Fully-automated image processing software to analyze calcium traces in populations of single cells
Fully-automated image processing software to analyze calcium traces in populations of single cells
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DOI：
10.1016/j.ceca.2010.09.008
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发表时间：
2010-11-01
期刊：
影响因子：
4
通讯作者：
Fivaz, Marc
中科院分区：
文献类型：
--
作者：
Wong, Loo Chin;Lu, Bo;Fivaz, Marc
在过去的十年中，荧光活细胞成像的进展通过提供对空间和时间中的单细胞信息的访问而彻底改变了细胞生物学。目前活细胞成像的一个局限性是缺乏自动化程序来分析大细胞群中的单细胞数据。大多数市售的图像处理软件没有内置的图像分割工具，可以自动准确地提取时间序列中的单细胞数据。因此，单个细胞通常是手动识别，这是一个耗时和固有的低通量的过程。我们已经开发了一个基于MATLAB的图像分割算法，可靠地检测密集群体中的单个细胞，并测量其荧光强度随时间的推移。为了证明该算法的价值，我们测量了数百个单个细胞中的钙库操作的钙进入（SOCE）。在大群体中快速获得单细胞钙信号使我们能够精确地确定SOCE活性与STIM 1水平之间的关系，STIM 1是SOCE的关键组成部分。我们的图像处理工具原则上可以应用于广泛的活细胞成像模式和基于细胞的药物筛选平台。(C)2010爱思唯尔有限公司版权所有。
Advances in fluorescence live cell imaging over the last decade have revolutionized cell biology by providing access to single-cell information in space and time. One current limitation of live-cell imaging is the lack of automated procedures to analyze single-cell data in large cell populations. Most commercially available image processing softwares do not have built-in image segmentation tools that can automatically and accurately extract single-cell data in a time series. Consequently, individual cells are usually identified manually, a process which is time consuming and inherently low-throughput.We have developed a MATLAB-based image segmentation algorithm that reliably detects individual cells in dense populations and measures their fluorescence intensity over time. To demonstrate the value of this algorithm, we measured store-operated calcium entry (SOCE) in hundreds of individual cells. Rapid access to single-cell calcium signals in large populations allowed us to precisely determine the relationship between SOCE activity and STIM1 levels, a key component of SOCE. Our image processing tool can in principle be applied to a wide range of live-cell imaging modalities and cell-based drug screening platforms. (C) 2010 Elsevier Ltd. All rights reserved.