Quantitative structure-retention relationship for reliable metabolite identification and quantification in metabolomics using ion-pair reversed-phase chromatography coupled with tandem mass spectrometry

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 reversedphase (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 liquidchromatography 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 randomforest 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.

Automated summary

This study developed a method for detecting hydrophilic metabolites using ion-pair reversed-phase liquid chromatography coupled with mass spectrometry, which can quantify more than 260 metabolites simultaneously and is applicable for various biological samples, providing an efficient approach for large-scale quantitative hydrophilic metabolomic profiling.