CellPhoneDB v5: inferring cell-cell communication from single-cell multiomics data

Kevin Troulé; Robert Petryszak; Martin Prete; James Cranley; Alicia Harasty; Zewen Kelvin Tuong; Sarah A Teichmann; Luz Garcia-Alonso; Roser Vento-Tormo

arXiv · 2311.04567

CellPhoneDB v5: inferring cell-cell communication from single-cell multiomics data

Kevin Troulé·Robert Petryszak·Martin Prete·James Cranley·Alicia Harasty·Zewen Kelvin Tuong·Sarah A Teichmann·Luz Garcia-Alonso·Roser Vento-Tormo

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Abstract

Cell-cell communication is essential for tissue development, regeneration and function, and its disruption can lead to diseases and developmental abnormalities. The revolution of single-cell genomics technologies offers unprecedented insights into cellular identities, opening new avenues to resolve the intricate cellular interactions present in tissue niches. CellPhoneDB is a bioinformatics toolkit designed to infer cell-cell communication by combining a curated repository of bona fide ligand-receptor interactions with a set of computational and statistical methods to integrate them with single-cell genomics data. Importantly, CellPhoneDB captures the multimeric nature of molecular complexes, thus representing cell-cell communication biology faithfully. Here we present CellPhoneDB v5, an updated version of the tool, which offers several new features. Firstly, the repository has been expanded by one-third with the addition of new interactions. These encompass interactions mediated by non-protein ligands such as endocrine hormones and GPCR ligands. Secondly, it includes a differentially expression-based methodology for more tailored interaction queries. Thirdly, it incorporates novel computational methods to prioritise specific cell-cell interactions, leveraging other single-cell modalities, such as spatial information or TF activities (i.e. CellSign module). Finally, we provide CellPhoneDBViz, a module to interactively visualise and share results amongst users. Altogether, CellPhoneDB v5 elevates the precision of cell-cell communication inference, ushering in new perspectives to comprehend tissue biology in both healthy and pathological states.

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Kevin Troulé, Robert Petryszak, Martin Prete, James Cranley, Alicia Harasty, Zewen Kelvin Tuong, Sarah A Teichmann, Luz Garcia-Alonso, Roser Vento-Tormo. 2023-11-08. CellPhoneDB v5: inferring cell-cell communication from single-cell multiomics data. https://arxiv.org/abs/2311.04567

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