Skip to main content

Automated Generic Analysis Tools for Protein Quantitation Using Stable Isotope Labeling

  • Protocol
  • First Online:
Proteome Bioinformatics

Part of the book series: Methods in Molecular Biology™ ((MIMB,volume 604))

  • 4650 Accesses

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.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Protocol
USD 49.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 159.00
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 109.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Griffin, T. J., Goodlett, D. R., and Aebersold, R. (2001) Advances in proteome analysis by mass spectrometry. Curr. Opin. Biotechnol. 12, 607-612.

    Article  CAS  PubMed  Google Scholar 

  2. Domon, B., and Aebersold, R. (2006) Mass spectrometry and protein analysis. Science 312, 212-217.

    Article  CAS  PubMed  Google Scholar 

  3. Nesvizhskii, A. I., and Aebersold, R. (2005) Interpretation of shotgun proteomic data: the protein inference problem. Mol. Cell. Proteomics 4, 1419-1440.

    Article  CAS  PubMed  Google Scholar 

  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.

    Article  CAS  PubMed  Google Scholar 

  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.

    Article  CAS  PubMed  Google Scholar 

  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.

    Article  CAS  PubMed  Google Scholar 

  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.

    Article  CAS  PubMed  Google Scholar 

  8. Ong, S. E., and Mann, M. (2005) Mass spectrometry-based proteomics turns quantitative. Nat. Chem. Biol. 1, 252-262.

    Article  CAS  PubMed  Google Scholar 

  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. 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. 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.

    Article  CAS  PubMed  Google Scholar 

  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.

    Article  CAS  PubMed  Google Scholar 

  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.

    Article  CAS  PubMed  Google Scholar 

  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.

    Article  CAS  PubMed  Google Scholar 

  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.

    Article  CAS  PubMed  Google Scholar 

  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. 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.

    Article  CAS  PubMed  Google Scholar 

  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.

    Article  PubMed  Google Scholar 

  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.

    Article  CAS  PubMed  Google Scholar 

  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.

    Article  CAS  PubMed  Google Scholar 

  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.

    Article  CAS  PubMed  Google Scholar 

  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.

    Article  CAS  PubMed  Google Scholar 

  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.

    Article  PubMed  Google Scholar 

  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. 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.

    Article  CAS  PubMed  Google Scholar 

  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.

    Article  CAS  PubMed  Google Scholar 

  27. Weisstein, Eric W. “Moving Average.” From MathWorld - A Wolfram Web Resource. http://mathworld.wolfram.com/MovingAverage.html

  28. Golub, G. H., Van Loan, C.F. (1996) Matrix Computations. 3rd edition, The Johns Hopkins University Press: USA.

    Google Scholar 

  29. Savitzky, A, and Marcel J.E. Golay (1964) Smoothing and differentiation of data by simplified least squares procedures. Anal. Chem. 36, 1627-1639.

    Article  CAS  Google Scholar 

  30. De Boor, C. (1978) A Practical Guide to Splines, 1st ed., pp. 114-115, Springer Verlag, NY.

    Google Scholar 

  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.

    Article  CAS  PubMed  Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Article  CAS  PubMed  Google Scholar 

  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.

    Article  CAS  PubMed  Google Scholar 

  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.

    Article  PubMed  Google Scholar 

  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.

    Article  CAS  PubMed  Google Scholar 

Download references

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.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Wen-Lian Hsu .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

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

About this protocol

Cite this protocol

Hsu, WL., Sung, TY. (2010). Automated Generic Analysis Tools for Protein Quantitation Using Stable Isotope Labeling. In: Hubbard, S., Jones, A. (eds) Proteome Bioinformatics. Methods in Molecular Biology™, vol 604. Humana Press. https://doi.org/10.1007/978-1-60761-444-9_17

Download citation

  • DOI: https://doi.org/10.1007/978-1-60761-444-9_17

  • Published:

  • Publisher Name: Humana Press

  • Print ISBN: 978-1-60761-443-2

  • Online ISBN: 978-1-60761-444-9

  • eBook Packages: Springer Protocols

Publish with us

Policies and ethics