Published online Jan 26, 2019. doi: 10.4252/wjsc.v11.i1.33
Peer-review started: October 30, 2018
First decision: November 15, 2018
Revised: November 24, 2018
Accepted: January 10, 2019
Article in press: January 10, 2019
Published online: January 26, 2019
Induced pluripotent stem cells (iPSCs) were first generated by Yamanaka and colleagues over a decade ago. Since then, iPSCs have been successfully differentiated into many distinct cell types, enabling tissue-, disease-, and patient-specific in vitro modelling. Cardiovascular disease is the greatest cause of mortality worldwide but encompasses rarer disorders of conduction and myocardial function for which a cellular model of study is ideal. Although methods to differentiate iPSCs into beating cardiomyocytes (iPSC-CMs) have recently been adequately optimized and commercialized, the resulting cells remain largely immature with regards to their structure and function, demonstrating fetal gene expression, disorganized morphology, reliance on predominantly glycolytic metabolism and contractile characteristics that differ from those of adult cardiomyocytes. As such, disease modelling using iPSC-CMs may be inaccurate and of limited utility. However, this limitation is widely recognized, and numerous groups have made substantial progress in addressing this problem. This review highlights successful methods that have been developed for the maturation of human iPSC-CMs using small molecules, environmental manipulation and 3-dimensional (3D) growth approaches.
Core tip: Induced pluripotent stem cells are key for generating disease-, patient-, and tissue-specific in vitro models. As such, induced pluripotent stem cells differentiated into cardiomyocytes offer a potential tool for the understanding of disease and the development of life-saving therapeutics. Currently, cardiomyocytes can be differentiated with high efficiency but remain immature in their structure and function. Maturation of these cells is possible using a variety of approaches and will allow for more accurate disease modeling.