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Stochastic protein expression in individual cells at the single molecule level

Author

Listed:
  • Long Cai

    (Harvard University)

  • Nir Friedman

    (Harvard University)

  • X. Sunney Xie

    (Harvard University)

Abstract

Single-Gene Biochemistry A central assumption of molecular biology is that cells work by transcribing DNA into messenger RNA, which is then translated into protein. That's familiar enough and uncontroversial. But gene expression has not been directly observed in real time in a live cell on a single-molecule basis. A new live-cell assay system has now been developed that makes such single-molecule observations possible, and can reveal the working of gene expression in live cells. The assay, tested in Escherichia coli, yeast and mouse embryonic stem cells, shows that protein molecules are produced in bursts. The distribution of molecules in each burst is a measure of gene expression levels, which can be compared under different conditions. This has the potential to take the sensitivity of gene expression profiling well beyond that possible today.

Suggested Citation

  • Long Cai & Nir Friedman & X. Sunney Xie, 2006. "Stochastic protein expression in individual cells at the single molecule level," Nature, Nature, vol. 440(7082), pages 358-362, March.
    • Handle: RePEc:nat:nature:v:440:y:2006:i:7082:d:10.1038_nature04599
    DOI: 10.1038/nature04599
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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. Chen Jia & Ramon Grima, 2024. "Holimap: an accurate and efficient method for solving stochastic gene network dynamics," Nature Communications, Nature, vol. 15(1), pages 1-14, December.
    3. Jérémie Bourdon & Damien Eveillard & Anne Siegel, 2011. "Integrating Quantitative Knowledge into a Qualitative Gene Regulatory Network," PLOS Computational Biology, Public Library of Science, vol. 7(9), pages 1-11, September.
    4. Robert J Prill & Robert Vogel & Guillermo A Cecchi & Grégoire Altan-Bonnet & Gustavo Stolovitzky, 2015. "Noise-Driven Causal Inference in Biomolecular Networks," PLOS ONE, Public Library of Science, vol. 10(6), pages 1-16, June.
    5. Bar Haim Y. & Booth James G. & Wells Martin T., 2012. "A Mixture-Model Approach for Parallel Testing for Unequal Variances," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 11(1), pages 1-21, January.
    6. Paolo Annibale & Stefano Vanni & Marco Scarselli & Ursula Rothlisberger & Aleksandra Radenovic, 2011. "Quantitative Photo Activated Localization Microscopy: Unraveling the Effects of Photoblinking," PLOS ONE, Public Library of Science, vol. 6(7), pages 1-8, July.
    7. Vera Bettenworth & Simon Vliet & Bartosz Turkowyd & Annika Bamberger & Heiko Wendt & Matthew McIntosh & Wieland Steinchen & Ulrike Endesfelder & Anke Becker, 2022. "Frequency modulation of a bacterial quorum sensing response," Nature Communications, Nature, vol. 13(1), pages 1-12, December.
    8. Jonathan Liu & Donald Hansen & Elizabeth Eck & Yang Joon Kim & Meghan Turner & Simon Alamos & Hernan Garcia, 2021. "Real-time single-cell characterization of the eukaryotic transcription cycle reveals correlations between RNA initiation, elongation, and cleavage," PLOS Computational Biology, Public Library of Science, vol. 17(5), pages 1-26, May.
    9. Karl P. Gerhardt & Satyajit D. Rao & Evan J. Olson & Oleg A. Igoshin & Jeffrey J. Tabor, 2021. "Independent control of mean and noise by convolution of gene expression distributions," Nature Communications, Nature, vol. 12(1), pages 1-10, December.
    10. Villaseñor, José A. & González-Estrada, Elizabeth, 2015. "A variance ratio test of fit for Gamma distributions," Statistics & Probability Letters, Elsevier, vol. 96(C), pages 281-286.
    11. Yann S Dufour & Sébastien Gillet & Nicholas W Frankel & Douglas B Weibel & Thierry Emonet, 2016. "Direct Correlation between Motile Behavior and Protein Abundance in Single Cells," PLOS Computational Biology, Public Library of Science, vol. 12(9), pages 1-25, September.
    12. 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.
    13. Piers J Ingram & Michael P H Stumpf & Jaroslav Stark, 2008. "Nonidentifiability of the Source of Intrinsic Noise in Gene Expression from Single-Burst Data," PLOS Computational Biology, Public Library of Science, vol. 4(10), pages 1-10, October.
    14. 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).
    15. Wadhwa, R.R. & Zalányi, L. & Szente, J. & Négyessy, L. & Érdi, P., 2017. "Stochastic kinetics of the circular gene hypothesis: Feedback effects and protein fluctuations," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 133(C), pages 326-336.
    16. Hao Ge & Pingping Wu & Hong Qian & Xiaoliang Sunney Xie, 2018. "Relatively slow stochastic gene-state switching in the presence of positive feedback significantly broadens the region of bimodality through stabilizing the uninduced phenotypic state," PLOS Computational Biology, Public Library of Science, vol. 14(3), pages 1-24, March.
    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. Shu Wang & Jia-Ren Lin & Eduardo D Sontag & Peter K Sorger, 2019. "Inferring reaction network structure from single-cell, multiplex data, using toric systems theory," PLOS Computational Biology, Public Library of Science, vol. 15(12), pages 1-25, December.

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