4.7 Article

Evaluation of coverage, retention patterns, and selectivity of seven liquid chromatographic methods for metabolomics

Journal

ANALYTICAL AND BIOANALYTICAL CHEMISTRY
Volume 408, Issue 22, Pages 6079-6091

Publisher

SPRINGER HEIDELBERG
DOI: 10.1007/s00216-016-9716-4

Keywords

High-performance liquid chromatography; Hydrophilic interaction liquid chromatography; Reversed phase; Mass spectrometry; Metabolomics; Selectivity

Funding

  1. National Institutes of Health [DK094292, DK089503, DK082841, DK081943, U2C-ES-026553, DK097153, UL1TR000433]

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Liquid chromatography-mass spectrometry-based metabolomics studies require highly selective and efficient chromatographic techniques. Typically employed reversed-phase (RP) methods fail to target polar metabolites, but the introduction of hydrophilic interaction liquid chromatography (HILIC) is slow due to perceived issues of reproducibility and ruggedness and a limited understanding of the complex retention mechanisms. In this study, we present a comparison of the chromatographic performance of a traditional RP-C18 column with zwitterionic, amide-, alkyl diol-, and aminoalkyl-based HILIC and mixed-mode columns. Our metabolite library represents one of the largest analyte sets available and consists of 764 authentic metabolite standards, including amino acids, nucleotides, sugars, and other metabolites, representing all major biological pathways and commonly observed exogenous metabolites (drugs). The coverage, retention patterns, and selectivity of the individual methods are highly diverse even between conceptually related HILIC methods. Furthermore, we show that HILIC sorbents having highly orthogonal selectivity and specificity enhance the coverage of major metabolite groups in (semi-) targeted applications compared to RP. Finally, we discuss issues encountered in the analysis of biological samples based on the results obtained with human plasma extracts. Our results demonstrate that fast and highly reproducible separations on zwitterionic columns are feasible, but knowledge of analyte properties is essential to avoid chromatographic bias and exclusion of key analytes in metabolomics studies.

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