Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Article
  • Published:

Mass-spectrometric identification and relative quantification of N-linked cell surface glycoproteins

A Corrigendum to this article was published on 01 September 2009

This article has been updated

Abstract

Although the classification of cell types often relies on the identification of cell surface proteins as differentiation markers, flow cytometry requires suitable antibodies and currently permits detection of only up to a dozen differentiation markers in a single measurement. We use multiplexed mass-spectrometric identification of several hundred N-linked glycosylation sites specifically from cell surface–exposed glycoproteins to phenotype cells without antibodies in an unbiased fashion and without a priori knowledge. We apply our cell surface–capturing (CSC) technology, which covalently labels extracellular glycan moieties on live cells, to the detection and relative quantitative comparison of the cell surface N-glycoproteomes of T and B cells, as well as to monitor changes in the abundance of cell surface N-glycoprotein markers during T-cell activation and the controlled differentiation of embryonic stem cells into the neural lineage. A snapshot view of the cell surface N-glycoproteins will enable detection of panels of N-glycoproteins as potential differentiation markers that are currently not accessible by other means.

This is a preview of subscription content, access via your institution

Access options

Buy this article

Prices may be subject to local taxes which are calculated during checkout

Figure 1: CSC uses a multistep tandem affinity labeling strategy to confer the desired specificity for the glycoproteins on the cell surface.
Figure 2: The CSC reaction is selective for glycans on the cell surface.
Figure 3: Specificity of the CSC technology and molecular functions of the proteins identified.
Figure 4: Cell surface glycoproteins differentially expressed on Jurkat T versus Ramos B lymphocytes.
Figure 5: Up- and downregulation of Jurkat T-cell proteins after CD3/CD28 co-stimulation for 24 h.
Figure 6: Up- and downregulation of glycoproteins on the surfaces of ES cells during their controlled differentiation into neural progenitors.

Similar content being viewed by others

Change history

  • 09 September 2009

    In the version of this article initially published, in Methods, p.385, line 5, the concentration of MgCl2, given as 0.5 M, is incorrect. The correct concentration is 0.5 mM MgCl2.The error has been corrected in the HTML and PDF versions of the article.

References

  1. von Heijne, G. The membrane protein universe: what's out there and why bother? J. Intern. Med. 261, 543–557 (2007).

    Article  CAS  Google Scholar 

  2. Wollscheid, B. et al. Lipid raft proteins and their identification in T lymphocytes. Subcell. Biochem. 37, 121–152 (2004).

    Article  CAS  Google Scholar 

  3. Hosen, N. et al. CD96 is a leukemic stem cell-specific marker in human acute myeloid leukemia. Proc. Natl. Acad. Sci. USA 104, 11008–11013 (2007).

    Article  CAS  Google Scholar 

  4. Hopkins, A.L. & Groom, C.R. The druggable genome. Nat. Rev. Drug Discov. 1, 727–730 (2002).

    Article  CAS  Google Scholar 

  5. Stewart, C.C. & Nicholson, J.K.A. Immunophenotyping (Wiley-Liss, New York, 2000).

  6. Zola, H. et al. CD molecules 2006—human cell differentiation molecules. J. Immunol. Methods 319, 1–5 (2007).

    Article  CAS  Google Scholar 

  7. Zola, H. Medical applications of leukocyte surface molecules—the CD molecules. Mol. Med. 12, 312–316 (2006).

    Article  CAS  Google Scholar 

  8. Ahram, M., Litou, Z.I., Fang, R. & Al-Tawallbeh, G. Estimation of membrane proteins in the human proteome. In Silico Biol. (Gedrukt) 6, 379–386 (2006).

    CAS  Google Scholar 

  9. Nicholson, I.C., Ayhan, M., Hoogenraad, N.J. & Zola, H. In silico evaluation of two mass spectrometry-based approaches for the identification of novel human leukocyte cell-surface proteins. J. Leukoc. Biol. 77, 190–198 (2005).

    Article  CAS  Google Scholar 

  10. Craig, F.E. & Foon, K.A. Flow cytometric immunophenotyping for hematologic neoplasms. Blood 111, 3941–3967 (2008).

    Article  CAS  Google Scholar 

  11. Evans, E.J. et al. The T cell surface—how well do we know it? Immunity 19, 213–223 (2003).

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

  13. Bantscheff, M., Schirle, M., Sweetman, G., Rick, J. & Kuster, B. Quantitative mass spectrometry in proteomics: a critical review. Anal. Bioanal. Chem. 389, 1017–1031 (2007).

    Article  CAS  Google Scholar 

  14. Macher, B.A. & Yen, T.Y. Proteins at membrane surfaces—a review of approaches. Mol. Biosyst. 3, 705–713 (2007).

    Article  CAS  Google Scholar 

  15. Josic, D. & Clifton, J.G. Mammalian plasma membrane proteomics. Proteomics 7, 3010–3029 (2007).

    Article  CAS  Google Scholar 

  16. Pasini, E.M. et al. In-depth analysis of the membrane and cytosolic proteome of red blood cells. Blood 108, 791–801 (2006).

    Article  CAS  Google Scholar 

  17. Andersen, J.S. & Mann, M. Organellar proteomics: turning inventories into insights. EMBO Rep. 7, 874–879 (2006).

    Article  CAS  Google Scholar 

  18. Han, D.K., Eng, J.K., Zhou, H. & Aebersold, R. Quantitative profiling of differentiation-induced microsomal proteins using isotope-coded affinity tags and mass spectrometry. Nat. Biotechnol. 19, 946–951 (2001).

    Article  CAS  Google Scholar 

  19. Nunomura, K. et al. Cell surface labeling and mass spectrometry reveal diversity of cell surface markers and signaling molecules expressed in undifferentiated mouse embryonic stem cells. Mol. Cell. Proteomics 4, 1968–1976 (2005).

    Article  CAS  Google Scholar 

  20. Rybak, J.N. et al. In vivo protein biotinylation for identification of organ-specific antigens accessible from the vasculature. Nat. Methods 2, 291–298 (2005).

    Article  CAS  Google Scholar 

  21. Zhang, W., Zhou, G., Zhao, Y., White, M.A. & Zhao, Y. Affinity enrichment of plasma membrane for proteomics analysis. Electrophoresis 24, 2855–2863 (2003).

    Article  CAS  Google Scholar 

  22. Arnott, D. et al. Selective detection of membrane proteins without antibodies: a mass spectrometric version of the Western blot. Mol. Cell. Proteomics 1, 148–156 (2002).

    Article  CAS  Google Scholar 

  23. Lewandrowski, U., Moebius, J., Walter, U. & Sickmann, A. Elucidation of N-glycosylation sites on human platelet proteins: a glycoproteomic approach. Mol. Cell. Proteomics 5, 226–233 (2006).

    Article  CAS  Google Scholar 

  24. Kaji, H., Yamauchi, Y., Takahashi, N. & Isobe, T. Mass spectrometric identification of N-linked glycopeptides using lectin-mediated affinity capture and glycosylation site-specific stable isotope tagging. Nat. Protocols 1, 3019–3027 (2006).

    Article  CAS  Google Scholar 

  25. Wu, C.C., MacCoss, M.J., Howell, K.E. & Yates, J.R. A method for the comprehensive proteomic analysis of membrane proteins. Nat. Biotechnol. 21, 532–538 (2003).

    Article  CAS  Google Scholar 

  26. Elortza, F. et al. Proteomic analysis of glycosylphosphatidylinositol-anchored membrane proteins. Mol. Cell. Proteomics 2, 1261–1270 (2003).

    Article  CAS  Google Scholar 

  27. Watarai, H. et al. Plasma membrane-focused proteomics: dramatic changes in surface expression during the maturation of human dendritic cells. Proteomics 5, 4001–4011 (2005).

    Article  CAS  Google Scholar 

  28. Zhang, H., Li, X.J., Martin, D.B. & Aebersold, R. Identification and quantification of N-linked glycoproteins using hydrazide chemistry, stable isotope labeling and mass spectrometry. Nat. Biotechnol. 21, 660–666 (2003).

    Article  CAS  Google Scholar 

  29. Varki, A., Cummings, R. & Esko, J. Essentials of Glycobiology (Cold Spring Harbor Laboratory Press, Cold Spring Harbor Press, NY, 2002).

  30. Bayer, E.A., Ben-Hur, H. & Wilchek, M. Biocytin hydrazide—a selective label for sialic acids, galactose, and other sugars in glycoconjugates using avidin-biotin technology. Anal. Biochem. 170, 271–281 (1988).

    Article  CAS  Google Scholar 

  31. Gahmberg, C.G. & Andersson, L.C. Selective radioactive labeling of cell surface sialoglycoproteins by periodate-tritiated borohydride. J. Biol. Chem. 252, 5888–5894 (1977).

    CAS  PubMed  Google Scholar 

  32. Yates, J.R., Eng, J.K. & McCormack, A.L. Mining genomes: correlating tandem mass spectra of modified and unmodified peptides to sequences in nucleotide databases. Anal. Chem. 67, 3202–3210 (1995).

    Article  CAS  Google Scholar 

  33. Keller, A., Eng, J., Zhang, N., Li, X.J. & Aebersold, R. A uniform proteomics MS/MS analysis platform utilizing open XML file formats. Mol. Syst. Biol. 1, 2005.0017 (2005).

    Article  Google Scholar 

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

    Article  CAS  Google Scholar 

  35. Shannon, P. et al. Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res. 13, 2498–2504 (2003).

    Article  CAS  Google Scholar 

  36. North, S.J. et al. Glycomic studies of Drosophila melanogaster embryos. Glycoconj. J. 23, 345–354 (2006).

    Article  CAS  Google Scholar 

  37. Thomas, P.D. et al. PANTHER: a browsable database of gene products organized by biological function, using curated protein family and subfamily classification. Nucleic Acids Res. 31, 334–341 (2003).

    Article  CAS  Google Scholar 

  38. Krogh, A., Larsson, B., von Heijne, G. & Sonnhammer, E.L. Predicting transmembrane protein topology with a hidden Markov model: application to complete genomes. J. Mol. Biol. 305, 567–580 (2001).

    Article  CAS  Google Scholar 

  39. Schamel, W.W. & Reth, M. Monomeric and oligomeric complexes of the B cell antigen receptor. Immunity 13, 5–14 (2000).

    Article  CAS  Google Scholar 

  40. Sancho, D., Gomez, M. & Sanchez-Madrid, F. CD69 is an immunoregulatory molecule induced following activation. Trends Immunol. 26, 136–140 (2005).

    Article  CAS  Google Scholar 

  41. Kim, J.E. & White, F.M. Quantitative analysis of phosphotyrosine signaling networks triggered by CD3 and CD28 costimulation in Jurkat cells. J. Immunol. 176, 2833–2843 (2006).

    Article  CAS  Google Scholar 

  42. Schiess, R. et al. Analysis of cell surface proteome changes via label-free, quantitative mass spectrometry. Mol. Cell Proteomics published online, doi:10.1074/mcp.M800172-MCP200 (25 November 2008).

    Google Scholar 

  43. Bibel, M. et al. Differentiation of mouse embryonic stem cells into a defined neuronal lineage. Nat. Neurosci. 7, 1003–1009 (2004).

    Article  CAS  Google Scholar 

  44. Bibel, M., Richter, J., Lacroix, E. & Barde, Y. Generation of a defined and uniform population of CNS progenitors and neurons from mouse embryonic stem cells. Nat. Protocols 2, 1034–1043 (2007).

    Article  CAS  Google Scholar 

  45. Mueller, L.N. et al. SuperHirn—a novel tool for high resolution LC-MS-based peptide/protein profiling. Proteomics 7, 3470–3480 (2007).

    Article  CAS  Google Scholar 

  46. Jarvis, D.L. Developing baculovirus-insect cell expression systems for humanized recombinant glycoprotein production. Virology 310, 1–7 (2003).

    Article  CAS  Google Scholar 

  47. Aumiller, J.J., Hollister, J.R. & Jarvis, D.L. A transgenic insect cell line engineered to produce CMP-sialic acid and sialylated glycoproteins. Glycobiology 13, 497–507 (2003).

    Article  CAS  Google Scholar 

  48. Viswanathan, K. et al. Engineering sialic acid synthetic ability into insect cells: identifying metabolic bottlenecks and devising strategies to overcome them. Biochemistry 42, 15215–15225 (2003).

    Article  CAS  Google Scholar 

  49. Chivian, D. et al. Automated prediction of CASP-5 structures using the Robetta server. Proteins 53 (Suppl 6), 524–533 (2003).

    Article  CAS  Google Scholar 

  50. Martin, D., Wollscheid, B., Aebersold, R. & Watts, J. Methods for characterizing glycoproteins and generating antibodies for same. US Patent 066661-0148 (2008).

Download references

Acknowledgements

This work has been funded in part with federal funds from the National Heart, Lung, and Blood Institute, National Institutes of Health (NIH), under contract no. N01-HV-28179 (to R.A.), from NIH RO1-AI51344-01 (to J.D.W.), from NIH N01-HV-28179-22 (to B.W.) and from National Center of Competence in Research (NCCR) Neural Plasticity and Repair (to B.W.). Thanks to Anne-Claude Gingras and Peter Zandstra for critical reading of the manuscript; to Andreas Hofmann and Thomas Bock for supplying information and graphics support; to Alexander Schmidt for LTQ-FT performance; and to Jimmy Eng, Andy Keller, Alexey Nesvizhskii, David Shteynberg, Luis Mendoza, Josh Tasman, James Eddes, Andreas Panagiotidis and Patrick Pedrioli for bioinformatic support.

Author information

Authors and Affiliations

Authors

Contributions

B.W., R.A. and J.D.W. planned the project. B.W., C.H., D.B.-F., M.B. and R.O. carried out experimental work. R.S. carried out the Drosophila experiments. B.W. and D.B.-F. analyzed the data. B.W., R.A. and J.D.W. wrote the paper. All authors discussed the results and commented on the manuscript.

Corresponding author

Correspondence to Bernd Wollscheid.

Supplementary information

Supplementary Figures

Supplementary Figures 1–4 (PDF 1152 kb)

Supplementary Table 1

CSC identified Jurkat T lymphocyte proteins (XLS 45 kb)

Supplementary Table 2

CSC identified Jurkat T lymphocyte glycoproteins upon initial Neuramidase treatment (XLS 49 kb)

Supplementary Table 3

CSC identified Kc167 cell proteins (XLS 38 kb)

Supplementary Table 4

CSC identified Kc167 cell peptides (XLS 50 kb)

Supplementary Table 5

CSC identified Jurkat T lymphocyte peptides (XLS 70 kb)

Supplementary Table 6

CSC identified mouse splenocyte proteins (XLS 39 kb)

Supplementary Table 7

CSC identified mouse splenocyte peptides (XLS 68 kb)

Supplementary Table 8

CSC identified and quantified human Ramos and Jurkat lymphocyte proteins (XLS 47 kb)

Supplementary Table 9

CSC identified human Ramos and Jurkat lymphocyte peptides (XLS 63 kb)

Supplementary Table 10

CSC identified and quantified proteins from unstimulated and CD3/CD28 stimulated human Jurkat T lymphopcytes (XLS 55 kb)

Supplementary Table 11

CSC identified peptides from unstimulated and CD3/CD28 stimulated human Jurkat T lymphopcytes (XLS 187 kb)

Supplementary Table 12

CSC identified proteins from embryonic stem cell, embroid bodies and neural precursors (XLS 185 kb)

Supplementary Table 13

CSC identified peptides from embryonic stem cell, embroid bodies and neural precursors (XLS 322 kb)

Rights and permissions

Reprints and permissions

About this article

Cite this article

Wollscheid, B., Bausch-Fluck, D., Henderson, C. et al. Mass-spectrometric identification and relative quantification of N-linked cell surface glycoproteins. Nat Biotechnol 27, 378–386 (2009). https://doi.org/10.1038/nbt.1532

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/nbt.1532

This article is cited by

Search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing