Extracellular vesicle-derived miRNA-mediated cell-cell communication inference for single-cell transcriptomic data with miRTalk

Abstract

AbstractMicroRNAs are released from cells in extracellular vesicles (EVs), representing an essential mode of cell-cell communication (CCC) via an inhibitory effect on gene expression. The advent of single-cell RNA-sequencing (scRNA-seq) technologies has ushered in an era of elucidating EV-derived miRNA-mediated CCC. However, the lack of computational methods to infer such CCC poses an outstanding challenge. Herein, we present miRTalk (https://github.com/multitalk/miRTalk), a pioneering framework for inferring EV-derived miRNA-mediated CCC with a probabilistic model and a curated database, miRTalkDB, which includes EV-derived miRNA-target associations. The benchmarking against simulated and real-world datasets demonstrated the remarkable accuracy and robustness of miRTalk. Subsequently, we employed miRTalk to uncover the in-depth CCC mechanisms underlying three disease scenarios. In summary, miRTalk represents the first approach for inferring EV-derived miRNA-mediated CCC with scRNA-seq data, providing invaluable insights into the CCC dynamics underpinning biological processes.