Learning the dynamics of cell-cell interactions in confined cell migration.
Learning the dynamics of cell-cell interactions in confined cell migration.
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DOI：
10.1073/pnas.2016602118
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发表时间：
2021-02-16
影响因子：
11.1
通讯作者：
Broedersz CP
中科院分区：
文献类型：
--
作者：
Brückner DB;Arlt N;Fink A;Ronceray P;Rädler JO;Broedersz CP
当细胞集体迁移，如愈合伤口或侵入组织时，它们通过细胞与细胞的相互作用进行协调。虽然对这些相互作用的分子基础了解很多，但相互作用的细胞行为的系统级随机动力学仍然知之甚少。在这里，我们设计了一个实验性的“细胞对撞机”，提供了大量相互作用的细胞轨迹。基于这些轨迹，我们推导出一个相互作用的运动方程，它准确地预测了不同细胞系的特征两两碰撞行为，包括反转、跟随或滑动事件。这种数据驱动的方法可以用来定量研究分子扰动如何控制细胞间的相互作用，并可能扩展到更大的细胞集体，其中推断的相互作用可以提供对多细胞动力学的关键见解。细胞在从伤口愈合到癌症转移的生理过程中的迁移动力学依赖于接触介导的细胞-细胞相互作用。这些相互作用在形成迁移细胞的随机轨迹方面起着关键作用。虽然数据驱动的单细胞随机迁移动力学的物理形式已经开发出来，但这样一个相互作用的细胞行为动力学的框架仍然难以捉摸。在这里，我们在一个最小的实验细胞对撞机中监测随机的细胞轨迹：一种哑铃形的微图案，细胞对在其上进行重复的细胞碰撞。我们观察到不同的特征行为，包括细胞在碰撞时相互反转、跟随和滑动。利用耦合细胞轨迹的大型实验数据集，我们推导出一个相互作用的随机运动方程，它准确地预测了观察到的相互作用行为。我们的方法揭示了相互作用的非癌症MCF10A细胞可以用排斥和摩擦相互作用来描述。相比之下，癌变的MDA-MB-231细胞表现出吸引和减摩相互作用，促进了这些细胞的主要相对滑动行为。基于这些实验推断的相互作用，我们展示了这个框架如何泛化，以提供不同细胞类型的不同细胞相互作用行为的统一理论描述。
When cells migrate collectively, such as to heal wounds or invade tissue, they coordinate through cell–cell interactions. While much is known about the molecular basis of these interactions, the system-level stochastic dynamics of interacting cell behavior remain poorly understood. Here, we design an experimental “cell collider,” providing a large ensemble of interacting cell trajectories. Based on these trajectories, we infer an interacting equation of motion, which accurately predicts characteristic pairwise collision behaviors of different cell lines, including reversal, following, or sliding events. This data-driven approach can be used to quantitatively study how molecular perturbations control cell–cell interactions and may be extended to larger cell collectives, where the inferred interactions could provide key insights into multicellular dynamics. The migratory dynamics of cells in physiological processes, ranging from wound healing to cancer metastasis, rely on contact-mediated cell–cell interactions. These interactions play a key role in shaping the stochastic trajectories of migrating cells. While data-driven physical formalisms for the stochastic migration dynamics of single cells have been developed, such a framework for the behavioral dynamics of interacting cells still remains elusive. Here, we monitor stochastic cell trajectories in a minimal experimental cell collider: a dumbbell-shaped micropattern on which pairs of cells perform repeated cellular collisions. We observe different characteristic behaviors, including cells reversing, following, and sliding past each other upon collision. Capitalizing on this large experimental dataset of coupled cell trajectories, we infer an interacting stochastic equation of motion that accurately predicts the observed interaction behaviors. Our approach reveals that interacting noncancerous MCF10A cells can be described by repulsion and friction interactions. In contrast, cancerous MDA-MB-231 cells exhibit attraction and antifriction interactions, promoting the predominant relative sliding behavior observed for these cells. Based on these experimentally inferred interactions, we show how this framework may generalize to provide a unifying theoretical description of the diverse cellular interaction behaviors of distinct cell types.
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影响因子：
12.5
作者：
Ferretti, Federica;Chardes, Victor;Giardina, Irene
通讯作者：
Giardina, Irene
DOI：
10.1103/physreve.89.062705
发表时间：
2014-06
期刊：
Physical review. E, Statistical, nonlinear, and soft matter physics
影响因子：
--
作者：
Camley BA;Rappel WJ
通讯作者：
Rappel WJ
影响因子：
4
作者：
Carey SP;Starchenko A;McGregor AL;Reinhart-King CA
通讯作者：
Reinhart-King CA
影响因子：
3.7
作者：
ABERCROMBIE, M;HEAYSMAN, JEM
通讯作者：
HEAYSMAN, JEM
影响因子：
19.6
作者：
Han, Yu Long;Pegoraro, Adrian F.;Guo, Ming
通讯作者：
Guo, Ming