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Single-cell NF-κB dynamics reveal digital activation and analogue information processing

Author

Listed:
  • Savaş Tay

    (Stanford University, Stanford, California 94305, USA
    Howard Hughes Medical Institute, Stanford, California 94305, USA)

  • Jacob J. Hughey

    (Stanford University, Stanford, California 94305, USA)

  • Timothy K. Lee

    (Stanford University, Stanford, California 94305, USA)

  • Tomasz Lipniacki

    (Institute of Fundamental Technological Research)

  • Stephen R. Quake

    (Stanford University, Stanford, California 94305, USA
    Howard Hughes Medical Institute, Stanford, California 94305, USA)

  • Markus W. Covert

    (Stanford University, Stanford, California 94305, USA)

Abstract

Single-cell information processing Multicellular organisms, particularly their immune systems, rely on complex cell-to-cell communications, the molecular mechanisms of which have been studied largely qualitatively, based on data averaged over heterogeneous cell populations. Now, Tay et al. have used high-throughput microfluidics to quantitatively measure the response of genes controlled by the DNA-binding protein NF-κB, in response to a wide (10,000-fold) range of doses of the cell–cell signalling molecule TNF-α, at the single-cell level in thousands of live cells. The results reveal all-or-none (digital) decisions of single cells at low levels of signalling, and graded (analogue) responses above.

Suggested Citation

  • Savaş Tay & Jacob J. Hughey & Timothy K. Lee & Tomasz Lipniacki & Stephen R. Quake & Markus W. Covert, 2010. "Single-cell NF-κB dynamics reveal digital activation and analogue information processing," Nature, Nature, vol. 466(7303), pages 267-271, July.
    • Handle: RePEc:nat:nature:v:466:y:2010:i:7303:d:10.1038_nature09145
    DOI: 10.1038/nature09145
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    Cited by:

    1. Gabriele Micali & Gerardo Aquino & David M Richards & Robert G Endres, 2015. "Accurate Encoding and Decoding by Single Cells: Amplitude Versus Frequency Modulation," PLOS Computational Biology, Public Library of Science, vol. 11(6), pages 1-21, June.
    2. Agne Tilūnaitė & Wayne Croft & Noah Russell & Tomas C Bellamy & Rüdiger Thul, 2017. "A Bayesian approach to modelling heterogeneous calcium responses in cell populations," PLOS Computational Biology, Public Library of Science, vol. 13(10), pages 1-25, October.
    3. Zhou, Peipei & Cai, Shuiming & Liu, Zengrong & Chen, Luonan & Wang, Ruiqi, 2013. "Coupling switches and oscillators as a means to shape cellular signals in biomolecular systems," Chaos, Solitons & Fractals, Elsevier, vol. 50(C), pages 115-126.
    4. Giorgos Minas & Dan J Woodcock & Louise Ashall & Claire V Harper & Michael R H White & David A Rand, 2020. "Multiplexing information flow through dynamic signalling systems," PLOS Computational Biology, Public Library of Science, vol. 16(8), pages 1-18, August.
    5. Martiny, Emil S. & Jensen, Mogens H. & Heltberg, Mathias S., 2022. "Detecting limit cycles in stochastic time series," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 605(C).
    6. Christopher C Govern & Arup K Chakraborty, 2013. "Stochastic Responses May Allow Genetically Diverse Cell Populations to Optimize Performance with Simpler Signaling Networks," PLOS ONE, Public Library of Science, vol. 8(8), pages 1-9, August.
    7. Johannes Witt & Fabian Konrath & Oliver Sawodny & Michael Ederer & Dagmar Kulms & Thomas Sauter, 2012. "Analysing the Role of UVB-Induced Translational Inhibition and PP2Ac Deactivation in NF-κB Signalling Using a Minimal Mathematical Model," PLOS ONE, Public Library of Science, vol. 7(7), pages 1-10, July.

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