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Published on: February 27, 2020
Two-Dimensional LC-MS/MS Determination of Chiral Amino Acids in Real-World Samples
1Graduate School of Pharmaceutical Sciences, Kyushu University, Fukuoka, Japan.
Chiral amino acid analysis is crucial for identifying D-amino acids, which act as biomarkers. Advanced two-dimensional liquid chromatography-tandem mass spectrometry (2D-LC-MS/MS) offers sensitive methods for their trace-level determination in biological samples.
Area of Science:
- Biochemistry
- Analytical Chemistry
- Biomarker Discovery
Background:
- D-amino acids are emerging as significant physiologically active substances and biomarkers.
- Their presence in biological samples is often at trace levels, necessitating highly sensitive analytical techniques.
- Chiral amino acid analysis is gaining importance in various scientific fields.
Purpose of the Study:
- To summarize the design and development of multidimensional liquid chromatography (LC) systems.
- To highlight the application of two-dimensional LC-tandem mass spectrometry (2D-LC-MS/MS) for chiral amino acid determination.
- To review methods for analyzing D-amino acids in complex biological and fermented matrices.
Main Methods:
- Development and application of multidimensional liquid chromatography (LC) systems.
- Utilization of two-dimensional LC coupled with tandem mass spectrometry (2D-LC-MS/MS).
- Analysis of chiral amino acids in diverse real-world samples.
Main Results:
- Successful application of 2D-LC-MS/MS for sensitive and selective determination of trace D-amino acids.
- Demonstration of the methods' efficacy across various matrices including urine, plasma, fermented products, and proteins.
- Validation of advanced LC systems for complex chiral analyses.
Conclusions:
- Multidimensional LC, particularly 2D-LC-MS/MS, provides essential sensitive and selective methods for D-amino acid determination.
- These analytical advancements are critical for utilizing D-amino acids as biomarkers and understanding their physiological roles.
- The described methods are applicable to a wide range of real-world samples, facilitating further research.
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