Single-Cell Transcriptomics of the Human Endocrine Pancreas

Yue J Wang, Jonathan Schug, Kyoung-Jae Won, Chengyang Liu, Ali Naji, Dana Avrahami, Maria L Golson, Klaus H Kaestner

157 Citations (Scopus)

Abstract

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 a- and β-Cells from children exhibit less well-defined gene signatures than those in adults. Remarkably, a- 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 a-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.

Original languageEnglish
JournalDiabetes
Volume65
Issue number10
Pages (from-to)3028-38
Number of pages11
ISSN0012-1797
DOIs
Publication statusPublished - 1 Oct 2016
Externally publishedYes

Keywords

  • Cell Cycle/genetics
  • Cell Proliferation/genetics
  • Computational Biology/methods
  • Diabetes Mellitus, Type 1/genetics
  • Diabetes Mellitus, Type 2/genetics
  • Glucagon-Secreting Cells/cytology
  • Humans
  • Insulin-Secreting Cells/cytology
  • Islets of Langerhans/cytology
  • Microfluidics/methods
  • Signal Transduction/genetics
  • Transcriptome/genetics

Fingerprint

Dive into the research topics of 'Single-Cell Transcriptomics of the Human Endocrine Pancreas'. Together they form a unique fingerprint.

Cite this