Persistent random deformation model of cells crawling on a gel surface

Ebata, Hiroyuki; Yamamoto, Azusa; Tsuji, Yasuo; Sasaki, Shigeo; Moriyama, Kosei; Kuboki, Thasaneeya; Kidoaki, Satoru

doi:10.1038/s41598-018-23540-x

Cited by 32 publications

(32 citation statements)

References 40 publications

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“…Observation of cell morphology and motility under label-free microscopes such as phase contrast is not so quantitative because of polymorphism of cell (Ebara et al, 2018; Li et al, 2006; Masuzzo et al, 2016). In this study, the damage of cell was detected by IFM even when the changes in the areas and shapes of the cell were not detected (Fig.…”

Section: Discussionmentioning

confidence: 99%

Quantitative Detection of Cell Activity by Measuring the Fluctuation of Intracellular Motility

Sakuma

Kondo

Higuchi

2019

Preprint

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13The measurement of cell activity changes during damage is important to 14 understand the process of cell death and evaluate the effect of medicines. To 15 evaluate cell activity generally, we extended the method of intensity fluctuation in 16 which intensity change in the pixel induced by the movement of organelles was 17 calculated. Cancer, endothelial and iPS cells were damaged by reactive oxygen 18 species (ROS) generated by a fluorescent dye (IR700), hydrogen peroxide, and 19 ultraviolet light. The intensity fluctuation in damaged cells gradually decreased 20 independent of the kind of cell, indicating that the decrease in the fluctuation is a 21 general phenomenon in damaged cells. The rupture of vesicles and 22 mitochondria in the cells were observed upon ROS production. The motility of 23 purified kinesin and dynein which transport vesicles and organelles was inhibited 24 by ROS. These suggest that ROS and cytotoxic molecules spreading from 25 ruptured organelles contribute to the reduction in cell activity which brings about 26 the decrease in the motility and intensity fluctuation of organelles driven by 27 kinesin and dynein.28 Liao et al., 2017). The motility of mitochondria gradually decreased just after the 65 addition of hydrogen peroxide, and the decrease in the motility reached 50% 66 within 10 minutes (Debattisti et al., 2017). These results suggested that the 67 effect on the motility of organelles in damaged cells occurred earlier than the 68 changes in cell morphology and motility. Thus, the method of detecting 69 intracellular motility under a conventional microscope would overcome the 70 disadvantages of previous fluorescence, morphological, and cell motility 71 methods. However, little is understood about the generality and mechanism of 72 motility reduction. 73In this study, various cells were damaged by several methods, including 74 photoactivation of IR700, irradiation with ultraviolet (UV) light and treatment with 75 hydrogen peroxide (H 2 O 2 ), and the change in the motility of organelles was 76 quantitatively evaluated by the developed intensity fluctuation method (IFM). We 77 found that all cells showed a decrease in motility caused by damage. To 78 elucidate the mechanisms of the decrease in motility in damaged cells, we 79 measured the reduction in the motile speeds of single vesicles in cells and in 80 purified kinesin and dynein in vitro assays. These measurements revealed that 81 the decrease detected by IMF resulted in the reduction in the organelle motility 82 driven by motor proteins. Since the transport system commonly exists in animal 83 cells, the decrease in the motility or transport of organelles would be a universal 84 phenomenon in various kinds of cells. 85 5 Results 86 87 1 Decrement of motility of organelles in damaged cells 88 Changes in the motility of organelles in damaged cells were evaluated 89 by the intensity fluctuation method (IFM) in various cell types. Six kinds of cells, 90 four cancer cell lines, one endothelial cell line, and one iPS cell line...

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Section: Discussionmentioning

confidence: 99%

Quantitative Detection of Cell Activity by Measuring the Fluctuation of Intracellular Motility

Sakuma

Kondo

Higuchi

2019

Preprint

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“…Cells that moved from the soft region toward the stiff domain of the surface elasticity boundary were selected for shape analysis based on our previous work (Ebata et al, 2018). The distance R(θ) from the centroid to the cell edge was calculated as a function of the angle θ, where θ is measured from the x axis.…”

Section: Quantification Of Cell Polarity At the Elasticity Boundarymentioning

confidence: 99%

“…On the other hand, the above analysis is valid only when the cell has elongated shape; it cannot adequately deal with less elongated shape like equilateral triangle that should appear in the process of development of motile polarity. Thus, we introduced more general analytical method by using the complex Fourier coefficient C n of cell-shape R (PRD model, see Method (Ebata et al, 2018)). C n denote Fig.…”

Section: Polarization Of Durotactic Cellsmentioning

confidence: 99%

“…To prepare a well-defined elasticity boundary, photolithographic microelasticity patterning of photocurable gelatin was used (Kidoaki and Matsuda, 2008;Kawano and Kidoaki, 2011;Kidoaki and Sakashita, 2013;Kuboki et al, 2014;Ueki and Kidoaki, 2015). The precise timing of the generation of cellular polarity when a cell crossed the boundary was quantified with a Fourier mode analysis of the cell shape (persistent random deformation (PRD) model (Ebata et al, 2018)), which makes it possible to precisely characterize the cell-shaping dynamics. On the other hand, the microscopic asymmetric activity in the turnover of FAs was characterized in the anterior part of durotactic cells using fluorescence recovery after photobleaching (FRAP).…”

Section: Introductionmentioning

confidence: 99%

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Hierarchical Development of Motile Polarity in Durotactic Cells Just Crossing an Elasticity Boundary

Kuboki

Ebata

Matsuda

et al. 2020

Cell Struct. Funct.

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Cellular durotaxis has been extensively studied in the field of mechanobiology. In principle, asymmetric mechanical field of a stiffness gradient generates motile polarity in a cell, which is a driving factor of durotaxis. However, the actual process by which the motile polarity in durotaxis develops is still unclear. In this study, to clarify the details of the kinetics of the development of durotactic polarity, we investigated the dynamics of both cell-shaping and the microscopic turnover of focal adhesions (FAs) for Venus-paxillin-expressing fibroblasts just crossing an elasticity boundary prepared on microelastically patterned gels. The Fourier mode analysis of cell-shaping based on a persistent random deformation model revealed that motile polarity at a cellbody scale was established within the first few hours after the leading edges of a moving cell passed through the boundary from the soft to the stiff regions. A fluorescence recovery after photobleaching (FRAP) analysis showed that the mobile fractions of paxillin at FAs in the anterior part of the cells exhibited an asymmetric increase within several tens of minutes after cells entered the stiff region. The results demonstrated that motile polarity in durotactic cells is established through the hierarchical step-wise development of different types of asymmetricity in the kinetics of FAs activity and cell-shaping with a several-hour time lag.

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“…Following Iwadate et al [4][5][6][7], it is useful to distinguish between slow crawling cells, such as fibroblasts, endothelial, and smooth muscle cells, and fast crawling cells such as Dictyostelium or neutrophil-like HL-60; where the typical migration velocities can differ by one to two orders of magnitude between the two types. For example, the average speed of fibroblasts is of the order of 10µm/h [8], whereas Dictyostelium can move at speeds on the order of 10µm/s [4]. In addition, slow crawling cells typically possess stress fibers, whereas fast crawling cells do not.…”

Section: Introductionmentioning

confidence: 99%

Modeling the mechanosensitivity of fast-crawling cells on cyclically stretched substrates

Molina

Yamamoto

2019

Soft Matter

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The mechanosensitivity of cells, which determines how they are able to respond to mechanical signals received from their environment, is crucial for the functioning of all biological systems. In experiments, cells placed on cyclically stretched substrates have been shown to reorient in a direction that depends not only on the type of cell, but also on the mechanical properties of the substrate, and the amplitude and rate of stretching. However, the underlying biochemical and mechanical mechanisms responsible for this realignment are still not completely understood. In this study, we introduce a computational model for fast crawling on cyclically stretched substrates that accounts for the sub-cellular processes responsible for the cell shape and motility, as well as the coupling to the substrate through the focal adhesion sites. In particular, we focus on the role of the focal adhesion dynamics, and show that the reorientation under cyclic stretching is strongly dependent on the frequency, as has been observed experimentally. Furthermore, we show that an asymmetry during the loading and unloading phases of the stretching, whether coming from the response of the cell itself, or from the stretching protocol, can be used to selectively align the cells in either the parallel or perpendicular directions. *

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Persistent random deformation model of cells crawling on a gel surface

Cited by 32 publications

References 40 publications

Quantitative Detection of Cell Activity by Measuring the Fluctuation of Intracellular Motility

Quantitative Detection of Cell Activity by Measuring the Fluctuation of Intracellular Motility

Hierarchical Development of Motile Polarity in Durotactic Cells Just Crossing an Elasticity Boundary

Modeling the mechanosensitivity of fast-crawling cells on cyclically stretched substrates

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