Decoding cell-cell communication in spatial transcriptomics: mechanistic insights, modeling constraints, and analytical caveats.

Spatial transcriptomics enables mechanistically grounded inference of cell-cell communication by preserving physical cell organization, improving ligand-receptor modeling despite ongoing analytical caveats.

• Why it matters: Inferring cell-cell communication from transcriptomic data has been limited by missing spatial context, risking biased or unreliable ligand-receptor interaction estimates.
• What they did: The review surveys statistical, optimal transport, and deep learning frameworks that incorporate spatial information to model ligand-receptor interactions and downstream signaling, and it summarizes shared mechanism-driven components.
• The result: It highlights context-dependent biases from tissue heterogeneity and spatial architecture—especially for permutation-based inference—and outlines mechanistic considerations and curated databases to support robust, interpretable CCC inference.