{"doi":"10.1101/2023.11.05.565674","title":"CellChat for systematic analysis of cell-cell communication from single-cell and spatially resolved transcriptomics","abstract":"Abstract 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.","journal":"bioRxiv (Cold Spring Harbor Laboratory)","year":2023,"id":389319,"datarank":0.7685945969104889,"base_score":5.123963979403259,"endowment":5.123963979403259,"self_citation_contribution":0.7685945969104889,"citation_network_contribution":0.0,"self_endowment_contribution":0.7685945969104889,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":167,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9064,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2023-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":19468,"name":"Maksim V. Plikus","orcid":"0000-0002-8845-2559","position":1,"is_corresponding":false},{"id":919507,"name":"Qing Nie","orcid":"0009-0007-6876-9843","position":2,"is_corresponding":false},{"id":19461,"name":"Suoqin Jin","orcid":"0000-0002-5131-0215","position":0,"is_corresponding":true}],"reference_count":47,"raw_metadata":{"citation_network_status":"fetched"},"created_at":"2026-07-19T01:18:22.323414Z","pmid":null,"pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}