Mapping spatial cell-cell communication programs by tailoring chains of cells for transformer neural networks

Abstract

Recent advances in spatial transcriptomics and computational modeling enable the study of cellular interactions in situ. However, existing methods quantify ligand-receptor activity pairwise or between predefined cell groups, yielding overlapping signals and limited ability to summarize concurrent interactions into programs while localizing communication hotspots. We introduce scCChain, a transformer-based framework that integrates ligand-receptor activity into spatially resolved communication programs and localizes hotspots at spot and single-cell resolution. scCChain first derives candidate programs using structured dimensionality reduction. Subsequently, it samples programspecific communication chains by linking transcriptionally similar sender cells to candidate receivers via weighted random walks on a distance-informed cell graph, borrowing signal from similar neighbors. Transformer-based modeling then scores chains to prioritize communication programs and pinpoint hotspots across the tissue. Applied to human breast cancer spatial transcriptomics data at spot and single-cell resolution, scCChain supports both exploratory communication program discovery and targeted analysis of user-specified ligand-receptor pairs. In spot-level data, it prioritizes a tumor-associated program enriched for pro-angiogenic signaling that localizes to invasive regions. In imaging-based data, it highlights CXCL12-CXCR4 communication hotspots at cellular resolution. Here, we demonstrate that chain-based transformer modeling enables interpretable discovery and mapping of biological meaningful spatial communication programs within complex tissues.