A Foundation Model for Spatial Proteomics

• URL: http://arxiv.org/abs/2506.03373v1
• Date: Tue, 03 Jun 2025 20:30:25 GMT
• Title: A Foundation Model for Spatial Proteomics
• Authors: Muhammad Shaban, Yuzhou Chang, Huaying Qiu, Yao Yu Yeo, Andrew H. Song, Guillaume Jaume, Yuchen Wang, Luca L. Weishaupt, Tong Ding, Anurag Vaidya, Abdallah Lamane, Daniel Shao, Mohammed Zidane, Yunhao Bai, Paige McCallum, Shuli Luo, Wenrui Wu, Yang Wang, Precious Cramer, Chi Ngai Chan, Pierre Stephan, Johanna Schaffenrath, Jia Le Lee, Hendrik A. Michel, Caiwei Tian, Cristina Almagro-Perez, Sophia J. Wagner, Sharifa Sahai, Ming Y. Lu, Richard J. Chen, Andrew Zhang, Mark Edward M. Gonzales, Ahmad Makky, Jia-Ying Joey Lee, Hao Cheng, Nourhan El Ahmar, Sayed Matar, Maximilian Haist, Darci Phillips, Yuqi Tan, Garry P. Nolan, W. Richard Burack, Jacob D. Estes, Jonathan T. C. Liu, Toni K Choueiri, Neeraj Agarwal, Marc Barry, Scott J. Rodig, Long Phi Le, Georg Gerber, Christian M. Schürch, Fabian J. Theis, Youn H Kim, Joe Yeong, Sabina Signoretti, Brooke E. Howitt, Lit-Hsin Loo, Qin Ma, Sizun Jiang, Faisal Mahmood,
• Abstract summary: Foundation models have begun to transform image analysis by acting as pretrained generalists backbones that can be adapted to many tasks.<n>Here, we introduce KRONOS, a foundation model built for spatial imaging.<n>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.
• Score: 12.03739424653581
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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.

Related papers

• SPATIA: Multimodal Model for Prediction and Generation of Spatial Cell Phenotypes [39.45743286683448]
  We introduce SPATIA, a multi-scale generative and predictive model for spatial transcriptomics.<n> SPATIA learns cell-level embeddings by fusing image-derived morphological tokens and transcriptomic vector tokens.<n>We benchmark SPATIA against 13 existing models across 12 individual tasks.
  arXiv  Detail & Related papers  (2025-07-07T06:54:02Z)
• PixCell: A generative foundation model for digital histopathology images [49.00921097924924]
  We introduce PixCell, the first diffusion-based generative foundation model for histopathology.<n>We train PixCell on PanCan-30M, a vast, diverse dataset derived from 69,184 H&E-stained whole slide images covering various cancer types.
  arXiv  Detail & Related papers  (2025-06-05T15:14:32Z)
• Molecular-driven Foundation Model for Oncologic Pathology [6.922502805825084]
  We introduce Threads, a slide-level foundation model capable of generating universal representations of whole-slide images of any size.<n> Threads was pre-trained using a multimodal learning approach on a diverse cohort of 47,171 hematoxylin and eosin (H&E)-stained tissue sections.
  arXiv  Detail & Related papers  (2025-01-28T02:35:02Z)
• Querying functional and structural niches on spatial transcriptomics data [7.240034062898855]
  spatial transcriptomics enables gene expression profiling in spatial contexts.<n>It has been revealed that spatial niches serve as cohesive and recurrent units in physiological and pathological processes.<n>We defined the Niche Query Task, which is to identify similar niches across ST samples given a niche of interest (NOI)<n>We developed QueST, a specialized method for solving this task.
  arXiv  Detail & Related papers  (2024-10-14T16:01:27Z)
• Denoising Diffusion Probabilistic Models for Image Inpainting of Cell Distributions in the Human Brain [0.0]
  We propose a denoising diffusion probabilistic model (DDPM) trained on light-microscopic scans of cell-body stained sections. We show that our trained DDPM is able to generate highly realistic image information for this purpose, generating plausible cell statistics and cytoarchitectonic patterns.
  arXiv  Detail & Related papers  (2023-11-28T14:34:04Z)
• Diffusion-based Data Augmentation for Nuclei Image Segmentation [68.28350341833526]
  We introduce the first diffusion-based augmentation method for nuclei segmentation. The idea is to synthesize a large number of labeled images to facilitate training the segmentation model. The experimental results show that by augmenting 10% labeled real dataset with synthetic samples, one can achieve comparable segmentation results.
  arXiv  Detail & Related papers  (2023-10-22T06:16:16Z)
• Affine-Consistent Transformer for Multi-Class Cell Nuclei Detection [76.11864242047074]
  We propose a novel Affine-Consistent Transformer (AC-Former), which directly yields a sequence of nucleus positions. We introduce an Adaptive Affine Transformer (AAT) module, which can automatically learn the key spatial transformations to warp original images for local network training. Experimental results demonstrate that the proposed method significantly outperforms existing state-of-the-art algorithms on various benchmarks.
  arXiv  Detail & Related papers  (2023-10-22T02:27:02Z)
• Realistic Data Enrichment for Robust Image Segmentation in Histopathology [2.248423960136122]
  We propose a new approach, based on diffusion models, which can enrich an imbalanced dataset with plausible examples from underrepresented groups. Our method can simply expand limited clinical datasets making them suitable to train machine learning pipelines.
  arXiv  Detail & Related papers  (2023-04-19T09:52:50Z)
• AMIGO: Sparse Multi-Modal Graph Transformer with Shared-Context Processing for Representation Learning of Giga-pixel Images [53.29794593104923]
  We present a novel concept of shared-context processing for whole slide histopathology images. AMIGO uses the celluar graph within the tissue to provide a single representation for a patient. We show that our model is strongly robust to missing information to an extent that it can achieve the same performance with as low as 20% of the data.
  arXiv  Detail & Related papers  (2023-03-01T23:37:45Z)
• Learning multi-scale functional representations of proteins from single-cell microscopy data [77.34726150561087]
  We show that simple convolutional networks trained on localization classification can learn protein representations that encapsulate diverse functional information. We also propose a robust evaluation strategy to assess quality of protein representations across different scales of biological function.
  arXiv  Detail & Related papers  (2022-05-24T00:00:07Z)
• Multi-Scale Representation Learning on Proteins [78.31410227443102]
  This paper introduces a multi-scale graph construction of a protein -- HoloProt. The surface captures coarser details of the protein, while sequence as primary component and structure captures finer details. Our graph encoder then learns a multi-scale representation by allowing each level to integrate the encoding from level(s) below with the graph at that level.
  arXiv  Detail & Related papers  (2022-04-04T08:29:17Z)
• Towards an Automatic Analysis of CHO-K1 Suspension Growth in Microfluidic Single-cell Cultivation [63.94623495501023]
  We propose a novel Machine Learning architecture, which allows us to infuse a neural deep network with human-powered abstraction on the level of data. Specifically, we train a generative model simultaneously on natural and synthetic data, so that it learns a shared representation, from which a target variable, such as the cell count, can be reliably estimated.
  arXiv  Detail & Related papers  (2020-10-20T08:36:51Z)
• Generative Modelling of 3D in-silico Spongiosa with Controllable Micro-Structural Parameters [1.0804061924593265]
  We propose to apply recent advances in generative adversarial networks to generate realistic bone structures in-silico. In a first step, we trained a volumetric generative model in a progressive manner using a Wasserstein objective and gradient penalty. We were able to simulate the resulting bone structure after deterioration or treatment effects of osteoporosis therapies.
  arXiv  Detail & Related papers  (2020-09-23T18:11:47Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.