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Using ProtMAX to create high-mass-accuracy precursor alignments from label-free quantitative mass spectrometry data generated in shotgun proteomics experiments

Nature Protocols volume 8, pages 595–601 (2013)Cite this article

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Abstract

Recently, new software tools have been developed for improved protein quantification using mass spectrometry (MS) data. However, there are still limitations especially in high-sample-throughput quantification methods, and most of these relate to extensive computational calculations. The mass accuracy precursor alignment (MAPA) strategy has been shown to be a robust method for relative protein quantification. Its major advantages are high resolution, sensitivity and sample throughput. Its accuracy is data dependent and thus best suited for precursor mass-to-charge precision of ∼1 p.p.m. This protocol describes how to use a software tool (ProtMAX) that allows for the automated alignment of precursors from up to several hundred MS runs within minutes without computational restrictions. It comprises features for 'ion intensity count' and 'target search' of a distinct set of peptides. This procedure also includes the recommended MS settings for complex quantitative MAPA analysis using ProtMAX (http://www.univie.ac.at/mosys/software.html).

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Figure 1
Figure 2: Target list example.
Figure 3: ProtMAX output file.
Figure 4: Graphical user interface of the Xcalibur MS instrument setup.
Figure 5: Graphical user interface of ProtMAX.

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References

  1. Michalski, A., Cox, J. & Mann, M. More than 100,000 detectable peptide species elute in single shotgun proteomics runs but the majority is inaccessible to data-dependent LC-MS/MS. J. Proteome Res. 10, 1785–1793 (2011).

    Article  CAS  Google Scholar 

  2. Michalski, A. et al. Mass spectrometry-based proteomics using Q Exactive, a high-performance benchtop quadrupole Orbitrap mass spectrometer. Mol. Cell. Proteomics 10, M111.011015 (2011).

    Article  Google Scholar 

  3. Thakur, S.S. et al. Deep and highly sensitive proteome coverage by LC-MS/MS without prefractionation. Mol. Cell Proteomics 10, M110.003699 (2011).

    Article  Google Scholar 

  4. Griffin, N.M. et al. Label-free, normalized quantification of complex mass spectrometry data for proteomic analysis. Nat. Biotechnol. 28, 83–89 (2010).

    Article  CAS  Google Scholar 

  5. Ishihama, Y. et al. Exponentially modified protein abundance index (emPAI) for estimation of absolute protein amount in proteomics by the number of sequenced peptides per protein. Mol. Cell Proteomics 4, 1265–1272 (2005).

    Article  CAS  Google Scholar 

  6. Liu, H., Sadygov, R.G. & Yates, J.R. A model for random sampling and estimation of relative protein abundance in shotgun proteomics. Anal. Chem. 76, 4193–4201 (2004).

    Article  CAS  Google Scholar 

  7. Lu, P., Vogel, C., Wang, R., Yao, X. & Marcotte, E.M. Absolute protein expression profiling estimates the relative contributions of transcriptional and translational regulation. Nat. Biotechnol. 25, 117–124 (2007).

    Article  CAS  Google Scholar 

  8. Paoletti, A.C. et al. Quantitative proteomic analysis of distinct mammalian Mediator complexes using normalized spectral abundance factors. Proc. Natl. Acad. Sci. USA 103, 18928–18933 (2006).

    Article  CAS  Google Scholar 

  9. Schulze, W.X. & Usadel, B. Quantitation in mass-spectrometry-based proteomics. Annu. Rev. Plant Biol. 61, 491–516 (2010).

    Article  CAS  Google Scholar 

  10. Schwanhausser, B. et al. Global quantification of mammalian gene expression control. Nature 473, 337–342 (2011).

    Article  Google Scholar 

  11. Silva, J.C., Gorenstein, M.V., Li, G.Z., Vissers, J.P.C. & Geromanos, S.J. Absolute quantification of proteins by LCMSE—a virtue of parallel MS acquisition. Mol. Cell Proteomics 5, 144–156 (2006).

    Article  CAS  Google Scholar 

  12. Baerenfaller, K. et al. Genome-scale proteomics reveals Arabidopsis thaliana gene models and proteome dynamics. Science 320, 938–941 (2008).

    Article  CAS  Google Scholar 

  13. Brunner, E. et al. A high-quality catalog of the Drosophila melanogaster proteome. Nat. Biotechnol. 25, 576–583 (2007).

    Article  CAS  Google Scholar 

  14. de Godoy, L.M.F. et al. Comprehensive mass-spectrometry-based proteome quantification of haploid versus diploid yeast. Nature 455, 1251–1260 (2008).

    Article  CAS  Google Scholar 

  15. Graumann, J. et al. Stable isotope labeling by amino acids in cell culture (SILAC) and proteome quantitation of mouse embryonic stem cells to a depth of 5,111 proteins. Mol. Cell Proteomics 7, 672–683 (2008).

    Article  CAS  Google Scholar 

  16. Malmstrom, J. et al. Proteome-wide cellular protein concentrations of the human pathogen Leptospira interrogans. Nature 460, 762–U112 (2009).

    Article  Google Scholar 

  17. Nagaraj, N. et al. Deep proteome and transcriptome mapping of a human cancer cell line. Mol. Syst. Biol. 7, 548 (2011).

    Article  Google Scholar 

  18. Pavelka, N. et al. Statistical similarities between transcriptomics and quantitative shotgun proteomics data. Mol. Cell Proteomics 7, 631–644 (2008).

    Article  CAS  Google Scholar 

  19. Cox, J. & Mann, M. MaxQuant enables high peptide identification rates, individualized p.p.b.-range mass accuracies and proteome-wide protein quantification. Nat. Biotechnol. 26, 1367–1372 (2008).

    Article  CAS  Google Scholar 

  20. De Vos, R.C.H. et al. Untargeted large-scale plant metabolomics using liquid chromatography coupled to mass spectrometry. Nat. Protoc. 2, 778–791 (2007).

    Article  CAS  Google Scholar 

  21. Hoehenwarter, W. et al. A rapid approach for phenotype-screening and database independent detection of cSNP/protein polymorphism using mass accuracy precursor alignment. Proteomics 8, 4214–4225 (2008).

    Article  CAS  Google Scholar 

  22. Hoehenwarter, W. et al. MAPA distinguishes genotype-specific variability of highly similar regulatory protein isoforms in potato tuber. J. Proteome Res. 10, 2979–2991 (2011).

    Article  CAS  Google Scholar 

  23. Chen, Y., Hoehenwarther, W. & Weckwerth, W. Comparative analysis of phytohormone-responsive phosphoproteins in Arabidopsis thaliana using TiO2-phosphopeptide enrichment and mass accuracy precursor alignment. Plant J. 63, 1–17 (2010).

    Article  CAS  Google Scholar 

  24. Doerfler, H. et al. Granger causality in integrated GC-MS and LC-MS metabolomics data reveals the interface of primary and secondary metabolism. Metabolomics. http://dx.doi.org/10.1007/s11306-012-0470-0 (25 October 2012).

  25. Mari, A. et al. Phytochemical composition of Potentilla anserina L. analyzed by an integrative GC-MS and LC-MS metabolomics platform. Metabolomics. http://dx.doi.org/10.1007/s11306-012-0473-x (17 November 2012).

  26. Lee, K.A., Farnsworth, C., Yu, W. & Bonilla, L.E. 24-hour lock mass protection. J. Proteome Res. 10, 880–885 (2011).

    Article  CAS  Google Scholar 

  27. Isaacson, T. et al. Sample extraction techniques for enhanced proteomic analysis of plant tissues. Nat. Protoc. 2, 769–774 (2006).

    Article  Google Scholar 

  28. Sheoran, I.S. et al. Compatibility of plant protein extraction methods with mass spectrometry for proteome analysis. Plant Sci. 176, 99–104 (2009).

    Article  CAS  Google Scholar 

  29. Sun, X. & Weckwerth, W. COVAIN: a toolbox for uni- and multivariate statistics, time-series and correlation network analysis and inverse estimation of the differential Jacobian from metabolomics covariance data. Metabolomics 8, 81–93 (2012).

    Article  CAS  Google Scholar 

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Acknowledgements

We thank J. Hummel for interesting comments.

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Author notes

  1. Wolfgang Hoehenwarter

    Present address: Present address: Proteome Analysis Research Group, Leibniz Institute for Plant Biochemistry, Halle, Germany.,

Authors and Affiliations

  1. Department of Molecular Systems Biology, University of Vienna, Vienna, Austria

    Volker Egelhofer, Wolfgang Hoehenwarter, David Lyon, Wolfram Weckwerth & Stefanie Wienkoop

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  1. Volker Egelhofer
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  2. Wolfgang Hoehenwarter
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  3. David Lyon
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Contributions

V.E. developed the algorithm of the ProtMAX tool. W.H. contributed to the concept and optimization of the ProtMAX tool of protein analysis and writing of the manuscript. D.L. contributed to the concept of the ProtMAX tool and writing of the manuscript. W.W. conceived the concept of the MAPA strategy for proteomics. S.W. conceived Preferences and feature upgrading with the target approach of the ProtMAX tool and was responsible for project coordination and writing of the manuscript.

Corresponding author

Correspondence to Stefanie Wienkoop.

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The authors declare no competing financial interests.

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Egelhofer, V., Hoehenwarter, W., Lyon, D. et al. Using ProtMAX to create high-mass-accuracy precursor alignments from label-free quantitative mass spectrometry data generated in shotgun proteomics experiments. Nat Protoc 8, 595–601 (2013). https://doi.org/10.1038/nprot.2013.013

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