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一种用于从基于摄像头的成像中自动检测、定位和测量局部钙信号的算法。

An algorithm for automated detection, localization and measurement of local calcium signals from camera-based imaging.

作者信息

Ellefsen Kyle L, Settle Brett, Parker Ian, Smith Ian F

机构信息

Department of Neurobiology & Behavior, University of California, Irvine, CA 92697, USA.

Department of Neurobiology & Behavior, University of California, Irvine, CA 92697, USA; Department of Physiology & Biophysics, University of California, Irvine, CA 92697, USA.

出版信息

Cell Calcium. 2014 Sep;56(3):147-56. doi: 10.1016/j.ceca.2014.06.003. Epub 2014 Jun 24.

Abstract

Local Ca(2+) transients such as puffs and sparks form the building blocks of cellular Ca(2+) signaling in numerous cell types. They have traditionally been studied by linescan confocal microscopy, but advances in TIRF microscopy together with improved electron-multiplied CCD (EMCCD) cameras now enable rapid (>500 frames s(-1)) imaging of subcellular Ca(2+) signals with high spatial resolution in two dimensions. This approach yields vastly more information (ca. 1 Gb min(-1)) than linescan imaging, rendering visual identification and analysis of local events imaged both laborious and subject to user bias. Here we describe a routine to rapidly automate identification and analysis of local Ca(2+) events. This features an intuitive graphical user-interfaces and runs under Matlab and the open-source Python software. The underlying algorithm features spatial and temporal noise filtering to reliably detect even small events in the presence of noisy and fluctuating baselines; localizes sites of Ca(2+) release with sub-pixel resolution; facilitates user review and editing of data; and outputs time-sequences of fluorescence ratio signals for identified event sites along with Excel-compatible tables listing amplitudes and kinetics of events.

摘要

局部钙瞬变,如钙团和钙火花,是多种细胞类型中细胞钙信号传导的基本组成部分。传统上,它们是通过线扫描共聚焦显微镜进行研究的,但随着全内反射荧光显微镜(TIRF显微镜)的发展以及电子倍增电荷耦合器件(EMCCD)相机的改进,现在能够以高空间分辨率在二维上对亚细胞钙信号进行快速(>500帧/秒)成像。这种方法产生的信息(约1GB/分钟)比线扫描成像多得多,使得对成像的局部事件进行视觉识别和分析既费力又容易受到用户偏见的影响。在这里,我们描述了一种快速自动识别和分析局部钙事件的程序。它具有直观的图形用户界面,可在Matlab和开源Python软件下运行。底层算法具有空间和时间噪声过滤功能,即使在基线噪声和波动的情况下也能可靠地检测到小事件;以亚像素分辨率定位钙释放位点;便于用户查看和编辑数据;并输出已识别事件位点的荧光比率信号的时间序列,以及列出事件幅度和动力学的Excel兼容表格。

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