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  Analysis of protrusion dynamics in amoeboid cell motility by means of regularized contour flows

Schindler, D., Moldenhawer, T., Stange, M., Lepro, V., Beta, C., Holschneider, M., et al. (2021). Analysis of protrusion dynamics in amoeboid cell motility by means of regularized contour flows. PLoS Computational Biology, 17(8): e1009268. doi:10.1371/journal.pcbi.1009268.

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Schindler, Daniel, Author
Moldenhawer, Ted, Author
Stange, Maike, Author
Lepro, Valentino1, Author              
Beta, Carsten, Author
Holschneider, Matthias, Author
Huisinga, Wilhelm, Author
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1Stefan Klumpp, Theorie & Bio-Systeme, Max Planck Institute of Colloids and Interfaces, Max Planck Society, ou_1863329              

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 Abstract: Amoeboid cell motility is essential for a wide range of biological processes including wound healing, embryonic morphogenesis, and cancer metastasis. It relies on complex dynamical patterns of cell shape changes that pose long-standing challenges to mathematical modeling and raise a need for automated and reproducible approaches to extract quantitative morphological features from image sequences. Here, we introduce a theoretical framework and a computational method for obtaining smooth representations of the spatiotemporal contour dynamics from stacks of segmented microscopy images. Based on a Gaussian process regression we propose a one-parameter family of regularized contour flows that allows us to continuously track reference points (virtual markers) between successive cell contours. We use this approach to define a coordinate system on the moving cell boundary and to represent different local geometric quantities in this frame of reference. In particular, we introduce the local marker dispersion as a measure to identify localized membrane expansions and provide a fully automated way to extract the properties of such expansions, including their area and growth time. The methods are available as an open-source software package called AmoePy, a Python-based toolbox for analyzing amoeboid cell motility (based on time-lapse microscopy data), including a graphical user interface and detailed documentation. Due to the mathematical rigor of our framework, we envision it to be of use for the development of novel cell motility models. We mainly use experimental data of the social amoeba Dictyostelium discoideum to illustrate and validate our approach.

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Language(s): eng - English
 Dates: 2021-08-232021
 Publication Status: Published in print
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 Identifiers: DOI: 10.1371/journal.pcbi.1009268
BibTex Citekey: 10.1371/journal.pcbi.1009268
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Title: PLoS Computational Biology
Source Genre: Journal
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Publ. Info: San Francisco, CA : Public Library of Science
Pages: - Volume / Issue: 17 (8) Sequence Number: e1009268 Start / End Page: - Identifier: ISSN: 1553-734X