Department of Anesthesiology, Division of Molecular Medicine, University of California at Los Angeles, California, USA.
Biophys J. 2010 Feb 3;98(3):493-504. doi: 10.1016/j.bpj.2009.10.037.
To quantify spatial protein-protein proximity (colocalization) in paired microscopic images of two sets of proteins labeled by distinct fluorophores, we showed that the cross-correlation and the autocorrelation functions of image intensity consisted of fast and slowly decaying components. The fast component resulted from clusters of proteins specifically labeled, and the slow component resulted from image heterogeneity and a broadly-distributed background. To better evaluate spatial proximity between the two specifically labeled proteins, we extracted the fast-decaying component by fitting the sharp peak in correlation functions to a Gaussian function, which was then used to obtain protein-protein proximity index and the Pearson's correlation coefficient. We also employed the median-filter method as a universal approach for background reduction to minimize nonspecific fluorescence. We illustrated our method by analyzing computer-simulated images and biological images.
为了定量分析通过两种不同荧光染料标记的两组蛋白质的微观共定位（空间蛋白质-蛋白质临近），我们发现图像强度的互相关函数和自相关函数包含快速衰减和缓慢衰减两个分量。快速衰减分量由特异性标记的蛋白质簇产生，而缓慢衰减分量则由图像异质性和广泛分布的背景产生。为了更好地评估两种特异性标记蛋白质之间的空间临近性，我们通过将相关函数中的尖锐峰拟合到高斯函数来提取快速衰减分量，然后使用该分量来获得蛋白质-蛋白质临近指数和皮尔逊相关系数。我们还使用中值滤波方法作为一种通用的背景减少方法，以最小化非特异性荧光。我们通过分析计算机模拟图像和生物图像来说明我们的方法。
