Single-cell expression profiling of islets generated by the Human Pancreas Analysis Program.
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ABSTRACT:
SUBMITTER: Patil AR
PROVIDER: S-EPMC10731597 | biostudies-literature | 2023 May
REPOSITORIES: biostudies-literature
Publications
Single-cell expression profiling of islets generated by the Human Pancreas Analysis Program.
Patil Abhijeet R AR Schug Jonathan J Naji Ali A Kaestner Klaus H KH Faryabi Robert B RB Vahedi Golnaz G
Nature metabolism 20230501 5
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Project description: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.
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Project description:The human endocrine pancreas consists of multiple cell types and plays a critical role in glucose homeostasis. Here, we apply mass cytometry technology to measure all major islet hormones, proliferative markers, and readouts of signaling pathways involved in proliferation at single-cell resolution. Using this innovative technology, we simultaneously examined baseline proliferation levels of all endocrine cell types from birth through adulthood, as well as in response to the mitogen harmine. High-dimensional analysis of our marker protein expression revealed three major clusters of beta cells within individuals. Proliferating beta cells are confined to two of the clusters.
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Project description:To decipher the populations of cells present in the human fetal pancreas and their lineage relationships, we developed strategies to isolate pancreatic progenitors, endocrine progenitors and endocrine cells. Transcriptome analysis of the individual populations revealed a large degree of conservation among vertebrates in the drivers of gene expression changes that occur at different steps of differentiation, although notably, sometimes, different members of the same gene family are expressed. The transcriptome analysis establishes a resource to identify novel genes and pathways involved in human pancreas development. Single-cell profiling further captured intermediate stages of differentiation and enabled us to decipher the sequence of transcriptional events occurring during human endocrine differentiation. Furthermore, we evaluate how well individual pancreatic cells derived in vitro from human pluripotent stem cells mirror the natural process occurring in human fetuses. This comparison uncovers a few differences at the progenitor steps, a convergence at the steps of endocrine induction, and the current inability to fully resolve endocrine cell subtypes in vitro.
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Project description:Aims/hypothesisDisruption of pancreatic islet function and glucose homeostasis can lead to the development of sustained hyperglycaemia, beta cell glucotoxicity and subsequently type 2 diabetes. In this study, we explored the effects of in vitro hyperglycaemic conditions on human pancreatic islet gene expression across 24 h in six pancreatic cell types: alpha; beta; gamma; delta; ductal; and acinar. We hypothesised that genes associated with hyperglycaemic conditions may be relevant to the onset and progression of diabetes.MethodsWe exposed human pancreatic islets from two donors to low (2.8 mmol/l) and high (15.0 mmol/l) glucose concentrations over 24 h in vitro. To assess the transcriptome, we performed single-cell RNA-seq (scRNA-seq) at seven time points. We modelled time as both a discrete and continuous variable to determine momentary and longitudinal changes in transcription associated with islet time in culture or glucose exposure. Additionally, we integrated genomic features and genetic summary statistics to nominate candidate effector genes. For three of these genes, we functionally characterised the effect on insulin production and secretion using CRISPR interference to knock down gene expression in EndoC-βH1 cells, followed by a glucose-stimulated insulin secretion assay.ResultsIn the discrete time models, we identified 1344 genes associated with time and 668 genes associated with glucose exposure across all cell types and time points. In the continuous time models, we identified 1311 genes associated with time, 345 genes associated with glucose exposure and 418 genes associated with interaction effects between time and glucose across all cell types. By integrating these expression profiles with summary statistics from genetic association studies, we identified 2449 candidate effector genes for type 2 diabetes, HbA1c, random blood glucose and fasting blood glucose. Of these candidate effector genes, we showed that three (ERO1B, HNRNPA2B1 and RHOBTB3) exhibited an effect on glucose-stimulated insulin production and secretion in EndoC-βH1 cells.Conclusions/interpretationThe findings of our study provide an in-depth characterisation of the 24 h transcriptomic response of human pancreatic islets to glucose exposure at a single-cell resolution. By integrating differentially expressed genes with genetic signals for type 2 diabetes and glucose-related traits, we provide insights into the molecular mechanisms underlying glucose homeostasis. Finally, we provide functional evidence to support the role of three candidate effector genes in insulin secretion and production.Data availabilityThe scRNA-seq data from the 24 h glucose exposure experiment performed in this study are available in the database of Genotypes and Phenotypes (dbGap; https://www.ncbi.nlm.nih.gov/gap/ ) with accession no. phs001188.v3.p1. Study metadata and summary statistics for the differential expression, gene set enrichment and candidate effector gene prediction analyses are available in the Zenodo data repository ( https://zenodo.org/ ) under accession number 11123248. The code used in this study is publicly available at https://github.com/CollinsLabBioComp/publication-islet_glucose_timecourse .
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Project description:Disruption of pancreatic islet function and glucose homeostasis can lead to the development of sustained hyperglycemia, beta cell glucotoxicity, and ultimately type 2 diabetes (T2D). In this study, we sought to explore the effects of hyperglycemia on human pancreatic islet (HPI) gene expression by exposing HPIs from two donors to low (2.8mM) and high (15.0mM) glucose concentrations over 24 hours, assaying the transcriptome at seven time points using single-cell RNA sequencing (scRNA-seq). We modeled time as both a discrete and continuous variable to determine momentary and longitudinal changes in transcription associated with islet time in culture or glucose exposure. Across all cell types, we identified 1,528 genes associated with time, 1,185 genes associated with glucose exposure, and 845 genes associated with interaction effects between time and glucose. We clustered differentially expressed genes across cell types and found 347 modules of genes with similar expression patterns across time and glucose conditions, including two beta cell modules enriched in genes associated with T2D. Finally, by integrating genomic features from this study and genetic summary statistics for T2D and related traits, we nominate 363 candidate effector genes that may underlie genetic associations for T2D and related traits.
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Project description:Human pancreatic islets consist of multiple endocrine cell types. To facilitate the detection of rare cellular states and uncover population heterogeneity, we performed single-cell RNA sequencing (RNA-seq) on islets from multiple deceased organ donors, including children, healthy adults, and individuals with type 1 or type 2 diabetes. We developed a robust computational biology framework for cell type annotation. Using this framework, we show that α- and β-cells from children exhibit less well-defined gene signatures than those in adults. Remarkably, α- and β-cells from donors with type 2 diabetes have expression profiles with features seen in children, indicating a partial dedifferentiation process. We also examined a naturally proliferating α-cell from a healthy adult, for which pathway analysis indicated activation of the cell cycle and repression of checkpoint control pathways. Importantly, this replicating α-cell exhibited activated Sonic hedgehog signaling, a pathway not previously known to contribute to human α-cell proliferation. Our study highlights the power of single-cell RNA-seq and provides a stepping stone for future explorations of cellular heterogeneity in pancreatic endocrine cells.
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Project description:Islet dysfunction is central in type 2 diabetes and full-blown type 2 diabetes develops first when the beta cells lose their ability to secrete adequate amounts of insulin in response to raised plasma glucose. Several mechanisms behind beta cell dysfunction have been put forward but many important questions still remain. Furthermore, our understanding of the contribution of each islet cell type in type 2 diabetes pathophysiology has been limited by technical boundaries. Closing this knowledge gap will lead to a leap forward in our understanding of the islet as an organ and potentially lead to improved treatments. The development of single-cell RNA sequencing (scRNAseq) has led to a breakthrough for characterising the transcriptome of each islet cell type and several important observations on the regulation of cell-type-specific gene expression have been made. When it comes to identifying type 2 diabetes disease mechanisms, the outcome is still limited. Several studies have identified differentially expressed genes, although there is very limited consensus between the studies. As with all new techniques, scRNAseq has limitations; in addition to being extremely expensive, genes expressed at low levels may not be detected, noise may not be appropriately filtered and selection biases for certain cell types are at hand. Furthermore, recent advances suggest that commonly used computational tools may be suboptimal for analysis of scRNAseq data in small-scale studies. Fortunately, development of new computational tools holds promise for harnessing the full potential of scRNAseq data. Here we summarise how scRNAseq has contributed to increasing the understanding of various aspects of islet biology as well as type 2 diabetes disease mechanisms. We also focus on challenges that remain and propose steps to promote the utilisation of the full potential of scRNAseq in this area.
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Project description:Human pluripotent stem cells differentiated to insulin-secreting β cells (SC-β cells) in islet organoids could provide an unlimited cell source for diabetes cell replacement therapy. However, current SC-β cells generated in vitro are transcriptionally and functionally immature compared to native adult β cells. Here, we use single-cell transcriptomic profiling to catalog changes that occur in transplanted SC-β cells. We find that transplanted SC-β cells exhibit drastic transcriptional changes and mature to more closely resemble adult β 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-α 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-β cells and other SC-islet cell types to enable future differentiation strategy improvements.
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Project description:Genome wide association studies (GWAS) have identified over 100 signals associated with type 1 diabetes (T1D). However, translating any given T1D GWAS signal into mechanistic insights, including putative causal variants and the context (cell type and cell state) in which they function, has been limited. Here, we present a comprehensive multi-omic integrative analysis of single-cell/nucleus resolution profiles of gene expression and chromatin accessibility in healthy and autoantibody+ (AAB+) human islets, as well as islets under multiple T1D stimulatory conditions. We broadly nominate effector cell types for all T1D GWAS signals. We further nominated higher-resolution contexts, including effector cell types, regulatory elements, and genes for three independent T1D risk variants acting through islet cells within the pancreas at the DLK1/MEG3, RASGRP1, and TOX loci. Subsequently, we created isogenic gene knockouts DLK1-/-, RASGRP1-/-, and TOX-/-, and the corresponding regulatory region knockout, RASGRP1Δ, and DLK1Δ hESCs. Loss of RASGRP1 or DLK1, as well as knockout of the regulatory region of RASGRP1 or DLK1, increased β cell apoptosis. Additionally, pancreatic β cells derived from isogenic hESCs carrying the risk allele of rs3783355A/A exhibited increased β cell death. Finally, RNA-seq and ATAC-seq identified five genes upregulated in both RASGRP1-/- and DLK1-/- β-like cells, four of which are associated with T1D. Together, this work reports an integrative approach for combining single cell multi-omics, GWAS, and isogenic hESC-derived β-like cells to prioritize the T1D associated signals and their underlying context-specific cell types, genes, SNPs, and regulatory elements, to illuminate biological functions and molecular mechanisms.
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Project description:Obesity is closely associated with adipose tissue inflammation and insulin resistance. Dysglycemia and type 2 diabetes results when islet β cells fail to maintain appropriate insulin secretion in the face of insulin resistance. To clarify the early transcriptional events leading to β-cell failure in the setting of obesity, we fed male C57BL/6J mice an obesogenic, high-fat diet (60% kcal from fat) or a control diet (10% kcal from fat) for one week, and islets from these mice (from four high-fat- and three control-fed mice) were subjected to single-cell RNA sequencing (sc-RNAseq) analysis. Islet endocrine cell types (α cells, β cells, δ cells, PP cells) and other resident cell types (macrophages, T cells) were annotated by transcript profiles and visualized using Uniform Manifold Approximation and Projection for Dimension Reduction (UMAP) plots. UMAP analysis revealed distinct cell clusters (11 for β cells, 5 for α cells, 3 for δ cells, PP cells, ductal cells, endothelial cells), emphasizing the heterogeneity of cell populations in the islet. Collectively, the clusters containing the majority of β cells showed the fewest gene expression changes, whereas clusters harboring the minority of β cells showed the most changes. We identified that distinct β-cell clusters downregulate genes associated with the endoplasmic reticulum stress response and upregulate genes associated with insulin secretion, whereas others upregulate genes that impair insulin secretion, cell proliferation, and cell survival. Moreover, all β-cell clusters negatively regulate genes associated with immune response activation. Glucagon-producing α cells exhibited patterns similar to β cells but, again, in clusters containing the minority of α cells. Our data indicate that an early transcriptional response in islets to an obesogenic diet reflects an attempt by distinct populations of β cells to augment or impair cellular function and/or reduce inflammatory responses as possible harbingers of ensuing insulin resistance.
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