Capillary Electrophoresis Mass Spectrometry-Based Metabolomics of Plasma Samples from Healthy Subjects in a Cross-Sectional Japanese Population Study.

Hiroyuki Yamamoto,Makoto Suzuki,Kazunori Sasaki,Kenjiro Kami,Shigeyuki Nakaji,Yota Tatara,Moon-Il Kang,Ken Itoh,Rira Matsuta

doi:10.3390/metabo11050314

Hiroyuki Yamamoto, Makoto Suzuki + Show 7 more

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https://doi.org/10.3390/metabo11050314

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Journal: Metabolites	Publication Date: May 13, 2021
Citations: 3	License type: CC BY 4.0

Affiliation: Hirosaki University

Abstract

For large-scale metabolomics, such as in cohort studies, normalization protocols using quality control (QC) samples have been established when using data from gas chromatography and liquid chromatography coupled to mass spectrometry. However, normalization protocols have not been established for capillary electrophoresis–mass spectrometry metabolomics. In this study, we performed metabolome analysis of 314 human plasma samples using capillary electrophoresis–mass spectrometry. QC samples were analyzed every 10 samples. The results of principal component analysis for the metabolome data from only the QC samples showed variations caused by capillary replacement in the first principal component score and linear variation with continuous measurement in the second principal component score. Correlation analysis between diagnostic blood tests and plasma metabolites normalized by the QC samples was performed for samples from 188 healthy subjects who participated in a Japanese population study. Five highly correlated pairs were identified, including two previously unidentified pairs in normal healthy subjects of blood urea nitrogen and guanidinosuccinic acid, and gamma-glutamyl transferase and cysteine glutathione disulfide. These results confirmed the validity of normalization protocols in capillary electrophoresis–mass spectrometry using large-scale metabolomics and comprehensive analysis.

Highlights

Many large-scale metabolomics studies have been performed for various purposes, such as prediction of the risk of developing diabetes [1,2], and evaluation of the associations between changes in specific groups of metabolites with antibiotic intervention and cardiovascular risk [3]
If we assume that the concentration of each metabolite in the quality control (QC) samples is identical, variations during measurement can be considered as the source of most variability in the QC sample results
principal component analysis (PCA) was performed for the metabolomic data of the QC samples only (Figure 1), and the factors causing variation that were associated with the first or second principal component (PC1 and PC2, respectively) scores were examined

Summary

Introduction

Many large-scale metabolomics studies have been performed for various purposes, such as prediction of the risk of developing diabetes [1,2], and evaluation of the associations between changes in specific groups of metabolites with antibiotic intervention and cardiovascular risk [3]. Association of metabolite levels with the genome [5] and basic background information such as sex [6], age [7], and body mass index (BMI) [8] have been reported. These studies provide useful information that could be used as a reference for analysis in clinical studies, such as biomarker discovery. It has been reported that there are three major problems during continuous sample measurement in large-scale metabolomics using gas chromatography (GC) and liquid chromatography (LC) coupled to mass spectrometry (MS): variation in the retention time, mass accuracy, and signal intensity [11,12]. High mass accuracy can be achieved by proper calibration during sample measurement

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Results

Conclusion