Inferring spatial single-cell-level interactions through interpreting cell state and niche correlations learned by self-supervised graph transformer.

Cell-cell interactions (CCI), driven by distance-dependent signaling, are important for tissue development and organ function. While imaging-based spatial transcriptomics (ST) offers unprecedented opportunities to unravel CCI at single-cell resolution, current analyses face challenges such as limited ligand-receptor pairs measured, insufficient spatial encoding, and low interpretability. We present GITIII, a lightweight, interpretable, self-supervised graph transformer-based model that conceptualizes cells as words and their surrounding cellular neighborhood as context that shapes the meaning or state of the central cell. GITIII infers CCI by examining the correlation between a cell's state and its niche, enabling us to understand how sender cells influence the gene expression of receiver cells, visualize spatial CCI patterns, perform CCI-informed cell clustering, and construct CCI networks. Applied to four ST datasets across multiple species, organs, and platforms, GITIII effectively identified and statistically interpreted CCI patterns in the brain and tumor microenvironments.