Multi-Task Graph Learning for Cell–Cell Communication Inference in Spatial Transcriptomics

Intercellular communication is fundamental to multicellular life, orchestrating tissue development, maintaining homeostasis, and driving the pathogenesis of complex diseases. Recent advances in spatial transcriptomics have enabled high-resolution, in situ mapping of intricate ligand-receptor (L-R) interactions within native tissue contexts. However, existing computational approaches often underutilize spatial topological information and overlook the distinct regulatory functions of different L-R interaction types, thereby limiting their capacity to capture the dynamic heterogeneity of cellular communication in complex microenvironments. To address these limitations, we propose CellMM, a novel multi-task learning framework powered by multi-relational graph neural networks. CellMM constructs a multi-relational graph that integrates gene expression profiles, spatial coordinates, and L-R interaction types. It employs relational graph convolutional networks to learn expressive node embeddings and leverages a multi-head attention mechanism to adaptively fuse multi-layer graph features, enabling the identification of active L-R signaling events between spatially adjacent cells. Experiments on three simulated datasets and applications to three real spatial transcriptomics datasets show that CellMM outperforms baseline methods in simulated evaluations and recovers biologically plausible communication patterns in real data.