DevelopmentDevelopmental BiologyPeer-reviewed journal article
A single-cell atlas of de novo β-cell regeneration reveals the contribution of hybrid β/δ cells to diabetes recovery in zebrafish
Sumeet Pal SinghiD, Prateek ChawlaiD, Alisa Hnatiuk, Margrit Kamel, Luis Fernando Delgadillo-SilvaiD, Bastiaan SpanjaardiD, Sema Elif EskiiD, Sharan JanjuhaiD, Pedro Olivares‐ChauvetiD, Oezge Kayisoglu, Fabian RostiD, Juliane Bläsche, Annekathrin Kränkel, Andreas PetzoldiD, Thomas KurthiD, Susanne ReinhardtiD, Jan Philipp JunkeriD, Nikolay NinoviD
Abstract
Regeneration-competent species possess the ability to reverse the progression of severe diseases by restoring the function of the damaged tissue. However, the cellular dynamics underlying this capability remain unexplored. Here, we use single-cell transcriptomics to map de novo {beta}-cell regeneration during induction and recovery from diabetes in zebrafish. We show that the zebrafish has evolved two distinct types of somatostatin-producing {delta}-cells, which we term {delta}1- and {delta}2-cells. Moreover, we characterize a small population of glucose-responsive islet cells, which share the hormones and fate-determinants of both {beta}- and {delta}1-cells. The transcriptomic analysis of {beta}-cell regeneration reveals that {beta}/{delta} hybrid cells constitute a prominent source of insulin-expression during diabetes recovery. Using in vivo calcium imaging and cell tracking, we further show that the hybrid cells form de novo and acquire glucose-responsiveness in the course of regeneration. The overexpression of dkk3, a gene enriched in hybrid cells, increases their formation in the absence of {beta}-cell injury. Finally, interspecies comparison shows that plastic {delta}1-cells are partially related to PP-cells in the human pancreas. Our work provides an atlas of {beta}-cell regeneration and indicates that the rapid formation of glucose-responsive hybrid cells contributes to the resolution of diabetes in zebrafish
- organism: zebrafish
- assay: scRNA-seq
- research focus: cell type annotation
- research focus: clustering
- approach: statistical model
- Citations
- 42
- FWCI
- 8.12
- Field percentile
- top 1%
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