STAGE: Deciphering Tissue Organization and Predicting Perturbation Responses through Transferable Spatial Representations

Spatial transcriptomics measures gene expression while preserving tissue architecture, providing a basis for investigating tissue microenvironments and their functional responses. Learning shared representations that capture spatial organization and expression information across heterogeneous measurements is a key challenge for tissue analysis and downstream prediction. We present STAGE, a spatial transcriptomics foundation model for spatial domain analysis and expression recovery. STAGE jointly encodes gene expression and spatial geometry in cellular neighborhood graphs and uses hierarchical prototypes to organize representations at multiple resolutions. The model is pretrained on approximately 32.44 million cells spanning 18 tissue types and six spatial transcriptomics platforms. Across cross-platform, technical-replicate, and normal–tumor evaluations, both zero-shot and fine-tuned STAGE outperform six comparison methods in scenario-averaged spatial label consistency and embedding separation. Task-specific modules extend these representations to masked-cell expression recovery and spatial expression completion under known perturbation conditions. On 1,314 masked validation cells from colon Xenium, STAGE spatial reconstruction improves nonzero-expression Pearson correlation from 0.2305 for CIFM to 0.4139. On Perturb-map, conditioning CONCERT on STAGE expression representations reduces nonzero-gene MSE by 5.9% and energy distance by 5.6% relative to CONCERT. These results establish spatial representations as a shared basis for tissue organization analysis and expression reconstruction under known perturbation conditions.