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

Autor: Wilhelm Huisinga, Daniel Schindler, Ted Moldenhawer, Carsten Beta, Maike Stange, Matthias Holschneider, Valentino Lepro
Jazyk: angličtina
Rok vydání: 2021
Předmět:
Computer science
Dictyosteliomycota
Cell Membranes
Coordinate system
Boundary (topology)
Quantitative Biology - Quantitative Methods
Frame of reference
Cell Behavior (q-bio.CB)
Image Processing
Computer-Assisted
Dictyostelium
Amoebas
Biology (General)
Quantitative Methods (q-bio.QM)
computer.programming_language
Graphical user interface
Protozoans
Ecology
Applied Mathematics
Simulation and Modeling
Physics
Dictyostelium Discoideum
Institut für Mathematik
Classical Mechanics
Eukaryota
Protists
Deformation
Cell Motility
Experimental Organism Systems
Slime Molds
Computational Theory and Mathematics
Modeling and Simulation
Physical Sciences
Cellular Structures and Organelles
Biological system
Algorithms
Research Article
Imaging Techniques
QH301-705.5
Movement
ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION
Geometry
Research and Analysis Methods
Models
Biological
Biophysical Phenomena
Cellular and Molecular Neuroscience
Model Organisms
ddc:570
Fluorescence Imaging
Embryonic morphogenesis
Genetics
Amoeba (mathematics)
ddc:610
Molecular Biology
Ecology
Evolution
Behavior and Systematics
Damage Mechanics
Curvature
Protozoan Models
business.industry
Organisms
Biology and Life Sciences
Institut für Physik und Astronomie
Cell Biology
Python (programming language)
Range (mathematics)
Microscopy
Fluorescence
FOS: Biological sciences
Animal Studies
Quantitative Biology - Cell Behavior
ddc:004
business
computer
Mathematics
Zdroj: PLoS Computational Biology, Vol 17, Iss 8, p e1009268 (2021)
PLoS Computational Biology
ISSN: 1553-7358
Popis: 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.
Comment: 31 pages, 12 figures, updated version after publication (see 10.1371/journal.pcbi.1009268), for supporting information, see 10.5281/zenodo.3984179, for software and data publication, see 10.5281/zenodo.3982371 and 10.5061/dryad.b5mkkwhbd
Databáze: OpenAIRE
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