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.

  • Letter
  • Published:

Comprehensive mass-spectrometry-based proteome quantification of haploid versus diploid yeast

Nature volume 455, pages 1251–1254 (2008)Cite this article

Abstract

Mass spectrometry is a powerful technology for the analysis of large numbers of endogenous proteins1,2. However, the analytical challenges associated with comprehensive identification and relative quantification of cellular proteomes have so far appeared to be insurmountable3. Here, using advances in computational proteomics, instrument performance and sample preparation strategies, we compare protein levels of essentially all endogenous proteins in haploid yeast cells to their diploid counterparts. Our analysis spans more than four orders of magnitude in protein abundance with no discrimination against membrane or low level regulatory proteins. Stable-isotope labelling by amino acids in cell culture (SILAC) quantification4,5 was very accurate across the proteome, as demonstrated by one-to-one ratios of most yeast proteins. Key members of the pheromone pathway were specific to haploid yeast but others were unaltered, suggesting an efficient control mechanism of the mating response. Several retrotransposon-associated proteins were specific to haploid yeast. Gene ontology analysis pinpointed a significant change for cell wall components in agreement with geometrical considerations: diploid cells have twice the volume but not twice the surface area of haploid cells. Transcriptome levels agreed poorly with proteome changes overall. However, after filtering out low confidence microarray measurements, messenger RNA changes and SILAC ratios correlated very well for pheromone pathway components. Systems-wide, precise quantification directly at the protein level opens up new perspectives in post-genomics and systems biology.

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

Access options

Buy this article

  • Purchase on SpringerLink
  • Instant access to full article PDF
Buy now

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

Figure 1: Three strategies for in-depth quantification of the yeast proteome by SILAC labelling and high-resolution mass spectrometry.
Figure 2: Proteome coverage.
Figure 3: Quantitative differences between the haploid and diploid yeast proteome.
Figure 4: Proteome and transcriptome changes of haploid versus diploid yeast.

Similar content being viewed by others

References

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

    Article  ADS  CAS  Google Scholar 

  2. Cravatt, B. F., Simon, G. M. & Yates, J. R. The biological impact of mass-spectrometry-based proteomics. Nature 450, 991–1000 (2007)

    Article  ADS  CAS  Google Scholar 

  3. Malmstrom, J., Lee, H. & Aebersold, R. Advances in proteomic workflows for systems biology. Curr. Opin. Biotechnol. 18, 378–384 (2007)

    Article  Google Scholar 

  4. Ong, S. E. et al. 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 (2002)

    Article  CAS  Google Scholar 

  5. Mann, M. Functional and quantitative proteomics using SILAC. Nature Rev. Mol. Cell Biol. 7, 952–958 (2006)

    Article  CAS  Google Scholar 

  6. Goffeau, A. et al. Life with 6000 genes. Science 274, 563–567 (1996)

    Article  Google Scholar 

  7. Shevchenko, A. et al. Linking genome and proteome by mass spectrometry: large-scale identification of yeast proteins from two dimensional gels. Proc. Natl Acad. Sci. USA 93, 14440–14445 (1996)

    Article  ADS  CAS  Google Scholar 

  8. Figeys, D. et al. Protein identification by solid phase microextraction-capillary zone electrophoresis-microelectrospray-tandem mass spectrometry. Nature Biotechnol. 14, 1579–1583 (1996)

    Article  CAS  Google Scholar 

  9. Washburn, M. P., Wolters, D. & Yates, J. R. Large-scale analysis of the yeast proteome by multidimensional protein identification technology. Nature Biotechnol. 19, 242–247 (2001)

    Article  CAS  Google Scholar 

  10. Peng, J. et al. Evaluation of multidimensional chromatography coupled with tandem mass spectrometry (LC/LC–MS/MS) for large-scale protein analysis: the yeast proteome. J. Proteome Res. 2, 43–50 (2003)

    Article  CAS  Google Scholar 

  11. King, N. L. et al. Analysis of the Saccharomyces cerevisiae proteome with PeptideAtlas. Genome Biol. 7, R106 (2006)

    Article  Google Scholar 

  12. de Godoy, L. M. et al. Status of complete proteome analysis by mass spectrometry: SILAC labeled yeast as a model system. Genome Biol. 7, R50 (2006)

    Article  Google Scholar 

  13. Gruhler, A. et al. Quantitative phosphoproteomics applied to the yeast pheromone signaling pathway. Mol. Cell. Proteomics 4, 310–327 (2005)

    Article  CAS  Google Scholar 

  14. Ghaemmaghami, S. et al. Global analysis of protein expression in yeast. Nature 425, 737–741 (2003)

    Article  ADS  CAS  Google Scholar 

  15. Huh, W. K. et al. Global analysis of protein localization in budding yeast. Nature 425, 686–691 (2003)

    Article  ADS  CAS  Google Scholar 

  16. Dohlman, H. G. & Slessareva, J. E. Pheromone signaling pathways in yeast. Sci. STKE 2006, cm6 (2006)

    Article  Google Scholar 

  17. Schwartz, M. A. & Madhani, H. D. Principles of MAP kinase signaling specificity in Saccharomyces cerevisiae . Annu. Rev. Genet. 38, 725–748 (2004)

    Article  CAS  Google Scholar 

  18. Blanc, V. M. & Adams, J. Evolution in Saccharomyces cerevisiae: identification of mutations increasing fitness in laboratory populations. Genetics 165, 975–983 (2003)

    CAS  PubMed  PubMed Central  Google Scholar 

  19. Company, M., Errede, B. & Ty, A. 1 cell-type-specific regulatory sequence is a recognition element for a constitutive binding factor. Mol. Cell. Biol. 8, 5299–5309 (1988)

    Article  CAS  Google Scholar 

  20. Ke, N., Irwin, P. A. & Voytas, D. F. The pheromone response pathway activates transcription of Ty5 retrotransposons located within silent chromatin of Saccharomyces cerevisiae . EMBO J. 16, 6272–6280 (1997)

    Article  CAS  Google Scholar 

  21. Tyers, M. & Mann, M. From genomics to proteomics. Nature 422, 193–197 (2003)

    Article  ADS  CAS  Google Scholar 

  22. Galitski, T. et al. Ploidy regulation of gene expression. Science 285, 251–254 (1999)

    Article  CAS  Google Scholar 

  23. Cox, J. & Mann, M. Is proteomics the new genomics? Cell 130, 395–398 (2007)

    Article  CAS  Google Scholar 

  24. Olsen, J. V. et al. Parts per million mass accuracy on an orbitrap mass spectrometer via lock mass injection into a C-trap. Mol. Cell. Proteomics 4, 2010–2021 (2005)

    Article  CAS  Google Scholar 

  25. Perkins, D. N. et al. Probability-based protein identification by searching sequence databases using mass spectrometry data. Electrophoresis 20, 3551–3567 (1999)

    Article  CAS  Google Scholar 

  26. Rappsilber, J., Ishihama, Y. & Mann, M. Stop and go extraction tips for matrix-assisted laser desorption/ionization, nanoelectrospray, and LC/MS sample pretreatment in proteomics. Anal. Chem. 75, 663–670 (2003)

    Article  CAS  Google Scholar 

  27. Shevchenko, A. et al. Mass spectrometric sequencing of proteins silver-stained polyacrylamide gels. Anal. Chem. 68, 850–858 (1996)

    Article  CAS  Google Scholar 

  28. Mann, M. & Wilm, M. Error-tolerant identification of peptides in sequence databases by peptide sequence tags. Anal. Chem. 66, 4390–4399 (1994)

    Article  CAS  Google Scholar 

Download references

Acknowledgements

G. de Souza measured part of the yeast proteome; C. Kumar contributed to bioinformatic analysis, G. Stoehr to proteome analysis. Z. Storchova provided the pGAL-HO plasmid. L.M.F.d.G. thanks D. Bertozzi for support and discussions. The Max-Planck Society and the DC-Thera and Interaction Proteome 6th framework projects of the European Union provided funding; T.C.W. is supported by the Human Frontier Science Program and M.L.N. by the European Molecular Biology Organization (EMBO).

Author information

Author notes

  1. Lyris M. F. de Godoy, Jesper V. Olsen, Jürgen Cox and Michael L. Nielsen: These authors contributed equally to this work.

Authors and Affiliations

  1. Proteomics and Signal Transduction, and,,

    Lyris M. F. de Godoy, Jesper V. Olsen, Jürgen Cox, Michael L. Nielsen, Nina C. Hubner & Matthias Mann

  2. Organelle Architecture and Dynamics, Max-Planck-Institute for Biochemistry, Am Klopferspitz 18, D-82152 Martinsried, Germany ,

    Florian Fröhlich & Tobias C. Walther

Authors
  1. Lyris M. F. de Godoy
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jesper V. Olsen
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jürgen Cox
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Michael L. Nielsen
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Nina C. Hubner
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Florian Fröhlich
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Tobias C. Walther
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Matthias Mann
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to Tobias C. Walther or Matthias Mann.

Supplementary information

Supplementary Information

This file contains Supplementary Materials, Supplementary References, Supplementary Figures 1-10 with Legends, Supplementary Tables 5, 9, 10, and a description of columns in Excel sheets for proteome identification and quantitation for Supplementary Tables 1,2,3,4, and 6 (protein lists), Supplementary Table 7 (peptides list) and Supplementary Table 8 (KEGG and GO analysis). (PDF 11070 kb)

Supplementary Table 1

Supplementary Table 1: List of identified proteins for strategy a (trypsin experiment). (XLS 4904 kb)

Supplementary Table 2

Supplementary Table 2: List of identified proteins for strategy b (IEF-Full mass range MS - Lys-C experiment). (XLS 5339 kb)

Supplementary Table 3

Supplementary Table 3: List of identified proteins for strategy c (IEF-Narrow MS ranges - Lys-C experiments). (XLS 5048 kb)

Supplementary Table 4

Supplementary Table 4: List of identified proteins for strategies abc (trypsin and Lys-C experiment). (XLS 5924 kb)

Supplementary Table 6

Supplementary Table 6: Quantitative data for Lys-C experiment (proteins). (XLS 5435 kb)

Supplementary Table 7

Supplementary Table 7: Quantitative data for Lys-C experiment (peptides). (XLS 12183 kb)

Supplementary Table 8

Supplementary Table 8: Gene Ontology and Kegg pathway analysis of protein classes. (XLS 39 kb)

Supplementary Table 11

Supplementary Table 11: Gene Ontology and Kegg pathway analysis of protein classes. (XLS 35 kb)

PowerPoint slides

Rights and permissions

Reprints and permissions

About this article

Cite this article

de Godoy, L., Olsen, J., Cox, J. et al. Comprehensive mass-spectrometry-based proteome quantification of haploid versus diploid yeast. Nature 455, 1251–1254 (2008). https://doi.org/10.1038/nature07341

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/nature07341

Search

Advanced 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