bioRxivCell BiologyPreprint
Single Cell RNA-Seq Analysis of Regenerative Drug-Treated Human Pancreatic Islets Identifies A Cycling Alpha Cell Population As Key Beta Cell Progenitors
E. Karakose, X. Wang, P. Wang, S. Carcamo, D. Demircioglu, O. Wood, R. Kang, L. Lambertini, G. Lu, D. K. Scott, A. Garcia-Ocana, C. Argmann, R. Sebra, D. Hasson, A. F. Stewart
Abstract
Diabetes ultimately results from an inadequate number of functional, insulin-producing human beta cells. Although current attempts to replenish the remaining beta cell pool in people with diabetes are encouraging, scalability and cost limit access for the millions of people with diabetes. The small molecule DYRK1A inhibitor class of beta cell regenerative drugs, either alone or in combination with GLP1 receptor agonists or TGF{beta} superfamily inhibitors, are capable of inducing beta cell replication in vitro and increasing beta cell mass in vivo. Despite these advances, the precise mechanisms of action of DYRK1A inhibitors remain incompletely understood. To address the mechanisms more deeply, we performed single cell RNA sequencing on human pancreatic islets treated with a DYRK1A inhibitor, either alone, or in combination with a GLP1 receptor agonist or a TGF{beta} superfamily inhibitor. We identify a cluster of Cycling Alpha Cells as the cells most responsive to DYRK1A inhibition. Velocity and pseudotime lineage trajectory analyses suggest that Cycling Alpha Cells serve as the primary target cell type for of DYRK1A inhibitors, and may serve as precursor cells that transdifferentiate into functional human beta cells in response to the DYRK1A inhibition. In addition to providing a novel mechanism of action for DYRK1A inhibitors, our findings suggest that efforts to target regenerative drugs to human beta cells may be mis-directed: the proper target may be Cycling Alpha Cells.
- organism: human
- assay: scRNA-seq
- research focus: cell type annotation
- research focus: trajectory inference
- research focus: differential expression
- approach: deep learning
- approach: statistical model
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