spora: A Unified Multimodal Dataset for Spatial Proteomics

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 12,596 standardized multiplex images from 5,254 patients across 31 cohorts and four 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- and patient-level tasks across 11 cohorts that places current SP foundation models on common ground against task-specific baselines for the first time. Data and documentation at: https://spora.epfl.ch (user: spora_reviewer password: Uh8aef0kiev9).