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Automated Generic Analysis Tools for Protein Quantitation Using Stable Isotope Labeling

  • Wen-Lian HsuEmail author
  • Ting-Yi Sung
Protocol
Part of the Methods in Molecular Biology™ book series (MIMB, volume 604)

Abstract

Isotope labeling combined with LC-MS/MS provides a robust platform for quantitative proteomics. Protein quantitation based on mass spectral data falls into two categories: one determined by MS/MS scans, e.g., iTRAQ-labeling quantitation, and the other by MS scans, e.g., quantitation using SILAC, ICAT, or 18O labeling. In large-scale LC-MS proteomic experiments, tens of thousands of MS and MS/MS spectra are generated and need to be analyzed. Data noise further complicates the data analysis. In this chapter, we present two automated tools, called Multi-Q and MaXIC-Q, for MS/MS- and MS-based quantitation analysis. They are designed as generic platforms that can accommodate search results from SEQUEST and Mascot, as well as mzXML files converted from raw files produced by various mass spectrometers. Toward accurate quantitation analysis, Multi-Q determines detection limits of the user’s instrument to filter out outliers and MaXIC-Q adopts stringent validation on our constructed projected ion mass spectra to ensure correct data for quantitation.

Key words

Computer software Stable isotope labeling Mass spectrometry Quantitative proteomics Quantitation analysis Dynamic range Extracted ion chromatogram Projected ion mass spectrum 

Notes

Acknowledgments

The authors gratefully acknowledge the financial support from the thematic program of Academia Sinica under Grant AS94B003 and AS95ASIA02 and the National Science Council of Taiwan under Grant NSC 95-3114-P-002-005-Y. We would also like to thank our collaborator Dr. Yu-Ju Chen’s lab in the Institute of Chemistry, Academia Sinica. Without their help and encouragement, this research would not have been possible.

References

  1. 1.
    Griffin, T. J., Goodlett, D. R., and Aebersold, R. (2001) Advances in proteome analysis by mass spectrometry. Curr. Opin. Biotechnol. 12, 607-612.CrossRefPubMedGoogle Scholar
  2. 2.
    Domon, B., and Aebersold, R. (2006) Mass spectrometry and protein analysis. Science 312, 212-217.CrossRefPubMedGoogle Scholar
  3. 3.
    Nesvizhskii, A. I., and Aebersold, R. (2005) Interpretation of shotgun proteomic data: the protein inference problem. Mol. Cell. Proteomics 4, 1419-1440.CrossRefPubMedGoogle Scholar
  4. 4.
    Washburn, M. P., Wolters, D., and Yates, J. R., III. (2001) Large-scale analysis of the yeast proteome by multidimensional protein identification technology. Nat. Biotechnol. 19, 242-247.CrossRefPubMedGoogle Scholar
  5. 5.
    Tao, W. A., and Aebersold, R. (2003) Advances in quantitative proteomics via stable isotope tagging and mass spectrometry. Curr. Opin. Biotechnol. 14, 110-118.CrossRefPubMedGoogle Scholar
  6. 6.
    Semmes, O. J., Malik, G., and Ward, M. (2006) Application of mass spectrometry to the discovery of biomarkers for detection of prostate cancer. J. Cell. Biochem. 98, 496-503.CrossRefPubMedGoogle Scholar
  7. 7.
    Thompson, A., Schäfer, J., Kuhn, K., Kienle, S., Schwarz, J., Schmidt, G., Neumann, T., and Hamon, C. (2003). Tandem mass tags: a novel quantification strategy for comparative analysis of complex protein mixtures by MS/MS. Anal. Chem. 75, 1895-1904.CrossRefPubMedGoogle Scholar
  8. 8.
    Ong, S. E., and Mann, M. (2005) Mass spectrometry-based proteomics turns quantitative. Nat. Chem. Biol. 1, 252-262.CrossRefPubMedGoogle Scholar
  9. 9.
    Islinger, M., Li, K. W., Loos, M., Lueers, G., and Voelkl, A. (2006) ITRAQ-quantification as an analytical tool to describe proteome changes in rat liver peroxisomes after bezafibrate treatment. Mol. Cell. Proteomics 5, S186.Google Scholar
  10. 10.
    Jabs, W., Lubeck, M., Schweiger-Hufnagel, U., Suckau, D., and Hahner, S. (2006) A comparative study of iTRAQ- and ICPL-based protein quantification. Mol. Cell. Proteomics 5, S248.Google Scholar
  11. 11.
    Gygi, S. P., Rist, B., Gerber, S. A., Turecek, F., Gelb, M. H., and Aebersold, R. (1999) Quantitative analysis of complex protein mixtures using isotope-coded affinity tags. Nat. Biotechnol. 17, 994-999.CrossRefPubMedGoogle Scholar
  12. 12.
    Yao, X., Freas, A., Ramirez, J., Demirev, P. A., and Fenselau, C. (2001) Proteolytic 18O labeling for comparative proteomics: model studies with two serotypes of adenovirus. Anal. Chem. 73, 2836-2842.CrossRefPubMedGoogle Scholar
  13. 13.
    Ong, S. E., Blagoev, B., Kratchmarova, I., Kristensen, D. B., Steen, H., Pandey, A., and Mann, M. (2002) Stable isotope labeling by amino acids in cell culture, SILAC, as a simple and accurate approach to expression proteomics. Mol. Cell. Proteomics 1, 376-386.CrossRefPubMedGoogle Scholar
  14. 14.
    Ong, S. E., Kratchmarova, I., and Mann, M. (2003) Properties of 13C-substituted arginine in stable isotope labeling by amino acids in cell culture (SILAC). J. Proteome Res. 2, 173-181.CrossRefPubMedGoogle Scholar
  15. 15.
    Ong, S. E., and Mann, M. (2006) A practical recipe for stable isotope labeling by amino acids in cell culture (SILAC). Nat. Protoc. 1, 2650-2660.CrossRefPubMedGoogle Scholar
  16. 16.
    Ong, S. E., and Mann, M. (2007) Stable isotope labeling by amino acids in cell culture for quantitative proteomics. In: Quantitative Proteomics by Mass Spectrometry, Sechi, S., ed., Methods Mol. Biol. 359, 37-52.Google Scholar
  17. 17.
    Callister, S. J., Barry R. C., Adkins, J. N., Johnson, E. T., Qian, W., Webb-Robertson B. M., Smith R. D., and Lipton M. S. (2006) Normalization approaches for removing systematic biases associated with mass spectrometry and label-free proteomics. J. Proteome Res. 5 , 277-286.CrossRefPubMedGoogle Scholar
  18. 18.
    Shadforth, I. P., Dunkley, T. P., Lilley, K. S., and Bessant, C. (2005) i-Tracker: for quantitative proteomics using iTRAQ. BMC Genomics 6, 145.CrossRefPubMedGoogle Scholar
  19. 19.
    Han, D. K., Eng, J., Zhou, H., and Aebersold, R. (2001) Quantitative profiling of differentiation-induced microsomal proteins using isotope-coded affinity tags and mass spectrometry. Nat. Biotechnol. 19, 946-951.CrossRefPubMedGoogle Scholar
  20. 20.
    Li, X. J., Zhang, H., Ranish, J. A., and Aebersold, R. (2003) Automated statistical analysis of protein abundance ratios from data generated by stable-isotope dilution and tandem mass spectrometry. Anal. Chem. 75, 6648-6657.CrossRefPubMedGoogle Scholar
  21. 21.
    MacCoss, M. J., Wu, C. C., Liu, H., Sadygov, R., and Yates, J. R., III. (2003) A correlation algorithm for the automated quantitative analysis of shotgun proteomics data. Anal. Chem. 75, 6912-6921.CrossRefPubMedGoogle Scholar
  22. 22.
    Lin, W. T., Hung, W. N., Yian, Y. H., Wu, K. P., Han, C. L., Chen, Y. R., Chen, Y. J., Sung, T. Y., and Hsu, W. L. (2006) Multi-Q: a fully automated tool for multiplexed protein quantitation. J. Proteome Res. 5, 2328-2338.CrossRefPubMedGoogle Scholar
  23. 23.
    Yu, C. Y., Tsui, Y. H., Yian, Y. H., Sung, T. Y., and Hsu, W. L. (2007) The Multi-Q web server for multiplexed protein quantitation. Nucleic Acids Res. 35, W707-W712.CrossRefPubMedGoogle Scholar
  24. 24.
    Tsou, C. C, Tsui, Y. H., Yian, Y. H., Chen, Y. J., Yang, H. Y., Yu, C. Y., Lynn, K. S., Chen, Y. J., Sung, T. Y., and Hsu, W. L. (2009) MaXIC-Q Web: a fully automated web service using statistical and computational methods for protein quantitation based on stable isotope labeling and LC-MS. Nucleic Acids Res. 37, suppl_2 W661-W669.Google Scholar
  25. 25.
    Keller, A., Nesvizhskii, A. I., Kolker, E., and Aebersold, R. (2002) Empirical statistical model to estimate the accuracy of peptide identifications made by MS/MS and database search. Anal. Chem. 74, 5383-5392.CrossRefPubMedGoogle Scholar
  26. 26.
    Nesvizhskii, A. I., Keller, A., Kolker, E., and Aebersold, R. (2003) A statistical model for identifying proteins by tandem mass spectrometry. Anal. Chem. 75, 4646-4658.CrossRefPubMedGoogle Scholar
  27. 27.
    Weisstein, Eric W. “Moving Average.” From MathWorld - A Wolfram Web Resource. http://mathworld.wolfram.com/MovingAverage.html
  28. 28.
    Golub, G. H., Van Loan, C.F. (1996) Matrix Computations. 3rd edition, The Johns Hopkins University Press: USA.Google Scholar
  29. 29.
    Savitzky, A, and Marcel J.E. Golay (1964) Smoothing and differentiation of data by simplified least squares procedures. Anal. Chem. 36, 1627-1639.CrossRefGoogle Scholar
  30. 30.
    De Boor, C. (1978) A Practical Guide to Splines, 1st ed., pp. 114-115, Springer Verlag, NY.Google Scholar
  31. 31.
    Lau, K. W., Jones, A. R., Swainston, N., Siepen, J.A., and Hubbard, S. J. (2007) Capture and analysis of quantitative proteomic data. Proteomics 7, 2787-2799.CrossRefPubMedGoogle Scholar
  32. 32.
    Muller, L. N., Brusniak, M.Y., Mani, D. R., and Aebersold, R. (2008). An assessment of software solutions for the analysis of mass spectrometry based quantitative proteomics data. J. Proteome Res. 7, 51-61.CrossRefGoogle Scholar
  33. 33.
    MacCoss, M. J., Toth, M. J., Matthews, D. E. (2001) Evaluation and optimization of ion-current ratio measurements by selected-ion-monitoring mass spectrometry. Anal. Chem. 73, 2976-2984.CrossRefPubMedGoogle Scholar
  34. 34.
    Aggarwal, K., Choe, L. H., Lee, K. H. (2005) Quantitative analysis of protein expression using amine-specific isobaric tags in Escherichia coli cells expressing rhsA elements. Proteomics 5, 2297-2308.CrossRefPubMedGoogle Scholar
  35. 35.
    Yang, Y. H., Dudoit, S., Luu, P., Lin, D. M., Peng, V., Ngai, J., Speed, T. P. (2002) Normalization for cDNA microarray data: a robust composite method addressing single and multiple slide systematic variation. Nucleic Acids Res. 30, e15.CrossRefPubMedGoogle Scholar
  36. 36.
    Ravin, N. V., and Ravin, V. K. (1999) Use of a linear multicopy vector based on the mini-replicon of temperate coliphage N15 for cloning DNA with abnormal secondary structures. Nucleic Acids Res. 27, e13.CrossRefPubMedGoogle Scholar

Copyright information

© Humana Press, a part of Springer Science+Business Media, LLC 2010

Authors and Affiliations

  1. 1.Institute of Information ScienceAcademia SinicaTaipeiTaiwan

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