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Cell Shape Dynamics: From Waves to Migration

Overview of attention for article published in PLoS Computational Biology, March 2012
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Title
Cell Shape Dynamics: From Waves to Migration
Published in
PLoS Computational Biology, March 2012
DOI 10.1371/journal.pcbi.1002392
Pubmed ID
Authors

Meghan K. Driscoll, Colin McCann, Rael Kopace, Tess Homan, John T. Fourkas, Carole Parent, Wolfgang Losert

Abstract

We observe and quantify wave-like characteristics of amoeboid migration. Using the amoeba Dictyostelium discoideum, a model system for the study of chemotaxis, we demonstrate that cell shape changes in a wave-like manner. Cells have regions of high boundary curvature that propagate from the leading edge toward the back, usually along alternating sides of the cell. Curvature waves are easily seen in cells that do not adhere to a surface, such as cells that are electrostatically repelled from surfaces or cells that extend over the edge of micro-fabricated cliffs. Without surface contact, curvature waves travel from the leading edge to the back of a cell at -35 µm/min. Non-adherent myosin II null cells do not exhibit these curvature waves. At the leading edge of adherent cells, curvature waves are associated with protrusive activity. Like regions of high curvature, protrusive activity travels along the boundary in a wave-like manner. Upon contact with a surface, the protrusions stop moving relative to the surface, and the boundary shape thus reflects the history of protrusive motion. The wave-like character of protrusions provides a plausible mechanism for the zig-zagging of pseudopods and for the ability of cells both to swim in viscous fluids and to navigate complex three dimensional topography.

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 201 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 6 3%
France 3 1%
Japan 2 <1%
Portugal 1 <1%
Unknown 189 94%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 58 29%
Researcher 50 25%
Student > Master 23 11%
Professor > Associate Professor 11 5%
Student > Bachelor 9 4%
Other 25 12%
Unknown 25 12%
Readers by discipline Count As %
Agricultural and Biological Sciences 56 28%
Physics and Astronomy 41 20%
Biochemistry, Genetics and Molecular Biology 23 11%
Engineering 19 9%
Computer Science 6 3%
Other 30 15%
Unknown 26 13%