Label‐free atherosclerosis diagnosis through a blood drop of apolipoprotein E knockout mouse model using surface‐enhanced Raman spectroscopy validated by machine learning algorithm

Author:

Lee Sanghwa1,Jue Miyeon1,Cho Minju1,Lee Kwanhee1,Paulson Bjorn1,Jo Hanjoong2,Song Joon Seon3,Kang Soo‐Jin4,Kim Jun Ki15ORCID

Affiliation:

1. Biomedical Engineering Research Center Asan Medical Center Seoul Republic of Korea

2. Wallace H. Coulter Department of Biomedical Engineering Emory University and Georgia Institute of Technology Atlanta Georgia USA

3. Department of Pathology University of Ulsan College of Medicine, Asan Medical Center Seoul Republic of Korea

4. Department of Cardiology University of Ulsan College of Medicine, Asan Medical Center Seoul Republic of Korea

5. Department of Biomedical Engineering University of Ulsan, College of Medicine Seoul Republic of Korea

Abstract

AbstractThe direct preventative detection of flow‐induced atherosclerosis remains a significant challenge, impeding the development of early treatments and prevention measures. This study proposes a method for diagnosing atherosclerosis in the carotid artery using nanometer biomarker measurements through surface‐enhanced Raman spectroscopy (SERS) from single‐drop blood samples. Atherosclerotic acceleration is induced in apolipoprotein E knockout mice which underwent a partial carotid ligation and were fed a high‐fat diet to rapidly induce disturbed flow‐induced atherosclerosis in the left common carotid artery while using the unligated, contralateral right carotid artery as control. The progressive atherosclerosis development of the left carotid artery was verified by micro‐magnetic resonance imaging (micro‐MRI) and histology in comparison to the right carotid artery. Single‐drop blood samples are deposited on chips of gold‐coated ZnO nanorods grown on silicon wafers that filter the nanometer markers and provide strong SERS signals. A diagnostic classifier was established based on principal component analysis (PCA), which separates the resultant spectra into the atherosclerotic and control groups. Scoring based on the principal components enabled the classification of samples into control, mild, and severe atherosclerotic disease. The PCA‐based analysis was validated against an independent test sample and compared against the PCA‐PLS‐DA machine learning algorithm which is known for applicability to Raman diagnosis. The accuracy of the PCA modification‐based diagnostic criteria was 94.5%, and that of the machine learning algorithm 97.5%. Using a mouse model, this study demonstrates that diagnosing and classifying the severity of atherosclerosis is possible using a single blood drop, SERS technology, and machine learning algorithm, indicating the detectability of biomarkers and vascular factors in the blood which correlate with the early stages of atherosclerosis development.

Funder

Asan Institute for Life Sciences, Asan Medical Center

National Research Foundation of Korea

Publisher

Wiley

Subject

Pharmaceutical Science,Biomedical Engineering,Biotechnology

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