George W. Woodruff School of Mechanical Engineering, Georgia Institute of Technology , Atlanta, Georgia.
Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology , Atlanta, Georgia.
J Neurophysiol. 2019 Jun 1;121(6):2341-2357. doi: 10.1152/jn.00738.2018. Epub 2019 Apr 10.
Patch clamping is the gold standard measurement technique for cell-type characterization in vivo, but it has low throughput, is difficult to scale, and requires highly skilled operation. We developed an autonomous robot that can acquire multiple consecutive patch-clamp recordings in vivo. In practice, 40 pipettes loaded into a carousel are sequentially filled and inserted into the brain, localized to a cell, used for patch clamping, and disposed. Automated visual stimulation and electrophysiology software enables functional cell-type classification of whole cell-patched cells, as we show for 37 cells in the anesthetized mouse in visual cortex (V1) layer 5. We achieved 9% yield, with 5.3 min per attempt over hundreds of trials. The highly variable and low-yield nature of in vivo patch-clamp recordings will benefit from such a standardized, automated, quantitative approach, allowing development of optimal algorithms and enabling scaling required for large-scale studies and integration with complementary techniques. In vivo patch-clamp is the gold standard for intracellular recordings, but it is a very manual and highly skilled technique. The robot in this work demonstrates the most automated in vivo patch-clamp experiment to date, by enabling production of multiple, serial intracellular recordings without human intervention. The robot automates pipette filling, wire threading, pipette positioning, neuron hunting, break-in, delivering sensory stimulus, and recording quality control, enabling in vivo cell-type characterization.
膜片钳技术是细胞类型鉴定的金标准测量技术，但它的通量低、难以扩展，并且需要高度熟练的操作。我们开发了一种自主机器人，它可以在体内进行多次连续的膜片钳记录。在实际操作中，40 个装在转盘中的移液器依次填充并插入大脑，定位到一个细胞，用于进行膜片钳记录，然后进行处理。自动化的视觉刺激和电生理学软件可以对全细胞封接细胞进行功能细胞类型分类，我们在麻醉小鼠视觉皮层（V1）第 5 层的 37 个细胞中展示了这一点。我们实现了 9%的产量，在数百次尝试中每次尝试耗时 5.3 分钟。体内膜片钳记录的高度可变性和低产量性质将受益于这种标准化、自动化、定量的方法，从而可以开发最佳算法，并实现大规模研究所需的扩展以及与互补技术的集成。体内膜片钳是细胞内记录的金标准，但它是一种非常手动和高度熟练的技术。这项工作中的机器人通过实现无需人工干预即可进行多次、连续的细胞内记录，展示了迄今为止最自动化的体内膜片钳实验。机器人实现了移液器填充、线穿线、移液器定位、神经元搜索、击穿、传递感觉刺激和记录质量控制的自动化，从而实现了体内细胞类型鉴定。
