Integration of autopatching with automated pipette and cell detection in vitro
Integration of autopatching with automated pipette and cell detection in vitro
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DOI：
10.1152/jn.00386.2016
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发表时间：
2016-10-01
影响因子：
2.5
通讯作者：
Chubykin, Alexander A.
中科院分区：
文献类型：
--
作者：
Wu, Qiuyu;Kolb, Ilya;Chubykin, Alexander A.
膜片钳是测量单个细胞电特性的主要技术。自1976年Neher和Sakmann发现膜片钳技术以来，它在拓宽我们对神经元中离子通道和突触的基本特性的理解方面发挥了重要作用。传统的膜片钳方法需要手动精确定位玻璃微量移液管对视觉识别的目标神经元的细胞膜。随后，在移液管和细胞膜之间建立紧密的“千兆密封”连接，并施加吸力以建立整个细胞贴片配置来进行电生理记录。对于每个单独的单元，手动重复该过程，使得其劳动密集且耗时。在这篇文章中，我们描述了一个新的自动膜片钳系统的脑切片的发展，它集成了所有步骤的膜片钳过程：通过显微镜，基于计算机视觉识别的膜片钳和荧光标记的神经元，显微操作器控制，和自动修补图像采集。我们验证了我们的系统在野生型和转基因小鼠的脑切片表达通道视紫红质2下的Thy1启动子（线18）或注射单纯疱疹病毒表达古视紫红质，ArchT。我们的基于计算机视觉的算法使得荧光细胞检测和定位独立于用户。与手工修补相比，我们的系统在成功率和平均试用时间上都具有上级优势。它提供了更可靠的修补过程的试验到试验控制，并提高了实验的再现性。
Patch clamp is the main technique for measuring electrical properties of individual cells. Since its discovery in 1976 by Neher and Sakmann, patch clamp has been instrumental in broadening our understanding of the fundamental properties of ion channels and synapses in neurons. The conventional patch-clamp method requires manual, precise positioning of a glass micropipette against the cell membrane of a visually identified target neuron. Subsequently, a tight "gigaseal" connection between the pipette and the cell membrane is established, and suction is applied to establish the whole cell patch configuration to perform electrophysiological recordings. This procedure is repeated manually for each individual cell, making it labor intensive and time consuming. In this article we describe the development of a new automatic patch-clamp system for brain slices, which integrates all steps of the patch-clamp process: image acquisition through a microscope, computer vision-based identification of a patch pipette and fluorescently labeled neurons, micromanipulator control, and automated patching. We validated our system in brain slices from wild-type and transgenic mice expressing channelrhodopsin 2 under the Thy1 promoter (line 18) or injected with a herpes simplex virus-expressing archaerhodopsin, ArchT. Our computer vision-based algorithm makes the fluorescent cell detection and targeting user independent. Compared with manual patching, our system is superior in both success rate and average trial duration. It provides more reliable trial-to-trial control of the patching process and improves reproducibility of experiments.