Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/159581
Title: Quantitative structure-retention relationship for reliable metabolite identification and quantification in metabolomics using ion-pair reversed-phase chromatography coupled with tandem mass spectrometry
Authors: Hu, Qingyu
Sun, Yuting
Yuan, Peihong
Lei, Hehua
Zhong, Huiqin
Wang, Yulan
Tang, Huiru
Keywords: Science::Medicine
Issue Date: 2022
Source: Hu, Q., Sun, Y., Yuan, P., Lei, H., Zhong, H., Wang, Y. & Tang, H. (2022). Quantitative structure-retention relationship for reliable metabolite identification and quantification in metabolomics using ion-pair reversed-phase chromatography coupled with tandem mass spectrometry. Talanta, 238 Pt 2, 123059-. https://dx.doi.org/10.1016/j.talanta.2021.123059
Journal: Talanta
Abstract: 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 (tR) for 183 hydrophilic metabolites. We found that tRs 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 tR. Subsequently, we developed a retention time predictive model using the random-forest regression algorithm (r2 = 0.93, q2 = 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.
URI: https://hdl.handle.net/10356/159581
ISSN: 0039-9140
DOI: 10.1016/j.talanta.2021.123059
Schools: Lee Kong Chian School of Medicine (LKCMedicine) 
Research Centres: Singapore Phenome Centre 
Rights: © 2021 Elsevier B.V. All rights reserved.
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:LKCMedicine Journal Articles

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