Drug screening at single-organoid resolution via bioprinting and interferometry
Open accessPreprint — published as 10.1038/s41467-023-38832-8
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.
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Phan and colleagues screened 240 targeted agents on organoids derived from patients with ovarian or peritoneal tumors, with results available within a week from surgery.
“Phan and colleagues screened 240 targeted agents on organoids derived from patients with ovarian or peritoneal tumors, with results available within a week from surgery.”
From Organoids (2022) · cites this paper as [151]
The Introduction (page unavailable) says a previously developed patient-derived organoid screening approach could yield results within one week of surgery, and the References list a study by Phan et al. The copy does not establish that Phan's study screened 240 targeted agents or used organoids from ovarian or peritoneal tumors; the one-week statement is not specifically attributed to Phan. (LLM, conf=0.78)
Experimental — accuracy not yet measuredOrganoids have been deployed to identify therapeutic leads in a number of tumors, including rare cancers that are understudied and poorly characterized at the molecular level.
“Organoids are ideally suited for FPM due to their ease of establishment, similarity with the tissue of origin and tractability158,167 and have been deployed to identify therapeutic leads in a number of tumors168, including rare cancers that tend to be understudied and poorly characterized at the molecular level16,151,169,170.”
From Organoids (2022) · cites this paper as [151]
The Introduction (page unavailable) describes tumor organoids as useful for drug development across varied tissue sources, and the References list organoid drug-discovery studies in several cancers, including chordoma. But this paper’s own drug-response experiments used breast cancer cell-line organoids; our copy does not establish that rare cancers are understudied or poorly characterized at the molecular level. (LLM, conf=0.84)
Experimental — accuracy not yet measuredTechnical challenges to rapid and high-throughput screening of 3D organoids have been addressed and surpassed by different means, including alternative plate design or seeding geometries.
“Technical challenges to rapid and high-throughput screening of 3D organoids have been addressed in recent years and surpassed by different means, including alternative plate design or seeding geometries16,151,152.”
From Organoids (2022) · cites this paper as [151]
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References 132 · Cited by 155
Research works printed but not linked: 4
- with next-generation functional diagnostics and pharmaceutical exploitation. Transl. Oncol. 14, 101126 (2021).
- Ronneberger, O., Fischer, P. & Brox, T. U-Net: Convolutional Networks for Biomedical Image Segmentation. in Medical Image Computing and Computer-Assisted Intervention -MICCAI 2015 (eds.
- TrackMate: An open and extensible platform for single-particle tracking -ScienceDirect. https:// www.sciencedirect.com/science/article/pii/ S1046202316303346.
- Smalley, K. S. M.) vol. 65 335-359 (Academic Press, 2012).
- Authors
- Tebon, P. J., Wang, B., Markowitz, A. L., Davarifar, A., Krawczuk, P., Murray, G., Nguyen, H. T. L., Tavanaie, N., Nguyen, T. L., Boutros, P. C., Teitell, M. A., & Soragni, A.
- Affiliations
- University of California, Los Angeles (US)
- University of Southern California (US)
- Virginia Commonwealth University (US)
- California NanoSystems Institute (US)
- Corresponding author
- Teitell, Michael A.
- Soragni, Alice
- ORCID
- Venue
- Nature Communications
- Publisher
- openRxiv
- Published
- 2021-10-04
- Volume
- 14
- Issue
- 1
- Pages
- 3168-3168
- ISSN
- 2041-1723 (ISSN-L)
- PMC
- PMC10244450
- OpenAlex
- W4379535850
- Cited by
- 155
- References
- 132
- Citations by year
- 2026: 44 · 2025: 59 · 2024: 45 · 2023: 6 · 2022: 1
- FWCI
- 12.14
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by
- Locations
- Nature Communications (publishedVersion, cc-by)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- eScholarship (California Digital Library) (submittedVersion, cc-by)
- RePEc: Research Papers in Economics (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- National Science Foundation (DGE-2,034,835, 2034835)
- U.S. Department of Defense (R01CA185189, P30CA016042, R01GM073981, W81XWH2110139, R21CA227480, R01GM127985)
- U.S. Department of Health and Human Services (R01GM073981, P30CA016042, R01GM114188, R01GM127985, U24CA248265, R01CA244729)
- National Institutes of Health (FA9550-15-1-0406)
- University of California, Los Angeles
- National Cancer Institute (R01CA185189, R01CA244729, U24CA248265, R21CA227480, P30CA016042, R01CA264248)
- National Institute of General Medical Sciences (R01GM073981, R01GM127985, R01GM114188)
- Jonsson Comprehensive Cancer Center (P30 CA016042)
- David Geffen School of Medicine, University of California, Los Angeles
- Air Force Office of Scientific Research (FA9550-, FA9550–15-1–0406)
- Topics
- Physical Sciences › Engineering › Biomedical Engineering › 3D Printing in Biomedical Research
- Physical Sciences › Physics and Astronomy › Atomic and Molecular Physics, and Optics › Digital Holography and Microscopy
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Cell Biology › Cellular Mechanics and Interactions
- Keywords
- tumor organoids, bioprinting, label-free imaging, drug screening, treatment resistance, intratumoral heterogeneity, organoid segmentation
- MeSH terms
- Drug Evaluation, Preclinical, Humans, Interferometry, Neoplasms, Organoids, Bioprinting
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