Cells Respond Digitally to Variation in Signal Intensity via Stochastic Activation of NF-κB

Abstract

Cells detect and process spatiotemporal signals and activate gene regulatory pathways in response. Here we use high-throughput microfluidic cell culture, quantitative gene expression analysis and mathematical modeling- to investigate how mammalian cells detect external concentrations of the signaling molecule TNF-α and relay information to the gene expression programs via the transcription factor NF-κB. We measured NF-κB activity in thousands of fluorescently labeled live cells with single-cell resolution with a temporal resolution of 6 minutes and for durations up to 12 hours under TNF-α concentrations covering 4 orders of magnitude. TNF-α induced mRNA levels of 23 genes were measured and quantified at the same concentration range and duration, linking the transcription factor dynamics to the gene expression. A stochastic model was developed that reproduces the single-cell dynamics and gene expression profiles at all measured conditions, constituting a broadly applicable model for TNF-α induced NF-κB signaling. We find, in contrast to population studies, that the activation is a discrete process at the single cell level with fewer cells responding at lower doses. Nevertheless, the activated cells respond robustly and early genes are upregulated even at the lowest TNF-α concentrations, indicating digital signaling to gene expression. Late gene expression requires persistent NF-κB activity that is induced only at highest signal levels. The measurements reveal the activation threshold, a hypersensitive dynamic range and saturation, and shows that as few as two bound receptors can activate the pathway. The cells further encode TNF-α concentration information by modulating the temporal dynamics of NF-κB, with higher concentrations resulting in faster activation and more oscillations. Our results -in addition to their biological significance- highlight the importance of high-quality, high-throughput measurements at the single-cell level in understanding how biological systems operate.