解析细胞通讯机制，梳理140+算法工具助力生物发现
Cell-cell Communication Inference and Analysis: Biological Mechanisms, Computational Approaches, and Future Opportunities
- 基于单细胞与空间转录组数据，整合已知配体受体对或从头推断通讯关系
- 系统梳理超过140种计算方法，覆盖多种建模框架与生物问题
- 提供交互式资源网站，方便方法对比与选择，适合生物医学研究者
多细胞生物中，细胞通过细胞间通讯（CCC）协调活动，对发育、组织稳态及疾病进展至关重要。单细胞与空间组学技术的进展为系统推断和分析CCC提供了前所未有的机遇，既可结合已知配体-受体相互作用（LRIs）先验知识，也可采用从头推断方法。已有大量计算方法被开发，聚焦于方法创新、复杂信号机制的精准建模以及更广泛的生物学问题探究。这些进展显著提升了我们分析CCC并生成生物学假说的能力。本文介绍了CCC的生物学机制与建模策略，并重点综述了超过140种从单细胞与空间转录组数据推断CCC的计算方法，强调方法框架与生物学问题的多样性。最后，讨论了该领域的当前挑战与未来机遇，并总结可用方法于交互式在线资源（https://cellchat.whu.edu.cn），以促进更高效的方法比较与选择。
原文摘要 · Abstract (English)
In multicellular organisms, cells coordinate their activities through cell-cell communication (CCC), which is crucial for development, tissue homeostasis, and disease progression. Recent advances in single-cell and spatial omics technologies provide unprecedented opportunities to systematically infer and analyze CCC from these omics data, either by integrating prior knowledge of ligand-receptor interactions (LRIs) or through de novo approaches. A variety of computational methods have been developed, focusing on methodological innovations, accurate modeling of complex signaling mechanisms, and investigation of broader biological questions. These advances have greatly enhanced our ability to analyze CCC and generate biological hypotheses. Here, we introduce the biological mechanisms and modeling strategies of CCC, and provide a focused overview of more than 140 computational methods for inferring CCC from single-cell and spatial transcriptomic data, emphasizing the diversity in methodological frameworks and biological questions. Finally, we discuss the current challenges and future opportunities in this rapidly evolving field, and summarize available methods in an interactive online resource (https://cellchat.whu.edu.cn) to facilitate more efficient method comparison and selection.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。