STTT: Test-Time Training for Cross-Domain Spatial Transcriptomics Prediction from Histology

High costs and limited availability of spatial transcriptomics data motivate predicting spatial gene expression directly from routine H&E histology. However, variations in staining, scanners, cohorts, platforms, and organs create substantial domain shifts. Most histology-to-spatial-transcriptomics predictors rely on static inference, leaving unlabeled target slides unable to guide adaptation at prediction time. We propose Spatial Test-Time Training (STTT), a framework for spatially structured adaptation at inference time. STTT combines masked-self reconstruction and bidirectional center–context reconstruction to adapt using unlabeled target-slide morphology and spatial neighborhoods. Low-rank query/value updates, representation damping, and slide-wise reset constrain this label-free adaptation. Across four cross-domain settings, STTT consistently improves mean PCC over source-trained SpaFactor and achieves the highest mean four-task PCC among seven compared TTT and TTA baselines.