Atherosclerosis is more than just a lipid-based disorder, and cell-mediated inflammation has emerged as a significant contributing factor for vascular plaque development and instability. Atherosclerotic lesions contain a complex mixture of immune cell populations, including T cells, neutrophils, eosinophils and dendritic cells (DCs) in addition to the well characterized macrophage component. Despite their relatively low cell numbers within vascular lesions, DCs are key regulators of inflammation that modulate the maturation and homeostasis of atheromas. DCs are found within atheromas at all stages of lesion development and their increased accumulation correlates with the level of plaque instability. We hypothesize that a diagnostic approach capable of targeting and quantifying plaque-resident immune cells, such as DCs, may provide a mechanism for assessing plaque stability and risk. Here, we propose a novel multiplexed magnetic resonance imaging (MRI) strategy that combines machine learning with nanomaterial probes for the non-invasive identification of unstable vascular plaques. As a proof of concept, we aim to demonstrate the potential of this cellular MRI methodology by calculating a new metric to assess plaque instability: the DC to macrophage ratio. Current cardiac MRI protocols cannot detect unstable plaques. Using a machine learning algorithm, we have developed a novel technique to generate multi-color (i.e. multiplexed) MRI images for the simultaneous identification of multiple nanomaterials with distinct T1/T2 relaxivities. Each color is assigned to a particular nanomaterial, allowing precise identification and quantification within live animals. Importantly, our team has also developed novel nanomaterials that selectively target DCs versus macrophages within vascular lesions. Combining these two technologies, we will separately and simultaneously mark lesion-resident DCs and macrophages to precisely quantify their ratios at multiple stages of atheroma development within Apoe-/-Fbn1c1039G+/- mice. Unlike most models, Apoe-/-Fbn1c1039G+/- mice develop highly inflamed vascular lesions that can spontaneously rupture due to a mutation in the fibrillin-1 (Fbn1) gene. Funding of this work will thus allow application of the latest developments in machine learning and nanoscale science to non-invasively assess vascular plaque instability.
|Effective start/end date||7/1/19 → 6/30/21|
- American Heart Association (19IPLOl34760491)
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