Matrix effects in untargeted LC-MS metabolomics: from creation to compensation with post-column infusion of standards

Abstract

Matrix effect is a well-known issue affecting accuracy and repeatability in metabolomics studies using liquid chromatography-electrospray ionization-mass spectrometry (LC-ESI-MS). Post-column infusion of standards (PCIS) is a promising strategy to monitor and correct matrix effect but has been rarely reported in untargeted metabolomics. The major challenges lie in selecting appropriate PCISs and identifying the most suitable PCIS to correct the matrix effect experienced by each feature. In this study, we aim to present a method for selecting suitable PCISs for matrix effect compensation based on the artificial matrix effect (MEart) created by post-column infusion of compounds that disrupt the ESI process. Our hypothesis is that the suitable PCIS for a given analyte can be identified by comparing the PCISs' ability in MEart compensation. We evaluated this approach using 19 stable-isotopically labeled (SIL) standards spiked in plasma, urine, and feces. PCISs selected based on MEart were compared to those selected by biological matrix effect (MEbio), with 17 out of 19 SIL standards (89 %) showing consistent PCIS selection, demonstrating the effectiveness of MEart in identifying suitable PCISs. Applying MEartselected PCISs to correct for the MEbio resulted in improved MEbio for most of the SILs affected by matrix effect and maintained MEbio for those experiencing no matrix effect. We demonstrated the efficacy of MEart in selecting suitable PCISs for MEbio correction within an LC-PCIS-MS method. Importantly, since MEart can be assessed for any detected feature, its application holds great potential for identifying suitable PCISs for matrix effect correction in untargeted metabolomics.