Multiscale model predicts increasing focal adhesion size with decreasing stiffness in fibrous matrices
Multiscale model predicts increasing focal adhesion size with decreasing stiffness in fibrous matrices
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DOI：
10.1073/pnas.1620486114
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发表时间：
2017-06-06
影响因子：
11.1
通讯作者：
Shenoy, Vivek B.
中科院分区：
文献类型：
--
作者：
Cao, Xuan;Ban, Ehsan;Shenoy, Vivek B.
我们描述了一个多尺度模型，该模型结合了由纤维间交联断裂和刚度依赖性细胞收缩引起的力依赖性机械塑性，以预测纤维细胞外基质（ECM）中的粘着斑（FA）生长和机械传感。该模型预测FA大小取决于ECM的刚度和可用于形成粘连的配体的密度。虽然这两个量在常用的水凝胶中是独立的，但收缩性细胞破坏软纤维基质中的交联，导致纤维的募集，这增加了细胞附近的配体密度。因此，虽然在非纤维性和弹性水凝胶中，病灶粘连的大小随ECM刚度的增加而增加，但纤维网络的可塑性导致偏离刚度和FA大小之间的良好正相关性。我们预测了一个相图，它描述了FA在ECM刚度和招募指数所跨越的空间中的非单调行为，该指数描述了细胞打破交联和招募纤维的能力。预测的FA尺寸随着ECM刚度的增加而减小与最近观察到的细胞在具有可调交联强度和力学的电纺纤维网络上扩散的结果非常一致。我们的模型提供了一个框架来分析细胞机械传感在非线性和非弹性ECM。
We describe a multiscale model that incorporates force-dependent mechanical plasticity induced by interfiber cross-link breakage and stiffness-dependent cellular contractility to predict focal adhesion (FA) growth and mechanosensing in fibrous extracellular matrices (ECMs). The model predicts that FA size depends on both the stiffness of ECM and the density of ligands available to form adhesions. Although these two quantities are independent in commonly used hydrogels, contractile cells break cross-links in soft fibrous matrices leading to recruitment of fibers, which increases the ligand density in the vicinity of cells. Consequently, although the size of focal adhesions increases with ECM stiffness in nonfibrous and elastic hydrogels, plasticity of fibrous networks leads to a departure from the well-described positive correlation between stiffness and FA size. We predict a phase diagram that describes nonmonotonic behavior of FA in the space spanned by ECM stiffness and recruitment index, which describes the ability of cells to break cross-links and recruit fibers. The predicted decrease in FA size with increasing ECM stiffness is in excellent agreement with recent observations of cell spreading on electrospun fiber networks with tunable cross-link strengths and mechanics. Our model provides a framework to analyze cell mechanosensing in nonlinear and inelastic ECMs.