1/1
2 files

Dynamic uni- and multicellular patterns encode biphasic activity in pancreatic islets

figure
posted on 19.01.2021, 12:35 by Manon Jaffredo, Eléonore Bertin, Antoine Pirog, Emilie Puginier, Julien Gaitan, Sandra Oucherif, Fanny Lebreton, Domenico Bosco, Bogdan Catargi, Daniel Cattaert, Sylvie Renaud, Jochen Lang, Matthieu Raoux
Biphasic secretion is an autonomous feature of many endocrine micro-organs to fulfill physiological demands. The biphasic activity of islet β-cells maintains glucose homeostasis and is altered in type-2 diabetes. Nevertheless, underlying cellular or multicellular functional organizations are only partially understood. High-resolution non-invasive multi-electrode array recordings permit simultaneous analysis of recruitment, of single-cell and of coupling activity within entire islets in long-time experiments. Using this approach, we addressed the organizational modes of both, 1st and 2nd phase, in mouse and human islets under physiological and pathophysiological conditions. Our data provide a new uni- and multicellular model of islet b-cell activation: during the 1st phase, small but highly active β-cell clusters are dominant, whereas during the 2nd phase electrical coupling generates large functional clusters via multicellular slow potentials to favor an economic sustained activity. Post-prandial levels of glucagon-like peptide-1 (GLP-1) favor coupling only in the 2nd phase, whereas aging and glucotoxicity alter coupled activity in both phases. In summary, biphasic activity is encoded upstream of vesicle pools at the micro-organ level by multicellular electrical signals and their dynamic synchronization between β-cells. The profound alteration of the electrical organization of islets in pathophysiological conditions may contribute to functional deficits in type-2 diabetes.

Funding

This study was supported by the following grants: FEDER Diaglyc (to JL and SR), ANR-18-CE17-0005 DIABLO (to JL and SR), French Ministry of Research Excellence PhD Scholarship (to MR and MJ), PEPS Idex/CNRS (to MR).

History

Exports