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Abstract Human-induced pluripotent stem cell (hiPSC)-derived cardiomyocytes have many promising applications, including the regeneration of injured heart muscles, cardiovascular disease modeling, and drug cardiotoxicity screening. Current differentiation protocols yield a heterogeneous cell population that includes pluripotent stem cells and different cardiac subtypes (pacemaking and contractile cells). The ability to purify these cells and obtain well-defined, controlled cell compositions is important for many downstream applications; however, there is currently no established and reliable method to identify hiPSC-derived cardiomyocytes and their subtypes. Here, we demonstrate that second harmonic generation (SHG) signals generated directly from the myosin rod bundles can be a label-free, intrinsic optical marker for identifying hiPSC-derived cardiomyocytes. A direct correlation between SHG signal intensity and cardiac subtype is observed, with pacemaker-like cells typically exhibiting ~70% less signal strength than atrial- and ventricular-like cardiomyocytes. These findings suggest that pacemaker-like cells can be separated from the heterogeneous population by choosing an SHG intensity threshold criteria. This work lays the foundation for developing an SHG-based high-throughput flow sorter for purifying hiPSC-derived cardiomyocytes and their subtypes.
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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 novelin vitrocardiac 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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- Award ID(s):
- 1804875
- PAR ID:
- 10153833
- Publisher / Repository:
- Nature Publishing Group
- Date Published:
- Journal Name:
- Scientific Reports
- Volume:
- 9
- Issue:
- 1
- ISSN:
- 2045-2322
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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