Visualizing and quantifying data from timelapse 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 timelapse 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 timelapse 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 timelapse imaging experiments.

Focus on biosensors: Looking through the lens of quantitative biology

“…The importance of a good segmentation becomes apparent in the subsequent quantification steps. For intensiometric sensors, this involves quantifying one signal channel, whereas for ratiometric sensors, this may involve dividing the signal from one channel by another (Mahlandt & Goedhart, 2021;Rizza et al, 2019). For ratiometric sensors, quantifying fluorescence emission in areas with poor/no signal can lead to artefactually large variation and a low signal-to-noise ratio as can including pixels where the detector Notes: Several image acquisition channels are used in FRET biosensor analysis.…”

“…This compromise means accurate fully automated segmentation and complete tissue reconstruction is difficult, but the development of deep learning segmentation tools, such as PlantSeg (Wolny et al, 2020) and Stardist (Weigert et al, 2020), may help increase segmentation accuracy and overcome some of these bottlenecks. After segmentation steps, biosensor image analysis workflows also require additional bespoke steps, such as background subtraction and the calculation of emission ratios, to process the data, so it can be quantified meaningfully (Mahlandt & Goedhart, 2021;Rizza et al, 2017;Rowe et al, 2021;Waadt et al, 2020).…”