Laboratory of Cellular Neurophysiology, Institute of Experimental Medicine, Hungarian Academy of Sciences, Budapest, Hungary.
János Szentágothai School of Neurosciences, Semmelweis University, Budapest, Hungary.
Sci Rep. 2017 Nov 10;7(1):15240. doi: 10.1038/s41598-017-15695-w.
Nanoscale distribution of molecules within small subcellular compartments of neurons critically influences their functional roles. Although, numerous ways of analyzing the spatial arrangement of proteins have been described, a thorough comparison of their effectiveness is missing. Here we present an open source software, GoldExt, with a plethora of measures for quantification of the nanoscale distribution of proteins in subcellular compartments (e.g. synapses) of nerve cells. First, we compared the ability of five different measures to distinguish artificial uniform and clustered patterns from random point patterns. Then, the performance of a set of clustering algorithms was evaluated on simulated datasets with predefined number of clusters. Finally, we applied the best performing methods to experimental data, and analyzed the nanoscale distribution of different pre- and postsynaptic proteins, revealing random, uniform and clustered sub-synaptic distribution patterns. Our results reveal that application of a single measure is sufficient to distinguish between different distributions.
神经元中小细胞区室内部的分子的纳米尺度分布对其功能角色有重要影响。虽然已经描述了许多分析蛋白质空间排列的方法,但它们的有效性缺乏全面的比较。在这里,我们提出了一个开源软件 GoldExt,它具有大量用于定量分析神经细胞中小细胞区室(如突触)中蛋白质纳米尺度分布的方法。首先,我们比较了五种不同方法区分人工均匀分布和聚类模式与随机点模式的能力。然后,我们在具有预定义聚类数的模拟数据集上评估了一组聚类算法的性能。最后,我们将性能最佳的方法应用于实验数据,并分析了不同的突触前和突触后蛋白的纳米尺度分布,揭示了随机、均匀和聚类的亚突触分布模式。我们的结果表明,应用单一方法足以区分不同的分布。