Literature DB >> 19400582

Global quantitative proteomic profiling through 18O-labeling in combination with MS/MS spectra analysis.

Carl A White1, Nicodemus Oey, Andrew Emili.   

Abstract

Several stable-isotope-based peptide labeling methods have been developed to support large-scale relative quantitation, through mass spectrometry, of proteins present in two different biological samples. In one of these, trypsin-catalyzed 18O-based labeling, quantitation is typically performed at the full scan (MS) level by comparing the peak intensities of sister precursor ions corresponding to the labeled and unlabeled forms of an intact peptide as they co-elute during liquid chromatography (LC) separations. We show here that measuring relative abundance at the product ion (MS/MS) level after fragmentation provides excellent accuracy, sensitivity and signal-to-noise, while combining quantitation with global shotgun protein identification. To facilitate routine data analysis using this approach, we have developed two specialized software programs, ySelect and yRatios, which draw upon database search results for 18O-based data sets and combine fragmentation spectra peak lists to (1) accurately determine protein ratios between two samples while applying a correction for incomplete labeling and (2) tabulate these results in both intuitive summary reports and in formats amenable to systematic pathway level analysis. To validate our process, we subjected simple and complex test protein mixtures to single-step and multistep LC-MS/MS profiling experiments. Ratio distributions approached the expected means, allowing empirical derivation of confidence level cutoffs for determining statistically significant fold-changes in protein abundance. A set of stringent criteria for detecting spurious ratios based on consistency checking between unlabeled and labeled y-ion pairs was found to highlight putative false positive identifications. In summary, this toolkit facilitates comparative proteomic quantitation under conditions that are optimized for making reliable protein inferences.

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Year:  2009        PMID: 19400582     DOI: 10.1021/pr8009098

Source DB:  PubMed          Journal:  J Proteome Res        ISSN: 1535-3893            Impact factor:   4.466


  3 in total

Review 1.  Multi-dimensional liquid chromatography in proteomics--a review.

Authors:  Xiang Zhang; Aiqin Fang; Catherine P Riley; Mu Wang; Fred E Regnier; Charles Buck
Journal:  Anal Chim Acta       Date:  2010-02-06       Impact factor: 6.558

2.  Bi-Linear Regression for O Quantification: Modeling across the Elution Profile.

Authors:  Jeanette E Eckel-Passow; Douglas W Mahoney; Ann L Oberg; Roman M Zenka; Kenneth L Johnson; K Sreekumaran Nair; Yogish C Kudva; H Robert Bergen; Terry M Therneau
Journal:  J Proteomics Bioinform       Date:  2010-12-15

Review 3.  Parameter Reliability and Understanding Enzyme Function.

Authors:  Andrew G McDonald; Keith F Tipton
Journal:  Molecules       Date:  2022-01-01       Impact factor: 4.411

  3 in total

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