UCLA

Human embryonic and induced pluripotent stem cell-derived cardiomyocytes (hESC-CM and hiPSC-CM, respectively) have held considerable scientific interest for their potential applications in the fields of drug screening, disease modeling, and tissue engineering. One of the most significant roadblocks currently hindering the use of hESC-CMs and hiPSC-CMs concerns their inability to achieve cellular phenotypic maturity. This roadblock is referred to as the “maturation block” problem: cardiomyocytes derived from stem cells are known to experience limitations in phenotypic expression, in which the cells demonstrate characteristics analogous to late fetal stage cells, rather than adult cells. If hESC-CM and hiPSC-CM cultures are to achieve their full potential in pharmacology and regenerative medicine applications, the maturation block problem must be resolved.

This dissertation sought to improve upon the current understanding of the mechanisms involved in stem cell-derived cardiomyocyte maturation. Here, analysis of microelectrode array (MEA) recordings of hESC-CM and hiPSC-CM cultures revealed that the pacemaker region often moves (translocates) across the MEA. The variable length of the quiescent period between translocation events was found to obey a power law probability distribution. Such distributions are a characteristic of critical systems, or systems that demonstrate complex spatiotemporal dynamics and emergent properties. The power law exponent obtained for pacemaker translocation quiescent periods (α = -1.58) closely mirrors the power law exponent observed in several critical systems (α = -1.5), indicating that critical dynamics may play a crucial role in the development of a stable pacemaker region in the cardiomyocyte culture.

The computational tools developed for cardiomyocyte power law analysis were expanded to investigate a variety of cardiomyocyte properties, including local activation time and conduction velocity, as well as spatial relationships between the pacemaker region and cardiomyocyte electrical properties. This led to the development of Cardio PyMEA, a free and open source, graphical user interface-based program that was written in Python for the analysis of MEA cardiomyocyte data. Cardio PyMEA was made available on Github for any interested individual to use for MEA-based cardiomyocyte analysis and could serve as an evolving platform for such analyses in the future.