Dataset Information

Rigidity-driven growth and migration of epithelial cells on microstructured anisotropic substrates.

ABSTRACT: The physical properties of the cellular environment are involved in regulating the formation and maintenance of tissues. In particular, substrate rigidity appears to be a key factor dictating cell response on culture surfaces. Here we study the behavior of epithelial cells cultured on microfabricated substrates engineered to exhibit an anisotropic stiffness. The substrate consists of a dense array of micropillars of oval cross-section, so that one direction is made stiffer than the other. We demonstrate how such an anisotropic rigidity can induce directional epithelial growth and guide cell migration along the direction of greatest rigidity. Regions of high tractional stress and large cellular deformations within the sheets of cells are concentrated at the edges, in particular at the two poles of the islands along their long axis, in correlation with the orientation of actin stress fibers and focal adhesions. By inducing scattering activity of epithelial cells, we show that isolated cells also migrate along the direction of greatest stiffness. Taken together, these findings show that the mechanical interactions of cells with their microenvironment can be tuned to engineer particular tissue properties.

SUBMITTER: Saez A

PROVIDER: S-EPMC1895941 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:Protein structural fluctuation, typically measured by Debye-Waller factors, or B-factors, is a manifestation of protein flexibility, which strongly correlates to protein function. The flexibility-rigidity index (FRI) is a newly proposed method for the construction of atomic rigidity functions required in the theory of continuum elasticity with atomic rigidity, which is a new multiscale formalism for describing excessively large biomolecular systems. The FRI method analyzes protein rigidity and flexibility and is capable of predicting protein B-factors without resorting to matrix diagonalization. A fundamental assumption used in the FRI is that protein structures are uniquely determined by various internal and external interactions, while the protein functions, such as stability and flexibility, are solely determined by the structure. As such, one can predict protein flexibility without resorting to the protein interaction Hamiltonian. Consequently, bypassing the matrix diagonalization, the original FRI has a computational complexity of O(N(2)). This work introduces a fast FRI (fFRI) algorithm for the flexibility analysis of large macromolecules. The proposed fFRI further reduces the computational complexity to O(N). Additionally, we propose anisotropic FRI (aFRI) algorithms for the analysis of protein collective dynamics. The aFRI algorithms permit adaptive Hessian matrices, from a completely global 3N × 3N matrix to completely local 3 × 3 matrices. These 3 × 3 matrices, despite being calculated locally, also contain non-local correlation information. Eigenvectors obtained from the proposed aFRI algorithms are able to demonstrate collective motions. Moreover, we investigate the performance of FRI by employing four families of radial basis correlation functions. Both parameter optimized and parameter-free FRI methods are explored. Furthermore, we compare the accuracy and efficiency of FRI with some established approaches to flexibility analysis, namely, normal mode analysis and Gaussian network model (GNM). The accuracy of the FRI method is tested using four sets of proteins, three sets of relatively small-, medium-, and large-sized structures and an extended set of 365 proteins. A fifth set of proteins is used to compare the efficiency of the FRI, fFRI, aFRI, and GNM methods. Intensive validation and comparison indicate that the FRI, particularly the fFRI, is orders of magnitude more efficient and about 10% more accurate overall than some of the most popular methods in the field. The proposed fFRI is able to predict B-factors for ?-carbons of the HIV virus capsid (313 236 residues) in less than 30 seconds on a single processor using only one core. Finally, we demonstrate the application of FRI and aFRI to protein domain analysis.

Dataset Information

Rigidity-driven growth and migration of epithelial cells on microstructured anisotropic substrates.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets