We develop a potential landscape approach to quantitatively describe experimental data from a fibroblast cell line that exhibits a wide range of GFP expression levels under the control of the promoter for tenascin-C. Time-lapse live-cell microscopy provides data about short-term fluctuations in promoter activity, and flow cytometry measurements provide data about the long-term kinetics, because isolated subpopulations of cells relax from a relatively narrow distribution of GFP expression back to the original broad distribution of responses. The landscape is obtained from the steady state distribution of GFP expression and connected to a potentiallike function using a stochastic differential equation description (Langevin/Fokker-Planck). The range of cell states is constrained by a force that is proportional to the gradient of the potential, and biochemical noise causes movement of cells within the landscape. Analyzing the mean square displacement of GFP intensity changes in live cells indicates that these fluctuations are described by a single diffusion constant in log GFP space. This finding allows application of the Kramers' model to calculate rates of switching between two attractor states and enables an accurate simulation of the dynamics of relaxation back to the steady state with no adjustable parameters. With this approach, it is possible to use the steady state distribution of phenotypes and a quantitative description of the shortterm fluctuations in individual cells to accurately predict the rates at which different phenotypes will arise from an isolated subpopulation of cells.population distribution | dynamical systems | stochastic protein expression | biological noise G enetically identical cells do not respond identically when exposed to nominally identical environmental conditions. Such nongenetic phenotypic variability has been widely observed in bacteria (1, 2), yeast (3), and mammalian cells (4-8). Population heterogeneity is thought to result from the inherently stochastic nature of intracellular events, which are subject to statistical fluctuations caused by small copy numbers of the constituent molecules, such as transcription factors (9). Many investigations into the origins and effects of stochastic gene expression have used engineered organisms and stochastic gene network models to determine the sources and magnitude of variability (10-12). These fluctuations, although causing continual change at the single-cell level, can lead to stable distributions of phenotypes within a population.The idea of a stable distribution of states in the presence of random fluctuations is reminiscent of statistical physics, where randomness results from thermal fluctuations and the stable distribution of states reflects a potential energy function. The popular concept of the epigenetic landscape suggested in the work by Waddington (13) (i.e., a surface of branching valleys and ridges on which cells explore phenotypic states) can be thought of as a series of potential energy functions. The epigenetic landscape,...
The analysis of fluorescence microscopy of cells often requires the determination of cell edges. This is typically done using segmentation techniques that separate the cell objects in an image from the surrounding background. This study compares segmentation results from nine different segmentation techniques applied to two different cell lines and five different sets of imaging conditions. Significant variability in the results of segmentation was observed that was due solely to differences in imaging conditions or applications of different algorithms. We quantified and compared the results with a novel bivariate similarity index metric that evaluates the degree of underestimating or overestimating a cell object. The results show that commonly used threshold-based segmentation techniques are less accurate than k-means clustering with multiple clusters. Segmentation accuracy varies with imaging conditions that determine the sharpness of cell edges and with geometric features of a cell. Based on this observation, we propose a method that quantifies cell edge character to provide an estimate of how accurately an algorithm will perform. The results of this study will assist the development of criteria for evaluating interlaboratory comparability. Published 2011 Wiley-Liss, Inc.y Key terms fluorescence microscopy; k-means cluster; image segmentation; cell edge; bivariate similarity index NUMEROUS areas of biomedical research rely on imaging of cells to provide information about molecular and phenotypic responses of cells to pharmaceuticals, toxins, and other environmental factors (1,2). Cell imaging is widely used in biological experiments because it can provide information on several relevant scales simultaneously. Molecular and supramolecular scales can be probed with the use of antibody and other specific affinity reagents and with fluorescent proteins. Gross phenotypic characteristics of cells that characterize their ultimate functional state can be examined in the same experiment, and often the temporal regime can be probed simultaneously. Together, these applications of cell imaging allow inference about the molecular details and the complex outcomes of the cellular biochemistry.Because of the enormous number of parameters that may influence a biological outcome, cell imaging experiments are often done in a ''high content '' mode (3,4), where large numbers of paired and replicate experiments are carried out simultaneously and result in very large (often gigabyte) image datasets. Such a large volume of image data precludes visual inspection of every image, and automated image processing and analysis is the only viable approach to data analysis.Segmentation of cell objects is a common image analysis operation that provides spatial and other features of identified objects and often precedes other operations to quantify parameters such as intracellular fluorescence. Segmentation can pose significant challenges to automated image processing and analysis. Because morphological features are often important in...
Background: A critical challenge in cell biology is quantifying the interactions of cells with their extracellular matrix (ECM) environment and the active remodeling by cells of their ECM. Fluorescence microscopy is a commonly employed technique for examining cell-matrix interactions. A label-free imaging method would provide an alternative that would eliminate the requirement of transfected cells and modified biological molecules, and if collected nondestructively, would allow long term observation and analysis of live cells.
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