Drug screening at single-organoid resolution via bioprinting and interferometry

Peyton J. Tebon, Bowen Wang, Alexander L. Markowitz, Ardalan Davarifar, Brandon L. Tsai, Patrycja Krawczuk, Alfredo E. Gonzalez, Sara Sartini, Graeme F. Murray, Huyen Thi Lam Nguyen, Nasrin Tavanaie, Thang L. Nguyen, Paul C. Boutros, Michael A. Teitell () and Alice Soragni ()
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Peyton J. Tebon: University of California Los Angeles
Bowen Wang: University of California Los Angeles
Alexander L. Markowitz: University of California Los Angeles
Ardalan Davarifar: University of California Los Angeles
Brandon L. Tsai: University of California Los Angeles
Patrycja Krawczuk: University of Southern California
Alfredo E. Gonzalez: University of California Los Angeles
Sara Sartini: University of California Los Angeles
Graeme F. Murray: Virginia Commonwealth University
Huyen Thi Lam Nguyen: University of California Los Angeles
Nasrin Tavanaie: University of California Los Angeles
Thang L. Nguyen: University of California Los Angeles
Paul C. Boutros: University of California Los Angeles
Michael A. Teitell: University of California Los Angeles
Alice Soragni: University of California Los Angeles

Nature Communications, 2023, vol. 14, issue 1, 1-16

Abstract: 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.

Date: 2023
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DOI: 10.1038/s41467-023-38832-8

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