# Gene expression prediction from histology

Frozen pathology foundation-model embeddings transfer poorly to single-cell spatial gene expression prediction from H&E: on the 5K gene panel, mean per-gene Pearson correlation falls from 0.375 at pseudo-spot resolution to 0.065 at single-cell resolution. A set of single-cell-oriented lightweight adaptations, namely cell-localized token pooling, 40x field-of-view inputs, Poisson count supervision and LoRA fine-tuning, raises the same panel to 0.174 and beats SpatialEx on the 5K panel with AUROC 0.800 versus 0.642. Gains concentrate in spatially variable and disease-relevant genes, while absolute correlation across the full panel stays low.

Frozen pathology foundation-model embeddings transfer poorly to single-cell spatial gene expression prediction from H&E: on the 5K gene panel, mean per-gene Pearson correlation falls from 0.375 at pseudo-spot resolution to 0.065 at single-cell resolution. A set of single-cell-oriented lightweight adaptations, namely cell-localized token pooling, 40x field-of-view inputs, Poisson count supervision and LoRA fine-tuning, raises the same panel to 0.174 and beats SpatialEx on the 5K panel with AUROC 0.800 versus 0.642. Gains concentrate in spatially variable and disease-relevant genes, while absolute correlation across the full panel stays low.

ALHENA distills spatial transcriptomic information into a routine H&E encoder that reads only images at inference. Rather than regressing individual genes, it trains the H&E representation to match the sample-to-sample similarity structure of two frozen transcriptomic models at cell and neighborhood scales. Across 110 cancer-type and signature combinations in TCGA, mean Spearman correlation was 0.341 versus 0.161 for unadapted UNI2-h; on the Thunder pathology benchmark the median change was −0.004.

ALHENA distills spatial transcriptomic information into a routine H&E encoder that reads only images at inference. Rather than regressing individual genes, it trains the H&E representation to match the sample-to-sample similarity structure of two frozen transcriptomic models at cell and neighborhood scales. Across 110 cancer-type and signature combinations in TCGA, mean Spearman correlation was 0.341 versus 0.161 for unadapted UNI2-h; on the Thunder pathology benchmark the median change was −0.004.

Volumetric spatial transcriptomics is expensive, and fixed-grid sampling measures redundant spots while missing informative regions in 3D tissue. STRATA co-optimizes sampling and prediction under a budget: on a 3D graph combining H&E morphology with single-cell priors, a reinforcement-learning agent picks morphologically sparse, transcriptomically informative spots, and a predictor fuses retrieval, regression and cross-layer imputation with per-spot weights. Across HER2, Breast, cSCC and a private Kidney cohort, STRATA has the highest PCC and lowest MSE; on HER2 its PCC is 0.361 and MSE 0.356, while the strongest-baseline figures stated in the text disagree with the table's baseline values, so that comparison is not quoted here.

Volumetric spatial transcriptomics is expensive, and fixed-grid sampling measures redundant spots while missing informative regions in 3D tissue. STRATA co-optimizes sampling and prediction under a budget: on a 3D graph combining H&E morphology with single-cell priors, a reinforcement-learning agent picks morphologically sparse, transcriptomically informative spots, and a predictor fuses retrieval, regression and cross-layer imputation with per-spot weights. Across HER2, Breast, cSCC and a private Kidney cohort, STRATA has the highest PCC and lowest MSE; on HER2 its PCC is 0.361 and MSE 0.356, while the strongest-baseline figures stated in the text disagree with the table's baseline values, so that comparison is not quoted here.