Key result
Nonlinear dimensional analysis of human stem cell-derived cardiomyocytes quantified contractile dynamic complexities and uniquely characterized drug-induced arrhythmic behaviors not apparent through conventional linear analysis.
Why the study?
Understanding the regulatory mechanisms and complex dynamic biological signals of cardiac contractile function is important for developing novel in vitro cardiac systems derived from hiPSCs.
Does nonlinear dimensional analysis quantify contractile dynamic complexities in hiPSC-CMs in response to drugs and electrical stimulation?
Does nonlinear dimensional analysis quantify contractile dynamic complexities in hiPSC-CMs in response to drugs and electrical stimulation?
Answers are grounded in this paper and cite their sources.
Nonlinear dimensional analysis provides a novel method to quantify the dynamic complexity of hiPSC-CM contractile signals, offering deeper insights into cardiac health and drug responses in vitro.
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.
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Hoang et al. (2019) studied In vitro human induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs). Nonlinear dimensional analysis (capacity and correlation dimensions) vs. Conventional linear amplitude and frequency analysis was evaluated on Contractile dynamic complexity (capacity and correlation dimensions). Nonlinear dimensional analysis of human stem cell-derived cardiomyocytes quantified contractile dynamic complexities and uniquely characterized drug-induced arrhythmic behaviors not apparent through conventional linear analysis.
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