Dissecting Tissue Architecture and Function through Mapping of Cellular Communication Networks

Abstract

Spatial transcriptome provides critical insights for inferring cell-cell communications. Based on the heuristic that physically adjacent cells are more likely to communicate with each other, we develop a computational framework that Infers the Cell-Cell Communication network (IC3) and subsequently identifies communication hotspots with rigorous error control. We demonstrate in simulations and real datasets that our method outperforms existing methods in accuracy and especially improves sensitivity to identify communications involving rare cell types. Applying IC3 in a mouse brain dataset, we recovered how cells communicate with each other to form a local structure that regulates the balance of the blood-brain barrier. These findings highlight that our method is effective in revealing tissue architecture and function from cellular communication networks.