ArticleSLAS discovery : advancing life sciences R & D2024
Development of an automated 3D high content cell screening platform for organoid phenotyping.
Article in SLAS discovery : advancing life sciences R & D, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
13 citing papers in PubMed.
- Beyond the Monolayer: Leveraging 3D Culture Systems to Decode Mechanisms of Drug Resistance and Therapeutic Vulnerabilities in Colorectal Cancer.Cancer medicine · 2026Review
- Organoids in Precision Radiotherapy: Methodological Foundations, Tumor-Specific Evidence, and Translational Roadmaps.Cancer medicine · 2026Review
- Organoids as brain tumour models: bridging the translational gap.Disease models & mechanisms · 2026Review
- Progress and new challenges in image-based profiling.Molecular systems biology · 2026Review
- DeShiftNet: a deformable-shifted cross-attention network for lightweight and robust organoid image segmentation.BMC bioinformatics · 2026Article
- Smart Nanoparticles Are Not Smart Enough (Yet): A Cell-Aware View of Cancer Nanomedicine.Cells · 2026Article
- An organoid-guided roadmap for precision delivery of epigallocatechin gallate in oral submucous fibrosis.Frontiers in bioengineering and biotechnology · 2026Review
- Decoding chemotherapy-induced peripheral neuropathy: ion channels, disease models, and future directions.Frontiers in pharmacology · 2026Review
- Patient-derived organoids in functional precision oncology: from experimental models to clinical decision-making.Frontiers in endocrinology · 2026Review
- HCS-3DX, a next-generation AI-driven automated 3D-oid high-content screening system.Nature communications · 2025Article
- From 2D to 3D and beyond: the evolution and impact of in vitro tumor models in cancer research.Nature methods · 2025Review
- Review
- Trends and challenges in organoid modeling and expansion with pluripotent stem cells and somatic tissue.PeerJ · 2024Review
Corrections and comments
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Authors and funding
12 authors.
Funding
Abstract
The use of organoid models in biomedical research has grown substantially since their inception. As they gain popularity among scientists seeking more complex and biologically relevant systems, there is a direct need to expand and clarify potential uses of such systems in diverse experimental contexts. Herein we outline a high-content screening (HCS) platform that allows researchers to screen drugs or other compounds against three-dimensional (3D) cell culture systems in a multi-well format (384-well). Furthermore, we compare the quality of robotic liquid handling with manual pipetting and characterize and contrast the phenotypic effects detected by confocal imaging and biochemical assays in response to drug treatment. We show that robotic liquid handling is more consistent and amendable to high throughput experimental designs when compared to manual pipetting due to improved precision and automated randomization capabilities. We also show that image-based techniques are more sensitive to detecting phenotypic changes within organoid cultures than traditional biochemical assays that evaluate cell viability, supporting their integration into organoid screening workflows. Finally, we highlight the enhanced capabilities of confocal imaging in this organoid screening platform as they relate to discerning organoid drug responses in single-well co-cultures of organoids derived from primary human biopsies and patient-derived xenograft (PDX) models. Altogether, this platform enables automated, imaging-based HCS of 3D cellular models in a non-destructive manner, opening the path to complementary analysis through integrated downstream methods.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.