Abstract

In the recent years, three-dimensional (3D) tumor spheroid models have been increasingly used for cancer drug discovery research.  The use of 3D tumor spheroid models may better represent the complex in vivo tumor microenvironments for cancer research.  Previously, we have published a novel high-throughput MCTS screening method using the Celigo Image Cytometer, where we described a method for measuring of end point viability and apoptosis of MCTS in 384-well ultra-low attachment (ULA) U-bottom microplates.   In this work, we demonstrate a high-throughput 3D MCTS screening method using the Celigo imaging cytometer to screen the real-time kinetic viability and apoptosis effects of 14 drug compounds (NIH/NCAT) on U87MG spheroids.  A dose response experiment is performed to screen the kinetic growth inhibitory effects, as well as viability and apoptosis effects induced by the drug compounds.  PI and caspase 3/7 fluorescent stains were used to stain the MCTS to measure the viability and apoptosis of the drug-treated tumor spheroids.  The results again showed that Celigo image cytometer can quickly generate accurate viability and apoptosis measurement of the drug-treated tumor spheroids, as well as growth inhibitory data to identify potential drug candidates.  The generated kinetic data allowed researchers to characterize the real-time cytotoxic or apoptotic effect, which may be more useful compared to only an end point readout.  Some of the drugs induced earlier cytotoxicity than other drugs, which can be corresponded directly with the change in PI and caspase fluorescent intensities.  At the end of the 9-day drug treatment, the tumor spheroids were also stained with calcein AM/PI or Hoechst/caspase 3/7 to repeat similar end point measurement as the previous publication.  The results were highly comparable, which indicated the screening method is highly repeatable.  By utilizing the real-time kinetic 3D spheroid screening method, researchers can better characterize and quantify drug effects on tumor spheroids in a time-dependent and high-throughput manner, which can improve the efficiency of identifying more qualified cancer drug candidates.