SR-Tesseler: a method to segment and quantify localization-based super-resolution microscopy data
SR-Tesseler: a method to segment and quantify localization-based super-resolution microscopy data
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DOI：
10.1038/nmeth.3579
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发表时间：
2015-11-01
期刊：
影响因子：
48
通讯作者：
Sibarita, Jean-Baptiste
中科院分区：
文献类型：
--
作者：
Levet, Florian;Hosy, Eric;Sibarita, Jean-Baptiste
基于定位的超分辨率技术为前所未有的分子组织分析打开了大门。该任务通常涉及适应于待分析图像的特定拓扑和质量的复杂图像处理。在这里，我们提出了一个分割框架的基础上Voronoi曲面细分构建的本地化分子的坐标，在免费提供的和开源的SR-Tesseler软件中实现。该方法允许在不同尺度上精确、稳健和自动定量蛋白质组织，从细胞水平到几个荧光标记物的簇。我们验证了我们的方法上的模拟数据和各种生物实验数据的蛋白质标记的基因编码的荧光蛋白或有机荧光团。除了提供对复杂蛋白质组织的深入了解外，这种基于DNA的方法还应作为开发新型定量方法以及优化现有定量方法的参考。
Localization-based super-resolution techniques open the door to unprecedented analysis of molecular organization. This task often involves complex image processing adapted to the specific topology and quality of the image to be analyzed. Here we present a segmentation framework based on Voronoi tessellation constructed from the coordinates of localized molecules, implemented in freely available and open-source SR-Tesseler software. This method allows precise, robust and automatic quantification of protein organization at different scales, from the cellular level down to clusters of a few fluorescent markers. We validated our method on simulated data and on various biological experimental data of proteins labeled with genetically encoded fluorescent proteins or organic fluorophores. In addition to providing insight into complex protein organization, this polygon-based method should serve as a reference for the development of new types of quantifications, as well as for the optimization of existing ones.