Evaluation of LC/MS Methods for Hydrophilic Metabolites to Enable Integration of Human Blood Metabolome Data.

Abstract

Information on candidate biomarker metabolites identified in recent disease biomarker discovery research is expected to play a key role in the future of personalized and precision medicine. Liquid chromatography mass spectrometry (LC/MS) is a powerful method for metabolomic analysis due to its comprehensive coverage and high detection sensitivity. However, the suitability of LC/MS methods for the identification and quantification of hydrophilic metabolites remains debatable. Here, we evaluated the performance of LC/MS methods combining four types of LC [hydrophilic interaction chromatography (HILIC), ion chromatography (IC) with an anion-exchange (AEX) column (AEX-IC), reversed-phase LC (RPLC) with a pentafluorophenylpropyl (PFPP) column (PFPP-RPLC), and unified-hydrophilic interaction AEX LC (unified-HILIC/AEX)], using the same Orbitrap mass spectrometer, with the aim of integrating future human plasma metabolome data. First, we conducted a qualitative performance evaluation of four LC/MS methods, HILIC/MS, AEX-IC/MS, PFPP-RPLC/MS, and unified-HILIC/AEX/MS, by analyzing 511 hydrophilic metabolite standards and NIST Standard Reference Material (SRM) 1950 (Metabolites in Frozen Human Plasma). The evaluation focused on metabolome coverage, peak width, sensitivity, and separation performance of isomers. Next, we thoroughly evaluated the quantitative performance of the four analytical methods for 63 hydrophilic metabolites in SRM 1950 using a stable isotope-labeled internal standard (SILIS) mixture derived from ¹³C-labeled Escherichia coli extracts. Furthermore, we successfully estimated new concentration values for 29 metabolites without certified values in SRM 1950 using quantitative data from the four LC/MS methods. We objectively evaluated the performance of the four LC/MS methods and demonstrated that absolute quantification using SILIS is effective for integrating hydrophilic metabolite data in metabolomics.