Organoids in image-based phenotypic chemical screens
Open accessAbstract
Image-based phenotypic screening relies on the extraction of multivariate information from cells cultured under a large variety of conditions. Technical advances in high-throughput microscopy enable screening in increasingly complex and biologically relevant model systems. To this end, organoids hold great potential for high-content screening because they recapitulate many aspects of parent tissues and can be derived from patient material. However, screening is substantially more difficult in organoids than in classical cell lines from both technical and analytical standpoints. In this review, we present an overview of studies employing organoids for screening applications. We discuss the promises and challenges of small-molecule treatments in organoids and give practical advice on designing, running, and analyzing high-content organoid-based phenotypic screens.
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Several examples have documented the power of chemical screens in organoids or directly in patient material.
“To that end, several examples have documented the power of chemical screens in organoids175,176 or directly in patient material.177−179”
From Advancing Targeted Protein Degradation via Multiomics Profiling and Artificial Intelligence (2023) · cites this paper as [176]
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Evolution and impact of high content imaging.
Way, G. P., Sailem, H., Shave, S., Kasprowicz, R., & Carragher, N. O. (2023) · SLAS Discovery · doi:10.1016/j.slasd.2023.08.009 · Cited by 67 · see citing works
Advancing Targeted Protein Degradation via Multiomics Profiling and Artificial Intelligence
Duran-Frigola, M., Cigler, M., & Winter, G. E. (2023) · Journal of the American Chemical Society · doi:10.1021/jacs.2c11098 · Cited by 50 · see citing works
Fuhr, A., Kurtz, A., Hiepen, C., & Müller, S. (2022) · Organoids · doi:10.3390/organoids1010003 · Cited by 10 · see citing works
- Authors
- Lukonin, I., Zinner, M., & Liberali, P.
- Affiliations
- Friedrich Miescher Institute (CH)
- University of Basel (CH)
- Corresponding author
- Liberali, Prisca
- Venue
- Experimental & Molecular Medicine
- Published
- 2021-10
- Volume
- 53
- Issue
- 10
- Pages
- 1495-1502
- ISSN
- 1226-3613 (ISSN-L)
- 2092-6413
- PMC
- PMC8569209
- OpenAlex
- W3206891048
- Cited by
- 99
- References
- 85
- Citations by year
- 2026: 19 · 2025: 20 · 2024: 17 · 2023: 25 · 2022: 17 · 2021: 1
- FWCI
- 2.04
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Experimental & Molecular Medicine (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Funders
- European Commission (HORIZON2020, 758617)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Biophysics › Cell Image Analysis Techniques
- Health Sciences › Medicine › Oncology › Cancer Cells and Metastasis
- Physical Sciences › Engineering › Biomedical Engineering › 3D Printing in Biomedical Research
- Keywords
- organoids, high-content screening, small molecule drug screening, high-throughput imaging, patient-derived models
- MeSH terms
- Humans, Organoids
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
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