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Mechanistic Study of the Deuterium Effect in Chromatographic Separation for Chemical-Tagging Metabolomics and Its Application to Biomarker Discovery in Metabolic Dysfunction-Associated Steatohepatitis

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posted on 2025-04-15, 16:33 authored by Yugo Akioka, Tomoya Higuchi, Takahiro Takayama, Mayuko Ichimura-Shimizu, Koichi Tsuneyama, Koichi Inoue
Over the past decade, numerous metabolomics techniques have been developed using liquid chromatography–mass spectrometry (LC-MS). These methodologies have yielded significant findings and facilitated the identification of biomarkers. Among these, chemical-tagging methodologies combined with isotope surrogate tags have garnered considerable attention as a leading approach. Chemical-tagging reduces labor and costs by eliminating the need for internal standard preparation. However, the chromatographic deuterium effect (CDE) has persisted as a significant challenge. CDE poses a risk of data misinterpretation in metabolomics due to potential differences in matrix effects. Although the CDE mechanism has been partially elucidated, it has primarily been attributed to differences in hydrophobicity. A detailed understanding of CDE mechanisms would be valuable for designing chemical tags and optimizing liquid chromatography (LC) conditions. Moreover, emphasizing the CDE could aid in the separation and purification of deuterated compounds. In this study, we investigated the mechanistic basis of the CDE. Initially, four chromatography columns with different separation modesoctadecyl, octadecyl with a positively charged surface, biphenyl, and pentafluorophenyl (PFP) groupswere evaluated based on retention differences between 1H- and 2H6-labeled chemically tagged metabolites. Among these, the PFP column demonstrated the most effective reduction of the CDE, suggesting that electronic interactions with fluorine stabilized 2H-labeled metabolites. Further optimization using the PFP column showed its efficacy in reducing the level of CDE in human serum samples. Finally, the optimized approach was successfully applied to global metabolomics analysis of serum from a mouse model of metabolic dysfunction-associated steatohepatitis.

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