Unified-Hydrophilic-Interaction/Anion-Exchange Liquid Chromatography Mass Spectrometry (Unified-HILIC/AEX/MS): A Single-Run Method for Comprehensive and Simultaneous Analysis of Polar Metabolome

Abstract

One of the technical challenges in the field of metabolomics is the development of a single-run method to detect the full complement of polar metabolites in biological samples. However, an ideal method to meet this demand has not yet been developed. Herein, we proposed a simple methodology that enables the comprehensive and simultaneous analysis of polar metabolites using unified-hydrophilic-interaction/anion-exchange liquid chromatography mass spectrometry (unified-HILIC/AEX/MS) with a polymer-based mixed amines column composed of methacrylate-based polymer particles with primary, secondary, tertiary, and quaternary amines as functional groups. The optimized unified-HILIC/AEX/MS method is composed of two consecutive chromatographic separations, HILIC-dominant separation for cationic, uncharged, and zwitterionic polar metabolites [retention times (RTs) = 0-12.8 min] and AEX-dominant separation for polar anionic metabolites (RTs = 12.8-26.5 min), by varying the ratio of acetonitrile to 40 mM ammonium bicarbonate solution (pH 9.8). A total of 400 polar metabolites were analyzed simultaneously through a combination of highly efficient separation using unified-HILIC/AEX and remarkably sensitive detection using multiple reaction monitoring-based triple quadrupole mass spectrometry (unified-HILIC/AEX/MS/MS). A nontargeted metabolomic approach using unified-HILIC/AEX high-resolution mass spectrometry (unified-HILIC/AEX/HRMS) also provided more comprehensive information on polar metabolites (3242 metabolic features) in HeLa cell extracts than the conventional HILIC/HRMS method (2068 metabolic features). Our established unified-HILIC/AEX/MS/MS and unified-HILIC/AEX/HRMS methods have several advantages over conventional techniques, including polar metabolome coverage, throughput, and accurate quantitative performance, and represent potentially useful tools for in-depth studies on metabolism and biomarker discovery.