基于小波分析的时变细胞运动参数提取
Deriving Time-varying Cellular Motility Parameters via Wavelet Analysis
摘要：
细胞迁移是正常组织发育和肿瘤转移不可缺少的生理和病理过程，受细胞内信号通路和细胞外微环境（ECM）的调控。然而，由于一些因素的影响，缺乏足够的工具来分析随时间变化的细胞迁移特性，即，ECM包括由迁移细胞引起的微结构重构引起的时间依赖性局部刚度。在这里，我们开发了一种方法来获得时间依赖性的运动参数从细胞轨迹，基于随时间变化的持续随机游走模型。特别地，我们采用小波去噪和小波变换来研究细胞迁移速度，并获得小波功率谱。随后通过洛伦兹功率谱推导出随时间变化的运动参数。分析表明，小波去噪、小波变换和Lorentzian功率谱的结合为精确提取时变运动参数提供了有力的工具，在一定程度上反映了微环境的时变特性。
英文摘要：
Cell migration is an indispensable physiological and pathological process for normal tissue development and cancer metastasis, which is greatly regulated by intracellular signal pathways and extracellular microenvironment (ECM). However, there is a lack of adequate tools to analyze the time-varying cell migration characteristics because of the effects of some factors, i.e., the ECM including the time-dependent local stiffness due to microstructural remodeling by migrating cells. Here, we develop an approach to derive the time-dependent motility parameters from cellular trajectories, based on the time-varying persistent random walk model. In particular, we employ the wavelet denoising and wavelet transform to investigate cell migration velocities and obtain the wavelet power spectrum. The time-dependent motility parameters are subsequently derived via Lorentzian power spectrum. Our analysis shows that the combination of wavelet denoising, wavelet transform and Lorentzian power spectrum provides a powerful tool to derive accurately the time-dependent motility parameters, which reflects the time-varying microenvironment characteristics to some extent.
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