Abstract
Microinjection enables the precise delivery of substances into specific areas of small animals, such as zebrafish, whose xenograft models can be a promising platform for developing rapid and personalized cancer therapies. However, manual microinjection exhibits experimental variability and low reproducibility, as it relies on the expertise of researchers. To address these problems, automated microinjection systems have been developed in recent years. In this study, we propose a microrobotic system based on an image recognition AI model that extracts key feature points to define the pericardial space in zebrafish larvae at 2 days post-fertilization. Using the geometric relationships among feature points, the system optimizes the glass capillary insertion motion for precise microinjection. We also introduced a batch agarose microplate that prevents dehydration while stabilizing the larvae, which improved the survival rate compared to the conventional plate (log-rank test, p < 0.0001). The proposed automation system achieved success rates of 80.8% (n = 1129) for microinjection and a 92.1% (n = 1143) for survival. Moreover, we successfully injected colorectal cancer cell lines (HCT116 and SW620) into the pericardial space, resulting in an engraftment success rate of 96.2% (n = 610). Our system exhibits higher success rates and reproducibility compared to manual microinjection, allowing even inexperienced researchers to perform stable injections. These results demonstrate that our system effectively enhances the efficiency and reproducibility of experiments involving zebrafish-based cancer research and xenograft model generation.