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Title: 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 more » cardiac signals, but also monitoring and diagnostics of cardiac health in response to external stress.

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Authors:
; ; ;
Award ID(s):
1804875
Publication Date:
NSF-PAR ID:
10153833
Journal Name:
Scientific Reports
Volume:
9
Issue:
1
ISSN:
2045-2322
Publisher:
Nature Publishing Group
Sponsoring Org:
National Science Foundation
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"Quantification of Contractile Dynamic Complexities Exhibited by Human Stem Cell-Derived Cardiomyocytes Using Nonlinear Dimensional Analysis". <em>Scientific Reports</em> 9 (1). Country unknown/Code not available: Nature Publishing Group. <a href="https://doi.org/10.1038/s41598-019-51197-7">https://doi.org/10.1038/s41598-019-51197-7.</a> <a href="https://par.nsf.gov/biblio/10153833">https://par.nsf.gov/biblio/10153833</a>. </div> <div class="modal-footer"> <button class="btn btn-sm btn-default" data-dismiss="modal" aria-hidden="true">Close</button> </div> </div> </div> </div></li> <li class="links-format"><a href="#cite-bib" data-toggle="modal">BibTeX</a> <div id="cite-bib" class="modal" tabindex="-1" role="dialog" aria-labelledby="cite-bib_label" aria-hidden="true"> <div class="modal-dialog"> <div class="modal-content"> <div class="modal-header"> <button type="button" class="close" data-dismiss="modal" aria-hidden="true">×</button> <strong id="cite-bib_label">Cite: BibTeX Format</strong> </div> <div class="modal-body"> @article{osti_10153833,<br/> place = {Country unknown/Code not available}, title = {Quantification of Contractile Dynamic Complexities Exhibited by Human Stem Cell-Derived Cardiomyocytes Using Nonlinear Dimensional Analysis}, url = {https://par.nsf.gov/biblio/10153833}, DOI = {10.1038/s41598-019-51197-7}, abstractNote = {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.}, journal = {Scientific Reports}, volume = {9}, number = {1}, publisher = {Nature Publishing Group}, author = {Hoang, Plansky and Jacquir, Sabir and Lemus, Stephanie and Ma, Zhen}, }</div> <div class="modal-footer"> <button class="btn btn-sm btn-default" data-dismiss="modal" aria-hidden="true">Close</button> </div> </div> </div> </div></li> <li class="divider"></li> </ul> <ul class="nav nav-list" style="font-size: 14px; font-family: Arial Regular;"> <li class="nav-header header-format">Export Metadata</li> <li class="links-format"><a href="https://par.nsf.gov/endnote?osti_id=10153833">EndNote</a></li> <li class="links-format"><a href="https://par.nsf.gov/export/format:excel/osti-id:10153833">Excel</a></li> <li class="links-format"><a href="https://par.nsf.gov/export/format:csv/osti-id:10153833">CSV</a></li> <li class="links-format"><a href="https://par.nsf.gov/export/format:xml/osti-id:10153833">XML</a></li> <li class="divider"></li> </ul> <ul class="nav nav-list" style="font-size: 14px; 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