DeSpaST: deconvoluting spatial transcriptomics signals to cell-level resolution using histology images

Advances in spatial transcriptomics (ST) technologies enable spatially resolved gene expression profiling, yet most platforms remain limited to spot-level resolution, where each measurement aggregates signals from multiple cells and obscures cell-specific programs and microenvironmental interactions. Existing computational approaches either provide limited sub-spot refinement or rely on matched single-cell RNA sequencing references that are costly and difficult to obtain. Here, we present DeSpaST (Deconvoluting Spatial Transcriptomics Signals to Cell-Level Resolution Using Histology Images), a dynamic edge-conditioned graph convolutional network that deconvolves spot-level ST data into cell-level gene expression profiles using only paired histology images. DeSpaST extracts nucleus-level morphological features, constructs directed cellular interaction graphs, and integrates spatial and transcriptional information through message passing. Validated across four cancer datasets with orthogonal Xenium, immunofluorescence, ablation, and interslice evaluations, DeSpaST enhances spatial resolution, facilitates downstream cellular-level analyses, and provides deeper insights into tissue biology.