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

Abstract

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.

Volcano-Shaped Scanning Probe Microscopy Probe for Combined Force-Electrogram Recordings from Excitable Cells

“…The bottom panel in Figure A represents the sum of the derivative for every pixel from the video in the region of interest presented in the top panel. This measure is conventionally used to quantify cardiomyocyte contractions , and serves as visual control for the nanovolcano measurements.…”

“…(A) Upper panel: Schematic drawing (left) and series of time lapse images showing the nanovolcano probe engaged on a beating cardiomyocyte (right). Pixels showing intensity variations compared to the previous frame are highlighted in blue and indicate cardiomyocyte contraction. , Lower panel: Temporal evolution of the sum of the derivatives of every pixels for the entire area depicted in the upper panel. (B) Cantilever displacement (top panel) and its absolute derivative (bottom panel) from the same cardiomyocyte.…”

SMN promotes mitochondrial metabolic maturation during myogenesis by regulating the MYOD-miRNA axis

“…For this, we isolated the cell population that included satellite cells and differentiated it into myotubes (Hayhurst et al, 2012). To evaluate the contraction ability of the skeletal myotubes, we stimulated them with a square wave electric current and measured the skeletal muscle contraction velocity (SMCV) using a cell motion imaging system ( Figure 7A ) (Hoang et al, 2019; Lin et al, 2019). Myotubes from Δ7-MuSCs showed a significantly slower SMCV than those from WT-MuSCs.…”

In vitro and In silico Models to Study SARS-CoV-2 Infection: Integrating Experimental and Computational Tools to Mimic “COVID-19 Cardiomyocyte”

“…In this scenario, hiPSC-CMs emerge as a promising platform for modeling COVID-19. However, these cells display limited maturation and biological heterogeneity, partly due to a lack of consensus protocols for their generation and characterization, negatively contributing to their clinical translation ( Lundy et al, 2013 ; Robertson et al, 2013 ; Koivumäki et al, 2018 ; Gintant et al, 2019 ; Hoang et al, 2019 ; Ribeiro et al, 2019 ). Several strategies have been proposed to overcome hiPSC-CMs maturation obstacles.…”