Hisashi Johno1, Kentaro Yoshimura2, Yuki Mori1, Tokuhide Kimura3, Manabu Niimi3, Masaki Yamada4, Tetsuo Tanigawa4, Jianglin Fan3, Sen Takeda5. 1. Department of Anatomy and Cell Biology, Interdisciplinary Graduate School of Medicine, University of Yamanashi, Chuo, Yamanashi, 409-3898, Japan. 2. Department of Anatomy and Cell Biology, Interdisciplinary Graduate School of Medicine, University of Yamanashi, Chuo, Yamanashi, 409-3898, Japan. kyoshimura@yamanashi.ac.jp. 3. Department of Molecular Pathology, Interdisciplinary Graduate School of Medicine, University of Yamanashi, Chuo, Yamanashi, 409-3898, Japan. 4. Analytical and Measuring Instruments Division, Global Application Development Center, Shimadzu Corporation, Kyoto, 604-8511, Japan. 5. Department of Anatomy and Cell Biology, Interdisciplinary Graduate School of Medicine, University of Yamanashi, Chuo, Yamanashi, 409-3898, Japan. stakeda@yamanashi.ac.jp.
Abstract
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.
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.
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