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

Author

Listed:
  • Lyris M. F. de Godoy

    (Proteomics and Signal Transduction, and,)

  • Jesper V. Olsen

    (Proteomics and Signal Transduction, and,)

  • Jürgen Cox

    (Proteomics and Signal Transduction, and,)

  • Michael L. Nielsen

    (Proteomics and Signal Transduction, and,)

  • Nina C. Hubner

    (Proteomics and Signal Transduction, and,)

  • Florian Fröhlich

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

  • Tobias C. Walther

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

  • Matthias Mann

    (Proteomics and Signal Transduction, and,)

Abstract

The yeast proteome quantified A combination of high-resolution mass spectrometry, 'SILAC' labelling and computational proteomics has been used to achieve an important goal in proteomics: the complete identification and quantification of a proteome. The analysis reveals a proteome made up of 4,399 individual endogenous proteins, essentially the complete proteome in terms of proteins expressed in normally growing yeast cells. The levels of these proteins in haploid cells were compared to the levels in diploid cells. Among other differences, cell wall components are significantly down-regulated in diploids — in line with the fact that diploid cells are twice as large as haploid cells but do not have twice the surface area.

Suggested Citation

  • Lyris M. F. de Godoy & Jesper V. Olsen & Jürgen Cox & Michael L. Nielsen & Nina C. Hubner & Florian Fröhlich & Tobias C. Walther & Matthias Mann, 2008. "Comprehensive mass-spectrometry-based proteome quantification of haploid versus diploid yeast," Nature, Nature, vol. 455(7217), pages 1251-1254, October.
    • Handle: RePEc:nat:nature:v:455:y:2008:i:7217:d:10.1038_nature07341
    DOI: 10.1038/nature07341
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    Cited by:

    1. Adam A Margolin & Shao-En Ong & Monica Schenone & Robert Gould & Stuart L Schreiber & Steven A Carr & Todd R Golub, 2009. "Empirical Bayes Analysis of Quantitative Proteomics Experiments," PLOS ONE, Public Library of Science, vol. 4(10), pages 1-15, October.
    2. Alexander M. Franks & Gábor Csárdi & D. Allan Drummond & Edoardo M. Airoldi, 2015. "Estimating a Structured Covariance Matrix From Multilab Measurements in High-Throughput Biology," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 110(509), pages 27-44, March.

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