几何线索在神经元生长中的作用
Role of geometrical cues in neuronal growth
摘要：
几何线索在神经元生长中起着重要作用。在这里，我们量化的轴突生长控制的几何形状的表面上，并报告一个一般的随机方法，定量描述的运动生长锥。我们发现，轴突显示一个强大的方向对齐微图案化的表面时，图案的周期性匹配的生长锥的尺寸。具有均匀几何形状的表面上的生长锥动力学描述的线性朗之万方程的确定性和随机的贡献。与此相反，轴突生长的表面上的周期性图案的特点是由两个广义朗之万方程的线性和二次速度依赖性和随机噪声的系统。我们结合实验数据和理论分析来测量生长锥运动的关键参数：角分布，关联函数，扩散系数，特征速度和阻尼系数。我们表明，轴突动力学显示一个交叉从一个Ornstein-Uhlenbeck过程的非线性随机制度时，图案的几何周期性接近线性尺寸的生长锥。生长对齐由表面几何形状决定，其由朗之万方程的确定性部分完全量化。这些结果提供了新的洞察曲率传感蛋白的作用和它们与几何线索的相互作用。
英文摘要：
Geometrical cues play an essential role in neuronal growth. Here, we quantify axonal growth on surfaces with controlled geometries and report a general stochastic approach that quantitatively describes the motion of growth cones. We show that axons display a strong directional alignment on micro-patterned surfaces when the periodicity of the patterns matches the dimension of the growth cone. The growth cone dynamics on surfaces with uniform geometry is described by a linear Langevin equation with both deterministic and stochastic contributions. In contrast, axonal growth on surfaces with periodic patterns is characterized by a system of two generalized Langevin equations with both linear and quadratic velocity dependence and stochastic noise. We combine experimental data with theoretical analysis to measure the key parameters of the growth cone motion: angular distributions, correlation functions, diffusion coefficients, characteristics speeds and damping coefficients. We demonstrate that axonal dynamics displays a cross-over from an Ornstein-Uhlenbeck process to a non-linear stochastic regime when the geometrical periodicity of the pattern approaches the linear dimension of the growth cone. Growth alignment is determined by surface geometry, which is fully quantified by the deterministic part of the Langevin equation. These results provide new insight into the role of curvature sensing proteins and their interactions with geometrical cues.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
