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 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_R) for 183 hydrophilic metabolites. We found that t_Rs 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_R. 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.

Quantification of more than 150 micropollutants including transformation products in aqueous samples by liquid chromatography-tandem mass spectrometry using scheduled multiple reaction monitoring

A direct injection, multi residue analytical method separated in two chromatographic runs was developed utilizing scheduled analysis to simultaneously quantify 154 compounds, 84 precursors and 70 transformation products (TPs)/metabolites. Improvements in the chromatographic data quality, sensitivity and reproducibility were achieved by scheduling the analysis of each analyte into pre-determined retention time windows. This study shows the influence of the scan time on the dwell time and the number of data points per peak as well as the effect on the precision of analysis. Lowering the scan time decreased dwell time to a minimal value, however, this had no negative effects on the precision. Increasing the number of data points per peak by decreasing the scan time led to more accurate peak shapes. A final set of parameters was chosen to obtain a minimum of 10 data points per peak to guarantee accurate peak shapes and thus reproducibility of analysis. A validation of the method was performed for different water matrices yielding very good linearity for all substances, with limits of quantification mainly in the lower to mid ng/L-range and recoveries mainly between 70 and 125% for surface water, bank filtrate as well as influents and effluents of wastewater treatment plants. The analysis of environmental samples and wastewater revealed the occurrence of selected precursors and TPs in all analyzed matrices: 95% of the compounds in the target list could be quantified in at least one sample. The relevance of TPs and metabolites such as valsartan acid and clopidogrel acid was also confirmed by their detection in all aqueous matrices. Wastewater indicators such as acesulfame and diclofenac were detected at elevated concentrations as well as substances such as oxipurinol which so far were not in the focus of monitoring programs. The developed method can be used for rapid analysis of various water matrices without any sample enrichment and can aid the assessment of water quality and water treatment processes.