Integrating omics into the cardiac differentiation of human pluripotent stem cells

Time-dependent extracellular manipulations of human pluripotent stem cells can yield as much as 90% pure populations of cardiomyocytes. While the extracellular control of differentiation generally entails dynamic regulation of well-known pathways such as Wnt, BMP, and Nodal signaling, the underlying genetic networks are far more complex and are poorly understood. Notably, the identification of these networks holds promise for understanding heart disease and regeneration. The availability of genome-wide experimentation, such as RNA and DNA sequencing, as well as high throughput surveying with small molecule and small interfering RNA libraries, now enables us to map the genetic interactions underlying cardiac differentiation on a global scale. Initial studies demonstrate the complexity of the genetic regulation of cardiac differentiation, exposing unanticipated novel mechanisms. However, the large datasets generated tend to be overwhelming and systematic approaches are needed to process the vast amount of data to improve our mechanistic understanding of the complex biology. Systems biology methods spur high hopes for parsing vast amounts of data into genetic interaction models that can be verified experimentally and ultimately yield functional networks that expose the genetic connections underlying biological processes.