Quantification of Local Morphodynamics and Local GTPase Activity by Edge Evolution Tracking

Advances in time-lapse fluorescence microscopy have enabled us to directly observe dynamic cellular phenomena. Although the techniques themselves have promoted the understanding of dynamic cellular functions, the vast number of images acquired has generated a need for automated processing tools to extract statistical information. A problem underlying the analysis of time-lapse cell images is the lack of rigorous methods to extract morphodynamic properties. Here, we propose an algorithm called edge evolution tracking (EET) to quantify the relationship between local morphological changes and local fluorescence intensities around a cell edge using time-lapse microscopy images. This algorithm enables us to trace the local edge extension and contraction by defining subdivided edges and their corresponding positions in successive frames. Thus, this algorithm enables the investigation of cross-correlations between local morphological changes and local intensity of fluorescent signals by considering the time shifts. By applying EET to fluorescence resonance energy transfer images of the Rho-family GTPases Rac1, Cdc42, and RhoA, we examined the cross-correlation between the local area difference and GTPase activity. The calculated correlations changed with time-shifts as expected, but surprisingly, the peak of the correlation coefficients appeared with a 6–8 min time shift of morphological changes and preceded the Rac1 or Cdc42 activities. Our method enables the quantification of the dynamics of local morphological change and local protein activity and statistical investigation of the relationship between them by considering time shifts in the relationship. Thus, this algorithm extends the value of time-lapse imaging data to better understand dynamics of cellular function

A diffusion-based neurite length-sensing mechanism involved in neuronal symmetry breaking

Shootin1, one of the earliest markers of neuronal symmetry breaking, accumulates in the neurite tips of polarizing neurons in a neurite length-dependent manner. Thus, neurons sense their neurites' length and translate this spatial information into a molecular signal, shootin1 concentration.Quantitative live cell imaging of shootin1 dynamics combined with mathematical modeling analyses reveals that its anterograde transport and retrograde diffusion in neurite shafts account for the neurite length-dependent accumulation of shootin1.The neurite length-dependent shootin1 accumulation and shootin1-induced neurite outgrowth constitute a positive feedback loop that amplifies stochastic shootin1 signals in neurite tips.Quantitative mathematical modeling shows that the above positive feedback loop, together with shootin1 upregulation, constitutes a core mechanism for neuronal symmetry breaking

Shootin1: a protein involved in the organization of an asymmetric signal for neuronal polarization

Neurons have the remarkable ability to polarize even in symmetrical in vitro environments. Although recent studies have shown that asymmetric intracellular signals can induce neuronal polarization, it remains unclear how these polarized signals are organized without asymmetric cues. We describe a novel protein, named shootin1, that became up-regulated during polarization of hippocampal neurons and began fluctuating accumulation among multiple neurites. Eventually, shootin1 accumulated asymmetrically in a single neurite, which led to axon induction for polarization. Disturbing the asymmetric organization of shootin1 by excess shootin1 disrupted polarization, whereas repressing shootin1 expression inhibited polarization. Overexpression and RNA interference data suggest that shootin1 is required for spatially localized phosphoinositide-3-kinase activity. Shootin1 was transported anterogradely to the growth cones and diffused back to the soma; inhibiting this transport prevented its asymmetric accumulation in neurons. We propose that shootin1 is involved in the generation of internal asymmetric signals required for neuronal polarization

Dynamic Regulation of Myosin Light Chain Phosphorylation by Rho-kinase

Myosin light chain (MLC) phosphorylation plays important roles in various cellular functions such as cellular morphogenesis, motility, and smooth muscle contraction. MLC phosphorylation is determined by the balance between activities of Rho-associated kinase (Rho-kinase) and myosin phosphatase. An impaired balance between Rho-kinase and myosin phosphatase activities induces the abnormal sustained phosphorylation of MLC, which contributes to the pathogenesis of certain vascular diseases, such as vasospasm and hypertension. However, the dynamic principle of the system underlying the regulation of MLC phosphorylation remains to be clarified. Here, to elucidate this dynamic principle whereby Rho-kinase regulates MLC phosphorylation, we developed a mathematical model based on the behavior of thrombin-dependent MLC phosphorylation, which is regulated by the Rho-kinase signaling network. Through analyzing our mathematical model, we predict that MLC phosphorylation and myosin phosphatase activity exhibit bistability, and that a novel signaling pathway leading to the auto-activation of myosin phosphatase is required for the regulatory system of MLC phosphorylation. In addition, on the basis of experimental data, we propose that the auto-activation pathway of myosin phosphatase occurs in vivo. These results indicate that bistability of myosin phosphatase activity is responsible for the bistability of MLC phosphorylation, and the sustained phosphorylation of MLC is attributed to this feature of bistability

Diagnosis by Volatile Organic Compounds in Exhaled Breath from Lung Cancer Patients Using Support Vector Machine Algorithm

Monitoring exhaled breath is a very attractive, noninvasive screening technique for early diagnosis of diseases, especially lung cancer. However, the technique provides insufficient accuracy because the exhaled air has many crucial volatile organic compounds (VOCs) at very low concentrations (ppb level). We analyzed the breath exhaled by lung cancer patients and healthy subjects (controls) using gas chromatography/mass spectrometry (GC/MS), and performed a subsequent statistical analysis to diagnose lung cancer based on the combination of multiple lung cancer-related VOCs. We detected 68 VOCs as marker species using GC/MS analysis. We reduced the number of VOCs and used support vector machine (SVM) algorithm to classify the samples. We observed that a combination of five VOCs (CHN, methanol, CH3CN, isoprene, 1-propanol) is sufficient for 89.0% screening accuracy, and hence, it can be used for the design and development of a desktop GC-sensor analysis system for lung cancer

Systems biology of symmetry breaking during neuronal polarity formation

Polarization, in which a single axon and multiple dendrites are formed, is crucial for neuronal functions, and symmetry breaking is the initial step of this process. Accumulating studies have revealed a number of molecules that act asymmetrically in neurons, and thereby regulate neuronal polarity. Thus, one of the major goals of current research is to understand how asymmetric signals are generated during the symmetry-breaking step. Current models of neuronal symmetry breaking generally involve local activation for induction of axon outgrowth and global inhibition to suppress formation of multiple axons and can be categorized into one-takes-all and activator-inhibitor models. Both types of model incorporate a positive feedback loop to execute local activation, but differ in the manner of global inhibition. Quantitative experimentation combined with computational modeling is a powerful strategy in systems biology, and analyses in this direction have begun to yield a more profound understanding of how neurons break their symmetry during polarity formation

Sensing of substratum rigidity and directional migration by fast-crawling cells

Living cells sense the mechanical properties of their surrounding environment and respond accordingly. Crawling cells detect the rigidity of their substratum and migrate in certain directions. They can be classified into two categories: slow-moving and fast-moving cell types. Slow-moving cell types, such as fibroblasts, smooth muscle cells, mesenchymal stem cells, etc., move toward rigid areas on the substratum in response to a rigidity gradient. However, there is not much information on rigidity sensing in fast-moving cell types whose size is ∼10 μm and migration velocity is ∼10 μm/min. In this study, we used both isotropic substrata with different rigidities and an anisotropic substratum that is rigid on the x axis but soft on the y axis to demonstrate rigidity sensing by fast-moving Dictyostelium cells and neutrophil-like differentiated HL-60 cells. Dictyostelium cells exerted larger traction forces on a more rigid isotropic substratum. Dictyostelium cells and HL-60 cells migrated in the “soft” direction on the anisotropic substratum, although myosin II–null Dictyostelium cells migrated in random directions, indicating that rigidity sensing of fast-moving cell types differs from that of slow types and is induced by a myosin II–related process