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

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.

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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.

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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

Authors and Affiliations

Authors

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)

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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

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