.com/anna alemany/transcriptomics/tree/master/mapandgo Tabular separated file indicating number of transcripts per section obtained as previously described Grün Dominic Lennart Kester and Alexander Van Oudenaarden. "Validation of noise models for single cell transcriptomics." Nature methods 11.6 2014: 637. We refer to transcripts as unique molecules based on UMI correction. *coutc* files contain number of reads *coutb* files contain number of observed UMI and *coutt* files contain number of unique UMI corrected transcripts per gene per cell The left read contains the barcode information: the first eight bases correspond to a cell specific barcode* followed by a 6bp unique molecular identifier UMI. The remainder of the left read contains a polyT stretch followed by a number <40 of transcript derived bases. Read 1 was not used for quantification. Genome build: zV9 Danio Rerio Supplementary files format and content: cell specific barcodes are specified in the file “cel seq2 barcodes.csv” Supplementary files format and content: transcriptCounts.tsv CHT   Cells were sorted into 384 well plates. All wells underwent Cel Seq2 protocol RNA libraries were prepared for sequencing using standard Illumina protocols   tissue:Hematopoietic stem and progenitor cells|developmental stage:5dpf|sort day:1|genotype:Ptpn11a D61G hom GSM5112476 GSM5112476: SBF 7; Danio rerio; RNA Seq GSM5112476   1 Cells were sorted into 384 well plates. All wells underwent Cel Seq2 protocol RNA libraries were prepared for sequencing using standard Illumina protocols GEO Accession:GSM5112476 RNA-Seq TRANSCRIPTOMIC cDNA PAIRED ILLUMINA NextSeq 500   SRP308406     HUB-SF-007_HGTH5BGX9_S1_L001_R1_001.fastq.gz HUB-SF-007_HGTH5BGX9_S1_L001_R2_001.fastq.gz fastq fastq 1157946140.0 13464490.0 GSM5112476 r1 0:26 1:60 A:293663997;C:219249074;G:222388449;T:422557074;N:87546 26 60     293663997 219249074 222388449 422557074 87546 SRX10181142 SRS8330082 SRA1200709 GEO Hubrecht Institute 2 0.10842 0.88791 0.10083 0.26914 0.99024 0.87596 0.72014 0.55545 26 60 T B sc-like readlen illumina nextseq unknown cdna_unspecified unknown sc single_cell_plate celseq   Netherlands 2021-02-26 Larval Larval Blood Hematopoietic System
63450 63450 SRR13796901 SRX10181142 SRS8330082 SRP308406 PRJNA705198 Inflammation potentiates JMML like blood defects in Shp2 mutant Noonan syndrome GSE167787 Transcriptome Analysis Hematopoietic stem and progenitor cells derived from a zebrafish model of Noonan syndrome carrying a patient associated Shp2 D61G mutation display an expansion of monocyte/macrophage progenitors with an inflammatory gene expression signature. Overall design: CHTs 5dpf were dissected and dissociated using FACS sorting HSPCs were isolated and single cell RNA seq was performed.   pubmed:35535491   SBF 7 GSM5112476   source name:CHT|tissue:Hematopoietic stem and progenitor cells|developmental stage:5dpf|sort day:1|genotype:Ptpn11a D61G hom SBF 7 Well numbers X001 X384 in *.TranscriptCounts.tsv refer to well numbers of the 384 well plate in the following order: A1 to A24 B1 to B24 until P24. The right mate of each read pair was mapped to the ensemble of all gene loci. Reads mapping to multiple loci were discarded. During sequencing Read 1 was assigned 26/75* base pairs and was used for identification of the Illumina library barcode cel barcode and UMI. R2 was assigned 60/75* base pairs and used to map to the reference transcriptome of Zv9 with BWA Anders and Huber 2010. Data was demultiplexed as described in Grün et al. 2014. Mapping and generation of count tables was automated using the MapAndGo script1. https://github.com/anna alemany/transcriptomics/tree/master/mapandgo Tabular separated file indicating number of transcripts per section obtained as previously described Grün Dominic Lennart Kester and Alexander Van Oudenaarden. "Validation of noise models for single cell transcriptomics." Nature methods 11.6 2014: 637. We refer to transcripts as unique molecules based on UMI correction. *coutc* files contain number of reads *coutb* files contain number of observed UMI and *coutt* files contain number of unique UMI corrected transcripts per gene per cell The left read contains the barcode information: the first eight bases correspond to a cell specific barcode* followed by a 6bp unique molecular identifier UMI. The remainder of the left read contains a polyT stretch followed by a number <40 of transcript derived bases. Read 1 was not used for quantification. Genome build: zV9 Danio Rerio Supplementary files format and content: cell specific barcodes are specified in the file “cel seq2 barcodes.csv” Supplementary files format and content: transcriptCounts.tsv CHT   Cells were sorted into 384 well plates. All wells underwent Cel Seq2 protocol RNA libraries were prepared for sequencing using standard Illumina protocols   tissue:Hematopoietic stem and progenitor cells|developmental stage:5dpf|sort day:1|genotype:Ptpn11a D61G hom GSM5112476 GSM5112476: SBF 7; Danio rerio; RNA Seq GSM5112476   1 Cells were sorted into 384 well plates. All wells underwent Cel Seq2 protocol RNA libraries were prepared for sequencing using standard Illumina protocols GEO Accession:GSM5112476 RNA-Seq TRANSCRIPTOMIC cDNA PAIRED ILLUMINA NextSeq 500   SRP308406     HUB-SF-007_HGTH5BGX9_S1_L002_R1_001.fastq.gz HUB-SF-007_HGTH5BGX9_S1_L002_R2_001.fastq.gz fastq fastq 1305620868.0 15181638.0 GSM5112476 r2 0:26 1:60 A:329590377;C:244593400;G:257683683;T:473667814;N:85594 26 60     329590377 244593400 257683683 473667814 85594 SRX10181142 SRS8330082 SRA1200709 GEO Hubrecht Institute 2 0.10792 0.88849 0.10019 0.26973 0.99056 0.88694 0.71095 0.5495 26 60 T B sc-like readlen illumina nextseq unknown cdna_unspecified unknown sc single_cell_plate celseq   Netherlands 2021-02-26 Larval Larval Blood Hematopoietic System
63451 63451 SRR13796902 SRX10181142 SRS8330082 SRP308406 PRJNA705198 Inflammation potentiates JMML like blood defects in Shp2 mutant Noonan syndrome GSE167787 Transcriptome Analysis Hematopoietic stem and progenitor cells derived from a zebrafish model of Noonan syndrome carrying a patient associated Shp2 D61G mutation display an expansion of monocyte/macrophage progenitors with an inflammatory gene expression signature. Overall design: CHTs 5dpf were dissected and dissociated using FACS sorting HSPCs were isolated and single cell RNA seq was performed.   pubmed:35535491   SBF 7 GSM5112476   source name:CHT|tissue:Hematopoietic stem and progenitor cells|developmental stage:5dpf|sort day:1|genotype:Ptpn11a D61G hom SBF 7 Well numbers X001 X384 in *.TranscriptCounts.tsv refer to well numbers of the 384 well plate in the following order: A1 to A24 B1 to B24 until P24. The right mate of each read pair was mapped to the ensemble of all gene loci. Reads mapping to multiple loci were discarded. During sequencing Read 1 was assigned 26/75* base pairs and was used for identification of the Illumina library barcode cel barcode and UMI. R2 was assigned 60/75* base pairs and used to map to the reference transcriptome of Zv9 with BWA Anders and Huber 2010. Data was demultiplexed as described in Grün et al. 2014. Mapping and generation of count tables was automated using the MapAndGo script1. https://github.com/anna alemany/transcriptomics/tree/master/mapandgo Tabular separated file indicating number of transcripts per section obtained as previously described Grün Dominic Lennart Kester and Alexander Van Oudenaarden. "Validation of noise models for single cell transcriptomics." Nature methods 11.6 2014: 637. We refer to transcripts as unique molecules based on UMI correction. *coutc* files contain number of reads *coutb* files contain number of observed UMI and *coutt* files contain number of unique UMI corrected transcripts per gene per cell The left read contains the barcode information: the first eight bases correspond to a cell specific barcode* followed by a 6bp unique molecular identifier UMI. The remainder of the left read contains a polyT stretch followed by a number <40 of transcript derived bases. Read 1 was not used for quantification. Genome build: zV9 Danio Rerio Supplementary files format and content: cell specific barcodes are specified in the file “cel seq2 barcodes.csv” Supplementary files format and content: transcriptCounts.tsv CHT   Cells were sorted into 384 well plates. All wells underwent Cel Seq2 protocol RNA libraries were prepared for sequencing using standard Illumina protocols   tissue:Hematopoietic stem and progenitor cells|developmental stage:5dpf|sort day:1|genotype:Ptpn11a D61G hom GSM5112476 GSM5112476: SBF 7; Danio rerio; RNA Seq GSM5112476   1 Cells were sorted into 384 well plates. All wells underwent Cel Seq2 protocol RNA libraries were prepared for sequencing using standard Illumina protocols GEO Accession:GSM5112476 RNA-Seq TRANSCRIPTOMIC cDNA PAIRED ILLUMINA NextSeq 500   SRP308406     HUB-SF-007_HGTH5BGX9_S1_L003_R1_001.fastq.gz HUB-SF-007_HGTH5BGX9_S1_L003_R2_001.fastq.gz fastq fastq 1082404600.0 12586100.0 GSM5112476 r3 0:26 1:60 A:273566909;C:204778843;G:209164350;T:394770468;N:124030 26 60     273566909 204778843 209164350 394770468 124030 SRX10181142 SRS8330082 SRA1200709 GEO Hubrecht Institute 2 0.1071 0.89293 0.0994 0.2726 0.9903 0.88158 0.70584 0.55056 26 60 T B sc-like readlen illumina nextseq unknown cdna_unspecified unknown sc single_cell_plate celseq   Netherlands 2021-02-26 Larval Larval Blood Hematopoietic System

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