{"database": "metadata", "table": "run_metadata", "rows": [[63083, "SRR13594979", "SRX9989399", "SRS8160234", "SRP304100", "PRJNA698538", "Heterogeneity effects of nanoplastics and lead on zebrafish intestine identified by single cell sequencing", "GSE165888", "Transcriptome Analysis", "Plastic particles in water environment can adsorb heavy metals  leading to combined toxicity to aquatic organisms. However  current conclusions are mostly obtained based on cell population average responses. Heterogeneity effects among cell populations in aquatic organisms remain unclear. This study analyzed the heterogeneity effects of 200 \u00b5g/L 100 nm polystyrene nanoplastics PS NPs  50 \u00b5g/L lead Pb  and their combined exposures on zebrafish intestine cells by single cell RNA sequencing.A total of 38640 cells in the zebrafish intestine was obtained and identified as seven cell populations  including enterocytes  macrophages  neutrophils  B cells  T cells  enteroendocrine cells  and goblet cells.Co exposure of PS NPs and Pb caused similar transcriptome profiles with PS NPs exposure in macrophages  which changed immunological recognition processes. The Pb exposure influenced the macrophages by direct cytotoxicity. However  the Pb alone and combined exposures induced similar modes of action in the enterocytes  including the generation of oxidative stress and abnormal lipid metabolism. Overall design: Single cell RNA sequencing were adopted to learn the transcriptional variation in zebrafish intestine followed exposure nanoplastics and lead.", null, "pubmed:34861263", null, "PS NPs+Pb scRNA seq", "GSM5057360", null, "source name:intestine cells|tissue:intestine single cell suspension|age:16 wpf|exposed pollutants:200\u03bcg/L PS NPs and 50\u03bcg/L Pb", "PS NPs+Pb scRNA seq", "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 91 Supplementary files format and content: gene barcode expression matrix", "intestine cells", "Exposure solutions were prepared by adding 200\u03bcg/L PS NPs  50\u03bcg/L Pb or both of them to culture water. The exposure solution were was replaced every 2 days.  post 21 d exposure  zebrafish were collected and intestine were rapidly extracted on ice.", "The isolated intestine tissue 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 a Nova6000.", null, "tissue:intestine single cell suspension|age:16 wpf|exposed pollutants:200\u03bcg/L PS NPs and 50\u03bcg/L Pb", "GSM5057360", "GSM5057360: PS NPs+Pb scRNA seq; Danio rerio; RNA Seq", "GSM5057360", null, "1", "The isolated intestine tissue 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 a Nova6000.", "GEO Accession:GSM5057360", "RNA-Seq", "TRANSCRIPTOMIC", "cDNA", "PAIRED", "ILLUMINA", "Illumina NovaSeq 6000", null, "SRP304100", null, null, "PS-NPs+Pb-3_S1_L002_R1_001.fastq.gz PS-NPs+Pb-3_S1_L002_R2_001.fastq.gz", "fastq fastq", 20835334200.0, 69451114.0, "GSM5057360 r3", "0:150 1:150", "A:7842229934;C:3755450675;G:3565661709;T:5671491296;N:500586", 150, 150, null, null, 7842229934, 3755450675, 3565661709, 5671491296, 500586, "SRX9989399", "SRS8160234", "SRA1189975", "GEO", "School of the Environment, Nanjing university", 2, 0.0, 0.9129, 0.0, 0.09663, 1.0, 0.82248, null, 0.56185, 150, 150, "T", "B", "mate1 technical by mapping diff", "illumina", "novaseq_era", "unknown", "cdna_unspecified", "unknown", "sc", "single_cell_droplet", "10x", null, "China", "2021-02-01", "Adult", "Adult", "Gut", "Digestive 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": ["63083"], "units": {}, "query_ms": 9.48126899311319}