Environmental and internal conditions expose cells to a multiplicity of stimuli whose consequences are difficult to predict. Here, we investigate the response to mating pheromone of yeast cells adapted to high osmolarity. Events downstream of pheromone binding involve two mitogen-activated protein kinase (MAPK) cascades: the pheromone response (PR) and the cell-wall integrity response (CWI). Although these MAPK pathways share components with each and a third MAPK pathway, the high osmolarity response (HOG), they are normally only activated by distinct stimuli, a phenomenon called insulation. We found that in cells adapted to high osmolarity, PR activated the HOG pathway in a pheromone- and osmolarity- dependent manner. Activation of HOG by the PR was not due to loss of insulation, but rather a response to a reduction in internal osmolarity, which resulted from an increase in glycerol release caused by the PR. By analyzing single-cell time courses, we found that stimulation of HOG occurred in discrete bursts that coincided with the “shmooing” morphogenetic process. Activation required the polarisome, the cell wall integrity MAPK Slt2, and the aquaglyceroporin Fps1. HOG activation resulted in high glycerol turnover that improved adaptability to rapid changes in osmolarity. Our work shows how a differentiation signal can recruit a second, unrelated sensory pathway to enable responses to yeast to multiple stimuli.

BackgroundStudies of cell-to-cell variation have in recent years grown in interest, due to improved bioanalytical techniques which facilitates determination of small changes with high uncertainty. Like much high-quality data, single-cell data is best analysed using a systems biology approach. The most common systems biology approach to single-cell data is the standard two-stage (STS) approach. In STS, data from each cell is analysed in a separate sub-problem, meaning that only data from the same cell is used to calculate the parameter values within that cell. Because only parts of the data are considered, problems with parameter unidentifiability are exaggerated in STS. In contrast, a related approach to data analysis has been developed for the studies of patient-to-patient variations. This approach, called nonlinear mixed-effects modelling (NLME), makes use of all data, when estimating the patient-specific parameters. NLME would therefore be advantageous compared to STS also for the study of cell-to-cell variation. However, no such systematic evaluation of the two approaches exists.ResultsHerein, such a systematic comparison between STS and NLME has been performed. Different examples, both linear and nonlinear, and both simulated and real experimental data, have been examined. With informative data, there is no significant difference in the results for either parameter or noise estimation. However, when data becomes uninformative, NLME is significantly superior to STS. These results hold independently of whether the loss of information is due to a low signal-to-noise ratio, too few data points, or a bad input signal. The improvement is shown to come from both the consideration of a joint likelihood (JLH) function, describing all parameters and data, and from an a priori postulated form of the population parameters. Finally, we provide a small tutorial that shows how to use NLME for single-cell analysis, using the free and user-friendly software Monolix.ConclusionsWhen considering uninformative single-cell data, NLME yields more accurate parameter and noise estimates, compared to more traditional approaches, such as STS and JLH.Electronic supplementary materialThe online version of this article (doi:10.1186/s12918-015-0203-x) contains supplementary material, which is available to authorized users.

Embryogenesis relies on instructions provided by spatially organized signaling molecules known as morphogens. Understanding the principles behind morphogen distribution and how cells interpret locally this information remains a major challenge in developmental biology. Here, we introduce morphogen‐age measurements as a novel approach to test models of morphogen gradient formation. Using a tandem fluorescent timer as a protein age sensor, we find a gradient of increasing age of Bicoid along the anterior–posterior axis in the early Drosophila embryo. Quantitative analysis of the protein age distribution across the embryo reveals that the synthesis–diffusion–degradation model is the most likely model underlying Bicoid gradient formation, and rules out other hypotheses for gradient formation. Moreover, we show that the timer can detect transitions in the dynamics associated with syncytial cellularization. Our results provide new insight into Bicoid gradient formation and demonstrate how morphogen‐age information can complement knowledge about movement, abundance, and distribution, which should be widely applicable to other systems.