FlyPhoneDB: an integrated web-based resource for cell–cell communication prediction in Drosophila

Abstract

Abstract Multicellular organisms rely on cell–cell communication to exchange information necessary for developmental processes and metabolic homeostasis. Cell–cell communication pathways can be inferred from transcriptomic datasets based on ligand–receptor expression. Recently, data generated from single-cell RNA sequencing have enabled ligand–receptor interaction predictions at an unprecedented resolution. While computational methods are available to infer cell–cell communication in vertebrates such a tool does not yet exist for Drosophila. Here, we generated a high-confidence list of ligand–receptor pairs for the major fly signaling pathways and developed FlyPhoneDB, a quantification algorithm that calculates interaction scores to predict ligand–receptor interactions between cells. At the FlyPhoneDB user interface, results are presented in a variety of tabular and graphical formats to facilitate biological interpretation. To illustrate that FlyPhoneDB can effectively identify active ligands and receptors to uncover cell–cell communication events, we applied FlyPhoneDB to Drosophila single-cell RNA sequencing data sets from adult midgut, abdomen, and blood, and demonstrate that FlyPhoneDB can readily identify previously characterized cell–cell communication pathways. Altogether, FlyPhoneDB is an easy-to-use framework that can be used to predict cell–cell communication between cell types from single-cell RNA sequencing data in Drosophila.