Visualizing and Quantifying Data from Time-Lapse Imaging Experiments

Abstract

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.

Keywords: Biosensor; Data Visualization; Dynamics; Fluorescence Imaging; Fluorescent protein; Image analysis; Open-source software; Optogenetics; Time-lapse.