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- Title
Drug screening at single-organoid resolution via bioprinting and interferometry.
- Authors
Tebon, Peyton J.; Wang, Bowen; Markowitz, Alexander L.; Davarifar, Ardalan; Tsai, Brandon L.; Krawczuk, Patrycja; Gonzalez, Alfredo E.; Sartini, Sara; Murray, Graeme F.; Nguyen, Huyen Thi Lam; Tavanaie, Nasrin; Nguyen, Thang L.; Boutros, Paul C.; Teitell, Michael A.; Soragni, Alice
- Abstract
High throughput drug screening is an established approach to investigate tumor biology and identify therapeutic leads. Traditional platforms use two-dimensional cultures 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 scale and screen. Manually seeded organoids coupled to destructive endpoint assays allow for the characterization of treatment response, but do not capture transitory changes and intra-sample heterogeneity underlying clinically observed resistance to therapy. We present a pipeline to generate bioprinted tumor organoids linked to label-free, time-resolved imaging via high-speed live cell interferometry (HSLCI) and machine learning-based quantitation of individual organoids. Bioprinting cells gives rise to 3D structures with unaltered tumor histology and gene expression profiles. HSLCI imaging in tandem with machine learning-based segmentation and classification tools enables accurate, label-free parallel mass measurements for thousands of organoids. We demonstrate that this strategy identifies organoids transiently or persistently sensitive or resistant to specific therapies, information that could be used to guide rapid therapy selection. Traditional 2D cell culture platforms do not accurately reflect the physiology of human tumors. Here, authors combine bioprinting and high-speed live cell interferometry with machine learning to measure drug sensitivity at single-organoid resolution in a label-free manner.
- Subjects
BIOPRINTING; MACHINE learning; MEDICAL screening; INTERFEROMETRY; HIGH throughput screening (Drug development); GENE expression profiling
- Publication
Nature Communications, 2023, Vol 14, Issue 1, p1
- ISSN
2041-1723
- Publication type
Article
- DOI
10.1038/s41467-023-38832-8