Introducing an automated high content confocal imaging approach for Organs-on-Chips

Area of Science:

• Biotechnology
• Drug Discovery
• Toxicology

Background:

• Organ-Chips are micro-engineered systems mimicking organ microenvironments for drug discovery.
• Current imaging methods for Organ-Chips are limited in scalability, hindering broad application.
• Automated imaging is crucial for enhancing pre-clinical drug efficacy and toxicity evaluation.

Purpose of the Study:

• To create an automated workflow for capturing and analyzing confocal images of Organ-Chips at scale.
• To establish a framework for statistical best practices in Organ-Chip imaging.
• To enable routine quantitative image data analysis for drug discovery decision-making.

Main Methods:

• Developed an end-to-end automated workflow for confocal image acquisition and analysis of multicellular Organ-Chips.
• Implemented the workflow on various Organ-Chips, including liver and kidney models.
• Tested the system with known toxic compounds (benzbromarone, staurosporine) and an AstraZeneca drug candidate.

Main Results:

• Significantly reduced image acquisition time and process variability.
• Minimized user bias in cellular phenotype assessment.
• Demonstrated the workflow's adaptability to different Organ-Chip designs and its utility in drug safety assessment.

Conclusions:

• The automated workflow enables scalable, quantitative imaging of Organ-Chips for drug discovery.
• This approach facilitates robust pre-clinical efficacy and toxicity prediction.
• Established a framework for statistical best practices in Organ-Chip imaging, advancing its routine use.