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光学显微镜下亚细胞共定位分析指南

A guided tour into subcellular colocalization analysis in light microscopy.

作者信息

Bolte S, Cordelières F P

机构信息

Plateforme d'Imagerie et de Biologie Cellulaire, IFR 87 la Plante et son Environnement, Institut des Sciences du Végétal, Avenue de la Terrasse, 91198 Gif-sur-Yvette Cedex, France.

出版信息

J Microsc. 2006 Dec;224(Pt 3):213-32. doi: 10.1111/j.1365-2818.2006.01706.x.

Abstract

It is generally accepted that the functional compartmentalization of eukaryotic cells is reflected by the differential occurrence of proteins in their compartments. The location and physiological function of a protein are closely related; local information of a protein is thus crucial to understanding its role in biological processes. The visualization of proteins residing on intracellular structures by fluorescence microscopy has become a routine approach in cell biology and is increasingly used to assess their colocalization with well-characterized markers. However, image-analysis methods for colocalization studies are a field of contention and enigma. We have therefore undertaken to review the most currently used colocalization analysis methods, introducing the basic optical concepts important for image acquisition and subsequent analysis. We provide a summary of practical tips for image acquisition and treatment that should precede proper colocalization analysis. Furthermore, we discuss the application and feasibility of colocalization tools for various biological colocalization situations and discuss their respective strengths and weaknesses. We have created a novel toolbox for subcellular colocalization analysis under ImageJ, named JACoP, that integrates current global statistic methods and a novel object-based approach.

摘要

人们普遍认为,真核细胞的功能区室化通过蛋白质在其区室中的差异分布得以体现。蛋白质的位置与其生理功能密切相关;因此,蛋白质的局部信息对于理解其在生物过程中的作用至关重要。通过荧光显微镜观察驻留在细胞内结构上的蛋白质已成为细胞生物学中的常规方法,并且越来越多地用于评估它们与特征明确的标志物的共定位情况。然而,用于共定位研究的图像分析方法是一个存在争议和谜团的领域。因此,我们着手回顾当前最常用的共定位分析方法,介绍对于图像采集和后续分析重要的基本光学概念。我们提供了在进行适当的共定位分析之前进行图像采集和处理的实用技巧总结。此外,我们讨论了共定位工具在各种生物共定位情况下的应用和可行性,并讨论了它们各自的优缺点。我们在ImageJ下创建了一个用于亚细胞共定位分析的新型工具箱,名为JACoP,它整合了当前的全局统计方法和一种基于对象的新方法。

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