{"database": "metadata", "table": "run_metadata", "rows": [[28599, "SRR26491379", "SRX22195228", "SRS19251243", "SRP468071", "PRJNA1031138", "Cebp1 and Cebp\u00df transcriptional axis controls eosinophilopoiesis in zebrafish [scRNA Seq]", "GSE246039", "Transcriptome Analysis", "Eosinophils are well known to regulate host protection from parasites and have been reported to paticipate many other physiologic and pathologic processes. Understanding the role of eosinophils in these processes requires a better understanding of eosinophilopoiesis. Using a zebrafish model  we have identified an eosinophil lineage specific marker  eslec. Using this marker we have established a Tgeslec:eGFP reporter line  which specifically labels zebrafish eosinophil lineage cells from early life through maturity. Spatial temporal analysis of eslec+ cells demonstrated organ distribution at the larval stage. By single cell RNA Seq of eslec+ cells  tissue distributed eosinophils were found to have similar differentiation paths but different tissue specific expression profiles. Genetic analysis demonstrated a Cebp1 and Cebp\u00df transcriptional axis that regulated eosinophilopoiesis  in which Cebp1 directly targeted cebpb to inhibit eosinophil differentiationthe commitment and differentiation of the eosinophil lineage. In summary  this study characterized eosinophil development in multiple dimensions including spatial temporal patterns  expression profiles  and genetic regulators. The results provide for a better understanding of eosinophilopoiesis. Overall design: For scRNA Seq analysis of eosinophils in kidney  eosinophils were sorted from adult Tgeslec:eGFP kidneys. For scRNA Seq analysis of renal cells of different genotypes  kidneys were collected from WT  cebp1 /   and cebpb /  individuals and prepared into cell suspensions. The cell suspensions were then applied to 10x three prime scRNA Seq analysis. The scRNA Seq data of WT  cebp1 /   and cebpb /  KM cells were integrated and analyzed together.", "parent bioproject:PRJNA814534", "pubmed:38280871", null, "cebp1 mut KM 3", "GSM7854250", null, "source name:renal cells|strain:AB strain|tissue:Kidney|genotype:cebp1 / |transgene:n1|geo loc name:missing|collection date:missing", "cebp1 mut KM 3", "Post basecalling  the fastq files were mapped to the zebrafish genome with the CellRanger package. Output files of CellRanger were applied to the R package \"Seurat\". Samples were filtered to remove low quality or contaminated cells. The renal cells from different genotypes sample 1 9 were integrated with the \"IntegrateData\" function of Seurat. Further analyses was performed based on the instructions of \"Seurat\" package. Assembly: GRCz11 Supplementary files format and content: Matrix table with raw gene counts for every gene and every cell  cell and gene metadata for integrated KM cells and KM eosinophils  and the relevant Seurat objects rds files.", "renal cells", null, "For scRNA Seq analysis of eosinophils from kidney  eosinophils were sorted from adult Tgeslec:eGFP kidneys. For scRNA Seq analysis of renal cells of different genotypes  kidneys were collected from WT  cebp1 /   and cebpb /  individuals and prepared into cell suspensions. The cell suspensions were then applied to 10x three prime scRNA Seq GEM generation. Post GEM generation  the library construction was performed strictly based on the manufacturer\u2019s instructions 10x Genomics  USA.", null, "strain:AB strain|tissue:Kidney|genotype:cebp1 / |transgene:n1", "GSM7854250", "GSM7854250: cebp1 mut KM 3; Danio rerio; RNA Seq", "GSM7854250 r1", "GSM7854250", "1", "For scRNA Seq analysis of eosinophils from kidney  eosinophils were sorted from adult Tgeslec:eGFP kidneys. For scRNA Seq analysis of renal cells of different genotypes  kidneys were collected from WT  cebp1 /   and cebpb /  individuals and prepared into cell suspensions. The cell suspensions were then applied to 10x three prime scRNA Seq GEM generation. Post GEM generation  the library construction was performed strictly based on the manufacturer's instructions 10x Genomics  USA.", null, "RNA-Seq", "TRANSCRIPTOMIC SINGLE CELL", "cDNA", "PAIRED", "ILLUMINA", "Illumina NovaSeq 6000", null, "SRP468071", null, "loader:fastq load.py", "e3_S0_L003_R1_001.fastq.gz e3_S0_L003_R2_001.fastq.gz", "fastq fastq", 106692684600.0, 355642282.0, "GSM7854250 r1", "0:150 1:150", "A:43760768907;C:19045349028;G:18701042387;T:25183159362;N:2364916", 150, 150, null, null, 43760768907, 19045349028, 18701042387, 25183159362, 2364916, "SRX22195228", "SRS19251243", "SRA1739642", "South China University of Technology", "South China University of Technology", 2, 0.0, 0.90163, 0.0, 0.08054, 1.0, 0.7908, null, 0.55652, 150, 150, "T", "B", "mate1 technical by mapping diff", "illumina", "novaseq_era", "3prime", "cdna_unspecified", "unknown", "sc", "single_cell_droplet", "10x", null, "China", "2023-10-23", "Adult", "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": ["28599"], "units": {}, "query_ms": 7.2415649992763065}