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<p>Cell–cell communication regulates complex biological processes in multicellular systems. Existing scRNA-seq-based methods typically aggregate gene expression by clusters, overlooking within-cluster heterogeneity. We present scComm, a computational framework that infers cell–cell communications between individual cells using supervised contrastive learning. In simulations, scComm outperforms other methods and achieves up to 95% accuracy. Applied to colorectal cancer, it reveals cell–cell communications linked to PD-1 blockade response and tertiary lymphoid structures. In liver cancer, it identifies three novel tumor subtypes and angiogenesis-promoting neutrophil subtypes that have unique tumor microenvironments. scComm enables high-resolution cell–cell communication analysis, uncovering biological insights missed by existing approaches.</p>

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scComm: a contrastive learning framework for deciphering cell–cell communications at single-cell resolution

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年3月24日

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  • Zijie Jin,
  • Zongli Tang,
  • Xinyi Li,
  • Kuangen Zhang,
  • Zhengwei Xie,
  • Ning Zhang

摘要

Cell–cell communication regulates complex biological processes in multicellular systems. Existing scRNA-seq-based methods typically aggregate gene expression by clusters, overlooking within-cluster heterogeneity. We present scComm, a computational framework that infers cell–cell communications between individual cells using supervised contrastive learning. In simulations, scComm outperforms other methods and achieves up to 95% accuracy. Applied to colorectal cancer, it reveals cell–cell communications linked to PD-1 blockade response and tertiary lymphoid structures. In liver cancer, it identifies three novel tumor subtypes and angiogenesis-promoting neutrophil subtypes that have unique tumor microenvironments. scComm enables high-resolution cell–cell communication analysis, uncovering biological insights missed by existing approaches.