Sparse RNA Prompts Calibrate Spatial Gene Expression from Histology

Histology-based models of spatial gene expression may capture within-slide variation while remaining miscalibrated across donors. We evaluated whether sparse same-slide RNA measurements reduce this cross-donor error and whether any improvement reflects local spatial adaptation or slide-level calibration. A frozen UNI2-h–Ridge predictor was evaluated on 70 slides from 47 donors across nine HEST cohorts. For each slide, 20 deterministic spatial partitions defined immutable target fields of view (FOVs) and disjoint prompt FOVs. We compared a no-prompt baseline with scalar, gene-wise, and low-rank empirical-Bayes offsets. At requested budget $B=5\%$ (mean achieved fraction, 5.94%; range, 4.97–14.48%), the prespecified low-rank comparison reduced donor-macro variance-normalized log-MSE from 1.441 to 1.239 (absolute difference, -0.202; paired bootstrap 95% interval, $[-0.500,-0.048]$), with improvement for 40 of 47 donors. Gene-wise offsets performed best, reducing normalized log-MSE to 1.200 ($-16.7\%$ relative to the no-prompt baseline) and mean Poisson deviance from 31.90 to 26.33 ($-17.5\%$), with improvement for all 47 donors. A scalar offset achieved normalized log-MSE of 1.206 and therefore recovered 97.3% of the gene-wise reduction relative to the no-prompt baseline; the low-rank estimator had 2.7% higher normalized log-MSE than the scalar estimator. Within-slide gene and program correlations were unchanged because each estimator adds a spot-invariant vector. These findings support same-slide calibration of expression levels, but not recovery of donor-specific spatial structure or identification of a uniquely technical source of the residual offset.