Single-Cell Transcriptome Profiling of Human Pancreatic Islets in Health and Type 2 Diabetes.
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ABSTRACT:
SUBMITTER: Segerstolpe A
PROVIDER: S-EPMC5069352 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:β cells are defined by the ability to produce and secret insulin. Recent studies have evaluated that human pancreatic β cells are heterogeneous and demonstrated the transcript alterations of β cell subpopulation in diabetes. Single-cell RNA sequence (scRNA-seq) analysis helps us to refine the cell types signatures and understand the role of the β cells during metabolic challenges and diseases. Here, we construct the pseudotime trajectory of β cells from publicly available scRNA-seq data in health and type 2 diabetes (T2D) based on highly dispersed and highly expressed genes using Monocle2. We identified three major states including 1) Normal branch, 2) Obesity-like branch and 3) T2D-like branch based on biomarker genes and genes that give rise to bifurcation in the trajectory. β cell function-maintain-related genes, insulin expression-related genes, and T2D-related genes enriched in three branches, respectively. Continuous pseudotime spectrum might suggest that β cells transition among different states. The application of pseudotime analysis is conducted to clarify the different cell states, providing novel insights into the pathology of β cells in T2D.Supplementary informationThe online version contains supplementary material is available at 10.1007/s43657-021-00024-z.
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Project description:Physiological variability in pancreatic cell type gene regulation and the impact on diabetes risk is poorly understood. In this study we mapped gene regulation in pancreatic cell types using single cell multiomic (joint RNA-seq and ATAC-seq) profiling in 28 non-diabetic donors in combination with single cell data from 35 non-diabetic donors in the Human Pancreas Analysis Program. We identified widespread associations with age, sex, BMI, and HbA1c, where gene regulatory responses were highly cell type- and phenotype-specific. In beta cells, donor age associated with hypoxia, apoptosis, unfolded protein response, and external signal-dependent transcriptional regulators, while HbA1c associated with inflammatory responses and gender with chromatin organization. We identified 10.8K loci where genetic variants were QTLs for cis regulatory element (cRE) accessibility, including 20% with lineage- or cell type-specific effects which disrupted distinct transcription factor motifs. Type 2 diabetes and glycemic trait associated variants were enriched in both phenotype- and QTL-associated beta cell cREs, whereas type 1 diabetes showed limited enrichment. Variants at 226 diabetes and glycemic trait loci were QTLs in beta and other cell types, including 40 that were statistically colocalized, and annotating target genes of colocalized QTLs revealed genes with putatively novel roles in disease. Our findings reveal diverse responses of pancreatic cell types to phenotype and genotype in physiology, and identify pathways, networks, and genes through which physiology impacts diabetes risk.
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Project description:Type 1 diabetes (T1D) is an autoimmune disease in which immune cells destroy insulin-producing beta cells. The aetiology of this complex disease is dependent on the interplay of multiple heterogeneous cell types in the pancreatic environment. Here, we provide a single-cell atlas of pancreatic islets of 24 T1D, autoantibody-positive and nondiabetic organ donors across multiple quantitative modalities including ~80,000 cells using single-cell transcriptomics, ~7,000,000 cells using cytometry by time of flight and ~1,000,000 cells using in situ imaging mass cytometry. We develop an advanced integrative analytical strategy to assess pancreatic islets and identify canonical cell types. We show that a subset of exocrine ductal cells acquires a signature of tolerogenic dendritic cells in an apparent attempt at immune suppression in T1D donors. Our multimodal analyses delineate cell types and processes that may contribute to T1D immunopathogenesis and provide an integrative procedure for exploration and discovery of human pancreatic function.
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Project description:Human pluripotent stem cells differentiated to insulin-secreting ? cells (SC-? cells) in islet organoids could provide an unlimited cell source for diabetes cell replacement therapy. However, current SC-? cells generated in vitro are transcriptionally and functionally immature compared to native adult ? cells. Here, we use single-cell transcriptomic profiling to catalog changes that occur in transplanted SC-? cells. We find that transplanted SC-? cells exhibit drastic transcriptional changes and mature to more closely resemble adult ? cells. Insulin and IAPP protein secretions increase upon transplantation, along with expression of maturation genes lacking with differentiation in vitro, including INS, MAFA, CHGB, and G6PC2. Other differentiated cell types, such as SC-? and SC-enterochromaffin (SC-EC) cells, also exhibit large transcriptional changes. This study provides a comprehensive resource for understanding human islet cell maturation and provides important insights into maturation of SC-? cells and other SC-islet cell types to enable future differentiation strategy improvements.
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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.
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Project description:Single-cell RNA sequencing (scRNA-seq) of human primary tissues is a rapidly emerging tool for investigating human health and disease at the molecular level. However, optimal processing of solid tissues presents a number of technical and logistical challenges, especially for tissues that are only available at autopsy, which includes pancreatic islets, a tissue that is highly relevant to diabetes. To assess the possible effects of different sample preparation protocols on fresh islet samples, we performed a detailed comparison of scRNA-seq data generated with islets isolated from a human donor but processed according to four treatment strategies, including fixation and cryopreservation. We found significant and reproducible differences in the proportion of cell types identified, and more minor effects on cell-specific patterns of gene expression. Fresh islets from a second donor confirmed gene expression signatures of alpha and beta subclusters. These findings may well apply to other tissues, emphasizing the need for careful consideration when choosing processing methods, comparing results between different studies, and/or interpreting data in the context of multiple cell types from preserved tissue.
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Project description:Perturbed secretion of insulin and other pancreatic hormones is the main cause of type 2 diabetes (T2D). The pancreatic islets harbor five cell types that are potentially altered differently by T2D. Whole-islet transcriptomics and single-cell RNA-sequencing (scRNAseq) studies have revealed differentially expressed genes but unfortunately without reaching consensus. We propose that, rather than analyzing expression of individual genes or structural changes in networks of genes, building and analyzing networks of differentially synchronized genes on single-cell level gives unprecedented insights to disease. To this end, we developed a differential gene synchronization network analysis (dGSNA) algorithm and used it to analyze a high-quality pancreatic islet scRNAseq dataset from T2D and non-T2D individuals. dGSNA on beta cells revealed T2D-induced changes in twelve networks of genes, representing both canonical and less studied biological processes in the context of T2D. Analysis of these networks suggested that in T2D, beta cell energy metabolism, cell growth, differentiation and proteolysis programs are perturbed, whereas exocytosis, insulin translation and the unfolded protein response programs instead are enhanced. To validate our method, we showed that ten out of eleven selected node genes regulated insulin mRNA levels and/or secretion in INS-1 832/13 cells. Further, knockout mouse models for two of these genes (Tmem176a/b and Cebpg) exhibited reduced beta cell mass and perturbed insulin secretion. The dGSNA algorithm thus predicts central disease processes in T2D, which was replicated in an independent data set, revealing targetable cellular functions. We conclude that analysis of networks of differentially synchronized genes provides insight into the pathophysiology of T2D. This approach is likely generally applicable to other diseases where scRNAseq data can be obtained.
2022-07-20 | GSE153855 | GEO
Project description:Understanding distinct cell-type specific gene expression in human pancreatic islets is important for developing islet regeneration strategies and therapies to improve β-cell function in type 1 diabetes (T1D). While numerous transcriptome-wide studies on human islet cell-types have focused on protein-coding genes, the non-coding repertoire, such as long non-coding RNA, including circular RNAs, remains mostly unexplored. Here, we explored transcriptional landscape of human α-, β-, and exocrine cells from published total RNA sequencing (RNA-seq) datasets to identify circular RNAs (circRNAs). Our analysis revealed that circRNAs are highly abundant in both α- and β-cells. We identified 10,830 high-confidence circRNAs expressed in human α-, β-, and exocrine cells. The most highly expressed candidates were MAN1A2, RMST, and HIPK3 across the three cell-types. Alternate circular isoforms were observed for circRNAs in the three cell-types, indicative of potential distinct functions. Highly selective α- and β-cell circRNAs were identified, which is suggestive of their potential role in regulating β-cell function.
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Project description:Aims/hypothesis?-Aminobutyric acid (GABA) is a signalling molecule in the interstitial space in pancreatic islets. We examined the expression and function of the GABA signalling system components in human pancreatic islets from normoglycaemic and type 2 diabetic individuals.MethodsExpression of GABA signalling system components was studied by microarray, quantitative PCR analysis, immunohistochemistry and patch-clamp experiments on cells in intact islets. Hormone release was measured from intact islets.ResultsThe GABA signalling system was compromised in islets from type 2 diabetic individuals, where the expression of the genes encoding the ?1, ?2, ?2 and ?3 GABA(A) channel subunits was downregulated. GABA originating within the islets evoked tonic currents in the cells. The currents were enhanced by pentobarbital and inhibited by the GABA(A) receptor antagonist, SR95531. The effects of SR95531 on hormone release revealed that activation of GABA(A) channels (GABA(A) receptors) decreased both insulin and glucagon secretion. The GABA(B) receptor antagonist, CPG55845, increased insulin release in islets (16.7 mmol/l glucose) from normoglycaemic and type 2 diabetic individuals.Conclusions/interpretationInterstitial GABA activates GABA(A) channels and GABA(B) receptors and effectively modulates hormone release in islets from type 2 diabetic and normoglycaemic individuals.
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