增殖驱动的机械反馈调节生长组织中的细胞动力学

Sumit Sinha, Xin Li, Abdul N Malmi-Kakkada, D. Thirumalai
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摘要

组织中的局部应力是一种集体属性,可调节细胞分裂和凋亡。反过来,细胞的生长和分裂也会引起组织中的活性应力。因此,细胞生长和局部应力之间存在反馈。然而,细胞动力学如何依赖于局部应力依赖的细胞分裂以及反馈强度还不完全清楚。在这里,我们通过对二维生长组织进行基于试剂的模拟,探究了应力介导的生长和细胞分裂对细胞动力学的影响。我们发现了单个细胞丰富的动力学行为,从干扰(平均位移平方,$\delta (t) \sim t^{\alpha}$,$\alpha$小于统一值)到超扩散($\alpha > 2$),这取决于细胞分裂率和机械反馈的强度。引人注目的是,$\Delta (t)$ 是由组织生长定律决定的,它量化了细胞增殖(细胞数量 $N(t)$是时间的函数)。生长规律(在长时间内,$N(t) \sim t^\{lambda}$)受临界压力调节,临界压力控制着机械反馈的强度以及细胞分裂与凋亡率之间的比率。我们可以看到,$\lambda \sim \alpha$意味着较高的生长速率会导致较大程度的细胞迁移。细胞运动性的变化与在多个细胞周期中出现的高度持久的力量有关。我们的预测可以通过细胞追踪成像技术进行检验。
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Proliferation-driven mechanical feedback regulates cell dynamics in growing tissues
Local stresses in a tissue, a collective property, regulate cell division and apoptosis. In turn, cell growth and division induce active stresses in the tissue. As a consequence, there is a feedback between cell growth and local stresses. However, how the cell dynamics depend on local stress-dependent cell division and the feedback strength is not fully understood. Here, we probe the consequences of stress-mediated growth and cell division on cell dynamics using agent-based simulations of a two-dimensional growing tissue. We discover a rich dynamical behavior of individual cells, ranging from jamming (mean square displacement, $\Delta (t) \sim t^{\alpha}$ with $\alpha$ less than unity), to hyperdiffusion ($\alpha > 2$) depending on cell division rate and the strength of the mechanical feedback. Strikingly, $\Delta (t)$ is determined by the tissue growth law, which quantifies cell proliferation (number of cells $N(t)$ as a function of time). The growth law ($N(t) \sim t^{\lambda}$ at long times) is regulated by the critical pressure that controls the strength of the mechanical feedback and the ratio between cell division-apoptosis rates. We show that $\lambda \sim \alpha$, which implies that higher growth rate leads to a greater degree of cell migration. The variations in cell motility are linked to the emergence of highly persistent forces extending over several cell cycle times. Our predictions are testable using cell-tracking imaging techniques.
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