Genetic predisposition is one of the factors that can lead to type 2 diabetes (T2D). Using genome-wide association studies and expression quantitative trait loci mapping approaches, recent studies have discovered >400 independent signals (>240 loci) associated with T2D. Pancreatic islets consist of a cluster of at least five different endocrine cell types (alpha, beta, delta, gamma, and epsilon), each producing a unique hormone in a coordinated manner. These clusters of cells work together to maintain insulin production and glucose homeostasis. Disruption of the interplay between the cell types is associated with T2D. However, the exact cellular mechanisms through which different risk factors contribute to disease risk are not completely understood.
Rai, Quang, et al. use single-cell combinatorial indexing ATAC-seq, a high-throughput epigenomic profiling method, to determine chromatin accessibility across samples in a tissue-wide manner, which enables them to deconvolve cell populations and identify cell-type-specific regulatory signatures underlying T2D. They found T2D single nucleotide polymorphisms to be significantly enriched in beta cell-specific and across cell-type shared islet open chromatin, but not in alpha or delta cell-specific open chromatin. They also developed a novel deep learning-based strategy to improve signal recovery and feature reconstruction for low abundance cell populations and apply it successfully to delta cells (<5% of the total islet population) identified in the study.Full Text