{"database": "metadata", "table": "run_metadata", "rows": [[52953, "SRR9609654", "SRX6373048", "SRS5034105", "SRP212225", "PRJNA551501", "Single cell RNA sequencing of zebrafish thyroid cells", "GSE133466", "Transcriptome Analysis", "The thyroid gland is responsible for supplying the thyroid hormones to the body. The gland is an endocrine organ with an intricate structure enabling production  storage and release of the thyroid hormones. The gland is composed of numerous spherical follicles of varying sizes  surrounded by thyroid follicular epithelial cells  or thyrocytes. The thyrocytes surrounding the follicles generate the thyroid hormones in a multi step process. Though the machinery responsible for the production of thyroid hormones by thyrocytes is well established  it remains unknown if all the thyrocytes resident in the thyroid gland are equally capable of generating thyroid hormones. In other words  the extent of molecular homogeneity between individual thyrocytes has not yet been investigated. To obtain an unbiased picture into the molecular heterogeneity present in zebrafish thyrocytes  we performed droplet based next generation sequencing of individual thyrod gland cells. Using unsupervised clustering  we could identify all the major cell types present in the thyroid gland. Moreover  we could define sub populations within the major cell types  demonstrating the presence of molecular heterogeneity within nominally homogenous cell populations. Overall design: We used 10x Genomics to profile thyroid cells from zebrafish. Thyroid gland was enzymatically dissociated and single cell library prepared using 10x Genomics Chromium pipeline. Sequencing was performed on llumina NextSeq 550 machine using a HighOutput flowcell in paired end mode R1: 26 cycles; I1: 8 cycles; R2: 57 cycles  thus generating 45 mio fragments. The raw sequencing data was then processed with the 'count' command of the Cell Ranger software v2.1.0 provided by 10X Genomics with the option '  expect cells' set to 6000 all other options were used as per default. To build the reference for Cell Ranger  zebrafish genome GRCz10 as well as gene annotation Ensembl 87 were downloaded from Ensembl and the annotation was filtered with the 'mkgtf' command of Cell Ranger options: '  attribute=gene biotype:protein coding   attribute=gene biotype:lincRNA \u2013attribute=gene biotype:antisense'. Genome sequence and filtered annotation were then used as input to the 'mkref' command of Cell Ranger to build the appropriate Cellranger Reference.", null, null, null, "8mpf Thyroid", "GSM3909772", null, "tissue:thyroid gland|treatment:8mpf|strain:Tgtg:mVenus T2A NTR|tag:Normal", "8mpf Thyroid", "The raw sequencing data was then processed with the \u2018count\u2019 command of the Cell Ranger software v2.1.0 provided by 10X Genomics with the option \u2018  expect cells\u2019 set to 6000 all other options were used as per default. To build the reference for Cell Ranger  zebrafish genome GRCz10 as well as gene annotation Ensembl 87 were downloaded from Ensembl and the annotation was filtered with the \u2018mkgtf\u2019 command of Cell Ranger options: \u2018  attribute=gene biotype:protein coding   attribute=gene biotype:lincRNA \u2013attribute=gene biotype:antisense\u2019. Genome sequence and filtered annotation were then used as input to the \u2018mkref\u2019 command of Cell Ranger to build the appropriate Cellranger Reference. Genome build: Zebrafish GRCz10 Supplementary files format and content: Matrix with rows as genes and columns as cells.", "thyroid gland", null, "Enzymatic Dissociation The single cell suspension was adjusted to a concentration of about 800 cells per microliter and diluted with nuclease free water according to the manufacturer\u2019s instructions to yield 12000 cells. Subsequently  the cells were carefully mixed with reverse transcription mix before loading the cells on the 10X Genomics Chromium system Zheng et al.  2016. post the gel emulsion bead suspension underwent the reverse transcription reaction  emulsion was broken and DNA purified using Silane beads. The cDNA was amplified with 10 cycles  following the guidelines of the 10x Genomics user manual. The 10X Genomics single cell RNA seq library preparation   involving fragmentation  dA Tailing  adapter ligation and indexing PCR \u2013 was performed based on the manufacturer\u2019s protocol.", null, "treatment:8mpf|strain:Tgtg:mVenus T2A NTR|tag:Normal", "GSM3909772", "GSM3909772: 8mpf Thyroid; Danio rerio; RNA Seq", "GSM3909772", null, "1", "Enzymatic Dissociation The single cell suspension was adjusted to a concentration of about 800 cells per microliter and diluted with nuclease free water according to the manufacturer's instructions to yield 12000 cells. Subsequently  the cells were carefully mixed with reverse transcription mix before loading the cells on the 10X Genomics Chromium system Zheng et al.  2016. post the gel emulsion bead suspension underwent the reverse transcription reaction  emulsion was broken and DNA purified using Silane beads. The cDNA was amplified with 10 cycles  following the guidelines of the 10x Genomics user manual. The 10X Genomics single cell RNA seq library preparation   involving fragmentation  dA Tailing  adapter ligation and indexing PCR \u2013 was performed based on the manufacturer's protocol.", null, "RNA-Seq", "TRANSCRIPTOMIC", "cDNA", "PAIRED", "ILLUMINA", "NextSeq 500", null, "SRP212225", null, null, "L38696_Track-76232_R1.fastq.gz L38696_Track-76232_R2.fastq.gz", "fastq fastq", 9752526168.0, 116101502.0, "GSM3909772 r1", "0:28 1:56", "A:2760523285;C:2129359977;G:2225570954;T:2632796182;N:4275770", 28, 56, null, null, 2760523285, 2129359977, 2225570954, 2632796182, 4275770, "SRX6373048", "SRS5034105", "SRA914541", "GEO", "Singh Lab, IRIBHM", 2, 0.00831, 0.90972, 0.00332, 0.2088, 0.99157, 0.80182, 0.40796, 0.56239, 28, 56, "T", "B", "sc-like readlen", "illumina", "nextseq", "unknown", "cdna_unspecified", "unknown", "sc", "single_cell_droplet", "10x", null, "Belgium", "2019-06-27", "Adult", "Adult", "Thyroid", "Endocrine 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": ["52953"], "units": {}, "query_ms": 7.216926000182866}