Single-cell multi-cohort dissection of the schizophrenia transcriptome

Abstract

Schizophrenia is a prevalent mental illness with a high societal burden, complex pathophysiology, and diverse genetic and environmental etiology. Its complexity, polygenicity, and heterogeneity have hindered mechanistic elucidation and the search for new therapeutics. We present a single-cell dissection of schizophrenia-associated transcriptomic changes in the human prefrontal cortex across two independent cohorts, one deeply profiling 48 subjects (361,996 cells), and the other broadly profiling 92 subjects (106,761 cells). We identified 25 cell types that we used to produce a high-resolution atlas of schizophrenia-altered genes and pathways. Excitatory neurons were the most affected cell group, with transcriptional changes converging on neurodevelopment and synapse-related molecular pathways. Differentially expressed gene sets implicate a coherently expressed module of trans-acting regulatory factors involved in neurodevelopment and genetically associated with schizophrenia risk. Transcriptional alterations significantly overlapped with known genetic risk factors, suggesting convergence of rare and common genomic variants on reproducible neuronal population specific alterations in schizophrenia. The severity of transcriptional pathology segregated two populations of schizophrenia subjects in a manner consistent with the expression of specific transcriptional patterns marked by genes involved in synaptic function and chromatin dynamics. Our results provide a high-resolution single cell atlas linking transcriptomic changes within specific cell populations to etiological genetic risk factors, contextualizing established knowledge within the cytoarchitecture of the human cortex and facilitating mechanistic understanding of schizophrenia pathophysiology and heterogeneity.

Multimodal joint deconvolution and integrative signature selection in proteomics

“…This result is consistent with previous findings about glia cell abundance in SCZ [16, 17]. A recent snRNA-seq study [18] reporting cellular subpopulations in human brain prefrontal cortex for SCZ vs. healthy controls (Figure 3 (B)) also validated our estimates in Figure 3 (A). However, the CBS RNA deconvolution with signature matrix built from scRNA-seq data of healthy human prefrontal cortex [19] failed to recover the expected case-control disparities or mean abundances in multiple cell types (Figure 3 (C)).…”

Integrated single cell and unsupervised spatial transcriptomic analysis defines molecular anatomy of the human dorsolateral prefrontal cortex

“…For example, we highlight the interaction between EFNA5 and EPHA5 in excitatory L5/6 and L6 neuron subtypes in deep cortical layers, which is consistent with results from the most recently released SCZ genome-wide association study (GWAS) that identified enrichment of SCZ risk genes in glutamatergic neurons (43). Not only is EFNA5 the locus of a GWAS-identified common SCZ risk variant, but it is also differentially expressed between individuals with SCZ and neurotypical controls in specific cell types (44) . Spatially mapping disease-relevant LR pairs, which are often highly specific and druggable targets, can give new insights into pathophysiology and can help prioritize spatially restricted targets for therapeutic development.…”

Evaluating performance and applications of sample-wise cell deconvolution methods on human brain transcriptomic data

“…To compare deconvoluted PAGs and PAGs from sc/snRNAseq data, PAGs for AD( 23 ) and SCZ( 59 ) in cell types were downloaded. For brain development, PAGs were identified in pseudo-bulk data.…”