Serge Pelet

Cytosolic pH is a second messenger for glucose and regulates the PKA pathway through V-ATPase

Glucose is the preferred carbon source for most cell types and a major determinant of cell growth. In yeast and certain mammalian cells, glucose activates the cAMP‐dependent protein kinase A (PKA), but the mechanisms of PKA activation remain unknown. Here, we identify cytosolic pH as a second messenger for glucose that mediates activation of the PKA pathway in yeast. We find that cytosolic pH is rapidly and reversibly regulated by glucose metabolism and identify the vacuolar ATPase (V‐ATPase), a proton pump required for the acidification of vacuoles, as a sensor of cytosolic pH. V‐ATPase assembly is regulated by cytosolic pH and is required for full activation of the PKA pathway in response to glucose, suggesting that it mediates, at least in part, the pH signal to PKA. Finally, V‐ATPase is also regulated by glucose in the Min6 β‐cell line and contributes to PKA activation and insulin secretion. Thus, these data suggest a novel and potentially conserved glucose‐sensing pathway and identify a mechanism how cytosolic pH can act as a signal to promote cell growth.

Moment-based inference predicts bimodality in transient gene expression

Recent computational studies indicate that the molecular noise of a cellular process may be a rich source of information about process dynamics and parameters. However, accessing this source requires stochastic models that are usually difficult to analyze. Therefore, parameter estimation for stochastic systems using distribution measurements, as provided for instance by flow cytometry, currently remains limited to very small and simple systems. Here we propose a new method that makes use of low-order moments of the measured distribution and thereby keeps the essential parts of the provided information, while still staying applicable to systems of realistic size. We demonstrate how cell-to-cell variability can be incorporated into the analysis obviating the need for the ubiquitous assumption that the measurements stem from a homogeneous cell population. We demonstrate the method for a simple example of gene expression using synthetic data generated by stochastic simulation. Subsequently, we use time-lapsed flow cytometry data for the osmo-stress induced transcriptional response in budding yeast to calibrate a stochastic model, which is then used as a basis for predictions. Our results show that measurements of the mean and the variance can be enough to determine the model parameters, even if the measured distributions are not well-characterized by low-order moments only—e.g., if they are bimodal.

Two-photon 3-D mapping of ex vivo human skin endogenous fluorescence species based on fluorescence emission spectra

Spectral resolved tissue imaging has a broad range of biomedical applications such as the minimally invasive diagnosis of diseases and the study of wound healing and tissue engineering processes. Two-photon microscopy imaging of endogenous fluorescence has been shown to be a powerful method for the quantification of tissue structure and biochemistry. While two-photon excited autofluorescence is observed ubiquitously, the identities and distributions of endogenous fluorophores have not been completely characterized in most tissues. We develop an image-guided spectral analysis method to analyze the distribution of fluorophores in human skin from 3-D resolved two-photon images. We identify five factors that contribute to most of the luminescence signals from human skin. Luminescence species identified include tryptophan, NAD(P)H, melanin, and elastin, which are autofluorescent, and collagen that contributes to a second harmonic signal.

Transient Activation of the HOG MAPK Pathway Regulates Bimodal Gene Expression

Bimodal expression of genes is activated in response to osmotic stress. Mitogen-activated protein kinase (MAPK) cascades are conserved signaling modules that control many cellular processes by integrating intra- and extracellular cues. The p38/Hog1 MAPK is transiently activated in response to osmotic stress, leading to rapid translocation into the nucleus and induction of a specific transcriptional program. When investigating the dynamic interplay between Hog1 activation and Hog1-driven gene expression, we found that Hog1 activation increases linearly with stimulus, whereas the transcriptional output is bimodal. Modeling predictions, corroborated by single-cell experiments, established that a slow stochastic transition from a repressed to an activated transcriptional state in conjunction with transient Hog1 activation generates this behavior. Together, these findings provide a molecular mechanism by which a cell can impose a transcriptional threshold in response to a linear signaling behavior.

Scalable inference of heterogeneous reaction kinetics from pooled single-cell recordings

Mathematical methods combined with measurements of single-cell dynamics provide a means to reconstruct intracellular processes that are only partly or indirectly accessible experimentally. To obtain reliable reconstructions, the pooling of measurements from several cells of a clonal population is mandatory. However, cell-to-cell variability originating from diverse sources poses computational challenges for such process reconstruction. We introduce a scalable Bayesian inference framework that properly accounts for population heterogeneity. The method allows inference of inaccessible molecular states and kinetic parameters; computation of Bayes factors for model selection; and dissection of intrinsic, extrinsic and technical noise. We show how additional single-cell readouts such as morphological features can be included in the analysis. We use the method to reconstruct the expression dynamics of a gene under an inducible promoter in yeast from time-lapse microscopy data.

Quantitative and dynamic assay of single cell chemotaxis

We have developed a single-cell assay platform that allows quantitative analysis of single cell chemotaxis by dynamic morphogenetic gradients, subcellular microscopic imaging and automated image analysis, and have applied these to measure cellular polarization of budding yeast. The computer-controlled microfluidic device regulates the gradient profile at any given time, and allows quantitative monitoring of cell morphology and the localization and expression of specific marker proteins during the dynamic polarization process. With this integrated experimental system, we compare the polarized signaling response of wild-type and far1-H7 mutant cells, which express a truncated Far1 protein unable to interact with Cdc24. Our results confirm that Far1 functions as an adaptor that recruits polarity establishment proteins to the site of extracellular signaling. Moreover, by changing the gradient profile and estimating the number of bound surface receptors, we quantitatively address why surprisingly small differences in pheromone concentration across yeast cells can be amplified into a robust polarity axis. This integrated single cell experimental platform thus opens the possibility to quantitatively investigate the molecular regulatory mechanism of chemotaxis in yeast, which serves as a paradigm to understand the fundamental processes involved in cancer metastasis, angiogenesis and axon generation.

An integrated image analysis platform to quantify signal transduction in single cells.

Microscopy can provide invaluable information about biological processes at the single cell level. It remains a challenge, however, to extract quantitative information from these types of datasets. We have developed an image analysis platform named YeastQuant to simplify data extraction by offering an integrated method to turn time-lapse movies into single cell measurements. This platform is based on a database with a graphical user interface where the users can describe their experiments. The database is connected to the engineering software Matlab, which allows extracting the desired information by automatically segmenting and quantifying the microscopy images. We implemented three different segmentation methods that recognize individual cells under different conditions, and integrated image analysis protocols that allow measuring and analyzing distinct cellular readouts. To illustrate the power and versatility of YeastQuant, we investigated dynamic signal transduction processes in yeast. First, we quantified the expression of fluorescent reporters induced by osmotic stress to study noise in gene expression. Second, we analyzed the dynamic relocation of endogenous proteins from the cytoplasm to the cell nucleus, which provides a fast measure of pathway activity. These examples demonstrate that YeastQuant provides a versatile and expandable database and an experimental framework that improves image analysis and quantification of diverse microscopy-based readouts. Such dynamic single cell measurements are highly needed to establish mathematical models of signal transduction pathways.

Real-time quantification of protein expression at the single-cell level via dynamic protein synthesis translocation reporters.

Protein expression is a dynamic process, which can be rapidly induced by extracellular signals. It is widely appreciated that single cells can display large variations in the level of gene induction. However, the variability in the dynamics of this process in individual cells is difficult to quantify using standard fluorescent protein (FP) expression assays, due to the slow maturation of their fluorophore. Here we have developed expression reporters that accurately measure both the levels and dynamics of protein synthesis in live single cells with a temporal resolution under a minute. Our system relies on the quantification of the translocation of a constitutively expressed FP into the nucleus. As a proof of concept, we used these reporters to measure the transient protein synthesis arising from two promoters responding to the yeast hyper osmolarity glycerol mitogen-activated protein kinase pathway (pSTL1 and pGPD1). They display distinct expression dynamics giving rise to strikingly different instantaneous expression noise.

Dynamic single cell measurements of kinase activity by synthetic kinase activity relocation sensors.

BackgroundMitogen activated protein kinases (MAPK) play an essential role in integrating extra-cellular signals and intra-cellular cues to allow cells to grow, adapt to stresses, or undergo apoptosis. Budding yeast serves as a powerful system to understand the fundamental regulatory mechanisms that allow these pathways to combine multiple signals and deliver an appropriate response. To fully comprehend the variability and dynamics of these signaling cascades, dynamic and quantitative single cell measurements are required. Microscopy is an ideal technique to obtain these data; however, novel assays have to be developed to measure the activity of these cascades.ResultsWe have generated fluorescent biosensors that allow the real-time measurement of kinase activity at the single cell level. Here, synthetic MAPK substrates were engineered to undergo nuclear-to-cytoplasmic relocation upon phosphorylation of a nuclear localization sequence. Combination of fluorescence microscopy and automated image analysis allows the quantification of the dynamics of kinase activity in hundreds of single cells. A large heterogeneity in the dynamics of MAPK activity between individual cells was measured. The variability in the mating pathway can be accounted for by differences in cell cycle stage, while, in the cell wall integrity pathway, the response to cell wall stress is independent of cell cycle stage.ConclusionsThese synthetic kinase activity relocation sensors allow the quantification of kinase activity in live single cells. The modularity of the architecture of these reporters will allow their application in many other signaling cascades. These measurements will allow to uncover new dynamic behaviour that previously could not be observed in population level measurements.

A Cellular System for Spatial Signal Decoding in Chemical Gradients.

Directional cell growth requires that cells read and interpret shallow chemical gradients, but how the gradient directional information is identified remains elusive. We use single-cell analysis and mathematical modeling to define the cellular gradient decoding network in yeast. Our results demonstrate that the spatial information of the gradient signal is read locally within the polarity site complex using double-positive feedback between the GTPase Cdc42 and trafficking of the receptor Ste2. Spatial decoding critically depends on low Cdc42 activity, which is maintained by the MAPK Fus3 through sequestration of the Cdc42 activator Cdc24. Deregulated Cdc42 or Ste2 trafficking prevents gradient decoding and leads to mis-oriented growth. Our work discovers how a conserved set of components assembles a network integrating signal intensity and directionality to decode the spatial information contained in chemical gradients.