Imaging-anchored multiomics in cardiovascular disease: integrating cardiac imaging, bulk, single-cell, and spatial transcriptomics.
Authors
Affiliations (9)
Affiliations (9)
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, 333 Cedar Street, New Haven, CT 06510, United States.
- Computational Biology Department, School of Computer Science, Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, PA 15213, United States.
- Department of Computer Science, Emory University, 201 Dowman Drive, Atlanta, GA 30322, United States.
- Department of Chemistry, Emory University, 1515 Dickey Drive, Atlanta, GA 30322, United States.
- Irell and Manella Graduate School of Biological Science, City of Hope, CA, United States.
- Medical Sciences Division, University of Oxford, John Radcliffe Hospital, Headington, Oxford OX3 9DU, Oxfordshire, United Kingdom.
- AIBioMed Research Group, Taipei Medical University, 250 Wu-Hsing Street, Xinyi District, Taipei 11031, Taiwan.
- In-Service Master Program in Artificial Intelligence in Medicine, College of Medicine, Taipei Medical University, 250 Wu-Hsing Street, Xinyi District, Taipei 11031, Taiwan.
- Translational Imaging Research Center, Taipei Medical University Hospital, 252 Wu-Hsing Street, Xinyi District, Taipei 11031, Taiwan.
Abstract
Cardiovascular disease arises from interactions between inherited risk, molecular programmes, and tissue-scale remodelling that are observed clinically through imaging. Cardiac MRI (CMR), computed tomography (CT), and echocardiography are integral to routine cardiovascular care, while bulk RNA sequencing, single-cell RNA sequencing, and spatial transcriptomics are providing increasingly detailed molecular characterization of cardiac tissue. Yet, these imaging and molecular data are still analysed in largely separate pipelines. This review examines joint representations that link cardiac imaging phenotypes to transcriptomic and spatially resolved molecular states. An imaging-anchored perspective is adopted in which echocardiography, CMR, and CT define a spatial phenotype of the heart, and bulk, single-cell and spatial transcriptomics provide cell-type- and location-specific molecular context. We define the representation requirements of each modality, compare multimodal fusion strategies, and synthesize integrative pipelines for radiogenomics, spatial alignment, and image-based gene-expression prediction, together with their validation requirements, limitations, and failure modes. Spatial multiomic maps of human myocardium and atherosclerotic plaque, together with single-cell, spatial, and multimodal medical foundation models, are advancing imaging-anchored multiomics; however, cost, scalability, and tissue availability remain substantial barriers to large-scale cardiovascular translation.