1. Proteomics and Signal Transduction, and,
2. Organelle Architecture and Dynamics, Max-Planck-Institute for Biochemistry, Am Klopferspitz 18, D-82152 Martinsried, Germany
3. These authors contributed equally to this work.

Correspondence to: Tobias C. Walther²Matthias Mann¹ Correspondence and requests for materials should be addressed to T.C.W. (Email: twalther@biochem.mpg.de) or M.M. (Email: mmann@biochem.mpg.de).

Abstract

Mass spectrometry is a powerful technology for the analysis of large numbers of endogenous proteins^{1, 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) quantification^{4, 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.

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