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对单个细胞和细胞区室中基因表达的自动跟踪。

Automated tracking of gene expression in individual cells and cell compartments.

作者信息

Shen Hailin, Nelson Glyn, Nelson David E, Kennedy Stephnie, Spiller David G, Griffiths Tony, Paton Norman, Oliver Stephen G, White Michael R H, Kell Douglas B

机构信息

School of Chemistry, The University of Manchester, Faraday Building, Sackville Street, PO Box 88, Manchester M60 1QD, UK.

出版信息

J R Soc Interface. 2006 Dec 22;3(11):787-94. doi: 10.1098/rsif.2006.0137.

Abstract

Many intracellular signal transduction processes involve the reversible translocation from the cytoplasm to the nucleus of transcription factors. The advent of fluorescently tagged protein derivatives has revolutionized cell biology, such that it is now possible to follow the location of such protein molecules in individual cells in real time. However, the quantitative analysis of the location of such proteins in microscopic images is very time consuming. We describe CellTracker, a software tool designed for the automated measurement of the cellular location and intensity of fluorescently tagged proteins. CellTracker runs in the MS Windows environment, is freely available (at http://www.dbkgroup.org/celltracker/), and combines automated cell tracking methods with powerful image-processing algorithms that are optimized for these applications. When tested in an application involving the nuclear transcription factor NF-kappaB, CellTracker is competitive in accuracy with the manual human analysis of such images but is more than 20 times faster, even on a small task where human fatigue is not an issue. This will lead to substantial benefits for time-lapse-based high-content screening.

摘要

许多细胞内信号转导过程涉及转录因子从细胞质到细胞核的可逆转运。荧光标记蛋白衍生物的出现彻底改变了细胞生物学,如今已能够实时追踪单个细胞中此类蛋白质分子的位置。然而,在显微图像中对这类蛋白质的位置进行定量分析非常耗时。我们介绍了CellTracker,这是一款用于自动测量荧光标记蛋白的细胞定位和强度的软件工具。CellTracker在微软视窗环境中运行,可免费获取(网址为http://www.dbkgroup.org/celltracker/),它将自动细胞追踪方法与针对这些应用进行优化的强大图像处理算法相结合。在涉及核转录因子NF-κB的应用测试中,CellTracker在准确性方面与人工手动分析此类图像具有竞争力,但速度要快20多倍,即使是在人工疲劳不是问题的小任务中也是如此。这将为基于延时的高内涵筛选带来巨大益处。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/51c3/1885368/f6fb21acb5c0/rsif20060137f01.jpg

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