Department of Mechanical Engineering, University of Hong Kong, Hong Kong SAR, China.
Soft Matter. 2019 Jan 2;15(2):166-174. doi: 10.1039/c8sm01747f.
Although the dynamic response of neurites is believed to play crucial roles in processes like axon outgrowth and formation of the neural network, the dynamic mechanical properties of such protrusions remain poorly understood. In this study, by using AFM (atomic force microscopy) indentation, we systematically examined the dynamic behavior of well-developed neurites on primary neurons under different loading modes (step loading, oscillating loading and ramp loading). Interestingly, the response was found to be strongly rate-dependent, with an apparent initial and long-term elastic modulus around 800 and 80 Pa, respectively. To better analyze the measurement data and extract information of key interest, the finite element simulation method (FEM) was also conducted where the neurite was treated as a viscoelastic solid consisting of multiple characteristic relaxation times. It was found that a minimum of three relaxation timescales, i.e. ∼0.01, 0.1 and 1 seconds, are needed to explain the observed relaxation curve as well as fit simulation results to the indentation and rheology data under different loading rates and driving frequencies. We further demonstrated that these three characteristic relaxation times likely originate from the thermal fluctuations of the microtubule, membrane relaxation and cytosol viscosity, respectively. By identifying key parameters describing the time-dependent behavior of neurites, as well as revealing possible physical mechanisms behind, this study could greatly help us understand how neural cells perform their biological duties over a wide spectrum of timescales.
虽然神经突的动态响应被认为在轴突生长和神经网络形成等过程中起着至关重要的作用，但这些突起的动态力学性质仍知之甚少。在这项研究中，我们通过使用原子力显微镜（AFM）压痕，系统地研究了原代神经元上发育良好的神经突在不同加载模式（阶跃加载、振荡加载和斜坡加载）下的动态行为。有趣的是，研究发现响应强烈地依赖于加载速率，其初始和长期弹性模量分别约为 800 和 80 Pa。为了更好地分析测量数据并提取关键感兴趣的信息，还进行了有限元模拟方法（FEM），其中将神经突视为由多个特征弛豫时间组成的粘弹性固体。研究发现，至少需要三个弛豫时间尺度，即约 0.01、0.1 和 1 秒，才能解释观察到的弛豫曲线，并根据不同的加载速率和驱动频率将模拟结果拟合到压痕和流变数据。我们进一步证明，这三个特征弛豫时间可能分别来源于微管的热波动、膜弛豫和细胞质粘度。通过确定描述神经突时变行为的关键参数，并揭示其背后可能的物理机制，这项研究可以帮助我们更好地理解神经细胞如何在广泛的时间尺度上执行其生物学功能。
