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.

  • Letter
  • Published:

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

Nature volume 466, pages 267–271 (2010)Cite this article

Subjects

Abstract

Cells operate in dynamic environments using extraordinary communication capabilities that emerge from the interactions of genetic circuitry. The mammalian immune response is a striking example of the coordination of different cell types1. Cell-to-cell communication is primarily mediated by signalling molecules that form spatiotemporal concentration gradients, requiring cells to respond to a wide range of signal intensities2. Here we use high-throughput microfluidic cell culture3 and fluorescence microscopy, quantitative gene expression analysis and mathematical modelling to investigate how single mammalian cells respond to different concentrations of the signalling molecule tumour-necrosis factor (TNF)-α, and relay information to the gene expression programs by means of the transcription factor nuclear factor (NF)-κB. We measured NF-κB activity in thousands of live cells under TNF-α doses covering four orders of magnitude. We find, in contrast to population-level studies with bulk assays2, that the activation is heterogeneous and is a digital process at the single-cell level with fewer cells responding at lower doses. Cells also encode a subtle set of analogue parameters to modulate the outcome; these parameters include NF-κB peak intensity, response time and number of oscillations. We developed a stochastic mathematical model that reproduces both the digital and analogue dynamics as well as most gene expression profiles at all measured conditions, constituting a broadly applicable model for TNF-α-induced NF-κB signalling in various types of cells. These results highlight the value of high-throughput quantitative measurements with single-cell resolution in understanding how biological systems operate.

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

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Figure 1: NF-κB single-cell microscopy measurements.
Figure 2: Time-dependent expression profiles of NF-κB target genes.
Figure 3: Mathematical model development and simulations.
Figure 4: Model predictions: stochastic and variable cell switching and target gene expression dynamics.

Similar content being viewed by others

References

  1. Hayden, M. S., West, A. P. & Ghosh, S. NF-κB and the immune response. Oncogene 25, 6758–6780 (2006)

    Article  CAS  Google Scholar 

  2. Cheong, R. et al. Transient IκB kinase activity mediates temporal NF-κB dynamics in response to a wide range of tumor necrosis factor-α doses. J. Biol. Chem. 281, 2945–2950 (2006)

    Article  CAS  Google Scholar 

  3. Gómez-Sjöberg, R., Leyrat, A. A., Pirone, D. M., Chen, C. S. & Quake, S. R. Versatile, fully automated, microfluidic cell culture system. Anal. Chem. 79, 8557–8563 (2007)

    Article  Google Scholar 

  4. Batchelor, E., Loewer, A. & Lahav, G. The ups and downs of p53: understanding protein dynamics in single cells. Nature Rev. Cancer 9, 371–377 (2009)

    Article  CAS  Google Scholar 

  5. Spencer, S. L., Gaudet, S., Albeck, J. G., Burke, J. M. & Sorger, P. K. Non-genetic origins of cell-to-cell variability in TRAIL-induced apoptosis. Nature 459, 428–432 (2009)

    Article  ADS  CAS  Google Scholar 

  6. Lahav, G. et al. Dynamics of the p53-mdm2 feedback loop in individual cells. Nature Genet. 36, 147–150 (2004)

    Article  CAS  Google Scholar 

  7. Covert, M. W., Leung, T. H., Gaston, J. E. & Baltimore, D. Achieving stability of lipopolysaccharide-induced NF-κB activation. Science 309, 1854–1857 (2005)

    Article  ADS  CAS  Google Scholar 

  8. Lee, T. K. et al. A noisy paracrine signal determines the cellular NF-κB response to LPS. Sci. Signal. 2, 93 (2009)

    Google Scholar 

  9. Cohen, A. A. et al. Dynamic proteomics of individual cancer cells in response to a drug. Science 322, 1511–1516 (2008)

    Article  ADS  CAS  Google Scholar 

  10. Hoffmann, A. & Baltimore, D. Circuitry of nuclear factor κB signaling. Immunol. Rev. 210, 171–186 (2006)

    Article  Google Scholar 

  11. Courtois, G. & Gilmore, T. D. Mutations in the NF-κB signaling pathway: implications for human disease. Oncogene 25, 6831–6843 (2006)

    Article  CAS  Google Scholar 

  12. Nelson, D. E. et al. Oscillations in NF-κB signaling control the dynamics of gene expression. Science 306, 704–708 (2004)

    Article  ADS  CAS  Google Scholar 

  13. Ashall, L. et al. Pulsatile stimulation determines timing and specificity of NF-κB-dependent transcription. Science 324, 242–246 (2009)

    Article  ADS  CAS  Google Scholar 

  14. St, Pierre, F. & Endy, D. Determination of cell-fate selection during phage lambda infection. Proc. Natl Acad. Sci. USA 105, 20705–20710 (2008)

    Article  ADS  Google Scholar 

  15. Snijder, B. et al. Population context determines cell-to-cell variability in endocytosis and virus infection. Nature 461, 520–523 (2009)

    Article  ADS  CAS  Google Scholar 

  16. Elowitz, M., 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 

  17. Hoffmann, A., Levchenko, A., Scott, M. L. & Baltimore, D. The IκB-NF-κB signaling module: temporal control and selective gene activation. Science 298, 1241–1245 (2002)

    Article  ADS  CAS  Google Scholar 

  18. Hao, S. & Baltimore, D. The stability of mRNA influences the temporal order of the induction of genes encoding inflammatory molecules. Nature Immunol. 10, 281–288 (2009)

    Article  CAS  Google Scholar 

  19. Giorgetti, L. et al. Noncooperative interactions between transcription factors and clustered DNA binding sites enable graded transcriptional responses to environmental inputs. Mol. Cell 37, 418–428 (2010)

    Article  CAS  Google Scholar 

  20. Bhat, S., Hermann, J., Armishaw, P., Corbisier, P. & Emslie, K. R. Single molecule detection in nanofluidic digital array allows accurate measurement of DNA copy number. Anal. Bioanal. Chem. 394, 457–467 (2009)

    Article  CAS  Google Scholar 

  21. Wilson, J. W., Catherine, B. & Christopher, S. (eds) in Apoptosis Genes (Springer, 1999)

    Book  Google Scholar 

  22. Lee, E. G. et al. Failure to regulate TNF-α-induced NF-κB and cell death responses in A20-deficient mice. Science 289, 2350–2354 (2000)

    Article  ADS  CAS  Google Scholar 

  23. Hutti, J. E. et al. IκB kinase beta phosphorylates the K63 deubiquitinase A20 to cause feedback inhibition of the NF-κB pathway. Mol. Cell. Biol. 27, 7451–7461 (2007)

    Article  CAS  Google Scholar 

  24. Lipniacki, T., Paszek, P., Brasier, A. R., Luxon, B. & Kimmel, M. Mathematical model of NF-κB regulatory module. J. Theor. Biol. 228, 195–215 (2004)

    Article  MathSciNet  CAS  Google Scholar 

  25. Lipniacki, T., Puszynski, K., Paszek, P., Brasier, A. R. & Kimmel, M. Single TNF-α trimers mediating NF-κB activation: Stochastic robustness of NF-κB signaling. BMC Bioinformatics 8, 376 (2007)

    Article  Google Scholar 

  26. Delhase, M., Hayakawa, M., Chen, Y. & Karin, M. Positive and negative regulation of IκB kinase activity through IKK subunit phosphorylation. Science 284, 309–313 (1998)

    Article  ADS  Google Scholar 

  27. Chen, Y.-M. et al. Dual regulation of TNF-α induced CCL2/monocyte chemoattractant protein-1 expression in vascular smooth muscle cells by NF-κB and AP-1: modulation by type III phosphodiesterase inhibition. J. Pharmacol. Exp. Ther. 103, 06262 (2004)

    Google Scholar 

  28. Toepke, M. W. & Beebe, D. J. PDMS absorption of small molecules and consequences in microfluidic applications. Lab Chip 6, 1484–1486 (2006)

    Article  CAS  Google Scholar 

Download references

Acknowledgements

We thank A. Leyrat and R. Gomez-Sjoberg for development of the automated cell culture system, assistance with the software, and for contributions to experimental design and preliminary data. This research was supported in part by an NIH Director’s Pioneer Award (to S.R.Q.), an NCI Pathway to Independence Award (K99CA125994) (to M.W.C.), the Foundation for Polish Science (TEAM 2009-3/6) and NSF/NIH grant no. R01-GM086885 (to T.L.), a Stanford Graduate Fellowship (to T.K.L.), and a Stanford Bio-X Graduate Fellowship (to J.J.H.).

Author information

Author notes

  1. Savaş Tay and Jacob J. Hughey: These authors contributed equally to this work.

Authors and Affiliations

  1. Department of Bioengineering, Stanford University, Stanford, California 94305, USA,

    Savaş Tay, Jacob J. Hughey, Timothy K. Lee, Stephen R. Quake & Markus W. Covert

  2. Howard Hughes Medical Institute, Stanford, California 94305, USA,

    Savaş Tay & Stephen R. Quake

  3. Institute of Fundamental Technological Research, Warsaw 02-106, Poland

    Tomasz Lipniacki

Authors
  1. Savaş Tay
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jacob J. Hughey
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Timothy K. Lee
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Tomasz Lipniacki
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Stephen R. Quake
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Markus W. Covert
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

S.T. and J.J.H. performed the experiments, S.T. and T.L. developed the mathematical models and performed the simulations, T.K.L. developed the image processing methods and all authors contributed to analysis of the data and to writing the manuscript.

Corresponding authors

Correspondence to Tomasz Lipniacki, Stephen R. Quake or Markus W. Covert.

Ethics declarations

Competing interests

S.R.Q. is a founder, shareholder and consultant for Fluidigm.

Supplementary information

Supplementary Information 1

This file contains Supplementary Figures 1-13 with legends and Supplementary Tables 1-3. (PDF 1436 kb)

Supplementary Information 2

This file contains Supplementary Mathematical Methods and Data, Supplementary Figures M1-M6 with legends, Supplementary Tables 1-3 and References. (PDF 3192 kb)

Supplementary Movie 1

This file contains a time-lapse fluorescent microscopy video of a single microfluidic chamber during stimulation with 10 ng/ml TNF-α, showing p65-DsRed nuclear localization oscillations in single-cells. (AVI 10231 kb)

Supplementary Movie 2

This file contains a time-lapse fluorescent microscopy video of a single microfluidic chamber during stimulation with 0.1 ng/ml TNF-α, showing p65-DsRed nuclear localization oscillations in single-cells. (AVI 6166 kb)

Supplementary Information 3

This zipped file contains the stochastic and deterministic model files. All code was written in Matlab. Deterministic model files are denoted with D, i.e. ModelD.m. The files include the following: (1) Main file that calls appropriate modeling functions (Mainfile.m, MainfileD.m). Start here to perform simulations, as this file calls all the other functions in the model. This file contains some commonly changed parameters such as the TNF-α dose; (2) Parameters file (Parameters.m, ParametersD.m). This file contains most of the biochemical parameters used in the simulations; (3) Model equations (Model.m, ModelD.m). ODE's corresponding to biochemical reactions; (4) Status change file (StatusChange.m): Calculates receptor and gene binding propensities in the stochastic version of the model; (5) Plotting files: AllCellPlotting.m and AllCellPlottingD.m plot individual cell traces and related parameters, while AverageCellPlotting.m and AverageCellPlottingD.m plot the population averages of the same simulated cells. (ZIP 17 kb)

PowerPoint slides

Rights and permissions

Reprints and permissions

About this article

Cite this article

Tay, S., Hughey, J., Lee, T. et al. Single-cell NF-κB dynamics reveal digital activation and analogue information processing. Nature 466, 267–271 (2010). https://doi.org/10.1038/nature09145

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/nature09145

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

Advanced 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