Bioinformatics Unit, Children's Medical Research Institute, The University of Sydney, Westmead, NSW, Australia.
Cancer Research Unit, Children's Medical Research Institute, The University of Sydney, Westmead, NSW, Australia.
Sci Rep. 2017 Aug 21;7(1):8879. doi: 10.1038/s41598-017-08786-1.
Protein colocalisation is often studied using pixel intensity-based coefficients such as Pearson, Manders, Li or Costes. However, these methods cannot be used to study object-based colocalisations in biological systems. Therefore, a novel method is required to automatically identify regions of fluorescent signal in two channels, identify the co-located parts of these regions, and calculate the statistical significance of the colocalisation. We have developed MatCol to address these needs. MatCol can be used to visualise protein and/or DNA colocalisations and fine tune user-defined parameters for the colocalisation analysis, including the application of median or Wiener filtering to improve the signal to noise ratio. Command-line execution allows batch processing of multiple images. Users can also calculate the statistical significance of the observed object colocalisations compared to overlap by random chance using Student's t-test. We validated MatCol in a biological setting. The colocalisations of telomeric DNA and TRF2 protein or TRF2 and PML proteins in >350 nuclei derived from three different cell lines revealed a highly significant correlation between manual and MatCol identification of colocalisations (linear regression R = 0.81, P < 0.0001). MatCol has the ability to replace manual colocalisation counting, and the potential to be applied to a wide range of biological areas.
蛋白质共定位通常使用基于像素强度的系数进行研究，如 Pearson、Manders、Li 或 Costes。然而，这些方法不能用于研究生物系统中的基于对象的共定位。因此，需要一种新的方法来自动识别两个通道中的荧光信号区域，识别这些区域的共定位部分，并计算共定位的统计显著性。我们已经开发了 MatCol 来满足这些需求。MatCol 可用于可视化蛋白质和/或 DNA 共定位，并微调用户定义的共定位分析参数，包括应用中值或维纳滤波来提高信号噪声比。命令行执行允许批量处理多个图像。用户还可以使用学生 t 检验计算观察到的对象共定位与随机重叠的统计学显著性。我们在生物学环境中验证了 MatCol。来自三个不同细胞系的>350 个核中端粒 DNA 和 TRF2 蛋白或 TRF2 和 PML 蛋白的共定位显示，手动和 MatCol 识别共定位之间具有高度显著的相关性（线性回归 R=0.81，P<0.0001）。MatCol 有能力取代手动共定位计数，并有可能应用于广泛的生物学领域。
