Liao Hsiao-Wei, Chen Guan-Yuan, Wu Ming-Shiang, Liao Wei-Chih, Lin Ching-Hung, Kuo Ching-Hua
School of Pharmacy, College of Medicine, National Taiwan University , Taipei 10051, Taiwan.
The Metabolomics Core Laboratory, Center of Genomic Medicine, National Taiwan University , Taipei 10055, Taiwan.
J Proteome Res. 2017 Feb 3;16(2):1097-1104. doi: 10.1021/acs.jproteome.6b01011. Epub 2017 Jan 19.
Quantitative metabolomics has become much more important in clinical research in recent years. Individual differences in matrix effects (MEs) and the injection order effect are two major factors that reduce the quantification accuracy in liquid chromatography-electrospray ionization-mass spectrometry-based (LC-ESI-MS) metabolomics studies. This study proposed a postcolumn infused-internal standard (PCI-IS) combined with a matrix normalization factor (MNF) strategy to improve the analytical accuracy of quantitative metabolomics. The PCI-IS combined with the MNF method was applied for a targeted metabolomics study of amino acids (AAs). D8-Phenylalanine was used as the PCI-IS, and it was postcolumn-infused into the ESI interface for calibration purposes. The MNF was used to bridge the AA response in a standard solution with the plasma samples. The MEs caused signal changes that were corrected by dividing the AA signal intensities by the PCI-IS intensities after adjustment with the MNF. After the method validation, we evaluated the method applicability for breast cancer research using 100 plasma samples. The quantification results revealed that the 11 tested AAs exhibit an accuracy between 88.2 and 110.7%. The principal component analysis score plot revealed that the injection order effect can be successfully removed, and most of the within-group variation of the tested AAs decreased after the PCI-IS correction. Finally, targeted metabolomics studies on the AAs showed that tryptophan was expressed more in malignant patients than in the benign group. We anticipate that a similar approach can be applied to other endogenous metabolites to facilitate quantitative metabolomics studies.
近年来，定量代谢组学在临床研究中变得越发重要。基质效应（MEs）的个体差异和进样顺序效应是降低基于液相色谱 - 电喷雾电离 - 质谱（LC - ESI - MS）的代谢组学研究中定量准确性的两个主要因素。本研究提出了一种柱后注入内标（PCI - IS）结合基质归一化因子（MNF）的策略，以提高定量代谢组学的分析准确性。PCI - IS与MNF方法被应用于氨基酸（AAs）的靶向代谢组学研究。D8 - 苯丙氨酸用作PCI - IS，并将其柱后注入ESI接口用于校准目的。MNF用于在标准溶液中的AA响应与血浆样品之间建立联系。MEs引起的信号变化通过在使用MNF进行调整后将AA信号强度除以PCI - IS强度来校正。在方法验证后，我们使用100份血浆样本评估了该方法在乳腺癌研究中的适用性。定量结果显示，所测试的11种AA的准确度在88.2%至110.7%之间。主成分分析得分图显示进样顺序效应能够成功消除，并且在PCI - IS校正后，所测试AA的组内变异大部分有所降低。最后，对AA的靶向代谢组学研究表明，恶性患者中色氨酸的表达高于良性组。我们预计类似的方法可应用于其他内源性代谢物，以促进定量代谢组学研究。
