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

• Type of Material: Article
• Authors: Sanghwa Lee, Asan Medical Center, Seoul Republic of Korea; Miyeon Jue, Asan Medical Center, Seoul Republic of Korea; Minju Cho, Asan Medical Center, Seoul Republic of Korea; Kwanhee Lee, Asan Medical Center, Seoul Republic of Korea; Bjorn Paulson, Asan Medical Center, Seoul Republic of Korea; Hanjoong Jo, Emory University; Joon Seon Song, University of Ulsan; Soo‐Jin Kang, University of Ulsan; Jun Ki Kim, Asan Medical Center, Seoul Republic of Korea
• Language: English
• Date: 2023-05-03
• Publisher: WILEY
• Publication Version: Final Published Version
• Copyright Statement: © 2023 The Authors. Bioengineering & Translational Medicine
• License: Creative Commons Attribution 4.0 International
• Final Published Version (URL): http://dx.doi.org/10.1002/btm2.10529
• Title of Journal or Parent Work: BIOENGINEERING & TRANSLATIONAL MEDICINE
• Volume: 8
• Issue: 4
• Start Page: e10529
• End Page: e10529
• Keywords: SPECTRA; LESIONS; INTRAVASCULAR ULTRASOUND; principal component analysis; Engineering, Biomedical; Pharmacology & Pharmacy; HEMODYNAMIC SHEAR-STRESS; Technology; Science & Technology; ApoE KO mouse; FLOW; CAROTID PLAQUE AREA; RISK-FACTORS; INFLAMMATION; MICE; Life Sciences & Biomedicine; Engineering; atherosclerosis; nano-sized biomarker; BIFURCATION; surface-enhanced Raman spectroscopy; Biotechnology & Applied Microbiology
• Research Categories: Health Sciences, Pharmacology; Health Sciences, Pathology; Engineering, Biomedical; Health Sciences, Pharmacy

Abstract

The 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.