BSPP predicts protein expression from spatial transcriptomics across tissue sections

Spatial transcriptomics (ST) has emerged as a powerful method for linking gene expression to tissue architecture. While recent efforts have focused on ST platforms that measure both RNA and protein levels simultaneously, these approaches suffer from several limitations: they are often restricted by the availability of protein markers, high cost, and the inability to capture large-scale tissue information due to memory and computational constraints. Furthermore, existing models either fail to incorporate spatial dependencies or oversimplify the relationship between RNA and protein expression by treating them as independent variables. In this work, we propose BSPP, a framework combining a frozen RNA-derived proteomic prior with benefit-aware spatial residual routing. BSPP leverages Universal Cell Embedding (UCE) to incorporate cross-species biological knowledge while requiring neither manual annotations nor anatomical registration. We evaluate BSPP on multiple benchmark datasets, where it outperforms baselines by achieving better improvement in protein expression prediction accuracy and recovering spatially organized protein patterns. Our results highlight the potential of BSPP in bridging the gap between RNA expression and protein prediction in spatial transcriptomics.