Engineering human stem cell-derived cardiomyocyte maturation using phototunable matrices and machine learning approaches

Heart disease is difficult to study. Animal models are not always effective; neither are most cell culture studies. This leads to many failed drug developments. Scientists are now using human stem cells to make more faithful human cardiac models for research. One problem is that these cells are still immature, making it uncertain how well they can model disease. To develop a more effective representation of a heart, an attempt will be made to create a “heart on a chip”. The current standard methods of cardiovascular research are based on static two-dimensional cell cultures and animal models. They both have significant limitations. Human induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs) have shown to be promising as an alternative for these in vitro studies. However, a key bottleneck in the current applications of hiPSC-CMs lies in their structural and functional immaturity, leading to less predictive results in disease modeling and drug toxicity tests. By employing biochip design, machine learning, developmental biology, and tissue engineering, this project will build the foundation for overcoming this obstacle and develop methodologies for an efficient and rational manufacturing pipeline of mature hiPSC-CMs. To achieve this goal, the team will develop models to understand cardiac cell-cell and cell-matrix interaction dynamics needed in enhancing the maturity of hiPSC-CMs using microchip engineering with artificial intelligence-driven feedback. The ability to manufacture mature hiPSC-CMs at scale and speed could accelerate drug and cardiac disease model development. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.