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

2025-12-03

AI Summary

Systematic characterization of cell–cell communication (CCC) in multicellular organisms is essential for understanding development, homeostasis, and disease pathogenesis; however, existing computational methods for inferring CCC from single-cell and spatial omics data suffer from a lack of standardized evaluation and biological interpretability. To address this, we systematically reviewed over 140 computational tools and propose the first unified evaluation framework integrating ligand–receptor prior knowledge with de novo network inference. Our framework quantitatively benchmarks tools across three critical dimensions: modeling signaling pathway complexity, incorporating spatial contextual information, and ensuring biological interpretability. By synergistically integrating single-cell transcriptomics, spatial transcriptomics, curated ligand–receptor databases, and machine learning models, we substantially improve both prediction accuracy and mechanistic traceability of CCC inference. The resulting framework establishes a reproducible analytical paradigm for developmental biology and precision medicine, while identifying key algorithmic challenges—including context-specific signal propagation and cross-modal integration—and charting future directions for methodological advancement.

Technology Category

• Knowledge Representation and Reasoning: Computational Complexity of Reasoning
• Cognitive Modeling & Cognitive Systems: Conceptual Inference and Reasoning
• Constraint Satisfaction and Optimization: Solvers and Tools

Application Category

• Semantics and Knowledge: Methods, algorithms and applications for the development of semantic models, knowledge graphs and other forms of structured data models with machine-interpretable semantics
• Social Networks and Social Media: Computational social science
• Graph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphs

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.

Problem

• Infer cell-cell communication from single-cell and spatial omics data.
• Model complex signaling mechanisms using computational methods.
• Address challenges in analyzing biological processes like development and disease.

Innovation

• Integrating ligand-receptor interactions from omics data
• Developing de novo computational inference approaches
• Modeling complex signaling mechanisms for biological hypotheses