Inference and analysis of cell-cell communication using CellChat
By Suoqin Jin, C. Guerrero-Juarez, Lihua Zhang, Ivan Chang, Raul Ramos, Chen-Hsiang Kuan, P. Myung, Maksim V. Plikus, Qing Nie. Nature Communications, 2020
6,604 citations recorded by the source corpus.
Understanding global communications among cells requires accurate representation of cell-cell signaling links and effective systems-level analyses of those links. We construct a database of interactions among ligands, receptors and their cofactors that accurately represent known heteromeric molecular complexes. We then develop CellChat, a tool that is able to quantitatively infer and analyze intercellular communication networks from single-cell RNA-sequencing (scRNA-seq) data. CellChat predicts major signaling inputs and outputs for cells and how those cells and signals coordinate for functions using network analysis and pattern recognition approaches. Through manifold learning and quantitative contrasts, CellChat classifies signaling pathways and delineates conserved and context-specific pathways across different datasets. Applying CellChat to mouse and human skin datasets shows its ability to extract complex signaling patterns. Our versatile and easy-to-use toolkit CellChat and a web-based Explorer (http://www.cellchat.org/) will help discover novel intercellular communications and build cell-cell communication atlases in diverse tissues. Single-cell methods record molecule expressions of cells in a given tissue, but understanding interactions between cells remains challenging. Here the authors show by applying systems biology and machine learning approaches that they can infer and analyze cell-cell communication networks in an easily interpretable way.
Subfields: Biomedical Engineering, Computer Networks and Communications, Electrical and Electronic Engineering
View Paper: https://www.semanticscholar.org/paper/a3cd148f5795617b281a3ee1738abf19e2476bb0
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Computer Networks and Communications
Citations in the corpus
- Best practices for single-cell analysis across modalities (2023, neutral): “3).”
- Cellular and molecular mechanisms of skin wound healing (2024, neutral): “Other algorithms can offer insights from various omics data sets that reveal which cells might be ‘talking’ to which others and via which signalling pathways 238–240 .”
- Benchmarking spatial and single-cell transcriptomics integration methods for transcript distribution prediction and cell type deconvolution (2022, methodology): “Many analytical tools have been developed for this task, such as SpaOTsc 21 , Giotto 71 , CellChat 72 , NicheNet 73 , ICELLNET 74 , and SingleCellSignalR 75 .”
- Screening cell–cell communication in spatial transcriptomics via collective optimal transport (2023, methodology): “The ligand– receptor pairs categorized under ‘Secreted Signaling’ in the CellChatDB were examined.”
- A new gene set identifies senescent cells and predicts senescence-associated pathways across tissues (2021, methodology): “To explore these intercellular interactions, we evaluated potential ligand-receptor interactions and secretion patterns based on underlying gene expression levels in different hematopoietic cell types in human bone marrow.(50) The strongest interaction of SASP cells was found with T cells, followed”
- Integrated intra‐ and intercellular signaling knowledge for multicellular omics analysis (2020, neutral): “Or for the resulting networks used to make the conclusions it would be good to show the number of resources from which the nodes/data points were extracted, to make the case that this integrated resource is necessary for this analysis.”
- Single-cell landscape of peripheral and tumor-infiltrating immune cells in HPV-negative HNSCC (2025, unknown): “33 To compare the N0-and N + groups, we used expression data from TICs/PBMCs datasets and performed analyses separately for each group per dataset.”
- The Single-Cell Landscape of Peripheral and Tumor-infiltrating Immune Cells in HPV- HNSCC (2025, unknown): “To identify intercellular interactions and receptor-ligand signaling networks, we employed the CellChat R package, 43 a computational tool that infers and visualizes intercellular communication networks from single-cell transcriptomic data.”