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细胞斑点:一种用于活细胞中钙通道自动检测和分析的软件。

CellSpecks: A Software for Automated Detection and Analysis of Calcium Channels in Live Cells.

机构信息

Department of Physics, University of South Florida, Tampa, Florida.

Pacific Biosciences, Menlo Park, California.

出版信息

Biophys J. 2018 Dec 4;115(11):2141-2151. doi: 10.1016/j.bpj.2018.10.015. Epub 2018 Oct 25.

Abstract

To couple the fidelity of patch-clamp recording with a more high-throughput screening capability, we pioneered a, to our knowledge, novel approach to single-channel recording that we named "optical patch clamp." By using highly sensitive fluorescent Ca indicator dyes in conjunction with total internal fluorescence microscopy techniques, we monitor Ca flux through individual Ca-permeable channels. This approach provides information about channel gating analogous to patch-clamp recording at a time resolution of ∼2 ms with the additional advantage of being massively parallel, providing simultaneous and independent recording from thousands of channels in the native environment. However, manual analysis of the data generated by this technique presents severe challenges because a video recording can include many thousands of frames. To overcome this bottleneck, we developed an image processing and analysis framework called CellSpecks capable of detecting and fully analyzing the kinetics of ion channels within a video sequence. By using randomly generated synthetic data, we tested the ability of CellSpecks to rapidly and efficiently detect and analyze the activity of thousands of ion channels, including openings for a few milliseconds. Here, we report the use of CellSpecks for the analysis of experimental data acquired by imaging muscle nicotinic acetylcholine receptors and the Alzheimer's disease-associated amyloid β pores with multiconductance levels in the plasma membrane of Xenopus laevis oocytes. We show that CellSpecks can accurately and efficiently generate location maps and create raw and processed fluorescence time traces; histograms of mean open times, mean close times, open probabilities, durations, and maximal amplitudes; and a "channel chip" showing the activity of all channels as a function of time. Although we specifically illustrate the application of CellSpecks for analyzing data from Ca channels, it can be easily customized to analyze other spatially and temporally localized signals.

摘要

为了将膜片钳记录的保真度与更高的高通量筛选能力相结合,我们开创了一种新颖的单通道记录方法,我们称之为“光学膜片钳”。我们使用高灵敏度的荧光 Ca 指示剂染料结合全内反射荧光显微镜技术,监测单个 Ca 通透性通道中的 Ca 流。这种方法提供了类似于膜片钳记录的通道门控信息,时间分辨率约为 2 ms,具有额外的优势,即大规模并行,提供了在天然环境中同时和独立记录数千个通道的能力。然而,这种技术产生的数据的手动分析带来了严峻的挑战,因为视频记录可能包含数千个帧。为了克服这个瓶颈,我们开发了一种图像处理和分析框架,称为 CellSpecks,能够检测和全面分析视频序列中离子通道的动力学。通过使用随机生成的合成数据,我们测试了 CellSpecks 快速有效地检测和分析数千个离子通道活动的能力,包括几毫秒的开放。在这里,我们报告了 CellSpecks 在分析通过成像肌肉烟碱型乙酰胆碱受体和阿尔茨海默病相关淀粉样β孔获得的实验数据中的应用,这些孔具有 Xenopus laevis 卵母细胞膜中的多电导水平。我们表明,CellSpecks 可以准确有效地生成位置图,并创建原始和处理后的荧光时间轨迹;平均开放时间、平均关闭时间、开放概率、持续时间和最大幅度的直方图;以及一个“通道芯片”,显示所有通道的活动作为时间的函数。虽然我们特别说明了 CellSpecks 用于分析 Ca 通道数据的应用,但它可以很容易地定制以分析其他空间和时间局部化的信号。

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本文引用的文献

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CaV1.2 sparklets in heart and vascular smooth muscle.心脏和血管平滑肌中的 Cav1.2 火花。
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