MIAAIM: Multi-omics image integration with dimensional reduction for tissue state mapping

Joshua M Hess, Richard K Dzeng, Iulian Ilieş, Denis Schapiro, John J Iskra, Divya Mirgh, John Nam, Erin H Seeley, David E Verrill, Walid M Abdelmoula, Michael S Regan, Georgios Theocharidis, Chin Lee Wu, Aristidis Veves, Nathalie Y R Agar, Ann E Sluder, Mark C Poznansky, Ruxandra F Sîrbulescu and Patrick M Reeves

PLOS Computational Biology, 2026, vol. 22, issue 5, 1-1

Abstract: High-parameter tissue imaging enables detailed molecular analysis of single cells within their spatial environment. A current challenge to more complete tissue and single-cell spatial profiling is in situ data alignment across imaging platforms that quantify multiple types of biomolecules at differing resolutions. Here, we describe MIAAIM (Multi-omics Image Alignment and Analysis by Information Manifolds), a modular framework to align and process data from separate imaging technologies with distinct imaging resolutions and data complexity. MIAAIM is designed to be applied to align and analyze images of clinical biopsies from histological staining, imaging mass cytometry, and mass spectrometry imaging. A key advantage of the MIAAIM approach is its capacity to identify unbiased molecular phenotypes that correlate with cell identities and states determined using high-resolution targeted immunodetection. In a large diabetic foot ulcer (DFU) biopsy, this strategy allowed the identification of unique molecular characteristics of infiltrating immune cells as a function of local tissue health. In multi-core tissue microarrays (TMAs) of prostate cancer, MIAAIM allowed the classification of adjacent tumor grades with high accuracy, with over 90% of classification signal sourced from spatial features, generated from segmented cells across multiple imaging modalities while revealing novel cell/ immune signatures of the disease state. MIAAIM provides a disease and cell type agnostic general framework to construct multimodal tissue imaging datasets, yielding novel insights into the association of molecular analytes with cell subsets and their activation states for the analysis of complex tissue states.Author summary: Integrating tissue imaging across different modalities and resolutions is a significant challenge that profiles the single cell and its local microenvironment within the tissue architecture. Here, we describe MIAAIM (Multi-omics Image Alignment and Analysis by Information Manifolds), a modular framework to align and combine data across imaging resolutions and multimodal imaging stacks. MIAAIM was used to integrate optical, imaging mass cytometry (IMC), and mass spectrometry imaging (MSI) of diabetic foot ulcer (DFU) clinical biopsies and prostate cancer tissue microarrays (TMAs). On the DFU sample, we explored immune niches within the tissue microenvironment. Using prostate cancer TMAs we generated single cell and cellular network profiles that led to state-of-the-art classification accuracies of Gleason score. MIAAIM provides a generalizable framework to integrate complex multimodal tissue imaging datasets, yielding novel insights into the association between molecular analytes and cellular populations across tissue structures.

Date: 2026
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pcbi00:1014274

DOI: 10.1371/journal.pcbi.1014274

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