Cell-cell Communication Inference and Analysis: Biological Mechanisms, Computational Approaches, and Future Opportunities
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arXiv
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| Auteurs principaux: | , , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866914412005163008 |
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| author | Cheng, Xiangzheng Huang, Haili Su, Ye Nie, Qing Zou, Xiufen Jin, Suoqin |
| author_facet | Cheng, Xiangzheng Huang, Haili Su, Ye Nie, Qing Zou, Xiufen Jin, Suoqin |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_03497 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Cell-cell Communication Inference and Analysis: Biological Mechanisms, Computational Approaches, and Future Opportunities Cheng, Xiangzheng Huang, Haili Su, Ye Nie, Qing Zou, Xiufen Jin, Suoqin Quantitative Methods Artificial Intelligence Cell Behavior 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. |
| title | Cell-cell Communication Inference and Analysis: Biological Mechanisms, Computational Approaches, and Future Opportunities |
| topic | Quantitative Methods Artificial Intelligence Cell Behavior |
| url | https://arxiv.org/abs/2512.03497 |