2026-10-28 スイス連邦工科大学ローザンヌ校（EPFL）
Imaging mass cytometry of a lung tumor core from a tissue microarray. 2026 EPFL/AIMM – CC-BY-SA 4.0
＜関連情報＞
- https://actu.epfl.ch/news/a-new-tissue-atlas-will-help-ai-understand-cance-2/
- https://arxiv.org/abs/2610.24002
spora：空間プロテオミクスのための統合マルチモーダルデータセットおよびプラットフォーム spora: A Unified Multimodal Dataset and Platform for Spatial Proteomics
Benedikt von Querfurth, Johann Wenckstern, Eeshaan Jain, Lukas Klein, Phil Cheng, Yexiang Cheng, Cedric Vincent-Cuaz, Martina Haberecker, Pascal Frossard, Andreas Wicki, Olivier Michielin, Charlotte Bunne
arXiv Submitted on 15 Oct 2026
DOI:https://doi.org/10.48550/arXiv.2610.24002
Abstract
Spatial proteomics (SP) captures the molecular composition and spatial organization of tissues at single-cell resolution, opening a direct view of the cellular ecosystems that drive disease. Protein panels, acquisition platforms, and tissue contexts differ from one cohort to the next, and a coherent picture of tissue biology can only emerge from models trained to bridge this heterogeneity. Foundation models offer the natural path forward, but their training depends on large, harmonized corpora that span platforms, panels, and cohorts. No public resource currently approaches that scale; available data sit in small, narrowly scoped releases with incompatible formats and conventions. We present spora, a large-scale multimodal SP dataset of 13,546 standardized multiplex images from 5,477 patients across 35 cohorts and five major SP technologies. Curated with biomedical experts, spora provides model training-ready formats together with cell and tissue segmentations, structured clinical metadata, and, where available, paired H&E enabling the training of virtual staining models. It is complemented by spora [io], a unified interface for tiling, sampling, and standardization across all cohorts and modalities, and spora [bench], a benchmark suite spanning cell phenotyping, diagnostic and therapeutic prediction as well as virtual staining tasks across twelve cohorts that places current SP foundation models on common ground against task specific baselines.
