A Robotic Blind Patch-Clamp System Based on Cranial Opening Localization and Micropipette Guidance
The blind patch-clamp is a powerful technique for studying ion channel function, offering crucial insights into the mechanisms underlying brain neural circuits and the factors contributing to neurological disorders of alive animals. However, existing blind patch-clamp systems usually lack an effective animal head fixation mechanism compatible with conventional in vitro patch-clamp system setup. Additionally, the random location of the craniotomy relative to the exposed skull makes manual positioning of the cranial opening time-consuming and labor-intensive. The small size of the cranial opening further increases the risk of the micropipette tip scraping against the adjacent skull during operation, leading to tip breakage and experimental failure. In this paper, we present a universal mouse head fixer and a robotic blind patch clamp operation process, including a robotic cranial opening positioning and a fast micropipette approaching trajectory generation method. In this process, the resistance value of micropipette tip is used as feedback to guide the its step-by-step operation in vivo. This method not only achieves faster operation speed, but also guarantees a success rate comparable to that of professional manual operation.
Authors
- Ruimin Li (ORCID: https://orcid.org/0000-0003-1610-2864)
- Xin Zhao (ORCID: https://orcid.org/0000-0003-0631-4628)
- Jinyu Qiu (ORCID: https://orcid.org/0000-0001-8480-744X)
- Zijian Guo (ORCID: https://orcid.org/0000-0003-4986-9308)
- Qili Zhao (ORCID: https://orcid.org/0000-0002-9517-7460)
- Mingzhu Sun (ORCID: https://orcid.org/0000-0003-0562-7665)
Institutions
- Nankai University (CN)
Publication Details
- Published
- 2024-12-10
- DOI
- https://doi.org/10.1109/robio64047.2024.10907311
- Primary Topic
- Soft Robotics and Applications
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- National Natural Science Foundation of China
- Natural Science Foundation of Guangdong Province