APPLICATION NOTE
An automated workflow for anti cancer drug toxicity evaluation using patient derived healthy intestinal organoids with suspension culture and AI enabled image analysis
Gain Deeper Toxicity Insights with Automated Human Intestinal Organoid Screening
Discover how an automated workflow combining the CellXpress.ai® Automated Cell Culture System, ImageXpress® HCS.ai High-Content Screening System, and IN Carta® Image Analysis Software streamlines anticancer drug toxicity evaluation in patient-derived human intestinal organoids. By integrating suspension organoid culture, high-content imaging, and AI-driven phenotypic analysis, researchers can improve assay consistency, reduce workflow complexity, and generate more comprehensive toxicity insights.
In this application note, you'll learn how to:
- Establish scalable toxicity assays using suspension-cultured human intestinal organoids
- Automate organoid culture, treatment, monitoring, imaging, and analysis to improve efficiency and reproducibility
- Capture multiparametric phenotypic data with high-content imaging to assess compound-induced effects
- Apply AI-driven image analysis to classify organoid health and identify toxicity-related phenotypic changes
- Generate more robust toxicity measurements using machine learning-based phenotypic profiling rather than single-readout approaches
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Product family
Imaging
Product primary application
CellXpress.ai Automated Cell Culture System; IN Carta Image Analysis Software; ImageXpress HCS.ai High-Content Screening System; Lab automation for high-throughput clone screening; 3D Cell Models; Automated Cell Culture; Cell Imaging & Analysis; Intestinal Organoids; Live Cell Imaging; Organoids; Spheroids; Cancer Research Solutions; Disease Modeling; Drug Discovery and Development; Organoid Innovation Center; Toxicology
CMP
701Nr00000xsPfQIAU
Title
An automated workflow for anti cancer drug toxicity evaluation using patient derived healthy intestinal organoids with suspension culture and AI enabled image analysis
Resource URL
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