CAS Key Laboratory of Magnetic Resonance in Biological Systems, State Key Laboratory of Magnetic Resonance and Atomic and Molecular Physics, National Centre for Magnetic Resonance in Wuhan, Wuhan Institute of Physics and Mathematics, Innovation Academy for Precision Measurement Science and Technology, Chinese Academy of Sciences, Wuhan, 430071, China; State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute, Metabonomics and Systems Biology Laboratory at Shanghai International Centre for Molecular Phenomics, Zhongshan Hospital, Fudan University, Shanghai, 200032, China; University of Chinese Academy of Sciences, Beijing, 100049, China.
CAS Key Laboratory of Magnetic Resonance in Biological Systems, State Key Laboratory of Magnetic Resonance and Atomic and Molecular Physics, National Centre for Magnetic Resonance in Wuhan, Wuhan Institute of Physics and Mathematics, Innovation Academy for Precision Measurement Science and Technology, Chinese Academy of Sciences, Wuhan, 430071, China; Department of Laboratory Medicine, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, China.
Talanta. 2022 Feb 1;238(Pt 2):123059. doi: 10.1016/j.talanta.2021.123059. Epub 2021 Nov 17.
Hydrophilic metabolites are essential for all biological systems with multiple functions and their quantitative analysis forms an important part of metabolomics. However, poor retention of these metabolites on reversed-phase (RP) chromatographic column hinders their effective analysis with RPLC-MS methods. Herein, we developed a method for detecting hydrophilic metabolites using the ion-pair reversed-phase liquid-chromatography coupled with mass spectrometry (IPRP-LC-MS/MS) in scheduled multiple-reaction-monitoring (sMRM) mode. We first developed a hexylamine-based IPRP-UHPLC-QTOFMS method and experimentally measured retention time (t) for 183 hydrophilic metabolites. We found that ts of these metabolites were dominated by their electrostatic potential depending upon the numbers and types of their ionizable groups. We then systematically investigated the quantitative structure-retention relationship (QSRR) and constructed QSRR models using the measured t. Subsequently, we developed a retention time predictive model using the random-forest regression algorithm (r = 0.93, q = 0.70, MAE = 1.28 min) for predicting metabolite retention time, which was applied in IPRP-UHPLC-MS/MS method in sMRM mode for quantitative metabolomic analysis. Our method can simultaneously quantify more than 260 metabolites. Moreover, we found that this method was applicable for multiple major biological matrices including biofluids and tissues. This approach offers an efficient method for large-scale quantitative hydrophilic metabolomic profiling even when metabolite standards are unavailable.
亲水代谢物是所有生物系统所必需的，具有多种功能，其定量分析是代谢组学的重要组成部分。然而，这些代谢物在反相（RP）色谱柱上的保留较差，阻碍了其与 RPLC-MS 方法的有效分析。在此，我们开发了一种使用离子对反相液相色谱-质谱联用（IPRP-LC-MS/MS）在预定多重反应监测（sMRM）模式下检测亲水代谢物的方法。我们首先开发了一种基于己胺的 IPRP-UHPLC-QTOFMS 方法，并实验测量了 183 种亲水代谢物的保留时间（t）。我们发现，这些代谢物的 ts 主要取决于其可电离基团的数量和类型的静电势。然后，我们系统地研究了定量结构-保留关系（QSRR），并使用测量的 t 构建了 QSRR 模型。随后，我们使用随机森林回归算法（r=0.93，q=0.70，MAE=1.28min）开发了一个保留时间预测模型，用于预测代谢物保留时间，该模型应用于 IPRP-UHPLC-MS/MS 方法中的 sMRM 模式，用于定量代谢组学分析。我们的方法可以同时定量超过 260 种代谢物。此外，我们发现该方法适用于多种主要生物基质，包括生物流体和组织。即使没有代谢物标准品，这种方法也为大规模定量亲水代谢组学分析提供了一种有效的方法。
