Boening Daniel, Gauthier-Kemper Anne, Gmeiner Benjamin, Klingauf Jürgen
Department of Cellular Biophysics, Institute of Medical Physics and Biophysics, University of Münster, 48149, Münster, Germany.
Max Planck Institute for the Science of Light, Nano-Optics Division, 91058, Erlangen, Germany.
Adv Biosyst. 2017 Oct;1(10):e1700091. doi: 10.1002/adbi.201700091. Epub 2017 Aug 17.
The advent of super-resolution microscopy opens up the opportunity to study biological structures in unprecedented detail. However, revealing quantitative information about the spatial organization of a set of labeled proteins requires sophisticated analysis. This study introduces a novel robust cluster recognition algorithm based on Delaunay triangulation (CRADT), which can handle complex datasets generated by 3D super-resolution microscopy. This algorithm allows determining volume and shape of protein clusters in 3D. The study demonstrates its performance by applying this algorithm on dual-color 3D super-resolved measurements of mouse hippocampal synapses, stained against the presynaptic active zone marker protein Bassoon and the opposing postsynaptic density protein Homer as well as the exo- and endocytosis machinery proteins Synaptobrevin and Clathrin.
超分辨率显微镜的出现为以前所未有的细节研究生物结构提供了机会。然而，要揭示一组标记蛋白质空间组织的定量信息需要复杂的分析。本研究引入了一种基于德劳内三角剖分的新型稳健聚类识别算法（CRADT），它可以处理由三维超分辨率显微镜生成的复杂数据集。该算法能够确定三维中蛋白质簇的体积和形状。该研究通过将此算法应用于小鼠海马突触的双色三维超分辨测量来证明其性能，这些突触用突触前活性区标记蛋白巴松管、相对的突触后致密蛋白荷马以及胞吐和胞吞机制蛋白突触小泡蛋白和网格蛋白进行染色。
