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Automated quantification tool for high-throughput proteomics using stable isotope labeling and LC-MSn.
Guanghui Wang1, Wells W Wu, Trairak Pisitkun
1Proteomics Core Facility and Laboratory of Kidney and Electrolyte Metabolism, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, Maryland 20892, USA.
Analytical Chemistry
|August 16, 2006
Summary
QUIL is a new software for automated protein quantification in proteomics, simplifying complex data analysis. It improves accuracy and reduces errors compared to existing tools, aiding high-throughput studies.
Area of Science:
- Proteomics
- Mass Spectrometry
- Computational Biology
Background:
- Liquid chromatography-tandem mass spectrometry (LC-MSn) is vital for high-throughput quantitative proteomics.
- Analyzing large datasets from LC-MSn, especially with multiple samples, poses significant quantification challenges.
Purpose of the Study:
- To develop QUIL, a computer program for automated protein quantification.
- To address challenges in post-acquisition data analysis for LC-MSn experiments.
Main Methods:
- QUIL automates protein quantification by accounting for dynamic spectral background.
- It minimizes coeluted peaks during elution profile identification and uses outlier-resistant methods for ratio estimation.
- Validation involved standard proteins and complex mixtures labeled with cICAT or 18O, analyzed on LCQ, LTQ, or FT-ICR instruments.
Main Results:
- For standard proteins, peptide ratio estimation showed a coefficient of variation (CV) of 6%.
- For complex mixtures, median CV for protein ratio calculations was below 10%.
- QUIL demonstrated improved ion chromatogram construction, peak integration, and reduced abundance ratio errors compared to commercial software.
Conclusions:
- QUIL effectively facilitates automated protein quantification in complex proteomics datasets.
- The software offers improved accuracy and reliability for quantitative proteomics analyses.
- QUIL's performance was validated against Western blotting, showing high correlation in protein abundance ratios.