Section Molecular Cytology, Swammerdam Institute for Life Sciences, University of Amsterdam, Amsterdam, The Netherlands.
Methods Mol Biol. 2022;2440:329-348. doi: 10.1007/978-1-0716-2051-9_19.
One obvious feature of life is that it is highly dynamic. The dynamics can be captured by movies that are made by acquiring images at regular time intervals, a method that is also known as time-lapse imaging. Looking at movies is a great way to learn more about the dynamics in cells, tissue, and organisms. However, science is different from Netflix, in that it aims for a quantitative understanding of the dynamics. The quantification is important for the comparison of dynamics and to study effects of perturbations. Here, we provide detailed processing and analysis methods that we commonly use to analyze and visualize our time-lapse imaging data. All methods use freely available open-source software and use example data that is available from an online data repository. The step-by-step guides together with example data allow for fully reproducible workflows that can be modified and adjusted to visualize and quantify other data from time-lapse imaging experiments.
生命的一个明显特征是其具有高度的动态性。这种动态性可以通过定期获取图像制作的电影来捕捉，这种方法也被称为延时成像。观看电影是了解细胞、组织和生物体动态的好方法。然而，科学与 Netflix 不同，它旨在对动态进行定量理解。这种量化对于比较动态和研究干扰效应很重要。在这里，我们提供了详细的处理和分析方法，我们通常使用这些方法来分析和可视化我们的延时成像数据。所有方法都使用免费的开源软件，并使用在线数据存储库中提供的示例数据。这些分步指南和示例数据允许进行完全可重复的工作流程，可以对其进行修改和调整，以可视化和量化来自延时成像实验的其他数据。
