Carotenoids co-localize with hydroxyapatite, cholesterol, and other lipids in calcified stenotic aortic valves. Ex vivo Raman maps compared to histological patterns
Unlike its application for atherosclerotic plaque analysis, Raman microspectroscopy was sporadically used to check the sole nature of bioapatite deposits in stenotic aortic valves, neglecting the involvement of accumulated lipids/lipoproteins in the calcific process. Here, Raman microspectroscopy was employed for examination of stenotic aortic valve leaflets to add information on nature and distribution of accumulated lipids and their correlation with mineralization in the light of its potential precocious diagnostic use. Cryosections from surgically explanted stenotic aortic valves (n=4) were studied matching Raman maps against specific histological patterns. Raman maps revealed the presence of phospholipids/triglycerides and cholesterol, which showed spatial overlapping with one another and Raman-identified hydroxyapatite. Moreover, the Raman patterns correlated with those displayed by both von-Kossa-calcium- and Nile-blue-stained serial cryosections. Raman analysis also provided the first identification of carotenoids, which co-localized with the identified lipid moieties. Additional fit concerned the distribution of collagen and elastin. The good correlation of Raman maps with high-affinity staining patterns proved that Raman microspectroscopy is a reliable tool in evaluating calcification degree, alteration/displacement of extracellular matrix components, and accumulation rate of different lipid forms in calcified heart valves. In addition, the novel identification of carotenoids supports the concept that valve stenosis is an atherosclerosis-like valve lesion, consistently with their previous Raman microspectroscopical identification inside atherosclerotic plaques.
PlumX Metrics provide insights into the ways people interact with individual pieces of research output (articles, conference proceedings, book chapters, and many more) in the online environment. Examples include, when research is mentioned in the news or is tweeted about. Collectively known as PlumX Metrics, these metrics are divided into five categories to help make sense of the huge amounts of data involved and to enable analysis by comparing like with like.
Copyright (c) 2015 A. Bonetti, A. Bonifacio, A. Della Mora, U. Livi, M. Marchini, F. Ortolani
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.