A Framework for Optimizing High-Content Imaging of 3D Models for Drug Discovery
Wardwell-Swanson, Suzuki, Dowell, Bieri, Thoma, Agarkova, Chiovaro, Strebel, Buschmann, Greve, Frey (2020) A Framework for Optimizing High-Content Imaging of 3D Models for Drug Discovery SLAS Discov (IF: 3.1) 25(7) 709-722Abstract
Three-dimensional (3D) spheroid models are rapidly gaining favor for drug discovery applications due to their improved morphological characteristics, cellular complexity, long lifespan in culture, and higher physiological relevance relative to two-dimensional (2D) cell culture models. High-content imaging (HCI) of 3D spheroid models has the potential to provide valuable information to help researchers untangle disease pathophysiology and assess novel therapies more effectively. The transition from 2D monolayer models to dense 3D spheroids in HCI applications is not trivial, however, and requires 3D-optimized protocols, instrumentation, and resources. Here, we discuss considerations for moving from 2D to 3D models and present a framework for HCI and analysis of 3D spheroid models in a drug discovery setting. We combined scaffold-free, multicellular spheroid models with scalable, automation-compatible plate technology enabling image-based applications ranging from high-throughput screening to more complex, lower-throughput microphysiological systems of organ networks. We used this framework in three case studies: investigation of lipid droplet accumulation in a human liver nonalcoholic steatohepatitis (NASH) model, real-time immune cell interactions in a multicellular 3D lung cancer model, and a high-throughput screening application using a 3D co-culture model of gastric carcinoma to assess dose-dependent drug efficacy and specificity. The results of these proof-of-concept studies demonstrate the potential for high-resolution image-based analysis of 3D spheroid models for drug discovery applications, and confirm that cell-level and temporal-spatial analyses that fully exploit multicellular features of spheroid models are not only possible but soon will be routine practice in drug discovery workflows.
Links
http://www.ncbi.nlm.nih.gov/pubmed/32484408http://dx.doi.org/10.1177/2472555220929291
