Cell Shape Dynamics: From Waves to Migration

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

doi:10.1371/journal.pcbi.1002392

Cited by 123 publications

(147 citation statements)

References 36 publications

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“…Without cell-surface contact, cells significantly change their collective migration pattern. However, the protrusions and retractions of individual cells are not significantly altered by inhibiting cell-surface contact, which is consistent with our previous observations of cells suspended in water [17]. Next, we investigate how cell-surface coupling affects cell-cell coupling and the spatial distribution of actin polymerization.…”

Section: Introductionsupporting

confidence: 89%

The interplay of cell–cell and cell–substrate adhesion in collective cell migration

Wang

Chowdhury

Driscoll

et al. 2014

J. R. Soc. Interface.

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Collective cell migration often involves notable cell-cell and cell-substrate adhesions and highly coordinated motion of touching cells. We focus on the interplay between cell-substrate adhesion and cell-cell adhesion. We show that the loss of cell-surface contact does not significantly alter the dynamic pattern of protrusions and retractions of fast migrating amoeboid cells (Dictyostelium discoideum), but significantly changes their ability to adhere to other cells. Analysis of the dynamics of cell shapes reveals that cells that are adherent to a surface may coordinate their motion with neighbouring cells through protrusion waves that travel across cell-cell contacts. However, while shape waves exist if cells are detached from surfaces, they do not couple cell to cell. In addition, our investigation of actin polymerization indicates that loss of cell-surface adhesion changes actin polymerization at cell-cell contacts. To further investigate cell-cell/cell-substrate interactions, we used optical micromanipulation to form cell-substrate contact at controlled locations. We find that both cell-shape dynamics and cytoskeletal activity respond rapidly to the formation of cell-substrate contact.

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Section: Introductionsupporting

confidence: 89%

The interplay of cell–cell and cell–substrate adhesion in collective cell migration

Wang

Chowdhury

Driscoll

et al. 2014

J. R. Soc. Interface.

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“…In particular, because zebrafish larvae are of fixed length and generate body movements in two dimensions, little modification would be required to adapt the approach described here. Although a representation based on skeleton angles would not be optimal for Drosophila larvae, a representation based directly on outline curvature could make Drosophila larva locomotion and in vitro cell motility (43) amenable to unsupervised motif discovery as well. Motifderived phenotypes are related to functional classes but are derived completely independently from other data; we therefore expect them to provide complementary information that may be usefully combined with proteomic and transcriptomic data in the future.…”

Section: Resultsmentioning

confidence: 99%

A dictionary of behavioral motifs reveals clusters of genes affecting Caenorhabditis elegans locomotion

Brown

Yemini

Grundy

et al. 2012

Proc. Natl. Acad. Sci. U.S.A.

210

252

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Visible phenotypes based on locomotion and posture have played a critical role in understanding the molecular basis of behavior and development in Caenorhabditis elegans and other model organisms. However, it is not known whether these human-defined features capture the most important aspects of behavior for phenotypic comparison or whether they are sufficient to discover new behaviors. Here we show that four basic shapes, or eigenworms, previously described for wild-type worms, also capture mutant shapes, and that this representation can be used to build a dictionary of repetitive behavioral motifs in an unbiased way. By measuring the distance between each individual's behavior and the elements in the motif dictionary, we create a fingerprint that can be used to compare mutants to wild type and to each other. This analysis has revealed phenotypes not previously detected by real-time observation and has allowed clustering of mutants into related groups. Behavioral motifs provide a compact and intuitive representation of behavioral phenotypes.phenotyping | imaging | ethology | nematode T he study of unconstrained spontaneous behavior is the core of ethology, and it has also made significant contributions to behavioral genetics in model organisms. A powerful approach has been the careful expert observation of mutants to identify those with visible locomotor phenotypes, as demonstrated for many model organisms (1-6). However, as with most manually scored experiments, subjectivity can reduce reproducibility, whereas subtle quantitative changes or those that happen on very short or long time-scales are likely to be missed. Furthermore, manual observations are not scalable, and this has led to a widening gap between our ability to sequence and manipulate genomes and our ability to assess the effects of genetic variation and mutation on behavior.Several recent reports describe systems that begin to address this gap by automatically recording and quantifying spontaneous behavior in animals ranging from worms (7-15) to flies (16-19), fish (20, 21), and mice (22,23). The advantage of these approaches is that they provide a means to quantify movement parameters such as velocity precisely and in some cases to automatically detect predefined behaviors based on a manually annotated training data set. This automated analysis eliminates some of the problems of a purely manual approach, but it still relies on preselected behavioral parameters that may not be optimal for phenotypic comparisons and precludes the discovery of new behaviors that have not already been observed by eye. An alternative approach is to use unsupervised learning, which attempts to use the inherent structure of a data set to identify informative patterns; to do this, we first needed to extract worm postures from movie data and have as compact and complete a representation of worm behavior as possible.

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“…The latter class of waves comprises periodic actin-based protrusions in various mouse and Drosophila cells (Döbereiner et al, 2006), and curvature waves in Dictyostelium that travel from the leading edge to the tail of a cell (Driscoll et al, 2012).…”

Section: Introductionmentioning

confidence: 99%

Actin and PIP3 waves in giant cells reveal the inherent length scale of an excited state

Gerhardt

Ecke

Walz

et al. 2014

Journal of Cell Science

106

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The membrane and actin cortex of a motile cell can autonomously differentiate into two states, one typical of the front, the other of the tail. On the substrate-attached surface of Dictyostelium discoideum cells, dynamic patterns of front-like and tail-like states are generated that are well suited to monitor transitions between these states. To image large-scale pattern dynamics independently of boundary effects, we produced giant cells by electric-pulse-induced cell fusion. In these cells, actin waves are coupled to the front and back of phosphatidylinositol (3,4,5)-trisphosphate (PIP3)-rich bands that have a finite width. These composite waves propagate across the plasma membrane of the giant cells with undiminished velocity. After any disturbance, the bands of PIP3 return to their intrinsic width. Upon collision, the waves locally annihilate each other and change direction; at the cell border they are either extinguished or reflected. Accordingly, expanding areas of progressing PIP3 synthesis become unstable beyond a critical radius, their center switching from a front-like to a tail-like state. Our data suggest that PIP3 patterns in normal-sized cells are segments of the selforganizing patterns that evolve in giant cells.

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Cell Shape Dynamics: From Waves to Migration

Cited by 123 publications

References 36 publications

The interplay of cell–cell and cell–substrate adhesion in collective cell migration

The interplay of cell–cell and cell–substrate adhesion in collective cell migration

A dictionary of behavioral motifs reveals clusters of genes affecting Caenorhabditis elegans locomotion

Actin and PIP3 waves in giant cells reveal the inherent length scale of an excited state

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