Search

Zebrafish microinjection is widely used in vascular biology, neurology, and other research areas. The microinjection of zebrafish larvae is a complicated 3D task because zebrafish larvae are independent individuals with complex body structures compared with biological cells. In this study, we propose an automatic high-throughput injection system for zebrafish larvae under an optical inverted microscope. The proposed system combines the fixing device design, object detection, visual positioning, and 3D injection path planning to improve the survival rate of zebrafish after injection. Experimental results demonstrated the capability and efficiency of the proposed system, which achieved a success rate of 95.0% and a survival rate of 98.7% for zebrafish injection. The system can be applied to various biomedical and biochemical experiments. Note to Practitioners—Zebrafish larvae have long been an important model organism in neuroscience, biomedicine, and drug discovery. In various methods for external substance transportation into the zebrafish, microinjection is more efficient but also more difficult. At present, high-throughput injection of zebrafish larvae is mainly based on traditional manual operations. The injection speed and success rate of manual injection gradually decrease with the increase in zebrafish numbers. In this study, an automatic injection system is designed and implemented for high-throughput zebrafish larvae injection. We first use a simply designed mold to fix batch zebrafish larvae. Then we propose a coarse-fine two-step positioning method and perform a 3D injection path planning to achieve precise microinjection. The proposed injection system frees the operators from the repetitive and tedious injection work, providing technical support for the life science experiment.

Patch clamp technique is a ”gold standard” approach to the study of ion channel biophysics and pharmacology. Due to the complexity of the operation and the heavy reliance on the experience of the experimenter, more and more researchers are focusing on patch clamping automation. In manual patch clamping, the operators patch the neurons in the specific brain region by experience, which lacks quantitative indicators of brain region selection. At present, the existing automated patch clamp focuses more on searching and measuring single cells under a high magnification lens, but ignores the localization of different brain regions under a low magnification lens, affecting the efficiency and application of the automated system. Addressing this, a 2D-3D registration method is proposed to register the experimental microscopic image of brain slices onto the brain atlases. This method is then embedded into the patch clamp system for brain region selection and electrophysiological recordings of neurons in multiple brain regions. It is the first time to achieve automated selection of brain regions in patch clamp experiments. The experimental results indicate that brain slice registration and brain region selection completely meet the requirements of the patch clamp experiment. Compared with the original robotic patch clamp process, the total time for the whole-cell patch clamp initialization with the improved process is reduced by 280 seconds, and the efficiency is increased by 42%. Meanwhile, the time for brain region switching is reduced to 1/6 of the original. In the future, this method will be applied to automatic image-guided patch clamp systems. Note to Practitioners—Calibrating the depth of the experimental brain slice in the brain and dividing the distribution of each brain region in the brain slice are the most important preparations for electrophysiological experiments. In this paper, we design 2D-3D registration to establish a method for brain region selection and robotic patch clamping of different brain regions in one brain slice. We have successfully detected the electrophysiological signals of neurons in 3 major brain regions, including the visual region, auditory region, and hippocampus. The experimental results show that the method proposed in this paper is suitable for the existing robotic patch clamp system and greatly improves the degree of automation.

Cellular shear adhesion, which is the adhesion between cells and the substrates, plays an important role in cell activities. This article aimed to develop a robotic system with automated cell aspiration and in situ micropipette cleaning for shear adhesion force measurement. We proposed a shear adhesion force measurement method based on the balance pressure model and designed a cell placement device with a double-Petri dish to improve the accuracy and efficiency of the measurement. Then we achieved the whole measurement automation by cell selection based on image texture analysis, contact detection based on motion history image (MHI) analysis, and cell aspiration state detection based on sub-pixel image registration. Three types of adherent cells (pancreatic cancer cell, osteoblast, and osteoclast) were used to validate the automated measurement system. The system demonstrated an overall success rate of 75.6% with an average measurement time of 5 min. The experimental results show that pancreatic cancer cells possessed higher shear adhesion force than osteoblasts and osteoclasts. There was a significant difference in adhesion between osteoblasts and osteoclasts. Conclusion: The experimental results demonstrate that the system was an efficient, reproducible approach for shear adhesion force measurement automatically. The robotic system eliminated the stringent skill requirements of operators and improved the consistency and efficiency of the shear adhesion measurement, which can be applied to life science.