Dataset Information

Relative quantification: characterization of bias, variability and fold changes in mass spectrometry data from iTRAQ-labeled peptides.

ABSTRACT: Shotgun proteomics via mass spectrometry (MS) is a powerful technology for biomarker discovery that has the potential to lead to noninvasive disease screening mechanisms. Successful application of MS-based proteomics technologies for biomarker discovery requires accurate expectations of bias, reproducibility, variance, and the true detectable differences in platforms chosen for analyses. Characterization of the variability inherent in MS assays is vital and should affect interpretation of measurements of observed differences in biological samples. Here we describe observed biases, variance structure, and the ability to detect known differences in spike-in data sets for which true relative abundance among defined samples were known and were subsequently measured with the iTRAQ technology on two MS platforms. Global biases were observed within these data sets. Measured variability was a function of mean abundance. Fold changes were biased toward the null and variance of a fold change was a function of protein mass and abundance. The information presented herein will be valuable for experimental design and analysis of the resulting data.

SUBMITTER: Mahoney DW

PROVIDER: S-EPMC3166364 | biostudies-literature | 2011 Sep

REPOSITORIES: biostudies-literature

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Relative quantification: characterization of bias, variability and fold changes in mass spectrometry data from iTRAQ-labeled peptides.

Mahoney Douglas W DW Therneau Terry M TM Heppelmann Carrie J CJ Higgins Leeann L Benson Linda M LM Zenka Roman M RM Jagtap Pratik P Nelsestuen Gary L GL Bergen H Robert HR Oberg Ann L AL

Journal of proteome research 20110802 9

Shotgun proteomics via mass spectrometry (MS) is a powerful technology for biomarker discovery that has the potential to lead to noninvasive disease screening mechanisms. Successful application of MS-based proteomics technologies for biomarker discovery requires accurate expectations of bias, reproducibility, variance, and the true detectable differences in platforms chosen for analyses. Characterization of the variability inherent in MS assays is vital and should affect interpretation of measur ...[more]

PMID: 21755926

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Project description:To facilitate accurate histone variant and post-translational modification (PTM) quantification via mass spectrometry, we present a library of 93 synthetic peptides using Protein-Aqua™ technology. The library contains 55 peptides representing different modified forms from histone H3 peptides, 23 peptides representing H4 peptides, 5 peptides representing canonical H2A peptides, 8 peptides representing H2A.Z peptides, and peptides for both macroH2A and H2A.X. The PTMs on these peptides include lysine mono- (me1), di- (me2), and tri-methylation (me3); lysine acetylation; arginine me1; serine/threonine phosphorylation; and N-terminal acetylation. The library was subjected to chemical derivatization with propionic anhydride, a widely employed protocol for histone peptide quantification. Subsequently, the detection efficiencies were quantified using mass spectrometry extracted ion chromatograms. The library yields a wide spectrum of detection efficiencies, with more than 1700-fold difference between the peptides with the lowest and highest efficiencies. In this paper, we describe the impact of different modifications on peptide detection efficiencies and provide a resource to correct for detection biases among the 93 histone peptides. In brief, there is no correlation between detection efficiency and molecular weight, hydrophobicity, basicity, or modification type. The same types of modifications may have very different effects on detection efficiencies depending on their positions within a peptide. We also observed antagonistic effects between modifications. In a study of mouse trophoblast stem cells, we utilized the detection efficiencies of the peptide library to correct for histone PTM/variant quantification. For most histone peptides examined, the corrected data did not change the biological conclusions but did alter the relative abundance of these peptides. For a low-abundant histone H2A variant, macroH2A, the corrected data led to a different conclusion than the uncorrected data. The peptide library and detection efficiencies presented here may serve as a resource to facilitate studies in the epigenetics and proteomics fields.

Dataset Information

Relative quantification: characterization of bias, variability and fold changes in mass spectrometry data from iTRAQ-labeled peptides.

Publications

Relative quantification: characterization of bias, variability and fold changes in mass spectrometry data from iTRAQ-labeled peptides.

Similar Datasets

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