Detection of potential new biomarkers of atherosclerosis by probe electrospray ionization mass spectrometry
Atherosclerotic diseases are the leading cause of death worldwide. Biomarkers of atherosclerosis are required to monitor and prevent disease progression. While mass spectrometry is a promising technique to search for such biomarkers, its clinical application is hampered by the laborious processes for sample preparation and analysis.
We developed a rapid method to detect plasma metabolites by probe electrospray ionization mass spectrometry (PESI-MS), which employs an ambient ionization technique enabling atmospheric pressure rapid mass spectrometry. To create an automatic diagnosis system of atherosclerotic disorders, we applied machine learning techniques to the obtained spectra.
Using our system, we successfully discriminated between rabbits with and without dyslipidemia. The causes of dyslipidemia (genetic lipoprotein receptor deficiency or dietary cholesterol overload) were also distinguishable by this method. Furthermore, after induction of atherosclerosis in rabbits with a cholesterol-rich diet, we were able to detect dynamic changes in plasma metabolites. The major metabolites detected by PESI-MS included cholesterol sulfate and a phospholipid (PE18:0/20:4), which are promising new biomarkers of atherosclerosis.
We developed a remarkably fast and easy method to detect potential new biomarkers of atherosclerosis in plasma using PESI-MS.
KeywordsProbe electrospray ionization mass spectrometry Atherosclerosis Dyslipidemia Blood plasma Machine learning
We thank Ayumi Iizuka for technical assistance with the PESI-MS analyses. This work was partially supported by JSPS KAKENHI Grant Number 16K08964 (Grant-in-Aid for Scientific Research (C) to K. Y.).
Availability of data
The datasets generated during and/or analyzed during the current study are available in the Figshare repository, [ https://doi.org/10.6084/m9.figshare.5783205].
Compliance with ethical standards
Conflict of interest
The authors declare no conflict of interest.
Animal experiments were performed with the approval of the Animal Care Committee of the University of Yamanashi and complied with the Guide for the Care and Use of Laboratory Animals published by the US National Institutes of Health.
- Bousquet, O., & Elisseeff, A. (2002). Stability and Generalization. Journal of Machine Learning Research, 2, 499–526.Google Scholar
- Dang, V. T., Huang, A., Zhong, L. H., Shi, Y., & Werstuck, G. H. (2016). Comprehensive plasma metabolomic analyses of atherosclerotic progression reveal alterations in glycerophospholipid and sphingolipid metabolism in apolipoprotein E-deficient mice. Scientific Reports, 6, 35037.CrossRefPubMedPubMedCentralGoogle Scholar
- Dang, V. T., & Werstuck, G. H. (2016). Metabolomics-based biomarkers of the pathogenesis of atherosclerosis. Biomarkers Journal, 2, 10.Google Scholar
- Grundy, S. M., Pasternak, R., Greenland, P., Smith, S. Jr., & Fuster, V. (1999). AHA/ACC scientific statement. Assessment of cardiovascular risk by use of multiple-risk-factor assessment equations: A statement for healthcare professionals from the American Heart Association and the American College of Cardiology. Journal of the American College of Cardiology, 34(4), 1348–1359.CrossRefPubMedGoogle Scholar
- Kolodgie, F. D., Katocs, A. S. Jr., Largis, E. E., Wrenn, S. M., Cornhill, J. F., Herderick, et al. (1996). Hypercholesterolemia in the rabbit induced by feeding graded amounts of low-level cholesterol. Methodological considerations regarding individual variability in response to dietary cholesterol and development of lesion type. Arteriosclerosis, Thrombosis, and Vascular Biology, 16(12), 1454–1464.CrossRefPubMedGoogle Scholar
- Mandal, M. K., Yoshimura, K., Chen, L. C., Yu, Z., Nakazawa, T., Katoh, R., et al. (2012). Application of probe electrospray ionization mass spectrometry (PESI-MS) to clinical diagnosis: Solvent effect on lipid analysis. Journal of The American Society for Mass Spectrometry, 23(11), 2043–2047.CrossRefPubMedGoogle Scholar
- Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., et al. (2011). Scikit-learn: Machine Learning in Python. Journal of Machine Learning Research, 12, 2825–2830.Google Scholar
- Takeda, S., Yoshimura, K., & Hiraoka, K. (2012). Innovations in Analytical oncology—Status quo of mass spectrometry-based diagnostics for malignant tumor. Journal of Analytical Oncology, 1(1), 74–80.Google Scholar