mirCCC: Repression-aware graph learning for miRNA-mediated cell-cell communication inference

Abstract

Cell-cell communication analyses usually focus on protein ligands and receptors and therefore miss extracellular vesicle transfer of microRNAs, an important route of signalling in cancer. Here we show that microRNA-mediated communication can be inferred from standard single-cell RNA sequencing by detecting coordinated decreases in the expression of validated miRNA target genes. We developed mirCCC, a computational framework that estimates cell-specific microRNA activity, models cellular sending and receiving capacity for extracellular vesicle transfer, and learns microRNA-resolved communication graphs from transcriptomic data. In synthetic benchmarks with strong confounding signals, mirCCC improved whereas all comparison methods declined. Applied to a human colorectal cancer atlas, mirCCC recovered known colorectal cancer-associated microRNAs and identified stromal- and myeloid-to-epithelial communication converging on a plasticity program linked to TGF-{beta} and Wnt/{beta}-catenin signalling. These results provide a practical route for studying extracellular vesicle-mediated communication in existing single-cell atlases. Author summaryCell-cell communication plays important roles across a wide range of biological processes. Most studies of cell-cell communication focus on interactions between ligands and receptors. However, cells can also use extracellular vesicles to deliver microRNAs (miRNAs), which regulate gene activity in receiving cells and contribute to cancer progression, immune regulation, and changes in the tissue environment. Since standard single-cell sequencing does not directly capture miRNAs, this mode of communication remains largely unexplored. Here we developed mirCCC, a computational method for inferring miRNA-mediated communication from standard single-cell transcriptomic data. Instead of detecting miRNAs directly, mirCCC examines the effects they leave in receiving cells. Active miRNAs often cause coordinated suppression of their target genes. mirCCC uses these coordinated reductions in the expression of known target genes to estimate miRNA activity and combines them with the ability of sending cells to package and release miRNAs and of receiving cells to process them. It can therefore reconstruct directional communication networks for individual miRNAs. Our evaluations show that mirCCC remains reliable in the presence of misleading signals and reveals biologically meaningful regulatory patterns in human colorectal cancer data. Overall, mirCCC provides a practical way to study miRNA-mediated cell-cell communication using existing single-cell transcriptomics datasets.