Optimizing High-Content Imaging of 3D Models for Drug Discovery

In vitro spheroid models are fast becoming the de facto standard for drug discovery applications, largely due to their human-like physiological and morphological characteristics, tissue-like cellular complexity, and long culture lifespan, which enables longitudinal studies that better reflect patient treatment plans in the clinic. High content imaging and analysis (HCA) of 3D spheroid models can provide valuable information to help researchers untangle disease pathophysiology and assess novel therapies more effectively. Making the move from simple monolayer 2D cell models to dense 3D spheroids in HCI applications, however, requires 3D-optimized protocols, instrumentation, and resources.

In this webinar, we will discuss considerations for high content imaging and analysis of 3D spheroid disease models for drug discovery, share lessons we learned while in setting up and conducting proof-of-concept studies designed to test the full potential for high resolution image-based analysis of 3D spheroid models, and provide a working checklist for researchers and core services groups planning to exploit these technologies in their work.

Learning Objectives:

• High Content assat development- considerations when transitioning from 2D cell models to 3D multicellular models including examples of the rich, physiologically-relevant data that can be extracted from multicellular 3D models 
• 3D image acquisition- high content instrument hardware/software and acquisition settings for efficient acquisition of high quality image stacks
• 3D image analysis- visualization and image analysis toolsets for 3D imaging, including the application of Deep Learning methods