CellChat for systematic analysis of cell-cell communication from single-cell and spatially resolved transcriptomics

This is the authors' abstract. We don't add key points for this paper.

Recent advances in single-cell sequencing technologies offer an opportunity to explore cell-cell communication in tissues systematically and with reduced bias. A key challenge is the integration between known molecular interactions and measurements into a framework to identify and analyze complex cell-cell communication networks. Previously, we developed a computational tool, named CellChat that infers and analyzes cell-cell communication networks from single-cell RNA-sequencing (scRNA-seq) data within an easily interpretable framework. CellChat quantifies the signaling communication probability between two cell groups using a simplified mass action-based model, which incorporates the core interaction between ligands and receptors with multi-subunit structure along with modulation by cofactors. CellChat v2 is an updated version that includes direct incorporation of spatial locations of cells, if available, to infer spatially proximal cell-cell communication, additional comparison functionalities, expanded database of ligand-receptor pairs along with rich annotations, and an Interactive CellChat Explorer. Here we provide a step-by-step protocol for using CellChat v2 that can be used for both scRNA-seq and spatially resolved transcriptomic data, including inference and analysis of cell-cell communication from one dataset and identification of altered signaling across different datasets. The key steps of applying CellChat v2 to spatially resolved transcriptomics are described in detail. The R implementation of CellChat v2 toolkit and tutorials with the graphic outputs are available at https://github.com/jinworks/CellChat . This protocol typically takes around 20 minutes, and no specialized prior bioinformatics training is required to complete the task.