Laboratory of Molecular Neurodegeneration, Peter the Great St. Petersburg Polytechnic University, Khlopina St. 11, St. Petersburg, Russia, 194021.
Department of Applied Mathematics, Peter the Great St. Petersburg Polytechnic University, Polytechnicheskaya St. 29, St. Petersburg, Russia, 195251.
Sci Rep. 2023 Jun 29;13(1):10561. doi: 10.1038/s41598-023-37406-4.
Dendritic spines form most excitatory synaptic inputs in neurons and these spines are altered in many neurodevelopmental and neurodegenerative disorders. Reliable methods to assess and quantify dendritic spines morphology are needed, but most existing methods are subjective and labor intensive. To solve this problem, we developed an open-source software that allows segmentation of dendritic spines from 3D images, extraction of their key morphological features, and their classification and clustering. Instead of commonly used spine descriptors based on numerical metrics we used chord length distribution histogram (CLDH) approach. CLDH method depends on distribution of lengths of chords randomly generated within dendritic spines volume. To achieve less biased analysis, we developed a classification procedure that uses machine-learning algorithm based on experts' consensus and machine-guided clustering tool. These approaches to unbiased and automated measurements, classification and clustering of synaptic spines that we developed should provide a useful resource for a variety of neuroscience and neurodegenerative research applications.
树突棘形成神经元中大多数兴奋性突触输入，并且这些树突棘在许多神经发育和神经退行性疾病中发生改变。需要可靠的方法来评估和量化树突棘形态，但大多数现有的方法都是主观的且劳动强度大。为了解决这个问题，我们开发了一个开源软件，允许从 3D 图像中分割树突棘，提取它们的关键形态特征，并对其进行分类和聚类。我们使用的是基于弦长分布直方图（CLDH）方法，而不是常用的基于数值度量的脊突描述符。CLDH 方法依赖于在树突棘体积内随机生成的弦长的分布。为了实现更少偏见的分析，我们开发了一种分类程序，该程序使用基于专家共识的机器学习算法和机器引导的聚类工具。我们开发的这种针对突触棘的无偏和自动化测量、分类和聚类的方法，应该为各种神经科学和神经退行性研究应用提供有用的资源。
