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Updated: Jan 12, 2026

A Strategy for Sensitive, Large Scale Quantitative Metabolomics
Published on: May 27, 2014
Absolute quantification of polar metabolites using matrix-free calibration curves and post-column infusion of
Mengle Zhu1, Lieke Lamont1, Pascal Maas1
1Metabolomics and Analytics Centre, Leiden Academic Centre for Drug Research, Leiden University, Leiden, The Netherlands.
Abstract:
Polar metabolites play essential roles in inflammation, immune regulation, and metabolism, and have emerged as important biomarkers in clinical research. However, accurate absolute quantification of these metabolites in complex biological matrices using hydrophilic interaction liquid chromatography-mass spectrometry (HILIC-MS) remains challenging, due to matrix effects that compromise measurement accuracy. While (stable isotope-labeled-) internal standards ((SIL-)IS) are commonly used to correct these effects, their limited availability and high cost can hinder their applicability. In this study, we present a robust approach using HILIC-MS coupled with post-column infusion of standards (PCIS) to enable absolute quantification of polar metabolites in plasma, using calibration curves prepared in neat solution without using SIL standards. First, the optimal PCIS for each metabolite was identified based on absolute matrix effect (AME) values, and method performance was systematically evaluated in terms of linearity, precision, accuracy, and matrix effects. Results showed that the PCIS approach consistently achieved comparable results to (SIL-)IS correction for analytes with SIL standards, and superior performance for analytes without available SIL standards. Application to a study cohort demonstrated consistency between this HILIC-PCIS-MS method and Nuclear Magnetic Resonance (NMR) quantification. Validation using NIST SRM 1950 plasma confirmed high quantification accuracy (80 % - 120 %) for most metabolites using a matrix-free calibration curve combined with PCIS in HILIC-MS. In summary, PCIS offers a promising and cost-effective alternative to (SIL-)IS correction, particularly benefiting metabolites lacking SIL standards, and provides a strategy for accurately correcting matrix effects and advancing quantitative metabolomics in complex biological matrices.
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