br Experimental Procedures br Author Contributions

Experimental Procedures
Author Contributions
Acknowledgments
Introduction Recent advances in stem cell technologies have enabled routine analysis of patient-derived cardiomyocytes, opening up new opportunities for drug testing and personalized health care. Numerous studies have demonstrated that induced pluripotent stem-cell-derived cardiomyocytes (iPS-CMs) display physiologically relevant characteristics and patient-derived iPS-CMs recapitulate aspects of patient cardiac pathology/phenotype in vitro (Harris et al., 2013; Navarrete et al., 2013; Sun et al., 2012). iPS-CMs can be used for preclinical testing of new drugs that may cause drug-induced arrhythmia or QT prolongation and cardiotoxicity, as well as for post-market safety testing or re-purposing of existing Food and Drug Administration-approved drugs (Guo et al., 2011; Liang et al., 2013; Sirenko et al., 2013; Himmel, 2013). Improved cell-culturing technologies now allow for the production of well-characterized cardiomyocytes at scale, hence providing a reliable source for routine screening applications. Therefore, accurate and reliable characterization of these cells, and their response to different chemical compounds plays a critical role in their successful utilization in drug development and safety testing. An ideal platform for characterizing iPS-CMs would ensure reproducibility, require small samples, provide a reliable and comprehensive quantitative profile of cell function, and be cost effective when run at large scales. Label-free video microscopy has already been recognized as a well-suited platform (Makino et al., 1999; Hossain et al., 2010). For example, Ting et al. (2014) created a video-management platform that determines whether a specific region is beating; it Silvestrol and counts the beating pattern/signal of differentiated cardiomycytes with a user-specified threshold on the average change in signal intensity. Also, Ahola et al. (2014) captured the beating activity of single cardiomyocytes by analyzing the motion vector field of individual cells manually segmented by the user. Similarly, researchers estimated beating profiles of cardiomyocytes with a block-matching optical flow approach (Huebsch et al., 2014). While this approach yields vector fields of cellular motion for beating monolayer and single-cell iPSC-CMs, it is computationally expensive and may require manual tuning of the expected motion parameters and signal thresholds for each video. These efforts show the promise of video microscopy and analysis; however, we need an integrated and fully automated solution to characterize iPS-CMs at larger scales. This solution must avoid manually tuning software parameters for each video and also handle a broad range of cell-culture conditions, such as varied cell densities and drug treatments. Finally, to facilitate real-time monitoring at relatively low cost, the algorithms used to identify motion must be rapid and suitable for computational implementation without the need for parallel computing. In current practice, patch-clamp assays are the standard reference for high-precision electrical measurements of iPS-CMs (Peng et al., 2010). However, patch-clamp analysis requires manual operation by a trained electrophysiologist. Such assays are inherently low-throughput and will not scale to meet the demands of large-scale drug testing. iPS-CMs can also be characterized using electrical potentials captured by a micro-electrode array (MEA) (Harris et al., 2013). With an MEA system, the local potential in a region consisting of electrically active cells is measured as a function of time in order to generate a beating signal that contains information such as frequency, irregularity, and QT interval. Such systems typically require high cell density in specialized plates and rely on direct contact between cells and electrodes. Other methods, such as fluorescence imaging of the calcium signals (Paredes et al., 2008), can be useful, but are prone to phototoxicity as well as potential interactions between calcium indicators and the chemical compounds being studied (Muschol et al., 1999).