在细胞链的物理模型中出现了多种集体运动模式。

IF 3.5 2区 生物学 Q1 MATHEMATICAL & COMPUTATIONAL BIOLOGY
Ying Zhang, Effie E Bastounis, Calina Copos
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引用次数: 0

摘要

集体细胞迁移是发育和癌症转移等过程的核心。虽然集体运动的机制越来越被理解,但它们的分类仍然不完整。在这里,我们研究小细胞链的迁移,即内聚对。对盘状盘基骨菌(Dd)的实验揭示了两种运动模式:个体贡献(IC)模式,其中每个细胞产生自己的牵引偶极子,以及超细胞(S)模式,其特征是单个偶极子。Dd对以IC模式为主,而Madin-Darby犬肾(MDCK)双偶以S模式为主。一个二维生物物理模型概括了许多实验观察;当两个细胞施加相似的牵引应力时,变形体Dd双晶中自然出现IC模式,而S模式则以较强的引线为主。与阿米巴相反,mdck样细胞链在增加细胞-细胞粘附时表现出对IC模式的偏好。将模型扩展到更长的链,我们展示了它在理解跨细胞类型和规模的紧急迁移模式方面的潜力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Emergence of multiple collective motility modes in a physical model of cell chains.

Collective cell migration is central to processes like development and cancer metastasis. While mechanisms of collective motility are increasingly understood, their classification remains incomplete. Here, we study the migration of small cell chains, namely cohesive pairs. Experiments with Dictyostelium discoideum (Dd) revealed two motility modes: the individual contributor (IC) mode, where each cell generates its own traction dipole, and the supracellular (S) mode, characterized by a single dipole. Dd pairs favored the IC mode, while Madin-Darby canine kidney (MDCK) doublets predominantly used the S mode. A 2D biophysical model recapitulated many experimental observations; the IC mode emerged naturally in ameboid Dd doublets when both cells exerted similar traction stresses, while the S mode dominated with stronger leaders. Contrary to amebas, MDCK-like cell chains showed a bias towards the IC mode when increasing cell-cell adhesion. Extending the model to longer chains, we show its potential for understanding emergent migration patterns across cell types and scales.

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来源期刊
NPJ Systems Biology and Applications
NPJ Systems Biology and Applications Mathematics-Applied Mathematics
CiteScore
5.80
自引率
0.00%
发文量
46
审稿时长
8 weeks
期刊介绍: npj Systems Biology and Applications is an online Open Access journal dedicated to publishing the premier research that takes a systems-oriented approach. The journal aims to provide a forum for the presentation of articles that help define this nascent field, as well as those that apply the advances to wider fields. We encourage studies that integrate, or aid the integration of, data, analyses and insight from molecules to organisms and broader systems. Important areas of interest include not only fundamental biological systems and drug discovery, but also applications to health, medical practice and implementation, big data, biotechnology, food science, human behaviour, broader biological systems and industrial applications of systems biology. We encourage all approaches, including network biology, application of control theory to biological systems, computational modelling and analysis, comprehensive and/or high-content measurements, theoretical, analytical and computational studies of system-level properties of biological systems and computational/software/data platforms enabling such studies.
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