Mathematical Institute, Leiden University, Leiden, The Netherlands.
Data Science, Institute for Computing and Information Sciences, Radboud University, Nijmegen, The Netherlands.
PLoS Comput Biol. 2022 Feb 14;18(2):e1009156. doi: 10.1371/journal.pcbi.1009156. eCollection 2022 Feb.
Lymphocytes have been described to perform different motility patterns such as Brownian random walks, persistent random walks, and Lévy walks. Depending on the conditions, such as confinement or the distribution of target cells, either Brownian or Lévy walks lead to more efficient interaction with the targets. The diversity of these motility patterns may be explained by an adaptive response to the surrounding extracellular matrix (ECM). Indeed, depending on the ECM composition, lymphocytes either display a floating motility without attaching to the ECM, or sliding and stepping motility with respectively continuous or discontinuous attachment to the ECM, or pivoting behaviour with sustained attachment to the ECM. Moreover, on the long term, lymphocytes either perform a persistent random walk or a Brownian-like movement depending on the ECM composition. How the ECM affects cell motility is still incompletely understood. Here, we integrate essential mechanistic details of the lymphocyte-matrix adhesions and lymphocyte intrinsic cytoskeletal induced cell propulsion into a Cellular Potts model (CPM). We show that the combination of de novo cell-matrix adhesion formation, adhesion growth and shrinkage, adhesion rupture, and feedback of adhesions onto cell propulsion recapitulates multiple lymphocyte behaviours, for different lymphocyte subsets and various substrates. With an increasing attachment area and increased adhesion strength, the cells' speed and persistence decreases. Additionally, the model predicts random walks with short-term persistent but long-term subdiffusive properties resulting in a pivoting type of motility. For small adhesion areas, the spatial distribution of adhesions emerges as a key factor influencing cell motility. Small adhesions at the front allow for more persistent motility than larger clusters at the back, despite a similar total adhesion area. In conclusion, we present an integrated framework to simulate the effects of ECM proteins on cell-matrix adhesion dynamics. The model reveals a sufficient set of principles explaining the plasticity of lymphocyte motility.
淋巴细胞表现出不同的运动模式，如布朗随机游动、持续随机游动和莱维游走。根据条件的不同，如受到限制或靶细胞的分布，布朗运动或莱维游走会导致与靶细胞的更有效相互作用。这些运动模式的多样性可以通过对周围细胞外基质 (ECM) 的适应性反应来解释。事实上，根据 ECM 的组成，淋巴细胞要么表现出不附着在 ECM 上的漂浮运动，要么表现出滑动和步进运动，分别具有连续或不连续的 ECM 附着，要么表现出具有持续 ECM 附着的枢转行为。此外，从长远来看，淋巴细胞要么表现出持续的随机游动，要么表现出类似于布朗运动，这取决于 ECM 的组成。ECM 如何影响细胞运动仍不完全清楚。在这里，我们将淋巴细胞-基质粘附的基本力学细节和淋巴细胞内在细胞骨架诱导的细胞推进整合到一个细胞 Potts 模型 (CPM) 中。我们表明，新形成的细胞-基质黏附、黏附的生长和收缩、黏附的破裂以及黏附对细胞推进的反馈的组合，再现了多种淋巴细胞行为，适用于不同的淋巴细胞亚群和各种基质。随着附着面积的增加和附着强度的增加，细胞的速度和持久性降低。此外，该模型还预测了具有短期持续但长期亚扩散特性的随机游动，从而导致枢转型运动。对于小的附着面积，附着的空间分布成为影响细胞运动的关键因素。与较大的后部簇相比，前部的小附着允许更持久的运动，尽管总附着面积相似。总之，我们提出了一个集成框架来模拟 ECM 蛋白对细胞-基质粘附动力学的影响。该模型揭示了一组足以解释淋巴细胞运动可塑性的原理。
