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Updated: Jan 12, 2026

Multimodal Study of Murine Cardiovascular Remodeling: Four-Dimensional Ultrasound and Mass Spectrometry Imaging
Published on: January 10, 2025
Multimodal landscape of atherosclerotic plaques: A spatial omics approach with mass spectrometry imaging
Robin Joshi1, Soon Yew Tang1, Ujjalkumar Subhash Das1
1Institute for Translational Medicine and Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
Abstract:
Atherosclerotic plaques are complex and heterogeneous structures, originating as fatty streaks in the vasculature and formed by the accumulation of lipids and foam cells. Over time, these lesions progress as inflammation, smooth muscle cell proliferation and phenotypic switching, and extracellular matrix deposition contribute to plaque growth, culminating in their fracture, reactive thrombogenesis, and a cardiovascular event such as myocardial infarction and stroke. Traditional bulk mass spectrometry (MS) analysis has yielded critical insights into the molecular mechanisms of plaque formation and disease progression, but it is unable to determine the spatial heterogeneity and microenvironmental complexity within the lesion. Recent advances in mass spectrometry imaging (MSI) based omics, including spatial lipidomics, proteomics, and metabolomics, have enabled unprecedented visualization of molecular distribution in atherosclerotic plaques at cellular resolution. These techniques promise to elucidate the distinct cellular crosstalk, lesion vulnerability, and sex-specific disease mechanisms that contribute to plaque development and rupture. This review examines the recent advances in MS-based spatial omics and their application to atherosclerotic plaques in both experimental models and human samples. We highlight recent findings, explore their implications for precision medicine and translational research, and discuss current challenges in sample preparation and data integration. Despite challenges, we suggest approaches for integration of MS-based spatial omics using artificial intelligence (AI) to enhance data integration, interpretation, and translational applications in atherosclerosis research. These advances promise to broaden our understanding of atherosclerosis and identify novel therapeutic targets to limit the burden of cardiovascular disease.
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