Unraveling cell-cell communication through spatial transcriptomics: a review of computational methods

Spatial transcriptomics (ST) has enabled direct interrogation of cell-cell communication (CCC) within intact tissues, providing critical spatial context that is lost in single-cell RNA-sequencing-based inference and allowing more accurate identification of physically plausible and spatially organized interactions. A rapidly expanding community of computational tools has emerged to decode CCC from ST data. Here, we provide a comprehensive review of the conceptual evolution and methodological landscape of spatial CCC inference, classifying existing approaches into two major trajectories. One trajectory, spatial pattern-based methods, assumes CCC events manifest as identifiable spatial patterns, such as colocalization, coordinated spatial signals, or higher-order spatial organization captured by deep learning models. The other trajectory, expression modulation-based approaches, assumes that CCC events influence the transcriptomic state of receiver cells. We systematically dissect their biological assumptions, statistical and deep learning frameworks, strengths, and limitations, and highlight emerging challenges in validation, benchmarking, multimodal integration, and tissue-specific modeling. Finally, we outline future directions toward achieving dynamic, multilayered reconstruction of inter- and intracellular communication, de novo signaling, and integrative multi-omics modeling.