A Foundation Model for Spatial Proteomics
Abstract
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.
Cite
@article{arxiv.2506.03373,
title = {A Foundation Model for Spatial Proteomics},
author = {Muhammad Shaban and Yuzhou Chang and Huaying Qiu and Yao Yu Yeo and Andrew H. Song and Guillaume Jaume and Yuchen Wang and Luca L. Weishaupt and Tong Ding and Anurag Vaidya and Abdallah Lamane and Daniel Shao and Mohammed Zidane and Yunhao Bai and Paige McCallum and Shuli Luo and Wenrui Wu and Yang Wang and Precious Cramer and Chi Ngai Chan and Pierre Stephan and Johanna Schaffenrath and Jia Le Lee and Hendrik A. Michel and Caiwei Tian and Cristina Almagro-Perez and Sophia J. Wagner and Sharifa Sahai and Ming Y. Lu and Richard J. Chen and Andrew Zhang and Mark Edward M. Gonzales and Ahmad Makky and Jia-Ying Joey Lee and Hao Cheng and Nourhan El Ahmar and Sayed Matar and Maximilian Haist and Darci Phillips and Yuqi Tan and Garry P. Nolan and W. Richard Burack and Jacob D. Estes and Jonathan T. C. Liu and Toni K Choueiri and Neeraj Agarwal and Marc Barry and Scott J. Rodig and Long Phi Le and Georg Gerber and Christian M. Schürch and Fabian J. Theis and Youn H Kim and Joe Yeong and Sabina Signoretti and Brooke E. Howitt and Lit-Hsin Loo and Qin Ma and Sizun Jiang and Faisal Mahmood},
journal= {arXiv preprint arXiv:2506.03373},
year = {2025}
}