Université Paris Descartes, Sorbonne Paris Cité, Paris, France; Equipe 11 Labellisée Ligue Nationale Contre le Cancer, Centre de Recherche des Cordeliers, Paris, France; Institut National de la Santé et de la Recherche Médicale, U1138, Paris, France; Université Pierre et Marie Curie, Paris, France; Metabolomics and Cell Biology Platforms, Gustave Roussy Cancer Campus, Villejuif, France.
Université Paris Descartes, Sorbonne Paris Cité, Paris, France; Equipe 11 Labellisée Ligue Nationale Contre le Cancer, Centre de Recherche des Cordeliers, Paris, France; Institut National de la Santé et de la Recherche Médicale, U1138, Paris, France; Université Pierre et Marie Curie, Paris, France; Metabolomics and Cell Biology Platforms, Gustave Roussy Cancer Campus, Villejuif, France; Faculty of Medicine, University of Paris Sud, Kremlin-Bicêtre, France; Pôle de Biologie, Hôpital Européen Georges Pompidou, AP-HP, Paris, France; Department of Women's and Children's Health, Karolinska University Hospital, Stockholm, Sweden.
Comput Biol Med. 2019 Apr;107:227-234. doi: 10.1016/j.compbiomed.2019.02.024. Epub 2019 Mar 2.
The microscopic assessment of the colocalization of fluorescent signals has been widely used in cell biology. Although imaging techniques have drastically improved over the past decades, the quantification of colocalization by measures such as the Pearson correlation coefficient or Manders overlap coefficient, has not changed. Here, we report the development of an R-based application that allows to (i) automatically segment cells and subcellular compartments, (ii) measure morphology and texture features, and (iii) calculate the degree of colocalization within each cell. Colocalization can thus be studied on a cell-by-cell basis, permitting to perform statistical analyses of cellular populations and subpopulations. ColocalizR has been designed to parallelize tasks, making it applicable to the analysis of large data sets. Its graphical user interface makes it suitable for researchers without specific knowledge in image analysis. Moreover, results can be exported into a wide range of formats rendering post-analysis adaptable to statistical requirements. This application and its source code are freely available at https://github.com/kroemerlab/ColocalizR.
荧光信号共定位的微观评估已被广泛应用于细胞生物学领域。尽管成像技术在过去几十年中得到了极大的改进,但共定位的定量分析,如 Pearson 相关系数或 Manders 重叠系数,并没有改变。在这里,我们报告了一个基于 R 的应用程序的开发,该应用程序允许 (i) 自动分割细胞和亚细胞区室,(ii) 测量形态和纹理特征,以及 (iii) 计算每个细胞内的共定位程度。因此,共定位可以在逐个细胞的基础上进行研究,从而可以对细胞群体和亚群体进行统计分析。ColocalizR 被设计为能够并行处理任务,使其适用于大型数据集的分析。其图形用户界面使其适合于没有图像分析专业知识的研究人员使用。此外,结果可以导出到多种格式,使后续分析能够适应统计需求。该应用程序及其源代码可在 https://github.com/kroemerlab/ColocalizR 上免费获得。