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A novel cell traction force microscopy to study multi-cellular system.

Authors :
Tang X
Tofangchi A
Anand SV
Saif TA
Source :
PLoS computational biology [PLoS Comput Biol] 2014 Jun 05; Vol. 10 (6), pp. e1003631. Date of Electronic Publication: 2014 Jun 05 (Print Publication: 2014).
Publication Year :
2014

Abstract

Traction forces exerted by adherent cells on their microenvironment can mediate many critical cellular functions. Accurate quantification of these forces is essential for mechanistic understanding of mechanotransduction. However, most existing methods of quantifying cellular forces are limited to single cells in isolation, whereas most physiological processes are inherently multi-cellular in nature where cell-cell and cell-microenvironment interactions determine the emergent properties of cell clusters. In the present study, a robust finite-element-method-based cell traction force microscopy technique is developed to estimate the traction forces produced by multiple isolated cells as well as cell clusters on soft substrates. The method accounts for the finite thickness of the substrate. Hence, cell cluster size can be larger than substrate thickness. The method allows computing the traction field from the substrate displacements within the cells' and clusters' boundaries. The displacement data outside these boundaries are not necessary. The utility of the method is demonstrated by computing the traction generated by multiple monkey kidney fibroblasts (MKF) and human colon cancerous (HCT-8) cells in close proximity, as well as by large clusters. It is found that cells act as individual contractile groups within clusters for generating traction. There may be multiple of such groups in the cluster, or the entire cluster may behave a single group. Individual cells do not form dipoles, but serve as a conduit of force (transmission lines) over long distances in the cluster. The cell-cell force can be either tensile or compressive depending on the cell-microenvironment interactions.

Details

Language :
English
ISSN :
1553-7358
Volume :
10
Issue :
6
Database :
MEDLINE
Journal :
PLoS computational biology
Publication Type :
Academic Journal
Accession number :
24901766
Full Text :
https://doi.org/10.1371/journal.pcbi.1003631