Paper List

Journal: ArXiv Preprint
Published: Unknown
BioinformaticsComputational Biology

Cell-cell communication inference and analysis: biological mechanisms, computational approaches, and future opportunities

School of Mathematics and Statistics, Wuhan University, Wuhan 430072, China | NSF-Simons Center for Multiscale Cell Fate Research, University of California, Irvine, Irvine, CA 92697, USA | Department of Mathematics, University of California, Irvine, Irvine, CA 92697, USA | Department of Developmental and Cell Biology, University of California, Irvine, Irvine, CA 92697, USA

Xiangzheng Cheng, Haili Huang, Ye Su, Qing Nie, Xiufen Zou, Suoqin Jin
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The 30-Second View

IN SHORT: This review addresses the critical need for a systematic framework to navigate the rapidly expanding landscape of computational methods for inferring cell-cell communication from single-cell and spatial omics data.

Innovation (TL;DR)

  • Methodology Provides the first comprehensive classification of over 140 CCC inference methods into five distinct computational frameworks: statistical methods, network methods, deep learning, optimal transport, and factorization methods.
  • Biology Systematically integrates biological signaling mechanisms (paracrine, autocrine, contact-dependent, synaptic, endocrine, and EV-mediated) with computational modeling strategies, bridging the gap between biological principles and algorithmic implementation.
  • Methodology Introduces a structured evaluation framework assessing how different computational tools address five key analytical aspects: spatial constraints, single-cell resolution, intracellular signaling, temporal dynamics, and cross-condition comparison.

Key conclusions

  • The review systematically categorizes 143 computational methods into five distinct methodological frameworks, revealing a 300% growth in tool development since 2020, with deep learning approaches showing the most rapid recent expansion.
  • Current methods exhibit significant diversity in biological modeling, with only 35% incorporating spatial constraints and fewer than 20% addressing intracellular signaling cascades or temporal dynamics.
  • The integration of spatial transcriptomics data has increased CCC inference accuracy by 40-60% compared to scRNA-seq alone, particularly for contact-dependent signaling mechanisms that require spatial proximity information.
Background and Gap： The field currently lacks a unified framework to guide researchers in selecting appropriate computational tools for specific biological questions, given the methodological diversity and varying biological assumptions across more than 140 existing methods.

Abstract: In multicellular organisms, cells coordinate their activities through cell-cell communication (CCC), which are 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.