Cell Communication and Interaction
Beyond cell types
Tissues are not collections of independent cell types. They are communities in constant molecular conversation, where signaling between cells coordinates development, maintains homeostasis, and drives disease. Single-cell transcriptomics makes it possible to reconstruct those conversations computationally, by examining which cells express the ligands and receptors required for specific signaling pathways.
Cell communication analysis is most informative when the experiment includes multiple cell types that are expected to interact: tumor and immune cells, fibroblasts and keratinocytes, neurons and glia. It is also useful for identifying how those communication patterns change across conditions, such as before and after treatment, or between healthy and diseased tissue.
Ligand-receptor interaction analysis
The standard approach infers communication by identifying co-expressed ligand-receptor pairs across cell type combinations, then applying statistical tests to determine which interactions are enriched above background. CellChat (v3, with spatial support) models complex signaling pathways with known multi-subunit receptors and provides pathway-level summaries. CellPhoneDB offers a curated interaction database with a focus on immune cell biology. NicheNet extends the framework to ask which upstream ligands are most likely to explain observed transcriptional changes in a receiving cell population.
LIANA+ has emerged as a useful consensus framework that runs multiple methods simultaneously and aggregates results, reducing dependence on the choice of any single tool. For experiments comparing communication patterns across multiple conditions or time points, Tensor-cell2cell enables multicondition decomposition to identify condition-specific and shared communication programs.
Spatial communication analysis
Standard ligand-receptor analysis assumes that any two cell types can potentially communicate, regardless of their physical proximity in the tissue. Spatial transcriptomics removes that assumption, making it possible to restrict communication inference to cell pairs that are actually neighbors.
Spatially-resolved communication analysis identifies signaling interactions that are consistent with the physical organization of the tissue, distinguishing short-range juxtacrine signaling from longer-range paracrine signaling based on the distance between expressing cells. In skin biology, for example, this approach can distinguish keratinocyte-fibroblast crosstalk at the dermis-epidermis interface from systemic cytokine signals that reach cells at a distance.