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

Author

Listed:
  • John R. S. Newman

    (Howard Hughes Medical Institute
    Department of Cellular and Molecular Pharmacology)

  • Sina Ghaemmaghami

    (Howard Hughes Medical Institute
    Department of Cellular and Molecular Pharmacology
    University of California, San Francisco)

  • Jan Ihmels

    (Howard Hughes Medical Institute
    Department of Cellular and Molecular Pharmacology)

  • David K. Breslow

    (Howard Hughes Medical Institute
    Department of Cellular and Molecular Pharmacology)

  • Matthew Noble

    (Howard Hughes Medical Institute)

  • Joseph L. DeRisi

    (Howard Hughes Medical Institute
    University of California
    California Institute for Quantitative Biomedical Research)

  • Jonathan S. Weissman

    (Howard Hughes Medical Institute
    Department of Cellular and Molecular Pharmacology)

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.

Suggested Citation

  • John R. S. Newman & Sina Ghaemmaghami & Jan Ihmels & David K. Breslow & Matthew Noble & Joseph L. DeRisi & Jonathan S. Weissman, 2006. "Single-cell proteomic analysis of S. cerevisiae reveals the architecture of biological noise," Nature, Nature, vol. 441(7095), pages 840-846, June.
    • Handle: RePEc:nat:nature:v:441:y:2006:i:7095:d:10.1038_nature04785
    DOI: 10.1038/nature04785
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    Cited by:

    1. Mohammad Soltani & Cesar A Vargas-Garcia & Duarte Antunes & Abhyudai Singh, 2016. "Intercellular Variability in Protein Levels from Stochastic Expression and Noisy Cell Cycle Processes," PLOS Computational Biology, Public Library of Science, vol. 12(8), pages 1-23, August.
    2. Abhyudai Singh & Mohammad Soltani, 2013. "Quantifying Intrinsic and Extrinsic Variability in Stochastic Gene Expression Models," PLOS ONE, Public Library of Science, vol. 8(12), pages 1-12, December.
    3. Jessica A Lee & Siavash Riazi & Shahla Nemati & Jannell V Bazurto & Andreas E Vasdekis & Benjamin J Ridenhour & Christopher H Remien & Christopher J Marx, 2019. "Microbial phenotypic heterogeneity in response to a metabolic toxin: Continuous, dynamically shifting distribution of formaldehyde tolerance in Methylobacterium extorquens populations," PLOS Genetics, Public Library of Science, vol. 15(11), pages 1-38, November.
    4. Arjun Raj & Charles S Peskin & Daniel Tranchina & Diana Y Vargas & Sanjay Tyagi, 2006. "Stochastic mRNA Synthesis in Mammalian Cells," PLOS Biology, Public Library of Science, vol. 4(10), pages 1-13, September.
    5. Leighton T Izu & Tamás Bányász & Ye Chen-Izu, 2015. "Optimizing Population Variability to Maximize Benefit," PLOS ONE, Public Library of Science, vol. 10(12), pages 1-17, December.
    6. Lit-Hsin Loo & Danai Laksameethanasan & Yi-Ling Tung, 2014. "Quantitative Protein Localization Signatures Reveal an Association between Spatial and Functional Divergences of Proteins," PLOS Computational Biology, Public Library of Science, vol. 10(3), pages 1-17, March.
    7. Niraj Kumar & Abhyudai Singh & Rahul V Kulkarni, 2015. "Transcriptional Bursting in Gene Expression: Analytical Results for General Stochastic Models," PLOS Computational Biology, Public Library of Science, vol. 11(10), pages 1-22, October.
    8. Ming Ni & Antoine L Decrulle & Fanette Fontaine & Alice Demarez & Francois Taddei & Ariel B Lindner, 2012. "Pre-Disposition and Epigenetics Govern Variation in Bacterial Survival upon Stress," PLOS Genetics, Public Library of Science, vol. 8(12), pages 1-11, December.
    9. Lee, Julian, 2023. "Poisson distributions in stochastic dynamics of gene expression: What events do they count?," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 630(C).
    10. Liang Qiao & Robert B Nachbar & Ioannis G Kevrekidis & Stanislav Y Shvartsman, 2007. "Bistability and Oscillations in the Huang-Ferrell Model of MAPK Signaling," PLOS Computational Biology, Public Library of Science, vol. 3(9), pages 1-8, September.
    11. Najme Khorasani & Mehdi Sadeghi & Abbas Nowzari-Dalini, 2020. "A computational model of stem cell molecular mechanism to maintain tissue homeostasis," PLOS ONE, Public Library of Science, vol. 15(7), pages 1-25, July.
    12. Louis-François Handfield & Yolanda T Chong & Jibril Simmons & Brenda J Andrews & Alan M Moses, 2013. "Unsupervised Clustering of Subcellular Protein Expression Patterns in High-Throughput Microscopy Images Reveals Protein Complexes and Functional Relationships between Proteins," PLOS Computational Biology, Public Library of Science, vol. 9(6), pages 1-19, June.
    13. Marc S Sherman & Barak A Cohen, 2014. "A Computational Framework for Analyzing Stochasticity in Gene Expression," PLOS Computational Biology, Public Library of Science, vol. 10(5), pages 1-13, May.
    14. 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.
    15. Alexey A Gritsenko & Marc Hulsman & Marcel J T Reinders & Dick de Ridder, 2015. "Unbiased Quantitative Models of Protein Translation Derived from Ribosome Profiling Data," PLOS Computational Biology, Public Library of Science, vol. 11(8), pages 1-26, August.
    16. Stuart Aitken & Marie-Cécile Robert & Ross D Alexander & Igor Goryanin & Edouard Bertrand & Jean D Beggs, 2010. "Processivity and Coupling in Messenger RNA Transcription," PLOS ONE, Public Library of Science, vol. 5(1), pages 1-12, January.
    17. Benjamin B Kaufmann & Qiong Yang & Jerome T Mettetal & Alexander van Oudenaarden, 2007. "Heritable Stochastic Switching Revealed by Single-Cell Genealogy," PLOS Biology, Public Library of Science, vol. 5(9), pages 1-8, September.
    18. Saurabh Modi & Supravat Dey & Abhyudai Singh, 2021. "Noise suppression in stochastic genetic circuits using PID controllers," PLOS Computational Biology, Public Library of Science, vol. 17(7), pages 1-25, July.

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