Mechanical model of cytoskeleton structuration during cell adhesion and spreading

Maurin, Bernard; Cañadas, Patrick; Baudriller, H.; Montcourrier, Philippe; Bettache, Nadir

doi:10.1016/j.jbiomech.2008.03.011

Cited by 47 publications

(53 citation statements)

References 15 publications

(21 reference statements)

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Section: Mechanical State Of Adherent Cell Depending On Its Morphologycontrasting

confidence: 75%

“…Nonetheless, other studies showed that, during cell adhesion and spreading, the growth of microtubules targeted early FAPs to orient and stabilise them (Krylyshkina et al, 2003). PFA-targeting microtubules in a biochemical purpose seems to have no mechanical action contrary to the hypothesis of Maurin et al and this was not taken into account in the present CDM model (Maurin et al, 2008).In the present study, the differentiation of the adherent stem cells cultured in vitro was not analysed. The computational results referred to morphological data which are known to have an influence on cell differentiation.…”

contrasting

confidence: 56%

“…McBeath et al showed that changes in cell shape alone are enough to mediate the switch in hMSC commitment from their adipogenic and osteogenic fate caused by the ROCK-mediated cytoskeletal tension and may raise the possibility that contractile activity increased with cell spreading (McBeath et al, 2004). We thus chose to investigate relations between intracellular tonus and cell shape.…”

Section: Mechanical State Of Adherent Cell Depending On Its Morphologymentioning

confidence: 99%

“…The differences between mechanical states were caused not only by cell shape but also by the number of focal adhesions, which influenced the amount of long Mechanotransduction during MSC adhesion as well as changes in CSK stiffness by the formation of stress fibres. Intracellular tonus as a function of adhesion conditions could explain the driving factor behind cell shape in cell differentiation (Kilian et al, 2010;McBeath et al, 2004). The more the CDM model spread, the more the intracellular tonus was transmitted directly to the substrate via focal adhesions.…”

Section: Mechanical State Of Adherent Cell Depending On Its Morphologymentioning

confidence: 99%

“…Moreover, stem cells, which are recruited to heal the implant interface, adhere on the surface and differentiate depending, among other factors, on their adherent morphology. For instance, stem cells which reach at the end of adhesion round, elongated or branched shapes depending on substrate topography or stiffness, differentiate preferentially into adipocytes, fibroblasts or osteoblasts, respectively (McBeath et al, 2004;Engler et al, 2006). Therefore, biomaterial surface must promote adhesion of stem cells and their differentiation into the desired phenotype so as to synthesise a periprosthetic tissue, ensuring efficient and viable biomaterial integration.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Computational model combined with in vitro experiments to analyse mechanotransduction during mesenchymal stem cell adhesion

Milan¹,

Lavenus²,

Pilet³

et al. 2013

eCM

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The shape that stem cells reach at the end of adhesion process influences their differentiation. Rearrangement of cytoskeleton and modification of intracellular tension may activate mechanotransduction pathways controlling cell commitment. In the present study, the mechanical signals involved in cell adhesion were computed in in vitro stem cells of different shapes using a single cell model, the so-called Cytoskeleton Divided Medium (CDM) model. In the CDM model, the filamentous cytoskeleton and nucleoskeleton networks were represented as a mechanical system of multiple tensile and compressive interactions between the nodes of a divided medium. The results showed that intracellular tonus, focal adhesion forces as well as nuclear deformation increased with cell spreading. The cell model was also implemented to simulate the adhesion process of a cell that spreads on protein-coated substrate by emitting filopodia and creating new distant focal adhesion points. As a result, the cell model predicted cytoskeleton reorganisation and reinforcement during cell spreading. The present model quantitatively computed the evolution of certain elements of mechanotransduction and may be a powerful tool for understanding cell mechanobiology and designing biomaterials with specific surface properties to control cell adhesion and differentiation. Keywords:Mesenchymal stem cells; cell adhesion; cell morphology; computational cell adhesion model; cytoskeleton; mechanical forces; mechanotransduction. IntroductionCell adhesion is fundamental in the field of biomaterials. Especially in orthopaedics, it is crucial for prosthesis osseointegration (Lavenus et al., 2010). Osteoblasts need to adhere on the implant to synthesise the bone tissue that is directly connected to it without the intervening soft tissue. Moreover, stem cells, which are recruited to heal the implant interface, adhere on the surface and differentiate depending, among other factors, on their adherent morphology. For instance, stem cells which reach at the end of adhesion round, elongated or branched shapes depending on substrate topography or stiffness, differentiate preferentially into adipocytes, fibroblasts or osteoblasts, respectively (McBeath et al., 2004;Engler et al., 2006). Therefore, biomaterial surface must promote adhesion of stem cells and their differentiation into the desired phenotype so as to synthesise a periprosthetic tissue, ensuring efficient and viable biomaterial integration. Biomaterials that mimic the natural composition of the extracellular matrix have been developed. They are composed of a biological phase or are coated with RGD complex and showed positive effects on cell adhesion (Von der Mark et al., 2010). Topography and surface chemistry, which play a key role in cell adhesion, may be controlled using nanotechnology. This announces the development of new implant surfaces with predictable tissue-integrative properties (Le Guéhennec et al., 2007;Lavenus et al., 2010).Cell adhesion results from the creation of several discrete focal a...

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Section: Mechanical State Of Adherent Cell Depending On Its Morphologycontrasting

confidence: 75%

contrasting

confidence: 56%

Section: Mechanical State Of Adherent Cell Depending On Its Morphologymentioning

confidence: 99%

Section: Mechanical State Of Adherent Cell Depending On Its Morphologymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Computational model combined with in vitro experiments to analyse mechanotransduction during mesenchymal stem cell adhesion

Milan¹,

Lavenus²,

Pilet³

et al. 2013

eCM

View full text Add to dashboard Cite

show abstract

A multiscale approach for determining the morphology of endothelial cells at a coronary artery

Pakravan

Saidi

Firoozabadi

2017

Numer Methods Biomed Eng

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The morphology of endothelial cells (ECs) may be an indication for determining atheroprone sites. Until now, there has been no clinical imaging technique to visualize the morphology of ECs in the arteries. The present study introduces a computational technique for determining the morphology of ECs. This technique is a multiscale simulation consisting of the artery scale and the cell scale. The artery scale is a fluid-structure interaction simulation. The input for the artery scale is the geometry of the coronary artery, that is, the dynamic curvature of the artery due to the cardiac motion, blood flow, blood pressure, heart rate, and the mechanical properties of the blood and the arterial wall, the measurements of which can be obtained for a specific patient. The results of the artery scale are wall shear stress (WSS) and cyclic strains as the mechanical stimuli of ECs. The cell scale is an inventive mass-and-spring model that is able to determine the morphological response of ECs to any combination of mechanical stimuli. The results of the multiscale simulation show the morphology of ECs at different locations of the coronary artery. The results indicate that the atheroprone sites have at least 1 of 3 factors: low time-averaged WSS, high angle of WSS, and high longitudinal strain. The most probable sites for atherosclerosis are located at the bifurcation region and lie on the myocardial side of the artery. The results also indicated that a higher dynamic curvature is a negative factor and a higher pulse pressure is a positive factor for protection against atherosclerosis.

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Correlations between the dielectric properties and exterior morphology of cells revealed by dielectrophoretic field‐flow fractionation

et al. 2013

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Although dielectrophoresis (DEP) has great potential for addressing clinical cell isolation problems based on cell dielectric differences, a biological basis for predicting the DEP behavior of cells has been lacking. Here, the dielectric properties of the NCI-60 panel of tumor cell types have been measured by dielectrophoretic (DEP) field-flow fractionation, correlated with the exterior morphologies of the cells during growth, and compared with the dielectric and morphological characteristics of the subpopulations of peripheral blood. In agreement with earlier findings, cell total capacitance varied with both cell size and plasma membrane folding and the dielectric properties of the NCI-60 cell types in suspension reflected the plasma membrane area and volume of the cells at their growth sites. Therefore, the behavior of cells in DEP-based manipulations is largely determined by their exterior morphological characteristics prior to release into suspension. As a consequence, DEP is able to discriminate between cells of similar size having different morphological origins, offering a significant advantage over size-based filtering for isolating circulating tumor cells, for example. The findings provide a framework for anticipating cell dielectric behavior on the basis of structure-function relationships and suggest that DEP should be widely applicable as a surface marker-independent method for sorting cells.

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Mechanical model of cytoskeleton structuration during cell adhesion and spreading

Cited by 47 publications

References 15 publications

Computational model combined with in vitro experiments to analyse mechanotransduction during mesenchymal stem cell adhesion

Computational model combined with in vitro experiments to analyse mechanotransduction during mesenchymal stem cell adhesion

A multiscale approach for determining the morphology of endothelial cells at a coronary artery

Correlations between the dielectric properties and exterior morphology of cells revealed by dielectrophoretic field‐flow fractionation

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