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.

Single-cell proteomic analysis of S. cerevisiae reveals the architecture of biological noise

Abstract

A major goal of biology is to provide a quantitative description of cellular behaviour. This task, however, has been hampered by the difficulty in measuring protein abundances and their variation. Here we present a strategy that pairs high-throughput flow cytometry and a library of GFP-tagged yeast strains to monitor rapidly and precisely protein levels at single-cell resolution. Bulk protein abundance measurements of >2,500 proteins in rich and minimal media provide a detailed view of the cellular response to these conditions, and capture many changes not observed by DNA microarray analyses. Our single-cell data argue that noise in protein expression is dominated by the stochastic production/destruction of messenger RNAs. Beyond this global trend, there are dramatic protein-specific differences in noise that are strongly correlated with a protein's mode of transcription and its function. For example, proteins that respond to environmental changes are noisy whereas those involved in protein synthesis are quiet. Thus, these studies reveal a remarkable structure to biological noise and suggest that protein noise levels have been selected to reflect the costs and potential benefits of this variation.

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

Relevant articles

Open Access articles citing this article.

Access options

Buy article

Get time limited or full article access on ReadCube.

$32.00

All prices are NET prices.

Figure 1: Quantitative analyses of protein abundance using flow cytometry.
Figure 2: Single-cell variation, gating and global trends in noise.
Figure 3: Biological structure of protein variation.
Figure 4: Overview of major factors contributing to biological noise.

References

  1. Florens, L. et al. A proteomic view of the Plasmodium falciparum life cycle. Nature 419, 520–526 (2002)

    Article  ADS  CAS  Google Scholar 

  2. Kumar, A. et al. Subcellular localization of the yeast proteome. Genes Dev. 16, 707–719 (2002)

    Article  CAS  Google Scholar 

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

    Article  ADS  CAS  Google Scholar 

  4. Andersen, J. S. et al. Nucleolar proteome dynamics. Nature 433, 77–83 (2005)

    Article  ADS  CAS  Google Scholar 

  5. Kellis, M., Patterson, N., Endrizzi, M., Birren, B. & Lander, E. S. Sequencing and comparison of yeast species to identify genes and regulatory elements. Nature 423, 241–254 (2003)

    Article  ADS  CAS  Google Scholar 

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

    Article  ADS  CAS  Google Scholar 

  7. Wei, J. et al. Global proteome discovery using an online three-dimensional LC–MS/MS. J. Proteome Res. 4, 801–808 (2005)

    Article  CAS  Google Scholar 

  8. Futcher, B., Latter, G. I., Monardo, P., McLaughlin, C. S. & Garrels, J. I. A sampling of the yeast proteome. Mol. Cell. Biol. 19, 7357–7368 (1999)

    Article  CAS  Google Scholar 

  9. Gygi, S. P., Rochon, Y., Franza, B. R. & Aebersold, R. Correlation between protein and mRNA abundance in yeast. Mol. Cell. Biol. 19, 1720–1730 (1999)

    Article  CAS  Google Scholar 

  10. Washburn, M. P. et al. Protein pathway and complex clustering of correlated mRNA and protein expression analyses in Saccharomyces cerevisiae. Proc. Natl Acad. Sci. USA 100, 3107–3112 (2003)

    Article  ADS  CAS  Google Scholar 

  11. Schrödinger, E. What is Life? The Physical Aspect of the Living Cell (Cambridge Univ. Press, Cambridge, UK, 1944)

    MATH  Google Scholar 

  12. Barkai, N. & Leibler, S. Circadian clocks limited by noise. Nature 403, 267–268 (2000)

    Article  ADS  CAS  Google Scholar 

  13. Balaban, N. Q., Merrin, J., Chait, R., Kowalik, L. & Leibler, S. Bacterial persistence as a phenotypic switch. Science 305, 1622–1625 (2004)

    Article  ADS  CAS  Google Scholar 

  14. Samoilov, M., Plyasunov, S. & Arkin, A. P. Stochastic amplification and signaling in enzymatic futile cycles through noise-induced bistability with oscillations. Proc. Natl Acad. Sci. USA 102, 2310–2315 (2005)

    Article  ADS  CAS  Google Scholar 

  15. Raser, J. M. & O'Shea, E. K. Noise in gene expression: origins, consequences, and control. Science 309, 2010–2013 (2005)

    Article  ADS  CAS  Google Scholar 

  16. Novick, A. & Weiner, M. Enzyme induction as an all-or-none phenomenon. Proc. Natl Acad. Sci. USA 43, 553–566 (1957)

    Article  ADS  CAS  Google Scholar 

  17. Ferrell, J. E. Jr & Machleder, E. M. The biochemical basis of an all-or-none cell fate switch in Xenopus oocytes. Science 280, 895–898 (1998)

    Article  ADS  CAS  Google Scholar 

  18. Biggar, S. R. & Crabtree, G. R. Cell signaling can direct either binary or graded transcriptional responses. EMBO J. 20, 3167–3176 (2001)

    Article  CAS  Google Scholar 

  19. Lahav, G. et al. Dynamics of the p53–Mdm2 feedback loop in individual cells. Nature Genet. 36, 147–150 (2004)

    Article  CAS  Google Scholar 

  20. Eitzman, P. D., Hendrick, J. L. & Srienc, F. Quantitative immunofluorescence in single Saccharomyces cerevisiae cells. Cytometry 10, 475–483 (1989)

    Article  CAS  Google Scholar 

  21. Edwards, B. S., Kuckuck, F. & Sklar, L. A. Plug flow cytometry: an automated coupling device for rapid sequential flow cytometric sample analysis. Cytometry 37, 156–159 (1999)

    Article  CAS  Google Scholar 

  22. Becskei, A., Kaufmann, B. B. & van Oudenaarden, A. Contributions of low molecule number and chromosomal positioning to stochastic gene expression. Nature Genet. 37, 937–944 (2005)

    Article  CAS  Google Scholar 

  23. Blake, W. J., Kaern, M., Cantor, C. R. & Collins, J. J. Noise in eukaryotic gene expression. Nature 422, 633–637 (2003)

    Article  ADS  CAS  Google Scholar 

  24. Elowitz, M. B., Levine, A. J., Siggia, E. D. & Swain, P. S. Stochastic gene expression in a single cell. Science 297, 1183–1186 (2002)

    Article  ADS  CAS  Google Scholar 

  25. Ozbudak, E. M., Thattai, M., Kurtser, I., Grossman, A. D. & van Oudenaarden, A. Regulation of noise in the expression of a single gene. Nature Genet. 31, 69–73 (2002)

    Article  CAS  Google Scholar 

  26. Paulsson, J. Prime movers of noisy gene expression. Nature Genet. 37, 925–926 (2005)

    Article  CAS  Google Scholar 

  27. Zaslaver, A. et al. Just-in-time transcription program in metabolic pathways. Nature Genet. 36, 486–491 (2004)

    Article  CAS  Google Scholar 

  28. Cormack, B. P., Valdivia, R. H. & Falkow, S. FACS-optimized mutants of the green fluorescent protein (GFP). Gene 173, 33–38 (1996)

    Article  CAS  Google Scholar 

  29. Shaner, N. C. et al. Improved monomeric red, orange and yellow fluorescent proteins derived from Discosoma sp. red fluorescent protein. Nature Biotechnol. 22, 1567–1572 (2004)

    Article  CAS  Google Scholar 

  30. Wu, J. Q. & Pollard, T. D. Counting cytokinesis proteins globally and locally in fission yeast. Science 310, 310–314 (2005)

    Article  ADS  CAS  Google Scholar 

  31. Hershko, A. & Ciechanover, A. The ubiquitin system. Annu. Rev. Biochem. 67, 425–479 (1998)

    Article  CAS  Google Scholar 

  32. Wodicka, L., Dong, H., Mittmann, M., Ho, M. H. & Lockhart, D. J. Genome-wide expression monitoring in Saccharomyces cerevisiae. Nature Biotechnol. 15, 1359–1367 (1997)

    Article  CAS  Google Scholar 

  33. Felice, M. R. et al. Post-transcriptional regulation of the yeast high affinity iron transport system. J. Biol. Chem. 280, 22181–22190 (2005)

    Article  CAS  Google Scholar 

  34. Colman-Lerner, A. et al. Regulated cell-to-cell variation in a cell-fate decision system. Nature 437, 699–706 (2005)

    Article  ADS  CAS  Google Scholar 

  35. Raser, J. M. & O'Shea, E. K. Control of stochasticity in eukaryotic gene expression. Science 304, 1811–1814 (2004)

    Article  ADS  CAS  Google Scholar 

  36. Paulsson, J. Summing up the noise in gene networks. Nature 427, 415–418 (2004)

    Article  ADS  CAS  Google Scholar 

  37. Fraser, H. B., Hirsh, A. E., Giaever, G., Kumm, J. & Eisen, M. B. Noise minimization in eukaryotic gene expression. PLoS Biol. 2, e137 (2004)

    Article  Google Scholar 

  38. Ashburner, M. et al. Gene ontology: tool for the unification of biology. The Gene Ontology Consortium. Nature Genet. 25, 25–29 (2000)

    Article  CAS  Google Scholar 

  39. Harbison, C. T. et al. Transcriptional regulatory code of a eukaryotic genome. Nature 431, 99–104 (2004)

    Article  ADS  CAS  Google Scholar 

  40. Yu, L. & Morse, R. H. Chromatin opening and transactivator potentiation by RAP1 in Saccharomyces cerevisiae. Mol. Cell. Biol. 19, 5279–5288 (1999)

    Article  CAS  Google Scholar 

  41. Huisinga, K. L. & Pugh, B. F. A genome-wide housekeeping role for TFIID and a highly regulated stress-related role for SAGA in Saccharomyces cerevisiae. Mol. Cell 13, 573–585 (2004)

    Article  CAS  Google Scholar 

  42. Warner, J. R. Synthesis of ribosomes in Saccharomyces cerevisiae. Microbiol. Rev. 53, 256–271 (1989)

    CAS  PubMed  PubMed Central  Google Scholar 

  43. Fagarasanu, M., Fagarasanu, A., Tam, Y. Y., Aitchison, J. D. & Rachubinski, R. A. Inp1p is a peroxisomal membrane protein required for peroxisome inheritance in Saccharomyces cerevisiae. J. Cell Biol. 169, 765–775 (2005)

    Article  CAS  Google Scholar 

  44. Holstege, F. C. et al. Dissecting the regulatory circuitry of a eukaryotic genome. Cell 95, 717–728 (1998)

    Article  CAS  Google Scholar 

  45. Wang, Y. et al. Precision and functional specificity in mRNA decay. Proc. Natl Acad. Sci. USA 99, 5860–5865 (2002)

    Article  ADS  CAS  Google Scholar 

Download references

Acknowledgements

The authors acknowledge M. Bigos, P. Dezain and S. Elmes for their help with cytometry; K. Uffenheimer and A. Carroll (A.C.) for assistance with automation; W. Wickner for an anti-GFP antibody; R. Tsien for a construct encoding tdTomato; A.C., W.-K. Huh, M. Jonikas, V. Zapeda and E. Griffis for experimental assistance; A. H. DePace for graphical assistance; S. Collins, V. Denic, H. El Samad, V. L. Newman, E. K. O'Shea and members of the Weissman laboratory for insightful comments; and the Hertz Foundation, the NIH, DARPA and HHMI for funding.

Author information

Authors and Affiliations

Authors

Corresponding authors

Correspondence to John R. S. Newman or Jonathan S. Weissman.

Ethics declarations

Competing interests

Reprints and permissions information is available at npg.nature.com/reprintsandpermissions. The authors declare no competing financial interests.

Supplementary information

Supplementary Notes 1

Instruction manual for custom software described in the main text. Instructions on how to use ‘HTSPro’ to control the delivery of samples to a flow cytometer, as well as to control the software that runs the cytometer. As noted in the Supplementary Text, the software is available to academic users from the Authors. (PDF 8360 kb)

Supplementary Notes 2

Supplementary information about experimental design, execution and interpretation. Also provided is a supplementary discussion and supplementary figures that support the Main Text. (PDF 1125 kb)

Supplementary Table 1

Abundance and variation measurements for strains grown in YEPD and SD. (XLS 1672 kb)

Supplementary Table 2

Supplementary Table 2 nature04785-s04.xls Changes in protein and mRNA levels for strains grown in YEPD and SD. (XLS 195 kb)

Supplementary Table 3

Statistics for calculating the number of false positive and false negative strains. (PDF 46 kb)

Supplementary Table 4

Primers Used for Tagging and Deletion of ORFs (PDF 54 kb)

Supplementary Table 5

Organization of GO-Term, transcription factor and transcription module correlations associated with low or high variation (PDF 120 kb)

Supplementary Table 6

References for data used for P-value calculations (PDF 99 kb)

Rights and permissions

Reprints and Permissions

About this article

Cite this article

Newman, J., Ghaemmaghami, S., Ihmels, J. et al. Single-cell proteomic analysis of S. cerevisiae reveals the architecture of biological noise. Nature 441, 840–846 (2006). https://doi.org/10.1038/nature04785

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/nature04785

This article is cited by

Comments

By submitting a comment you agree to abide by our Terms and Community Guidelines. If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.

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