Automated microinjection for zebrafish xenograft models

Area of Science:

• * Comparative medicine and experimental therapeutics.
• * Zebrafish models in oncology and drug development.

Background:

• * Zebrafish xenograft models show promise for predicting patient responses to cancer therapies.
• * Manual microinjection into zebrafish larvae is labor-intensive, requires specialized training, and suffers from variability, hindering broader application.
• * Current limitations impede the full potential of zebrafish models in clinical diagnostics and drug discovery pipelines.

Purpose of the Study:

• * To develop and validate an automated microinjection robot for zebrafish xenograft models.
• * To overcome the challenges associated with manual microinjection, including labor, training, and reproducibility.
• * To enhance the efficiency, accuracy, and scalability of generating zebrafish xenograft models for research and diagnostics.

Main Methods:

• * Design, construction, and validation of a novel microinjection robot system.
• * Implementation of both fully automated and semi-automated injection modes.
• * Injections targeted to specific zebrafish larval tissues: vasculature, perivitelline space, and hindbrain ventricle.

Main Results:

• * The automated robot achieved an average injection success rate of approximately 60%.
• * Larval survival rates exceeded 70%, demonstrating comparability to manual injection techniques.
• * The fully automated mode operated twice as fast as manual methods, significantly improving throughput.

Conclusions:

• * The automated microinjection robot successfully addresses key limitations of manual zebrafish xenograft model generation.
• * Automation enhances reproducibility, efficiency, and accuracy, reducing the need for extensive operator training.
• * This technology facilitates wider adoption of zebrafish xenograft models for accelerated drug discovery and personalized cancer diagnostics.