Literature DB >> 24136177

Motility efficiency and spatiotemporal synchronization in non-metastatic vs. metastatic breast cancer cells.

Thomas M Hermans1, Didzis Pilans, Sabil Huda, Patrick Fuller, Kristiana Kandere-Grzybowska, Bartosz A Grzybowski.   

Abstract

Metastatic breast cancer cells move not only more rapidly and persistently than their non-metastatic variants but in doing so use the mechanical work of the cytoskeleton more efficiently. The efficiency of the cell motions is defined for entire cells (rather than parts of the cell membrane) and is related to the work expended in forming membrane protrusions and retractions. This work, in turn, is estimated by integrating the protruded and retracted areas along the entire cell perimeter and is standardized with respect to the net translocation of the cell. A combination of cross-correlation, Granger causality, and morphodynamic profiling analyses is then used to relate the efficiency to the cell membrane dynamics. In metastatic cells, the protrusions and retractions are highly "synchronized" both in space and in time and these cells move efficiently. In contrast, protrusions and retractions formed by non-metastatic cells are not "synchronized" corresponding to low motility efficiencies. Our work provides a link between the kinematics of cell motions and their energetics. It also suggests that spatiotemporal synchronization might be one of the hallmarks of invasiveness of cancerous cells.

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Year:  2013        PMID: 24136177      PMCID: PMC4122865          DOI: 10.1039/c3ib40144h

Source DB:  PubMed          Journal:  Integr Biol (Camb)        ISSN: 1757-9694            Impact factor:   2.192


  46 in total

1.  Cell migration in development and disease.

Authors:  Clemens M Franz; Gareth E Jones; Anne J Ridley
Journal:  Dev Cell       Date:  2002-02       Impact factor: 12.270

Review 2.  Mathematics of cell motility: have we got its number?

Authors:  Alex Mogilner
Journal:  J Math Biol       Date:  2008-05-07       Impact factor: 2.259

3.  A computational model of cell migration coupling the growth of focal adhesions with oscillatory cell protrusions.

Authors:  Angélique Stéphanou; Eleni Mylona; Mark Chaplain; Philippe Tracqui
Journal:  J Theor Biol       Date:  2008-05-04       Impact factor: 2.691

4.  A MATLAB toolbox for Granger causal connectivity analysis.

Authors:  Anil K Seth
Journal:  J Neurosci Methods       Date:  2009-12-02       Impact factor: 2.390

5.  Cell migration: PKA and RhoA set the pace.

Authors:  Karen A Newell-Litwa; Alan Rick Horwitz
Journal:  Curr Biol       Date:  2011-08-09       Impact factor: 10.834

6.  Methods for comparing the means of two independent log-normal samples.

Authors:  X H Zhou; S Gao; S L Hui
Journal:  Biometrics       Date:  1997-09       Impact factor: 2.571

7.  Fourier analysis of cell motility: correlation of motility with metastatic potential.

Authors:  A W Partin; J S Schoeniger; J L Mohler; D S Coffey
Journal:  Proc Natl Acad Sci U S A       Date:  1989-02       Impact factor: 11.205

Review 8.  Cell migration in tumors.

Authors:  Hideki Yamaguchi; Jeffrey Wyckoff; John Condeelis
Journal:  Curr Opin Cell Biol       Date:  2005-10       Impact factor: 8.382

9.  Effects of linoleic acid on the growth and metastasis of two human breast cancer cell lines in nude mice and the invasive capacity of these cell lines in vitro.

Authors:  D P Rose; J M Connolly; X H Liu
Journal:  Cancer Res       Date:  1994-12-15       Impact factor: 12.701

10.  Dynamics of fibroblast spreading.

Authors:  G A Dunn; D Zicha
Journal:  J Cell Sci       Date:  1995-03       Impact factor: 5.285

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  6 in total

Review 1.  Emerging machine learning approaches to phenotyping cellular motility and morphodynamics.

Authors:  Hee June Choi; Chuangqi Wang; Xiang Pan; Junbong Jang; Mengzhi Cao; Joseph A Brazzo; Yongho Bae; Kwonmoo Lee
Journal:  Phys Biol       Date:  2021-06-17       Impact factor: 2.959

2.  Application of a co‑expression network for the analysis of aggressive and non‑aggressive breast cancer cell lines to predict the clinical outcome of patients.

Authors:  Ling Guo; Kun Zhang; Zhitong Bing
Journal:  Mol Med Rep       Date:  2017-09-25       Impact factor: 2.952

3.  QuimP: analyzing transmembrane signalling in highly deformable cells.

Authors:  Piotr Baniukiewicz; Sharon Collier; Till Bretschneider
Journal:  Bioinformatics       Date:  2018-08-01       Impact factor: 6.937

4.  A deep learning-based segmentation pipeline for profiling cellular morphodynamics using multiple types of live cell microscopy.

Authors:  Junbong Jang; Chuangqi Wang; Xitong Zhang; Hee June Choi; Xiang Pan; Bolun Lin; Yudong Yu; Carly Whittle; Madison Ryan; Yenyu Chen; Kwonmoo Lee
Journal:  Cell Rep Methods       Date:  2021-10-27

5.  Profiling cellular morphodynamics by spatiotemporal spectrum decomposition.

Authors:  Xiao Ma; Onur Dagliyan; Klaus M Hahn; Gaudenz Danuser
Journal:  PLoS Comput Biol       Date:  2018-08-02       Impact factor: 4.475

6.  Lévy-like movement patterns of metastatic cancer cells revealed in microfabricated systems and implicated in vivo.

Authors:  Sabil Huda; Bettina Weigelin; Katarina Wolf; Konstantin V Tretiakov; Konstantin Polev; Gary Wilk; Masatomo Iwasa; Fateme S Emami; Jakub W Narojczyk; Michal Banaszak; Siowling Soh; Didzis Pilans; Amir Vahid; Monika Makurath; Peter Friedl; Gary G Borisy; Kristiana Kandere-Grzybowska; Bartosz A Grzybowski
Journal:  Nat Commun       Date:  2018-10-31       Impact factor: 14.919

  6 in total

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