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 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.
You will learn:
- Core advantages of 3D models and 3D in vitro technology engineered for drug discovery applications, and the range of rich, physiologically relevant data you can extract from 3D disease models using HCA methods.
- Considerations when upgrading high-content imaging from 2D to 3D, including fixation, staining, and clearing methods.
Tips and tricks for image acquisition, including HCA instrumentation requirements, algorithm optimization, and our guidelines for automated confocal image settings and parameters. - HCA data analysis methodologies for visualization of 3D image stacks, volumetric data, segmentation of 3D samples impacted by reduced light penetration, segmentation of individual cells to extract population data.
- How to harness the power of deep learning HCA capabilities to analyze and digitize highly complex imaging experiment data extracted from 3D models and live cell systems.