{"database": "metadata", "table": "run_metadata", "is_view": false, "human_description_en": "where devstage_curation = \"Undetermined\" and technology = \"celseq\"", "rows": [[32858, "SRR29482326", "SRX24993370", "SRS21694834", "SRP515140", "PRJNA1126247", "Specific oncogene activation of the cell of origin in mucosal melanoma [SORT seq]", "GSE270356", "Other", "Mucosal melanoma MM is a deadly cancer derived from mucosal melanocytes. To test the consequences of MM genetics  we develop a zebrafish model in which all melanocytes experience CCND1 expression and loss of PTEN and TP53. Surprisingly  melanoma only develops from melanocytes lining internal organs  analogous to the location of patient MM. We find that zebrafish MMs have a unique chromatin landscape from cutaneous melanoma. Internal melanocytes are labeled using a MM specific transcriptional enhancer. Normal zebrafish internal melanocytes share a gene expression signature with MMs. Patient and zebrafish MMs show increased migratory neural crest gene and decreased antigen presentation gene expression  consistent with the increased metastatic behavior and decreased immunotherapy sensitivity of MM. Our work suggests the cell state of the originating melanocyte influences the behavior of derived melanomas. Our animal model phenotypically and transcriptionally mimics patient tumors  allowing this model to be used for MM therapeutic discovery. As this is a non MAPK driven genetically engineered model of melanoma  our work also has implications for the 15% of cutaneous melanoma patients who lack MAPK driving mutations.  Overall design: Single cell RNA sequencing was done on internal vs. external normal adult zebrafish melanocytes using the SORT seq platform.", null, null, null, "Internal melanocytes", "GSM8340241", null, "source name:Internal melanocytes|tissue:Internal melanocytes|cell type:melanocytes|genotype:mitfa / ; roy /  fish injected with mitfa:GFP|geo loc name:missing|collection date:missing", "Internal melanocytes", "BWA was used to align paired end read to danRer11. Count tables were generated using MapAndGo. Count tables were corrected using UMI to remove duplicate reads. Transcript counts were adjusted using Poissonian counting statistics to yield the number of UMIs detected per cell. Counts were imported into R using the Seurat suite version 3.0 Assembly: danRer11 Supplementary files format and content: .tsv file contains count matrix file used for data normalization and visualization using Seurat Library strategy: SORT seq", "Internal melanocytes", null, "Respective tissues were mechanically dissociated  digested in TrypLE for 45 min Invitrogen  12563011  40uM filtered  spun down  and resuspended in FACs buffer dPBS Mg+/Ca+ free  with 2% FBS  pen/strep. Cells were heat lysed at 65\u00b0C followed by cDNA synthesis with barcodes. All the barcoded material from one plate was pooled into one library and amplified using in vitro transcription. Following amplification  library preparation was done following the CEL Seq2 protocol to prepare a cDNA library for sequencing using TruSeq small RNA primers Illumina.", "mitfa / ; roy /  zebrafish injected with mitfa:GFP were Raised to maturity to obtain tissues", "tissue:Internal melanocytes|cell type:melanocytes|genotype:mitfa / ; roy /  fish injected with mitfa:GFP", "GSM8340241", "GSM8340241: Internal melanocytes; Danio rerio; OTHER", "GSM8340241 r1", "GSM8340241", "1", "Respective tissues were mechanically dissociated  digested in TrypLE for 45 min Invitrogen  12563011  40uM filtered  spun down  and resuspended in FACs buffer dPBS Mg+/Ca+ free  with 2% FBS  pen/strep. Cells were heat lysed at 65\u00b0C followed by cDNA synthesis with barcodes. All the barcoded material from one plate was pooled into one library and amplified using in vitro transcription. Following amplification  library preparation was done following the CEL Seq2 protocol to prepare a cDNA library for sequencing using TruSeq small RNA primers Illumina.", null, "OTHER", "TRANSCRIPTOMIC", "other", "PAIRED", "ILLUMINA", "NextSeq 500", null, "SRP515140", null, null, "HAR-MI-002_H5GCWBGXF_R2.fastq.gz HAR-MI-002_H5GCWBGXF_R1.fastq.gz", "fastq fastq", 2957975418.0, 34395063.0, "GSM8340241 r1", "0:26 1:60", "A:738954503;C:560052455;G:520746060;T:1137116132;N:1106268", 26, 60, null, null, 738954503, 560052455, 520746060, 1137116132, 1106268, "SRX24993370", "SRS21694834", "SRA1904773", "Insco Lab, Medical Oncology, Dana Farber Cancer Institute", "Insco Lab, Medical Oncology, Dana Farber Cancer Institute", 2, 0.11637, 0.84116, 0.10894, 0.31188, 0.98851, 0.71526, 0.6688, 0.60221, 26, 60, "T", "B", "sc-like readlen", "illumina", "nextseq", "unknown", "small_rna", "trueseq", "sc", "single_cell_plate", "celseq", null, "United States", "2024-06-20", "Undetermined", "Adult", "Skin", "Surface Structure"], [32859, "SRR29482327", "SRX24993369", "SRS21694833", "SRP515140", "PRJNA1126247", "Specific oncogene activation of the cell of origin in mucosal melanoma [SORT seq]", "GSE270356", "Other", "Mucosal melanoma MM is a deadly cancer derived from mucosal melanocytes. To test the consequences of MM genetics  we develop a zebrafish model in which all melanocytes experience CCND1 expression and loss of PTEN and TP53. Surprisingly  melanoma only develops from melanocytes lining internal organs  analogous to the location of patient MM. We find that zebrafish MMs have a unique chromatin landscape from cutaneous melanoma. Internal melanocytes are labeled using a MM specific transcriptional enhancer. Normal zebrafish internal melanocytes share a gene expression signature with MMs. Patient and zebrafish MMs show increased migratory neural crest gene and decreased antigen presentation gene expression  consistent with the increased metastatic behavior and decreased immunotherapy sensitivity of MM. Our work suggests the cell state of the originating melanocyte influences the behavior of derived melanomas. Our animal model phenotypically and transcriptionally mimics patient tumors  allowing this model to be used for MM therapeutic discovery. As this is a non MAPK driven genetically engineered model of melanoma  our work also has implications for the 15% of cutaneous melanoma patients who lack MAPK driving mutations.  Overall design: Single cell RNA sequencing was done on internal vs. external normal adult zebrafish melanocytes using the SORT seq platform.", null, null, null, "Cutaneous melanocytes", "GSM8340240", null, "source name:Cutaneous melanocytes|tissue:Cutaneous melanocytes|cell type:melanocytes|genotype:mitfa / ; roy /  fish injected with mitfa:GFP|geo loc name:missing|collection date:missing", "Cutaneous melanocytes", "BWA was used to align paired end read to danRer11. Count tables were generated using MapAndGo. Count tables were corrected using UMI to remove duplicate reads. Transcript counts were adjusted using Poissonian counting statistics to yield the number of UMIs detected per cell. Counts were imported into R using the Seurat suite version 3.0 Assembly: danRer11 Supplementary files format and content: .tsv file contains count matrix file used for data normalization and visualization using Seurat Library strategy: SORT seq", "Cutaneous melanocytes", null, "Respective tissues were mechanically dissociated  digested in TrypLE for 45 min Invitrogen  12563011  40uM filtered  spun down  and resuspended in FACs buffer dPBS Mg+/Ca+ free  with 2% FBS  pen/strep. Cells were heat lysed at 65\u00b0C followed by cDNA synthesis with barcodes. All the barcoded material from one plate was pooled into one library and amplified using in vitro transcription. Following amplification  library preparation was done following the CEL Seq2 protocol to prepare a cDNA library for sequencing using TruSeq small RNA primers Illumina.", "mitfa / ; roy /  zebrafish injected with mitfa:GFP were Raised to maturity to obtain tissues", "tissue:Cutaneous melanocytes|cell type:melanocytes|genotype:mitfa / ; roy /  fish injected with mitfa:GFP", "GSM8340240", "GSM8340240: Cutaneous melanocytes; Danio rerio; OTHER", "GSM8340240 r1", "GSM8340240", "1", "Respective tissues were mechanically dissociated  digested in TrypLE for 45 min Invitrogen  12563011  40uM filtered  spun down  and resuspended in FACs buffer dPBS Mg+/Ca+ free  with 2% FBS  pen/strep. Cells were heat lysed at 65\u00b0C followed by cDNA synthesis with barcodes. All the barcoded material from one plate was pooled into one library and amplified using in vitro transcription. Following amplification  library preparation was done following the CEL Seq2 protocol to prepare a cDNA library for sequencing using TruSeq small RNA primers Illumina.", null, "OTHER", "TRANSCRIPTOMIC", "other", "PAIRED", "ILLUMINA", "NextSeq 500", null, "SRP515140", null, null, "HAR-MI-001_H5GCWBGXF_R2.fastq.gz HAR-MI-001_H5GCWBGXF_R1.fastq.gz", "fastq fastq", 2680460212.0, 31168142.0, "GSM8340240 r1", "0:26 1:60", "A:704357434;C:519158361;G:467098420;T:988838604;N:1007393", 26, 60, null, null, 704357434, 519158361, 467098420, 988838604, 1007393, "SRX24993369", "SRS21694833", "SRA1904773", "Insco Lab, Medical Oncology, Dana Farber Cancer Institute", "Insco Lab, Medical Oncology, Dana Farber Cancer Institute", 2, 0.11679, 0.81545, 0.10693, 0.5033, 0.9808, 0.76404, 0.44749, 0.57623, 26, 60, "T", "B", "sc-like readlen", "illumina", "nextseq", "unknown", "small_rna", "trueseq", "sc", "single_cell_plate", "celseq", null, "United States", "2024-06-20", "Undetermined", "Adult", "Skin", "Surface Structure"], [41278, "SRR6039223", "SRX3187382", "SRS2515225", "SRP082370", "PRJNA339266", "Single cell sequencing reveals dissociation induced gene expression in tissue subpopulations", "GSE85755", "Other", "In many gene expression studies  cells are extracted by tissue dissociation and Fluorescence Activated Cell Sorting FACS  but the effect of these protocols on cellular transcriptomes is not well characterized and often ignored. Here  we applied single cell mRNA sequencing scRNA seq to muscle stem cells  and unexpectedly found a subpopulation that is strongly affected by the widely used dissociation protocol that we employed. One implication of this finding is that several published transcriptomics studies may need to be reinterpreted. Importantly  we detected similar subpopulations in other single cell datasets  suggesting that cells from other tissues might be affected by this artefact as well. Overall design: Mouse satellite cells and zebrafish fin cells were extracted from Tibialis Anterior muscles of Pax7nGFP mice and wildtype zebrafish fins  respectively. For cell extraction  traditional Supplementary Methods dissociation protocols that combine mechanical and enzymatic dissociation were employed  and live cells were subsequently sorted into plates using FACS. Next  single cell mRNA sequencing CEL Seq or SORT Seq robotized version of CEL Seq2 was applied  and data was analyzed with RaceID2 to identify clusters. CEL Seq samples: Manual CEL Seq; Satellite cells unstained; Male Pax7nGFP mice 5 mpf 7 mpf; 1h collagenase treated default dissociation protocol; 96 cells per plate with 96 different barcodes see \"Cel seq barcodes 96.csv\"; some primes numbers are bulk samples see \"BulkSamples BarcodesAndNrOfCellsUsed perCEL Seq1 library\"; Spike ins included see \"ERCC92.fa\"; No mitochondrial reads in count tables; Sequencing lanes not concatenated in fastq files uploaded here; \"Merged CEL Seq AllMiceAndLibrariesMerged.csv\" file is count table with reads from all mice and libraries merged Annotation of columns: Zx.y  where Z = mouse  x = library and y = cell barcode; bulk samples are not included any more in this file; See Supplementary Methods for details. SORT Seq 1h and 2h dissociated samples: Robotized CEL Seq2; Satellite cells unstained; Male Pax7nGFP mice 5 mpf 7 mpf; 8 muscles from 4 mice; One plate of 1h default dissociation protocol and one plate of 2h collagenase treated cells; 384 cells per plate with each of the 96 barcodes see \"Cel seq barcodes 96.csv\" used 4 times per plate therefore  each plate has 4 libraries; No bulk samples included; Spike ins included see \"ERCC92.fa\"; No mitochondrial reads in count table; In some wells  we sorted no cell internal negative control; barcodes #95 and #96 were used for empty wells; Sequencing lanes not concatenated in fastq files uploaded here; \"Merged SORT Seq DissociationTimecourse.csv\" file is count table were reads from all dissociation timepoints are merged Annotation columns: DZhx y  where Z = 1 or 2 hours collagenase treated  x = library and y = cell barcode; See Supplementary Methods for details. SORT Seq MitoTracker stained samples pilot and repeat: Robotized CEL Seq2 samples; Satellite cells stained with MitoTracker; Female Pax7nGFP mice 1 4.7 mpf mouse for pilot experiment; 3 mpf 6 mpf mice for repeat experiment; 1h collagenase treated default dissociation protocol; 384 cells per plate with each of the 384 barcodes see \"Cel seq barcodes 384.csv\" used 1 times per plate therefore  each plate has 1 library; Note: pilot experiment has 263 cells so plate was partly empty  repeat experiment done with 4 full plates; No bulk samples included; Spike ins included see \"ERCC92.fa\"; Mitochondrial reads rows named \"*  chrM\" included in count tables these were removed prior to RaceID2; In some wells  we sorted no cell barcodes #357 #360 and #381 #384 were used for empty wells; Sequencing lanes concatenated in fastq files uploaded here; No merged file was generated for pilot experiment as only one library  \"Merged MitoTracker Repeat.csv\" file is count table were reads from all plates of repeat experiment were merged Annotation of columns: Plx Welly  where x = plate number 1 4 and x = cell barcode; See Supplementary Methods for details. SORT Seq zebrafish fin samples: Robotized CEL Seq2; Fin cells unstained; all live cells; Wildtype zebrafish; Dissociated using default fin dissociation protocol Supplementary Methods; 384 cells per plate with each of the 384 barcodes see \"Cel seq barcodes 384.csv\" used 1 times per plate therefore  each plate has 1 library; Note: only merged count table file \"fin C E count table.csv\"  Annotation columns: Xx.py.prim.finZ  where x = cell barcode  y = plate number and Z is fish C or E and no individual library count table files were uploaded to GEO for zebrafish fin data; No bulk samples included; Spike ins not included in merged count tables file; Mitochondrial reads not included in merged count tables file; In some wells  we sorted no cell barcodes #357 #360 and #381 #384 were used for empty wells; Sequencing lanes concatenated in fastq files uploaded here; See Supplementary Methods for details.", null, "pubmed:28960196", null, "SORT Seq zebrafish fin merged", "GSM2781033", null, "tissue:All cells from caudal fin|strain:Wildtype", "SORT Seq zebrafish fin merged", "Reads 2 were mapped to the reference transcriptome created from the genomes downloaded from the UCSC genome browser; ERCC Spike in sequences and mitochondrial sequences were added in sense direction using bwa version 0.6.2 r126 with default parameters. All isoforms of the same gene were merged to a single gene locus and reads mapping to multiple loci in the transcriptome were discarded. Reads 1 contains the cel specific barcode information first 8 bases followed by a UMI sequence 4bp for unstained satellite cell data; 6bp for MitoTracker Stained satellite cells and zebrafish data and a polyT stretch. Reads 1 were thus used to extract the cell barcode sequences see \u201cCel seq barcodes 96.csv\u201d file for sequences used for unstained satellite cell data and see \"Cel seq barcodes 384\" for MitoTracker stained satellite cells and zebrafish data and UMIs; see Supplementary Methods for details. post mapping  a UMI correction was applied to the read and barcode counts files to generate unique transcript count tables as described before see Supplementary Methods section for details. Note that processed files for satellite cell data here still contain ERCC Spike in molecules  bulk samples bulk samples are only included in CEL Seq experiments; see \u201cBulkSamples BarcodesAndNrOfCellsUsed perCEL Seq1 library\u201d file for description of how many cells were used as bulk and which primer barcode sequence was used for bulk sample in each library and mitochondrial reads only for MitoTracker stained satellite cells. Genome build: mus musculus: mm10; danio rerio: Zv9 Ensembl release 74; both were extended with spike ins see \"ERCC92.fa\" Supplementary files format and content: *.coutt.csv  *TranscriptCounts.tsv and *count table.csv: tab separated data files listing how many reads of which transcripts were detected in all sequenced cells post UMI correction. Column names refer to cells sequenced in this library numbers refer the the CEL Seq primer barcode used for that cell. The first column lists official gene symbols followed by the chromosome name  separated by a double underscore. Note that processed files for satellite cell data here still contain ERCC Spike in molecules  bulk samples bulk samples are only included in CEL Seq experiments; see \u201cBulkSamples BarcodesAndNrOfCellsUsed perCEL Seq1 library\u201d file for description of how many cells were used as bulk and which primer barcode sequence was used for bulk sample in each library and mitochondrial reads only for MitoTracker stained satellite cells.", "All cells from caudal fin", null, "Cells were sorted into Vapor Lock Qiagen containing a droplet with primers and dNTPs. Cells were lysed at 65 degrees Celsius  post which SORT Seq Muraro et al.  2016  with minor modifications as desribed in Supplementary Methods of van den Brink et al.  2017 was applied. As in SORT Seq protocol Muraro et al.  2016  with minor modifications as desribed in Supplementary Methods of van den Brink et al.  2017.", null, "strain:Wildtype", "GSM2781033", "GSM2781033: SORT Seq zebrafish fin merged; Danio rerio; RNA Seq", "GSM2781033", null, "1", "Cells were sorted into Vapor Lock Qiagen containing a droplet with primers and dNTPs. Cells were lysed at 65 degrees Celsius  post which SORT Seq Muraro et al.  2016  with minor modifications as desribed in Supplementary Methods of van den Brink et al.  2017 was applied. As in SORT Seq protocol Muraro et al.  2016  with minor modifications as desribed in Supplementary Methods of van den Brink et al.  2017.", "GEO Accession:GSM2781033", "RNA-Seq", "TRANSCRIPTOMIC", "cDNA", "PAIRED", "ILLUMINA", "NextSeq 500", null, "SRP082370", null, null, "FinC_plate01_R1.fastq.gz FinC_plate01_R2.fastq.gz", "fastq fastq", 4156649914.0, 27498834.0, "GSM2781033 r1", "0:75.66 1:75.50", "A:1184667722;C:812111779;G:946047137;T:1213491214;N:332062", 75, 75, null, null, 1184667722, 812111779, 946047137, 1213491214, 332062, "SRX3187382", "SRS2515225", "SRA453335", "GEO", "Alexander van Oudenaarden, Hubrecht Institute", 2, 0.13191, 0.2761, 0.11086, 0.21281, 0.97997, 0.9586, 0.48699, 0.512, 76, 76, "B", "B", "mate1-mate2 similar by mapping diff", "illumina", "nextseq", "unknown", "cdna_unspecified", "unknown", "sc", "single_cell_plate", "celseq", null, "Netherlands", "2017-09-12", "Undetermined", "Adult", "Fin", "Surface Structure"], [41279, "SRR6039224", "SRX3187382", "SRS2515225", "SRP082370", "PRJNA339266", "Single cell sequencing reveals dissociation induced gene expression in tissue subpopulations", "GSE85755", "Other", "In many gene expression studies  cells are extracted by tissue dissociation and Fluorescence Activated Cell Sorting FACS  but the effect of these protocols on cellular transcriptomes is not well characterized and often ignored. Here  we applied single cell mRNA sequencing scRNA seq to muscle stem cells  and unexpectedly found a subpopulation that is strongly affected by the widely used dissociation protocol that we employed. One implication of this finding is that several published transcriptomics studies may need to be reinterpreted. Importantly  we detected similar subpopulations in other single cell datasets  suggesting that cells from other tissues might be affected by this artefact as well. Overall design: Mouse satellite cells and zebrafish fin cells were extracted from Tibialis Anterior muscles of Pax7nGFP mice and wildtype zebrafish fins  respectively. For cell extraction  traditional Supplementary Methods dissociation protocols that combine mechanical and enzymatic dissociation were employed  and live cells were subsequently sorted into plates using FACS. Next  single cell mRNA sequencing CEL Seq or SORT Seq robotized version of CEL Seq2 was applied  and data was analyzed with RaceID2 to identify clusters. CEL Seq samples: Manual CEL Seq; Satellite cells unstained; Male Pax7nGFP mice 5 mpf 7 mpf; 1h collagenase treated default dissociation protocol; 96 cells per plate with 96 different barcodes see \"Cel seq barcodes 96.csv\"; some primes numbers are bulk samples see \"BulkSamples BarcodesAndNrOfCellsUsed perCEL Seq1 library\"; Spike ins included see \"ERCC92.fa\"; No mitochondrial reads in count tables; Sequencing lanes not concatenated in fastq files uploaded here; \"Merged CEL Seq AllMiceAndLibrariesMerged.csv\" file is count table with reads from all mice and libraries merged Annotation of columns: Zx.y  where Z = mouse  x = library and y = cell barcode; bulk samples are not included any more in this file; See Supplementary Methods for details. SORT Seq 1h and 2h dissociated samples: Robotized CEL Seq2; Satellite cells unstained; Male Pax7nGFP mice 5 mpf 7 mpf; 8 muscles from 4 mice; One plate of 1h default dissociation protocol and one plate of 2h collagenase treated cells; 384 cells per plate with each of the 96 barcodes see \"Cel seq barcodes 96.csv\" used 4 times per plate therefore  each plate has 4 libraries; No bulk samples included; Spike ins included see \"ERCC92.fa\"; No mitochondrial reads in count table; In some wells  we sorted no cell internal negative control; barcodes #95 and #96 were used for empty wells; Sequencing lanes not concatenated in fastq files uploaded here; \"Merged SORT Seq DissociationTimecourse.csv\" file is count table were reads from all dissociation timepoints are merged Annotation columns: DZhx y  where Z = 1 or 2 hours collagenase treated  x = library and y = cell barcode; See Supplementary Methods for details. SORT Seq MitoTracker stained samples pilot and repeat: Robotized CEL Seq2 samples; Satellite cells stained with MitoTracker; Female Pax7nGFP mice 1 4.7 mpf mouse for pilot experiment; 3 mpf 6 mpf mice for repeat experiment; 1h collagenase treated default dissociation protocol; 384 cells per plate with each of the 384 barcodes see \"Cel seq barcodes 384.csv\" used 1 times per plate therefore  each plate has 1 library; Note: pilot experiment has 263 cells so plate was partly empty  repeat experiment done with 4 full plates; No bulk samples included; Spike ins included see \"ERCC92.fa\"; Mitochondrial reads rows named \"*  chrM\" included in count tables these were removed prior to RaceID2; In some wells  we sorted no cell barcodes #357 #360 and #381 #384 were used for empty wells; Sequencing lanes concatenated in fastq files uploaded here; No merged file was generated for pilot experiment as only one library  \"Merged MitoTracker Repeat.csv\" file is count table were reads from all plates of repeat experiment were merged Annotation of columns: Plx Welly  where x = plate number 1 4 and x = cell barcode; See Supplementary Methods for details. SORT Seq zebrafish fin samples: Robotized CEL Seq2; Fin cells unstained; all live cells; Wildtype zebrafish; Dissociated using default fin dissociation protocol Supplementary Methods; 384 cells per plate with each of the 384 barcodes see \"Cel seq barcodes 384.csv\" used 1 times per plate therefore  each plate has 1 library; Note: only merged count table file \"fin C E count table.csv\"  Annotation columns: Xx.py.prim.finZ  where x = cell barcode  y = plate number and Z is fish C or E and no individual library count table files were uploaded to GEO for zebrafish fin data; No bulk samples included; Spike ins not included in merged count tables file; Mitochondrial reads not included in merged count tables file; In some wells  we sorted no cell barcodes #357 #360 and #381 #384 were used for empty wells; Sequencing lanes concatenated in fastq files uploaded here; See Supplementary Methods for details.", null, "pubmed:28960196", null, "SORT Seq zebrafish fin merged", "GSM2781033", null, "tissue:All cells from caudal fin|strain:Wildtype", "SORT Seq zebrafish fin merged", "Reads 2 were mapped to the reference transcriptome created from the genomes downloaded from the UCSC genome browser; ERCC Spike in sequences and mitochondrial sequences were added in sense direction using bwa version 0.6.2 r126 with default parameters. All isoforms of the same gene were merged to a single gene locus and reads mapping to multiple loci in the transcriptome were discarded. Reads 1 contains the cel specific barcode information first 8 bases followed by a UMI sequence 4bp for unstained satellite cell data; 6bp for MitoTracker Stained satellite cells and zebrafish data and a polyT stretch. Reads 1 were thus used to extract the cell barcode sequences see \u201cCel seq barcodes 96.csv\u201d file for sequences used for unstained satellite cell data and see \"Cel seq barcodes 384\" for MitoTracker stained satellite cells and zebrafish data and UMIs; see Supplementary Methods for details. post mapping  a UMI correction was applied to the read and barcode counts files to generate unique transcript count tables as described before see Supplementary Methods section for details. Note that processed files for satellite cell data here still contain ERCC Spike in molecules  bulk samples bulk samples are only included in CEL Seq experiments; see \u201cBulkSamples BarcodesAndNrOfCellsUsed perCEL Seq1 library\u201d file for description of how many cells were used as bulk and which primer barcode sequence was used for bulk sample in each library and mitochondrial reads only for MitoTracker stained satellite cells. Genome build: mus musculus: mm10; danio rerio: Zv9 Ensembl release 74; both were extended with spike ins see \"ERCC92.fa\" Supplementary files format and content: *.coutt.csv  *TranscriptCounts.tsv and *count table.csv: tab separated data files listing how many reads of which transcripts were detected in all sequenced cells post UMI correction. Column names refer to cells sequenced in this library numbers refer the the CEL Seq primer barcode used for that cell. The first column lists official gene symbols followed by the chromosome name  separated by a double underscore. Note that processed files for satellite cell data here still contain ERCC Spike in molecules  bulk samples bulk samples are only included in CEL Seq experiments; see \u201cBulkSamples BarcodesAndNrOfCellsUsed perCEL Seq1 library\u201d file for description of how many cells were used as bulk and which primer barcode sequence was used for bulk sample in each library and mitochondrial reads only for MitoTracker stained satellite cells.", "All cells from caudal fin", null, "Cells were sorted into Vapor Lock Qiagen containing a droplet with primers and dNTPs. Cells were lysed at 65 degrees Celsius  post which SORT Seq Muraro et al.  2016  with minor modifications as desribed in Supplementary Methods of van den Brink et al.  2017 was applied. As in SORT Seq protocol Muraro et al.  2016  with minor modifications as desribed in Supplementary Methods of van den Brink et al.  2017.", null, "strain:Wildtype", "GSM2781033", "GSM2781033: SORT Seq zebrafish fin merged; Danio rerio; RNA Seq", "GSM2781033", null, "1", "Cells were sorted into Vapor Lock Qiagen containing a droplet with primers and dNTPs. Cells were lysed at 65 degrees Celsius  post which SORT Seq Muraro et al.  2016  with minor modifications as desribed in Supplementary Methods of van den Brink et al.  2017 was applied. As in SORT Seq protocol Muraro et al.  2016  with minor modifications as desribed in Supplementary Methods of van den Brink et al.  2017.", "GEO Accession:GSM2781033", "RNA-Seq", "TRANSCRIPTOMIC", "cDNA", "PAIRED", "ILLUMINA", "NextSeq 500", null, "SRP082370", null, null, "FinE_plate07_R1.fastq.gz FinE_plate07_R2.fastq.gz", "fastq fastq", 7045442122.0, 46667895.0, "GSM2781033 r10", "0:75.65 1:75.32", "A:1992737539;C:1481998828;G:1584855122;T:1984679859;N:1170774", 75, 75, null, null, 1992737539, 1481998828, 1584855122, 1984679859, 1170774, "SRX3187382", "SRS2515225", "SRA453335", "GEO", "Alexander van Oudenaarden, Hubrecht Institute", 2, 0.08863, 0.2093, 0.07731, 0.18045, 0.97934, 0.95655, 0.51176, 0.52167, 76, 76, "B", "B", "mate1-mate2 similar by mapping diff", "illumina", "nextseq", "unknown", "cdna_unspecified", "unknown", "sc", "single_cell_plate", "celseq", null, "Netherlands", "2017-09-12", "Undetermined", "Adult", "Fin", "Surface Structure"], [41280, "SRR6039225", "SRX3187382", "SRS2515225", "SRP082370", "PRJNA339266", "Single cell sequencing reveals dissociation induced gene expression in tissue subpopulations", "GSE85755", "Other", "In many gene expression studies  cells are extracted by tissue dissociation and Fluorescence Activated Cell Sorting FACS  but the effect of these protocols on cellular transcriptomes is not well characterized and often ignored. Here  we applied single cell mRNA sequencing scRNA seq to muscle stem cells  and unexpectedly found a subpopulation that is strongly affected by the widely used dissociation protocol that we employed. One implication of this finding is that several published transcriptomics studies may need to be reinterpreted. Importantly  we detected similar subpopulations in other single cell datasets  suggesting that cells from other tissues might be affected by this artefact as well. Overall design: Mouse satellite cells and zebrafish fin cells were extracted from Tibialis Anterior muscles of Pax7nGFP mice and wildtype zebrafish fins  respectively. For cell extraction  traditional Supplementary Methods dissociation protocols that combine mechanical and enzymatic dissociation were employed  and live cells were subsequently sorted into plates using FACS. Next  single cell mRNA sequencing CEL Seq or SORT Seq robotized version of CEL Seq2 was applied  and data was analyzed with RaceID2 to identify clusters. CEL Seq samples: Manual CEL Seq; Satellite cells unstained; Male Pax7nGFP mice 5 mpf 7 mpf; 1h collagenase treated default dissociation protocol; 96 cells per plate with 96 different barcodes see \"Cel seq barcodes 96.csv\"; some primes numbers are bulk samples see \"BulkSamples BarcodesAndNrOfCellsUsed perCEL Seq1 library\"; Spike ins included see \"ERCC92.fa\"; No mitochondrial reads in count tables; Sequencing lanes not concatenated in fastq files uploaded here; \"Merged CEL Seq AllMiceAndLibrariesMerged.csv\" file is count table with reads from all mice and libraries merged Annotation of columns: Zx.y  where Z = mouse  x = library and y = cell barcode; bulk samples are not included any more in this file; See Supplementary Methods for details. SORT Seq 1h and 2h dissociated samples: Robotized CEL Seq2; Satellite cells unstained; Male Pax7nGFP mice 5 mpf 7 mpf; 8 muscles from 4 mice; One plate of 1h default dissociation protocol and one plate of 2h collagenase treated cells; 384 cells per plate with each of the 96 barcodes see \"Cel seq barcodes 96.csv\" used 4 times per plate therefore  each plate has 4 libraries; No bulk samples included; Spike ins included see \"ERCC92.fa\"; No mitochondrial reads in count table; In some wells  we sorted no cell internal negative control; barcodes #95 and #96 were used for empty wells; Sequencing lanes not concatenated in fastq files uploaded here; \"Merged SORT Seq DissociationTimecourse.csv\" file is count table were reads from all dissociation timepoints are merged Annotation columns: DZhx y  where Z = 1 or 2 hours collagenase treated  x = library and y = cell barcode; See Supplementary Methods for details. SORT Seq MitoTracker stained samples pilot and repeat: Robotized CEL Seq2 samples; Satellite cells stained with MitoTracker; Female Pax7nGFP mice 1 4.7 mpf mouse for pilot experiment; 3 mpf 6 mpf mice for repeat experiment; 1h collagenase treated default dissociation protocol; 384 cells per plate with each of the 384 barcodes see \"Cel seq barcodes 384.csv\" used 1 times per plate therefore  each plate has 1 library; Note: pilot experiment has 263 cells so plate was partly empty  repeat experiment done with 4 full plates; No bulk samples included; Spike ins included see \"ERCC92.fa\"; Mitochondrial reads rows named \"*  chrM\" included in count tables these were removed prior to RaceID2; In some wells  we sorted no cell barcodes #357 #360 and #381 #384 were used for empty wells; Sequencing lanes concatenated in fastq files uploaded here; No merged file was generated for pilot experiment as only one library  \"Merged MitoTracker Repeat.csv\" file is count table were reads from all plates of repeat experiment were merged Annotation of columns: Plx Welly  where x = plate number 1 4 and x = cell barcode; See Supplementary Methods for details. SORT Seq zebrafish fin samples: Robotized CEL Seq2; Fin cells unstained; all live cells; Wildtype zebrafish; Dissociated using default fin dissociation protocol Supplementary Methods; 384 cells per plate with each of the 384 barcodes see \"Cel seq barcodes 384.csv\" used 1 times per plate therefore  each plate has 1 library; Note: only merged count table file \"fin C E count table.csv\"  Annotation columns: Xx.py.prim.finZ  where x = cell barcode  y = plate number and Z is fish C or E and no individual library count table files were uploaded to GEO for zebrafish fin data; No bulk samples included; Spike ins not included in merged count tables file; Mitochondrial reads not included in merged count tables file; In some wells  we sorted no cell barcodes #357 #360 and #381 #384 were used for empty wells; Sequencing lanes concatenated in fastq files uploaded here; See Supplementary Methods for details.", null, "pubmed:28960196", null, "SORT Seq zebrafish fin merged", "GSM2781033", null, "tissue:All cells from caudal fin|strain:Wildtype", "SORT Seq zebrafish fin merged", "Reads 2 were mapped to the reference transcriptome created from the genomes downloaded from the UCSC genome browser; ERCC Spike in sequences and mitochondrial sequences were added in sense direction using bwa version 0.6.2 r126 with default parameters. All isoforms of the same gene were merged to a single gene locus and reads mapping to multiple loci in the transcriptome were discarded. Reads 1 contains the cel specific barcode information first 8 bases followed by a UMI sequence 4bp for unstained satellite cell data; 6bp for MitoTracker Stained satellite cells and zebrafish data and a polyT stretch. Reads 1 were thus used to extract the cell barcode sequences see \u201cCel seq barcodes 96.csv\u201d file for sequences used for unstained satellite cell data and see \"Cel seq barcodes 384\" for MitoTracker stained satellite cells and zebrafish data and UMIs; see Supplementary Methods for details. post mapping  a UMI correction was applied to the read and barcode counts files to generate unique transcript count tables as described before see Supplementary Methods section for details. Note that processed files for satellite cell data here still contain ERCC Spike in molecules  bulk samples bulk samples are only included in CEL Seq experiments; see \u201cBulkSamples BarcodesAndNrOfCellsUsed perCEL Seq1 library\u201d file for description of how many cells were used as bulk and which primer barcode sequence was used for bulk sample in each library and mitochondrial reads only for MitoTracker stained satellite cells. Genome build: mus musculus: mm10; danio rerio: Zv9 Ensembl release 74; both were extended with spike ins see \"ERCC92.fa\" Supplementary files format and content: *.coutt.csv  *TranscriptCounts.tsv and *count table.csv: tab separated data files listing how many reads of which transcripts were detected in all sequenced cells post UMI correction. Column names refer to cells sequenced in this library numbers refer the the CEL Seq primer barcode used for that cell. The first column lists official gene symbols followed by the chromosome name  separated by a double underscore. Note that processed files for satellite cell data here still contain ERCC Spike in molecules  bulk samples bulk samples are only included in CEL Seq experiments; see \u201cBulkSamples BarcodesAndNrOfCellsUsed perCEL Seq1 library\u201d file for description of how many cells were used as bulk and which primer barcode sequence was used for bulk sample in each library and mitochondrial reads only for MitoTracker stained satellite cells.", "All cells from caudal fin", null, "Cells were sorted into Vapor Lock Qiagen containing a droplet with primers and dNTPs. Cells were lysed at 65 degrees Celsius  post which SORT Seq Muraro et al.  2016  with minor modifications as desribed in Supplementary Methods of van den Brink et al.  2017 was applied. As in SORT Seq protocol Muraro et al.  2016  with minor modifications as desribed in Supplementary Methods of van den Brink et al.  2017.", null, "strain:Wildtype", "GSM2781033", "GSM2781033: SORT Seq zebrafish fin merged; Danio rerio; RNA Seq", "GSM2781033", null, "1", "Cells were sorted into Vapor Lock Qiagen containing a droplet with primers and dNTPs. Cells were lysed at 65 degrees Celsius  post which SORT Seq Muraro et al.  2016  with minor modifications as desribed in Supplementary Methods of van den Brink et al.  2017 was applied. As in SORT Seq protocol Muraro et al.  2016  with minor modifications as desribed in Supplementary Methods of van den Brink et al.  2017.", "GEO Accession:GSM2781033", "RNA-Seq", "TRANSCRIPTOMIC", "cDNA", "PAIRED", "ILLUMINA", "NextSeq 500", null, "SRP082370", null, null, "FinE_plate08_R1.fastq.gz FinE_plate08_R2.fastq.gz", "fastq fastq", 18530449971.0, 122665653.0, "GSM2781033 r11", "0:75.68 1:75.38", "A:4667348302;C:4333487274;G:4681098112;T:4848415162;N:101121", 75, 75, null, null, 4667348302, 4333487274, 4681098112, 4848415162, 101121, "SRX3187382", "SRS2515225", "SRA453335", "GEO", "Alexander van Oudenaarden, Hubrecht Institute", 2, 0.05595, 0.12249, 0.04637, 0.10243, 0.98415, 0.96451, 0.51093, 0.52879, 75, 75, "B", "B", "mate1-mate2 similar by mapping diff", "illumina", "nextseq", "unknown", "cdna_unspecified", "unknown", "sc", "single_cell_plate", "celseq", null, "Netherlands", "2017-09-12", "Undetermined", "Adult", "Fin", "Surface Structure"], [41281, "SRR6039226", "SRX3187382", "SRS2515225", "SRP082370", "PRJNA339266", "Single cell sequencing reveals dissociation induced gene expression in tissue subpopulations", "GSE85755", "Other", "In many gene expression studies  cells are extracted by tissue dissociation and Fluorescence Activated Cell Sorting FACS  but the effect of these protocols on cellular transcriptomes is not well characterized and often ignored. Here  we applied single cell mRNA sequencing scRNA seq to muscle stem cells  and unexpectedly found a subpopulation that is strongly affected by the widely used dissociation protocol that we employed. One implication of this finding is that several published transcriptomics studies may need to be reinterpreted. Importantly  we detected similar subpopulations in other single cell datasets  suggesting that cells from other tissues might be affected by this artefact as well. Overall design: Mouse satellite cells and zebrafish fin cells were extracted from Tibialis Anterior muscles of Pax7nGFP mice and wildtype zebrafish fins  respectively. For cell extraction  traditional Supplementary Methods dissociation protocols that combine mechanical and enzymatic dissociation were employed  and live cells were subsequently sorted into plates using FACS. Next  single cell mRNA sequencing CEL Seq or SORT Seq robotized version of CEL Seq2 was applied  and data was analyzed with RaceID2 to identify clusters. CEL Seq samples: Manual CEL Seq; Satellite cells unstained; Male Pax7nGFP mice 5 mpf 7 mpf; 1h collagenase treated default dissociation protocol; 96 cells per plate with 96 different barcodes see \"Cel seq barcodes 96.csv\"; some primes numbers are bulk samples see \"BulkSamples BarcodesAndNrOfCellsUsed perCEL Seq1 library\"; Spike ins included see \"ERCC92.fa\"; No mitochondrial reads in count tables; Sequencing lanes not concatenated in fastq files uploaded here; \"Merged CEL Seq AllMiceAndLibrariesMerged.csv\" file is count table with reads from all mice and libraries merged Annotation of columns: Zx.y  where Z = mouse  x = library and y = cell barcode; bulk samples are not included any more in this file; See Supplementary Methods for details. SORT Seq 1h and 2h dissociated samples: Robotized CEL Seq2; Satellite cells unstained; Male Pax7nGFP mice 5 mpf 7 mpf; 8 muscles from 4 mice; One plate of 1h default dissociation protocol and one plate of 2h collagenase treated cells; 384 cells per plate with each of the 96 barcodes see \"Cel seq barcodes 96.csv\" used 4 times per plate therefore  each plate has 4 libraries; No bulk samples included; Spike ins included see \"ERCC92.fa\"; No mitochondrial reads in count table; In some wells  we sorted no cell internal negative control; barcodes #95 and #96 were used for empty wells; Sequencing lanes not concatenated in fastq files uploaded here; \"Merged SORT Seq DissociationTimecourse.csv\" file is count table were reads from all dissociation timepoints are merged Annotation columns: DZhx y  where Z = 1 or 2 hours collagenase treated  x = library and y = cell barcode; See Supplementary Methods for details. SORT Seq MitoTracker stained samples pilot and repeat: Robotized CEL Seq2 samples; Satellite cells stained with MitoTracker; Female Pax7nGFP mice 1 4.7 mpf mouse for pilot experiment; 3 mpf 6 mpf mice for repeat experiment; 1h collagenase treated default dissociation protocol; 384 cells per plate with each of the 384 barcodes see \"Cel seq barcodes 384.csv\" used 1 times per plate therefore  each plate has 1 library; Note: pilot experiment has 263 cells so plate was partly empty  repeat experiment done with 4 full plates; No bulk samples included; Spike ins included see \"ERCC92.fa\"; Mitochondrial reads rows named \"*  chrM\" included in count tables these were removed prior to RaceID2; In some wells  we sorted no cell barcodes #357 #360 and #381 #384 were used for empty wells; Sequencing lanes concatenated in fastq files uploaded here; No merged file was generated for pilot experiment as only one library  \"Merged MitoTracker Repeat.csv\" file is count table were reads from all plates of repeat experiment were merged Annotation of columns: Plx Welly  where x = plate number 1 4 and x = cell barcode; See Supplementary Methods for details. SORT Seq zebrafish fin samples: Robotized CEL Seq2; Fin cells unstained; all live cells; Wildtype zebrafish; Dissociated using default fin dissociation protocol Supplementary Methods; 384 cells per plate with each of the 384 barcodes see \"Cel seq barcodes 384.csv\" used 1 times per plate therefore  each plate has 1 library; Note: only merged count table file \"fin C E count table.csv\"  Annotation columns: Xx.py.prim.finZ  where x = cell barcode  y = plate number and Z is fish C or E and no individual library count table files were uploaded to GEO for zebrafish fin data; No bulk samples included; Spike ins not included in merged count tables file; Mitochondrial reads not included in merged count tables file; In some wells  we sorted no cell barcodes #357 #360 and #381 #384 were used for empty wells; Sequencing lanes concatenated in fastq files uploaded here; See Supplementary Methods for details.", null, "pubmed:28960196", null, "SORT Seq zebrafish fin merged", "GSM2781033", null, "tissue:All cells from caudal fin|strain:Wildtype", "SORT Seq zebrafish fin merged", "Reads 2 were mapped to the reference transcriptome created from the genomes downloaded from the UCSC genome browser; ERCC Spike in sequences and mitochondrial sequences were added in sense direction using bwa version 0.6.2 r126 with default parameters. All isoforms of the same gene were merged to a single gene locus and reads mapping to multiple loci in the transcriptome were discarded. Reads 1 contains the cel specific barcode information first 8 bases followed by a UMI sequence 4bp for unstained satellite cell data; 6bp for MitoTracker Stained satellite cells and zebrafish data and a polyT stretch. Reads 1 were thus used to extract the cell barcode sequences see \u201cCel seq barcodes 96.csv\u201d file for sequences used for unstained satellite cell data and see \"Cel seq barcodes 384\" for MitoTracker stained satellite cells and zebrafish data and UMIs; see Supplementary Methods for details. post mapping  a UMI correction was applied to the read and barcode counts files to generate unique transcript count tables as described before see Supplementary Methods section for details. Note that processed files for satellite cell data here still contain ERCC Spike in molecules  bulk samples bulk samples are only included in CEL Seq experiments; see \u201cBulkSamples BarcodesAndNrOfCellsUsed perCEL Seq1 library\u201d file for description of how many cells were used as bulk and which primer barcode sequence was used for bulk sample in each library and mitochondrial reads only for MitoTracker stained satellite cells. Genome build: mus musculus: mm10; danio rerio: Zv9 Ensembl release 74; both were extended with spike ins see \"ERCC92.fa\" Supplementary files format and content: *.coutt.csv  *TranscriptCounts.tsv and *count table.csv: tab separated data files listing how many reads of which transcripts were detected in all sequenced cells post UMI correction. Column names refer to cells sequenced in this library numbers refer the the CEL Seq primer barcode used for that cell. The first column lists official gene symbols followed by the chromosome name  separated by a double underscore. Note that processed files for satellite cell data here still contain ERCC Spike in molecules  bulk samples bulk samples are only included in CEL Seq experiments; see \u201cBulkSamples BarcodesAndNrOfCellsUsed perCEL Seq1 library\u201d file for description of how many cells were used as bulk and which primer barcode sequence was used for bulk sample in each library and mitochondrial reads only for MitoTracker stained satellite cells.", "All cells from caudal fin", null, "Cells were sorted into Vapor Lock Qiagen containing a droplet with primers and dNTPs. Cells were lysed at 65 degrees Celsius  post which SORT Seq Muraro et al.  2016  with minor modifications as desribed in Supplementary Methods of van den Brink et al.  2017 was applied. As in SORT Seq protocol Muraro et al.  2016  with minor modifications as desribed in Supplementary Methods of van den Brink et al.  2017.", null, "strain:Wildtype", "GSM2781033", "GSM2781033: SORT Seq zebrafish fin merged; Danio rerio; RNA Seq", "GSM2781033", null, "1", "Cells were sorted into Vapor Lock Qiagen containing a droplet with primers and dNTPs. Cells were lysed at 65 degrees Celsius  post which SORT Seq Muraro et al.  2016  with minor modifications as desribed in Supplementary Methods of van den Brink et al.  2017 was applied. As in SORT Seq protocol Muraro et al.  2016  with minor modifications as desribed in Supplementary Methods of van den Brink et al.  2017.", "GEO Accession:GSM2781033", "RNA-Seq", "TRANSCRIPTOMIC", "cDNA", "PAIRED", "ILLUMINA", "NextSeq 500", null, "SRP082370", null, null, "FinC_plate02_R1.fastq.gz FinC_plate02_R2.fastq.gz", "fastq fastq", 5961095878.0, 39431358.0, "GSM2781033 r2", "0:75.66 1:75.51", "A:1678045732;C:1185915367;G:1391829821;T:1704836561;N:468397", 75, 75, null, null, 1678045732, 1185915367, 1391829821, 1704836561, 468397, "SRX3187382", "SRS2515225", "SRA453335", "GEO", "Alexander van Oudenaarden, Hubrecht Institute", 2, 0.13015, 0.27027, 0.11023, 0.2177, 0.97985, 0.95964, 0.5491, 0.51821, 76, 76, "B", "B", "mate1-mate2 similar by mapping diff", "illumina", "nextseq", "unknown", "cdna_unspecified", "unknown", "sc", "single_cell_plate", "celseq", null, "Netherlands", "2017-09-12", "Undetermined", "Adult", "Fin", "Surface Structure"], [41282, "SRR6039227", "SRX3187382", "SRS2515225", "SRP082370", "PRJNA339266", "Single cell sequencing reveals dissociation induced gene expression in tissue subpopulations", "GSE85755", "Other", "In many gene expression studies  cells are extracted by tissue dissociation and Fluorescence Activated Cell Sorting FACS  but the effect of these protocols on cellular transcriptomes is not well characterized and often ignored. Here  we applied single cell mRNA sequencing scRNA seq to muscle stem cells  and unexpectedly found a subpopulation that is strongly affected by the widely used dissociation protocol that we employed. One implication of this finding is that several published transcriptomics studies may need to be reinterpreted. Importantly  we detected similar subpopulations in other single cell datasets  suggesting that cells from other tissues might be affected by this artefact as well. Overall design: Mouse satellite cells and zebrafish fin cells were extracted from Tibialis Anterior muscles of Pax7nGFP mice and wildtype zebrafish fins  respectively. For cell extraction  traditional Supplementary Methods dissociation protocols that combine mechanical and enzymatic dissociation were employed  and live cells were subsequently sorted into plates using FACS. Next  single cell mRNA sequencing CEL Seq or SORT Seq robotized version of CEL Seq2 was applied  and data was analyzed with RaceID2 to identify clusters. CEL Seq samples: Manual CEL Seq; Satellite cells unstained; Male Pax7nGFP mice 5 mpf 7 mpf; 1h collagenase treated default dissociation protocol; 96 cells per plate with 96 different barcodes see \"Cel seq barcodes 96.csv\"; some primes numbers are bulk samples see \"BulkSamples BarcodesAndNrOfCellsUsed perCEL Seq1 library\"; Spike ins included see \"ERCC92.fa\"; No mitochondrial reads in count tables; Sequencing lanes not concatenated in fastq files uploaded here; \"Merged CEL Seq AllMiceAndLibrariesMerged.csv\" file is count table with reads from all mice and libraries merged Annotation of columns: Zx.y  where Z = mouse  x = library and y = cell barcode; bulk samples are not included any more in this file; See Supplementary Methods for details. SORT Seq 1h and 2h dissociated samples: Robotized CEL Seq2; Satellite cells unstained; Male Pax7nGFP mice 5 mpf 7 mpf; 8 muscles from 4 mice; One plate of 1h default dissociation protocol and one plate of 2h collagenase treated cells; 384 cells per plate with each of the 96 barcodes see \"Cel seq barcodes 96.csv\" used 4 times per plate therefore  each plate has 4 libraries; No bulk samples included; Spike ins included see \"ERCC92.fa\"; No mitochondrial reads in count table; In some wells  we sorted no cell internal negative control; barcodes #95 and #96 were used for empty wells; Sequencing lanes not concatenated in fastq files uploaded here; \"Merged SORT Seq DissociationTimecourse.csv\" file is count table were reads from all dissociation timepoints are merged Annotation columns: DZhx y  where Z = 1 or 2 hours collagenase treated  x = library and y = cell barcode; See Supplementary Methods for details. SORT Seq MitoTracker stained samples pilot and repeat: Robotized CEL Seq2 samples; Satellite cells stained with MitoTracker; Female Pax7nGFP mice 1 4.7 mpf mouse for pilot experiment; 3 mpf 6 mpf mice for repeat experiment; 1h collagenase treated default dissociation protocol; 384 cells per plate with each of the 384 barcodes see \"Cel seq barcodes 384.csv\" used 1 times per plate therefore  each plate has 1 library; Note: pilot experiment has 263 cells so plate was partly empty  repeat experiment done with 4 full plates; No bulk samples included; Spike ins included see \"ERCC92.fa\"; Mitochondrial reads rows named \"*  chrM\" included in count tables these were removed prior to RaceID2; In some wells  we sorted no cell barcodes #357 #360 and #381 #384 were used for empty wells; Sequencing lanes concatenated in fastq files uploaded here; No merged file was generated for pilot experiment as only one library  \"Merged MitoTracker Repeat.csv\" file is count table were reads from all plates of repeat experiment were merged Annotation of columns: Plx Welly  where x = plate number 1 4 and x = cell barcode; See Supplementary Methods for details. SORT Seq zebrafish fin samples: Robotized CEL Seq2; Fin cells unstained; all live cells; Wildtype zebrafish; Dissociated using default fin dissociation protocol Supplementary Methods; 384 cells per plate with each of the 384 barcodes see \"Cel seq barcodes 384.csv\" used 1 times per plate therefore  each plate has 1 library; Note: only merged count table file \"fin C E count table.csv\"  Annotation columns: Xx.py.prim.finZ  where x = cell barcode  y = plate number and Z is fish C or E and no individual library count table files were uploaded to GEO for zebrafish fin data; No bulk samples included; Spike ins not included in merged count tables file; Mitochondrial reads not included in merged count tables file; In some wells  we sorted no cell barcodes #357 #360 and #381 #384 were used for empty wells; Sequencing lanes concatenated in fastq files uploaded here; See Supplementary Methods for details.", null, "pubmed:28960196", null, "SORT Seq zebrafish fin merged", "GSM2781033", null, "tissue:All cells from caudal fin|strain:Wildtype", "SORT Seq zebrafish fin merged", "Reads 2 were mapped to the reference transcriptome created from the genomes downloaded from the UCSC genome browser; ERCC Spike in sequences and mitochondrial sequences were added in sense direction using bwa version 0.6.2 r126 with default parameters. All isoforms of the same gene were merged to a single gene locus and reads mapping to multiple loci in the transcriptome were discarded. Reads 1 contains the cel specific barcode information first 8 bases followed by a UMI sequence 4bp for unstained satellite cell data; 6bp for MitoTracker Stained satellite cells and zebrafish data and a polyT stretch. Reads 1 were thus used to extract the cell barcode sequences see \u201cCel seq barcodes 96.csv\u201d file for sequences used for unstained satellite cell data and see \"Cel seq barcodes 384\" for MitoTracker stained satellite cells and zebrafish data and UMIs; see Supplementary Methods for details. post mapping  a UMI correction was applied to the read and barcode counts files to generate unique transcript count tables as described before see Supplementary Methods section for details. Note that processed files for satellite cell data here still contain ERCC Spike in molecules  bulk samples bulk samples are only included in CEL Seq experiments; see \u201cBulkSamples BarcodesAndNrOfCellsUsed perCEL Seq1 library\u201d file for description of how many cells were used as bulk and which primer barcode sequence was used for bulk sample in each library and mitochondrial reads only for MitoTracker stained satellite cells. Genome build: mus musculus: mm10; danio rerio: Zv9 Ensembl release 74; both were extended with spike ins see \"ERCC92.fa\" Supplementary files format and content: *.coutt.csv  *TranscriptCounts.tsv and *count table.csv: tab separated data files listing how many reads of which transcripts were detected in all sequenced cells post UMI correction. Column names refer to cells sequenced in this library numbers refer the the CEL Seq primer barcode used for that cell. The first column lists official gene symbols followed by the chromosome name  separated by a double underscore. Note that processed files for satellite cell data here still contain ERCC Spike in molecules  bulk samples bulk samples are only included in CEL Seq experiments; see \u201cBulkSamples BarcodesAndNrOfCellsUsed perCEL Seq1 library\u201d file for description of how many cells were used as bulk and which primer barcode sequence was used for bulk sample in each library and mitochondrial reads only for MitoTracker stained satellite cells.", "All cells from caudal fin", null, "Cells were sorted into Vapor Lock Qiagen containing a droplet with primers and dNTPs. Cells were lysed at 65 degrees Celsius  post which SORT Seq Muraro et al.  2016  with minor modifications as desribed in Supplementary Methods of van den Brink et al.  2017 was applied. As in SORT Seq protocol Muraro et al.  2016  with minor modifications as desribed in Supplementary Methods of van den Brink et al.  2017.", null, "strain:Wildtype", "GSM2781033", "GSM2781033: SORT Seq zebrafish fin merged; Danio rerio; RNA Seq", "GSM2781033", null, "1", "Cells were sorted into Vapor Lock Qiagen containing a droplet with primers and dNTPs. Cells were lysed at 65 degrees Celsius  post which SORT Seq Muraro et al.  2016  with minor modifications as desribed in Supplementary Methods of van den Brink et al.  2017 was applied. As in SORT Seq protocol Muraro et al.  2016  with minor modifications as desribed in Supplementary Methods of van den Brink et al.  2017.", "GEO Accession:GSM2781033", "RNA-Seq", "TRANSCRIPTOMIC", "cDNA", "PAIRED", "ILLUMINA", "NextSeq 500", null, "SRP082370", null, null, "FinC_plate03_R1.fastq.gz FinC_plate03_R2.fastq.gz", "fastq fastq", 6170232577.0, 40846092.0, "GSM2781033 r3", "0:75.56 1:75.50", "A:1866777846;C:1148020988;G:1151073641;T:2003993265;N:366837", 75, 75, null, null, 1866777846, 1148020988, 1151073641, 2003993265, 366837, "SRX3187382", "SRS2515225", "SRA453335", "GEO", "Alexander van Oudenaarden, Hubrecht Institute", 2, 0.21215, 0.40881, 0.18316, 0.33198, 0.95919, 0.91102, 0.49711, 0.53044, 75, 76, "T", "B", "mate1 technical by mapping diff", "illumina", "nextseq", "unknown", "cdna_unspecified", "unknown", "sc", "single_cell_plate", "celseq", null, "Netherlands", "2017-09-12", "Undetermined", "Adult", "Fin", "Surface Structure"], [41283, "SRR6039228", "SRX3187382", "SRS2515225", "SRP082370", "PRJNA339266", "Single cell sequencing reveals dissociation induced gene expression in tissue subpopulations", "GSE85755", "Other", "In many gene expression studies  cells are extracted by tissue dissociation and Fluorescence Activated Cell Sorting FACS  but the effect of these protocols on cellular transcriptomes is not well characterized and often ignored. Here  we applied single cell mRNA sequencing scRNA seq to muscle stem cells  and unexpectedly found a subpopulation that is strongly affected by the widely used dissociation protocol that we employed. One implication of this finding is that several published transcriptomics studies may need to be reinterpreted. Importantly  we detected similar subpopulations in other single cell datasets  suggesting that cells from other tissues might be affected by this artefact as well. Overall design: Mouse satellite cells and zebrafish fin cells were extracted from Tibialis Anterior muscles of Pax7nGFP mice and wildtype zebrafish fins  respectively. For cell extraction  traditional Supplementary Methods dissociation protocols that combine mechanical and enzymatic dissociation were employed  and live cells were subsequently sorted into plates using FACS. Next  single cell mRNA sequencing CEL Seq or SORT Seq robotized version of CEL Seq2 was applied  and data was analyzed with RaceID2 to identify clusters. CEL Seq samples: Manual CEL Seq; Satellite cells unstained; Male Pax7nGFP mice 5 mpf 7 mpf; 1h collagenase treated default dissociation protocol; 96 cells per plate with 96 different barcodes see \"Cel seq barcodes 96.csv\"; some primes numbers are bulk samples see \"BulkSamples BarcodesAndNrOfCellsUsed perCEL Seq1 library\"; Spike ins included see \"ERCC92.fa\"; No mitochondrial reads in count tables; Sequencing lanes not concatenated in fastq files uploaded here; \"Merged CEL Seq AllMiceAndLibrariesMerged.csv\" file is count table with reads from all mice and libraries merged Annotation of columns: Zx.y  where Z = mouse  x = library and y = cell barcode; bulk samples are not included any more in this file; See Supplementary Methods for details. SORT Seq 1h and 2h dissociated samples: Robotized CEL Seq2; Satellite cells unstained; Male Pax7nGFP mice 5 mpf 7 mpf; 8 muscles from 4 mice; One plate of 1h default dissociation protocol and one plate of 2h collagenase treated cells; 384 cells per plate with each of the 96 barcodes see \"Cel seq barcodes 96.csv\" used 4 times per plate therefore  each plate has 4 libraries; No bulk samples included; Spike ins included see \"ERCC92.fa\"; No mitochondrial reads in count table; In some wells  we sorted no cell internal negative control; barcodes #95 and #96 were used for empty wells; Sequencing lanes not concatenated in fastq files uploaded here; \"Merged SORT Seq DissociationTimecourse.csv\" file is count table were reads from all dissociation timepoints are merged Annotation columns: DZhx y  where Z = 1 or 2 hours collagenase treated  x = library and y = cell barcode; See Supplementary Methods for details. SORT Seq MitoTracker stained samples pilot and repeat: Robotized CEL Seq2 samples; Satellite cells stained with MitoTracker; Female Pax7nGFP mice 1 4.7 mpf mouse for pilot experiment; 3 mpf 6 mpf mice for repeat experiment; 1h collagenase treated default dissociation protocol; 384 cells per plate with each of the 384 barcodes see \"Cel seq barcodes 384.csv\" used 1 times per plate therefore  each plate has 1 library; Note: pilot experiment has 263 cells so plate was partly empty  repeat experiment done with 4 full plates; No bulk samples included; Spike ins included see \"ERCC92.fa\"; Mitochondrial reads rows named \"*  chrM\" included in count tables these were removed prior to RaceID2; In some wells  we sorted no cell barcodes #357 #360 and #381 #384 were used for empty wells; Sequencing lanes concatenated in fastq files uploaded here; No merged file was generated for pilot experiment as only one library  \"Merged MitoTracker Repeat.csv\" file is count table were reads from all plates of repeat experiment were merged Annotation of columns: Plx Welly  where x = plate number 1 4 and x = cell barcode; See Supplementary Methods for details. SORT Seq zebrafish fin samples: Robotized CEL Seq2; Fin cells unstained; all live cells; Wildtype zebrafish; Dissociated using default fin dissociation protocol Supplementary Methods; 384 cells per plate with each of the 384 barcodes see \"Cel seq barcodes 384.csv\" used 1 times per plate therefore  each plate has 1 library; Note: only merged count table file \"fin C E count table.csv\"  Annotation columns: Xx.py.prim.finZ  where x = cell barcode  y = plate number and Z is fish C or E and no individual library count table files were uploaded to GEO for zebrafish fin data; No bulk samples included; Spike ins not included in merged count tables file; Mitochondrial reads not included in merged count tables file; In some wells  we sorted no cell barcodes #357 #360 and #381 #384 were used for empty wells; Sequencing lanes concatenated in fastq files uploaded here; See Supplementary Methods for details.", null, "pubmed:28960196", null, "SORT Seq zebrafish fin merged", "GSM2781033", null, "tissue:All cells from caudal fin|strain:Wildtype", "SORT Seq zebrafish fin merged", "Reads 2 were mapped to the reference transcriptome created from the genomes downloaded from the UCSC genome browser; ERCC Spike in sequences and mitochondrial sequences were added in sense direction using bwa version 0.6.2 r126 with default parameters. All isoforms of the same gene were merged to a single gene locus and reads mapping to multiple loci in the transcriptome were discarded. Reads 1 contains the cel specific barcode information first 8 bases followed by a UMI sequence 4bp for unstained satellite cell data; 6bp for MitoTracker Stained satellite cells and zebrafish data and a polyT stretch. Reads 1 were thus used to extract the cell barcode sequences see \u201cCel seq barcodes 96.csv\u201d file for sequences used for unstained satellite cell data and see \"Cel seq barcodes 384\" for MitoTracker stained satellite cells and zebrafish data and UMIs; see Supplementary Methods for details. post mapping  a UMI correction was applied to the read and barcode counts files to generate unique transcript count tables as described before see Supplementary Methods section for details. Note that processed files for satellite cell data here still contain ERCC Spike in molecules  bulk samples bulk samples are only included in CEL Seq experiments; see \u201cBulkSamples BarcodesAndNrOfCellsUsed perCEL Seq1 library\u201d file for description of how many cells were used as bulk and which primer barcode sequence was used for bulk sample in each library and mitochondrial reads only for MitoTracker stained satellite cells. Genome build: mus musculus: mm10; danio rerio: Zv9 Ensembl release 74; both were extended with spike ins see \"ERCC92.fa\" Supplementary files format and content: *.coutt.csv  *TranscriptCounts.tsv and *count table.csv: tab separated data files listing how many reads of which transcripts were detected in all sequenced cells post UMI correction. Column names refer to cells sequenced in this library numbers refer the the CEL Seq primer barcode used for that cell. The first column lists official gene symbols followed by the chromosome name  separated by a double underscore. Note that processed files for satellite cell data here still contain ERCC Spike in molecules  bulk samples bulk samples are only included in CEL Seq experiments; see \u201cBulkSamples BarcodesAndNrOfCellsUsed perCEL Seq1 library\u201d file for description of how many cells were used as bulk and which primer barcode sequence was used for bulk sample in each library and mitochondrial reads only for MitoTracker stained satellite cells.", "All cells from caudal fin", null, "Cells were sorted into Vapor Lock Qiagen containing a droplet with primers and dNTPs. Cells were lysed at 65 degrees Celsius  post which SORT Seq Muraro et al.  2016  with minor modifications as desribed in Supplementary Methods of van den Brink et al.  2017 was applied. As in SORT Seq protocol Muraro et al.  2016  with minor modifications as desribed in Supplementary Methods of van den Brink et al.  2017.", null, "strain:Wildtype", "GSM2781033", "GSM2781033: SORT Seq zebrafish fin merged; Danio rerio; RNA Seq", "GSM2781033", null, "1", "Cells were sorted into Vapor Lock Qiagen containing a droplet with primers and dNTPs. Cells were lysed at 65 degrees Celsius  post which SORT Seq Muraro et al.  2016  with minor modifications as desribed in Supplementary Methods of van den Brink et al.  2017 was applied. As in SORT Seq protocol Muraro et al.  2016  with minor modifications as desribed in Supplementary Methods of van den Brink et al.  2017.", "GEO Accession:GSM2781033", "RNA-Seq", "TRANSCRIPTOMIC", "cDNA", "PAIRED", "ILLUMINA", "NextSeq 500", null, "SRP082370", null, null, "FinC_plate04_R1.fastq.gz FinC_plate04_R2.fastq.gz", "fastq fastq", 11725606474.0, 77611342.0, "GSM2781033 r4", "0:75.62 1:75.46", "A:3255674293;C:2567072026;G:2847190480;T:3054954945;N:714730", 75, 75, null, null, 3255674293, 2567072026, 2847190480, 3054954945, 714730, "SRX3187382", "SRS2515225", "SRA453335", "GEO", "Alexander van Oudenaarden, Hubrecht Institute", 2, 0.06975, 0.13988, 0.05976, 0.119, 0.98533, 0.97165, 0.50512, 0.46229, 75, 75, "B", "B", "mate1-mate2 similar by mapping diff", "illumina", "nextseq", "unknown", "cdna_unspecified", "unknown", "sc", "single_cell_plate", "celseq", null, "Netherlands", "2017-09-12", "Undetermined", "Adult", "Fin", "Surface Structure"], [41284, "SRR6039229", "SRX3187382", "SRS2515225", "SRP082370", "PRJNA339266", "Single cell sequencing reveals dissociation induced gene expression in tissue subpopulations", "GSE85755", "Other", "In many gene expression studies  cells are extracted by tissue dissociation and Fluorescence Activated Cell Sorting FACS  but the effect of these protocols on cellular transcriptomes is not well characterized and often ignored. Here  we applied single cell mRNA sequencing scRNA seq to muscle stem cells  and unexpectedly found a subpopulation that is strongly affected by the widely used dissociation protocol that we employed. One implication of this finding is that several published transcriptomics studies may need to be reinterpreted. Importantly  we detected similar subpopulations in other single cell datasets  suggesting that cells from other tissues might be affected by this artefact as well. Overall design: Mouse satellite cells and zebrafish fin cells were extracted from Tibialis Anterior muscles of Pax7nGFP mice and wildtype zebrafish fins  respectively. For cell extraction  traditional Supplementary Methods dissociation protocols that combine mechanical and enzymatic dissociation were employed  and live cells were subsequently sorted into plates using FACS. Next  single cell mRNA sequencing CEL Seq or SORT Seq robotized version of CEL Seq2 was applied  and data was analyzed with RaceID2 to identify clusters. CEL Seq samples: Manual CEL Seq; Satellite cells unstained; Male Pax7nGFP mice 5 mpf 7 mpf; 1h collagenase treated default dissociation protocol; 96 cells per plate with 96 different barcodes see \"Cel seq barcodes 96.csv\"; some primes numbers are bulk samples see \"BulkSamples BarcodesAndNrOfCellsUsed perCEL Seq1 library\"; Spike ins included see \"ERCC92.fa\"; No mitochondrial reads in count tables; Sequencing lanes not concatenated in fastq files uploaded here; \"Merged CEL Seq AllMiceAndLibrariesMerged.csv\" file is count table with reads from all mice and libraries merged Annotation of columns: Zx.y  where Z = mouse  x = library and y = cell barcode; bulk samples are not included any more in this file; See Supplementary Methods for details. SORT Seq 1h and 2h dissociated samples: Robotized CEL Seq2; Satellite cells unstained; Male Pax7nGFP mice 5 mpf 7 mpf; 8 muscles from 4 mice; One plate of 1h default dissociation protocol and one plate of 2h collagenase treated cells; 384 cells per plate with each of the 96 barcodes see \"Cel seq barcodes 96.csv\" used 4 times per plate therefore  each plate has 4 libraries; No bulk samples included; Spike ins included see \"ERCC92.fa\"; No mitochondrial reads in count table; In some wells  we sorted no cell internal negative control; barcodes #95 and #96 were used for empty wells; Sequencing lanes not concatenated in fastq files uploaded here; \"Merged SORT Seq DissociationTimecourse.csv\" file is count table were reads from all dissociation timepoints are merged Annotation columns: DZhx y  where Z = 1 or 2 hours collagenase treated  x = library and y = cell barcode; See Supplementary Methods for details. SORT Seq MitoTracker stained samples pilot and repeat: Robotized CEL Seq2 samples; Satellite cells stained with MitoTracker; Female Pax7nGFP mice 1 4.7 mpf mouse for pilot experiment; 3 mpf 6 mpf mice for repeat experiment; 1h collagenase treated default dissociation protocol; 384 cells per plate with each of the 384 barcodes see \"Cel seq barcodes 384.csv\" used 1 times per plate therefore  each plate has 1 library; Note: pilot experiment has 263 cells so plate was partly empty  repeat experiment done with 4 full plates; No bulk samples included; Spike ins included see \"ERCC92.fa\"; Mitochondrial reads rows named \"*  chrM\" included in count tables these were removed prior to RaceID2; In some wells  we sorted no cell barcodes #357 #360 and #381 #384 were used for empty wells; Sequencing lanes concatenated in fastq files uploaded here; No merged file was generated for pilot experiment as only one library  \"Merged MitoTracker Repeat.csv\" file is count table were reads from all plates of repeat experiment were merged Annotation of columns: Plx Welly  where x = plate number 1 4 and x = cell barcode; See Supplementary Methods for details. SORT Seq zebrafish fin samples: Robotized CEL Seq2; Fin cells unstained; all live cells; Wildtype zebrafish; Dissociated using default fin dissociation protocol Supplementary Methods; 384 cells per plate with each of the 384 barcodes see \"Cel seq barcodes 384.csv\" used 1 times per plate therefore  each plate has 1 library; Note: only merged count table file \"fin C E count table.csv\"  Annotation columns: Xx.py.prim.finZ  where x = cell barcode  y = plate number and Z is fish C or E and no individual library count table files were uploaded to GEO for zebrafish fin data; No bulk samples included; Spike ins not included in merged count tables file; Mitochondrial reads not included in merged count tables file; In some wells  we sorted no cell barcodes #357 #360 and #381 #384 were used for empty wells; Sequencing lanes concatenated in fastq files uploaded here; See Supplementary Methods for details.", null, "pubmed:28960196", null, "SORT Seq zebrafish fin merged", "GSM2781033", null, "tissue:All cells from caudal fin|strain:Wildtype", "SORT Seq zebrafish fin merged", "Reads 2 were mapped to the reference transcriptome created from the genomes downloaded from the UCSC genome browser; ERCC Spike in sequences and mitochondrial sequences were added in sense direction using bwa version 0.6.2 r126 with default parameters. All isoforms of the same gene were merged to a single gene locus and reads mapping to multiple loci in the transcriptome were discarded. Reads 1 contains the cel specific barcode information first 8 bases followed by a UMI sequence 4bp for unstained satellite cell data; 6bp for MitoTracker Stained satellite cells and zebrafish data and a polyT stretch. Reads 1 were thus used to extract the cell barcode sequences see \u201cCel seq barcodes 96.csv\u201d file for sequences used for unstained satellite cell data and see \"Cel seq barcodes 384\" for MitoTracker stained satellite cells and zebrafish data and UMIs; see Supplementary Methods for details. post mapping  a UMI correction was applied to the read and barcode counts files to generate unique transcript count tables as described before see Supplementary Methods section for details. Note that processed files for satellite cell data here still contain ERCC Spike in molecules  bulk samples bulk samples are only included in CEL Seq experiments; see \u201cBulkSamples BarcodesAndNrOfCellsUsed perCEL Seq1 library\u201d file for description of how many cells were used as bulk and which primer barcode sequence was used for bulk sample in each library and mitochondrial reads only for MitoTracker stained satellite cells. Genome build: mus musculus: mm10; danio rerio: Zv9 Ensembl release 74; both were extended with spike ins see \"ERCC92.fa\" Supplementary files format and content: *.coutt.csv  *TranscriptCounts.tsv and *count table.csv: tab separated data files listing how many reads of which transcripts were detected in all sequenced cells post UMI correction. Column names refer to cells sequenced in this library numbers refer the the CEL Seq primer barcode used for that cell. The first column lists official gene symbols followed by the chromosome name  separated by a double underscore. Note that processed files for satellite cell data here still contain ERCC Spike in molecules  bulk samples bulk samples are only included in CEL Seq experiments; see \u201cBulkSamples BarcodesAndNrOfCellsUsed perCEL Seq1 library\u201d file for description of how many cells were used as bulk and which primer barcode sequence was used for bulk sample in each library and mitochondrial reads only for MitoTracker stained satellite cells.", "All cells from caudal fin", null, "Cells were sorted into Vapor Lock Qiagen containing a droplet with primers and dNTPs. Cells were lysed at 65 degrees Celsius  post which SORT Seq Muraro et al.  2016  with minor modifications as desribed in Supplementary Methods of van den Brink et al.  2017 was applied. As in SORT Seq protocol Muraro et al.  2016  with minor modifications as desribed in Supplementary Methods of van den Brink et al.  2017.", null, "strain:Wildtype", "GSM2781033", "GSM2781033: SORT Seq zebrafish fin merged; Danio rerio; RNA Seq", "GSM2781033", null, "1", "Cells were sorted into Vapor Lock Qiagen containing a droplet with primers and dNTPs. Cells were lysed at 65 degrees Celsius  post which SORT Seq Muraro et al.  2016  with minor modifications as desribed in Supplementary Methods of van den Brink et al.  2017 was applied. As in SORT Seq protocol Muraro et al.  2016  with minor modifications as desribed in Supplementary Methods of van den Brink et al.  2017.", "GEO Accession:GSM2781033", "RNA-Seq", "TRANSCRIPTOMIC", "cDNA", "PAIRED", "ILLUMINA", "NextSeq 500", null, "SRP082370", null, null, "FinC_plate05_R1.fastq.gz FinC_plate05_R2.fastq.gz", "fastq fastq", 10721896827.0, 71080622.0, "GSM2781033 r5", "0:75.44 1:75.41", "A:3568478635;C:1747554798;G:1675757438;T:3729472140;N:633816", 75, 75, null, null, 3568478635, 1747554798, 1675757438, 3729472140, 633816, "SRX3187382", "SRS2515225", "SRA453335", "GEO", "Alexander van Oudenaarden, Hubrecht Institute", 2, 0.32303, 0.52716, 0.2796, 0.43703, 0.94276, 0.90057, 0.51543, 0.52404, 75, 75, "T", "B", "mate1 technical by mapping diff", "illumina", "nextseq", "unknown", "cdna_unspecified", "unknown", "sc", "single_cell_plate", "celseq", null, "Netherlands", "2017-09-12", "Undetermined", "Adult", "Fin", "Surface Structure"], [41285, "SRR6039230", "SRX3187382", "SRS2515225", "SRP082370", "PRJNA339266", "Single cell sequencing reveals dissociation induced gene expression in tissue subpopulations", "GSE85755", "Other", "In many gene expression studies  cells are extracted by tissue dissociation and Fluorescence Activated Cell Sorting FACS  but the effect of these protocols on cellular transcriptomes is not well characterized and often ignored. Here  we applied single cell mRNA sequencing scRNA seq to muscle stem cells  and unexpectedly found a subpopulation that is strongly affected by the widely used dissociation protocol that we employed. One implication of this finding is that several published transcriptomics studies may need to be reinterpreted. Importantly  we detected similar subpopulations in other single cell datasets  suggesting that cells from other tissues might be affected by this artefact as well. Overall design: Mouse satellite cells and zebrafish fin cells were extracted from Tibialis Anterior muscles of Pax7nGFP mice and wildtype zebrafish fins  respectively. For cell extraction  traditional Supplementary Methods dissociation protocols that combine mechanical and enzymatic dissociation were employed  and live cells were subsequently sorted into plates using FACS. Next  single cell mRNA sequencing CEL Seq or SORT Seq robotized version of CEL Seq2 was applied  and data was analyzed with RaceID2 to identify clusters. CEL Seq samples: Manual CEL Seq; Satellite cells unstained; Male Pax7nGFP mice 5 mpf 7 mpf; 1h collagenase treated default dissociation protocol; 96 cells per plate with 96 different barcodes see \"Cel seq barcodes 96.csv\"; some primes numbers are bulk samples see \"BulkSamples BarcodesAndNrOfCellsUsed perCEL Seq1 library\"; Spike ins included see \"ERCC92.fa\"; No mitochondrial reads in count tables; Sequencing lanes not concatenated in fastq files uploaded here; \"Merged CEL Seq AllMiceAndLibrariesMerged.csv\" file is count table with reads from all mice and libraries merged Annotation of columns: Zx.y  where Z = mouse  x = library and y = cell barcode; bulk samples are not included any more in this file; See Supplementary Methods for details. SORT Seq 1h and 2h dissociated samples: Robotized CEL Seq2; Satellite cells unstained; Male Pax7nGFP mice 5 mpf 7 mpf; 8 muscles from 4 mice; One plate of 1h default dissociation protocol and one plate of 2h collagenase treated cells; 384 cells per plate with each of the 96 barcodes see \"Cel seq barcodes 96.csv\" used 4 times per plate therefore  each plate has 4 libraries; No bulk samples included; Spike ins included see \"ERCC92.fa\"; No mitochondrial reads in count table; In some wells  we sorted no cell internal negative control; barcodes #95 and #96 were used for empty wells; Sequencing lanes not concatenated in fastq files uploaded here; \"Merged SORT Seq DissociationTimecourse.csv\" file is count table were reads from all dissociation timepoints are merged Annotation columns: DZhx y  where Z = 1 or 2 hours collagenase treated  x = library and y = cell barcode; See Supplementary Methods for details. SORT Seq MitoTracker stained samples pilot and repeat: Robotized CEL Seq2 samples; Satellite cells stained with MitoTracker; Female Pax7nGFP mice 1 4.7 mpf mouse for pilot experiment; 3 mpf 6 mpf mice for repeat experiment; 1h collagenase treated default dissociation protocol; 384 cells per plate with each of the 384 barcodes see \"Cel seq barcodes 384.csv\" used 1 times per plate therefore  each plate has 1 library; Note: pilot experiment has 263 cells so plate was partly empty  repeat experiment done with 4 full plates; No bulk samples included; Spike ins included see \"ERCC92.fa\"; Mitochondrial reads rows named \"*  chrM\" included in count tables these were removed prior to RaceID2; In some wells  we sorted no cell barcodes #357 #360 and #381 #384 were used for empty wells; Sequencing lanes concatenated in fastq files uploaded here; No merged file was generated for pilot experiment as only one library  \"Merged MitoTracker Repeat.csv\" file is count table were reads from all plates of repeat experiment were merged Annotation of columns: Plx Welly  where x = plate number 1 4 and x = cell barcode; See Supplementary Methods for details. SORT Seq zebrafish fin samples: Robotized CEL Seq2; Fin cells unstained; all live cells; Wildtype zebrafish; Dissociated using default fin dissociation protocol Supplementary Methods; 384 cells per plate with each of the 384 barcodes see \"Cel seq barcodes 384.csv\" used 1 times per plate therefore  each plate has 1 library; Note: only merged count table file \"fin C E count table.csv\"  Annotation columns: Xx.py.prim.finZ  where x = cell barcode  y = plate number and Z is fish C or E and no individual library count table files were uploaded to GEO for zebrafish fin data; No bulk samples included; Spike ins not included in merged count tables file; Mitochondrial reads not included in merged count tables file; In some wells  we sorted no cell barcodes #357 #360 and #381 #384 were used for empty wells; Sequencing lanes concatenated in fastq files uploaded here; See Supplementary Methods for details.", null, "pubmed:28960196", null, "SORT Seq zebrafish fin merged", "GSM2781033", null, "tissue:All cells from caudal fin|strain:Wildtype", "SORT Seq zebrafish fin merged", "Reads 2 were mapped to the reference transcriptome created from the genomes downloaded from the UCSC genome browser; ERCC Spike in sequences and mitochondrial sequences were added in sense direction using bwa version 0.6.2 r126 with default parameters. All isoforms of the same gene were merged to a single gene locus and reads mapping to multiple loci in the transcriptome were discarded. Reads 1 contains the cel specific barcode information first 8 bases followed by a UMI sequence 4bp for unstained satellite cell data; 6bp for MitoTracker Stained satellite cells and zebrafish data and a polyT stretch. Reads 1 were thus used to extract the cell barcode sequences see \u201cCel seq barcodes 96.csv\u201d file for sequences used for unstained satellite cell data and see \"Cel seq barcodes 384\" for MitoTracker stained satellite cells and zebrafish data and UMIs; see Supplementary Methods for details. post mapping  a UMI correction was applied to the read and barcode counts files to generate unique transcript count tables as described before see Supplementary Methods section for details. Note that processed files for satellite cell data here still contain ERCC Spike in molecules  bulk samples bulk samples are only included in CEL Seq experiments; see \u201cBulkSamples BarcodesAndNrOfCellsUsed perCEL Seq1 library\u201d file for description of how many cells were used as bulk and which primer barcode sequence was used for bulk sample in each library and mitochondrial reads only for MitoTracker stained satellite cells. Genome build: mus musculus: mm10; danio rerio: Zv9 Ensembl release 74; both were extended with spike ins see \"ERCC92.fa\" Supplementary files format and content: *.coutt.csv  *TranscriptCounts.tsv and *count table.csv: tab separated data files listing how many reads of which transcripts were detected in all sequenced cells post UMI correction. Column names refer to cells sequenced in this library numbers refer the the CEL Seq primer barcode used for that cell. The first column lists official gene symbols followed by the chromosome name  separated by a double underscore. Note that processed files for satellite cell data here still contain ERCC Spike in molecules  bulk samples bulk samples are only included in CEL Seq experiments; see \u201cBulkSamples BarcodesAndNrOfCellsUsed perCEL Seq1 library\u201d file for description of how many cells were used as bulk and which primer barcode sequence was used for bulk sample in each library and mitochondrial reads only for MitoTracker stained satellite cells.", "All cells from caudal fin", null, "Cells were sorted into Vapor Lock Qiagen containing a droplet with primers and dNTPs. Cells were lysed at 65 degrees Celsius  post which SORT Seq Muraro et al.  2016  with minor modifications as desribed in Supplementary Methods of van den Brink et al.  2017 was applied. As in SORT Seq protocol Muraro et al.  2016  with minor modifications as desribed in Supplementary Methods of van den Brink et al.  2017.", null, "strain:Wildtype", "GSM2781033", "GSM2781033: SORT Seq zebrafish fin merged; Danio rerio; RNA Seq", "GSM2781033", null, "1", "Cells were sorted into Vapor Lock Qiagen containing a droplet with primers and dNTPs. Cells were lysed at 65 degrees Celsius  post which SORT Seq Muraro et al.  2016  with minor modifications as desribed in Supplementary Methods of van den Brink et al.  2017 was applied. As in SORT Seq protocol Muraro et al.  2016  with minor modifications as desribed in Supplementary Methods of van den Brink et al.  2017.", "GEO Accession:GSM2781033", "RNA-Seq", "TRANSCRIPTOMIC", "cDNA", "PAIRED", "ILLUMINA", "NextSeq 500", null, "SRP082370", null, null, "FinC_plate06_R1.fastq.gz FinC_plate06_R2.fastq.gz", "fastq fastq", 4861560177.0, 32218130.0, "GSM2781033 r6", "0:75.46 1:75.44", "A:1606516776;C:766379277;G:687731226;T:1800636189;N:296709", 75, 75, null, null, 1606516776, 766379277, 687731226, 1800636189, 296709, "SRX3187382", "SRS2515225", "SRA453335", "GEO", "Alexander van Oudenaarden, Hubrecht Institute", 2, 0.40204, 0.66185, 0.35053, 0.55748, 0.94637, 0.89148, 0.48866, 0.50482, 75, 75, "T", "B", "mate1 technical by mapping diff", "illumina", "nextseq", "unknown", "cdna_unspecified", "unknown", "sc", "single_cell_plate", "celseq", null, "Netherlands", "2017-09-12", "Undetermined", "Adult", "Fin", "Surface Structure"], [41286, "SRR6039231", "SRX3187382", "SRS2515225", "SRP082370", "PRJNA339266", "Single cell sequencing reveals dissociation induced gene expression in tissue subpopulations", "GSE85755", "Other", "In many gene expression studies  cells are extracted by tissue dissociation and Fluorescence Activated Cell Sorting FACS  but the effect of these protocols on cellular transcriptomes is not well characterized and often ignored. Here  we applied single cell mRNA sequencing scRNA seq to muscle stem cells  and unexpectedly found a subpopulation that is strongly affected by the widely used dissociation protocol that we employed. One implication of this finding is that several published transcriptomics studies may need to be reinterpreted. Importantly  we detected similar subpopulations in other single cell datasets  suggesting that cells from other tissues might be affected by this artefact as well. Overall design: Mouse satellite cells and zebrafish fin cells were extracted from Tibialis Anterior muscles of Pax7nGFP mice and wildtype zebrafish fins  respectively. For cell extraction  traditional Supplementary Methods dissociation protocols that combine mechanical and enzymatic dissociation were employed  and live cells were subsequently sorted into plates using FACS. Next  single cell mRNA sequencing CEL Seq or SORT Seq robotized version of CEL Seq2 was applied  and data was analyzed with RaceID2 to identify clusters. CEL Seq samples: Manual CEL Seq; Satellite cells unstained; Male Pax7nGFP mice 5 mpf 7 mpf; 1h collagenase treated default dissociation protocol; 96 cells per plate with 96 different barcodes see \"Cel seq barcodes 96.csv\"; some primes numbers are bulk samples see \"BulkSamples BarcodesAndNrOfCellsUsed perCEL Seq1 library\"; Spike ins included see \"ERCC92.fa\"; No mitochondrial reads in count tables; Sequencing lanes not concatenated in fastq files uploaded here; \"Merged CEL Seq AllMiceAndLibrariesMerged.csv\" file is count table with reads from all mice and libraries merged Annotation of columns: Zx.y  where Z = mouse  x = library and y = cell barcode; bulk samples are not included any more in this file; See Supplementary Methods for details. SORT Seq 1h and 2h dissociated samples: Robotized CEL Seq2; Satellite cells unstained; Male Pax7nGFP mice 5 mpf 7 mpf; 8 muscles from 4 mice; One plate of 1h default dissociation protocol and one plate of 2h collagenase treated cells; 384 cells per plate with each of the 96 barcodes see \"Cel seq barcodes 96.csv\" used 4 times per plate therefore  each plate has 4 libraries; No bulk samples included; Spike ins included see \"ERCC92.fa\"; No mitochondrial reads in count table; In some wells  we sorted no cell internal negative control; barcodes #95 and #96 were used for empty wells; Sequencing lanes not concatenated in fastq files uploaded here; \"Merged SORT Seq DissociationTimecourse.csv\" file is count table were reads from all dissociation timepoints are merged Annotation columns: DZhx y  where Z = 1 or 2 hours collagenase treated  x = library and y = cell barcode; See Supplementary Methods for details. SORT Seq MitoTracker stained samples pilot and repeat: Robotized CEL Seq2 samples; Satellite cells stained with MitoTracker; Female Pax7nGFP mice 1 4.7 mpf mouse for pilot experiment; 3 mpf 6 mpf mice for repeat experiment; 1h collagenase treated default dissociation protocol; 384 cells per plate with each of the 384 barcodes see \"Cel seq barcodes 384.csv\" used 1 times per plate therefore  each plate has 1 library; Note: pilot experiment has 263 cells so plate was partly empty  repeat experiment done with 4 full plates; No bulk samples included; Spike ins included see \"ERCC92.fa\"; Mitochondrial reads rows named \"*  chrM\" included in count tables these were removed prior to RaceID2; In some wells  we sorted no cell barcodes #357 #360 and #381 #384 were used for empty wells; Sequencing lanes concatenated in fastq files uploaded here; No merged file was generated for pilot experiment as only one library  \"Merged MitoTracker Repeat.csv\" file is count table were reads from all plates of repeat experiment were merged Annotation of columns: Plx Welly  where x = plate number 1 4 and x = cell barcode; See Supplementary Methods for details. SORT Seq zebrafish fin samples: Robotized CEL Seq2; Fin cells unstained; all live cells; Wildtype zebrafish; Dissociated using default fin dissociation protocol Supplementary Methods; 384 cells per plate with each of the 384 barcodes see \"Cel seq barcodes 384.csv\" used 1 times per plate therefore  each plate has 1 library; Note: only merged count table file \"fin C E count table.csv\"  Annotation columns: Xx.py.prim.finZ  where x = cell barcode  y = plate number and Z is fish C or E and no individual library count table files were uploaded to GEO for zebrafish fin data; No bulk samples included; Spike ins not included in merged count tables file; Mitochondrial reads not included in merged count tables file; In some wells  we sorted no cell barcodes #357 #360 and #381 #384 were used for empty wells; Sequencing lanes concatenated in fastq files uploaded here; See Supplementary Methods for details.", null, "pubmed:28960196", null, "SORT Seq zebrafish fin merged", "GSM2781033", null, "tissue:All cells from caudal fin|strain:Wildtype", "SORT Seq zebrafish fin merged", "Reads 2 were mapped to the reference transcriptome created from the genomes downloaded from the UCSC genome browser; ERCC Spike in sequences and mitochondrial sequences were added in sense direction using bwa version 0.6.2 r126 with default parameters. All isoforms of the same gene were merged to a single gene locus and reads mapping to multiple loci in the transcriptome were discarded. Reads 1 contains the cel specific barcode information first 8 bases followed by a UMI sequence 4bp for unstained satellite cell data; 6bp for MitoTracker Stained satellite cells and zebrafish data and a polyT stretch. Reads 1 were thus used to extract the cell barcode sequences see \u201cCel seq barcodes 96.csv\u201d file for sequences used for unstained satellite cell data and see \"Cel seq barcodes 384\" for MitoTracker stained satellite cells and zebrafish data and UMIs; see Supplementary Methods for details. post mapping  a UMI correction was applied to the read and barcode counts files to generate unique transcript count tables as described before see Supplementary Methods section for details. Note that processed files for satellite cell data here still contain ERCC Spike in molecules  bulk samples bulk samples are only included in CEL Seq experiments; see \u201cBulkSamples BarcodesAndNrOfCellsUsed perCEL Seq1 library\u201d file for description of how many cells were used as bulk and which primer barcode sequence was used for bulk sample in each library and mitochondrial reads only for MitoTracker stained satellite cells. Genome build: mus musculus: mm10; danio rerio: Zv9 Ensembl release 74; both were extended with spike ins see \"ERCC92.fa\" Supplementary files format and content: *.coutt.csv  *TranscriptCounts.tsv and *count table.csv: tab separated data files listing how many reads of which transcripts were detected in all sequenced cells post UMI correction. Column names refer to cells sequenced in this library numbers refer the the CEL Seq primer barcode used for that cell. The first column lists official gene symbols followed by the chromosome name  separated by a double underscore. Note that processed files for satellite cell data here still contain ERCC Spike in molecules  bulk samples bulk samples are only included in CEL Seq experiments; see \u201cBulkSamples BarcodesAndNrOfCellsUsed perCEL Seq1 library\u201d file for description of how many cells were used as bulk and which primer barcode sequence was used for bulk sample in each library and mitochondrial reads only for MitoTracker stained satellite cells.", "All cells from caudal fin", null, "Cells were sorted into Vapor Lock Qiagen containing a droplet with primers and dNTPs. Cells were lysed at 65 degrees Celsius  post which SORT Seq Muraro et al.  2016  with minor modifications as desribed in Supplementary Methods of van den Brink et al.  2017 was applied. As in SORT Seq protocol Muraro et al.  2016  with minor modifications as desribed in Supplementary Methods of van den Brink et al.  2017.", null, "strain:Wildtype", "GSM2781033", "GSM2781033: SORT Seq zebrafish fin merged; Danio rerio; RNA Seq", "GSM2781033", null, "1", "Cells were sorted into Vapor Lock Qiagen containing a droplet with primers and dNTPs. Cells were lysed at 65 degrees Celsius  post which SORT Seq Muraro et al.  2016  with minor modifications as desribed in Supplementary Methods of van den Brink et al.  2017 was applied. As in SORT Seq protocol Muraro et al.  2016  with minor modifications as desribed in Supplementary Methods of van den Brink et al.  2017.", "GEO Accession:GSM2781033", "RNA-Seq", "TRANSCRIPTOMIC", "cDNA", "PAIRED", "ILLUMINA", "NextSeq 500", null, "SRP082370", null, null, "FinE_plate02_R1.fastq.gz FinE_plate02_R2.fastq.gz", "fastq fastq", 7812360278.0, 51697480.0, "GSM2781033 r7", "0:75.54 1:75.58", "A:2098454376;C:1641311761;G:1943503636;T:2128270555;N:819950", 75, 75, null, null, 2098454376, 1641311761, 1943503636, 2128270555, 819950, "SRX3187382", "SRS2515225", "SRA453335", "GEO", "Alexander van Oudenaarden, Hubrecht Institute", 2, 0.0105, 0.02592, 0.00852, 0.02011, 0.99626, 0.98754, 0.33762, 0.53206, 76, 75, "T", "T", "mates < 9% mapping rate", "illumina", "nextseq", "unknown", "cdna_unspecified", "unknown", "sc", "single_cell_plate", "celseq", null, "Netherlands", "2017-09-12", "Undetermined", "Adult", "Fin", "Surface Structure"], [41287, "SRR6039232", "SRX3187382", "SRS2515225", "SRP082370", "PRJNA339266", "Single cell sequencing reveals dissociation induced gene expression in tissue subpopulations", "GSE85755", "Other", "In many gene expression studies  cells are extracted by tissue dissociation and Fluorescence Activated Cell Sorting FACS  but the effect of these protocols on cellular transcriptomes is not well characterized and often ignored. Here  we applied single cell mRNA sequencing scRNA seq to muscle stem cells  and unexpectedly found a subpopulation that is strongly affected by the widely used dissociation protocol that we employed. One implication of this finding is that several published transcriptomics studies may need to be reinterpreted. Importantly  we detected similar subpopulations in other single cell datasets  suggesting that cells from other tissues might be affected by this artefact as well. Overall design: Mouse satellite cells and zebrafish fin cells were extracted from Tibialis Anterior muscles of Pax7nGFP mice and wildtype zebrafish fins  respectively. For cell extraction  traditional Supplementary Methods dissociation protocols that combine mechanical and enzymatic dissociation were employed  and live cells were subsequently sorted into plates using FACS. Next  single cell mRNA sequencing CEL Seq or SORT Seq robotized version of CEL Seq2 was applied  and data was analyzed with RaceID2 to identify clusters. CEL Seq samples: Manual CEL Seq; Satellite cells unstained; Male Pax7nGFP mice 5 mpf 7 mpf; 1h collagenase treated default dissociation protocol; 96 cells per plate with 96 different barcodes see \"Cel seq barcodes 96.csv\"; some primes numbers are bulk samples see \"BulkSamples BarcodesAndNrOfCellsUsed perCEL Seq1 library\"; Spike ins included see \"ERCC92.fa\"; No mitochondrial reads in count tables; Sequencing lanes not concatenated in fastq files uploaded here; \"Merged CEL Seq AllMiceAndLibrariesMerged.csv\" file is count table with reads from all mice and libraries merged Annotation of columns: Zx.y  where Z = mouse  x = library and y = cell barcode; bulk samples are not included any more in this file; See Supplementary Methods for details. SORT Seq 1h and 2h dissociated samples: Robotized CEL Seq2; Satellite cells unstained; Male Pax7nGFP mice 5 mpf 7 mpf; 8 muscles from 4 mice; One plate of 1h default dissociation protocol and one plate of 2h collagenase treated cells; 384 cells per plate with each of the 96 barcodes see \"Cel seq barcodes 96.csv\" used 4 times per plate therefore  each plate has 4 libraries; No bulk samples included; Spike ins included see \"ERCC92.fa\"; No mitochondrial reads in count table; In some wells  we sorted no cell internal negative control; barcodes #95 and #96 were used for empty wells; Sequencing lanes not concatenated in fastq files uploaded here; \"Merged SORT Seq DissociationTimecourse.csv\" file is count table were reads from all dissociation timepoints are merged Annotation columns: DZhx y  where Z = 1 or 2 hours collagenase treated  x = library and y = cell barcode; See Supplementary Methods for details. SORT Seq MitoTracker stained samples pilot and repeat: Robotized CEL Seq2 samples; Satellite cells stained with MitoTracker; Female Pax7nGFP mice 1 4.7 mpf mouse for pilot experiment; 3 mpf 6 mpf mice for repeat experiment; 1h collagenase treated default dissociation protocol; 384 cells per plate with each of the 384 barcodes see \"Cel seq barcodes 384.csv\" used 1 times per plate therefore  each plate has 1 library; Note: pilot experiment has 263 cells so plate was partly empty  repeat experiment done with 4 full plates; No bulk samples included; Spike ins included see \"ERCC92.fa\"; Mitochondrial reads rows named \"*  chrM\" included in count tables these were removed prior to RaceID2; In some wells  we sorted no cell barcodes #357 #360 and #381 #384 were used for empty wells; Sequencing lanes concatenated in fastq files uploaded here; No merged file was generated for pilot experiment as only one library  \"Merged MitoTracker Repeat.csv\" file is count table were reads from all plates of repeat experiment were merged Annotation of columns: Plx Welly  where x = plate number 1 4 and x = cell barcode; See Supplementary Methods for details. SORT Seq zebrafish fin samples: Robotized CEL Seq2; Fin cells unstained; all live cells; Wildtype zebrafish; Dissociated using default fin dissociation protocol Supplementary Methods; 384 cells per plate with each of the 384 barcodes see \"Cel seq barcodes 384.csv\" used 1 times per plate therefore  each plate has 1 library; Note: only merged count table file \"fin C E count table.csv\"  Annotation columns: Xx.py.prim.finZ  where x = cell barcode  y = plate number and Z is fish C or E and no individual library count table files were uploaded to GEO for zebrafish fin data; No bulk samples included; Spike ins not included in merged count tables file; Mitochondrial reads not included in merged count tables file; In some wells  we sorted no cell barcodes #357 #360 and #381 #384 were used for empty wells; Sequencing lanes concatenated in fastq files uploaded here; See Supplementary Methods for details.", null, "pubmed:28960196", null, "SORT Seq zebrafish fin merged", "GSM2781033", null, "tissue:All cells from caudal fin|strain:Wildtype", "SORT Seq zebrafish fin merged", "Reads 2 were mapped to the reference transcriptome created from the genomes downloaded from the UCSC genome browser; ERCC Spike in sequences and mitochondrial sequences were added in sense direction using bwa version 0.6.2 r126 with default parameters. All isoforms of the same gene were merged to a single gene locus and reads mapping to multiple loci in the transcriptome were discarded. Reads 1 contains the cel specific barcode information first 8 bases followed by a UMI sequence 4bp for unstained satellite cell data; 6bp for MitoTracker Stained satellite cells and zebrafish data and a polyT stretch. Reads 1 were thus used to extract the cell barcode sequences see \u201cCel seq barcodes 96.csv\u201d file for sequences used for unstained satellite cell data and see \"Cel seq barcodes 384\" for MitoTracker stained satellite cells and zebrafish data and UMIs; see Supplementary Methods for details. post mapping  a UMI correction was applied to the read and barcode counts files to generate unique transcript count tables as described before see Supplementary Methods section for details. Note that processed files for satellite cell data here still contain ERCC Spike in molecules  bulk samples bulk samples are only included in CEL Seq experiments; see \u201cBulkSamples BarcodesAndNrOfCellsUsed perCEL Seq1 library\u201d file for description of how many cells were used as bulk and which primer barcode sequence was used for bulk sample in each library and mitochondrial reads only for MitoTracker stained satellite cells. Genome build: mus musculus: mm10; danio rerio: Zv9 Ensembl release 74; both were extended with spike ins see \"ERCC92.fa\" Supplementary files format and content: *.coutt.csv  *TranscriptCounts.tsv and *count table.csv: tab separated data files listing how many reads of which transcripts were detected in all sequenced cells post UMI correction. Column names refer to cells sequenced in this library numbers refer the the CEL Seq primer barcode used for that cell. The first column lists official gene symbols followed by the chromosome name  separated by a double underscore. Note that processed files for satellite cell data here still contain ERCC Spike in molecules  bulk samples bulk samples are only included in CEL Seq experiments; see \u201cBulkSamples BarcodesAndNrOfCellsUsed perCEL Seq1 library\u201d file for description of how many cells were used as bulk and which primer barcode sequence was used for bulk sample in each library and mitochondrial reads only for MitoTracker stained satellite cells.", "All cells from caudal fin", null, "Cells were sorted into Vapor Lock Qiagen containing a droplet with primers and dNTPs. Cells were lysed at 65 degrees Celsius  post which SORT Seq Muraro et al.  2016  with minor modifications as desribed in Supplementary Methods of van den Brink et al.  2017 was applied. As in SORT Seq protocol Muraro et al.  2016  with minor modifications as desribed in Supplementary Methods of van den Brink et al.  2017.", null, "strain:Wildtype", "GSM2781033", "GSM2781033: SORT Seq zebrafish fin merged; Danio rerio; RNA Seq", "GSM2781033", null, "1", "Cells were sorted into Vapor Lock Qiagen containing a droplet with primers and dNTPs. Cells were lysed at 65 degrees Celsius  post which SORT Seq Muraro et al.  2016  with minor modifications as desribed in Supplementary Methods of van den Brink et al.  2017 was applied. As in SORT Seq protocol Muraro et al.  2016  with minor modifications as desribed in Supplementary Methods of van den Brink et al.  2017.", "GEO Accession:GSM2781033", "RNA-Seq", "TRANSCRIPTOMIC", "cDNA", "PAIRED", "ILLUMINA", "NextSeq 500", null, "SRP082370", null, null, "FinE_plate03_R1.fastq.gz FinE_plate03_R2.fastq.gz", "fastq fastq", 6704494628.0, 44436630.0, "GSM2781033 r8", "0:75.49 1:75.39", "A:2087008571;C:1124579474;G:1252171872;T:2240541544;N:193167", 75, 75, null, null, 2087008571, 1124579474, 1252171872, 2240541544, 193167, "SRX3187382", "SRS2515225", "SRA453335", "GEO", "Alexander van Oudenaarden, Hubrecht Institute", 2, 0.24968, 0.47949, 0.2198, 0.40276, 0.95154, 0.89238, 0.48976, 0.54355, 75, 75, "T", "B", "mate1 technical by mapping diff", "illumina", "nextseq", "unknown", "cdna_unspecified", "unknown", "sc", "single_cell_plate", "celseq", null, "Netherlands", "2017-09-12", "Undetermined", "Adult", "Fin", "Surface Structure"], [41288, "SRR6039233", "SRX3187382", "SRS2515225", "SRP082370", "PRJNA339266", "Single cell sequencing reveals dissociation induced gene expression in tissue subpopulations", "GSE85755", "Other", "In many gene expression studies  cells are extracted by tissue dissociation and Fluorescence Activated Cell Sorting FACS  but the effect of these protocols on cellular transcriptomes is not well characterized and often ignored. Here  we applied single cell mRNA sequencing scRNA seq to muscle stem cells  and unexpectedly found a subpopulation that is strongly affected by the widely used dissociation protocol that we employed. One implication of this finding is that several published transcriptomics studies may need to be reinterpreted. Importantly  we detected similar subpopulations in other single cell datasets  suggesting that cells from other tissues might be affected by this artefact as well. Overall design: Mouse satellite cells and zebrafish fin cells were extracted from Tibialis Anterior muscles of Pax7nGFP mice and wildtype zebrafish fins  respectively. For cell extraction  traditional Supplementary Methods dissociation protocols that combine mechanical and enzymatic dissociation were employed  and live cells were subsequently sorted into plates using FACS. Next  single cell mRNA sequencing CEL Seq or SORT Seq robotized version of CEL Seq2 was applied  and data was analyzed with RaceID2 to identify clusters. CEL Seq samples: Manual CEL Seq; Satellite cells unstained; Male Pax7nGFP mice 5 mpf 7 mpf; 1h collagenase treated default dissociation protocol; 96 cells per plate with 96 different barcodes see \"Cel seq barcodes 96.csv\"; some primes numbers are bulk samples see \"BulkSamples BarcodesAndNrOfCellsUsed perCEL Seq1 library\"; Spike ins included see \"ERCC92.fa\"; No mitochondrial reads in count tables; Sequencing lanes not concatenated in fastq files uploaded here; \"Merged CEL Seq AllMiceAndLibrariesMerged.csv\" file is count table with reads from all mice and libraries merged Annotation of columns: Zx.y  where Z = mouse  x = library and y = cell barcode; bulk samples are not included any more in this file; See Supplementary Methods for details. SORT Seq 1h and 2h dissociated samples: Robotized CEL Seq2; Satellite cells unstained; Male Pax7nGFP mice 5 mpf 7 mpf; 8 muscles from 4 mice; One plate of 1h default dissociation protocol and one plate of 2h collagenase treated cells; 384 cells per plate with each of the 96 barcodes see \"Cel seq barcodes 96.csv\" used 4 times per plate therefore  each plate has 4 libraries; No bulk samples included; Spike ins included see \"ERCC92.fa\"; No mitochondrial reads in count table; In some wells  we sorted no cell internal negative control; barcodes #95 and #96 were used for empty wells; Sequencing lanes not concatenated in fastq files uploaded here; \"Merged SORT Seq DissociationTimecourse.csv\" file is count table were reads from all dissociation timepoints are merged Annotation columns: DZhx y  where Z = 1 or 2 hours collagenase treated  x = library and y = cell barcode; See Supplementary Methods for details. SORT Seq MitoTracker stained samples pilot and repeat: Robotized CEL Seq2 samples; Satellite cells stained with MitoTracker; Female Pax7nGFP mice 1 4.7 mpf mouse for pilot experiment; 3 mpf 6 mpf mice for repeat experiment; 1h collagenase treated default dissociation protocol; 384 cells per plate with each of the 384 barcodes see \"Cel seq barcodes 384.csv\" used 1 times per plate therefore  each plate has 1 library; Note: pilot experiment has 263 cells so plate was partly empty  repeat experiment done with 4 full plates; No bulk samples included; Spike ins included see \"ERCC92.fa\"; Mitochondrial reads rows named \"*  chrM\" included in count tables these were removed prior to RaceID2; In some wells  we sorted no cell barcodes #357 #360 and #381 #384 were used for empty wells; Sequencing lanes concatenated in fastq files uploaded here; No merged file was generated for pilot experiment as only one library  \"Merged MitoTracker Repeat.csv\" file is count table were reads from all plates of repeat experiment were merged Annotation of columns: Plx Welly  where x = plate number 1 4 and x = cell barcode; See Supplementary Methods for details. SORT Seq zebrafish fin samples: Robotized CEL Seq2; Fin cells unstained; all live cells; Wildtype zebrafish; Dissociated using default fin dissociation protocol Supplementary Methods; 384 cells per plate with each of the 384 barcodes see \"Cel seq barcodes 384.csv\" used 1 times per plate therefore  each plate has 1 library; Note: only merged count table file \"fin C E count table.csv\"  Annotation columns: Xx.py.prim.finZ  where x = cell barcode  y = plate number and Z is fish C or E and no individual library count table files were uploaded to GEO for zebrafish fin data; No bulk samples included; Spike ins not included in merged count tables file; Mitochondrial reads not included in merged count tables file; In some wells  we sorted no cell barcodes #357 #360 and #381 #384 were used for empty wells; Sequencing lanes concatenated in fastq files uploaded here; See Supplementary Methods for details.", null, "pubmed:28960196", null, "SORT Seq zebrafish fin merged", "GSM2781033", null, "tissue:All cells from caudal fin|strain:Wildtype", "SORT Seq zebrafish fin merged", "Reads 2 were mapped to the reference transcriptome created from the genomes downloaded from the UCSC genome browser; ERCC Spike in sequences and mitochondrial sequences were added in sense direction using bwa version 0.6.2 r126 with default parameters. All isoforms of the same gene were merged to a single gene locus and reads mapping to multiple loci in the transcriptome were discarded. Reads 1 contains the cel specific barcode information first 8 bases followed by a UMI sequence 4bp for unstained satellite cell data; 6bp for MitoTracker Stained satellite cells and zebrafish data and a polyT stretch. Reads 1 were thus used to extract the cell barcode sequences see \u201cCel seq barcodes 96.csv\u201d file for sequences used for unstained satellite cell data and see \"Cel seq barcodes 384\" for MitoTracker stained satellite cells and zebrafish data and UMIs; see Supplementary Methods for details. post mapping  a UMI correction was applied to the read and barcode counts files to generate unique transcript count tables as described before see Supplementary Methods section for details. Note that processed files for satellite cell data here still contain ERCC Spike in molecules  bulk samples bulk samples are only included in CEL Seq experiments; see \u201cBulkSamples BarcodesAndNrOfCellsUsed perCEL Seq1 library\u201d file for description of how many cells were used as bulk and which primer barcode sequence was used for bulk sample in each library and mitochondrial reads only for MitoTracker stained satellite cells. Genome build: mus musculus: mm10; danio rerio: Zv9 Ensembl release 74; both were extended with spike ins see \"ERCC92.fa\" Supplementary files format and content: *.coutt.csv  *TranscriptCounts.tsv and *count table.csv: tab separated data files listing how many reads of which transcripts were detected in all sequenced cells post UMI correction. Column names refer to cells sequenced in this library numbers refer the the CEL Seq primer barcode used for that cell. The first column lists official gene symbols followed by the chromosome name  separated by a double underscore. Note that processed files for satellite cell data here still contain ERCC Spike in molecules  bulk samples bulk samples are only included in CEL Seq experiments; see \u201cBulkSamples BarcodesAndNrOfCellsUsed perCEL Seq1 library\u201d file for description of how many cells were used as bulk and which primer barcode sequence was used for bulk sample in each library and mitochondrial reads only for MitoTracker stained satellite cells.", "All cells from caudal fin", null, "Cells were sorted into Vapor Lock Qiagen containing a droplet with primers and dNTPs. Cells were lysed at 65 degrees Celsius  post which SORT Seq Muraro et al.  2016  with minor modifications as desribed in Supplementary Methods of van den Brink et al.  2017 was applied. As in SORT Seq protocol Muraro et al.  2016  with minor modifications as desribed in Supplementary Methods of van den Brink et al.  2017.", null, "strain:Wildtype", "GSM2781033", "GSM2781033: SORT Seq zebrafish fin merged; Danio rerio; RNA Seq", "GSM2781033", null, "1", "Cells were sorted into Vapor Lock Qiagen containing a droplet with primers and dNTPs. Cells were lysed at 65 degrees Celsius  post which SORT Seq Muraro et al.  2016  with minor modifications as desribed in Supplementary Methods of van den Brink et al.  2017 was applied. As in SORT Seq protocol Muraro et al.  2016  with minor modifications as desribed in Supplementary Methods of van den Brink et al.  2017.", "GEO Accession:GSM2781033", "RNA-Seq", "TRANSCRIPTOMIC", "cDNA", "PAIRED", "ILLUMINA", "NextSeq 500", null, "SRP082370", null, null, "FinE_plate04_R2.fastq.gz FinE_plate04_R1.fastq.gz", "fastq fastq", 8402262674.0, 55692887.0, "GSM2781033 r9", "0:75.48 1:75.39", "A:2618499207;C:1444061167;G:1613776766;T:2725680678;N:244856", 75, 75, null, null, 2618499207, 1444061167, 1613776766, 2725680678, 244856, "SRX3187382", "SRS2515225", "SRA453335", "GEO", "Alexander van Oudenaarden, Hubrecht Institute", 2, 0.26069, 0.43322, 0.23296, 0.36903, 0.95663, 0.90542, 0.50653, 0.52921, 76, 75, "T", "B", "mate1 technical by mapping diff", "illumina", "nextseq", "unknown", "cdna_unspecified", "unknown", "sc", "single_cell_plate", "celseq", null, "Netherlands", "2017-09-12", "Undetermined", "Adult", "Fin", "Surface Structure"], [44015, "SRR6211474", "SRX3320751", "SRS2626325", "SRP121343", "PRJNA415636", "Simultaneous lineage tracing and cell type identification using CRISPR/Cas9 induced genetic scars", "GSE106121", "Other", "A key goal of developmental biology is to understand how a single cell transforms into a full grown organism consisting of many different cell types. Single cell RNA sequencing scRNA seq has become a widely used method due to its ability to identify all cell types in a tissue or organ in a systematic manner. However  a major challenge is to organize the resulting taxonomy of cell types into lineage trees revealing the developmental origin of cells. Here  we present a strategy for simultaneous lineage tracing and transcriptome profiling in thousands of single cells. By combining scRNA seq with computational analysis of lineage barcodes generated by genome editing of transgenic reporter genes  we reconstruct developmental lineage trees in zebrafish larvae and adult fish. In future analyses  LINNAEUS LINeage tracing by Nuclease Activated Editing of Ubiquitous Sequences can be used as a systematic approach for identifying the lineage origin of novel cell types  or of known cell types under different conditions. Overall design: Combining scRNA seq with computational analysis of lineage barcodes generated by genome editing of transgenic reporter genes.", null, "pubmed:29644996", null, "Time course 10h24h mRNA", "GSM2830047", null, "source name:Full organism|strain/background:Zebrabow M|tissue:Whole body|developmental stage:Embryo|time point:10h  24h", "Time course 10h24h mRNA", "Library strategy: Targeted amplification Embryos were injected with Cas9 and sgRNA at the 1 cell stage. post 1  2  3  4  6  8  10  and 24 hours  2 3 embryos were collected and RNA and/or DNA were extracted using TRIzol Reagent according to the manufacturer\u2019s protocols. Bulk scar libraries were produced similarly to the bulk libraries for the scar probabilities. For each sample  we calculated the percentage of unscarred RFP. We fit a negative exponential to this data  assuming that the fraction of unscarred RFP at t=0 was one. Genome build: N/A Supplementary files format and content: List of scar sequences with CIGAR and cell barcode.", "Full organism", null, "Trizol extraction of RNA. CEL seq", null, "strain/background:Zebrabow M|tissue:Whole body|developmental stage:Embryo|time point:10h  24h", "GSM2830047", "GSM2830047: Time course 10h24h mRNA; Danio rerio; OTHER", "GSM2830047", null, "1", "Trizol extraction of RNA. CEL seq", "GEO Accession:GSM2830047", "OTHER", "TRANSCRIPTOMIC", "other", "PAIRED", "ILLUMINA", "NextSeq 500", null, "SRP121343", null, null, "dyn_RNA_10h24h_S13_R2_001.fastq.gz dyn_RNA_10h24h_S13_R1_001.fastq.gz", "fastq fastq", 1456176300.0, 9707842.0, "GSM2830047 r1", "0:100 1:50", "A:216943159;C:398650961;G:516656316;T:323899428;N:26436", 100, 50, null, null, 216943159, 398650961, 516656316, 323899428, 26436, "SRX3320751", "SRS2626325", "SRA623333", "GEO", "Max Delbr\u00fcck Center", 2, 3e-05, 0.00111, 0.0, 0.0011, 0.99995, 1.0, 0.75, null, 100, 50, "T", "T", "mates < 9% mapping rate", "illumina", "nextseq", "unknown", "other", "unknown", "sc", "single_cell_plate", "celseq", null, "Germany", "2017-10-24", "Undetermined", "Embryo", "Trunk", "Surface Structure"], [44016, "SRR6211473", "SRX3320750", "SRS2626324", "SRP121343", "PRJNA415636", "Simultaneous lineage tracing and cell type identification using CRISPR/Cas9 induced genetic scars", "GSE106121", "Other", "A key goal of developmental biology is to understand how a single cell transforms into a full grown organism consisting of many different cell types. Single cell RNA sequencing scRNA seq has become a widely used method due to its ability to identify all cell types in a tissue or organ in a systematic manner. However  a major challenge is to organize the resulting taxonomy of cell types into lineage trees revealing the developmental origin of cells. Here  we present a strategy for simultaneous lineage tracing and transcriptome profiling in thousands of single cells. By combining scRNA seq with computational analysis of lineage barcodes generated by genome editing of transgenic reporter genes  we reconstruct developmental lineage trees in zebrafish larvae and adult fish. In future analyses  LINNAEUS LINeage tracing by Nuclease Activated Editing of Ubiquitous Sequences can be used as a systematic approach for identifying the lineage origin of novel cell types  or of known cell types under different conditions. Overall design: Combining scRNA seq with computational analysis of lineage barcodes generated by genome editing of transgenic reporter genes.", null, "pubmed:29644996", null, "Time course 3h6h8h mRNA", "GSM2830046", null, "source name:Full organism|strain/background:Zebrabow M|tissue:Whole body|developmental stage:Embryo|time point:3h  6h  8h", "Time course 3h6h8h mRNA", "Library strategy: Targeted amplification Embryos were injected with Cas9 and sgRNA at the 1 cell stage. post 1  2  3  4  6  8  10  and 24 hours  2 3 embryos were collected and RNA and/or DNA were extracted using TRIzol Reagent according to the manufacturer\u2019s protocols. Bulk scar libraries were produced similarly to the bulk libraries for the scar probabilities. For each sample  we calculated the percentage of unscarred RFP. We fit a negative exponential to this data  assuming that the fraction of unscarred RFP at t=0 was one. Genome build: N/A Supplementary files format and content: List of scar sequences with CIGAR and cell barcode.", "Full organism", null, "Trizol extraction of RNA. CEL seq", null, "strain/background:Zebrabow M|tissue:Whole body|developmental stage:Embryo|time point:3h  6h  8h", "GSM2830046", "GSM2830046: Time course 3h6h8h mRNA; Danio rerio; OTHER", "GSM2830046", null, "1", "Trizol extraction of RNA. CEL seq", "GEO Accession:GSM2830046", "OTHER", "TRANSCRIPTOMIC", "other", "PAIRED", "ILLUMINA", "NextSeq 500", null, "SRP121343", null, null, "dyn_RNA_3h6h8h_S12_R1_001.fastq.gz dyn_RNA_3h6h8h_S12_R2_001.fastq.gz", "fastq fastq", 1382400000.0, 9216000.0, "GSM2830046 r1", "0:100 1:50", "A:219040774;C:378529638;G:475287684;T:309516830;N:25074", 100, 50, null, null, 219040774, 378529638, 475287684, 309516830, 25074, "SRX3320750", "SRS2626324", "SRA623333", "GEO", "Max Delbr\u00fcck Center", 2, 5e-05, 0.00116, 0.0, 0.00115, 0.99987, 1.0, 0.5, null, 100, 50, "T", "T", "mates < 9% mapping rate", "illumina", "nextseq", "unknown", "other", "unknown", "sc", "single_cell_plate", "celseq", null, "Germany", "2017-10-24", "Undetermined", "Embryo", "Trunk", "Surface Structure"], [47861, "SRR6908751", "SRX3856810", "SRS3100403", "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.", null, "pubmed:31585086", null, "WKM9 unenriched", "GSM3070148", null, "tissue:WKM9 unenriched|FISHid:WKM9|cell type:whole kidney marrow single cells|cell subtype:unenriched", "WKM9 unenriched", "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 unenriched", null, "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", null, "FISHid:WKM9|cell type:whole kidney marrow single cells|cell subtype:unenriched", "GSM3070148", "GSM3070148: WKM9 unenriched; Danio rerio; RNA Seq", "GSM3070148", null, "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:GSM3070148", "RNA-Seq", "TRANSCRIPTOMIC", "cDNA", "PAIRED", "ILLUMINA", "NextSeq 500", null, "SRP136633", null, null, "WKM9_unenriched_L001_R1_001.fastq.gz WKM9_unenriched_L001_R2_001.fastq.gz", "fastq fastq", 1346805481.0, 8925729.0, "GSM3070148 r1", "0:75.41 1:75.48", "A:389424098;C:209314278;G:233588167;T:514471922;N:7016", 75, 75, null, null, 389424098, 209314278, 233588167, 514471922, 7016, "SRX3856810", "SRS3100403", "SRA675960", "GEO", "Hubrecht Institute", 2, 0.43527, 0.79133, 0.35777, 0.52091, 0.95361, 0.87156, 0.48343, 0.54164, 76, 75, "T", "B", "mate1 technical by mapping diff", "illumina", "nextseq", "unknown", "random_priming", "trueseq", "sc", "single_cell_plate", "celseq", null, "Netherlands", "2018-03-28", "Undetermined", "Undetermined", "Kidney", "Renal System"], [47862, "SRR6908752", "SRX3856810", "SRS3100403", "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.", null, "pubmed:31585086", null, "WKM9 unenriched", "GSM3070148", null, "tissue:WKM9 unenriched|FISHid:WKM9|cell type:whole kidney marrow single cells|cell subtype:unenriched", "WKM9 unenriched", "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 unenriched", null, "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", null, "FISHid:WKM9|cell type:whole kidney marrow single cells|cell subtype:unenriched", "GSM3070148", "GSM3070148: WKM9 unenriched; Danio rerio; RNA Seq", "GSM3070148", null, "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:GSM3070148", "RNA-Seq", "TRANSCRIPTOMIC", "cDNA", "PAIRED", "ILLUMINA", "NextSeq 500", null, "SRP136633", null, null, "WKM9_unenriched_L002_R1_001.fastq.gz WKM9_unenriched_L002_R2_001.fastq.gz", "fastq fastq", 1325332076.0, 8783303.0, "GSM3070148 r2", "0:75.41 1:75.48", "A:380077587;C:204687058;G:234896688;T:505666915;N:3828", 75, 75, null, null, 380077587, 204687058, 234896688, 505666915, 3828, "SRX3856810", "SRS3100403", "SRA675960", "GEO", "Hubrecht Institute", 2, 0.42931, 0.78191, 0.35315, 0.51028, 0.95787, 0.87493, 0.49522, 0.49685, 75, 75, "T", "B", "mate1 technical by mapping diff", "illumina", "nextseq", "unknown", "random_priming", "trueseq", "sc", "single_cell_plate", "celseq", null, "Netherlands", "2018-03-28", "Undetermined", "Undetermined", "Kidney", "Renal System"], [47863, "SRR6908753", "SRX3856810", "SRS3100403", "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.", null, "pubmed:31585086", null, "WKM9 unenriched", "GSM3070148", null, "tissue:WKM9 unenriched|FISHid:WKM9|cell type:whole kidney marrow single cells|cell subtype:unenriched", "WKM9 unenriched", "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 unenriched", null, "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", null, "FISHid:WKM9|cell type:whole kidney marrow single cells|cell subtype:unenriched", "GSM3070148", "GSM3070148: WKM9 unenriched; Danio rerio; RNA Seq", "GSM3070148", null, "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:GSM3070148", "RNA-Seq", "TRANSCRIPTOMIC", "cDNA", "PAIRED", "ILLUMINA", "NextSeq 500", null, "SRP136633", null, null, "WKM9_unenriched_L003_R1_001.fastq.gz WKM9_unenriched_L003_R2_001.fastq.gz", "fastq fastq", 1209174922.0, 8013226.0, "GSM3070148 r3", "0:75.42 1:75.47", "A:347525516;C:187142186;G:212384943;T:462099359;N:22918", 75, 75, null, null, 347525516, 187142186, 212384943, 462099359, 22918, "SRX3856810", "SRS3100403", "SRA675960", "GEO", "Hubrecht Institute", 2, 0.4232, 0.78709, 0.34991, 0.51583, 0.9614, 0.88215, 0.50753, 0.49857, 76, 76, "T", "B", "mate1 technical by mapping diff", "illumina", "nextseq", "unknown", "random_priming", "trueseq", "sc", "single_cell_plate", "celseq", null, "Netherlands", "2018-03-28", "Undetermined", "Undetermined", "Kidney", "Renal System"], [47864, "SRR6908754", "SRX3856810", "SRS3100403", "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.", null, "pubmed:31585086", null, "WKM9 unenriched", "GSM3070148", null, "tissue:WKM9 unenriched|FISHid:WKM9|cell type:whole kidney marrow single cells|cell subtype:unenriched", "WKM9 unenriched", "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 unenriched", null, "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", null, "FISHid:WKM9|cell type:whole kidney marrow single cells|cell subtype:unenriched", "GSM3070148", "GSM3070148: WKM9 unenriched; Danio rerio; RNA Seq", "GSM3070148", null, "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:GSM3070148", "RNA-Seq", "TRANSCRIPTOMIC", "cDNA", "PAIRED", "ILLUMINA", "NextSeq 500", null, "SRP136633", null, null, "WKM9_unenriched_L004_R1_001.fastq.gz WKM9_unenriched_L004_R2_001.fastq.gz", "fastq fastq", 1177710986.0, 7804805.0, "GSM3070148 r4", "0:75.42 1:75.47", "A:337640779;C:180611864;G:209653010;T:449785638;N:19695", 75, 75, null, null, 337640779, 180611864, 209653010, 449785638, 19695, "SRX3856810", "SRS3100403", "SRA675960", "GEO", "Hubrecht Institute", 2, 0.42938, 0.78452, 0.35813, 0.51351, 0.96435, 0.89035, 0.46765, 0.55409, 75, 76, "T", "B", "mate1 technical by mapping diff", "illumina", "nextseq", "unknown", "random_priming", "trueseq", "sc", "single_cell_plate", "celseq", null, "Netherlands", "2018-03-28", "Undetermined", "Undetermined", "Kidney", "Renal System"], [47865, "SRR6908747", "SRX3856809", "SRS3100401", "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.", null, "pubmed:31585086", null, "WKM9 monocytes", "GSM3070147", null, "tissue:WKM9 monocytes|FISHid:WKM9|cell type:whole kidney marrow single cells|cell subtype:monocytes", "WKM9 monocytes", "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 monocytes", null, "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", null, "FISHid:WKM9|cell type:whole kidney marrow single cells|cell subtype:monocytes", "GSM3070147", "GSM3070147: WKM9 monocytes; Danio rerio; RNA Seq", "GSM3070147", null, "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:GSM3070147", "RNA-Seq", "TRANSCRIPTOMIC", "cDNA", "PAIRED", "ILLUMINA", "NextSeq 500", null, "SRP136633", null, null, "WKM9_monocytes_L001_R1_001.fastq.gz WKM9_monocytes_L001_R2_001.fastq.gz", "fastq fastq", 1661471905.0, 11011987.0, "GSM3070147 r1", "0:75.40 1:75.48", "A:483808436;C:239092096;G:277634836;T:660929005;N:7532", 75, 75, null, null, 483808436, 239092096, 277634836, 660929005, 7532, "SRX3856809", "SRS3100401", "SRA675960", "GEO", "Hubrecht Institute", 2, 0.36065, 0.81129, 0.26031, 0.39596, 0.96376, 0.87584, 0.46482, 0.51218, 76, 76, "T", "B", "mate1 technical by mapping diff", "illumina", "nextseq", "unknown", "random_priming", "trueseq", "sc", "single_cell_plate", "celseq", null, "Netherlands", "2018-03-28", "Undetermined", "Undetermined", "Kidney", "Renal System"], [47866, "SRR6908748", "SRX3856809", "SRS3100401", "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.", null, "pubmed:31585086", null, "WKM9 monocytes", "GSM3070147", null, "tissue:WKM9 monocytes|FISHid:WKM9|cell type:whole kidney marrow single cells|cell subtype:monocytes", "WKM9 monocytes", "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 monocytes", null, "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", null, "FISHid:WKM9|cell type:whole kidney marrow single cells|cell subtype:monocytes", "GSM3070147", "GSM3070147: WKM9 monocytes; Danio rerio; RNA Seq", "GSM3070147", null, "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:GSM3070147", "RNA-Seq", "TRANSCRIPTOMIC", "cDNA", "PAIRED", "ILLUMINA", "NextSeq 500", null, "SRP136633", null, null, "WKM9_monocytes_L002_R1_001.fastq.gz WKM9_monocytes_L002_R2_001.fastq.gz", "fastq fastq", 1643858405.0, 10895074.0, "GSM3070147 r2", "0:75.40 1:75.48", "A:474610672;C:235181134;G:281183851;T:652878617;N:4131", 75, 75, null, null, 474610672, 235181134, 281183851, 652878617, 4131, "SRX3856809", "SRS3100401", "SRA675960", "GEO", "Hubrecht Institute", 2, 0.35844, 0.80892, 0.26121, 0.38966, 0.96631, 0.88008, 0.45688, 0.49263, 74, 75, "T", "B", "mate1 technical by mapping diff", "illumina", "nextseq", "unknown", "random_priming", "trueseq", "sc", "single_cell_plate", "celseq", null, "Netherlands", "2018-03-28", "Undetermined", "Undetermined", "Kidney", "Renal System"], [47867, "SRR6908749", "SRX3856809", "SRS3100401", "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.", null, "pubmed:31585086", null, "WKM9 monocytes", "GSM3070147", null, "tissue:WKM9 monocytes|FISHid:WKM9|cell type:whole kidney marrow single cells|cell subtype:monocytes", "WKM9 monocytes", "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 monocytes", null, "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", null, "FISHid:WKM9|cell type:whole kidney marrow single cells|cell subtype:monocytes", "GSM3070147", "GSM3070147: WKM9 monocytes; Danio rerio; RNA Seq", "GSM3070147", null, "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:GSM3070147", "RNA-Seq", "TRANSCRIPTOMIC", "cDNA", "PAIRED", "ILLUMINA", "NextSeq 500", null, "SRP136633", null, null, "WKM9_monocytes_L003_R1_001.fastq.gz WKM9_monocytes_L003_R2_001.fastq.gz", "fastq fastq", 1503593728.0, 9965072.0, "GSM3070147 r3", "0:75.41 1:75.48", "A:435191543;C:215315484;G:255248972;T:597807800;N:29929", 75, 75, null, null, 435191543, 215315484, 255248972, 597807800, 29929, "SRX3856809", "SRS3100401", "SRA675960", "GEO", "Hubrecht Institute", 2, 0.3504, 0.80674, 0.25495, 0.3888, 0.96974, 0.88402, 0.4596, 0.50684, 76, 76, "T", "B", "mate1 technical by mapping diff", "illumina", "nextseq", "unknown", "random_priming", "trueseq", "sc", "single_cell_plate", "celseq", null, "Netherlands", "2018-03-28", "Undetermined", "Undetermined", "Kidney", "Renal System"], [47868, "SRR6908750", "SRX3856809", "SRS3100401", "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.", null, "pubmed:31585086", null, "WKM9 monocytes", "GSM3070147", null, "tissue:WKM9 monocytes|FISHid:WKM9|cell type:whole kidney marrow single cells|cell subtype:monocytes", "WKM9 monocytes", "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 monocytes", null, "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", null, "FISHid:WKM9|cell type:whole kidney marrow single cells|cell subtype:monocytes", "GSM3070147", "GSM3070147: WKM9 monocytes; Danio rerio; RNA Seq", "GSM3070147", null, "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:GSM3070147", "RNA-Seq", "TRANSCRIPTOMIC", "cDNA", "PAIRED", "ILLUMINA", "NextSeq 500", null, "SRP136633", null, null, "WKM9_monocytes_L004_R1_001.fastq.gz WKM9_monocytes_L004_R2_001.fastq.gz", "fastq fastq", 1474164037.0, 9770113.0, "GSM3070147 r4", "0:75.41 1:75.48", "A:425654229;C:209255453;G:253849148;T:585380972;N:24235", 75, 75, null, null, 425654229, 209255453, 253849148, 585380972, 24235, "SRX3856809", "SRS3100401", "SRA675960", "GEO", "Hubrecht Institute", 2, 0.36215, 0.81922, 0.26584, 0.39594, 0.97096, 0.89171, 0.46223, 0.51731, 75, 75, "T", "B", "mate1 technical by mapping diff", "illumina", "nextseq", "unknown", "random_priming", "trueseq", "sc", "single_cell_plate", "celseq", null, "Netherlands", "2018-03-28", "Undetermined", "Undetermined", "Kidney", "Renal System"], [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.", null, "pubmed:31585086", null, "WKM9 eosinophils", "GSM3070146", null, "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", null, "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", null, "FISHid:WKM9|cell type:whole kidney marrow single cells|cell subtype:eosinophils", "GSM3070146", "GSM3070146: WKM9 eosinophils; Danio rerio; RNA Seq", "GSM3070146", null, "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", null, "SRP136633", null, null, "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, null, null, 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", null, "Netherlands", "2018-03-28", "Undetermined", "Undetermined", "Blood", "Hematopoietic System"], [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.", null, "pubmed:31585086", null, "WKM9 eosinophils", "GSM3070146", null, "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", null, "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", null, "FISHid:WKM9|cell type:whole kidney marrow single cells|cell subtype:eosinophils", "GSM3070146", "GSM3070146: WKM9 eosinophils; Danio rerio; RNA Seq", "GSM3070146", null, "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", null, "SRP136633", null, null, "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, null, null, 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", null, "Netherlands", "2018-03-28", "Undetermined", "Undetermined", "Blood", "Hematopoietic System"], [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.", null, "pubmed:31585086", null, "WKM9 eosinophils", "GSM3070146", null, "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", null, "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", null, "FISHid:WKM9|cell type:whole kidney marrow single cells|cell subtype:eosinophils", "GSM3070146", "GSM3070146: WKM9 eosinophils; Danio rerio; RNA Seq", "GSM3070146", null, "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", null, "SRP136633", null, null, "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, null, null, 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", null, "Netherlands", "2018-03-28", "Undetermined", "Undetermined", "Blood", "Hematopoietic System"], [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.", null, "pubmed:31585086", null, "WKM9 eosinophils", "GSM3070146", null, "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", null, "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", null, "FISHid:WKM9|cell type:whole kidney marrow single cells|cell subtype:eosinophils", "GSM3070146", "GSM3070146: WKM9 eosinophils; Danio rerio; RNA Seq", "GSM3070146", null, "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", null, "SRP136633", null, null, "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, null, null, 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", null, "Netherlands", "2018-03-28", "Undetermined", "Undetermined", "Blood", "Hematopoietic System"], [47873, "SRR6908739", "SRX3856807", "SRS3100399", "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.", null, "pubmed:31585086", null, "WKM8 unenriched", "GSM3070145", null, "tissue:WKM8 unenriched|FISHid:WKM8|cell type:whole kidney marrow single cells|cell subtype:unenriched", "WKM8 unenriched", "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", "WKM8 unenriched", null, "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", null, "FISHid:WKM8|cell type:whole kidney marrow single cells|cell subtype:unenriched", "GSM3070145", "GSM3070145: WKM8 unenriched; Danio rerio; RNA Seq", "GSM3070145", null, "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:GSM3070145", "RNA-Seq", "TRANSCRIPTOMIC", "cDNA", "PAIRED", "ILLUMINA", "NextSeq 500", null, "SRP136633", null, null, "WKM8_unenriched_L001_R1_001.fastq.gz WKM8_unenriched_L001_R2_001.fastq.gz", "fastq fastq", 1667087336.0, 11048469.0, "GSM3070145 r1", "0:75.40 1:75.49", "A:475560256;C:240713376;G:276116899;T:674689422;N:7383", 75, 75, null, null, 475560256, 240713376, 276116899, 674689422, 7383, "SRX3856807", "SRS3100399", "SRA675960", "GEO", "Hubrecht Institute", 2, 0.39157, 0.8295, 0.26694, 0.3429, 0.95806, 0.85117, 0.47471, 0.5079, 76, 76, "T", "B", "mate1 technical by mapping diff", "illumina", "nextseq", "unknown", "random_priming", "trueseq", "sc", "single_cell_plate", "celseq", null, "Netherlands", "2018-03-28", "Undetermined", "Undetermined", "Kidney", "Renal System"], [47874, "SRR6908740", "SRX3856807", "SRS3100399", "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.", null, "pubmed:31585086", null, "WKM8 unenriched", "GSM3070145", null, "tissue:WKM8 unenriched|FISHid:WKM8|cell type:whole kidney marrow single cells|cell subtype:unenriched", "WKM8 unenriched", "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", "WKM8 unenriched", null, "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", null, "FISHid:WKM8|cell type:whole kidney marrow single cells|cell subtype:unenriched", "GSM3070145", "GSM3070145: WKM8 unenriched; Danio rerio; RNA Seq", "GSM3070145", null, "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:GSM3070145", "RNA-Seq", "TRANSCRIPTOMIC", "cDNA", "PAIRED", "ILLUMINA", "NextSeq 500", null, "SRP136633", null, null, "WKM8_unenriched_L002_R1_001.fastq.gz WKM8_unenriched_L002_R2_001.fastq.gz", "fastq fastq", 1643985440.0, 10895186.0, "GSM3070145 r2", "0:75.40 1:75.49", "A:465022994;C:236298663;G:278363355;T:664296206;N:4222", 75, 75, null, null, 465022994, 236298663, 278363355, 664296206, 4222, "SRX3856807", "SRS3100399", "SRA675960", "GEO", "Hubrecht Institute", 2, 0.38307, 0.82794, 0.26079, 0.33908, 0.95966, 0.85573, 0.47886, 0.53225, 76, 74, "T", "B", "mate1 technical by mapping diff", "illumina", "nextseq", "unknown", "random_priming", "trueseq", "sc", "single_cell_plate", "celseq", null, "Netherlands", "2018-03-28", "Undetermined", "Undetermined", "Kidney", "Renal System"], [47875, "SRR6908741", "SRX3856807", "SRS3100399", "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.", null, "pubmed:31585086", null, "WKM8 unenriched", "GSM3070145", null, "tissue:WKM8 unenriched|FISHid:WKM8|cell type:whole kidney marrow single cells|cell subtype:unenriched", "WKM8 unenriched", "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", "WKM8 unenriched", null, "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", null, "FISHid:WKM8|cell type:whole kidney marrow single cells|cell subtype:unenriched", "GSM3070145", "GSM3070145: WKM8 unenriched; Danio rerio; RNA Seq", "GSM3070145", null, "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:GSM3070145", "RNA-Seq", "TRANSCRIPTOMIC", "cDNA", "PAIRED", "ILLUMINA", "NextSeq 500", null, "SRP136633", null, null, "WKM8_unenriched_L003_R1_001.fastq.gz WKM8_unenriched_L003_R2_001.fastq.gz", "fastq fastq", 1497995831.0, 9927192.0, "GSM3070145 r3", "0:75.41 1:75.48", "A:423994108;C:216014805;G:251009522;T:606949772;N:27624", 75, 75, null, null, 423994108, 216014805, 251009522, 606949772, 27624, "SRX3856807", "SRS3100399", "SRA675960", "GEO", "Hubrecht Institute", 2, 0.37662, 0.82297, 0.26009, 0.33859, 0.96382, 0.8616, 0.4713, 0.53853, 75, 75, "T", "B", "mate1 technical by mapping diff", "illumina", "nextseq", "unknown", "random_priming", "trueseq", "sc", "single_cell_plate", "celseq", null, "Netherlands", "2018-03-28", "Undetermined", "Undetermined", "Kidney", "Renal System"], [47876, "SRR6908742", "SRX3856807", "SRS3100399", "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.", null, "pubmed:31585086", null, "WKM8 unenriched", "GSM3070145", null, "tissue:WKM8 unenriched|FISHid:WKM8|cell type:whole kidney marrow single cells|cell subtype:unenriched", "WKM8 unenriched", "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", "WKM8 unenriched", null, "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", null, "FISHid:WKM8|cell type:whole kidney marrow single cells|cell subtype:unenriched", "GSM3070145", "GSM3070145: WKM8 unenriched; Danio rerio; RNA Seq", "GSM3070145", null, "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:GSM3070145", "RNA-Seq", "TRANSCRIPTOMIC", "cDNA", "PAIRED", "ILLUMINA", "NextSeq 500", null, "SRP136633", null, null, "WKM8_unenriched_L004_R1_001.fastq.gz WKM8_unenriched_L004_R2_001.fastq.gz", "fastq fastq", 1462948321.0, 9695189.0, "GSM3070145 r4", "0:75.41 1:75.48", "A:413526172;C:209432303;G:247960945;T:592005236;N:23665", 75, 75, null, null, 413526172, 209432303, 247960945, 592005236, 23665, "SRX3856807", "SRS3100399", "SRA675960", "GEO", "Hubrecht Institute", 2, 0.39195, 0.83802, 0.26623, 0.34329, 0.96301, 0.86659, 0.47028, 0.53168, 76, 75, "T", "B", "mate1 technical by mapping diff", "illumina", "nextseq", "unknown", "random_priming", "trueseq", "sc", "single_cell_plate", "celseq", null, "Netherlands", "2018-03-28", "Undetermined", "Undetermined", "Kidney", "Renal System"], [47877, "SRR6908735", "SRX3856806", "SRS3100398", "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.", null, "pubmed:31585086", null, "WKM8 monocytes", "GSM3070144", null, "tissue:WKM8 monocytes|FISHid:WKM8|cell type:whole kidney marrow single cells|cell subtype:monocytes", "WKM8 monocytes", "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", "WKM8 monocytes", null, "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", null, "FISHid:WKM8|cell type:whole kidney marrow single cells|cell subtype:monocytes", "GSM3070144", "GSM3070144: WKM8 monocytes; Danio rerio; RNA Seq", "GSM3070144", null, "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:GSM3070144", "RNA-Seq", "TRANSCRIPTOMIC", "cDNA", "PAIRED", "ILLUMINA", "NextSeq 500", null, "SRP136633", null, null, "WKM8_monocytes_L001_R1_001.fastq.gz WKM8_monocytes_L001_R2_001.fastq.gz", "fastq fastq", 930630033.0, 6164203.0, "GSM3070144 r1", "0:75.50 1:75.47", "A:258982746;C:129522885;G:148763386;T:393303395;N:57621", 75, 75, null, null, 258982746, 129522885, 148763386, 393303395, 57621, "SRX3856806", "SRS3100398", "SRA675960", "GEO", "Hubrecht Institute", 2, 0.32023, 0.84288, 0.2067, 0.29948, 0.98628, 0.9247, 0.4816, 0.56903, 75, 75, "T", "B", "mate1 technical by mapping diff", "illumina", "nextseq", "unknown", "random_priming", "trueseq", "sc", "single_cell_plate", "celseq", null, "Netherlands", "2018-03-28", "Undetermined", "Undetermined", "Kidney", "Renal System"], [47878, "SRR6908736", "SRX3856806", "SRS3100398", "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.", null, "pubmed:31585086", null, "WKM8 monocytes", "GSM3070144", null, "tissue:WKM8 monocytes|FISHid:WKM8|cell type:whole kidney marrow single cells|cell subtype:monocytes", "WKM8 monocytes", "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", "WKM8 monocytes", null, "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", null, "FISHid:WKM8|cell type:whole kidney marrow single cells|cell subtype:monocytes", "GSM3070144", "GSM3070144: WKM8 monocytes; Danio rerio; RNA Seq", "GSM3070144", null, "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:GSM3070144", "RNA-Seq", "TRANSCRIPTOMIC", "cDNA", "PAIRED", "ILLUMINA", "NextSeq 500", null, "SRP136633", null, null, "WKM8_monocytes_L002_R1_001.fastq.gz WKM8_monocytes_L002_R2_001.fastq.gz", "fastq fastq", 840139689.0, 5565664.0, "GSM3070144 r2", "0:75.50 1:75.45", "A:236179715;C:116541374;G:133572472;T:353806731;N:39397", 75, 75, null, null, 236179715, 116541374, 133572472, 353806731, 39397, "SRX3856806", "SRS3100398", "SRA675960", "GEO", "Hubrecht Institute", 2, 0.28608, 0.81641, 0.18643, 0.30235, 0.99159, 0.94799, 0.46036, 0.51728, 75, 76, "T", "B", "mate1 technical by mapping diff", "illumina", "nextseq", "unknown", "random_priming", "trueseq", "sc", "single_cell_plate", "celseq", null, "Netherlands", "2018-03-28", "Undetermined", "Undetermined", "Kidney", "Renal System"], [47879, "SRR6908737", "SRX3856806", "SRS3100398", "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.", null, "pubmed:31585086", null, "WKM8 monocytes", "GSM3070144", null, "tissue:WKM8 monocytes|FISHid:WKM8|cell type:whole kidney marrow single cells|cell subtype:monocytes", "WKM8 monocytes", "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", "WKM8 monocytes", null, "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", null, "FISHid:WKM8|cell type:whole kidney marrow single cells|cell subtype:monocytes", "GSM3070144", "GSM3070144: WKM8 monocytes; Danio rerio; RNA Seq", "GSM3070144", null, "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:GSM3070144", "RNA-Seq", "TRANSCRIPTOMIC", "cDNA", "PAIRED", "ILLUMINA", "NextSeq 500", null, "SRP136633", null, null, "WKM8_monocytes_L003_R2_001.fastq.gz WKM8_monocytes_L003_R1_001.fastq.gz", "fastq fastq", 829094700.0, 5491203.0, "GSM3070144 r3", "0:75.52 1:75.47", "A:229473245;C:115123563;G:132541877;T:351944469;N:11546", 75, 75, null, null, 229473245, 115123563, 132541877, 351944469, 11546, "SRX3856806", "SRS3100398", "SRA675960", "GEO", "Hubrecht Institute", 2, 0.27624, 0.83541, 0.18571, 0.30162, 0.99358, 0.93693, 0.46624, 0.58337, 76, 76, "T", "B", "mate1 technical by mapping diff", "illumina", "nextseq", "unknown", "random_priming", "trueseq", "sc", "single_cell_plate", "celseq", null, "Netherlands", "2018-03-28", "Undetermined", "Undetermined", "Kidney", "Renal System"], [47880, "SRR6908738", "SRX3856806", "SRS3100398", "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.", null, "pubmed:31585086", null, "WKM8 monocytes", "GSM3070144", null, "tissue:WKM8 monocytes|FISHid:WKM8|cell type:whole kidney marrow single cells|cell subtype:monocytes", "WKM8 monocytes", "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", "WKM8 monocytes", null, "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", null, "FISHid:WKM8|cell type:whole kidney marrow single cells|cell subtype:monocytes", "GSM3070144", "GSM3070144: WKM8 monocytes; Danio rerio; RNA Seq", "GSM3070144", null, "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:GSM3070144", "RNA-Seq", "TRANSCRIPTOMIC", "cDNA", "PAIRED", "ILLUMINA", "NextSeq 500", null, "SRP136633", null, null, "WKM8_monocytes_L004_R1_001.fastq.gz WKM8_monocytes_L004_R2_001.fastq.gz", "fastq fastq", 717316840.0, 4751642.0, "GSM3070144 r4", "0:75.51 1:75.45", "A:201002776;C:99055550;G:115888860;T:301362855;N:6799", 75, 75, null, null, 201002776, 99055550, 115888860, 301362855, 6799, "SRX3856806", "SRS3100398", "SRA675960", "GEO", "Hubrecht Institute", 2, 0.24919, 0.82101, 0.16961, 0.29185, 0.99543, 0.94882, 0.43582, 0.58134, 76, 75, "T", "B", "mate1 technical by mapping diff", "illumina", "nextseq", "unknown", "random_priming", "trueseq", "sc", "single_cell_plate", "celseq", null, "Netherlands", "2018-03-28", "Undetermined", "Undetermined", "Kidney", "Renal System"], [47881, "SRR6908731", "SRX3856805", "SRS3100397", "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.", null, "pubmed:31585086", null, "WKM8 eosinophils and lymphocytes", "GSM3070143", null, "tissue:WKM8 eosinophils and lymphocytes|FISHid:WKM8|cell type:whole kidney marrow single cells|cell subtype:lymphocytes", "WKM8 eosinophils and lymphocytes", "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", "WKM8 eosinophils and lymphocytes", null, "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", null, "FISHid:WKM8|cell type:whole kidney marrow single cells|cell subtype:lymphocytes", "GSM3070143", "GSM3070143: WKM8 eosinophils and lymphocytes; Danio rerio; RNA Seq", "GSM3070143", null, "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:GSM3070143", "RNA-Seq", "TRANSCRIPTOMIC", "cDNA", "PAIRED", "ILLUMINA", "NextSeq 500", null, "SRP136633", null, null, "WKM8_eosinophils_and_lymphocytes_L001_R2_001.fastq.gz WKM8_eosinophils_and_lymphocytes_L001_R1_001.fastq.gz", "fastq fastq", 1375061469.0, 9111725.0, "GSM3070143 r1", "0:75.43 1:75.48", "A:397639171;C:207855377;G:230813601;T:538747052;N:6268", 75, 75, null, null, 397639171, 207855377, 230813601, 538747052, 6268, "SRX3856805", "SRS3100397", "SRA675960", "GEO", "Hubrecht Institute", 2, 0.4147, 0.81295, 0.33835, 0.48636, 0.94951, 0.85942, 0.48903, 0.52414, 76, 75, "T", "B", "mate1 technical by mapping diff", "illumina", "nextseq", "unknown", "random_priming", "trueseq", "sc", "single_cell_plate", "celseq", null, "Netherlands", "2018-03-28", "Undetermined", "Undetermined", "Multi-tissue", "Multi-system"], [47882, "SRR6908732", "SRX3856805", "SRS3100397", "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.", null, "pubmed:31585086", null, "WKM8 eosinophils and lymphocytes", "GSM3070143", null, "tissue:WKM8 eosinophils and lymphocytes|FISHid:WKM8|cell type:whole kidney marrow single cells|cell subtype:lymphocytes", "WKM8 eosinophils and lymphocytes", "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", "WKM8 eosinophils and lymphocytes", null, "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", null, "FISHid:WKM8|cell type:whole kidney marrow single cells|cell subtype:lymphocytes", "GSM3070143", "GSM3070143: WKM8 eosinophils and lymphocytes; Danio rerio; RNA Seq", "GSM3070143", null, "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:GSM3070143", "RNA-Seq", "TRANSCRIPTOMIC", "cDNA", "PAIRED", "ILLUMINA", "NextSeq 500", null, "SRP136633", null, null, "WKM8_eosinophils_and_lymphocytes_L002_R1_001.fastq.gz WKM8_eosinophils_and_lymphocytes_L002_R2_001.fastq.gz", "fastq fastq", 1349675695.0, 8943335.0, "GSM3070143 r2", "0:75.43 1:75.48", "A:387332778;C:203252981;G:231137130;T:527949327;N:3479", 75, 75, null, null, 387332778, 203252981, 231137130, 527949327, 3479, "SRX3856805", "SRS3100397", "SRA675960", "GEO", "Hubrecht Institute", 2, 0.40287, 0.80137, 0.3286, 0.47191, 0.95446, 0.86354, 0.4748, 0.49423, 76, 74, "T", "B", "mate1 technical by mapping diff", "illumina", "nextseq", "unknown", "random_priming", "trueseq", "sc", "single_cell_plate", "celseq", null, "Netherlands", "2018-03-28", "Undetermined", "Undetermined", "Multi-tissue", "Multi-system"], [47883, "SRR6908733", "SRX3856805", "SRS3100397", "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.", null, "pubmed:31585086", null, "WKM8 eosinophils and lymphocytes", "GSM3070143", null, "tissue:WKM8 eosinophils and lymphocytes|FISHid:WKM8|cell type:whole kidney marrow single cells|cell subtype:lymphocytes", "WKM8 eosinophils and lymphocytes", "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", "WKM8 eosinophils and lymphocytes", null, "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", null, "FISHid:WKM8|cell type:whole kidney marrow single cells|cell subtype:lymphocytes", "GSM3070143", "GSM3070143: WKM8 eosinophils and lymphocytes; Danio rerio; RNA Seq", "GSM3070143", null, "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:GSM3070143", "RNA-Seq", "TRANSCRIPTOMIC", "cDNA", "PAIRED", "ILLUMINA", "NextSeq 500", null, "SRP136633", null, null, "WKM8_eosinophils_and_lymphocytes_L003_R1_001.fastq.gz WKM8_eosinophils_and_lymphocytes_L003_R2_001.fastq.gz", "fastq fastq", 1228039195.0, 8136994.0, "GSM3070143 r3", "0:75.44 1:75.48", "A:352677291;C:185534334;G:208351378;T:481454385;N:21807", 75, 75, null, null, 352677291, 185534334, 208351378, 481454385, 21807, "SRX3856805", "SRS3100397", "SRA675960", "GEO", "Hubrecht Institute", 2, 0.40303, 0.80734, 0.33097, 0.48111, 0.95812, 0.86709, 0.47702, 0.5199, 76, 75, "T", "B", "mate1 technical by mapping diff", "illumina", "nextseq", "unknown", "random_priming", "trueseq", "sc", "single_cell_plate", "celseq", null, "Netherlands", "2018-03-28", "Undetermined", "Undetermined", "Multi-tissue", "Multi-system"], [47884, "SRR6908734", "SRX3856805", "SRS3100397", "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.", null, "pubmed:31585086", null, "WKM8 eosinophils and lymphocytes", "GSM3070143", null, "tissue:WKM8 eosinophils and lymphocytes|FISHid:WKM8|cell type:whole kidney marrow single cells|cell subtype:lymphocytes", "WKM8 eosinophils and lymphocytes", "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", "WKM8 eosinophils and lymphocytes", null, "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", null, "FISHid:WKM8|cell type:whole kidney marrow single cells|cell subtype:lymphocytes", "GSM3070143", "GSM3070143: WKM8 eosinophils and lymphocytes; Danio rerio; RNA Seq", "GSM3070143", null, "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:GSM3070143", "RNA-Seq", "TRANSCRIPTOMIC", "cDNA", "PAIRED", "ILLUMINA", "NextSeq 500", null, "SRP136633", null, null, "WKM8_eosinophils_and_lymphocytes_L004_R1_001.fastq.gz WKM8_eosinophils_and_lymphocytes_L004_R2_001.fastq.gz", "fastq fastq", 1192405777.0, 7901058.0, "GSM3070143 r4", "0:75.44 1:75.48", "A:341927110;C:179070080;G:204590907;T:466800667;N:17013", 75, 75, null, null, 341927110, 179070080, 204590907, 466800667, 17013, "SRX3856805", "SRS3100397", "SRA675960", "GEO", "Hubrecht Institute", 2, 0.40935, 0.80524, 0.33588, 0.48, 0.95899, 0.87377, 0.47516, 0.52985, 75, 75, "T", "B", "mate1 technical by mapping diff", "illumina", "nextseq", "unknown", "random_priming", "trueseq", "sc", "single_cell_plate", "celseq", null, "Netherlands", "2018-03-28", "Undetermined", "Undetermined", "Multi-tissue", "Multi-system"], [47885, "SRR6908727", "SRX3856804", "SRS3100396", "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.", null, "pubmed:31585086", null, "WKM7 unenriched", "GSM3070142", null, "tissue:WKM7 unenriched|FISHid:WKM7|cell type:whole kidney marrow single cells|cell subtype:unenriched", "WKM7 unenriched", "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", "WKM7 unenriched", null, "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", null, "FISHid:WKM7|cell type:whole kidney marrow single cells|cell subtype:unenriched", "GSM3070142", "GSM3070142: WKM7 unenriched; Danio rerio; RNA Seq", "GSM3070142", null, "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:GSM3070142", "RNA-Seq", "TRANSCRIPTOMIC", "cDNA", "PAIRED", "ILLUMINA", "NextSeq 500", null, "SRP136633", null, null, "WKM7_unenriched_L001_R1_001.fastq.gz WKM7_unenriched_L001_R2_001.fastq.gz", "fastq fastq", 1470115941.0, 9738991.0, "GSM3070142 r1", "0:75.44 1:75.51", "A:398948568;C:202294647;G:231760451;T:636827163;N:285112", 75, 75, null, null, 398948568, 202294647, 231760451, 636827163, 285112, "SRX3856804", "SRS3100396", "SRA675960", "GEO", "Hubrecht Institute", 2, 0.40309, 0.87046, 0.32221, 0.26166, 0.99233, 0.84327, 0.29642, 0.52123, 76, 76, "T", "B", "mate1 technical by mapping diff", "illumina", "nextseq", "unknown", "random_priming", "trueseq", "sc", "single_cell_plate", "celseq", null, "Netherlands", "2018-03-28", "Undetermined", "Undetermined", "Kidney", "Renal System"], [47886, "SRR6908728", "SRX3856804", "SRS3100396", "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.", null, "pubmed:31585086", null, "WKM7 unenriched", "GSM3070142", null, "tissue:WKM7 unenriched|FISHid:WKM7|cell type:whole kidney marrow single cells|cell subtype:unenriched", "WKM7 unenriched", "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", "WKM7 unenriched", null, "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", null, "FISHid:WKM7|cell type:whole kidney marrow single cells|cell subtype:unenriched", "GSM3070142", "GSM3070142: WKM7 unenriched; Danio rerio; RNA Seq", "GSM3070142", null, "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:GSM3070142", "RNA-Seq", "TRANSCRIPTOMIC", "cDNA", "PAIRED", "ILLUMINA", "NextSeq 500", null, "SRP136633", null, null, "WKM7_unenriched_L002_R1_001.fastq.gz WKM7_unenriched_L002_R2_001.fastq.gz", "fastq fastq", 1461695240.0, 9683144.0, "GSM3070142 r2", "0:75.44 1:75.51", "A:394008699;C:201030493;G:233919014;T:632480364;N:256670", 75, 75, null, null, 394008699, 201030493, 233919014, 632480364, 256670, "SRX3856804", "SRS3100396", "SRA675960", "GEO", "Hubrecht Institute", 2, 0.38758, 0.86795, 0.30719, 0.26558, 0.99255, 0.84607, 0.30994, 0.51718, 76, 76, "T", "B", "mate1 technical by mapping diff", "illumina", "nextseq", "unknown", "random_priming", "trueseq", "sc", "single_cell_plate", "celseq", null, "Netherlands", "2018-03-28", "Undetermined", "Undetermined", "Kidney", "Renal System"], [47887, "SRR6908729", "SRX3856804", "SRS3100396", "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.", null, "pubmed:31585086", null, "WKM7 unenriched", "GSM3070142", null, "tissue:WKM7 unenriched|FISHid:WKM7|cell type:whole kidney marrow single cells|cell subtype:unenriched", "WKM7 unenriched", "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", "WKM7 unenriched", null, "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", null, "FISHid:WKM7|cell type:whole kidney marrow single cells|cell subtype:unenriched", "GSM3070142", "GSM3070142: WKM7 unenriched; Danio rerio; RNA Seq", "GSM3070142", null, "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:GSM3070142", "RNA-Seq", "TRANSCRIPTOMIC", "cDNA", "PAIRED", "ILLUMINA", "NextSeq 500", null, "SRP136633", null, null, "WKM7_unenriched_L003_R1_001.fastq.gz WKM7_unenriched_L003_R2_001.fastq.gz", "fastq fastq", 1418045610.0, 9393861.0, "GSM3070142 r3", "0:75.45 1:75.51", "A:383399208;C:195487055;G:222663120;T:616484616;N:11611", 75, 75, null, null, 383399208, 195487055, 222663120, 616484616, 11611, "SRX3856804", "SRS3100396", "SRA675960", "GEO", "Hubrecht Institute", 2, 0.41019, 0.87176, 0.32985, 0.2642, 0.99338, 0.8452, 0.2589, 0.52225, 76, 76, "T", "B", "mate1 technical by mapping diff", "illumina", "nextseq", "unknown", "random_priming", "trueseq", "sc", "single_cell_plate", "celseq", null, "Netherlands", "2018-03-28", "Undetermined", "Undetermined", "Kidney", "Renal System"], [47888, "SRR6908730", "SRX3856804", "SRS3100396", "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.", null, "pubmed:31585086", null, "WKM7 unenriched", "GSM3070142", null, "tissue:WKM7 unenriched|FISHid:WKM7|cell type:whole kidney marrow single cells|cell subtype:unenriched", "WKM7 unenriched", "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", "WKM7 unenriched", null, "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", null, "FISHid:WKM7|cell type:whole kidney marrow single cells|cell subtype:unenriched", "GSM3070142", "GSM3070142: WKM7 unenriched; Danio rerio; RNA Seq", "GSM3070142", null, "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:GSM3070142", "RNA-Seq", "TRANSCRIPTOMIC", "cDNA", "PAIRED", "ILLUMINA", "NextSeq 500", null, "SRP136633", null, null, "WKM7_unenriched_L004_R1_001.fastq.gz WKM7_unenriched_L004_R2_001.fastq.gz", "fastq fastq", 1401330195.0, 9283131.0, "GSM3070142 r4", "0:75.44 1:75.51", "A:376049927;C:192808560;G:224900456;T:607563117;N:8135", 75, 75, null, null, 376049927, 192808560, 224900456, 607563117, 8135, "SRX3856804", "SRS3100396", "SRA675960", "GEO", "Hubrecht Institute", 2, 0.40071, 0.86588, 0.31811, 0.26185, 0.99263, 0.84569, 0.27227, 0.5224, 75, 75, "T", "B", "mate1 technical by mapping diff", "illumina", "nextseq", "unknown", "random_priming", "trueseq", "sc", "single_cell_plate", "celseq", null, "Netherlands", "2018-03-28", "Undetermined", "Undetermined", "Kidney", "Renal System"], [47889, "SRR6908723", "SRX3856803", "SRS3100395", "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.", null, "pubmed:31585086", null, "WKM7 monocytes", "GSM3070141", null, "tissue:WKM7 monocytes|FISHid:WKM7|cell type:whole kidney marrow single cells|cell subtype:monocytes", "WKM7 monocytes", "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", "WKM7 monocytes", null, "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", null, "FISHid:WKM7|cell type:whole kidney marrow single cells|cell subtype:monocytes", "GSM3070141", "GSM3070141: WKM7 monocytes; Danio rerio; RNA Seq", "GSM3070141", null, "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:GSM3070141", "RNA-Seq", "TRANSCRIPTOMIC", "cDNA", "PAIRED", "ILLUMINA", "NextSeq 500", null, "SRP136633", null, null, "WKM7_monocytes_L001_R1_001.fastq.gz WKM7_monocytes_L001_R2_001.fastq.gz", "fastq fastq", 1791125985.0, 11864169.0, "GSM3070141 r1", "0:75.46 1:75.51", "A:485867889;C:238293947;G:271424293;T:795194496;N:345360", 75, 75, null, null, 485867889, 238293947, 271424293, 795194496, 345360, "SRX3856803", "SRS3100395", "SRA675960", "GEO", "Hubrecht Institute", 2, 0.42246, 0.87038, 0.34993, 0.25475, 0.99302, 0.86906, 0.3, 0.50725, 76, 75, "T", "B", "mate1 technical by mapping diff", "illumina", "nextseq", "unknown", "random_priming", "trueseq", "sc", "single_cell_plate", "celseq", null, "Netherlands", "2018-03-28", "Undetermined", "Undetermined", "Kidney", "Renal System"], [47890, "SRR6908724", "SRX3856803", "SRS3100395", "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.", null, "pubmed:31585086", null, "WKM7 monocytes", "GSM3070141", null, "tissue:WKM7 monocytes|FISHid:WKM7|cell type:whole kidney marrow single cells|cell subtype:monocytes", "WKM7 monocytes", "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", "WKM7 monocytes", null, "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", null, "FISHid:WKM7|cell type:whole kidney marrow single cells|cell subtype:monocytes", "GSM3070141", "GSM3070141: WKM7 monocytes; Danio rerio; RNA Seq", "GSM3070141", null, "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:GSM3070141", "RNA-Seq", "TRANSCRIPTOMIC", "cDNA", "PAIRED", "ILLUMINA", "NextSeq 500", null, "SRP136633", null, null, "WKM7_monocytes_L002_R1_001.fastq.gz WKM7_monocytes_L002_R2_001.fastq.gz", "fastq fastq", 1792721494.0, 11874637.0, "GSM3070141 r2", "0:75.46 1:75.51", "A:482977185;C:238180503;G:276068534;T:795172908;N:322364", 75, 75, null, null, 482977185, 238180503, 276068534, 795172908, 322364, "SRX3856803", "SRS3100395", "SRA675960", "GEO", "Hubrecht Institute", 2, 0.42187, 0.86585, 0.347, 0.25727, 0.99261, 0.86983, 0.32168, 0.51044, 76, 75, "T", "B", "mate1 technical by mapping diff", "illumina", "nextseq", "unknown", "random_priming", "trueseq", "sc", "single_cell_plate", "celseq", null, "Netherlands", "2018-03-28", "Undetermined", "Undetermined", "Kidney", "Renal System"], [47891, "SRR6908725", "SRX3856803", "SRS3100395", "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.", null, "pubmed:31585086", null, "WKM7 monocytes", "GSM3070141", null, "tissue:WKM7 monocytes|FISHid:WKM7|cell type:whole kidney marrow single cells|cell subtype:monocytes", "WKM7 monocytes", "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", "WKM7 monocytes", null, "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", null, "FISHid:WKM7|cell type:whole kidney marrow single cells|cell subtype:monocytes", "GSM3070141", "GSM3070141: WKM7 monocytes; Danio rerio; RNA Seq", "GSM3070141", null, "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:GSM3070141", "RNA-Seq", "TRANSCRIPTOMIC", "cDNA", "PAIRED", "ILLUMINA", "NextSeq 500", null, "SRP136633", null, null, "WKM7_monocytes_L003_R1_001.fastq.gz WKM7_monocytes_L003_R2_001.fastq.gz", "fastq fastq", 1726273948.0, 11434355.0, "GSM3070141 r3", "0:75.46 1:75.51", "A:466721054;C:229970045;G:260445487;T:769123115;N:14247", 75, 75, null, null, 466721054, 229970045, 260445487, 769123115, 14247, "SRX3856803", "SRS3100395", "SRA675960", "GEO", "Hubrecht Institute", 2, 0.43817, 0.86963, 0.36713, 0.25464, 0.99409, 0.87136, 0.28011, 0.50765, 75, 73, "T", "B", "mate1 technical by mapping diff", "illumina", "nextseq", "unknown", "random_priming", "trueseq", "sc", "single_cell_plate", "celseq", null, "Netherlands", "2018-03-28", "Undetermined", "Undetermined", "Kidney", "Renal System"], [47892, "SRR6908726", "SRX3856803", "SRS3100395", "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.", null, "pubmed:31585086", null, "WKM7 monocytes", "GSM3070141", null, "tissue:WKM7 monocytes|FISHid:WKM7|cell type:whole kidney marrow single cells|cell subtype:monocytes", "WKM7 monocytes", "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", "WKM7 monocytes", null, "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", null, "FISHid:WKM7|cell type:whole kidney marrow single cells|cell subtype:monocytes", "GSM3070141", "GSM3070141: WKM7 monocytes; Danio rerio; RNA Seq", "GSM3070141", null, "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:GSM3070141", "RNA-Seq", "TRANSCRIPTOMIC", "cDNA", "PAIRED", "ILLUMINA", "NextSeq 500", null, "SRP136633", null, null, "WKM7_monocytes_L004_R1_001.fastq.gz WKM7_monocytes_L004_R2_001.fastq.gz", "fastq fastq", 1706923699.0, 11306267.0, "GSM3070141 r4", "0:75.46 1:75.51", "A:458067525;C:226793433;G:263424046;T:758628361;N:10334", 75, 75, null, null, 458067525, 226793433, 263424046, 758628361, 10334, "SRX3856803", "SRS3100395", "SRA675960", "GEO", "Hubrecht Institute", 2, 0.42414, 0.86508, 0.34964, 0.25009, 0.99375, 0.87198, 0.28225, 0.51467, 76, 76, "T", "B", "mate1 technical by mapping diff", "illumina", "nextseq", "unknown", "random_priming", "trueseq", "sc", "single_cell_plate", "celseq", null, "Netherlands", "2018-03-28", "Undetermined", "Undetermined", "Kidney", "Renal System"], [47893, "SRR6908717", "SRX3856802", "SRS3100394", "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.", null, "pubmed:31585086", null, "WKM6 unenriched", "GSM3070140", null, "tissue:WKM6 unenriched|FISHid:WKM6|cell type:whole kidney marrow single cells|cell subtype:unenriched", "WKM6 unenriched", "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", "WKM6 unenriched", null, "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", null, "FISHid:WKM6|cell type:whole kidney marrow single cells|cell subtype:unenriched", "GSM3070140", "GSM3070140: WKM6 unenriched; Danio rerio; RNA Seq", "GSM3070140", null, "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:GSM3070140", "RNA-Seq", "TRANSCRIPTOMIC", "cDNA", "PAIRED", "ILLUMINA", "NextSeq 500", null, "SRP136633", null, null, "WKM6_unenriched_L001_R1_001.fastq.gz WKM6_unenriched_L001_R2_001.fastq.gz", "fastq fastq", 1091182818.0, 7228537.0, "GSM3070140 r1", "0:75.49 1:75.46", "A:305460604;C:155608830;G:177642198;T:452400520;N:70666", 75, 75, null, null, 305460604, 155608830, 177642198, 452400520, 70666, "SRX3856802", "SRS3100394", "SRA675960", "GEO", "Hubrecht Institute", 2, 0.32983, 0.81725, 0.21464, 0.30328, 0.98524, 0.91348, 0.47654, 0.51388, 76, 76, "T", "B", "mate1 technical by mapping diff", "illumina", "nextseq", "unknown", "random_priming", "trueseq", "sc", "single_cell_plate", "celseq", null, "Netherlands", "2018-03-28", "Undetermined", "Undetermined", "Kidney", "Renal System"], [47894, "SRR6908718", "SRX3856802", "SRS3100394", "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.", null, "pubmed:31585086", null, "WKM6 unenriched", "GSM3070140", null, "tissue:WKM6 unenriched|FISHid:WKM6|cell type:whole kidney marrow single cells|cell subtype:unenriched", "WKM6 unenriched", "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", "WKM6 unenriched", null, "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", null, "FISHid:WKM6|cell type:whole kidney marrow single cells|cell subtype:unenriched", "GSM3070140", "GSM3070140: WKM6 unenriched; Danio rerio; RNA Seq", "GSM3070140", null, "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:GSM3070140", "RNA-Seq", "TRANSCRIPTOMIC", "cDNA", "PAIRED", "ILLUMINA", "NextSeq 500", null, "SRP136633", null, null, "WKM6_unenriched_L002_R1_001.fastq.gz WKM6_unenriched_L002_R2_001.fastq.gz", "fastq fastq", 992904107.0, 6578507.0, "GSM3070140 r2", "0:75.49 1:75.44", "A:280993567;C:141013446;G:160470422;T:410379926;N:46746", 75, 75, null, null, 280993567, 141013446, 160470422, 410379926, 46746, "SRX3856802", "SRS3100394", "SRA675960", "GEO", "Hubrecht Institute", 2, 0.3103, 0.79506, 0.20764, 0.30662, 0.99076, 0.94178, 0.49304, 0.53389, 76, 76, "T", "B", "mate1 technical by mapping diff", "illumina", "nextseq", "unknown", "random_priming", "trueseq", "sc", "single_cell_plate", "celseq", null, "Netherlands", "2018-03-28", "Undetermined", "Undetermined", "Kidney", "Renal System"], [47895, "SRR6908720", "SRX3856802", "SRS3100394", "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.", null, "pubmed:31585086", null, "WKM6 unenriched", "GSM3070140", null, "tissue:WKM6 unenriched|FISHid:WKM6|cell type:whole kidney marrow single cells|cell subtype:unenriched", "WKM6 unenriched", "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", "WKM6 unenriched", null, "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", null, "FISHid:WKM6|cell type:whole kidney marrow single cells|cell subtype:unenriched", "GSM3070140", "GSM3070140: WKM6 unenriched; Danio rerio; RNA Seq", "GSM3070140", null, "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:GSM3070140", "RNA-Seq", "TRANSCRIPTOMIC", "cDNA", "PAIRED", "ILLUMINA", "NextSeq 500", null, "SRP136633", null, null, "WKM6_unenriched_L003_R1_001.fastq.gz WKM6_unenriched_L003_R2_001.fastq.gz", "fastq fastq", 967648040.0, 6409722.0, "GSM3070140 r3", "0:75.51 1:75.46", "A:269841606;C:137530059;G:157502274;T:402760480;N:13621", 75, 75, null, null, 269841606, 137530059, 157502274, 402760480, 13621, "SRX3856802", "SRS3100394", "SRA675960", "GEO", "Hubrecht Institute", 2, 0.304, 0.80424, 0.20488, 0.29857, 0.99383, 0.92894, 0.44008, 0.5232, 76, 76, "T", "B", "mate1 technical by mapping diff", "illumina", "nextseq", "unknown", "random_priming", "trueseq", "sc", "single_cell_plate", "celseq", null, "Netherlands", "2018-03-28", "Undetermined", "Undetermined", "Kidney", "Renal System"], [47896, "SRR6908722", "SRX3856802", "SRS3100394", "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.", null, "pubmed:31585086", null, "WKM6 unenriched", "GSM3070140", null, "tissue:WKM6 unenriched|FISHid:WKM6|cell type:whole kidney marrow single cells|cell subtype:unenriched", "WKM6 unenriched", "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", "WKM6 unenriched", null, "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", null, "FISHid:WKM6|cell type:whole kidney marrow single cells|cell subtype:unenriched", "GSM3070140", "GSM3070140: WKM6 unenriched; Danio rerio; RNA Seq", "GSM3070140", null, "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:GSM3070140", "RNA-Seq", "TRANSCRIPTOMIC", "cDNA", "PAIRED", "ILLUMINA", "NextSeq 500", null, "SRP136633", null, null, "WKM6_unenriched_L004_R1_001.fastq.gz WKM6_unenriched_L004_R2_001.fastq.gz", "fastq fastq", 843966623.0, 5591352.0, "GSM3070140 r4", "0:75.50 1:75.44", "A:238568640;C:119216971;G:138770810;T:347402234;N:7968", 75, 75, null, null, 238568640, 119216971, 138770810, 347402234, 7968, "SRX3856802", "SRS3100394", "SRA675960", "GEO", "Hubrecht Institute", 2, 0.28014, 0.78908, 0.18667, 0.29821, 0.99496, 0.94424, 0.46594, 0.52932, 75, 72, "T", "B", "mate1 technical by mapping diff", "illumina", "nextseq", "unknown", "random_priming", "trueseq", "sc", "single_cell_plate", "celseq", null, "Netherlands", "2018-03-28", "Undetermined", "Undetermined", "Kidney", "Renal System"], [47897, "SRR6908713", "SRX3856801", "SRS3100393", "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.", null, "pubmed:31585086", null, "WKM6 lymphocytes", "GSM3070139", null, "tissue:WKM6 lymphocytes|FISHid:WKM6|cell type:whole kidney marrow single cells|cell subtype:lymphocytes", "WKM6 lymphocytes", "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", "WKM6 lymphocytes", null, "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", null, "FISHid:WKM6|cell type:whole kidney marrow single cells|cell subtype:lymphocytes", "GSM3070139", "GSM3070139: WKM6 lymphocytes; Danio rerio; RNA Seq", "GSM3070139", null, "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:GSM3070139", "RNA-Seq", "TRANSCRIPTOMIC", "cDNA", "PAIRED", "ILLUMINA", "NextSeq 500", null, "SRP136633", null, null, "WKM6_lymphocytes_L001_R1_001.fastq.gz WKM6_lymphocytes_L001_R2_001.fastq.gz", "fastq fastq", 1092718271.0, 7238113.0, "GSM3070139 r1", "0:75.49 1:75.48", "A:308417237;C:162459915;G:183666585;T:438104581;N:69953", 75, 75, null, null, 308417237, 162459915, 183666585, 438104581, 69953, "SRX3856801", "SRS3100393", "SRA675960", "GEO", "Hubrecht Institute", 2, 0.33032, 0.81969, 0.25647, 0.49446, 0.98587, 0.93105, 0.47765, 0.53939, 75, 76, "T", "B", "mate1 technical by mapping diff", "illumina", "nextseq", "unknown", "random_priming", "trueseq", "sc", "single_cell_plate", "celseq", null, "Netherlands", "2018-03-28", "Undetermined", "Undetermined", "Lymphatic System", "Cardiovascular System"], [47898, "SRR6908714", "SRX3856801", "SRS3100393", "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.", null, "pubmed:31585086", null, "WKM6 lymphocytes", "GSM3070139", null, "tissue:WKM6 lymphocytes|FISHid:WKM6|cell type:whole kidney marrow single cells|cell subtype:lymphocytes", "WKM6 lymphocytes", "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", "WKM6 lymphocytes", null, "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", null, "FISHid:WKM6|cell type:whole kidney marrow single cells|cell subtype:lymphocytes", "GSM3070139", "GSM3070139: WKM6 lymphocytes; Danio rerio; RNA Seq", "GSM3070139", null, "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:GSM3070139", "RNA-Seq", "TRANSCRIPTOMIC", "cDNA", "PAIRED", "ILLUMINA", "NextSeq 500", null, "SRP136633", null, null, "WKM6_lymphocytes_L002_R1_001.fastq.gz WKM6_lymphocytes_L002_R2_001.fastq.gz", "fastq fastq", 988686695.0, 6550079.0, "GSM3070139 r2", "0:75.49 1:75.46", "A:281384679;C:146335731;G:165878017;T:395040397;N:47871", 75, 75, null, null, 281384679, 146335731, 165878017, 395040397, 47871, "SRX3856801", "SRS3100393", "SRA675960", "GEO", "Hubrecht Institute", 2, 0.31311, 0.79325, 0.23663, 0.48843, 0.99034, 0.95471, 0.47178, 0.54622, 75, 76, "T", "B", "mate1 technical by mapping diff", "illumina", "nextseq", "unknown", "random_priming", "trueseq", "sc", "single_cell_plate", "celseq", null, "Netherlands", "2018-03-28", "Undetermined", "Undetermined", "Lymphatic System", "Cardiovascular System"], [47899, "SRR6908715", "SRX3856801", "SRS3100393", "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.", null, "pubmed:31585086", null, "WKM6 lymphocytes", "GSM3070139", null, "tissue:WKM6 lymphocytes|FISHid:WKM6|cell type:whole kidney marrow single cells|cell subtype:lymphocytes", "WKM6 lymphocytes", "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", "WKM6 lymphocytes", null, "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", null, "FISHid:WKM6|cell type:whole kidney marrow single cells|cell subtype:lymphocytes", "GSM3070139", "GSM3070139: WKM6 lymphocytes; Danio rerio; RNA Seq", "GSM3070139", null, "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:GSM3070139", "RNA-Seq", "TRANSCRIPTOMIC", "cDNA", "PAIRED", "ILLUMINA", "NextSeq 500", null, "SRP136633", null, null, "WKM6_lymphocytes_L003_R1_001.fastq.gz WKM6_lymphocytes_L003_R2_001.fastq.gz", "fastq fastq", 965970467.0, 6397966.0, "GSM3070139 r3", "0:75.51 1:75.47", "A:271037832;C:143188198;G:162584210;T:389145889;N:14338", 75, 75, null, null, 271037832, 143188198, 162584210, 389145889, 14338, "SRX3856801", "SRS3100393", "SRA675960", "GEO", "Hubrecht Institute", 2, 0.2983, 0.81397, 0.23255, 0.49479, 0.99377, 0.94379, 0.43187, 0.54702, 76, 76, "T", "B", "mate1 technical by mapping diff", "illumina", "nextseq", "unknown", "random_priming", "trueseq", "sc", "single_cell_plate", "celseq", null, "Netherlands", "2018-03-28", "Undetermined", "Undetermined", "Lymphatic System", "Cardiovascular System"], [47900, "SRR6908716", "SRX3856801", "SRS3100393", "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.", null, "pubmed:31585086", null, "WKM6 lymphocytes", "GSM3070139", null, "tissue:WKM6 lymphocytes|FISHid:WKM6|cell type:whole kidney marrow single cells|cell subtype:lymphocytes", "WKM6 lymphocytes", "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", "WKM6 lymphocytes", null, "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", null, "FISHid:WKM6|cell type:whole kidney marrow single cells|cell subtype:lymphocytes", "GSM3070139", "GSM3070139: WKM6 lymphocytes; Danio rerio; RNA Seq", "GSM3070139", null, "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:GSM3070139", "RNA-Seq", "TRANSCRIPTOMIC", "cDNA", "PAIRED", "ILLUMINA", "NextSeq 500", null, "SRP136633", null, null, "WKM6_lymphocytes_L004_R1_001.fastq.gz WKM6_lymphocytes_L004_R2_001.fastq.gz", "fastq fastq", 845107841.0, 5598430.0, "GSM3070139 r4", "0:75.50 1:75.46", "A:239787549;C:124312776;G:144002735;T:336997076;N:7705", 75, 75, null, null, 239787549, 124312776, 144002735, 336997076, 7705, "SRX3856801", "SRS3100393", "SRA675960", "GEO", "Hubrecht Institute", 2, 0.27606, 0.8077, 0.21352, 0.49895, 0.99502, 0.95511, 0.37654, 0.5537, 76, 76, "T", "B", "mate1 technical by mapping diff", "illumina", "nextseq", "unknown", "random_priming", "trueseq", "sc", "single_cell_plate", "celseq", null, "Netherlands", "2018-03-28", "Undetermined", "Undetermined", "Lymphatic System", "Cardiovascular System"], [47901, "SRR6908709", "SRX3856800", "SRS3100392", "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.", null, "pubmed:31585086", null, "WKM5 unenriched", "GSM3070138", null, "tissue:WKM5 unenriched|FISHid:WKM5|cell type:whole kidney marrow single cells|cell subtype:unenriched", "WKM5 unenriched", "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", "WKM5 unenriched", null, "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", null, "FISHid:WKM5|cell type:whole kidney marrow single cells|cell subtype:unenriched", "GSM3070138", "GSM3070138: WKM5 unenriched; Danio rerio; RNA Seq", "GSM3070138", null, "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:GSM3070138", "RNA-Seq", "TRANSCRIPTOMIC", "cDNA", "PAIRED", "ILLUMINA", "NextSeq 500", null, "SRP136633", null, null, "WKM5_unenriched_L001_R1_001.fastq.gz WKM5_unenriched_L001_R2_001.fastq.gz", "fastq fastq", 1616915014.0, 10719306.0, "GSM3070138 r1", "0:75.41 1:75.43", "A:470483106;C:225755405;G:263766190;T:656561040;N:349273", 75, 75, null, null, 470483106, 225755405, 263766190, 656561040, 349273, "SRX3856800", "SRS3100392", "SRA675960", "GEO", "Hubrecht Institute", 2, 0.10729, 0.72403, 0.07307, 0.20274, 0.99017, 0.84524, 0.5068, 0.51693, 76, 76, "T", "B", "mate1 technical by mapping diff", "illumina", "nextseq", "unknown", "random_priming", "trueseq", "sc", "single_cell_plate", "celseq", null, "Netherlands", "2018-03-28", "Undetermined", "Undetermined", "Kidney", "Renal System"], [47902, "SRR6908710", "SRX3856800", "SRS3100392", "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.", null, "pubmed:31585086", null, "WKM5 unenriched", "GSM3070138", null, "tissue:WKM5 unenriched|FISHid:WKM5|cell type:whole kidney marrow single cells|cell subtype:unenriched", "WKM5 unenriched", "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", "WKM5 unenriched", null, "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", null, "FISHid:WKM5|cell type:whole kidney marrow single cells|cell subtype:unenriched", "GSM3070138", "GSM3070138: WKM5 unenriched; Danio rerio; RNA Seq", "GSM3070138", null, "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:GSM3070138", "RNA-Seq", "TRANSCRIPTOMIC", "cDNA", "PAIRED", "ILLUMINA", "NextSeq 500", null, "SRP136633", null, null, "WKM5_unenriched_L002_R1_001.fastq.gz WKM5_unenriched_L002_R2_001.fastq.gz", "fastq fastq", 1573355252.0, 10430670.0, "GSM3070138 r2", "0:75.40 1:75.44", "A:458302389;C:217385315;G:261327273;T:636024406;N:315869", 75, 75, null, null, 458302389, 217385315, 261327273, 636024406, 315869, "SRX3856800", "SRS3100392", "SRA675960", "GEO", "Hubrecht Institute", 2, 0.11064, 0.71559, 0.07574, 0.19872, 0.98981, 0.85005, 0.45203, 0.52838, 75, 76, "T", "B", "mate1 technical by mapping diff", "illumina", "nextseq", "unknown", "random_priming", "trueseq", "sc", "single_cell_plate", "celseq", null, "Netherlands", "2018-03-28", "Undetermined", "Undetermined", "Kidney", "Renal System"], [47903, "SRR6908711", "SRX3856800", "SRS3100392", "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.", null, "pubmed:31585086", null, "WKM5 unenriched", "GSM3070138", null, "tissue:WKM5 unenriched|FISHid:WKM5|cell type:whole kidney marrow single cells|cell subtype:unenriched", "WKM5 unenriched", "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", "WKM5 unenriched", null, "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", null, "FISHid:WKM5|cell type:whole kidney marrow single cells|cell subtype:unenriched", "GSM3070138", "GSM3070138: WKM5 unenriched; Danio rerio; RNA Seq", "GSM3070138", null, "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:GSM3070138", "RNA-Seq", "TRANSCRIPTOMIC", "cDNA", "PAIRED", "ILLUMINA", "NextSeq 500", null, "SRP136633", null, null, "WKM5_unenriched_L003_R2_001.fastq.gz WKM5_unenriched_L003_R1_001.fastq.gz", "fastq fastq", 1566951274.0, 10386628.0, "GSM3070138 r3", "0:75.42 1:75.44", "A:451321309;C:220819392;G:256230144;T:638495895;N:84534", 75, 75, null, null, 451321309, 220819392, 256230144, 638495895, 84534, "SRX3856800", "SRS3100392", "SRA675960", "GEO", "Hubrecht Institute", 2, 0.10189, 0.72988, 0.07219, 0.20488, 0.99281, 0.83899, 0.46012, 0.51971, 75, 76, "T", "B", "mate1 technical by mapping diff", "illumina", "nextseq", "unknown", "random_priming", "trueseq", "sc", "single_cell_plate", "celseq", null, "Netherlands", "2018-03-28", "Undetermined", "Undetermined", "Kidney", "Renal System"], [47904, "SRR6908712", "SRX3856800", "SRS3100392", "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.", null, "pubmed:31585086", null, "WKM5 unenriched", "GSM3070138", null, "tissue:WKM5 unenriched|FISHid:WKM5|cell type:whole kidney marrow single cells|cell subtype:unenriched", "WKM5 unenriched", "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", "WKM5 unenriched", null, "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", null, "FISHid:WKM5|cell type:whole kidney marrow single cells|cell subtype:unenriched", "GSM3070138", "GSM3070138: WKM5 unenriched; Danio rerio; RNA Seq", "GSM3070138", null, "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:GSM3070138", "RNA-Seq", "TRANSCRIPTOMIC", "cDNA", "PAIRED", "ILLUMINA", "NextSeq 500", null, "SRP136633", null, null, "WKM5_unenriched_L004_R1_001.fastq.gz WKM5_unenriched_L004_R2_001.fastq.gz", "fastq fastq", 1470255063.0, 9745929.0, "GSM3070138 r4", "0:75.41 1:75.45", "A:425542718;C:206068561;G:242779977;T:595798063;N:65744", 75, 75, null, null, 425542718, 206068561, 242779977, 595798063, 65744, "SRX3856800", "SRS3100392", "SRA675960", "GEO", "Hubrecht Institute", 2, 0.10318, 0.71368, 0.07165, 0.20008, 0.99159, 0.8508, 0.46642, 0.51043, 75, 76, "T", "B", "mate1 technical by mapping diff", "illumina", "nextseq", "unknown", "random_priming", "trueseq", "sc", "single_cell_plate", "celseq", null, "Netherlands", "2018-03-28", "Undetermined", "Undetermined", "Kidney", "Renal System"], [47905, "SRR6908705", "SRX3856799", "SRS3100391", "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.", null, "pubmed:31585086", null, "WKM5 lymphocytes", "GSM3070137", null, "tissue:WKM5 lymphocytes|FISHid:WKM5|cell type:whole kidney marrow single cells|cell subtype:lymphocytes", "WKM5 lymphocytes", "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", "WKM5 lymphocytes", null, "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", null, "FISHid:WKM5|cell type:whole kidney marrow single cells|cell subtype:lymphocytes", "GSM3070137", "GSM3070137: WKM5 lymphocytes; Danio rerio; RNA Seq", "GSM3070137", null, "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:GSM3070137", "RNA-Seq", "TRANSCRIPTOMIC", "cDNA", "PAIRED", "ILLUMINA", "NextSeq 500", null, "SRP136633", null, null, "WKM5_lymphocytes_L001_R1_001.fastq.gz WKM5_lymphocytes_L001_R2_001.fastq.gz", "fastq fastq", 989705998.0, 6559370.0, "GSM3070137 r1", "0:75.45 1:75.43", "A:297302138;C:149495824;G:174348330;T:368343979;N:215727", 75, 75, null, null, 297302138, 149495824, 174348330, 368343979, 215727, "SRX3856799", "SRS3100391", "SRA675960", "GEO", "Hubrecht Institute", 2, 0.24742, 0.6658, 0.20828, 0.41062, 0.98214, 0.85423, 0.46204, 0.47999, 76, 76, "T", "B", "mate1 technical by mapping diff", "illumina", "nextseq", "unknown", "random_priming", "trueseq", "sc", "single_cell_plate", "celseq", null, "Netherlands", "2018-03-28", "Undetermined", "Undetermined", "Lymphatic System", "Cardiovascular System"], [47906, "SRR6908706", "SRX3856799", "SRS3100391", "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.", null, "pubmed:31585086", null, "WKM5 lymphocytes", "GSM3070137", null, "tissue:WKM5 lymphocytes|FISHid:WKM5|cell type:whole kidney marrow single cells|cell subtype:lymphocytes", "WKM5 lymphocytes", "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", "WKM5 lymphocytes", null, "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", null, "FISHid:WKM5|cell type:whole kidney marrow single cells|cell subtype:lymphocytes", "GSM3070137", "GSM3070137: WKM5 lymphocytes; Danio rerio; RNA Seq", "GSM3070137", null, "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:GSM3070137", "RNA-Seq", "TRANSCRIPTOMIC", "cDNA", "PAIRED", "ILLUMINA", "NextSeq 500", null, "SRP136633", null, null, "WKM5_lymphocytes_L002_R1_001.fastq.gz WKM5_lymphocytes_L002_R2_001.fastq.gz", "fastq fastq", 966752947.0, 6406981.0, "GSM3070137 r2", "0:75.44 1:75.45", "A:289601664;C:144620642;G:173993992;T:358339663;N:196986", 75, 75, null, null, 289601664, 144620642, 173993992, 358339663, 196986, "SRX3856799", "SRS3100391", "SRA675960", "GEO", "Hubrecht Institute", 2, 0.23604, 0.65405, 0.20033, 0.39718, 0.98196, 0.8636, 0.50139, 0.50729, 74, 76, "T", "B", "mate1 technical by mapping diff", "illumina", "nextseq", "unknown", "random_priming", "trueseq", "sc", "single_cell_plate", "celseq", null, "Netherlands", "2018-03-28", "Undetermined", "Undetermined", "Lymphatic System", "Cardiovascular System"], [47907, "SRR6908707", "SRX3856799", "SRS3100391", "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.", null, "pubmed:31585086", null, "WKM5 lymphocytes", "GSM3070137", null, "tissue:WKM5 lymphocytes|FISHid:WKM5|cell type:whole kidney marrow single cells|cell subtype:lymphocytes", "WKM5 lymphocytes", "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", "WKM5 lymphocytes", null, "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", null, "FISHid:WKM5|cell type:whole kidney marrow single cells|cell subtype:lymphocytes", "GSM3070137", "GSM3070137: WKM5 lymphocytes; Danio rerio; RNA Seq", "GSM3070137", null, "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:GSM3070137", "RNA-Seq", "TRANSCRIPTOMIC", "cDNA", "PAIRED", "ILLUMINA", "NextSeq 500", null, "SRP136633", null, null, "WKM5_lymphocytes_L003_R1_001.fastq.gz WKM5_lymphocytes_L003_R2_001.fastq.gz", "fastq fastq", 958861430.0, 6354224.0, "GSM3070137 r3", "0:75.46 1:75.44", "A:286188763;C:145686623;G:169054587;T:357878024;N:53433", 75, 75, null, null, 286188763, 145686623, 169054587, 357878024, 53433, "SRX3856799", "SRS3100391", "SRA675960", "GEO", "Hubrecht Institute", 2, 0.25215, 0.67286, 0.20951, 0.41571, 0.98707, 0.84611, 0.45707, 0.51523, 75, 75, "T", "B", "mate1 technical by mapping diff", "illumina", "nextseq", "unknown", "random_priming", "trueseq", "sc", "single_cell_plate", "celseq", null, "Netherlands", "2018-03-28", "Undetermined", "Undetermined", "Lymphatic System", "Cardiovascular System"], [47908, "SRR6908708", "SRX3856799", "SRS3100391", "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.", null, "pubmed:31585086", null, "WKM5 lymphocytes", "GSM3070137", null, "tissue:WKM5 lymphocytes|FISHid:WKM5|cell type:whole kidney marrow single cells|cell subtype:lymphocytes", "WKM5 lymphocytes", "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", "WKM5 lymphocytes", null, "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", null, "FISHid:WKM5|cell type:whole kidney marrow single cells|cell subtype:lymphocytes", "GSM3070137", "GSM3070137: WKM5 lymphocytes; Danio rerio; RNA Seq", "GSM3070137", null, "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:GSM3070137", "RNA-Seq", "TRANSCRIPTOMIC", "cDNA", "PAIRED", "ILLUMINA", "NextSeq 500", null, "SRP136633", null, null, "WKM5_lymphocytes_L004_R2_001.fastq.gz WKM5_lymphocytes_L004_R1_001.fastq.gz", "fastq fastq", 907053718.0, 6010618.0, "GSM3070137 r4", "0:75.45 1:75.46", "A:270238988;C:137187310;G:162483694;T:337104230;N:39496", 75, 75, null, null, 270238988, 137187310, 162483694, 337104230, 39496, "SRX3856799", "SRS3100391", "SRA675960", "GEO", "Hubrecht Institute", 2, 0.2382, 0.66089, 0.20055, 0.40225, 0.98537, 0.86109, 0.4862, 0.51182, 75, 74, "T", "B", "mate1 technical by mapping diff", "illumina", "nextseq", "unknown", "random_priming", "trueseq", "sc", "single_cell_plate", "celseq", null, "Netherlands", "2018-03-28", "Undetermined", "Undetermined", "Lymphatic System", "Cardiovascular System"], [47909, "SRR6908701", "SRX3856798", "SRS3100390", "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.", null, "pubmed:31585086", null, "WKM5 hspcs", "GSM3070136", null, "tissue:WKM5 hspcs|FISHid:WKM5|cell type:whole kidney marrow single cells|cell subtype:hspcs", "WKM5 hspcs", "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", "WKM5 hspcs", null, "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", null, "FISHid:WKM5|cell type:whole kidney marrow single cells|cell subtype:hspcs", "GSM3070136", "GSM3070136: WKM5 hspcs; Danio rerio; RNA Seq", "GSM3070136", null, "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:GSM3070136", "RNA-Seq", "TRANSCRIPTOMIC", "cDNA", "PAIRED", "ILLUMINA", "NextSeq 500", null, "SRP136633", null, null, "WKM5_hspcs_L001_R2_001.fastq.gz WKM5_hspcs_L001_R1_001.fastq.gz", "fastq fastq", 6064422368.0, 40205564.0, "GSM3070136 r1", "0:75.42 1:75.41", "A:1792101497;C:906329224;G:1061291018;T:2303411628;N:1289001", 75, 75, null, null, 1792101497, 906329224, 1061291018, 2303411628, 1289001, "SRX3856798", "SRS3100390", "SRA675960", "GEO", "Hubrecht Institute", 2, 0.07707, 0.61908, 0.04536, 0.14979, 0.98711, 0.86186, 0.54256, 0.52115, 75, 76, "T", "B", "mate1 technical by mapping diff", "illumina", "nextseq", "unknown", "random_priming", "trueseq", "sc", "single_cell_plate", "celseq", null, "Netherlands", "2018-03-28", "Undetermined", "Undetermined", "Kidney", "Renal System"], [47910, "SRR6908702", "SRX3856798", "SRS3100390", "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.", null, "pubmed:31585086", null, "WKM5 hspcs", "GSM3070136", null, "tissue:WKM5 hspcs|FISHid:WKM5|cell type:whole kidney marrow single cells|cell subtype:hspcs", "WKM5 hspcs", "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", "WKM5 hspcs", null, "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", null, "FISHid:WKM5|cell type:whole kidney marrow single cells|cell subtype:hspcs", "GSM3070136", "GSM3070136: WKM5 hspcs; Danio rerio; RNA Seq", "GSM3070136", null, "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:GSM3070136", "RNA-Seq", "TRANSCRIPTOMIC", "cDNA", "PAIRED", "ILLUMINA", "NextSeq 500", null, "SRP136633", null, null, "WKM5_hspcs_L002_R1_001.fastq.gz WKM5_hspcs_L002_R2_001.fastq.gz", "fastq fastq", 5921476240.0, 39257421.0, "GSM3070136 r2", "0:75.41 1:75.43", "A:1748991850;C:876000140;G:1055863619;T:2239402733;N:1217898", 75, 75, null, null, 1748991850, 876000140, 1055863619, 2239402733, 1217898, "SRX3856798", "SRS3100390", "SRA675960", "GEO", "Hubrecht Institute", 2, 0.08014, 0.61413, 0.04634, 0.14581, 0.98654, 0.8717, 0.51292, 0.50066, 76, 75, "T", "B", "mate1 technical by mapping diff", "illumina", "nextseq", "unknown", "random_priming", "trueseq", "sc", "single_cell_plate", "celseq", null, "Netherlands", "2018-03-28", "Undetermined", "Undetermined", "Kidney", "Renal System"], [47911, "SRR6908703", "SRX3856798", "SRS3100390", "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.", null, "pubmed:31585086", null, "WKM5 hspcs", "GSM3070136", null, "tissue:WKM5 hspcs|FISHid:WKM5|cell type:whole kidney marrow single cells|cell subtype:hspcs", "WKM5 hspcs", "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", "WKM5 hspcs", null, "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", null, "FISHid:WKM5|cell type:whole kidney marrow single cells|cell subtype:hspcs", "GSM3070136", "GSM3070136: WKM5 hspcs; Danio rerio; RNA Seq", "GSM3070136", null, "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:GSM3070136", "RNA-Seq", "TRANSCRIPTOMIC", "cDNA", "PAIRED", "ILLUMINA", "NextSeq 500", null, "SRP136633", null, null, "WKM5_hspcs_L003_R1_001.fastq.gz WKM5_hspcs_L003_R2_001.fastq.gz", "fastq fastq", 5866299339.0, 38885339.0, "GSM3070136 r3", "0:75.43 1:75.43", "A:1717556539;C:883848150;G:1028347614;T:2236229877;N:317159", 75, 75, null, null, 1717556539, 883848150, 1028347614, 2236229877, 317159, "SRX3856798", "SRS3100390", "SRA675960", "GEO", "Hubrecht Institute", 2, 0.06878, 0.62531, 0.04189, 0.15336, 0.99113, 0.85888, 0.5, 0.51754, 75, 76, "T", "B", "mate1 technical by mapping diff", "illumina", "nextseq", "unknown", "random_priming", "trueseq", "sc", "single_cell_plate", "celseq", null, "Netherlands", "2018-03-28", "Undetermined", "Undetermined", "Kidney", "Renal System"], [47912, "SRR6908704", "SRX3856798", "SRS3100390", "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.", null, "pubmed:31585086", null, "WKM5 hspcs", "GSM3070136", null, "tissue:WKM5 hspcs|FISHid:WKM5|cell type:whole kidney marrow single cells|cell subtype:hspcs", "WKM5 hspcs", "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", "WKM5 hspcs", null, "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", null, "FISHid:WKM5|cell type:whole kidney marrow single cells|cell subtype:hspcs", "GSM3070136", "GSM3070136: WKM5 hspcs; Danio rerio; RNA Seq", "GSM3070136", null, "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:GSM3070136", "RNA-Seq", "TRANSCRIPTOMIC", "cDNA", "PAIRED", "ILLUMINA", "NextSeq 500", null, "SRP136633", null, null, "WKM5_hspcs_L004_R1_001.fastq.gz WKM5_hspcs_L004_R2_001.fastq.gz", "fastq fastq", 5566177327.0, 36895726.0, "GSM3070136 r4", "0:75.42 1:75.44", "A:1631791302;C:834536884;G:988788873;T:2110815191;N:245077", 75, 75, null, null, 1631791302, 834536884, 988788873, 2110815191, 245077, "SRX3856798", "SRS3100390", "SRA675960", "GEO", "Hubrecht Institute", 2, 0.07494, 0.61393, 0.04705, 0.14806, 0.99032, 0.87178, 0.49704, 0.51802, 76, 76, "T", "B", "mate1 technical by mapping diff", "illumina", "nextseq", "unknown", "random_priming", "trueseq", "sc", "single_cell_plate", "celseq", null, "Netherlands", "2018-03-28", "Undetermined", "Undetermined", "Kidney", "Renal System"], [47913, "SRR6908697", "SRX3856797", "SRS3100389", "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.", null, "pubmed:31585086", null, "WKM4 unenriched", "GSM3070135", null, "tissue:WKM4 unenriched|FISHid:WKM4|cell type:whole kidney marrow single cells|cell subtype:unenriched", "WKM4 unenriched", "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 unenriched", null, "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", null, "FISHid:WKM4|cell type:whole kidney marrow single cells|cell subtype:unenriched", "GSM3070135", "GSM3070135: WKM4 unenriched; Danio rerio; RNA Seq", "GSM3070135", null, "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:GSM3070135", "RNA-Seq", "TRANSCRIPTOMIC", "cDNA", "PAIRED", "ILLUMINA", "NextSeq 500", null, "SRP136633", null, null, "WKM4_unenriched_L001_R2_001.fastq.gz WKM4_unenriched_L001_R1_001.fastq.gz", "fastq fastq", 1000182622.0, 6625590.0, "GSM3070135 r1", "0:75.50 1:75.46", "A:281062086;C:163533727;G:177503446;T:378045390;N:37973", 75, 75, null, null, 281062086, 163533727, 177503446, 378045390, 37973, "SRX3856797", "SRS3100389", "SRA675960", "GEO", "Hubrecht Institute", 2, 0.32578, 0.64735, 0.27911, 0.49563, 0.94596, 0.8396, 0.51648, 0.52037, 76, 75, "T", "B", "mate1 technical by mapping diff", "illumina", "nextseq", "unknown", "random_priming", "trueseq", "sc", "single_cell_plate", "celseq", null, "Netherlands", "2018-03-28", "Undetermined", "Undetermined", "Kidney", "Renal System"], [47914, "SRR6908698", "SRX3856797", "SRS3100389", "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.", null, "pubmed:31585086", null, "WKM4 unenriched", "GSM3070135", null, "tissue:WKM4 unenriched|FISHid:WKM4|cell type:whole kidney marrow single cells|cell subtype:unenriched", "WKM4 unenriched", "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 unenriched", null, "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", null, "FISHid:WKM4|cell type:whole kidney marrow single cells|cell subtype:unenriched", "GSM3070135", "GSM3070135: WKM4 unenriched; Danio rerio; RNA Seq", "GSM3070135", null, "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:GSM3070135", "RNA-Seq", "TRANSCRIPTOMIC", "cDNA", "PAIRED", "ILLUMINA", "NextSeq 500", null, "SRP136633", null, null, "WKM4_unenriched_L002_R2_001.fastq.gz WKM4_unenriched_L002_R1_001.fastq.gz", "fastq fastq", 1024598143.0, 6787724.0, "GSM3070135 r2", "0:75.49 1:75.46", "A:287879816;C:166711858;G:184760618;T:385194933;N:50918", 75, 75, null, null, 287879816, 166711858, 184760618, 385194933, 50918, "SRX3856797", "SRS3100389", "SRA675960", "GEO", "Hubrecht Institute", 2, 0.30906, 0.63868, 0.26505, 0.49323, 0.9498, 0.84977, 0.508, 0.53041, 75, 75, "T", "B", "mate1 technical by mapping diff", "illumina", "nextseq", "unknown", "random_priming", "trueseq", "sc", "single_cell_plate", "celseq", null, "Netherlands", "2018-03-28", "Undetermined", "Undetermined", "Kidney", "Renal System"], [47915, "SRR6908699", "SRX3856797", "SRS3100389", "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.", null, "pubmed:31585086", null, "WKM4 unenriched", "GSM3070135", null, "tissue:WKM4 unenriched|FISHid:WKM4|cell type:whole kidney marrow single cells|cell subtype:unenriched", "WKM4 unenriched", "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 unenriched", null, "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", null, "FISHid:WKM4|cell type:whole kidney marrow single cells|cell subtype:unenriched", "GSM3070135", "GSM3070135: WKM4 unenriched; Danio rerio; RNA Seq", "GSM3070135", null, "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:GSM3070135", "RNA-Seq", "TRANSCRIPTOMIC", "cDNA", "PAIRED", "ILLUMINA", "NextSeq 500", null, "SRP136633", null, null, "WKM4_unenriched_L003_R2_001.fastq.gz WKM4_unenriched_L003_R1_001.fastq.gz", "fastq fastq", 987354885.0, 6540694.0, "GSM3070135 r3", "0:75.50 1:75.46", "A:277978811;C:161185677;G:175345183;T:372839184;N:6030", 75, 75, null, null, 277978811, 161185677, 175345183, 372839184, 6030, "SRX3856797", "SRS3100389", "SRA675960", "GEO", "Hubrecht Institute", 2, 0.32206, 0.64379, 0.27584, 0.49734, 0.94608, 0.84764, 0.51258, 0.52761, 76, 75, "T", "B", "mate1 technical by mapping diff", "illumina", "nextseq", "unknown", "random_priming", "trueseq", "sc", "single_cell_plate", "celseq", null, "Netherlands", "2018-03-28", "Undetermined", "Undetermined", "Kidney", "Renal System"], [47916, "SRR6908700", "SRX3856797", "SRS3100389", "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.", null, "pubmed:31585086", null, "WKM4 unenriched", "GSM3070135", null, "tissue:WKM4 unenriched|FISHid:WKM4|cell type:whole kidney marrow single cells|cell subtype:unenriched", "WKM4 unenriched", "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 unenriched", null, "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", null, "FISHid:WKM4|cell type:whole kidney marrow single cells|cell subtype:unenriched", "GSM3070135", "GSM3070135: WKM4 unenriched; Danio rerio; RNA Seq", "GSM3070135", null, "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:GSM3070135", "RNA-Seq", "TRANSCRIPTOMIC", "cDNA", "PAIRED", "ILLUMINA", "NextSeq 500", null, "SRP136633", null, null, "WKM4_unenriched_L004_R1_001.fastq.gz WKM4_unenriched_L004_R2_001.fastq.gz", "fastq fastq", 1001846281.0, 6636299.0, "GSM3070135 r4", "0:75.50 1:75.46", "A:280586899;C:163214034;G:180353077;T:377687027;N:5244", 75, 75, null, null, 280586899, 163214034, 180353077, 377687027, 5244, "SRX3856797", "SRS3100389", "SRA675960", "GEO", "Hubrecht Institute", 2, 0.32088, 0.64536, 0.27364, 0.4929, 0.94663, 0.84822, 0.51406, 0.53384, 76, 76, "T", "B", "mate1 technical by mapping diff", "illumina", "nextseq", "unknown", "random_priming", "trueseq", "sc", "single_cell_plate", "celseq", null, "Netherlands", "2018-03-28", "Undetermined", "Undetermined", "Kidney", "Renal System"], [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.", null, "pubmed:31585086", null, "WKM4 eosinophils", "GSM3070134", null, "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", null, "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", null, "FISHid:WKM4|cell type:whole kidney marrow single cells|cell subtype:eosinophils", "GSM3070134", "GSM3070134: WKM4 eosinophils; Danio rerio; RNA Seq", "GSM3070134", null, "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", null, "SRP136633", null, null, "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, null, null, 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", null, "Netherlands", "2018-03-28", "Undetermined", "Undetermined", "Blood", "Hematopoietic System"], [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.", null, "pubmed:31585086", null, "WKM4 eosinophils", "GSM3070134", null, "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", null, "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", null, "FISHid:WKM4|cell type:whole kidney marrow single cells|cell subtype:eosinophils", "GSM3070134", "GSM3070134: WKM4 eosinophils; Danio rerio; RNA Seq", "GSM3070134", null, "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", null, "SRP136633", null, null, "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, null, null, 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", null, "Netherlands", "2018-03-28", "Undetermined", "Undetermined", "Blood", "Hematopoietic System"], [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.", null, "pubmed:31585086", null, "WKM4 eosinophils", "GSM3070134", null, "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", null, "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", null, "FISHid:WKM4|cell type:whole kidney marrow single cells|cell subtype:eosinophils", "GSM3070134", "GSM3070134: WKM4 eosinophils; Danio rerio; RNA Seq", "GSM3070134", null, "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", null, "SRP136633", null, null, "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, null, null, 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", null, "Netherlands", "2018-03-28", "Undetermined", "Undetermined", "Blood", "Hematopoietic System"], [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.", null, "pubmed:31585086", null, "WKM4 eosinophils", "GSM3070134", null, "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", null, "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", null, "FISHid:WKM4|cell type:whole kidney marrow single cells|cell subtype:eosinophils", "GSM3070134", "GSM3070134: WKM4 eosinophils; Danio rerio; RNA Seq", "GSM3070134", null, "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", null, "SRP136633", null, null, "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, null, null, 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", null, "Netherlands", "2018-03-28", "Undetermined", "Undetermined", "Blood", "Hematopoietic System"], [47921, "SRR6908689", "SRX3856795", "SRS3100387", "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.", null, "pubmed:31585086", null, "WKM3 unenriched", "GSM3070133", null, "tissue:WKM3 unenriched|FISHid:WKM3|cell type:whole kidney marrow single cells|cell subtype:unenriched", "WKM3 unenriched", "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 unenriched", null, "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", null, "FISHid:WKM3|cell type:whole kidney marrow single cells|cell subtype:unenriched", "GSM3070133", "GSM3070133: WKM3 unenriched; Danio rerio; RNA Seq", "GSM3070133", null, "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:GSM3070133", "RNA-Seq", "TRANSCRIPTOMIC", "cDNA", "PAIRED", "ILLUMINA", "NextSeq 500", null, "SRP136633", null, null, "WKM3_unenriched_L001_R1_001.fastq.gz WKM3_unenriched_L001_R2_001.fastq.gz", "fastq fastq", 1418323523.0, 9398733.0, "GSM3070133 r1", "0:75.43 1:75.47", "A:422643558;C:211337836;G:207870633;T:576229967;N:241529", 75, 75, null, null, 422643558, 211337836, 207870633, 576229967, 241529, "SRX3856795", "SRS3100387", "SRA675960", "GEO", "Hubrecht Institute", 2, 0.38916, 0.82774, 0.31564, 0.36709, 0.96169, 0.9177, 0.50178, 0.54487, 75, 76, "T", "B", "mate1 technical by mapping diff", "illumina", "nextseq", "unknown", "random_priming", "trueseq", "sc", "single_cell_plate", "celseq", null, "Netherlands", "2018-03-28", "Undetermined", "Undetermined", "Kidney", "Renal System"], [47922, "SRR6908690", "SRX3856795", "SRS3100387", "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.", null, "pubmed:31585086", null, "WKM3 unenriched", "GSM3070133", null, "tissue:WKM3 unenriched|FISHid:WKM3|cell type:whole kidney marrow single cells|cell subtype:unenriched", "WKM3 unenriched", "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 unenriched", null, "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", null, "FISHid:WKM3|cell type:whole kidney marrow single cells|cell subtype:unenriched", "GSM3070133", "GSM3070133: WKM3 unenriched; Danio rerio; RNA Seq", "GSM3070133", null, "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:GSM3070133", "RNA-Seq", "TRANSCRIPTOMIC", "cDNA", "PAIRED", "ILLUMINA", "NextSeq 500", null, "SRP136633", null, null, "WKM3_unenriched_L002_R1_001.fastq.gz WKM3_unenriched_L002_R2_001.fastq.gz", "fastq fastq", 1297380885.0, 8597567.0, "GSM3070133 r2", "0:75.43 1:75.47", "A:384413707;C:192495936;G:194053875;T:526229304;N:188063", 75, 75, null, null, 384413707, 192495936, 194053875, 526229304, 188063, "SRX3856795", "SRS3100387", "SRA675960", "GEO", "Hubrecht Institute", 2, 0.38661, 0.82306, 0.31693, 0.35768, 0.96171, 0.92468, 0.48342, 0.54858, 75, 76, "T", "B", "mate1 technical by mapping diff", "illumina", "nextseq", "unknown", "random_priming", "trueseq", "sc", "single_cell_plate", "celseq", null, "Netherlands", "2018-03-28", "Undetermined", "Undetermined", "Kidney", "Renal System"], [47923, "SRR6908691", "SRX3856795", "SRS3100387", "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.", null, "pubmed:31585086", null, "WKM3 unenriched", "GSM3070133", null, "tissue:WKM3 unenriched|FISHid:WKM3|cell type:whole kidney marrow single cells|cell subtype:unenriched", "WKM3 unenriched", "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 unenriched", null, "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", null, "FISHid:WKM3|cell type:whole kidney marrow single cells|cell subtype:unenriched", "GSM3070133", "GSM3070133: WKM3 unenriched; Danio rerio; RNA Seq", "GSM3070133", null, "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:GSM3070133", "RNA-Seq", "TRANSCRIPTOMIC", "cDNA", "PAIRED", "ILLUMINA", "NextSeq 500", null, "SRP136633", null, null, "WKM3_unenriched_L003_R1_001.fastq.gz WKM3_unenriched_L003_R2_001.fastq.gz", "fastq fastq", 1470091205.0, 9741084.0, "GSM3070133 r3", "0:75.44 1:75.48", "A:432738050;C:218429286;G:217928653;T:600846627;N:148589", 75, 75, null, null, 432738050, 218429286, 217928653, 600846627, 148589, "SRX3856795", "SRS3100387", "SRA675960", "GEO", "Hubrecht Institute", 2, 0.39516, 0.83773, 0.32452, 0.36027, 0.96834, 0.91662, 0.49614, 0.5268, 76, 76, "T", "B", "mate1 technical by mapping diff", "illumina", "nextseq", "unknown", "random_priming", "trueseq", "sc", "single_cell_plate", "celseq", null, "Netherlands", "2018-03-28", "Undetermined", "Undetermined", "Kidney", "Renal System"], [47924, "SRR6908692", "SRX3856795", "SRS3100387", "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.", null, "pubmed:31585086", null, "WKM3 unenriched", "GSM3070133", null, "tissue:WKM3 unenriched|FISHid:WKM3|cell type:whole kidney marrow single cells|cell subtype:unenriched", "WKM3 unenriched", "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 unenriched", null, "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", null, "FISHid:WKM3|cell type:whole kidney marrow single cells|cell subtype:unenriched", "GSM3070133", "GSM3070133: WKM3 unenriched; Danio rerio; RNA Seq", "GSM3070133", null, "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:GSM3070133", "RNA-Seq", "TRANSCRIPTOMIC", "cDNA", "PAIRED", "ILLUMINA", "NextSeq 500", null, "SRP136633", null, null, "WKM3_unenriched_L004_R1_001.fastq.gz WKM3_unenriched_L004_R2_001.fastq.gz", "fastq fastq", 1400378972.0, 9280367.0, "GSM3070133 r4", "0:75.43 1:75.46", "A:412590948;C:206892406;G:210433218;T:570347127;N:115273", 75, 75, null, null, 412590948, 206892406, 210433218, 570347127, 115273, "SRX3856795", "SRS3100387", "SRA675960", "GEO", "Hubrecht Institute", 2, 0.3812, 0.82835, 0.31027, 0.35634, 0.96889, 0.93608, 0.50533, 0.53883, 76, 76, "T", "B", "mate1 technical by mapping diff", "illumina", "nextseq", "unknown", "random_priming", "trueseq", "sc", "single_cell_plate", "celseq", null, "Netherlands", "2018-03-28", "Undetermined", "Undetermined", "Kidney", "Renal System"], [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.", null, "pubmed:31585086", null, "WKM3 eosinophils", "GSM3070132", null, "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", null, "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", null, "FISHid:WKM3|cell type:whole kidney marrow single cells|cell subtype:eosinophils", "GSM3070132", "GSM3070132: WKM3 eosinophils; Danio rerio; RNA Seq", "GSM3070132", null, "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", null, "SRP136633", null, null, "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, null, null, 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", null, "Netherlands", "2018-03-28", "Undetermined", "Undetermined", "Blood", "Hematopoietic System"], [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.", null, "pubmed:31585086", null, "WKM3 eosinophils", "GSM3070132", null, "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", null, "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", null, "FISHid:WKM3|cell type:whole kidney marrow single cells|cell subtype:eosinophils", "GSM3070132", "GSM3070132: WKM3 eosinophils; Danio rerio; RNA Seq", "GSM3070132", null, "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", null, "SRP136633", null, null, "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, null, null, 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", null, "Netherlands", "2018-03-28", "Undetermined", "Undetermined", "Blood", "Hematopoietic System"], [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.", null, "pubmed:31585086", null, "WKM3 eosinophils", "GSM3070132", null, "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", null, "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", null, "FISHid:WKM3|cell type:whole kidney marrow single cells|cell subtype:eosinophils", "GSM3070132", "GSM3070132: WKM3 eosinophils; Danio rerio; RNA Seq", "GSM3070132", null, "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", null, "SRP136633", null, null, "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, null, null, 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", null, "Netherlands", "2018-03-28", "Undetermined", "Undetermined", "Blood", "Hematopoietic System"], [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.", null, "pubmed:31585086", null, "WKM3 eosinophils", "GSM3070132", null, "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", null, "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", null, "FISHid:WKM3|cell type:whole kidney marrow single cells|cell subtype:eosinophils", "GSM3070132", "GSM3070132: WKM3 eosinophils; Danio rerio; RNA Seq", "GSM3070132", null, "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", null, "SRP136633", null, null, "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, null, null, 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", null, "Netherlands", "2018-03-28", "Undetermined", "Undetermined", "Blood", "Hematopoietic System"], [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.", null, "pubmed:31585086", null, "WKM3 classicalgate eosinophils", "GSM3070131", null, "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", null, "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", null, "FISHid:WKM3|cell type:whole kidney marrow single cells|cell subtype:classicalgate eosinophils", "GSM3070131", "GSM3070131: WKM3 classicalgate eosinophils; Danio rerio; RNA Seq", "GSM3070131", null, "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", null, "SRP136633", null, null, "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, null, null, 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", null, "Netherlands", "2018-03-28", "Undetermined", "Undetermined", "Blood", "Hematopoietic System"], [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.", null, "pubmed:31585086", null, "WKM3 classicalgate eosinophils", "GSM3070131", null, "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", null, "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", null, "FISHid:WKM3|cell type:whole kidney marrow single cells|cell subtype:classicalgate eosinophils", "GSM3070131", "GSM3070131: WKM3 classicalgate eosinophils; Danio rerio; RNA Seq", "GSM3070131", null, "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", null, "SRP136633", null, null, "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, null, null, 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", null, "Netherlands", "2018-03-28", "Undetermined", "Undetermined", "Blood", "Hematopoietic System"], [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.", null, "pubmed:31585086", null, "WKM3 classicalgate eosinophils", "GSM3070131", null, "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", null, "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", null, "FISHid:WKM3|cell type:whole kidney marrow single cells|cell subtype:classicalgate eosinophils", "GSM3070131", "GSM3070131: WKM3 classicalgate eosinophils; Danio rerio; RNA Seq", "GSM3070131", null, "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", null, "SRP136633", null, null, "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, null, null, 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", null, "Netherlands", "2018-03-28", "Undetermined", "Undetermined", "Blood", "Hematopoietic System"], [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.", null, "pubmed:31585086", null, "WKM3 classicalgate eosinophils", "GSM3070131", null, "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", null, "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", null, "FISHid:WKM3|cell type:whole kidney marrow single cells|cell subtype:classicalgate eosinophils", "GSM3070131", "GSM3070131: WKM3 classicalgate eosinophils; Danio rerio; RNA Seq", "GSM3070131", null, "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", null, "SRP136633", null, null, "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, null, null, 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", null, "Netherlands", "2018-03-28", "Undetermined", "Undetermined", "Blood", "Hematopoietic System"], [47933, "SRR6908677", "SRX3856791", "SRS3100383", "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.", null, "pubmed:31585086", null, "WKM2 unenriched", "GSM3070130", null, "tissue:WKM2 unenriched|FISHid:WKM2|cell type:whole kidney marrow single cells|cell subtype:unenriched", "WKM2 unenriched", "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 unenriched", null, "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", null, "FISHid:WKM2|cell type:whole kidney marrow single cells|cell subtype:unenriched", "GSM3070130", "GSM3070130: WKM2 unenriched; Danio rerio; RNA Seq", "GSM3070130", null, "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:GSM3070130", "RNA-Seq", "TRANSCRIPTOMIC", "cDNA", "PAIRED", "ILLUMINA", "NextSeq 500", null, "SRP136633", null, null, "WKM2_unenriched_L001_R2_001.fastq.gz WKM2_unenriched_L001_R1_001.fastq.gz", "fastq fastq", 1050338787.0, 6957998.0, "GSM3070130 r1", "0:75.46 1:75.50", "A:307962198;C:162998859;G:159301612;T:419897804;N:178314", 75, 75, null, null, 307962198, 162998859, 159301612, 419897804, 178314, "SRX3856791", "SRS3100383", "SRA675960", "GEO", "Hubrecht Institute", 2, 0.40419, 0.79829, 0.34944, 0.46358, 0.95463, 0.92715, 0.5107, 0.52123, 75, 76, "T", "B", "mate1 technical by mapping diff", "illumina", "nextseq", "unknown", "random_priming", "trueseq", "sc", "single_cell_plate", "celseq", null, "Netherlands", "2018-03-28", "Undetermined", "Undetermined", "Kidney", "Renal System"], [47934, "SRR6908678", "SRX3856791", "SRS3100383", "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.", null, "pubmed:31585086", null, "WKM2 unenriched", "GSM3070130", null, "tissue:WKM2 unenriched|FISHid:WKM2|cell type:whole kidney marrow single cells|cell subtype:unenriched", "WKM2 unenriched", "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 unenriched", null, "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", null, "FISHid:WKM2|cell type:whole kidney marrow single cells|cell subtype:unenriched", "GSM3070130", "GSM3070130: WKM2 unenriched; Danio rerio; RNA Seq", "GSM3070130", null, "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:GSM3070130", "RNA-Seq", "TRANSCRIPTOMIC", "cDNA", "PAIRED", "ILLUMINA", "NextSeq 500", null, "SRP136633", null, null, "WKM2_unenriched_L002_R1_001.fastq.gz WKM2_unenriched_L002_R2_001.fastq.gz", "fastq fastq", 954172584.0, 6321045.0, "GSM3070130 r2", "0:75.45 1:75.50", "A:277724691;C:147647025;G:148192193;T:380472896;N:135779", 75, 75, null, null, 277724691, 147647025, 148192193, 380472896, 135779, "SRX3856791", "SRS3100383", "SRA675960", "GEO", "Hubrecht Institute", 2, 0.40099, 0.79668, 0.34548, 0.45475, 0.95491, 0.93168, 0.512, 0.51844, 75, 76, "T", "B", "mate1 technical by mapping diff", "illumina", "nextseq", "unknown", "random_priming", "trueseq", "sc", "single_cell_plate", "celseq", null, "Netherlands", "2018-03-28", "Undetermined", "Undetermined", "Kidney", "Renal System"], [47935, "SRR6908679", "SRX3856791", "SRS3100383", "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.", null, "pubmed:31585086", null, "WKM2 unenriched", "GSM3070130", null, "tissue:WKM2 unenriched|FISHid:WKM2|cell type:whole kidney marrow single cells|cell subtype:unenriched", "WKM2 unenriched", "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 unenriched", null, "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", null, "FISHid:WKM2|cell type:whole kidney marrow single cells|cell subtype:unenriched", "GSM3070130", "GSM3070130: WKM2 unenriched; Danio rerio; RNA Seq", "GSM3070130", null, "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:GSM3070130", "RNA-Seq", "TRANSCRIPTOMIC", "cDNA", "PAIRED", "ILLUMINA", "NextSeq 500", null, "SRP136633", null, null, "WKM2_unenriched_L003_R1_001.fastq.gz WKM2_unenriched_L003_R2_001.fastq.gz", "fastq fastq", 1095988368.0, 7259975.0, "GSM3070130 r3", "0:75.47 1:75.50", "A:318145632;C:169141931;G:167581935;T:441001916;N:116954", 75, 75, null, null, 318145632, 169141931, 167581935, 441001916, 116954, "SRX3856791", "SRS3100383", "SRA675960", "GEO", "Hubrecht Institute", 2, 0.4016, 0.81167, 0.34543, 0.45854, 0.96193, 0.92427, 0.51235, 0.51464, 76, 74, "T", "B", "mate1 technical by mapping diff", "illumina", "nextseq", "unknown", "random_priming", "trueseq", "sc", "single_cell_plate", "celseq", null, "Netherlands", "2018-03-28", "Undetermined", "Undetermined", "Kidney", "Renal System"], [47936, "SRR6908680", "SRX3856791", "SRS3100383", "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.", null, "pubmed:31585086", null, "WKM2 unenriched", "GSM3070130", null, "tissue:WKM2 unenriched|FISHid:WKM2|cell type:whole kidney marrow single cells|cell subtype:unenriched", "WKM2 unenriched", "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 unenriched", null, "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", null, "FISHid:WKM2|cell type:whole kidney marrow single cells|cell subtype:unenriched", "GSM3070130", "GSM3070130: WKM2 unenriched; Danio rerio; RNA Seq", "GSM3070130", null, "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:GSM3070130", "RNA-Seq", "TRANSCRIPTOMIC", "cDNA", "PAIRED", "ILLUMINA", "NextSeq 500", null, "SRP136633", null, null, "WKM2_unenriched_L004_R1_001.fastq.gz WKM2_unenriched_L004_R2_001.fastq.gz", "fastq fastq", 1028881429.0, 6816251.0, "GSM3070130 r4", "0:75.46 1:75.49", "A:298430122;C:158105868;G:160593676;T:411666228;N:85535", 75, 75, null, null, 298430122, 158105868, 160593676, 411666228, 85535, "SRX3856791", "SRS3100383", "SRA675960", "GEO", "Hubrecht Institute", 2, 0.3924, 0.79036, 0.33854, 0.45285, 0.96386, 0.94568, 0.5146, 0.50952, 75, 76, "T", "B", "mate1 technical by mapping diff", "illumina", "nextseq", "unknown", "random_priming", "trueseq", "sc", "single_cell_plate", "celseq", null, "Netherlands", "2018-03-28", "Undetermined", "Undetermined", "Kidney", "Renal System"], [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.", null, "pubmed:31585086", null, "WKM2 eosinophils", "GSM3070129", null, "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", null, "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", null, "FISHid:WKM2|cell type:whole kidney marrow single cells|cell subtype:eosinophils", "GSM3070129", "GSM3070129: WKM2 eosinophils; Danio rerio; RNA Seq", "GSM3070129", null, "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", null, "SRP136633", null, null, "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, null, null, 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", null, "Netherlands", "2018-03-28", "Undetermined", "Undetermined", "Blood", "Hematopoietic System"], [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.", null, "pubmed:31585086", null, "WKM2 eosinophils", "GSM3070129", null, "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", null, "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", null, "FISHid:WKM2|cell type:whole kidney marrow single cells|cell subtype:eosinophils", "GSM3070129", "GSM3070129: WKM2 eosinophils; Danio rerio; RNA Seq", "GSM3070129", null, "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", null, "SRP136633", null, null, "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, null, null, 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", null, "Netherlands", "2018-03-28", "Undetermined", "Undetermined", "Blood", "Hematopoietic System"], [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.", null, "pubmed:31585086", null, "WKM2 eosinophils", "GSM3070129", null, "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", null, "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", null, "FISHid:WKM2|cell type:whole kidney marrow single cells|cell subtype:eosinophils", "GSM3070129", "GSM3070129: WKM2 eosinophils; Danio rerio; RNA Seq", "GSM3070129", null, "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", null, "SRP136633", null, null, "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, null, null, 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", null, "Netherlands", "2018-03-28", "Undetermined", "Undetermined", "Blood", "Hematopoietic System"], [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.", null, "pubmed:31585086", null, "WKM2 eosinophils", "GSM3070129", null, "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", null, "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", null, "FISHid:WKM2|cell type:whole kidney marrow single cells|cell subtype:eosinophils", "GSM3070129", "GSM3070129: WKM2 eosinophils; Danio rerio; RNA Seq", "GSM3070129", null, "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", null, "SRP136633", null, null, "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, null, null, 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", null, "Netherlands", "2018-03-28", "Undetermined", "Undetermined", "Blood", "Hematopoietic System"], [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.", null, "pubmed:31585086", null, "WKM2 classicalgate eosinophils", "GSM3070128", null, "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", null, "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", null, "FISHid:WKM2|cell type:whole kidney marrow single cells|cell subtype:classicalgate eosinophils", "GSM3070128", "GSM3070128: WKM2 classicalgate eosinophils; Danio rerio; RNA Seq", "GSM3070128", null, "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", null, "SRP136633", null, null, "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, null, null, 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", null, "Netherlands", "2018-03-28", "Undetermined", "Undetermined", "Blood", "Hematopoietic System"], [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.", null, "pubmed:31585086", null, "WKM2 classicalgate eosinophils", "GSM3070128", null, "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. 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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.", null, "pubmed:31585086", null, "WKM2 classicalgate eosinophils", "GSM3070128", null, "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. 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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.", null, "pubmed:31585086", null, "WKM2 classicalgate eosinophils", "GSM3070128", null, "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. 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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.", null, "pubmed:31585086", null, "WKM1 unenriched and eosinphils", "GSM3070127", null, "tissue:WKM1 unenriched|FISHid:WKM1|cell type:whole kidney marrow single cells|cell subtype:unenriched", "WKM1 unenriched and eosinphils", "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. 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