ReDiM: Decodable Multi-view Representation Learning of RNA–Protein Discordance with Spatial and Metabolic Context

Spatial multi-omics connects transcriptomic, proteomic, and metabolic organization within intact tissue, but spatial proteomics remains more limited in molecular coverage and availability than spatial transcriptomics. Predicting spatial protein abundance from RNA is essential for bridging this measurement gap. However, RNA is not a quantitative proxy for protein: translation, degradation, and metabolic state create context-dependent discordance across tissue regions. Existing predictors typically learn a direct RNA-to-protein mapping and absorb such deviations into undifferentiated prediction error. We introduce ReDiM, a decodable representation-learning model that casts context-dependent RNA–protein correction as a supervised, low-dimensional learning problem. ReDiM freezes a reference RNA-to-protein predictor, defines correction as the difference between observed and reference-expected protein abundance, and represents it in a training-fitted 16-dimensional protein basis. It predicts correction coordinates from three RNA-accessible views: reference protein expectation, spatially contextualized RNA, and RNA-derived reaction-level flux enrichment scores (FES). FES maps RNA onto a genome-scale metabolic network, enabling FES-associated corrections to be traced across protein, reaction, and pathway levels. A fixed decoder maps corrections to a common 31-protein space. Across held-out liver sections, amyotrophic lateral sclerosis (ALS) donors, and brain regions, training-derived bases reconstruct held-out discordance, and ReDiM improves prediction beyond the frozen reference. Its two contextual inputs play distinct roles: spatial RNA drives the recovery of protein rank structure, whereas FES further refines absolute prediction error, with larger gains in target spots that differ more from the training data. To characterize this metabolic contribution, we analyze FES attribution in ALS and identify protein- and pathway-level correction patterns that recur across donors. By learning transferable RNA–protein corrections from complementary spatial and metabolic context, ReDiM improves protein prediction in unseen biological domains. ReDiM thus recasts spatial RNA-to-protein prediction as learning a transferable, decodable correction rather than treating discordance as undifferentiated error.