Correlation-Based Contraction Tracking of Cardiomyocyte Monolayers
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Cardiac tissue engineering has made great strides in combating cardiovascular disease in modern society utilizing cardiac regenerative medicine as well as in vitro disease modeling. Stem cell-derived cardiomyocytes are a promising path to tissue regeneration, but the technology is not fully developed, as fully functional cardiac tissue phenotypically similar to the native tissue in humans has not yet been created. Many engineered cardiac tissues lack the orientation of highly aligned native cardiac tissue, and lack the mature sarcomeric structures that adult human cardiomyocytes have. Therefore, quantification of cardiomyocyte function and maturity is of critical importance in order to measure the effectiveness of these engineered tissues in mimicking the phenotype of native cardiac tissues. Specifically, the quality of contractile motion is very important, as it is a direct indicator of the status of the structural maturity of the sarcomeres--the organized contractile motor units. Past research has implemented a variety of methods to track and quantify cardiomyocyte contractions, but many of them share limitations derived from either the methodological parameters or from the available resultant physiologically relevant endpoints. In this thesis we present an application of entirely optical contraction analysis that expands upon previous applications. Using this MATLAB and C++-based software utilizing Particle Image Velocimetry (PIV) and Digital Image Correlation (DIC) algorithms, a variety of physiologically relevant contractile endpoints can be determined from brightfield video data of engineered cardiomyocyte tissues. We will validate the accuracy of this method Using micropost arrays as well as nanopatterned substrates in conjunction with primary cardiomyocytes, human embryonic stem cell-derived cardiomyocytes, and human induced pluripotent stem cell-derived cardiomyocytes. We will then apply these algorithms, referred to as Correlation-based Contraction Quantification (CCQ) to a case study to analyze the behavior of Duchenne Muscular Dystrophy, a genetic cardiomyopathy, in in vitro tissues to illustrate the benefit of such software in applications to disease models.
- Bioengineering