Skip to main content

Protein profiling of sickle cell versus control RBC core membrane skeletons by ICAT technology and tandem mass spectrometry

Abstract

A proteomic approach using a cleavable ICAT reagent and nano-LC ESI tandem mass spectrometry was used to perform protein profiling of core RBC membrane skeleton proteins between sickle cell patients (SS) and controls (AA), and determine the efficacy of this technology. The data was validated through Peptide/Protein Prophet and protein ratios were calculated through ASAPratio. Through an ANOVA test, it was determined that there is no significant difference in the mean ratios from control populations (AA1/AA2) and sickle cell versus control populations (AA/SS). The mean ratios were not significantly different from 1.0 in either comparison for the core skeleton proteins (α spectrin, β spectrin, band 4.1 and actin). On the natural-log scale, the variation (standard deviation) of the method was determined to be 14.1% and the variation contributed by the samples was 13.8% which together give a total variation of 19.7% in the ratios.

Abbreviations

ASAPratio:

automated statistical analysis of protein abundance ratios+

cICAT:

cleavable isotope coded affinity tag

CID:

collision induced dissociation

2D DIGE:

two dimension differential gel electrophoresis

ESI:

electrospray ionization source

ICAT:

isotope coded affinity tag

LC:

liquid chromatography

RBC:

red blood cell

SD:

standard deviation

SILAC:

stable isotope labeled amino acids in cell culture

WBCs:

white blood cells

References

  1. 1.

    Kakhniashvili, D.G., Bulla, L.A. Jr and Goodman, S.R. The human erythrocyte proteome: analysis by ion trap mass spectrometry. Mol. Cell. Proteomics 3 (2004) 501–509.

    PubMed  CAS  Article  Google Scholar 

  2. 2.

    Aebersold, R. and Mann, M. Mass spectrometry-based proteomics. Nature 422 (2003) 198–207.

    PubMed  CAS  Article  Google Scholar 

  3. 3.

    Shao-En, O., Blagoev, B., Kratchmarova, I., Kristensen, D. B., Hanno Steen, Pandey, A. and Mann M. Stable isotope labeling by amino acids in cell culture, SILAC, as a simple and accurate approach to expression proteomics. Mol. Cell. Proteomics 1 (2002) 376–386.

    Article  Google Scholar 

  4. 4.

    Patterson, S.D. and Aebersold, R.H. Proteomics: the first decade and beyond. Nat. Genet. 33 (2003) 311–323.

    PubMed  CAS  Article  Google Scholar 

  5. 5.

    Yi, E.C., Li, X.J., Cooke, K., Lee, H., Raught, B., Page, A., Aneliunas, V., Hieter, P., Goodlett, D.R., and Aebersold R. Increased quantitative proteome coverage with (13)C/(12)C-based, acid-cleavable isotope-coded affinity tag reagent and modified data acquisition scheme. Proteomics 5 (2005) 380–387.

    PubMed  CAS  Article  Google Scholar 

  6. 6.

    Li, J., Steen, H. and Gygi, S.P. Protein profiling with cleavable isotope-coded affinity tag (cICAT) reagents the yeast salinity stress response. Mol. Cell. Proteomics 2 (2003) 1198–1204.

    PubMed  CAS  Article  Google Scholar 

  7. 7.

    Molloy, M.P., Donohoe, S., Brzezinski, E.E., Kilby, G.W., Stevenson, T.I., Baker, J.D., Goodlett, R.D. and Gage, D.A. Large-scale evaluation of quantitative reproducibility and proteome coverage using acid cleavable isotope coded affinity tag mass spectrometry for proteomic profiling. Proteomics 5 (2005) 1204–1208.

    PubMed  Article  Google Scholar 

  8. 8.

    Eng, J.K., McCormack, A. L., and Yates, J. R. 3rd. An approach to correlate tandem mass spectral data of peptides with amino acid sequences in a protein database. J. Am. Soc. Mass Spectrom. 5 (1994) 976–998.

    CAS  Article  Google Scholar 

  9. 9.

    Keller, A., Nesvihzkii, A., Li, X., Pedroli, P., Eng, J., Hubbley, R., Mallick, P., Zhang, N., Shannon, P., Deutsch, E., Watts, J. and Aebersold R. Computational tools for high throughput proteomics. ISB Symposium, 2003.

  10. 10.

    Keller, A., Nesvizhskii, A.I., Kolker E. and Aebersold R. Empirical statistical model to estimate the accuracy of peptide identifications made by MS/MS and database search. Anal. Chem. 74 (2002) 5383–5392.

    PubMed  CAS  Article  Google Scholar 

  11. 11.

    Li, X.J., Zhang, H., Ranish, J.A. and Aebersold R. Automated statistical analysis of protein abundance ratios from data generated by stable-isotope dilution and tandem mass spectrometry. Anal. Chem. 75 (2003) 6648–6657.

    PubMed  CAS  Article  Google Scholar 

  12. 12.

    Nesvizhskii A.I., Keller A., Kolker E. and Aebersold R. A Statistical model for identifying proteins by tandem mass spectrometry. Anal. Chem. 75 (2003) 4646–4658.

    PubMed  CAS  Article  Google Scholar 

  13. 13.

    Searle, S.R., Casella, G. and McCulloch Variance Components, John Wiley, New York, 1992, 168–231.

    Google Scholar 

  14. 14.

    Pinheiro, J.C. and Bates, D. M. Mixed-Effects Models in S and S-PLUS, Springer Verlag, New York, 2000, 57–96.

    Google Scholar 

  15. 15.

    Lehman, E.L. Elements of Large Sample Theory, Springer Verlag, New York, 1998, 277–362.

    Google Scholar 

  16. 16.

    Gygi, S.P., Rist, B., Gerber, S.A., Turecek, F., Gelb, M. and Aebersold R. Quantitative analysis of complex protein mixtures using isotope-coded affinity tags. Nat. Biotechnol. 17 (1999) 994–999.

    PubMed  CAS  Article  Google Scholar 

  17. 17.

    Zhang, R., Sioma, C.S., Wang, S. and Regnier, F.E. Fractionation of isotopically labeled peptides in quantitative proteomics. Anal. Chem. 73 (2001) 5142–5149.

    PubMed  CAS  Article  Google Scholar 

  18. 18.

    Oda, Y., Owa, T., Sato, T. and Boucher, B. Quantitative chemical proteomics for identifying candidate drug targets. Anal. Chem. 75 (2003) 2159–2165.

    PubMed  CAS  Article  Google Scholar 

  19. 19.

    Hansen, K.C., Schmitt-Ulms, G., Chalkley, R. J., Hirsch, J., Baldwin, M.A. and Burlingame, A.L. Mass spectrometric analysis of protein mixtures at low levels using cleavable 13C-isotope-coded affinity tag and multidimensional chromatography. Mol. Cell Proteomics 2 (2003) 299–314.

    PubMed  CAS  Google Scholar 

  20. 20.

    Gygi, S.P., Corthals, G.L., Zhang, Y., Rochon, Y. and Aebersold, R. Evaluation of two-dimensional gel electrophoresis-based proteome analysis technology. Proc. Natl. Acad. Sci. USA 97 (2000) 9390–9395.

    PubMed  CAS  Article  Google Scholar 

  21. 21.

    Wilkins, M.R., Gasteiger, E., Sanchez, J.C., Bairoch, A. and Hochstrasser, D.F. Electrophoresis 19 (1998) 1501–1505.

    PubMed  CAS  Article  Google Scholar 

  22. 22.

    Santoni, V., Molloy, M. and Rabilloud, T. Membrane proteins and proteomics: Un amour impossible? Electrophoresis 21 (2000) 1054–1070.

    PubMed  CAS  Article  Google Scholar 

  23. 23.

    Molloy, M.P., Phadke, N.D., Chen, H., Tyldesley, R. Garfin D.E., Maddock, J.R. and Andrews P.C. Profiling the alkaline membrane proteome of Caulobacter crescentus with two-dimensional electrophoresis and mass spectrometry. Proteomics 2 (2002) 899–910.

    PubMed  CAS  Article  Google Scholar 

  24. 24.

    Kakhniashvili, D.G., Griko, N.B., Bulla, L.A. Jr and Goodman, S.R. The proteomics of sickle cell disease: profiling of erythrocyte membrane proteins by 2D-DIGE and tandem mass spectrometry. Exp. Biol. Med. 230 (2005) 787–792.

    CAS  Google Scholar 

Download references

Author information

Affiliations

Authors

Corresponding author

Correspondence to Steven R. Goodman.

Additional information

Invited paper

Rights and permissions

Reprints and Permissions

About this article

Cite this article

Chou, J., Choudhary, P.K. & Goodman, S.R. Protein profiling of sickle cell versus control RBC core membrane skeletons by ICAT technology and tandem mass spectrometry. Cell Mol Biol Lett 11, 326–337 (2006). https://doi.org/10.2478/s11658-006-0026-2

Download citation

Key words

  • Proteomics
  • Cleavable ICAT
  • Ion trap mass spectrometry
  • RBC membrane skeleton
  • Sickle cell