4.7 Article

Label-Free Characterization of Atherosclerotic Plaques Via High-Resolution Multispectral Fluorescence Lifetime Imaging Microscopy

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LIPPINCOTT WILLIAMS & WILKINS
DOI: 10.1161/ATVBAHA.123.319339

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atherosclerosis; autofluorescence imaging; fluorescence microscopy; lipids; machine learning; macrophages

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Multispectral fluorescence lifetime imaging microscopy (FLIM) was used to investigate the complex composition of coronary artery and atheroma at the pixel level. Automated visualization of multiple plaque components without the need for histological staining and analysis was achieved using this FLIM strategy.
Background:Autofluorescence lifetime (AFL) imaging, a robust technique that enables label-free molecular investigation of biological tissues, is being introduced into the field of cardiovascular diagnostics. However, detailed AFL characteristics of coronary arteries remain elusive and there is a lack of methodology enabling such characterization. Methods:We developed multispectral fluorescence lifetime imaging microscopy (FLIM) based on analog-mean-delay. Freshly sectioned coronary arteries and atheromas, harvested from 5 swine models, were imaged using FLIM and stained to label lipids, macrophages, collagen, and smooth muscle cells. The components were quantitated from digitized histological images and compared with the corresponding FLIM. Multispectral AFL parameters derived from 2 different spectral bands (390 nm and 450 nm) were analyzed. Results:FLIM provided a wide field-of-view, high-resolution AFL imaging of frozen sections. Principal compositions of coronary arteries, such as tunica media, tunica adventitia, elastic laminas, smooth muscle cell-enriched fibrous plaque, lipid-rich core, and foamy macrophages, were well visualized in FLIM images and were found to have each different AFL spectra. In particular, proatherogenic components including lipids and foamy macrophages exhibited significantly different AFL values compared with plaque-stabilizing collagen- or smooth muscle cell-enriched tissues (P<0.0001). Pairwise comparisons showed that each composition was distinguishable from another by the difference in multispectral AFL parameters. Pixel-level analysis based on coregistered FLIM-histology dataset showed that each component of atherosclerosis (lipids, macrophages, collagen, and smooth muscle cells) had distinct correlation pattern with AFL parameters. Random forest regressors trained with the dataset allowed automated, simultaneous visualization of the key atherosclerotic components with high accuracy (r>0.87). Conclusions:FLIM provided detailed pixel-level AFL investigation of the complex composition of coronary artery and atheroma. Our FLIM strategy enabling an automated, comprehensive visualization of multiple plaque components from unlabeled sections will be highly useful to efficiently evaluate ex vivo samples without the need for histological staining and analysis.

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