CellChat for systematic analysis of cell-cell communication from single-cell transcriptomics.
Journal
Nature protocols
ISSN: 1750-2799
Titre abrégé: Nat Protoc
Pays: England
ID NLM: 101284307
Informations de publication
Date de publication:
16 Sep 2024
16 Sep 2024
Historique:
received:
31
07
2023
accepted:
27
06
2024
medline:
18
9
2024
pubmed:
18
9
2024
entrez:
17
9
2024
Statut:
aheadofprint
Résumé
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 integrating 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 transcriptomic 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 multisubunit structure along with modulation by cofactors. Importantly, CellChat performs a systematic and comparative analysis of cell-cell communication using a variety of quantitative metrics and machine-learning approaches. CellChat v2 is an updated version that includes additional comparison functionalities, an expanded database of ligand-receptor pairs along with rich functional annotations, and an Interactive CellChat Explorer. Here we provide a step-by-step protocol for using CellChat v2 on single-cell transcriptomic data, including inference and analysis of cell-cell communication from one dataset and identification of altered intercellular communication, signals and cell populations from different datasets across biological conditions. The R implementation of CellChat v2 toolkit and its tutorials together with the graphic outputs are available at https://github.com/jinworks/CellChat . This protocol typically takes ~5 min depending on dataset size and requires a basic understanding of R and single-cell data analysis but no specialized bioinformatics training for its implementation.
Identifiants
pubmed: 39289562
doi: 10.1038/s41596-024-01045-4
pii: 10.1038/s41596-024-01045-4
doi:
Types de publication
Journal Article
Review
Langues
eng
Sous-ensembles de citation
IM
Subventions
Organisme : National Science Foundation (NSF)
ID : MCB2028424
Organisme : Foundation for the National Institutes of Health (Foundation for the National Institutes of Health, Inc.)
ID : R01AR079150
Organisme : Foundation for the National Institutes of Health (Foundation for the National Institutes of Health, Inc.)
ID : R01DE030565
Organisme : National Natural Science Foundation of China (National Science Foundation of China)
ID : 92374108
Informations de copyright
© 2024. Springer Nature Limited.
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