# A Foundation Model for Spatial Proteomics

arXiv: https://arxiv.org/abs/2506.03373 · HTML: https://mlsanity.com/paper/2506.03373/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Jun 2025.

## Built on by (most cited first, 12 of 12)

- [SciReasoner: Laying the Scientific Reasoning Ground Across Disciplines](https://mlsanity.com/paper/2509.21320/) (2025)
- [Computational Pathology in the Era of Emerging Foundation and Agentic AI -- International Expert Perspectives on Clinical Integration and Translational Readiness](https://mlsanity.com/paper/2603.05884/) (2026)
- [Evaluating Vision Foundation Models for Pixel and Object Classification in Microscopy](https://mlsanity.com/paper/2603.19802/) (2026)
- [Preserving Marker Specificity with Lightweight Channel-Independent Representation Learning](https://mlsanity.com/paper/2512.15410/) (2025)
- [ImmuVis: Hyperconvolutional Foundation Model for Imaging Mass Cytometry](https://mlsanity.com/paper/2602.04585/) (2026)
- [HGP-Mamba: Integrating Histology and Generated Protein Features for Mamba-based Multimodal Survival Risk Prediction](https://mlsanity.com/paper/2603.16421/) (2026)
- [Linking spatial biology and clinical histology via Haiku](https://mlsanity.com/paper/2605.00925/) (2026)
- [SVC-Probe: A Framework for Evaluating Perturbation Generalization in Spatial Foundation-Model Embeddings](https://mlsanity.com/paper/2606.28465/) (2026)
- [Mitigating Batch Effects in Histopathology via Language-Mediated Robust Embedding Generation](https://mlsanity.com/paper/2606.28697/) (2026)
- [Building artificial intelligence virtual tissue (AIVT) for tissue state representation, feature prediction, and dynamic simulation](https://mlsanity.com/paper/2606.29883/) (2026)
- [MUL-T: Decoding Spatial Cellular Architecture in Multiplexed Tissue Images](https://mlsanity.com/paper/2607.28030/) (2026)
- [CytoFormer: A Molecularly Supervised Cell Foundation Model for Histopathology Cell Classification](https://mlsanity.com/paper/2608.16718/) (2026)

## Abstract (arXiv)

Foundation models have begun to transform image analysis by acting as pretrained generalist backbones that can be adapted to many tasks even when post-training data are limited, yet their impact on spatial proteomics, imaging that maps proteins at single-cell resolution, remains limited. Here, we introduce KRONOS, a foundation model built for spatial proteomics. KRONOS was trained in a self-supervised manner on over 47 million image patches covering 175 protein markers, 16 tissue types, and 8 fluorescence-based imaging platforms. We introduce key architectural adaptations to address the high-dimensional, multi-channel, and heterogeneous nature of multiplex imaging. We demonstrate that KRONOS learns biologically meaningful representations across multiple scales, ranging from cellular and microenvironment to tissue levels, enabling it to address diverse downstream tasks, including cell phenotyping, region classification, and patient stratification. Evaluated across 11 independent cohorts, KRONOS achieves state-of-the-art performance across cell phenotyping, treatment response prediction, and retrieval tasks, and is highly data-efficient. KRONOS also introduces the paradigm of segmentation-free patch-level processing for efficient and scalable spatial proteomics analysis, allowing cross-institutional comparisons, and as an image reverse search engine for spatial patterns. Together, these results position KRONOS as a flexible and scalable tool for spatial proteomics. The model is publicly accessible at https://github.com/mahmoodlab/KRONOS.