NanoBRET™ Live-Cell Kinase Selectivity Profiling Adapted for High-Throughput Screening

Kinases represent one of the most therapeutically tractable targets for drug discovery in the twenty-first century. However, confirming engagement and achieving intracellular kinase selectivity for small-molecule kinase inhibitors can represent noteworthy challenges. The NanoBRETTM platform enables broad-spectrum live-cell kinase selectivity profiling in most laboratory settings, without advanced instrumentation or expertise. However, the prototype workflow for this selectivity profiling is currently limited to manual liquid handling and 96-well plates. Herein, we describe a scalable workflow with automation and acoustic dispensing, thus dramatically improving the throughput. Such adaptations enable profiling of larger compound sets against 192 full-length protein kinases in live cells, with statistical robustness supporting quantitative analysis.

Live multicellular tumor spheroid models for high-content imaging and screening in cancer drug discovery

The multi cellular tumor spheroid (MCTS) model has been used for decades with proven superiority over monolayer cell culture models at recapitulating in vivo tumor growth. Yet its use in high-throughput drug discovery has been limited, particularly with image based screening, due to practical and technical hurdles. Here we report a significant advance in utilizing live MCTS models for high-content image based drug discovery. Using a validated GFP reporter (CK5Pro-GFP) of luminal breast cancer stem cells (CSC), we developed an algorithm to quantify changes in CK5Pro-GFP expression levels for individual Z-stack planes (local) or as maximal projections of the summed Z-stacks (global) of MCTS. From these image sets, we can quantify the cross-sectional area of GFP positive cells, the fluorescence intensity of the GFP positive cells, and the percent of spheroid cross-sectional area that expresses CK5Pro-GFP.We demonstrate that acquiring data in this manner can be done in real time and is statistically robust (Z’=0.85) for use in primary high-content screening cancer drug discovery.