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Eliminating the dication-induced intersample chemical-shift variations for NMR-based biofluid metabonomic analysis
Limiao Jiang1, Jing Huang, Yulan Wang
1State Key Laboratory of Magnetic Resonance and Atomic and Molecular Physics, Center for Biospectroscopy and Metabonomics, Wuhan Institute of Physics and Mathematics, Chinese Academy of Sciences, PR China.
The Analyst
|August 4, 2012
Summary
This study presents optimized methods to eliminate variations in NMR-based urinary metabonomics caused by pH and dication concentration differences. These validated techniques ensure high-quality data for high-throughput studies by reducing intersample chemical-shift variations.
Area of Science:
- Systems Biology
- Metabonomics
- Analytical Chemistry
Background:
- NMR-based urinary metabonomics is crucial for understanding physiology and pathophysiology.
- Intersample chemical-shift variations due to pH and dication concentration pose significant challenges.
- Accurate analysis requires methods to standardize these variations.
Purpose of the Study:
- To develop and validate optimized methods for eliminating intersample chemical-shift variations in NMR-based urinary metabonomics.
- To address variations caused by differing pH and dication concentrations in urine samples.
- To improve data quality and reliability for high-throughput metabonomics studies.
Main Methods:
- Optimization of additives including potassium fluoride, phosphate buffer, and K(3)EDTA for urine samples.
- Systematic adjustment of additive amounts considering pH control, signal-to-noise ratio, and metabolite chemical-shift uniformity.
- Validation of optimized procedures for rat, mouse, and human urine samples.
Main Results:
- Two methods were optimized and validated to eliminate intersample chemical-shift variations.
- Combined treatment with potassium fluoride, phosphate buffer, and K(3)EDTA effectively removed variations.
- The optimized methods reduced intersample chemical-shift variations to 1.5 Hz for all metabolites.
- EDTA treatment followed by phosphate buffer also showed promise, though with some signal obscuration.
Conclusions:
- The developed methods effectively eliminate pH and dication effects, significantly reducing intersample chemical-shift variations.
- These validated procedures ensure high data quality for high-throughput and robotic urinary metabonomics.
- The methods eliminate the need for peak alignments or corrections, streamlining data analysis.
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