Recent advances in multimodal deep learning have successfully begun to link molecular data with tissue morphology. To date, this work has largely focused on transcriptomics, a limited surrogate for protein expression. The direct prediction of proteins, the functional endpoints of gene expression, remains underexplored, largely due to the difficulty in generating spatially resolved proteomic data. Early efforts to predict proteomic data from tissue morphology were hindered by low specificity and limited multiplexing capacity, constraints inherent to immunohistochemistry and multiplexed immunofluorescence techniques. This proof-of-concept study presents a novel data generation pipeline to address these throughput and specificity challenges and decode the proteome from H&E slides via high-throughput tile-level mass spectrometry profiling. This approach enables a direct one-to-one correspondence between histological features and proteomic measurements, which is an essential prerequisite for training robust foundation models. By applying this pipeline to gastric cancer biopsies, the study demonstrated that morphologically distinct clusters corresponded to distinct proteomic profiles. Notably, tumor regions were enriched for clinically relevant markers such as HMGB1, LGALS3, and ERBB2, all of which are associated with poor prognosis. This work establishes the foundation for a new generation of AI-driven proteomics, demonstrating that routine histological images contain sufficient information to predict thousands of proteins across diverse biological conditions. TileDVP represents a paradigm shift toward accessible, high-throughput spatial proteomics that could transform biomarker discovery and precision medicine applications.