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Anal Chem. 2006 Aug 15;78(16):5752-61.

Automated quantification tool for high-throughput proteomics using stable isotope labeling and LC-MSn.

Author information

1
Proteomics Core Facility and Laboratory of Kidney and Electrolyte Metabolism, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, Maryland 20892, USA.

Abstract

LC-MSn has become a popular option for high-throughput quantitative proteomics, thanks to the availability of stable-isotope labeling reagents. However, the vast quantity of data generated from LC-MSn continues to make the postacquisition quantification analyses challenging, especially in experiments involving multiple samples per experimental condition. To facilitate data analysis, we developed a computer program, QUIL, for automated protein quantification. QUIL accounts for the dynamic nature of spectral background and subtracts this background accordingly during ion chromatogram reconstruction. For elution profile identification, QUIL minimizes the inclusion of coeluted neighbor peaks, yet tolerates imperfect peak shapes. Outlier-resistant methods have been implemented for better protein ratio estimation. The utility of QUIL was validated by quantitative analyses of a standard protein as well as complex protein mixtures, which were labeled with cICAT or 18O and analyzed using LCQ, LTQ, or FT-ICR instruments. For samples that no prior knowledge of relative protein quantities was available, Western blotting was performed for confirmation. For the standard protein, the coefficient of variation (CV) of peptide ratio estimation was 6%. For complex mixtures, the median CV for protein ratio calculations was less than 10%. Computed protein abundance ratios exhibited a relatively high degree of correlation with those obtained from Western blot analyses. Compared with a widely used commercial software tool, QUIL showed improvement in ion chromatogram construction and peak integration and significantly reduced relative errors in abundance ratio assessment.

PMID:
16906720
DOI:
10.1021/ac060611v
[Indexed for MEDLINE]

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