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

By Åsa Segerstolpe, Athanasia G. Palasantza, Pernilla Eliasson, Eva-Marie Andersson, A. Andréasson, Xiaoyan Sun, S. Picelli, A. Sabirsh, Maryam Clausen, Magnus K. Bjursell and 4 more. Cell Metabolism, 2016

1,332 citations recorded by the source corpus.

Summary 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.

Subfields: Genetics, Immunology, Surgery

View Paper: https://www.semanticscholar.org/paper/fe24e816c2e777d73e203c6283d018f211d9b194

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Citations in the corpus

A benchmark of batch-effect correction methods for single-cell RNA sequencing data (2020, methodology): “Dataset 4 was constructed using human pancreatic data from five different sources, available under accession numbers GSE85241 [33], E-MTAB-5061 [34], GSE84133 [35] GSE83139 [36], and GSE81608 [37].”
Obesity and type 2 diabetes mellitus: connections in epidemiology, pathogenesis, and treatments (2023, background): “In addition, a decline in the transcript encoding insulin in b-cells of patients with T2DM was also shown by single-cell RNA sequencing (23).”
Comprehensive integration of single-cell data (2018, positive): “We examined a collection of eight previously published datasets using tissue from human pancreatic islets spanning 27 donors, ﬁve technologies, and four laboratories (Baron et al., 2016; Law-lor et al., 2017; Gru¨n et al., 2016; Muraro et al., 2016; Segerstolpe et al., 2016).”
Dissecting Immune Circuits by Linking CRISPR-Pooled Screens with Single-Cell RNA-Seq. (2016, positive): “…in various tissues and disease states (Gru¨n et al., 2015; Gury-BenAri et al., 2016; Jaitin et al., 2014; Junker and van Oudenaarden, 2014; La Manno et al., 2016; Paul et al., 2015; Segerstolpe et al., 2016; Shalek et al., 2014; Stegle et al., 2015; Treutlein et al., 2014; Zei-sel et al., 2015).”
Batch effects in single-cell RNA sequencing data are corrected by matching mutual nearest neighbours (2018, methodology): “GSE81076, GSE85241, GSE86473 and the ArrayExpress accession number E-MTAB-5061 for the pancreas data sets.”
A comparison of automatic cell identification methods for single-cell RNA sequencing data (2019, background): “All 5 pancreatic data-sets were obtained from https://hemberg-lab.github.io/ scRNA.seq.datasets/ (Baron Mouse: GSE84133, Baron Human: GSE84133, Muraro: GSE85241, Segerstolpe: E-MTAB-5061, Xin: GSE81608).”
Mechanisms of Action and Therapeutic Application of Glucagon-like Peptide-1. (2018, background): “Nevertheless, the GLP1R is expressed at very low levels or is undetectable in the majority of human a cells using singlecell RNA sequencing (RNA-seq) or immunohistochemistry for detection of the GLP-1R protein (Muraro et al., 2016; Segerstolpe et al., 2016; Waser et al., 2015).”