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.
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References
Long F, Zhou J, Peng H (2012) Visualization and analysis of 3D microscopic images. PLoS Comput Biol 8:e1002519
Pietzsch T, Saalfeld S, Preibisch S et al (2015) BigDataViewer: visualization and processing for large image data sets. Nat Methods 12:481–483
Schindelin J, Arganda-Carreras I, Frise E et al (2012) Fiji: an open-source platform for biological-image analysis. Nat Methods 9:676–682
Goedhart J (2020) PlotTwist: a web app for plotting and annotating continuous data. PLoS Biol 18:e3000581
Tinevez J-Y, Perry N, Schindelin J et al (2017) TrackMate: an open and extensible platform for single-particle tracking. Methods 115:80–90
Meddens MBM, Pandzic E, Slotman JA et al (2016) Actomyosin-dependent dynamic spatial patterns of cytoskeletal components drive mesoscale podosome organization. Nat Commun 7:13127
Chertkova AO, Mastop M, Postma M et al (2020) Robust and bright genetically encoded fluorescent markers for highlighting structures and compartments in mammalian cells. bioRxiv. https://doi.org/10.1101/160374
Arts JJG, Mahlandt EK, Grönloh MLB et al (2021) Endothelial junctional membrane protrusions serve as hotspots for neutrophil transmigration. elife. https://doi.org/10.7554/eLife.66074
Geissbuehler M, Lasser T (2013) How to display data by color schemes compatible with red-green color perception deficiencies. Opt Express 21:9862–9874
Reinhard NR, Mastop M, Yin T et al (2017) The balance between Gαi-Cdc42/Rac and Gα12/13-RhoA pathways determines endothelial barrier regulation by sphingosine-1-phosphate. Mol Biol Cell 28(23):3371–3382
Mastop M, Reinhard NR, Zuconelli CR et al (2018) A FRET-based biosensor for measuring Gα13 activation in single cells. PLoS One 13(3):e0193705
Niopek D, Wehler P, Roensch J et al (2016) Optogenetic control of nuclear protein export. Nat Commun 7:10624
Nagai T, Yamada S, Tominaga T et al (2004) Expanded dynamic range of fluorescent indicators for Ca2+ by circularly permuted yellow fluorescent proteins. Proc Natl Acad Sci U S A 101:10554
Van Unen J, Rashidfarrokhi A, Hoogendoorn E et al (2016) Quantitative single-cell analysis of signaling pathways activated immediately downstream of histamine receptor subtypes. Mol Pharmacol (3):90, 162–176
Jakobs MA, Dimitracopoulos A, Franze K (2019) KymoButler, a deep learning software for automated kymograph analysis. elife 8:e42288
Acknowledgments
We thank Janine Arts and Jaap van Buul (Sanquin Research and Landsteiner Laboratory, Amsterdam, the Netherlands) for providing the data of the membrane dynamics and Marten Postma (University of Amsterdam, the Netherlands) for useful discussions.
This work was supported by an NWO ALW-OPEN grant ALWOP.306 (EKM). We are grateful for all the input, comments, and solutions from the active communities on Stack Overflow, Twitter, and other fora that share their knowledge and expertise (you know who you are).
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Mahlandt, E.K., Goedhart, J. (2022). Visualizing and Quantifying Data from Time-Lapse Imaging Experiments. In: Heit, B. (eds) Fluorescent Microscopy. Methods in Molecular Biology, vol 2440. Humana, New York, NY. https://doi.org/10.1007/978-1-0716-2051-9_19
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DOI: https://doi.org/10.1007/978-1-0716-2051-9_19
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