{"database": "metadata", "table": "run_metadata", "rows": [[42607, "SRR5810676", "SRX2989235", "SRS2341158", "SRP111339", "PRJNA393429", "Dissecting hematopoietic and renal cell heterogeneity in adult zebrafish at single cell resolution using RNA sequencing [inDrops]", "GSE100910", "Transcriptome Analysis", "Recent advances in single cell transcriptomic profiling have provided unprecedented access to investigate cell heterogeneity during tissue and organ development. Here  we utilized massively parallel single cell RNA sequencing to define cell heterogeneity within the zebrafish kidney marrow  constructing a comprehensive molecular atlas of definitive hematopoiesis and functionally distinct renal cells found in adult zebrafish. Because our method analyzed blood and kidney cells in an unbiased manner  our approach was useful in characterizing immune cell deficiencies within prkdcD3612fs  il2rgaY91fs and double homozygous mutant fish  identifying blood cell losses in T  B  and natural killer cells within specific genetic mutants.  Our analysis also uncovered novel cell types including two classes of natural killer immune cells  classically defined and erythroid primed hematopoietic stem and progenitor cells  mucin secreting kidney cells  and kidney stem/progenitor cells. In total  our work provides the first comprehensive single cell transcriptomic analysis of kidney and marrow cells in the adult zebrafish. Overall design: The goal of our study is to establish the transcriptional profiles of hematopoietic and kidney cell lineages residing in the zebrafish whole kidney marrow. Firstly  we performed single cell RNA sequencing by a modified Smart seq2 protocol on sorted single cells from fluorescent transgenic zebrafish lines  which label distinct blood cell types n = 246 cells total. Secondly  we utilized droplet based single cell RNA sequencing inDrop to investigate unmarked  comprehensive hematopoietic lineage structure within wild type  casper strain zebrafish N=3 animals  n=3 782 cells total. From this  we identified ten distinct hematopoietic groups of blood and immune identities. Thirdly  we confirmed blood lineage interpretations by comparing hematopoietic lineages within wild type fish with mutant zebrafish with known immunodeficiencies  including prkdcD3612fs N=3 animals  n=3 201 cells  il2rgaY91fs N=2 animals  n=2 068 cells and prkdcD3612fs  il2rgaY91fs double compound mutant fish N=2 animals  n=2 276 cells. Lastly  we identified seven structural and functional cell lineages of kidney identities in the whole kidney marrow n=1 699 kidney cells.", "parent bioproject:PRJNA393415", "pubmed:28878000", null, "inDrop sequencing of prkdcD3612fs homozygous mutant zebrafish sample animal #2", "GSM2696102", null, "source name:Whole kidney marrow|indrop version:inDrop V2 protocol|tissue:kidney marrow|genotype:prkdcD3612fs", "inDrop sequencing of prkdcD3612fs homozygous mutant zebrafish sample animal #2", "Reads were processed using the indrops pipelinehttps://github.com/indrops/indrops. Briefly  reads were filtered according to structure and quality criteria. Filtered reads were then they were sorted by barcode. Demultiplexed reads were aligned to the GRCz10 transcriptome using Bowtie. Please see file \"inDrop GEO supplementary.xlsx \" available on the series record for information on de multiplexing and barcodes for individual samples and cells. Genome build: GRCz10 Supplementary files format and content: text file with read counts.", "Whole kidney marrow", null, "RNA from individual cells were reverse transcribed  barcoded  and unique transcripts are imprinted with UMI  all within the droplet. RNA from 1 500 cells of each animal were pooled and subsequently processed together. cDNA products were pre amplified to produce a cDNA library compatible with the Illumina Nextseq platform. Three sequencing runs were completed using the Nextseq 500 High Output V2 kit 75 cycles Illumina on a NextSeq 500 platform Illumina. Zebrafish cells are isolated from the kidney marrow into single cell suspensions  and treated with 15% Optiprep Sigma. Single cells are microfluidically sorted into droplets containing reverse transcription and barcoding reagents.  This method is outlined in Klein et al. PMID 26000487 and Zilionis et al. PMID 27929523.", null, "indrop version:inDrop V2 protocol|tissue:kidney marrow|genotype:prkdcD3612fs", "GSM2696102", "GSM2696102: inDrop sequencing of prkdcD3612fs homozygous mutant zebrafish sample animal #2; Danio rerio; RNA Seq", "GSM2696102", null, "1", "RNA from individual cells were reverse transcribed  barcoded  and unique transcripts are imprinted with UMI  all within the droplet. RNA from 1 500 cells of each animal were pooled and subsequently processed together. cDNA products were pre amplified to produce a cDNA library compatible with the Illumina Nextseq platform. Three sequencing runs were completed using the Nextseq 500 High Output V2 kit 75 cycles Illumina on a NextSeq 500 platform Illumina. Zebrafish cells are isolated from the kidney marrow into single cell suspensions  and treated with 15% Optiprep Sigma. Single cells are microfluidically sorted into droplets containing reverse transcription and barcoding reagents.  This method is outlined in Klein et al. PMID 26000487 and Zilionis et al. PMID 27929523.", "GEO Accession:GSM2696102", "RNA-Seq", "TRANSCRIPTOMIC", "cDNA", "SINGLE", "ILLUMINA", "NextSeq 500", null, "SRP111339", null, null, "PRKDC2_R1.fastq.gz", "fastq", 1987929396.0, 55220261.0, "GSM2696102 r1", "0:36 1:0", "A:506372087;C:413396954;G:415448050;T:652683387;N:28918", 36, 0, null, null, 506372087, 413396954, 415448050, 652683387, 28918, "SRX2989235", "SRS2341158", "SRA584587", "GEO", "Pathology, Massachusetts General Hospital", 1, 0.86587, null, 0.12447, null, 0.80336, null, 0.54126, null, 36, null, "B", null, "usable mapping rate", "illumina", "nextseq", "unknown", "random_priming", "unknown", "sc", "single_cell_droplet", "indrops", null, "United States", "2017-07-07", "Undetermined", "Adult", "Kidney", "Renal System"]], "columns": ["rowid", "run.accession", "experiment.accession", "sample.accession", "study.accession", "bioproject", "study.title", "study.alias", "study.type", "study.abstract", "study.attributes", "study.PMIDs", "sample.description", "sample.title", "sample.alias", "sample.centername", "sample.attributes", "GEOsample.title", "GEOsample.dataprocessing", "GEOsample.source", "GEOsample.treatmentprotocol", "GEOsample.extractprotocol", "GEOsample.growthprotocol", "GEOsample.characteristics", "GEOsample.accession", "experiment.title", "experiment.alias", "experiment.library_name", "experiment.design_description", "experiment.library_construction_protocol", "experiment.attributes", "experiment.library_strategy", "experiment.library_source", "experiment.library_selection", "experiment.library_layout", "experiment.platform", "experiment.instrument_model", "experiment.spot_descriptor", "experiment.study_ref", "run.title", "run.attributes", "run.filename", "run.semantic_name", "run.total_bases", "run.total_spots", "run.alias", "run.read_lengths", "run.base_counts", "run.r1_length", "run.r2_length", "run.r3_length", "run.r4_length", "run.Acount", "run.Ccount", "run.Gcount", "run.Tcount", "run.Ncount", "run.experiment", "run.pool_member", "submission.accession", "submission.srasource", "submission.bioprojectsource", "seqdetective.n_mates", "seqdetective.mapping_rate.mate1", "seqdetective.mapping_rate.mate2", "seqdetective.nofeature_rate.mate1", "seqdetective.nofeature_rate.mate2", "seqdetective.sparsity.mate1", "seqdetective.sparsity.mate2", "seqdetective.pos_strand_rate.mate1", "seqdetective.pos_strand_rate.mate2", "seqdetective.readlen.mate1", "seqdetective.readlen.mate2", "seqdetective.judgement.mate1", "seqdetective.judgement.mate2", "seqdetective.judgement.reason", "platform_family", "instrument_generation", "read_bias", "selection_class", "prep_kit", "sc_or_bulk", "tech_class", "technology", "tech_variant", "submission.bioprojectsource.country", "earliest_date", "devstage_curation", "devstage_curation_coarse", "tissue_curation", "tissue_curation_coarse"], "primary_keys": ["rowid"], "primary_key_values": ["42607"], "units": {}, "query_ms": 9.472578996792436}