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Single-Cell Transcriptome Profiling of Human Pancreatic Islets in Health and Type 2 Diabetes.

ABSTRACT: Hormone-secreting cells within pancreatic islets of Langerhans play important roles in metabolic homeostasis and disease. However, their transcriptional characterization is still incomplete. Here, we sequenced the transcriptomes of thousands of human islet cells from healthy and type 2 diabetic donors. We could define specific genetic programs for each individual endocrine and exocrine cell type, even for rare δ, γ, ε, and stellate cells, and revealed subpopulations of α, β, and acinar cells. Intriguingly, δ cells expressed several important receptors, indicating an unrecognized importance of these cells in integrating paracrine and systemic metabolic signals. Genes previously associated with obesity or diabetes were found to correlate with BMI. Finally, comparing healthy and T2D transcriptomes in a cell-type resolved manner uncovered candidates for future functional studies. Altogether, our analyses demonstrate the utility of the generated single-cell gene expression resource.

SUBMITTER: Segerstolpe A

PROVIDER: S-EPMC5069352 | biostudies-literature | 2016 Oct

REPOSITORIES: biostudies-literature

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Single-Cell Transcriptome Profiling of Human Pancreatic Islets in Health and Type 2 Diabetes.

Segerstolpe Åsa Å Palasantza Athanasia A Eliasson Pernilla P Andersson Eva-Marie EM Andréasson Anne-Christine AC Sun Xiaoyan X Picelli Simone S Sabirsh Alan A Clausen Maryam M Bjursell Magnus K MK Smith David M DM Kasper Maria M Ämmälä Carina C Sandberg Rickard R

Cell metabolism 20160922 4

Hormone-secreting cells within pancreatic islets of Langerhans play important roles in metabolic homeostasis and disease. However, their transcriptional characterization is still incomplete. Here, we sequenced the transcriptomes of thousands of human islet cells from healthy and type 2 diabetic donors. We could define specific genetic programs for each individual endocrine and exocrine cell type, even for rare δ, γ, ε, and stellate cells, and revealed subpopulations of α, β, and acinar cells. In ...[more]

PMID: 27667667

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Project description:ObjectiveType 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.MethodsWe 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.ResultsWe 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.ConclusionsCollectively, 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.

Dataset Information

Single-Cell Transcriptome Profiling of Human Pancreatic Islets in Health and Type 2 Diabetes.

Publications

Single-Cell Transcriptome Profiling of Human Pancreatic Islets in Health and Type 2 Diabetes.

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