The Virtual Tissues foundation model resolves spatial proteomics across scales.

August 5, 2026| Source: Nature

Spatial proteomics technologies have transformed our understanding of complex tissue architecture in cancer but present unique challenges for computational analysis 1 . Each study uses a different marker panel and protocol, and most methods are tailored to single cohorts, which limits knowledge transfer and robust biomarker discovery. Here we present Virtual Tissues (VirTues), a general-purpose foundation model for spatial proteomics that learns marker-aware, multi-scale representations of proteins, cells, niches and tissues directly from multiplex imaging data. From a single pretrained backbone, VirTues supports marker reconstruction, cell segmentation and typing, niche annotation, spatial biomarker discovery and patient stratification, including zero-shot annotation across heterogeneous panels and datasets. In triple-negative breast cancer, VirTues-derived biomarkers predict anti-PD-L1 chemo-immunotherapy response 2 and stratify disease-free survival in an independent cohort 3 , outperforming state-of-the-art biomarkers derived from the same datasets and current clinical stratification schemes.

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The Virtual Tissues foundation model resolves spatial proteomics across scales.

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Where intention goes, responsibility flows: Grounding AI accountability in the machine learning paradigm.

Protein resource allocation across subcellular compartments revealed by large-scale absolute quantitative proteomics in yeast.

Quantifying ancient architecture: Stone-level machine learning reveals architectural variation and settlement growth at Shivta.

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Data-driven discovery and rapid, direct synthesis of MXenes.