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用于球柱细胞双色成像的修正 Pearson 相关系数。

Modified Pearson correlation coefficient for two-color imaging in spherocylindrical cells.

机构信息

Department of Chemistry, University of Wisconsin-Madison, Madison, WI, 53706, USA.

Present Address: Department of Biophysics and Biophysical Chemistry, Johns Hopkins School of Medicine, Baltimore, 21205, USA.

出版信息

BMC Bioinformatics. 2018 Nov 16;19(1):428. doi: 10.1186/s12859-018-2444-3.

Abstract

The revolution in fluorescence microscopy enables sub-diffraction-limit ("superresolution") localization of hundreds or thousands of copies of two differently labeled proteins in the same live cell. In typical experiments, fluorescence from the entire three-dimensional (3D) cell body is projected along the z-axis of the microscope to form a 2D image at the camera plane. For imaging of two different species, here denoted "red" and "green", a significant biological question is the extent to which the red and green spatial distributions are positively correlated, anti-correlated, or uncorrelated. A commonly used statistic for assessing the degree of linear correlation between two image matrices R and G is the Pearson Correlation Coefficient (PCC). PCC should vary from - 1 (perfect anti-correlation) to 0 (no linear correlation) to + 1 (perfect positive correlation). However, in the special case of spherocylindrical bacterial cells such as E. coli or B. subtilis, we show that the PCC fails both qualitatively and quantitatively. PCC returns the same + 1 value for 2D projections of distributions that are either perfectly correlated in 3D or completely uncorrelated in 3D. The PCC also systematically underestimates the degree of anti-correlation between the projections of two perfectly anti-correlated 3D distributions. The problem is that the projection of a random spatial distribution within the 3D spherocylinder is non-random in 2D, whereas PCC compares every matrix element of R or G with the constant mean value [Formula: see text] or [Formula: see text]. We propose a modified Pearson Correlation Coefficient (MPCC) that corrects this problem for spherocylindrical cell geometry by using the proper reference matrix for comparison with R and G. Correct behavior of MPCC is confirmed for a variety of numerical simulations and on experimental distributions of HU and RNA polymerase in live E. coli cells. The MPCC concept should be generalizable to other cell shapes.

摘要

荧光显微镜的革命使得在同一个活细胞中能够对数百或数千个两种不同标记的蛋白质进行亚衍射极限(“超分辨率”)定位。在典型的实验中,来自整个三维(3D)细胞体的荧光沿着显微镜的 z 轴投影,在相机平面上形成 2D 图像。对于两种不同物种的成像,这里表示为“红色”和“绿色”,一个重要的生物学问题是红色和绿色空间分布之间存在正相关、负相关还是不相关。用于评估两个图像矩阵 R 和 G 之间线性相关性程度的常用统计量是 Pearson 相关系数(PCC)。PCC 应从 -1(完美的负相关)变化到 0(没有线性相关性)到 +1(完美的正相关)。然而,在球形圆柱状细菌细胞(如大肠杆菌或枯草芽孢杆菌)的特殊情况下,我们表明 PCC 在质量和数量上都失败了。对于在 3D 中完全相关或完全不相关的分布的 2D 投影,PCC 返回相同的 +1 值。PCC 还系统地低估了两个完美负相关 3D 分布投影之间的负相关性程度。问题在于,在 3D 球形圆柱体内的随机空间分布的投影在 2D 中是随机的,而 PCC 将 R 或 G 的每个矩阵元素与常数平均值 [公式:见正文] 或 [公式:见正文] 进行比较。我们提出了一种修正的 Pearson 相关系数(MPCC),通过使用与 R 和 G 进行比较的适当参考矩阵来纠正球形圆柱细胞几何形状的这个问题。MPCC 在各种数值模拟和活大肠杆菌细胞中 HU 和 RNA 聚合酶的实验分布上的正确行为得到了确认。MPCC 概念应该可以推广到其他细胞形状。

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