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活细胞中蛋白质-蛋白质共定位的自动定量测量。

Automatic and quantitative measurement of protein-protein colocalization in live cells.

作者信息

Costes Sylvain V, Daelemans Dirk, Cho Edward H, Dobbin Zachary, Pavlakis George, Lockett Stephen

机构信息

Image Analysis Laboratory, National Cancer Institute, Frederick, Maryland, USA.

出版信息

Biophys J. 2004 Jun;86(6):3993-4003. doi: 10.1529/biophysj.103.038422.

Abstract

We introduce a novel statistical approach that quantifies, for the first time, the amount of colocalization of two fluorescent-labeled proteins in an image automatically, removing the bias of visual interpretation. This is done by estimating simultaneously the maximum threshold of intensity for each color below which pixels do not show any statistical correlation. The sensitivity of the method was illustrated on simulated data by statistically confirming the existence of true colocalization in images with as little as 3% colocalization. This method was then tested on a large three-dimensional set of fixed cells cotransfected with CFP/YFP pairs of proteins that either co-compartmentalized, interacted, or were just randomly localized in the nucleolus. In this test, the algorithm successfully distinguished random color overlap from colocalization due to either co-compartmentalization or interaction, and results were verified by fluorescence resonance energy transfer. The accuracy and consistency of our algorithm was further illustrated by measuring, for the first time in live cells, the dissociation rate (k(d)) of the HIV-1 Rev/CRM1 export complex induced by the cytotoxin leptomycin B. Rev/CRM1 colocalization in nucleoli dropped exponentially after addition of leptomycin B at a rate of 1.25 x 10(-3) s(-1). More generally, this algorithm can be used to answer a variety of biological questions involving protein-protein interactions or co-compartmentalization and can be generalized to colocalization of more than two colors.

摘要

我们引入了一种全新的统计方法,该方法首次能够自动量化图像中两种荧光标记蛋白质的共定位量,消除了视觉解读的偏差。这是通过同时估计每种颜色强度的最大阈值来实现的,低于该阈值的像素不显示任何统计相关性。通过统计确认在共定位低至3%的图像中存在真正的共定位,在模拟数据上展示了该方法的灵敏度。然后,该方法在大量三维固定细胞数据集上进行了测试,这些细胞共转染了CFP/YFP对蛋白质,这些蛋白质要么共分隔、相互作用,要么只是随机定位于核仁中。在这个测试中,该算法成功地区分了由于共分隔或相互作用导致的共定位与随机颜色重叠,并且结果通过荧光共振能量转移得到了验证。通过首次在活细胞中测量细胞毒素雷帕霉素B诱导的HIV-1 Rev/CRM1输出复合物的解离速率(k(d)),进一步说明了我们算法的准确性和一致性。添加雷帕霉素B后,核仁中Rev/CRM1的共定位以1.25×10(-3) s(-1)的速率呈指数下降。更一般地说,该算法可用于回答各种涉及蛋白质-蛋白质相互作用或共分隔的生物学问题,并且可以推广到两种以上颜色的共定位。

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