APPLICATION NOTE
Understanding organoid morphology using 3D image analysis
This study assesses patient-derived organoid size and cell count from 3D image analysis.
Patient-derived Organoids are the drug discovery platform of the future because of their greater physiological and clinical relevance as compared to existing 2D models used in screening. Importantly, this work demonstrates the principle that morphological parameters can be assessed and quantified in a high-throughput format.
Once key morphometric signatures for clinical efficacy have been defined, 3D imaging has the potential to revolutionize drug discovery, leading to the early selection of hit compounds that will progress through to the clinic. Assays for changes in organoid morphology following drug-treatment have the potential to identify treatments targeted to specific cell-types such as cancer stem cells, that would be missed in the metabolic assays used currently, since there is no bulk cell-killing effect.
Learn more about the systems behind the methods
ImageXpress Micro Confocal system
High-content confocal imaging solution with proprietary spinning disk technology and water objective options
- Ideal for 3D organoid and spheroid imaging
- Proprietary AgileOptix™ spinning disk technology
- QuickID reduces image acquisition time and data storage requirements
- Water immersion objectives enhance resolution, sensitivity and throughput of complex
3D assays - Lasers and deep tissue confocal disk provide deeper penetration into 3D samples
- 3D volumetric analysis
MetaXpress software for ImageXpress high-content imaging systems
Multi-level analysis tools for a wide range of applications
- Meet high throughput requirements with a scalable, streamlined workflow
- Adapt your analysis tools to tackle your toughest problems, including 3D analysis
- Schedule automatic data transfer between third-party hardware sources and secure database
- Set up hundreds of routinely used HCS assays using MetaXpress software modules
- Export data to IN Carta software, leveraging intuitive, modern machine learning
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