A Foundation Model for Spatial Proteomics
Paper • 2506.03373 • Published
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KRONOS: A Foundation Model for Spatial Proteomics
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Abstract
Foundation models have transformed image analysis by acting as pretrained generalist backbones that can be adapted to many downstream tasks, with the recent advances in histopathology showing significant clinical promise. Their utility for spatial proteomics, the measurement of proteins at the subcellular resolution, has not been fully realized due to the lack of diverse, large-scale pretraining datasets and architectures to incorporate marker-specific subtleties. We introduce KRONOS, a foundation model built for spatial proteomics, trained in a self-supervised manner on over 56 million image patches covering 268 protein markers. KRONOS introduces key architectural innovations to account for heterogeneous marker panels that substantially vary across datasets, while modeling cross-marker interactions to better capture the underlying tissue biology. We demonstrate that KRONOS learns biologically meaningful representations across multiple scales from cells and tumor microenvironments to entire tissue, thereby enabling a wide range of spatial biology tasks. Evaluated comprehensively across diverse tasks such as cell phenotyping, region-level cell composition prediction, and patient risk stratification, KRONOS achieves consistently superior performance against raw marker-based approaches, which constitute the standard spatial proteomics analysis pipelines, and other image foundation models. Beyond benchmarking performance, KRONOS demonstrates new paradigms for data-driven clinical research workflow for biomarker discovery, supported by simultaneous superior risk stratification and interpretation of risk-modulating biomarkers, with the capabilities to "de-plex" high-dimensional discovery marker panels for effective downstream clinical deployment. We envision KRONOS as a foundational tool for data-driven, scalable spatial proteomics workflows.
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Contact
For any additional questions or comments, contact Andrew H. Song (asong2@mdanderson.org),
Anurag Vaidya (avaidya@mit.edu),
Sizun Jiang (sjiang3@bidmc.harvard.edu),
Faisal Mahmood (FaisalMahmood@bwh.harvard.edu).
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