Isotope Labeling-Assisted Evaluation of Hydrophilic and Hydrophobic Liquid Chromatograph–Mass Spectrometry for Metabolomics Profiling

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 hydrophili...

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Bibliographic Details
Published in:Analytical chemistry (Washington) 2018-07, Vol.90 (14), p.8538-8545
Main Authors: Xie, Boer, Wang, Yuanyuan, Jones, Drew R, Dey, Kaushik Kumar, Wang, Xusheng, Li, Yuxin, Cho, Ji-Hoon, Shaw, Timothy I, Tan, Haiyan, Peng, Junmin
Format: Article
Language:English
Subjects:
Amino acids
Amino Acids - analysis
Amino Acids - metabolism
Animals
Brain - metabolism
Brain Chemistry
Chemistry
Chromatography
Chromatography, Liquid - methods
Chromatography, Reverse-Phase - methods
Contaminants
Evaluation
Feature extraction
Flow velocity
Hydrophilic surfaces
Hydrophobic and Hydrophilic Interactions
Hydrophobic surfaces
Hydrophobicity
Ionization
Isotope Labeling - methods
Isotopes
Liquid chromatography
Mass spectrometry
Mass spectroscopy
Metabolism
Metabolites
Metabolome
Metabolomics
Metabolomics - methods
Nitrogen
Nitrogen - analysis
Nitrogen - metabolism
Optimization
Rats
Spectroscopy
Stable isotopes
Tandem Mass Spectrometry - methods
Uridine
Yeast
Yeasts - chemistry
Yeasts - metabolism
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Summary: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.
ISSN:0003-2700
1520-6882
DOI:10.1021/acs.analchem.8b01591