Jin Suoqin, Plikus Maksim V, Nie Qing
School of Mathematics and Statistics, Wuhan University, Wuhan, China.
Hubei Key Laboratory of Computational Science, Wuhan University, Wuhan, China.
Nat Protoc. 2025 Jan;20(1):180-219. doi: 10.1038/s41596-024-01045-4. Epub 2024 Sep 16.
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.
单细胞测序技术的最新进展为系统地、减少偏差地探索组织中的细胞间通讯提供了机会。一个关键挑战是将已知的分子相互作用和测量结果整合到一个框架中，以识别和分析复杂的细胞间通讯网络。此前，我们开发了一种名为CellChat的计算工具，它可以在一个易于解释的框架内从单细胞转录组数据中推断和分析细胞间通讯网络。CellChat使用基于简化质量作用的模型来量化两个细胞群之间的信号通讯概率，该模型将具有多亚基结构的配体和受体之间的核心相互作用与辅因子的调节作用结合在一起。重要的是，CellChat使用各种定量指标和机器学习方法对细胞间通讯进行系统的比较分析。CellChat v2是一个更新版本，包括额外的比较功能、扩展的配体-受体对数据库以及丰富的功能注释，还有一个交互式CellChat浏览器。在这里，我们提供了一个在单细胞转录组数据上使用CellChat v2的分步方案，包括从一个数据集中推断和分析细胞间通讯，以及识别不同生物条件下不同数据集之间细胞间通讯、信号和细胞群的变化。CellChat v2工具包的R实现及其教程以及图形输出可在https://github.com/jinworks/CellChat上获取。根据数据集大小，该方案通常需要约5分钟，并且需要对R和单细胞数据分析有基本的了解，但实施过程中不需要专门的生物信息学培训。
