Annecchino Luca A, Morris Alexander R, Copeland Caroline S, Agabi Oshiorenoya E, Chadderton Paul, Schultz Simon R
Department of Bioengineering and Centre for Neurotechnology, Imperial College London, London SW7 2AZ, UK.
Department of Bioengineering and Centre for Neurotechnology, Imperial College London, London SW7 2AZ, UK.
Neuron. 2017 Aug 30;95(5):1048-1055.e3. doi: 10.1016/j.neuron.2017.08.018.
Whole-cell patch-clamp electrophysiological recording is a powerful technique for studying cellular function. While in vivo patch-clamp recording has recently benefited from automation, it is normally performed "blind," meaning that throughput for sampling some genetically or morphologically defined cell types is unacceptably low. One solution to this problem is to use two-photon microscopy to target fluorescently labeled neurons. Combining this with robotic automation is difficult, however, as micropipette penetration induces tissue deformation, moving target cells from their initial location. Here we describe a platform for automated two-photon targeted patch-clamp recording, which solves this problem by making use of a closed loop visual servo algorithm. Our system keeps the target cell in focus while iteratively adjusting the pipette approach trajectory to compensate for tissue motion. We demonstrate platform validation with patch-clamp recordings from a variety of cells in the mouse neocortex and cerebellum.
全细胞膜片钳电生理记录是研究细胞功能的一项强大技术。虽然体内膜片钳记录最近受益于自动化,但它通常是“盲目”进行的,这意味着对某些基因或形态学定义的细胞类型进行采样的通量低得令人无法接受。解决这个问题的一种方法是使用双光子显微镜来靶向荧光标记的神经元。然而,将其与机器人自动化相结合很困难,因为微吸管穿透会引起组织变形,使目标细胞从其初始位置移动。在这里,我们描述了一个用于自动双光子靶向膜片钳记录的平台,该平台通过使用闭环视觉伺服算法解决了这个问题。我们的系统在迭代调整吸管进针轨迹以补偿组织运动的同时,使目标细胞保持在焦点上。我们通过对小鼠新皮层和小脑中各种细胞进行膜片钳记录来证明该平台的有效性。