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

TL;DR

CellPhoneDB v5 is an advanced bioinformatics toolkit that enhances the inference of cell-cell communication from single-cell multiomics data by expanding its interaction database and integrating new computational and visualization features.

Contribution

The paper introduces CellPhoneDB v5, which significantly expands the interaction repository, adds new analysis methods, and incorporates multimodal data integration for more accurate cell communication inference.

Findings

Expanded interaction database with one-third more entries

New methods for differential expression-based interaction queries

Integration of spatial and transcription factor activity data

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.