Cell‐specific responses to the cytokine TGF β are determined by variability in protein levels

Abstract

The cytokine TGFβ provides important information during embryonic development, adult tissue homeostasis, and regeneration. Alterations in the cellular response to TGFβ are involved in severe human diseases. To understand how cells encode the extracellular input and transmit its information to elicit appropriate responses, we acquired quantitative time‐resolved measurements of pathway activation at the single‐cell level. We established dynamic time warping to quantitatively compare signaling dynamics of thousands of individual cells and described heterogeneous single‐cell responses by mathematical modeling. Our combined experimental and theoretical study revealed that the response to a given dose of TGFβ is determined cell specifically by the levels of defined signaling proteins. This heterogeneity in signaling protein expression leads to decomposition of cells into classes with qualitatively distinct signaling dynamics and phenotypic outcome. Negative feedback regulators promote heterogeneous signaling, as a SMAD7 knock‐out specifically affected the signal duration in a subpopulation of cells. Taken together, we propose a quantitative framework that allows predicting and testing sources of cellular signaling heterogeneity.

Quantitative relationships between SMAD dynamics and target gene activation kinetics in single live cells

“…To determine whether the ligand dose-response is analog (graded response of all cells) or digital (modulation of the fraction of responding cells) we performed stimulation experiments with a range of TGF-β doses (5pM-5nM) and quantified ctgf responses together with SMAD4/2 profiles in single-cells. These experiments revealed a dosedependent (analog) profile of the response characterized by gradually altered SMAD4/2translocation and the target gene activity ( Fig.4A-B), consistent with previous reports 16 . Figure 1C showed clearly defined transients, reaching a peak after 3-4 hours, and then returning to basal levels after around 8 hours ( Fig.5A).…”

“…Continuous TGF-β stimulation has been described as generating transient responses, while consecutive pulse stimulations was shown to result in sustained activation 15,17 . This heterogeneity in the response profile of individual cells could be due to differences in negative feedback efficiency 16,27,34 or secondary mechanisms of SMADs recruiting activators and suppressors, thereby shaping the transcriptional response later after stimulation 35 . Surprisingly, we found that SMAD4 but not SMAD2 expression levels regulate the probability of cells to display a transient versus a sustained ctgf transcriptional response.…”

“…pSMAD2/3 subsequently heterodimerizes with SMAD4 to translocate into the nucleus and activate hundreds of target genes in different cellular contexts [12][13][14] . Single-cell studies have revealed the pulsatile nature of SMAD shuttling dynamics and the heterogeneity of signaling determined by varying protein levels of individual cells 15,16 . Yet, how cells interpret SMAD signaling and elicit a response remains unclear, mostly due to the scarcity of experimental systems allowing simultaneous measurements of SMAD dynamics and transcriptional output in the same cells.…”

Cell-to-cell variability in AMPK activation reveals autonomous cycles in cellular energy balance

“…The similarity in AMPKAR2 PHOS between daughters decayed gradually, such that it approached the level of unrelated cells with a half-life of ~29 hours. Such patterns of decaying correlation have been observed in other processes, including cell death and signaling pathways (Spencer et al, 2009;Strasen et al, 2018), where they reflect shifts in protein or gene expression complement of each cell that alter cellular phenotype on the scale of 1-2 days (Sigal et al, 2006b).…”

Noisy Perturbation Models Distinguish Network Specific from Embedding Variability

“…As such the observed embedding variability in these rates can be caused by deterministic or stochastic sources [34]. For example, the population context (cell density and morphology) and nonlinear reactions, such as the the cell cycle are deterministic sources of variability [35,36,37,38], while variability in many protein levels is of stochastic nature [32,39,40,41].…”