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滞留:用于能量最小化和基质机械感知的细胞黏附模式。

Sticking around: Cell adhesion patterning for energy minimization and substrate mechanosensing.

机构信息

Department of Mathematics, University of Surrey, Guildford, UK.

Department of Mathematics, University of Surrey, Guildford, UK; Centre for Mathematical and Computational Biology, University of Surrey, Guildford, UK.

出版信息

Biophys J. 2022 May 3;121(9):1777-1786. doi: 10.1016/j.bpj.2022.03.017. Epub 2022 Mar 16.

Abstract

Tissue stiffness (Young's modulus) is a key control parameter in cell behavior and bioengineered gels where defined mechanical properties have become an essential part of the toolkit for interrogating mechanotransduction. Here, we show using a mechanical cell model that the effective substrate stiffness experienced by a cell depends, not just on the engineered mechanical properties of the substrate but critically also on the particular arrangement of adhesions between cell and substrate. In particular, we find that cells with different adhesion patterns can experience two different gel stiffnesses as equivalent and will generate the same mean cell deformations. In considering small patches of adhesion, which mimic focal adhesion complexes, we show how the experimentally observed focal adhesion growth and elongation on stiff substrates can be explained by energy considerations. Relatedly, energy arguments also provide a reason why nascent adhesions do not establish into focal adhesions on soft substrates, as has been commonly observed. Fewer and larger adhesions are predicted to be preferred over more and smaller, an effect enhanced by random spot placing with the simulations predicting qualitatively realistic cell shapes in this case.

摘要

组织硬度(杨氏模量)是细胞行为和生物工程凝胶的关键控制参数,其中定义明确的机械性能已成为机械转导研究工具包的重要组成部分。在这里,我们使用机械细胞模型表明,细胞所经历的有效基底硬度不仅取决于基底的工程机械性能,而且还取决于细胞和基底之间特定的粘附排列。具体来说,我们发现具有不同粘附模式的细胞可以将两种不同的凝胶硬度视为等效,并且会产生相同的平均细胞变形。在考虑小面积的粘附时,模拟了焦点粘附复合物,我们展示了如何通过能量考虑来解释在硬基底上观察到的焦点粘附的生长和伸长。相关地,能量论点也提供了为什么在软基底上不会形成新的粘附点作为焦点粘附的原因,这是常见的观察结果。与更多且更小的粘附点相比,预测到更少且更大的粘附点更受青睐,这种情况通过模拟中随机点状放置来增强,预测出在这种情况下具有定性逼真的细胞形状。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2e7a/9117892/b6e89d33f0d5/gr4.jpg

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