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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., eLife digest 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.

Cell division plays an important role in animal tissue morphogenesis, which depends, critically, on the orientation of divisions. In isolated adherent cells, the orientation of mitotic spindles is sensitive to interphase cell shape and the direction of extrinsic mechanical forces. In epithelia, the relative importance of these two factors is challenging to assess. To do this, we used suspended monolayers devoid of ECM, where divisions become oriented following a stretch, allowing the regulation and function of epithelial division orientation in stress relaxation to be characterized. Using this system, we found that divisions align better with the long, interphase cell axis than with the monolayer stress axis. Nevertheless, because the application of stretch induces a global realignment of interphase long axes along the direction of extension, this is sufficient to bias the orientation of divisions in the direction of stretch. Each division redistributes the mother cell mass along the axis of division. Thus, the global bias in division orientation enables cells to act collectively to redistribute mass along the axis of stretch, helping to return the monolayer to its resting state. Further, this behavior could be quantitatively reproduced using a model designed to assess the impact of autonomous changes in mitotic cell mechanics within a stretched monolayer. In summary, the propensity of cells to divide along their long axis preserves epithelial homeostasis by facilitating both stress relaxation and isotropic growth without the need for cells to read or transduce mechanical signals.