Mechanical adaptions of collective cells nearby free tissue boundaries

Lv, Jian-Qing; Chen, Pengcheng; Góźdź, Wojciech T.; Li, Bo

doi:10.1016/j.jbiomech.2020.109763

Cited by 8 publications

(13 citation statements)

References 44 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Compared to continuum models [38], stochasticity enables our model to reproduce the observed fast irregular oscillation of cell velocities in fingers [43] and the spatial autocorrelation of the velocity [11]. Our underdamped AVM predicts that cells at the interface and the fingers have larger area than those well inside the tissue, which has been corroborated by recent experiments [44]. We also observe in numerical simulations of tissue spreading that the velocity of the fastest cells in a finger may oscillate with a short period in a range between 30 minutes to about one hour.…”

Section: Discussionsupporting

confidence: 71%

“…Areas of cells during a simulation of a spreading configuration: (a) Area of cells near the interface, (b) area of cells far from the interface. Our simulations exhibit the same trend as measurements reported in Ref [44]…”

supporting

confidence: 88%

“…Our underdamped dynamics also predicts that cells inside an aggregate spreading onto an empty space have smaller area than those at the tissue interface. This prediction has been corroborated by experiments [44]. Simulating the AVM with overdamped dynamics, we observe the opposite: cells at the interface and fingers have smaller are than cells inside the tissue [39].…”

Section: Introductionsupporting

confidence: 82%

“…This prediction of the underdamped AVM with dynamics as in Eq (8) has been observed in experiments; see Fig 4 of Ref. [44]. In experiments, the area of finger cells reaches larger values than in the simulations, which is related to the fact that we use a fixed target area for all cells and the cell area cannot depart arbitrarily far from target in the AVM.…”

Section: Plos Computational Biologysupporting

confidence: 70%

See 3 more Smart Citations

Tracking collective cell motion by topological data analysis

2020

View full text Add to dashboard Cite

By modifying and calibrating an active vertex model to experiments, we have simulated numerically a confluent cellular monolayer spreading on an empty space and the collision of two monolayers of different cells in an antagonistic migration assay. Cells are subject to inertial forces and to active forces that try to align their velocities with those of neighboring ones. In agreement with experiments in the literature, the spreading test exhibits formation of fingers in the moving interfaces, there appear swirls in the velocity field, and the polar order parameter and the correlation and swirl lengths increase with time. Numerical simulations show that cells inside the tissue have smaller area than those at the interface, which has been observed in recent experiments. In the antagonistic migration assay, a population of fluidlike Ras cells invades a population of wild type solidlike cells having shape parameters above and below the geometric critical value, respectively. Cell mixing or segregation depends on the junction tensions between different cells. We reproduce the experimentally observed antagonistic migration assays by assuming that a fraction of cells favor mixing, the others segregation, and that these cells are randomly distributed in space. To characterize and compare the structure of interfaces between cell types or of interfaces of spreading cellular monolayers in an automatic manner, we apply topological data analysis to experimental data and to results of our numerical simulations. We use time series of data generated by numerical simulations to automatically group, track and classify the advancing interfaces of cellular aggregates by means of bottleneck or Wasserstein distances of persistent homologies. These techniques of topological data analysis are scalable and could be used in studies involving large amounts of data. Besides applications to wound healing and metastatic cancer, these studies are relevant for tissue engineering, biological effects of materials, tissue and organ regeneration.

show abstract

Section: Discussionsupporting

confidence: 71%

supporting

confidence: 88%

Section: Introductionsupporting

confidence: 82%

Section: Plos Computational Biologysupporting

confidence: 70%

See 2 more Smart Citations

Tracking collective cell motion by topological data analysis

2020

View full text Add to dashboard Cite

show abstract

“…Thus, it is important to be able to model the shape transformation at the level of a single cell to understand the behavior of large adhering collections of cells. The results of our mathematical modeling of adhesion may be useful in explanation of the behavior of cells cultures confined and grown on a flat substrate [23], as well as in biomedical applications such as protection of the adhesion of platelets to vascular stents [24,25].…”

Section: Discussionmentioning

confidence: 99%

Investigation of Shape Transformations of Vesicles, Induced by Their Adhesion to Flat Substrates Characterized by Different Adhesion Strength

Raval

Iglič

Góźdź

2021

IJMS

View full text Add to dashboard Cite

The adhesion of lipid vesicles to a rigid flat surface is investigated. We examine the influence of the membrane spontaneous curvature, adhesion strength, and the reduced volume on the stability and shape transformations of adhered vesicles. The minimal strength of the adhesion necessary to stabilize the shapes of adhered vesicles belonging to different shape classes is determined. It is shown that the budding of an adhered vesicle may be induced by the change of the adhesion strength. The importance of the free vesicle shape for its susceptibility to adhesion is discussed.

show abstract

Collective Motion of Epithelial Cells in Tissues and the Active Vertex Model

Bonilla

2024

Springer Proceedings in Mathematics &Amp; Statistics

View full text Add to dashboard Cite

Mechanical adaptions of collective cells nearby free tissue boundaries

Cited by 8 publications

References 44 publications

Tracking collective cell motion by topological data analysis

Tracking collective cell motion by topological data analysis

Investigation of Shape Transformations of Vesicles, Induced by Their Adhesion to Flat Substrates Characterized by Different Adhesion Strength

Collective Motion of Epithelial Cells in Tissues and the Active Vertex Model

Contact Info

Product

Resources

About