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

Area of Science:

• Biotechnology
• Drug Discovery
• Cell Biology

Background:

• Three-dimensional (3D) spheroid models are increasingly adopted in drug discovery due to enhanced physiological relevance, cellular complexity, and longevity compared to 2D cultures.
• High-content imaging (HCI) of 3D spheroids offers potential for deeper insights into disease pathophysiology and therapeutic evaluation.
• Transitioning from 2D to 3D models for HCI requires optimized protocols, instrumentation, and resources.

Purpose of the Study:

• To discuss considerations for implementing 3D spheroid models in HCI for drug discovery.
• To present a framework for HCI and analysis of 3D spheroid models.
• To demonstrate the utility of this framework through case studies in liver, lung, and gastric cancer models.

Main Methods:

• Development of a scaffold-free, multicellular spheroid model integrated with scalable, automation-compatible plate technology.
• Application of the framework to three distinct case studies: NASH liver model, lung cancer model, and gastric carcinoma co-culture model.
• Utilizing high-resolution imaging for cell-level and temporal-spatial analyses.

Main Results:

• Demonstrated the potential of high-resolution image-based analysis for 3D spheroid models in drug discovery.
• Successfully investigated lipid droplet accumulation in a NASH model.
• Observed real-time immune cell interactions in a 3D lung cancer model.
• Assessed dose-dependent drug efficacy and specificity in a 3D gastric carcinoma co-culture model.

Conclusions:

• The presented framework enables advanced, image-based analysis of 3D spheroid models for drug discovery.
• Cell-level and temporal-spatial analyses of multicellular spheroid features are feasible and poised to become standard in drug discovery workflows.
• 3D spheroid models coupled with HCI offer a powerful platform for evaluating novel therapies and understanding disease mechanisms.