Quantification of Contractile Dynamic Complexities Exhibited by Human Stem Cell-Derived Cardiomyocytes Using Nonlinear Dimensional Analysis
Quantification of Contractile Dynamic Complexities Exhibited by Human Stem Cell-Derived Cardiomyocytes Using Nonlinear Dimensional Analysis
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DOI：
10.1038/s41598-019-51197-7
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发表时间：
2019-10-11
影响因子：
4.6
通讯作者：
Ma, Zhen
中科院分区：
文献类型：
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作者：
Hoang, Plansky;Jacquir, Sabir;Ma, Zhen
由于对生物信号中存在的非线性的认识不断增加，理解生物信号的复杂性已经得到了广泛的关注。众所周知，心脏信号表现出高度复杂的动力学，包括高度相互依赖的调节心脏收缩功能的机制。了解这些调控机制对于开发新的体外心脏系统非常重要，尤其是随着直接从人诱导多能干细胞(HiPSCs)获得心脏组织的指数性增长。这项工作描述了一种独特的分析方法，它集成了对物理心脏收缩的线性幅度和频率分析，以及对收缩信号的非线性分析，以衡量信号的复杂性。我们产生了反映HiPSC来源的心肌细胞(HiPSC-CMS)物理收缩的收缩运动波形，并将这些信号进行非线性分析，以计算容量和关联维度。这些参数使我们能够在重构到相空间时表征心脏信号的动力学，并提供信号复杂性的测量以补充收缩生理学数据。因此，我们应用这种方法来评估药物反应，并观察到收缩生理学和动态复杂性之间的关系对于每种受试药物都是独一无二的。这说明这种方法不仅适用于表征心脏信号，而且还适用于监测和诊断对外部应激的心脏健康状况。
Understanding the complexity of biological signals has been gaining widespread attention due to increasing knowledge on the nonlinearity that exists in these systems. Cardiac signals are known to exhibit highly complex dynamics, consisting of high degrees of interdependency that regulate the cardiac contractile functions. These regulatory mechanisms are important to understand for the development of novel in vitro cardiac systems, especially with the exponential growth in deriving cardiac tissue directly from human induced pluripotent stem cells (hiPSCs). This work describes a unique analytical approach that integrates linear amplitude and frequency analysis of physical cardiac contraction, with nonlinear analysis of the contraction signals to measure the signals' complexity. We generated contraction motion waveforms reflecting the physical contraction of hiPSC-derived cardiomyocytes (hiPSC-CMs) and implemented these signals to nonlinear analysis to compute the capacity and correlation dimensions. These parameters allowed us to characterize the dynamics of the cardiac signals when reconstructed into a phase space and provided a measure of signal complexity to supplement contractile physiology data. Thus, we applied this approach to evaluate drug response and observed that relationships between contractile physiology and dynamic complexity were unique to each tested drug. This illustrated the applicability of this approach in not only characterization of cardiac signals, but also monitoring and diagnostics of cardiac health in response to external stress.