{"database": "metadata", "table": "run_metadata", "rows": [[62678, "SRR13302564", "SRX9731496", "SRS7924278", "SRP299270", "PRJNA687691", "The alx3 gene functions to regulate skeletal cell differentiation timing during zebrafish neurocranium development.", "GSE163826", "Other", "During craniofacial development  different populations of cartilage and bone forming cells develop in precise locations in the head. Most of these cells are derived from pluripotent cranial neural crest cells. The mechanisms that divide neural crest cells into distinct populations are not fully understood. Here we use single cell RNA sequencing to transcriptomically define different populations of cranial neural crest cells. We discovered that the transcription factor encoding alx gene family is restricted to the frontonasal population of neural crest cells. Furthermore  genetic mutant analyses indicate that alx3 functions to subdivide the frontonasal population into medial versus lateral subpopulations. Our results support a mechanism in which the alx gene family functions as an identity code  subdividing frontonasal neural crest cells into distinct subpopulations. This study furthers our understanding of how different skeletal cell fates are established during craniofacial development and how these mechanisms can go awry in genetic diseases. Overall design: neural crest cell profiling of 24 hpf WT D. rerio", null, "pubmed:33741714;pubmed:36134886", null, "WT rep 1", "GSM4988014", null, "tissue:Nerual crest cells|tissue source:neuroectoderm|strain:AB strain|age:24 hpf", "WT rep 1", "make ref / make fastq files / aggr command / count command; CellRanger 2.2.0 merge WT and mut data; Seurat v2; e10x <  MergeSeuratobject1 = wt  object2 = mut  project = \"n10x\"  min.cells = 5  min.genes = 100 filter for <250 genes per cell and for any mitochondrial UMIs at less than or equal to 2.5% per cell; Seurat v2; e10x <  FilterCellsobject = e10x  subset.names = c\"nGene\"  \"percent.mito\"  low.thresholds = c250   Inf  high.thresholds = cInf  0.025 cluster projection/dim red. Via PCA/UMAP post WT subset; Seurat v2; nichols TSNE <  RunTSNEnichols dr seurat WT  dims.use = 1:10  do.fast= TRUE  seed.use = 123/nichols umap clusters <  FindClustersobject = nichols TSNE  reduction.type = \"pca\"  dims.use = 1:15  resolution = 0.2  save.SNN = TRUE  n.start = 10  nn.eps = 0.5  print.output = FALSE  force.recalc = TRUE/nichols umap clusters <  FindClustersobject = nichols TSNE  reduction.type = \"pca\"  dims.use = 1:15  resolution = 0.2  save.SNN = TRUE  n.start = 10  nn.eps = 0.5  print.output = FALSE  force.recalc = TRUE main dim reduction plot; Seurat v2; DimPlotobject = nichols umap clusters  reduction.use = \"umap\"  do.return = TRUE  pt.size = 1.3 differential expression marker gene ID/localization of expression; Seurat v2; nichols umap.markers1 <  FindAllMarkersnichols umap clusters  only.pos = TRUE  min.pct = 0.1  thresh.use = 0.25/FeaturePlot organizing genes by descending ascending pct.2; Seurat v2; subset based on ascending pct.2 visualize distribution of alx genes across each cluster; Seurat v2; VlnPlotnichols umap clusters  features.plot = <each alx gene>  single.legend = TRUE  y.max = 2 + ggplot2::theme classic + ylab\"Expression Level log TPM\" pseudotemporal and lineage tracing analyses; mnonocle3 v3; Follows from pseudotime and lineage trajectory mapping vignette for Monocle 3 Trapnell et al.  2014 seurat /featureplot app; Seurat v2  RShiny; FeaturePlotnichols umap clusters diet  features = input$gene name  reduction = \"umap\"  cols = c\"grey\" \"blue\"  label = TRUE label.size = 4.5  pt.size = 1.5  ncol = 3 + xlim 10  10 + ylim 10  15; used the diet seurat function native to seurat to generate the \"skiny\" object used here lineage tracing/trajectory analysis app; monocle3 v3  Rshiny; plot cellsNichols cds subset  genes = input$gene name  cell size = 1.5  graph label size = 3  group label size = 4 + themeplot.title = element textsize = 25; uses the main cell data set object as in our pseudotemporal and lineage tracing analyses Genome build: GRCz11/danRer11 Supplementary files format and content: counts.csv Supplementary files format and content: raw UMI counts per gene per cell", "Nerual crest cells", "Crispr/cas9 mutagenesis of alx3 gene Jao et al.  2013  Barske et al.  2016", "32 double transgenic flia:EGFP;sox10:mRFP embryos were dissociated into single cell suspension using cold active protease from Bacillus licheniformis  DNase  EDTA  and trituration Adam et al.  2017 10X Genomics libraries", "Animals were maintained and staged according to established protocols Westerfield  1993  Kimmel et al.  1995.", "tissue source:neuroectoderm|strain:AB strain|age:24 hpf", "GSM4988014", "GSM4988014: WT rep 1; Danio rerio; RNA Seq", "GSM4988014", null, "1", "32 double transgenic flia:EGFP;sox10:mRFP embryos were dissociated into single cell suspension using cold active protease from Bacillus licheniformis  DNase  EDTA  and trituration Adam et al.  2017 10X Genomics libraries", "GEO Accession:GSM4988014", "RNA-Seq", "TRANSCRIPTOMIC", "cDNA", "PAIRED", "ILLUMINA", "Illumina NovaSeq 6000", null, "SRP299270", null, null, "2_mef_wt_High_24hpf_R1_001.fastq.gz 2_mef_wt_High_24hpf_R2_001.fastq.gz", "fastq fastq", 233119629204.0, 771919302.0, "GSM4988014 r1", "0:151 1:151", "A:69457580771;C:37624255978;G:33980317215;T:87522302829;N:4535172411", 151, 151, null, null, 69457580771, 37624255978, 33980317215, 87522302829, 4535172411, "SRX9731496", "SRS7924278", "SRA1177732", "GEO", "Nichols Lab, Craniofacial Biology, University of Colorado - Anschutz Medical Campus", 2, 0.05174, 0.87764, 0.00382, 0.06802, 0.98435, 0.84443, 0.4489, 0.49905, 151, 151, "T", "B", "mate1 technical by mapping diff", "illumina", "novaseq_era", "unknown", "cdna_unspecified", "unknown", "sc", "single_cell_droplet", "10x", null, "United States", "2020-12-24", "Pharyngula", "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": ["62678"], "units": {}, "query_ms": 11.311350999676506}