{"database": "metadata", "table": "run_metadata", "rows": [[43632, "SRR5961760", "SRX3119871", "SRS2454730", "SRP116018", "PRJNA399711", "Whole organism clone tracing using single cell sequencing", "GSE102990", "Transcriptome Analysis", "We present ScarTrace  a single cell sequencing strategy that allows us to simultaneously quantify information on clonal history and cell type for thousands of single cells obtained from different organs from adult zebrafish. Using this approach we show that all blood cells types in the kidney marrow arise from a small set of multipotent embryonic. In contrast  we find that cells in the eyes  brain  and caudal tail fin arise from many embryonic progenitors  which are more restricted and produce specific cell types in the adult tissue. Next we use ScarTrace to explore when embryonic cells commit to forming either left or right organs using the eyes and brain as a model system. Lastly we monitor regeneration of the caudal tail fin and identify a subpopulation of resident macrophages that have a clonal origin that is distinct from other blood cell types. Overall design: Single cell sequencing data from cells isolated from zebrafish organs whole kidney marrow  forebrain  hindbrain  left eye  right eye  left midbrain  right midbrain  and regenerated fin. For each cell  we provide libraries with transcritpome and with clonal information  respectively.", null, "pubmed:29590089", null, "P1 forebrain Scars", "GSM2752194", null, "source name:forebrain single cells|cas9 injection:protein|FISH id:P1|tissue:forebrain|sorted plates:2", "P1 forebrain Scars", "In scar libraries  first read contains cell barcode 8 first nucleotides and second read contains the scar. Second reads with a valid cell barcode in first read are mapped to the reference GFP sequence and only reads that map to GFP and contain the reverse PCR primer with at least three mismatches are taken into account for downstream analysis. These reads are mapped again using the pairwise2.align.globalms function from Biopython with match score set to 1; mismatch score to 0.25; open gap penalty to  1; and extending gap penalty to  0.1. Subsequently  we pool scars per cell and by cigar. For each scar library  a double Gaussian is fitted to the distribution of reads per cell to find the threshold in number of reads to select cells. Next  scars were filtered according to their frequency of detection in the full library. For each cell  reads are normalized to 100 and scars representing less than 3.5% are removed.                                        Genome build: In scar libraries: eGFP sequence extended with ERCC92 Supplementary files format and content: *scarclones.txt: tabular separated files  with scar percentage columns per cell rows. Cells from the same fish have been named according to barcode ID  plate and organ of origin.  Last column hclust referes to ID for the clone cells sharing hclust label have the same scar pattern", "forebrain single cells", null, "post organ isolation  live single cells are sorted into 384 well plates containing mineral oil  uniquely barcoded cell specific primers for independent scar and mRNA detection  Spike in controls and and RNAse inhibitor. Scartrace Illumina TruSeq adapaters", null, "cas9 injection:protein|FISH id:P1|tissue:forebrain|sorted plates:2", "GSM2752194", "GSM2752194: P1 forebrain Scars; Danio rerio; RNA Seq", "GSM2752194", null, "1", "post organ isolation  live single cells are sorted into 384 well plates containing mineral oil  uniquely barcoded cell specific primers for independent scar and mRNA detection  Spike in controls and and RNAse inhibitor. Scartrace Illumina TruSeq adapaters", "GEO Accession:GSM2752194", "RNA-Seq", "TRANSCRIPTOMIC", "cDNA", "PAIRED", "ILLUMINA", "NextSeq 500", null, "SRP116018", null, null, "P1_forebrain_Scar_p01_R1.fastq.gz P1_forebrain_Scar_p01_R2.fastq.gz", "fastq fastq", 111111360.0, 731095.0, "GSM2752194 r1", "0:75.99 1:75.99", "A:20001359;C:32539820;G:41186423;T:17376363;N:7395", 75, 75, null, null, 20001359, 32539820, 41186423, 17376363, 7395, "SRX3119871", "SRS2454730", "SRA602108", "GEO", "AVO, Hubrecht Institue", 2, 2e-05, 2e-05, 0.0, 0.0, 0.99997, 0.99997, 0.0, 0.0, 76, 76, "T", "T", "mates < 9% mapping rate", "illumina", "nextseq", "unknown", "random_priming", "trueseq", "sc", "other_seq", "scartrace", null, "Netherlands", "2017-08-23", "Undetermined", "Multi-stage", "Brain", "Nervous 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": ["43632"], "units": {}, "query_ms": 9.336005998193286}