ArticleAnalytical chemistry2018
Isotope Labeling-Assisted Evaluation of Hydrophilic and Hydrophobic Liquid Chromatograph-Mass Spectrometry for Metabolomics Profiling.
Article in Analytical chemistry, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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4 citing papers in PubMed.
- CYP3A5 unexpectedly regulates glucose metabolism through the AKT-TXNIP-GLUT1 axis in pancreatic cancer.Genes & diseases · 2024Article
- Sulfhydryl functionalized carbon quantum dots as a turn-off fluorescent probe for sensitive detection of HgRSC advances · 2021Article
- JUMPm: A Tool for Large-Scale Identification of Metabolites in Untargeted Metabolomics.Metabolites · 2020Article
- A large-scale analysis of targeted metabolomics data from heterogeneous biological samples provides insights into metabolite dynamics.Metabolomics : Official journal of the Metabolomic Society · 2019Article
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10 authors.
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Abstract
High throughput untargeted metabolomics usually relies on complementary liquid chromatography-mass spectrometry (LC-MS) methods to expand the coverage of diverse metabolites, but the integration of those methods is not fully characterized. We systematically investigated the performance of hydrophilic interaction liquid chromatography (HILIC)-MS and nanoflow reverse-phase liquid chromatography (nRPLC)-MS under 8 LC-MS settings, varying stationary phases (HILIC and C18), mobile phases (acidic and basic pH), and MS ionization modes (positive and negative). Whereas nRPLC-MS optimization was previously reported, we found in HILIC-MS (2.1 mm × 150 mm) that the optimal performance was achieved in a 90 min gradient with 100 μL/min flow rate by loading metabolite extracts from 2 mg of cell/tissue samples. Since peak features were highly compromised by contaminants, we used stable isotope labeled yeast to enhance formula identification for comparing different LC-MS conditions. The 8 LC-MS settings enabled the detection of a total of 1050 formulas, among which 78%, 73%, and 62% formulas were recovered by the best combination of 4, 3, and 2 LC-MS settings, respectively. Moreover, these yeast samples were harvested in the presence or absence of nitrogen starvation, enabling quantitative comparisons of altered formulas and metabolite structures, followed by validation with selected synthetic metabolites. The results revealed that nitrogen starvation downregulated amino acid components but upregulated uridine-related metabolism. In summary, this study introduces a thorough evaluation of hydrophilicity and hydrophobicity based LC-MS and provides information for selecting complementary settings to balance throughput and efficiency during metabolomics experiments.
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