Automation in microinjection for zebrafish pericardial space with image-based motion control and batch agarose...

Area of Science:

• Biomedical Engineering
• Zebrafish Xenograft Models
• Cancer Research

Background:

• Microinjection is crucial for zebrafish xenograft models in cancer therapy research.
• Manual microinjection suffers from variability and low reproducibility due to reliance on researcher expertise.
• Automated systems are needed to overcome these limitations.

Purpose of the Study:

• To develop an automated microrobotic system for precise microinjection in zebrafish.
• To improve the efficiency and reproducibility of generating zebrafish xenograft models for cancer research.
• To enable even inexperienced researchers to perform stable microinjections.

Main Methods:

• An AI image recognition model identifies key feature points in zebrafish larvae for precise targeting.
• A microrobotic system optimizes capillary insertion based on geometric relationships of feature points.
• A batch agarose microplate was developed to enhance larval survival during the process.

Main Results:

• The automated system achieved 80.8% microinjection success and 92.1% survival rates.
• Successful injection of colorectal cancer cell lines (HCT116, SW620) with a 96.2% engraftment rate.
• The system demonstrated higher success rates and reproducibility compared to manual microinjection.

Conclusions:

• The proposed microrobotic system significantly enhances the efficiency and reproducibility of zebrafish xenograft model generation.
• This automation facilitates rapid and personalized cancer therapy development using zebrafish models.
• The system empowers researchers, regardless of experience, to conduct stable and reliable microinjections.