Saigusa Daisuke, Hishinuma Eiji, Matsukawa Naomi, Takahashi Masatomo, Inoue Jin, Tadaka Shu, Motoike Ikuko N, Hozawa Atsushi, Izumi Yoshihiro, Bamba Takeshi, Kinoshita Kengo, Ekroos Kim, Koshiba Seizo, Yamamoto Masayuki
Department of Integrative Genomics, Tohoku University Tohoku Medical Megabank Organization, 2-1 Seiryo-machi, Aoba-ku, Sendai 980-8573, Japan.
Medical Biochemistry, Tohoku University Graduate School of Medicine, 2-1 Seiryo-machi, Aoba-ku, Sendai 980-8575, Japan.
Metabolites. 2021 Sep 24;11(10):652. doi: 10.3390/metabo11100652.
Metabolic profiling is an omics approach that can be used to observe phenotypic changes, making it particularly attractive for biomarker discovery. Although several candidate metabolites biomarkers for disease expression have been identified in recent clinical studies, the reference values of healthy subjects have not been established. In particular, the accuracy of concentrations measured by mass spectrometry (MS) is unclear. Therefore, comprehensive metabolic profiling in large-scale cohorts by MS to create a database with reference ranges is essential for evaluating the quality of the discovered biomarkers. In this study, we tested 8700 plasma samples by commercial kit-based metabolomics and separated them into two groups of 6159 and 2541 analyses based on the different ultra-high-performance tandem mass spectrometry (UHPLC-MS/MS) systems. We evaluated the quality of the quantified values of the detected metabolites from the reference materials in the group of 2541 compared with the quantified values from other platforms, such as nuclear magnetic resonance (NMR), supercritical fluid chromatography tandem mass spectrometry (SFC-MS/MS) and UHPLC-Fourier transform mass spectrometry (FTMS). The values of the amino acids were highly correlated with the NMR results, and lipid species such as phosphatidylcholines and ceramides showed good correlation, while the values of triglycerides and cholesterol esters correlated less to the lipidomics analyses performed using SFC-MS/MS and UHPLC-FTMS. The evaluation of the quantified values by MS-based techniques is essential for metabolic profiling in a large-scale cohort.
代谢谱分析是一种组学方法，可用于观察表型变化，这使其在生物标志物发现方面极具吸引力。尽管在最近的临床研究中已经确定了几种用于疾病表达的候选代谢物生物标志物，但尚未确定健康受试者的参考值。特别是，质谱（MS）测量浓度的准确性尚不清楚。因此，通过质谱在大规模队列中进行全面的代谢谱分析以创建具有参考范围的数据库，对于评估所发现生物标志物的质量至关重要。在本研究中，我们使用基于商业试剂盒的代谢组学方法对8700份血浆样本进行了检测，并根据不同的超高效串联质谱（UHPLC-MS/MS）系统将它们分为6159份和2541份分析两组。我们将2541组中参考物质检测到的代谢物定量值的质量与其他平台（如核磁共振（NMR）、超临界流体色谱串联质谱（SFC-MS/MS）和UHPLC-傅里叶变换质谱（FTMS））的定量值进行了比较。氨基酸的值与NMR结果高度相关，磷脂酰胆碱和神经酰胺等脂质种类显示出良好的相关性，而甘油三酯和胆固醇酯的值与使用SFC-MS/MS和UHPLC-FTMS进行的脂质组学分析的相关性较小。基于质谱技术的定量值评估对于大规模队列中的代谢谱分析至关重要。
