Single-Cell Heterogeneity Analysis and CRISPR Screen Identify Key β-Cell-Specific Disease Genes

Abstract

Summary: Identification of human disease signature genes typically requires samples from many donors to achieve statistical significance. Here, we show that single-cell heterogeneity analysis may overcome this hurdle by significantly improving the test sensitivity. We analyzed the transcriptome of 39,905 single islets cells from 9 donors and observed distinct β cell heterogeneity trajectories associated with obesity or type 2 diabetes (T2D). We therefore developed RePACT, a sensitive single-cell analysis algorithm to identify both common and specific signature genes for obesity and T2D. We mapped both β-cell-specific genes and disease signature genes to the insulin regulatory network identified from a genome-wide CRISPR screen. Our integrative analysis discovered the previously unrecognized roles of the cohesin loading complex and the NuA4/Tip60 histone acetyltransferase complex in regulating insulin transcription and release. Our study demonstrated the power of combining single-cell heterogeneity analysis and functional genomics to dissect the etiology of complex diseases. : Fang et al. found that β cells from healthy, obese, and diabetic donors have a distinct cellular heterogeneity pattern, which allows sensitive identification of disease signature genes from a small number of donors. Combined with results from a genome-wide CRISPR screen, they further annotated signature genes with insulin regulatory functions. Keywords: Cellular heterogeneity, single cell, CRISPR screen, Drop-seq, pancreatic islet, β cell, diabetes, obesity, functional genomics, bioinformatics