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Raman spectroscopy imaging reveals interplay between atherosclerosis and medial calcification in the human aorta.

ABSTRACT: Medial calcification in the human aorta accumulates during aging and is known to be aggravated in several diseases. Atherosclerosis, another major cause of cardiovascular calcification, shares some common aggravators. However, the mechanisms of cardiovascular calcification remain poorly understood. To elucidate the relationship between medial aortic calcification and atherosclerosis, we characterized the cross-sectional distributions of the predominant minerals in aortic tissue, apatite and whitlockite, and the associated extracellular matrix. We also compared the cellular changes between atherosclerotic and nonatherosclerotic human aortic tissues. This was achieved through the development of Raman spectroscopy imaging methods that adapted algorithms to distinguish between the major biomolecules present within these tissues. We present a relationship between apatite, cholesterol, and triglyceride in atherosclerosis, with the relative amount of all molecules concurrently increased in the atherosclerotic plaque. Further, the increase in apatite was disproportionately large in relation to whitlockite in the aortic media directly underlying a plaque, indicating that apatite is more pathologically significant in atherosclerosis-aggravated medial calcification. We also discovered a reduction of ?-carotene in the whole aortic intima, including a plaque in atherosclerotic aortic tissues compared to nonatherosclerotic tissues. This unprecedented biomolecular characterization of the aortic tissue furthers our understanding of pathological and physiological cardiovascular calcification events in humans.

SUBMITTER: You AYF

PROVIDER: S-EPMC5721727 | biostudies-literature | 2017 Dec

REPOSITORIES: biostudies-literature

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Raman spectroscopy imaging reveals interplay between atherosclerosis and medial calcification in the human aorta.

You Amanda Y F AYF Bergholt Mads S MS St-Pierre Jean-Philippe JP Kit-Anan Worrapong W Pence Isaac J IJ Chester Adrian H AH Yacoub Magdi H MH Bertazzo Sergio S Stevens Molly M MM

Science advances 20171206 12

Medial calcification in the human aorta accumulates during aging and is known to be aggravated in several diseases. Atherosclerosis, another major cause of cardiovascular calcification, shares some common aggravators. However, the mechanisms of cardiovascular calcification remain poorly understood. To elucidate the relationship between medial aortic calcification and atherosclerosis, we characterized the cross-sectional distributions of the predominant minerals in aortic tissue, apatite and whit ...[more]

PMID: 29226241

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Project description:The accurate analytical characterization of metastatic phenotype at primary tumor diagnosis and its evolution with time are critical for controlling metastatic progression of cancer. Here, we report a label-free optical strategy using Raman spectroscopy and machine learning to identify distinct metastatic phenotypes observed in tumors formed by isogenic murine breast cancer cell lines of progressively increasing metastatic propensities. Methods: We employed the 4T1 isogenic panel of murine breast cancer cells to grow tumors of varying metastatic potential and acquired label-free spectra using a fiber probe-based portable Raman spectroscopy system. We used MCR-ALS and random forests classifiers to identify putative spectral markers and predict metastatic phenotype of tumors based on their optical spectra. We also used tumors derived from 4T1 cells silenced for the expression of TWIST, FOXC2 and CXCR3 genes to assess their metastatic phenotype based on their Raman spectra. Results: The MCR-ALS spectral decomposition showed consistent differences in the contribution of components that resembled collagen and lipids between the non-metastatic 67NR tumors and the metastatic tumors formed by FARN, 4T07, and 4T1 cells. Our Raman spectra-based random forest analysis provided evidence that machine learning models built on spectral data can allow the accurate identification of metastatic phenotype of independent test tumors. By silencing genes critical for metastasis in highly metastatic cell lines, we showed that the random forest classifiers provided predictions consistent with the observed phenotypic switch of the resultant tumors towards lower metastatic potential. Furthermore, the spectral assessment of lipid and collagen content of these tumors was consistent with the observed phenotypic switch. Conclusion: Overall, our findings indicate that Raman spectroscopy may offer a novel strategy to evaluate metastatic risk during primary tumor biopsies in clinical patients.

Dataset Information

Raman spectroscopy imaging reveals interplay between atherosclerosis and medial calcification in the human aorta.

Publications

Raman spectroscopy imaging reveals interplay between atherosclerosis and medial calcification in the human aorta.

Similar Datasets

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