Genomic profiling in post-mortem brain from autistic individuals has consistently revealed convergent molecular changes. What drives these changes and how they relate to genetic susceptibility in this complex condition is not understood. We performed deep single nuclear RNA sequencing (snRNAseq) to examine cell composition and transcriptomics, identifying dysregulation of cell type-specific gene regulatory networks (GRNs) in autism, which we corroborated using snATAC-seq and spatial transcriptomics. Transcriptomic changes were primarily cell type-specific, involving multiple cell types, most prominently interhemispheric and callosal-projecting neurons, interneurons within superficial laminae, and distinct glial reactive states involving oligodendrocytes, microglia, and astrocytes. Autism-associated GRN drivers and their targets were enriched in rare and common genetic risk variants, connecting autism genetic susceptibility and cellular and circuit alterations in the human brain. This data is the raw differential gene expression comparing ASD versus CTL subjects for each cell cluster. 

Please see Manuscript for detailed information.

In Brief: we generated Pseudobulk expression ASD vs CTL analysis by cell type. We generated pseudobulk counts for each sample by adding counts from the same cell type. Then pseudobulk counts are normalized by variance stabilizing transformation method. To identify genes differentially expressed in ASD compared to control in each cell type, we examined covariates with top 5 PCs from normalized pseudo-bulk expression matrix. We identified the following covariates consistently correlated with top 5PCs for each cell type: age, PMI, BrainRegion, SeqBatch, Mito_perc, and ngenes. We then randomly selected subjects 500 times and calculated average beta to regress out effects of these covariates. Then we used limma-voom to identify differentially expressed genes for each cluster.