A Foundation Model for Spatial Proteomics

TL;DR

KRONOS is a novel foundation model for spatial proteomics that learns meaningful biological representations from large-scale multiplex imaging data, enabling diverse tasks with high efficiency and scalability.

Contribution

The paper introduces KRONOS, a self-supervised foundation model specifically designed for spatial proteomics, with architectural adaptations for high-dimensional multiplex imaging.

Findings

Achieves state-of-the-art performance in cell phenotyping and treatment response prediction.

Demonstrates high data efficiency and scalability across multiple cohorts.

Enables segmentation-free, patch-level analysis for spatial pattern retrieval.

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.