2020 - A Scalable Approach Reveals Functional Responses of iPSC Cardiomyocyte 3D Spheroids
CardioExcyte 96 publication in SLAS Discovery (2020)
Burnham M.P., Harvey R., Sargeant R., Fertig N., Haddrick M.
SLAS Discovery (2020) doi: 10.1177/2472555220975332
Cardiomyocytes (CMs) derived from induced pluripotent stem cells (iPSCs) provide an in vitro model of the human myocardium. Complex 3D scaffolded culture methods improve the phenotypical maturity of iPSC-CMs, although typically at the expense of throughput. We have developed a novel, scalable approach that enables the use of iPSC-CM 3D spheroid models in a label-free readout system in a standard 96-well plate-based format. Spheroids were accurately positioned onto recording electrodes using a magnetic gold–iron oxide nanoparticle approach. Remarkably, both contractility (impedance) and extracellular field potentials (EFPs) could be detected from the actively beating spheroids over long durations and after automated dosing with pharmacological agents. The effects on these parameters of factors, such as co-culture (including human primary cardiac fibroblasts), extracellular buffer composition, and electrical pacing, were investigated. Beat amplitudes were increased greater than 15-fold by co-culture with fibroblasts. Optimization of extracellular Ca2+ fluxes and electrical pacing promoted the proper physiological response to positive inotropic agonists of increased beat amplitude (force) rather than the increased beat rate often observed in iPSC-CM studies. Mechanistically divergent repolarizations in different spheroid models were indicated by their responses to BaCl2 compared with E-4031. These studies demonstrate a new method that enables the pharmacological responses of 3D iPSC-CM spheroids to be determined in a label-free, standardized, 96-well plate-based system. This approach could have discovery applications across cardiovascular efficacy and safety, where parameters typically sought as readouts of iPSC-CM maturity or physiological relevance have the potential to improve assay predictivity.