Clinical Proteomics pp 209-230

Part of the Methods in Molecular Biology™ book series (MIMB, volume 428)

Label-Free LC-MS Method for the Identification of Biomarkers

  • Richard E. Higgs
  • Michael D. Knierman
  • Valentina Gelfanova
  • Jon P. Butler
  • John E. Hale

Summary

Pharmaceutical companies and regulatory agencies are pursuing biomarkers as a means to increase the productivity of drug development. Quantifying differential levels of proteins from complex biological samples like plasma or cerebrospinal fluid is one specific approach being used to identify markers of drug action, efficacy, toxicity, etc. Academic investigators are also interested in markers that are diagnostic or prognostic of disease states. We report a comprehensive, fully automated, and label-free approach to relative protein quantification including: sample preparation, proteolytic protein digestion, LC-MS/MS data acquisition, de-noising, mass and charge state estimation, chromatographic alignment, and peptide quantification via integration of extracted ion chromatograms. Additionally, we describe methods for transformation and normalization of the quantitative peptide levels in multiplexed measurements to improve precision for statistical analysis. Lastly, we outline how the described methods can be used to design and power biomarker discovery studies.

Key Words

relative quantification label-free quantification biomarkers proteomics LC-MS/MS 

References

  1. 1.
    FDA Critical Path Initiative 2006 (http://www.fda.gov/oc/initiatives/criticalpath).
  2. 2.
    NIH Road Map for Medical Research 2006 (http://www.nihroadmap.nih.gov/index.asp).
  3. 3.
    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.PubMedCrossRefGoogle Scholar
  4. 4.
    Aggarwal, K., Choe, L.H., and Lee, K.H. 2006. Shotgun proteomics using the iTRAQ isobaric tags. Brief. Funct. Genomic. Proteomic. 5: 112–120.PubMedCrossRefGoogle Scholar
  5. 5.
    Petricoin, E.F., Ardekani, A.M., Hitt, B.A., Levine, P.J., Fusaro, V.A., Steinberg, S.M., Mills, G.B., Simone, C., Fishman, D.A., Kohn, E.C. et al 2002. Use of proteomic patterns in serum to identify ovarian cancer. Lancet 359: 572–577.PubMedCrossRefGoogle Scholar
  6. 6.
    Radulovic, D., Jelveh, S., Ryu, S., Hamilton, T.G., Foss, E., Mao, Y., and Emili, A. 2004. Informatics platform for global proteomic profiling and biomarker discovery using liquid chromatography-tandem mass spectrometry. Mol Cell Proteomics 3: 984–997.PubMedCrossRefGoogle Scholar
  7. 7.
    Wiener, M.C., Sachs, J.R., Deyanova, E.G., and Yates, N.A. 2004. Differential mass spectrometry: a label-free LC-MS method for finding significant differences in complex peptide and protein mixtures. Anal. Chem. 76: 6085–6096.PubMedCrossRefGoogle Scholar
  8. 8.
    Gao, J., Opiteck, G.J., Friedrichs, M.S., Dongre, A.R., and Hefta, S.A. 2003. Changes in the protein expression of yeast as a function of carbon source. J. Proteome. Res. 2: 643–649.PubMedCrossRefGoogle Scholar
  9. 9.
    Colinge, J., Chiappe, D., Lagache, S., Moniatte, M., and Bougueleret, L. 2005. Differential Proteomics via probabilistic peptide identification scores. Anal. Chem. 77: 596–606.PubMedCrossRefGoogle Scholar
  10. 10.
    Higgs, R.E., Knierman, M.D., Gelfanova, V., Butler, J.P., and Hale, J.E. 2005. Comprehensive label-free method for the relative quantification of proteins from biological samples. J. Proteome. Res. 4: 1442–1450.PubMedCrossRefGoogle Scholar
  11. 11.
    Higgs, R.E., Knierman, M.D., Freeman, A.B., Gelbert, L.M., Patil, S.T., and Hale, J.E. 2007. Estimating the statistical significance of peptide identifications from shotgun proteomics experiments. J. Proteome. Res. 6: 1758–1767.PubMedCrossRefGoogle Scholar
  12. 12.
    Patil, S.T., Higgs, R.E., Brandt, J.E., Knierman, M.D., Gelfanova, V., Butler, J.P., Downing, A.M., Dorocke, J., Dean, R.A., Potter, W.Z. et al. 2007. Identifying pharmacodynamic protein markers of centrally active drugs in humans: a pilot study in a novel clinical model. J. Proteome. Res. 6: 955–966.PubMedCrossRefGoogle Scholar
  13. 13.
    Anderson, L., and Hunter, C.L. 2006. Quantitative mass spectrometric multiple reaction monitoring assays for major plasma proteins. Mol Cell Proteomics 5: 573–588.PubMedGoogle Scholar
  14. 14.
    Anderson, N.L., and Anderson, N.G. 2002. The human plasma proteome: history, character, and diagnostic prospects. Mol Cell Proteomics 1: 845–867.PubMedCrossRefGoogle Scholar
  15. 15.
    Gutman, S., and Kessler, L.G. 2006. The US Food and Drug Administration perspective on cancer biomarker development. Nat. Rev. Cancer 6: 565–571.PubMedCrossRefGoogle Scholar
  16. 16.
    Rifai, N., Gillette, M.A., and Carr, S.A. 2006. Protein biomarker discovery and validation: the long and uncertain path to clinical utility. Nat. Biotechnol. 24: 971–983.PubMedCrossRefGoogle Scholar
  17. 17.
    Hale, J.E., Butler, J.P., Gelfanova, V., You, J.S., and Knierman, M.D. 2004. A simplified procedure for the reduction and alkylation of cysteine residues in proteins prior to proteolytic digestion and mass spectral analysis. Anal. Biochem. 333: 174–181.PubMedCrossRefGoogle Scholar
  18. 18.
    Proakis, J.G., and Manolakis, D.G. 1992. Digital Signal Processing – Principles, Algorithms and Applications. Prentice Hall, New York, NY.Google Scholar
  19. 19.
    Eng, J.K., Mccormack, A.L., and Yates, J.R. 1994. An approach to correlate tandem mass spectral data of peptides with amino acid sequences in a protein database. Journal of the American Society for Mass Spectrometry 5: 976–989.CrossRefGoogle Scholar
  20. 20.
    Craig, R., and Beavis, R.C. 2003. A method for reducing the time required to match protein sequences with tandem mass spectra. Rapid Commun. Mass Spectrom. 17: 2310–2316.PubMedCrossRefGoogle Scholar
  21. 21.
    Ulintz, P.J., Zhu, J., Qin, Z.S., and Andrews, P.C. 2006. Improved classification of mass spectrometry database search results using newer machine learning approaches. Mol Cell Proteomics 5: 497–509.PubMedGoogle Scholar
  22. 22.
    Benjamini, Y., and Hochberg, Y. 1995. Controlling the false discovery rate - a practical and powerful approach to multiple testing. Journal of the Royal Statistical Society Series B-Methodological 57: 289–300.Google Scholar
  23. 23.
    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.PubMedCrossRefGoogle Scholar
  24. 24.
    Cleveland, W.S., Grosse, E., and Shyu, W.M. 1992. Local regression models. In Statistical Models in S. J.M. Chambers and T.J. Hastie, eds. Wadsworth & Brooks/Cole, Pacific Grove, CA.Google Scholar
  25. 25.
    Boelens, H.F., Dijkstra, R.J., Eilers, P.H., Fitzpatrick, F., and Westerhuis, J.A. 2004. New background correction method for liquid chromatography with diode array detection, infrared spectroscopic detection and Raman spectroscopic detection. J. Chromatogr. A 1057: 21–30.PubMedCrossRefGoogle Scholar
  26. 26.
    Bolstad, B.M., Irizarry, R.A., Astrand, M., and Speed, T.P. 2003. A comparison of normalization methods for high density oligonucleotide array data based on variance and bias. Bioinformatics 19: 185–193.PubMedCrossRefGoogle Scholar
  27. 27.
    Miller, R.G., Jr. 1991. Simultaneous Statistical Inference. Springer-Verlag, New York.Google Scholar
  28. 28.
    Butler, K.W., Deslauriers, R., Geoffrion, Y., Storey, J.M., Storey, K.B., Smith, I.C., and Somorjai, R.L. 1985. 31P nuclear magnetic resonance studies of crayfish (Orconectes virilis). The use of inversion spin transfer to monitor enzyme kinetics in vivo. Eur. J. Biochem. 149: 79–83.PubMedCrossRefGoogle Scholar
  29. 29.
    Efron, B. 2004. Large-scale simultaneous hypothesis testing: the choice of a null distribution. J. Am. Stat. Soc. 99: 96–104.Google Scholar
  30. 30.
    Pounds, S., and Cheng, C. 2005. Sample size determination for the false discovery rate. Bioinformatics 21: 4263–4271.PubMedCrossRefGoogle Scholar
  31. 31.
    Hu, J., Zou, F., and Wright, F.A. 2005. Practical FDR-based sample size calculations in microarray experiments. Bioinformatics 21: 3264–3272.PubMedCrossRefGoogle Scholar
  32. 32.
    Jung, S.H. 2005. Sample size for FDR-control in microarray data analysis. Bioinformatics 21: 3097–3104.PubMedCrossRefGoogle Scholar
  33. 33.
    Li, S.S., Bigler, J., Lampe, J.W., Potter, J.D., and Feng, Z. 2005. FDR-controlling testing procedures and sample size determination for microarrays. Stat. Med. 24: 2267–2280.PubMedCrossRefGoogle Scholar
  34. 34.
    Bemis, K.G. 2005. Statistical Issues with Mass Spectrometry Proteomics for Biomarker Discovery. In International Workshop on Statistical Methodology in Clinical and Nonclinical R&DDIA conference, Nice, France.Google Scholar

Copyright information

© Humana Press, a part of Springer Science+Business Media, LLC 2008

Authors and Affiliations

  • Richard E. Higgs
    • 1
  • Michael D. Knierman
    • 2
  • Valentina Gelfanova
    • 3
  • Jon P. Butler
    • 4
  • John E. Hale
    • 5
  1. 1.School of Life SciencesUniversity of Hertfordshire College LaneHatfieldHertsUK
  2. 2.School of Life SciencesUniversity of Hertfordshire College LaneHatfieldHertsUK
  3. 3.School of Life SciencesUniversity of Hertfordshire College LaneHatfieldHertsUK
  4. 4.School of Life SciencesUniversity of Hertfordshire College LaneHatfieldHertsUK
  5. 5.School of Life SciencesUniversity of Hertfordshire College LaneHatfieldHertsUK

Personalised recommendations