Abstract
A sensible control of hormone secretion from pancreatic islets requires concerted inter-cellular communications, but a comprehensive picture of the whole islet interactome is presently missing. Single-cell transcriptomics allows to overcome this and we used here a single-cell dataset from type 2 diabetic (T2D) and non-diabetic (ND) donors to leverage islet interaction networks. The single-cell dataset contains 3046 cells classified in 7 cell types. The interactions across cell types in T2D and ND were obtained and resulting networks analysed to identify high-centrality genes and altered interactions in T2D. The T2D interactome displayed a higher number of interactions (10 787) than ND (9707); 1289 interactions involved beta cells (1147 in ND). High-centrality genes included EGFR, FGFR1 and FGFR2, important for cell survival and proliferation. In conclusion, this analysis represents the first in silico model of the human islet interactome, enabling the identification of signatures potentially relevant for T2D pathophysiology.