Scientists from Qing Nie’s group in the Departments of Mathematics and Developmental and Cell Biology at UC Irvine have developed a machine learning method called SigXTalk to analyze how cells communicate. Using single-cell sequencing data, the tool can track how signals from one cell cascade through networks and interact with other pathways — even revealing how these patterns change in disease. The research could help identify therapeutic targets by uncovering how cells miscommunicate in disorders ranging from cancer to immune disease.
The study, entitled “Dissecting crosstalk induced by cell-cell communication using single-cell transcriptomic data”, was published in the July 2025 issue of Nature Communications.