Detection of potential new biomarkers of atherosclerosis by probe electrospray ionization mass spectrometry

Introduction 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...

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Bibliographic Details
Published in:Metabolomics 2018-04, Vol.14 (4), p.38-11, Article 38
Main Authors: Johno, Hisashi, Yoshimura, Kentaro, Mori, Yuki, Kimura, Tokuhide, Niimi, Manabu, Yamada, Masaki, Tanigawa, Tetsuo, Fan, Jianglin, Takeda, Sen
Format: Article
Language:English
Subjects:
Animals
Arteriosclerosis
Atherosclerosis
Atherosclerosis - blood
Atherosclerosis - diagnosis
Atherosclerosis - metabolism
Biochemistry
Biomarkers
Biomarkers - blood
Biomarkers - metabolism
Biomedical and Life Sciences
Biomedicine
Cell Biology
Cholesterol
Cholesterol Esters - blood
Cholesterol Esters - metabolism
Chromatography, Liquid
Developmental Biology
Dyslipidemia
Ions
Learning algorithms
Life Sciences
Machine Learning
Mass spectrometry
Mass spectroscopy
Metabolic disorders
Metabolites
Metabolomics
Molecular Medicine
Original Article
Phospholipids
Phospholipids - blood
Phospholipids - metabolism
Rabbits
Scientific imaging
Spectrometry, Mass, Electrospray Ionization
Sulfates
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Summary:Introduction 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. Methods 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. Results 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. Conclusion We developed a remarkably fast and easy method to detect potential new biomarkers of atherosclerosis in plasma using PESI-MS.
ISSN:1573-3882
1573-3890
DOI:10.1007/s11306-018-1334-z