FlyPhoneDB2: A computational framework for analyzing cell-cell communication in Drosophila scRNA-seq data integrating AlphaFold-multimer predictions

Cell-cell communication (CCC) plays a critical role in the physiological regulation of organisms and has been implicated in numerous diseases. Previously, we introduced FlyPhoneDB, a tool designed to explore CCC in <i>Drosophila</i> single-cell RNA-sequencing datasets. The core algorithm of FlyPhoneDB infers tissue-specific signaling events between cell types by calculating cell-cell interaction scores based on curated ligand-receptor (L-R) expression across major signaling pathways. However, the utility of FlyPhoneDB was limited by the relatively small number of available L-R pairs. Here, we present FlyPhoneDB2, a major upgrade featuring a significantly expanded knowledgebase that includes a greater number of L-R pairs, incorporating annotations from mammalian species and structural predictions from AlphaFold-Multimer. In addition, the algorithm has been optimized for improved performance and more effective noise filtering. New functionalities have also been introduced, such as the addition of downstream reporter genes to evaluate pathway activity, multi-sample CCC comparison, and enhanced visualizations summarizing communication at a network level. We demonstrate the utility of FlyPhoneDB2 by analyzing whole-body single-nuclei RNA-seq datasets from flies with gut tumors induced by the Yorkie oncogene. We show that FlyPhoneDB2 not only recapitulates established biological insights into the <i>Drosophila</i> Yorkie tumor model, but also identifies novel potential L-R pairs that may play important roles in tumor-induced cachexia. FlyPhoneDB2 is available at https://www.flyrnai.org/tools/fly_phone_v2/.