Lagache Thibault, Sauvonnet Nathalie, Danglot Lydia, Olivo-Marin Jean-Christophe
Cell Biology and Infection Department, BioImage Analysis Unit, Institut Pasteur, 75724 Paris Cedex 15, France.
Cell Biology and Infection Department, Molecular Microbial Pathogenesis Unit, Institut Pasteur, 75724 Paris Cedex 15, France.
Cytometry A. 2015 Jun;87(6):568-79. doi: 10.1002/cyto.a.22629. Epub 2015 Jan 20.
The quantitative analysis of molecule interactions in bioimaging is key for understanding the molecular orchestration of cellular processes and is generally achieved through the study of the spatial colocalization between the different populations of molecules. Colocalization methods are traditionally divided into pixel-based methods that measure global correlation coefficients from the overlap between pixel intensities in different color channels, and object-based methods that first segment molecule spots and then analyze their spatial distributions with second-order statistics. Here, we present a review of such colocalization methods and give a quantitative comparison of their relative merits in different types of biological applications and contexts. We show on synthetic and biological images that object-based methods are more robust statistically than pixel-based methods, and allow moreover to quantify accurately the number of colocalized molecules.
生物成像中分子相互作用的定量分析是理解细胞过程分子调控的关键,通常通过研究不同分子群体之间的空间共定位来实现。传统上,共定位方法分为基于像素的方法和基于对象的方法。基于像素的方法通过测量不同颜色通道中像素强度重叠的全局相关系数来进行分析,而基于对象的方法则首先分割分子斑点,然后使用二阶统计量分析其空间分布。在此,我们对这些共定位方法进行综述,并对它们在不同类型生物学应用和背景下的相对优缺点进行定量比较。我们在合成图像和生物图像上表明,基于对象的方法在统计上比基于像素的方法更稳健,而且能够准确量化共定位分子的数量。