Studying Nuclear Dynamics in Response to Actin Disruption in Planta.

The plant nucleus and the actin cytoskeleton are intimately connected. The actin cytoskeleton is pivotal for nuclear positioning, shape, and dynamics. These properties of the nucleus are important for its functions during normal development and in response to external cues such as biotic and abiotic stresses. Moreover, we know that there is a direct physical connection between the actin cytoskeleton and the nucleus which spans the double-membraned nuclear envelope into the nuclear lamina, and this connection is called the linker of nucleoskeleton and cytoskeleton (LINC) complex. Recently a role for actin in regulating inter-nuclear organization via the control of nuclear invaginations has emerged. Therefore, a detailed understanding of nuclear shape, organization, and dynamics and the techniques used to measure and quantify these metrics will allow us to determine and further understand the contribution made by actin to these parameters. The protocols described here will allow researchers to determine the circularity index of a nucleus, quantify nuclear deformations, and determine dynamics of nuclei within plant cells.

Image-Based Experimental Measurement Techniques to Characterize Velocity Fields in Blood Microflows.

Predicting blood microflow in both simple and complex geometries is challenging because of the composition and behavior of the blood at microscale. However, characterization of the velocity in microchannels is the key for gaining insights into cellular interactions at the microscale, mechanisms of diseases, and efficacy of therapeutic solutions. Image-based measurement techniques are a subset of methods for measuring the local flow velocity that typically utilize tracer particles for flow visualization. In the most basic form, a high-speed camera and microscope setup are the only requirements for data acquisition; however, the development of image processing algorithms and equipment has made current image-based techniques more sophisticated. This mini review aims to provide a succinct and accessible overview of image-based experimental measurement techniques to characterize the velocity field of blood microflow. The following techniques are introduced: cell tracking velocimetry, kymographs, micro-particle velocimetry, and dual-slit photometry as entry techniques for measuring various velocity fields either in vivo or in vitro.

GR24, A Synthetic Strigolactone Analog, and Light Affect the Organization of Cortical Microtubules in Arabidopsis Hypocotyl Cells.

Strigolactones are plant hormones regulating cytoskeleton-mediated developmental events in roots, such as lateral root formation and elongation of root hairs and hypocotyls. The latter process was addressed herein by the exogenous application of a synthetic strigolactone, GR24, and an inhibitor of strigolactone biosynthesis, TIS108, on hypocotyls of wild-type Arabidopsis and a strigolactone signaling mutant max2-1 (more axillary growth 2-1). Owing to the interdependence between light and strigolactone signaling, the present work was extended to seedlings grown under a standard light/dark regime, or under continuous darkness. Given the essential role of the cortical microtubules in cell elongation, their organization and dynamics were characterized under the conditions of altered strigolactone signaling using fluorescence microscopy methods with different spatiotemporal capacities, such as confocal laser scanning microscopy (CLSM) and structured illumination microscopy (SIM). It was found that GR24-dependent inhibition of hypocotyl elongation correlated with changes in cortical microtubule organization and dynamics, observed in living wild-type and max2-1 seedlings stably expressing genetically encoded fluorescent molecular markers for microtubules. Quantitative assessment of microscopic datasets revealed that chemical and/or genetic manipulation of strigolactone signaling affected microtubule remodeling, especially under light conditions. The application of GR24 in dark conditions partially alleviated cytoskeletal rearrangement, suggesting a new mechanistic connection between cytoskeletal behavior and the light-dependence of strigolactone signaling.

KymoButler, a deep learning software for automated kymograph analysis.

Kymographs are graphical representations of spatial position over time, which are often used in biology to visualise the motion of fluorescent particles, molecules, vesicles, or organelles moving along a predictable path. Although in kymographs tracks of individual particles are qualitatively easily distinguished, their automated quantitative analysis is much more challenging. Kymographs often exhibit low signal-to-noise-ratios (SNRs), and available tools that automate their analysis usually require manual supervision. Here we developed KymoButler, a Deep Learning-based software to automatically track dynamic processes in kymographs. We demonstrate that KymoButler performs as well as expert manual data analysis on kymographs with complex particle trajectories from a variety of different biological systems. The software was packaged in a web-based 'one-click' application for use by the wider scientific community (http://kymobutler.deepmirror.ai). Our approach significantly speeds up data analysis, avoids unconscious bias, and represents another step towards the widespread adaptation of Machine Learning techniques in biological data analysis.

Many molecules and structures within cells have to move about to do their job. Studying these movements is important to understand many biological processes, including the development of the brain or the spread of viruses. Kymographs are images that represent the movement of particles in time and space. Unfortunately, tracing the lines that represent movement in kymographs of biological particles is hard to do automatically, so currently this analysis is done by hand. Manually annotating kymographs is tedious, time-consuming and prone to the researcher's unconscious bias. In an effort to simplify the analysis of kymographs, Jakobs et al. have developed KymoButler, a software tool that can do it automatically. KymoButler uses artificial intelligence to trace the lines in a kymograph and extract the information about particle movement. It speeds up analysis of kymographs by between 50 and 250 times, and comparisons show that it is as reliable as manual analysis. KymoButler is also significantly more effective than any previously existing automatic kymograph analysis programme. To make KymoButler accessible, Jakobs et al. have also created a website with a drag-and-drop facility that allows researchers to easily use the tool. KymoButler has been tested in many areas of biological research, from quantifying the movement of molecules in neurons to analysing the dynamics of the scaffolds that help cells keep their shape. This variety of applications showcases KymoButler's versatility, and its potential applications. Jakobs et al. are further contributing to the field of machine learning in biology with 'deepmirror.ai', an online hub with the goal of accelerating the adoption of artificial intelligence in biology.

Analyzing mitochondrial dynamics in mouse organotypic slice cultures.

Mitochondria are mobile organelles that dynamically remodel their membranes and actively migrate along cytoskeletal tracks. There is overwhelming evidence that regulators of mitochondrial dynamics are critical for the survival and function of neural tissues. In multiple animal models, ablation of genes regulating mitochondrial shape result in stunted neural development and neurodegeneration. Organotypic cultures serve as ideal in vitro tissue models to further dissect the mechanisms of mitochondrial function in neuronal survival. Slice cultures preserve the three-dimensional cytoarchitecture of neural networks and can survive for prolonged periods in culture. In addition, these cultures allow long-term assessment of genetic or pharmacologic perturbations on neuronal function. Organotypic preparations from murine and rat models have been developed for many regions of the brain. In this chapter, we describe our methods for preparing basal ganglia and cerebellar slice cultures suitable for studying mitochondrial function in Parkinson's disease and cerebellar ataxia, respectively. With such slices, we describe a robust method for live imaging of mitochondrial dynamics. To quantitatively analyze mitochondrial motility, we show how to generate kymographs using the open source image analysis program ImageJ. These techniques provide a powerful platform for assessing mitochondrial activity in neural networks.