Year: 2026
Type: article
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.
Source: PLoS Computational Biology
Authors: 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, Patrick M. Reeves
Institutions: Harvard University, Massachusetts General Hospital, Weill Cornell Medicine, Tufts University, Northeastern University, Broad Institute, Heidelberg University, University Hospital Heidelberg, Massachusetts Institute of Technology, Emory and Henry College, The University of Texas at Austin, Brigham and Women's Hospital, Beth Israel Deaconess Medical Center
Language: English
Subfield: Molecular Biology
Domain: Life Sciences
Open Access status: gold
Estimated APC paid: $3,043
Funders: National Science Foundation, Foundation for the National Institutes of Health, Damon Runyon Cancer Research Foundation, Massachusetts General Hospital, Yale University, Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung, Bundesministerium für Bildung und Forschung, Universität Zürich, National Institutes of Health, Berrikuntza + Ikerketa + Osasuna Eusko Fundazioa, National Cancer Institute, DOD Prostate Cancer Research Program
Awards: G2708535327, MIT/Mayo Physical Sciences Center for Drug Distribution and Efficacy in Brain Tumors, G4426893147, G4477544551, G4694221852, Using functional “cancer-geography” to build a predictive model for the melanoma precancerous ecosystem, G5717550216, G5850682332, G6162127319, G6579435028, HiGHmed - Medizininformatik-Konsortium - Nachwuchsgruppe 'Digitale Bildanalytik (HiGHDiBi)': Universität Heidelberg, Graduate Research Fellowship Program (GRFP), G9000002381, G9000172682
OpenAlex ID: W7162473155