temporal-quantification-mapk-induced-expression-single-yeast

Temporal Quantification of MAPK Induced Expression in Single Yeast Cells

The quantification of gene expression at the  single cell level uncovers novel regulatory mechanisms obscured in measurements  performed at the population ...

University of Lausanne

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.

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

The measurement of Forster resonance energy transfer (FRET) in microscopes can be realized by different imaging modalities. In the present work, reference FRET constructs are developed to allow the comparison of FRET microscopy measurements using intensity, spectral, and lifetime imaging. Complimentary DNA strands are respectively labeled with Oregon Green 488 (OG488) or tetramethylrhodamine (TMR). The OG488 dye is fixed at the 5(') end of one strand, and the TMR label position is allowed to vary along the complimentary strand. Since OG488 and TMR are FRET pairs, the FRET efficiency can be determined theoretically from the distance separating the two dyes of the double-stranded DNA molecules. Microscopic images are formed by imaging microcapillaries containing various mixtures of oligonucleotides labeled with the FRET fluorophore pair, only the donor, or only acceptor. Traditional two-channel intensity measurements are compared with spectrally resolved imaging and fluorescence lifetime imaging by calculating a FRET index. The latter proves to be the best method to quantify FRET efficiency in the image. More importantly, the intensity fraction of molecules undergoing FRET can be quantitatively measured in each pixel of the image.

Comparing the quantification of Forster resonance energy transfer measurement accuracies based on intensity, spectral, and lifetime imaging.

In the femtoliter observation volume of a two-photon microscope, multiple fluorophores can be present and complex photophysics can take place. Combined detection of the fluorescence emission spectra and lifetimes can provide deeper insight into specimen properties than these two imaging modalities taken separately. Therefore, we have developed a detection scheme based on a frequency-modulated multichannel photomultiplier, which measures simultaneously the spectrum and the lifetime of the emitted fluorescence. Experimentally, the efficiency of the frequency domain lifetime measurement was compared to a time domain set-up. The performance of this spectrally and lifetime-resolved microscope was evaluated on reference specimens and living cells labeled with three different stains targeting the membrane, the mitochondria, and the nucleus.

Frequency domain lifetime and spectral imaging microscopy.

Multicolor fluorescence correlation spectroscopy has been recently developed to study chemical interactions of multiple chemical species labeled with spectrally distinct fluorophores. In the presence of spectral overlap, there exists a lower detectability limit for reaction products with multicolor fluorophores. In addition, the ability to separate bound product from reactants allows thermodynamic properties such as dissociation constants to be measured for chemical reactions. In this report, we utilize a spectrally resolved two-photon microscope with single-photon counting sensitivity to acquire spectral and temporal information from multiple chemical species. Further, we have developed a global fitting analysis algorithm that simultaneously analyzes all distinct auto- and cross-correlation functions from 15 independent spectral channels. We have demonstrated that the global analysis approach allows the concentration and diffusion coefficients of fluorescent particles to be resolved despite the presence of overlapping emission spectra.

Spectrally resolved fluorescence correlation spectroscopy based on global analysis.

Parameterized models of biophysical and mechanical cell properties are important for predictive mathematical modeling of cellular processes. The concepts of turgor, cell wall elasticity, osmotically active volume, and intracellular osmolarity have been investigated for decades, but a consistent rigorous parameterization of these concepts is lacking. Here, we subjected several data sets of minimum volume measurements in yeast obtained after hyper-osmotic shock to a thermodynamic modeling framework. We estimated parameters for several relevant biophysical cell properties and tested alternative hypotheses about these concepts using a model discrimination approach. In accordance with previous reports, we estimated an average initial turgor of 0.6 Â± 0.2 MPa and found that turgor becomes negligible at a relative volume of 93.3 Â± 6.3% corresponding to an osmotic shock of 0.4 Â± 0.2 Osm/l. At high stress levels (4 Osm/l), plasmolysis may occur. We found that the volumetric elastic modulus, a measure of cell wall elasticity, is 14.3 Â± 10.4 MPa. Our model discrimination analysis suggests that other thermodynamic quantities affecting the intracellular water potential, for example the matrix potential, can be neglected under physiological conditions. The parameterized turgor models showed that activation of the osmosensing high osmolarity glycerol (HOG) signaling pathway correlates with turgor loss in a 1:1 relationship. This finding suggests that mechanical properties of the membrane trigger HOG pathway activation, which can be represented and quantitatively modeled by turgor.

Biophysical properties of Saccharomyces cerevisiae and their relationship with HOG pathway activation.

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 beta-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.

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

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.

Transient activation of the HOG MAPK pathway regulates bimodal gene expression.

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.

Quantitative and dynamic assay of single cell chemotaxis.

Osmotic stress triggers the activation of the HOG (high osmolarity glycerol) pathway in Saccharomyces cerevisiae. This signaling cascade culminates in the activation of the MAPK (mitogen-activated protein kinase) Hog1. Quantitative single cell measurements revealed a discrepancy between kinase- and transcriptional activities of Hog1. While kinase activity increases proportionally to stress stimulus, gene expression is inhibited under low stress conditions. Interestingly, a slow stochastic gene activation process is responsible for setting a tunable threshold for gene expression under basal or low stress conditions, which generates a bimodal expression pattern at intermediate stress levels.

Dynamic processes at stress promoters regulate the bimodal expression of HOG response genes.

Global algorithms can improve the analysis of fluorescence energy transfer (FRET) measurement based on fluorescence lifetime microscopy. However, global analysis of FRET data is also susceptible to experimental artifacts. This work examines several common artifacts and suggests remedial experimental protocols. Specifically, we examined the accuracy of different methods for instrument response extraction and propose an adaptive method based on the mean lifetime of fluorescent proteins. We further examined the effects of image segmentation and a priori constraints on the accuracy of lifetime extraction. Methods to test the applicability of global analysis on cellular data are proposed and demonstrated. The accuracy of global fitting degrades with lower photon count. By systematically tracking the effect of the minimum photon count on lifetime and FRET prefactors when carrying out global analysis, we demonstrate a correction procedure to recover the correct FRET parameters, allowing us to obtain protein interaction information even in dim cellular regions with photon counts as low as 100 per decay curve.

Methodological considerations for global analysis of cellular FLIM/FRET measurements.

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.

Moment-based inference predicts bimodality in transient gene expression.

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.

Serge Pelet

Department of Fundamental Microbiology

University of Lausanne

Temporal Quantification of MAPK Induced Expression in Single Yeast Cells