Drug screening at single-organoid resolution via bioprinting and interferometry

Abstract

High-throughput drug screening is an established approach to investigate tumor biology and identify therapeutic leads. Traditional platforms for high-throughput screening use two-dimensional cultures of immortalized cell lines which do not accurately reflect the biology of human tumors. More clinically relevant model systems, such as three-dimensional tumor organoids, can be difficult to screen and scale. For example, manually seeded organoids coupled to destructive endpoint assays allow for the characterization of response to treatment, but do not capture the transitory changes and intra-sample heterogeneity underlying clinically observed resistance to therapy. We therefore developed a pipeline to generate bioprinted tumor organoids linked to label-free, real-time imaging via high-speed live cell interferometry (HSLCI) and machine learning-based quantitation of individual organoids. Bioprinting cells gives rise to 3D organoid structures that preserve tumor histology and gene expression. HSLCI imaging in tandem with machine learning-based image segmentation and organoid classification tools enables accurate, label-free parallel mass measurements for thousands of bioprinted organoids. We demonstrate that our method quantitatively identifies individual organoids as insensitive, transiently sensitive, or persistently sensitive to specific treatments. This opens new avenues for rapid, actionable therapeutic selection using automated tumor organoid screening.