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Updated: Dec 14, 2025

A Strategy for Sensitive, Large Scale Quantitative Metabolomics
Published on: May 27, 2014
Large-scale targeted metabolomics method for metabolite profiling of human samples
Guodong Cao1, Zhengbo Song2, Yanjun Hong3
1State Key Laboratory of Environmental and Biological Analysis, Department of Chemistry, Hong Kong Baptist University, Hong Kong, China.
We developed a large-scale targeted metabolomics method for broad human sample analysis. This approach offers enhanced sensitivity and coverage for biomarker discovery in diseases like lung cancer.
Area of Science:
- Metabolomics
- Biochemistry
- Analytical Chemistry
Background:
- Targeted metabolomics offers quantification but limited metabolite coverage.
- Unbiased metabolite identification is crucial for expanding metabolomic analysis.
Purpose of the Study:
- Develop a large-scale targeted metabolomics method for diverse human samples.
- Enhance metabolite coverage and analytical performance compared to existing methods.
- Apply the method for biomarker discovery in lung cancer.
Main Methods:
- Utilized ultra-high-performance liquid chromatography (UHPLC) coupled with high-resolution Orbitrap mass spectrometry (MS) for initial metabolite identification.
- Established a targeted metabolomics method using UHPLC-triple quadrupole MS for profiling over 400 metabolites across 92 metabolic pathways.
- Validated method performance in human cells, tissues, and body fluids.
Main Results:
- The developed method profiles >400 metabolites in 92 pathways with high sensitivity, repeatability, and linearity.
- Successfully identified 107 differential metabolites distinguishing lung cancer from normal tissues.
- Results implicate the Warburg effect, altered redox state, and nucleotide metabolism in lung cancer.
Conclusions:
- The new large-scale targeted metabolomics method provides wide metabolite coverage and excellent analytical performance.
- Demonstrates significant utility and flexibility for biomarker discovery in clinical and basic research.
- Offers advantages over untargeted metabolomics for sensitive and reproducible quantification.
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