RESOLVE: Residual Decomposition with Single-Cell Prototype Guidance for Spatial Gene Expression Prediction

Predicting spatial gene expression from H&E-stained histology offers a promising low-cost alternative for reconstructing spatial transcriptomic maps. However, existing methods typically model all expression variation jointly, conflating stable morphology-associated patterns with slide-specific cell-state deviations and thereby limiting the recovery of signals that are not directly explained by histology. To this end, we propose RESOLVE, which reformulates spatial gene expression prediction as two complementary processes, a morphology-driven base prediction and a cell-state-driven residual correction. A lightweight base predictor learns the morphology-expression mapping shared across slides, capturing stable tissue-level expression patterns. A prototype-guided residual pathway builds cell-state prototypes with explicit biological meaning from a single-cell reference and uses prototype routing to correct the slide-specific deviations that the base prediction cannot adequately explain. A per-spot gate modulates the residual magnitude according to the local tissue state, and an orthogonality constraint encourages the two pathways to learn complementary and decoupled representations, explicitly separating stable morphological signals from slide-specific cell-state changes. Experiments on three public ST datasets show that RESOLVE attains the best results on Her2ST and the best PCC on ST-Net and SCC. The gains of the residual pathway are interpretable. Genes that benefit most improve mainly by correcting slide-level differences and concentrate in interferon-$\alpha /\gamma$ and estrogen response programs. This pattern indicates that the residual corrects cell-state deviations rather than re-fitting what morphology already states.