{"database": "metadata", "table": "run_metadata", "rows": [[55653, "SRR10611682", "SRX7290954", "SRS5784119", "SRP235262", "PRJNA594336", "Single cell sequencing reveals heterogeneity effects of bisphenol A on zebrafish embryonic development", "GSE141664", "Transcriptome Analysis", "Embryonic period is sensitive window of bisphenol A BPA exposure. However  embryonic development is a highly dynamic process with changing cell populations and gene expression profiles. Heterogeneity effects of BPA on fish embryonic development remain not clear. This study applied single cell RNA sequencing to analyze the impact of BPA exposure on transcriptome heterogeneity of 64683 cells from zebrafish embryos at 8  12 hpf and 30 hpf. A total of 38 cell populations were identified  and gene expression profiles of 16 cell populations were significantly altered by BPA exposure. The strongest toxic effects of BPA were found at 12 hpf of segmentation stage  which is an active stage of cell differentiation. At 8 hpf  BPA mainly influenced the outer layer cell populations of embryos  such as neural plate border and enveloping layer cells. At 12 hpf and 30 hpf  nervous system formation and heart morphogenesis were disturbed. Differential process of neural plate border  neural crest  and neuron cells was altered  leading to increased neurogenesis. For the forebrain  midbrain  neurons  and optic cells  the altered cell division and DNA replication and repair were identified. Our study for the first time provides the comprehensive understanding of BPA toxicity on fish embryo development at single cell levels. Overall design: Single cell RNA sequencing were adopted to learn the transcriptional variation in zebrafish embryonic development following exposure to bisphenol A at different stage gastrulation  segmentation  and pharyngula.", null, null, null, "CK 12h", "GSM4210781", null, "source name:zebrafish embryo|tissue:embryonic single cell suspension|age:12hpf|exposed pollutants:n1", "CK 12h", "We use FastQC to perform basic statistics on the quality of the raw reads. Then  those read sequences produced by the Illumina pipeline in FASTQ format were pre processed through Trimmomatic software which can be summarized as below:1 Remove low quality reads: scan the read with a 4 base wide sliding window  cutting when the average quality per base drops below 10 SLIDINGWINDOW:  4:10 2 Remove trailing low quality or N bases below quality 3 TRAILING:3 3 Remove adapters : there are two modes to remove the adapter sequence: a.  alignment with the adapter sequence  the number of matching bases were greater than 7 and mismatch=2; b.when read1 and read2 overlapping base scoring  greater than 30  removed non overlapping portions ILLUMINACLIP: adapter.fa:  2: 30: 7 4 Drop reads below the 26 bases long 5 Discard those reads that can not form paired The remaining reads that passed all the filtering steps was counted as clean reads and all subsequent analyses were based on this. At last  we use FastQC to perform basic statistics on the quality of the clean reads. Cell Ranger uses an aligner called STAR  which peforms splicing aware alignment of reads to the genome. Cell Ranger then uses the transcript annotation GTF to bucket the reads into exonic  intronic  and intergenic  and by whether the reads align confidently to the genome. A read is exonic if at least 50% of it intersects an exon  intronic if it is non exonic and intersects an intron  and intergenic otherwise. For reads that align to a single exonic locus but also align to 1 or more non exonic loci  the exonic locus is prioritized and the read is considered to be confidently mapped to the exonic locus with MAPQ 255. Cell Ranger further aligns exonic reads to annotated transcripts  looking for compatibility. A read that is compatible with the exons of an annotated transcript  and aligned to the same strand  is considered mapped to the transcriptome. If the read is compatible with a single gene annotation  it is considered uniquely confidently mapped to the transcriptome. Only reads that  are confidently mapped to the transcriptome are used for UMI counting. Cell Ranger takes as input the expected number of recovered cells  N see    expect cells. Let m be a robust estimate of the maximum total UMI counts  taken as the 99th percentile of the top N barcodes by total UMI counts. All barcodes whose total UMI counts exceed m/10 are called as cells. This is performed separately for each GEM group library and  if the reference contains multiple genomes  for each genome. Genome build: Danio rerio Ensemble 95\uff08GRCz11 Supplementary files format and content: gene barcode expression matrix", "zebrafish embryo", "Exposure solutions were prepared by adding 100 \u03bcg/L BPA to culture medium. The exposure solution were was replaced every 12 hours.At three different stages gasturla \u00a0segmentation\u00a0and\u00a0pharyngula   zebrafish embryo were collected.", "The zebrafish embryo was digested into  cell suspension with dispase. Cells were loaded on a GemCode Single Cell Instrument10x Genomics  USA to generate single cell Gel bead in Emulsion GEMs. ScRNA seq libraries were prepared using the GemCode Single Cell 3\u2019 Gel Bead  Chip and Library Kits 10x Genomics  USA as per the manufacturer\u2019s protocol. Libraries were sequenced on an illumina Xten.", null, "tissue:embryonic single cell suspension|age:12hpf|exposed pollutants:n1", "GSM4210781", "GSM4210781: CK 12h; Danio rerio; RNA Seq", "GSM4210781", null, "1", "The zebrafish embryo was digested into  cell suspension with dispase. Cells were loaded on a GemCode Single Cell Instrument10x Genomics  USA to generate single cell Gel bead in Emulsion GEMs. ScRNA seq libraries were prepared using the GemCode Single Cell three prime Gel Bead  Chip and Library Kits 10x Genomics  USA as per the manufacturer's protocol. Libraries were sequenced on an illumina Xten.", "GEO Accession:GSM4210781", "RNA-Seq", "TRANSCRIPTOMIC", "cDNA", "PAIRED", "ILLUMINA", "HiSeq X Ten", null, "SRP235262", null, null, "CK-12h_S3_L003_R1_001.fastq.gz CK-12h_S3_L003_R2_001.fastq.gz", "fastq fastq", 104307666146.0, 345389623.0, "GSM4210781 r1", "0:151 1:151", "A:25979928759;C:17362219827;G:18048535744;T:42903273069;N:13708747", 151, 151, null, null, 25979928759, 17362219827, 18048535744, 42903273069, 13708747, "SRX7290954", "SRS5784119", "SRA1008496", "GEO", "Nanjing University", 2, 0.08003, 0.91078, 0.01149, 0.08744, 0.98561, 0.81152, 0.53825, 0.48534, 151, 151, "T", "B", "mate1 technical by mapping diff", "illumina", "hiseq_era", "unknown", "cdna_unspecified", "unknown", "sc", "single_cell_droplet", "10x", null, "China", "2019-12-09", "Segmentation", "Embryo", "Embryo Imprecise", "All anatomical structures"]], "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": ["55653"], "units": {}, "query_ms": 6.8111520013189875}