Department of Mathematics, University of California, Irvine, Irvine, CA, USA.
NSF-Simons Center for Multiscale Cell Fate Research, University of California, Irvine, Irvine, CA, USA.
Nat Commun. 2021 Feb 17;12(1):1088. doi: 10.1038/s41467-021-21246-9.
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.
理解细胞间的全球通讯需要准确表示细胞间信号传递的联系，并对这些联系进行有效的系统级分析。我们构建了一个配体、受体及其辅助因子相互作用的数据库，该数据库能够准确地表示已知的异源分子复合物。然后，我们开发了 CellChat，这是一种能够从单细胞 RNA 测序 (scRNA-seq) 数据中定量推断和分析细胞间通讯网络的工具。CellChat 通过网络分析和模式识别方法预测细胞的主要信号输入和输出，以及这些细胞和信号如何协调功能。通过流形学习和定量对比，CellChat 对信号通路进行分类，并在不同的数据集之间划定保守和特定于上下文的通路。将 CellChat 应用于小鼠和人类皮肤数据集，表明其能够提取复杂的信号模式。我们的多功能且易于使用的 CellChat 工具包和基于网络的浏览器（http://www.cellchat.org/）将有助于发现新的细胞间通讯，并在不同组织中构建细胞间通讯图谱。
