Matrix effect evaluation using multi-component post-column infusion in untargeted hydrophilic interaction liquid chromatography-mass spectrometry plasma metabolomics.

Abstract

Metabolomics based on hydrophilic interaction liquid chromatography (HILIC) coupled with mass spectrometry (MS) is a powerful tool for polar metabolite identification and quantification to further contribute to biomarker discovery and disease mechanism elucidation. However, matrix effect (ME), which may lead to altered ionization efficiency due to co-eluting compounds, is a significant challenge during biological analysis. Therefore, ME evaluation plays a crucial role during method development. Two approaches to evaluate ME are using stable isotope labelled-internal standards (SIL-IS) and post-column infusion (PCI) of standards. In this study, we developed an untargeted HILIC-MS method by applying four PCI standards for ME evaluation. We found PCI is a compelling approach for ME assessment compared to SIL-IS method due to its advantage in untargeted analysis. Through the ME evaluation and chromatographic performance comparison of 18 SIL standards across three columns and three different mobile phase pH conditions, our findings revealed that the BEH-Z-HILIC column operated at pH 4 with 10 mM ammonium formate exhibited minimal ME and superior performance. The method showed exceptional linearity (R² > 0.98), reliable repeatability (RSD < 15 %), good inter-day precision (RSD < 30 %), and acceptable recovery (>75 %) for all SIL standards. Absolute matrix effect (AME) and relative matrix effect (RME) assessment in three plasma donors revealed a high consistency between PCI and SIL-IS approaches. Finally, this method coupled with the PCI approach was applied to 40 plasma samples. Fifty endogenous compounds were detected and their AME and RME were evaluated. Results showed that many compounds experienced severe ion suppression, though their ME variation between 40 samples is low. In conclusion, PCI method is a robust alternative for monitoring ME and evaluating ME of endogenous compounds during untargeted method optimization and biological analysis.