OBJECTIVE: Type 2 diabetes (T2D) is a complex disease characterized by 
pancreatic islet dysfunction, insulin resistance, and disruption of blood 
glucose levels. Genome-wide association studies (GWAS) have identified > 400 
independent signals that encode genetic predisposition. More than 90% of 
associated single-nucleotide polymorphisms (SNPs) localize to non-coding regions 
and are enriched in chromatin-defined islet enhancer elements, indicating a 
strong transcriptional regulatory component to disease susceptibility. 
Pancreatic islets are a mixture of cell types that express distinct hormonal 
programs, so each cell type may contribute differentially to the underlying 
regulatory processes that modulate T2D-associated transcriptional circuits. 
Existing chromatin profiling methods such as ATAC-seq and DNase-seq, applied to 
islets in bulk, produce aggregate profiles that mask important cellular and 
regulatory heterogeneity.
METHODS: We present genome-wide single-cell chromatin accessibility profiles in 
>1,600 cells derived from a human pancreatic islet sample using single-cell 
combinatorial indexing ATAC-seq (sci-ATAC-seq). We also developed a deep 
learning model based on U-Net architecture to accurately predict open chromatin 
peak calls in rare cell populations.
RESULTS: We show that sci-ATAC-seq profiles allow us to deconvolve alpha, beta, 
and delta cell populations and identify cell-type-specific regulatory signatures 
underlying T2D. Particularly, T2D GWAS SNPs are significantly enriched in beta 
cell-specific and across cell-type shared islet open chromatin, but not in alpha 
or delta cell-specific open chromatin. We also demonstrate, using less abundant 
delta cells, that deep learning models can improve signal recovery and feature 
reconstruction of rarer cell populations. Finally, we use co-accessibility 
measures to nominate the cell-specific target genes at 104 non-coding T2D GWAS 
signals.
CONCLUSIONS: Collectively, we identify the islet cell type of action across 
genetic signals of T2D predisposition and provide higher-resolution mechanistic 
insights into genetically encoded risk pathways.