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Integrative Spatial Omics for Systems-Level Mapping of Pathological Niches

Kruse, A. R. S.; Lardenoije, R.; Migas, L. G.; Scott, C. F.; Marshall, C.; Malek, M. C.; Eskaros, A.; Pham, T.; Aamodt, K.; Colley, M.; Ventura-Antunes, L.; Farrow, M. A.; Van de Plas, R.; Goncalves, J.; Schrag, M.; Powers, A. C.; Spraggins, J. M.

2025-09-17 systems biology
10.1101/2025.09.12.675904 bioRxiv
Show abstract

Spatial omics technologies are a powerful tool for mapping the relationship between cellular organization and molecular distributions in healthy and diseased tissue microenvironments. Here, we describe a novel multimodal pipeline that represents experimental and computational advances for spatiomolecular analysis of tissue samples across molecular classes. This adaptable method integrates matrix-assisted laser desorption/ionization imaging mass spectrometry spatial lipidomics, spatial transcriptomics, protein imaging via multiplexed immunofluorescence microscopy, and histopathological staining to uncover spatiomolecular profiles associated with unique cellular niches and pathological features. We demonstrate the power of this approach using two different complex human disease systems: Alzheimers disease in human brain tissue and type 2 diabetes mellitus in the human pancreas. This work establishes and demonstrates a generalizable framework for multimodal spatial integration, enabling precise mapping of molecular mechanisms that underlie complex tissue pathologies.

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"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.