Institute of Robotics and Automatic Information System, Tianjin Key Laboratory of Intelligent Robotics, Nankai University, Tianjin 300350, China.
Institute of Intelligence Technology and Robotic Systems, Shenzhen Research Institute of Nankai University, Shenzhen 518083, China.
Sensors (Basel). 2023 Sep 28;23(19):8144. doi: 10.3390/s23198144.
A patch clamp is the "gold standard" method for studying ion-channel biophysics and pharmacology. Due to the complexity of the operation and the heavy reliance on experimenter experience, more and more researchers are focusing on patch-clamp automation. The existing automated patch-clamp system focuses on the process of completing the experiment; the detection method in each step is relatively simple, and the robustness of the complex brain film environment is lacking, which will increase the detection error in the microscopic environment, affecting the success rate of the automated patch clamp. To address these problems, we propose a method that is suitable for the contact between pipette tips and neuronal cells in automated patch-clamp systems. It mainly includes two key steps: precise positioning of pipettes and contact judgment. First, to obtain the precise coordinates of the tip of the pipette, we use the Mixture of Gaussian (MOG) algorithm for motion detection to focus on the tip area under the microscope. We use the object detection model to eliminate the encirclement frame of the pipette tip to reduce the influence of different shaped tips, and then use the sweeping line algorithm to accurately locate the pipette tip. We also use the object detection model to obtain a three-dimensional bounding frame of neuronal cells. When the microscope focuses on the maximum plane of the cell, which is the height in the middle of the enclosing frame, we detect the focus of the tip of the pipette to determine whether the contact between the tip and the cell is successful, because the cell and the pipette will be at the same height at this time. We propose a multitasking network CU-net that can judge the focus of pipette tips in complex contexts. Finally, we design an automated contact sensing process in combination with resistance constraints and apply it to our automated patch-clamp system. The experimental results show that our method can increase the success rate of pipette contact with cells in patch-clamp experiments.
膜片钳是研究离子通道生物物理学和药理学的“金标准”方法。由于操作复杂且严重依赖实验者经验,越来越多的研究人员将注意力集中在膜片钳自动化上。现有的自动化膜片钳系统专注于完成实验的过程;每个步骤的检测方法相对简单,缺乏对复杂脑膜环境的鲁棒性,这将增加微观环境中的检测误差,影响自动化膜片钳的成功率。针对这些问题,我们提出了一种适用于自动化膜片钳系统中管尖与神经元细胞接触的方法。它主要包括两个关键步骤:微管尖端的精确定位和接触判断。首先,为了获得微管尖端的精确坐标,我们使用混合高斯(MOG)算法进行运动检测,将显微镜下的尖端区域聚焦。我们使用目标检测模型消除微管尖端的包围框,以减少不同形状尖端的影响,然后使用扫描线算法精确定位微管尖端。我们还使用目标检测模型获取神经元细胞的三维包围框。当显微镜聚焦于细胞的最大平面,即包围框中间的高度时,我们检测微管尖端的焦点,以确定尖端与细胞的接触是否成功,因为此时细胞和微管将处于同一高度。我们提出了一个可以在复杂环境下判断微管尖端焦点的多任务网络 CU-net。最后,我们结合电阻约束设计了一个自动化接触感应过程,并将其应用于我们的自动化膜片钳系统。实验结果表明,我们的方法可以提高膜片钳实验中微管与细胞接触的成功率。