Protocol for FRAP-based estimation of nuclear import and export rates in single yeast cells.

Here, we present a protocol for estimating nuclear transport parameters in single cells. We describe steps for performing four consecutive fluorescence recovery after photobleaching experiments, fitting the obtained data to an ordinary differential equations model, and statistical analysis of the fittings using a specialized R package. This protocol permits the estimation of import and export rates, nuclear or cytosolic fixed fractions, and total number of molecules. For complete details on the use and execution of this protocol, please refer to Durrieu et al.1.

Metabolic FRET sensors in intact organs: Applying spectral unmixing to acquire reliable signals.

In multicellular organisms, metabolic coordination across multiple tissues and cell types is essential to satisfy regionalized energetic requirements and respond coherently to changing environmental conditions. However, most metabolic assays require the destruction of the biological sample, with a concomitant loss of spatial information. Fluorescent metabolic sensors and probes are among the most user-friendly techniques for collecting metabolic information with spatial resolution. In a previous work, we have adapted to an animal system, Drosophila melanogaster, genetically encoded metabolic FRET-based sensors that had been previously developed in single-cell systems. These sensors provide semi-quantitative data on the stationary concentrations of key metabolites of the bioenergetic metabolism: lactate, pyruvate, and 2-oxoglutarate. The use of these sensors in intact organs required the development of an image processing method that minimizes the contribution of spatially complex autofluorescence patterns, that would obscure the FRET signals. In this article, we show step by step how to design FRET-based sensor experiments and how to process the fluorescence signal to obtain reliable FRET values.

Characterization of cell-to-cell variation in nuclear transport rates and identification of its sources.

Nuclear transport is an essential part of eukaryotic cell function. Here, we present scFRAP, a model-assisted fluorescent recovery after photobleaching (FRAP)- based method to determine nuclear import and export rates independently in individual live cells. To overcome the inherent noise of single-cell measurements, we performed sequential FRAPs on the same cell. We found large cell-to-cell variation in transport rates within isogenic yeast populations. For passive transport, the variability in NPC number might explain most of the variability. Using this approach, we studied mother-daughter cell asymmetry in the active nuclear shuttling of the transcription factor Ace2, which is specifically concentrated in daughter cell nuclei in early G1. Rather than reduced export in the daughter cell, as previously hypothesized, we found that this asymmetry is mainly due to an increased import in daughters. These results shed light on cell-to-cell variation in cellular dynamics and its sources.

Bicoid gradient formation mechanism and dynamics revealed by protein lifetime analysis.

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.

Nonlinear mixed-effects modelling for single cell estimation: when, why, and how to use it.

BACKGROUND: Studies 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. RESULTS: Herein, 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. CONCLUSIONS: When considering uninformative single-cell data, NLME yields more accurate parameter and noise estimates, compared to more traditional approaches, such as STS and JLH.

Pheromone-induced morphogenesis improves osmoadaptation capacity by activating the HOG MAPK pathway.

Environmental and internal conditions expose cells to a multiplicity of stimuli whose consequences are difficult to predict. 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 (CWI) response. Although the PR MAPK pathway shares components with a third MAPK pathway, the high osmolarity (HOG) response, each one is normally only activated by its cognate stimulus, 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 CWI MAPK Slt2, and the aquaglyceroporin Fps1. HOG activation resulted in high glycerol turnover, which improved adaptability to rapid changes in osmolarity. Our work shows how a differentiation signal can recruit a second, unrelated sensory pathway to fine-tune yeast response in a complex environment.