Analysis of an experimental model of in vitro cardiac tissue using phase space reconstruction

Quantification of Contractile Dynamic Complexities Exhibited by Human Stem Cell-Derived Cardiomyocytes Using Nonlinear Dimensional Analysis

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.

Quantitatively characterizing drug-induced arrhythmic contractile motions of human stem cell-derived cardiomyocytes

Quantification of abnormal contractile motions of cardiac tissue has been a noteworthy challenge and significant limitation in assessing and classifying the drug-induced arrhythmias (i.e., Torsades de pointes). To overcome these challenges, researchers have taken advantage of computational image processing tools to measure contractile motion from cardiomyocytes derived from human induced pluripotent stem cells (hiPSC-CMs). However, the amplitude and frequency analysis of contractile motion waveforms does not produce sufficient information to objectively classify the degree of variations between two or more sets of cardiac contractile motions. In this paper, we generated contractile motion data from beating hiPSC-CMs using motion tracking software based on optical flow analysis, and then implemented a computational algorithm, phase space reconstruction (PSR), to derive parameters (embedding, regularity, and fractal dimensions) to further characterize the dynamic nature of the cardiac contractile motions. Application of drugs known to cause cardiac arrhythmia induced significant changes to these resultant dimensional parameters calculated from PSR analysis. Integrating this new computational algorithm with the existing analytical toolbox of cardiac contractile motions will allow us to expand current assessments of cardiac tissue physiology into an automated, high-throughput, and quantifiable manner which will allow more objective assessments of drug-induced proarrhythmias.

Heart-on-a-chip based on stem cell biology

Heart diseases are one of the main causes of death around the world. The great challenge for scientists is to develop new therapeutic methods for these types of ailments. Stem cells (SCs) therapy could be one of a promising technique used for renewal of cardiac cells and treatment of heart diseases. Conventional in vitro techniques utilized for investigation of heart regeneration do not mimic natural cardiac physiology. Lab-on-a-chip systems may be the solution which could allow the creation of a heart muscle model, enabling the growth of cardiac cells in conditions similar to in vivo conditions. Microsystems can be also used for differentiation of stem cells into heart cells, successfully. It will help better understand of proliferation and regeneration ability of these cells. In this review, we present Heart-on-a-chip systems based on cardiac cell culture and stem cell biology. This review begins with the description of the physiological environment and the functions of the heart. Next, we shortly described conventional techniques of stem cells differentiation into the cardiac cells. This review is mostly focused on describing Lab-on-a-chip systems for cardiac tissue engineering. Therefore, in the next part of this article, the microsystems for both cardiac cell culture and SCs differentiation into cardiac cells are described. The section about SCs differentiation into the heart cells is divided in sections describing biochemical, physical and mechanical stimulations. Finally, we outline present challenges and future research concerning Heart-on-a-chip based on stem cell biology.