Deep Visual Proteomics with AI: A Spatial Approach to Decode Disease Mechanisms

Early detection and molecular characterization of disease progression are persistent challenges in modern medicine. Although genomic and transcriptomic profiling of tissue lesions and precursors has advanced, spatial proteomic mapping of these areas remains largely unexplored. We introduce a platform that combines Deep Visual Proteomics (DVP) with AI-powered pathology foundation models for systematic, high-resolution characterization of key regions in clinical tissue samples.

 

Using AI trained on extensive histopathological datasets, we identify and segment cellular regions within complex tissues. These regions undergo spatially-resolved, mass-spectrometry-based proteomics, allowing direct comparison of molecular signatures across diverse tissue states. DVP enables the identification of thousands of proteins from minimal, phenotype-matched cell populations, forming detailed, spatially-resolved proteomic maps of disease progression.

 

This approach offers deep insights into disease dynamics at the proteomic level, revealing complex signaling pathways and molecular signatures that drive the transition from health to disease. We identify potential biomarkers and therapeutic targets that could facilitate early detection and intervention across various diseases. By merging AI-driven pathology with the depth of DVP, we establish a new paradigm for spatial medicine, enhancing understanding and clinical tools for studying disease initiation and progression.

 

Learning Objectives