Graph-Based Inference of Cell-Cell Communication Networks from Single-Cell Transcriptomic Profiles for Disease Mechanism Discovery and Biomarker Prioritization across Tissues
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Abstract
The advent of single-cell transcriptomic technologies has revolutionized our understanding of cellular heterogeneity, offering unprecedented resolution into the molecular states of individual cells. However, tissues are not mere collections of isolated cells; they are highly coordinated ecosystems driven by intricate networks of cell-cell communication. Traditional analyses often overlook the complex topological structures of these interaction networks. This paper presents a comprehensive framework for the graph-based inference of cell-cell communication networks using single-cell transcriptomic profiles. By mapping ligand-receptor expression patterns onto mathematically rigorous graph structures, we facilitate the discovery of novel disease mechanisms and the prioritization of clinical biomarkers across diverse tissue types. This methodology conceptualizes cells as nodes and molecular interactions as weighted edges, enabling the application of advanced graph theory algorithms to decode spatial and functional signaling dynamics. The study explores the theoretical foundations of transcriptomic network inference, evaluates algorithmic approaches for graph construction, and demonstrates the utility of network topology in identifying pathological rewiring in complex diseases such as cancer and autoimmune disorders. Furthermore, centrality metrics and modularity analyses are deployed to pinpoint keystone cellular populations and prioritize robust biomarkers for clinical translation. Ultimately, this graph-centric paradigm bridges the gap between single-cell molecular profiling and systems-level tissue biology, providing a powerful analytical engine for translational medicine and therapeutic target discovery
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