Single-cell genomics identifies cell type–specific molecular changes in autism

Autism spectrum disorder (ASD) is a phenotypically and genetically heterogeneous neurodevelopmental disorder. Despite this heterogeneity, previous studies have shown patterns of molecular convergence in post-mortem brain tissue from autistic subjects. Here, we integrate genome-wide measures of mRNA expression, miRNA expression, DNA methylation, and histone acetylation from ASD and control brains to identify a convergent molecular subtype of ASD with shared dysregulation across both the epigenome and transcriptome. Focusing on this convergent subtype, we substantially expand the repertoire of differentially expressed genes in ASD and identify a component of upregulated immune processes that are associated with hypomethylation. We utilize eQTL and chromosome conformation datasets to link differentially acetylated regions with their cognate genes and identify an enrichment of ASD genetic risk variants in hyperacetylated noncoding regulatory regions linked to neuronal genes. These findings help elucidate how diverse genetic risk factors converge onto specific molecular processes in ASD.

Characterizing the developmental trajectory of oligodendrocyte progenitor cells (OPC) is of great interest given the importance of these cells in the remyelination process. However, studies of human OPC development remain limited by the availability of whole cell samples and material that encompasses a wide age range, including time of peak myelination. In this study, we apply single cell RNA sequencing to viable whole cells across the age span and link transcriptomic signatures of oligodendrocyte‐lineage cells with stage‐specific functional properties. Cells were isolated from surgical tissue samples of second‐trimester fetal, 2‐year‐old pediatric, 13‐year‐old adolescent, and adult donors by mechanical and enzymatic digestion, followed by percoll gradient centrifugation. Gene expression was analyzed using droplet‐based RNA sequencing (10X Chromium). Louvain clustering analysis identified three distinct cellular subpopulations based on 5,613 genes, comprised of an early OPC (e‐OPC) group, a late OPC group (l‐OPC), and a mature OL (MOL) group. Gene ontology terms enriched for e‐OPCs included cell cycle and development, for l‐OPCs included extracellular matrix and cell adhesion, and for MOLs included myelination and cytoskeleton. The e‐OPCs were mostly confined to the premyelinating fetal group, and the l‐OPCs were most highly represented in the pediatric age group, corresponding to the peak age of myelination. Cells expressing a signature characteristic of l‐OPCs were identified in the adult brain in situ using RNAScope. These findings highlight the transcriptomic variability in OL‐lineage cells before, during, and after peak myelination and contribute to identifying novel pathways required to achieve remyelination.

Statistical deconvolution strategies have emerged over the past decade to estimate the proportion of various cell populations in homogenate tissue sources like brain using gene expression data. Here we show that several existing deconvolution algorithms which estimate the RNA composition of homogenate tissue, relates to the amount of RNA attributable to each cell type, and not the cellular composition relating to the underlying fraction of cells. Incorporating "cell size" parameters into RNA-based deconvolution algorithms can successfully recover cellular fractions in homogenate brain RNA-seq data. We lastly show that using both cell sizes and cell type-specific gene expression profiles from brain regions other than the target/user-provided bulk tissue RNA-seq dataset consistently results in biased cell fractions. We report several independently constructed cell size estimates as a community resource and extend the MuSiC framework to accommodate these cell size estimates ( https://github.com/xuranw/MuSiC/ ).