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

Sanghwa Lee; Miyeon Jue; Minju Cho; Kwanhee Lee; Bjorn Paulson; Hanjoong Jo; Joon Seon Song; Soo-Jin Kang; Jun Ki Kim

doi:10.1002/btm2.10529

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

Bioeng Transl Med. 2023 May 3;8(4):e10529. doi: 10.1002/btm2.10529. eCollection 2023 Jul.

Authors

Sanghwa Lee¹, Miyeon Jue¹, Minju Cho¹, Kwanhee Lee¹, Bjorn Paulson¹, Hanjoong Jo², Joon Seon Song³, Soo-Jin Kang⁴, Jun Ki Kim^{1

5}

Affiliations

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

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.

Keywords: ApoE KO mouse; atherosclerosis; nano‐sized biomarker; principal component analysis; surface‐enhanced Raman spectroscopy.