Design of a High-Throughput Robotic Batch Microinjection System for Zebrafish Larvae-Based on Image Potential Energy

Abstract

Microinjection of zebrafish larvae is widely used in vaccine development, drug screening, gene research, etc. Conventional manual injection has the disadvantages of low efficiency and operator-skill dependence. In this article, a high-throughput robotic microinjection system is proposed for zebrafish larvae. For the first time, the image contour-based potential energy algorithm is introduced to judge whether the microneedle has successfully pierced into the zebrafish larva, so as to further decide whether to inject materials into the target sample. The customized microstructured agarose medium can be used to fix batch zebrafish larvae with different poses in standard array for microinjection. A deep learning machine vision approach based on convolutional neural networks is employed to recognize multiple injection target points at one time. The multidevice collaboration achieves continuous and accurate microinjection operations. A prototype system has been fabricated for experimental testing. The results show that the system can quickly inject a batch of zebrafish larvae with a high success rate and high survival rate. Owing to a high degree of automation, the proposed microinjection system greatly reduces the workload of experimenters, saves the experimental cost, and shortens the relevant experimental study period.