\nWe aimed to gain insight into the molecular pathology of specific\nneuronal and glial subtypes by analyzing single cells in the cortex of\nASD patients. To this end, we performed unbiased nuclei isolation and\nsnRNA-seq 48 post-mortem tissue samples from the prefrontal (PFC),\nanterior cingulate (ACC) and insular (IC) cortical regions of 16\npatients with ASD and 16 control subjects followed by high-throughput\nsingle-nucleus RNA sequencing.
\n\n\nWe generated more that 120,000 single-nuclei RNA-seq profiles and identified 11\nneuronal and six glial cell types; these included known subtypes of excitatory\nneurons and interneurons, main types of glial cells and brain endothelial\ncells. To identify genes dysregulated in ASD in a cell type-specific manner, we\ncompared nuclei profiles from ASD samples and control subjects. In total, we\nidentified 707 differentially expressed genes (DEGs); 81% of these genes were\ndifferentially expressed in a single cell type.
\n\n\nOur findings show that\ndevelopment and synaptic signaling of upper-layer excitatory neurons is\nespecially affected in ASD. Moreover, we find significant dysregulation of grey\nmatter astrocyte-encoded genes responsible for astrocyte development and\ncell-cell signaling. These findings indicate molecular pathological changes in\nupper-layer cortical neuron circuits and aberrant astroglia-neuronal\ninteractions in ASD and identify potential cellular and molecular therapeutic\ntargets.
\n", "paper_url": "https://science.sciencemag.org/content/364/6441/685 Velmeshev et al. Science. 2019.", "pmid": "31097668", "bioproject": "PRJNA434002", "rawMatrixFile": "rawMatrix.zip", "rawMatrixNote": "Matrix with raw read counts, includes a copy of the meta data", "submitter": "Dmitry Velmeshev", "version": 3, "submission_date": "2019-10-18", "lab": "Kriegstein", "unitDesc": "10x UMI counts from cellranger, log2-transformed", "inDir": "", "coordFiles": [ "tSNE.coords.tsv.gz" ], "matrixFile": "/usr/local/apache/htdocs-cells/autism/exprMatrix.tsv.gz", "methods": "We utilized 10X Genomics CellRanger software to perform cellular\nde-multiplexing, read alignment and Unique Molecular Identifier (UMI)\nquantification. Matrices containing UMI counts were filtered based on number of\ngenes expressed per cell (>=500) and fraction of mitochondrial and ribosomal\ntranscripts (<5%). Matrices from individual runs were combined, normalized\nto total UMIs per nucleus and log transformed. Genes expressed in more than\nfive cells were considered for downstream analysis.\n\nDimensionality reduction was performed using truncated principle component\nanalysis (PCA); significant principle components were selected using scree plot\nmethod. PC scores were used to perform t-Distributed Stochastic Neighbor\nEmbedding (t-SNE), as well as to calculate nearest neighbor (nn) distances. NN\nedges were Jaccard distance-weighed and used to perform Louvain clustering.\nClustering information was combined with t-SNE coordinates to visualize\nclusters.
\n\nTo determine cell types constituting each cluster, a combination of expression\nplots for know cell type markers, regional t-SNE plots and unbiased marker\ndiscovery with Model-based Analysis of Single-cell Transcriptomics (MAST) was\nutilized.
\n\nTo perform discovery of genes differentially expressed in ASD subjects compared\nto controls in each cell type, MAST was used to fit a linear mixed model and\ncontrol for individual as a random effect factor and age, sex, RNA integrity\nnumber (RIN), cortical region, mitochondrial and ribosomal RNA ratios,\nsequencing platform, 10X capture and sequencing batch as fixed effect factors.\nGenes with Fold Change >=0.2 and FDR<0.01 were considered differentially\nexpressed.
\n", "imageMd5": "ae73e65834eec7bdb9d94058acc63566" }