Deciphering cell-cell communication from spatially resolved transcriptomic data at single-cell resolution with subgraph-based attentional graph neural network

The inference of cell-cell communication (CCC) is crucial for a better understanding of complex cellular behavior and regulatory mechanisms in biological systems. However, current computational methods still encounter substantial constraints in inferring spatially resolved CCC at the single-cell level, hampered by their focus on cell-type-centric communications and struggles with handling the limitations of spatial transcriptomics (ST) data. To address this issue, we present a versatile method, called DeepTalk, to infer spatial CCC at single-cell resolution by integrating single-cell RNA sequencing (scRNA-seq) data and ST data. DeepTalk utilizes graph attention network (GAT) to integrate scRNA-seq and ST data, which enables accurate cell-type identification for single-cell ST data and deconvolution for spot-based ST data. Then, DeepTalk can capture the connections among cells at multiple levels using subgraph-based GAT, and further achieve spatially resolved CCC inference at single-cell resolution. Application of DeepTalk to diverse datasets from different platforms demonstrates its promising performance and robustness in discovering meaningful spatial CCCs, which can provide a novel avenue for the exploration and interpretation of various biological processes.