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

Authors :
Eva-Marie Andersson
David M. Smith
Carina Ämmälä
Pernilla Eliasson
Anne-Christine Andréasson
Åsa Segerstolpe
Magnus K. Bjursell
Maryam Clausen
Alan Sabirsh
Simone Picelli
Rickard Sandberg
Maria Kasper
Athanasia Palasantza
Xiaoyan Sun
Source :
Cell Metabolism
Publication Year :
2016
Publisher :
Elsevier BV, 2016.

Abstract

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.<br />Graphical Abstract Image 1<br />Highlights • Single-cell RNA-seq enabled molecular profiling of rare human pancreatic cells • Subpopulations were identified within endocrine and exocrine cell types • Genes associated with obesity or diabetes had correlating expression with BMI • Transcriptional alterations found in type 2 diabetic individuals<br />Segerstolpe et al. use single-cell transcriptomics to generate transcriptional profiles of individual pancreatic endocrine and exocrine cells of healthy and type 2 diabetic donors. They revealed cell-type-specific gene expression and novel subpopulations, as well as gene correlations to BMI and gene expression alterations in diabetes.

Details

ISSN :
15504131
Volume :
24
Database :
OpenAIRE
Journal :
Cell Metabolism
Accession number :
edsair.doi.dedup.....bed553225a56e235cc9127551eff2b90