生成和分析三维树突脊柱形态数据集与SpineTool软件。

Anita Ustinova, Ekaterina Volkova, Anastasiya Rakovskaya, Daria Smirnova, Olesya Korovina, Ekaterina Pchitskaya
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引用次数: 0

摘要

树突棘的形态与突触和神经元的当前状态有关,并在突触可塑性响应刺激的过程中发生变化。同时,在各种神经退行性和神经发育障碍以及其他脑状态下,树突状脊柱的改变也被报道。在正常和病理条件下研究突触过程和分子通路,以及在临床前研究中测试突触保护策略,迫切需要准确和信息的脊柱形状分析。原代神经元培养可以实现树突棘的高质量成像,并提供广泛的实验操作。本文概述了在正常状态和低淀粉样蛋白毒性条件下(阿尔茨海默病体外模型)用三维共聚焦显微镜分离、培养、荧光标记和成像小鼠初级海马神经元的方案。另一种方案描述了在行- m转基因小鼠脑切片中使用表达EGFP的神经元进行神经元形态学分析。由于树突棘是相对较小的结构,靠近共聚焦显微镜的分辨率极限,它们在图像上的正确分割是具有挑战性的。该方案强调了图像预处理步骤,包括生成理论点扩展函数和反卷积,从而提高了分辨率并消除了噪声,从而提高了三维脊柱重建的效果。SpineTool是一个基于python的开源脚本,支持树突和脊柱的3D分割以及数值度量计算,包括关键度量,如脊柱长度、体积和表面积,具有新特性弦长分布直方图,可以改善聚类结果。SpineTool支持手动和机器学习脊柱分类(即蘑菇,细,粗,丝状)和使用k-means和DBSCAN方法的自动聚类。本协议提供了使用SpineTool分析和分类对照组和实验组树突棘的详细说明,增强了我们对不同实验条件下脊柱形态的理解。©2024 Wiley期刊有限责任公司基本方案1:获得正常状态和低淀粉样蛋白毒性条件下海马神经元培养的三维共聚焦树突脊柱图像备用方案:从固定脑切片中获得小鼠海马神经元共聚焦树突脊柱图像支持方案:共聚焦图像的后处理反褶积基本方案2:使用SpineTool对树突脊柱进行分割基本方案3:使用SpineTool准备脊柱数据集基本协议4:使用SpineTool聚类树突棘基本协议5:使用SpineTool对树突棘进行机器分类。
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Generate and Analyze Three-Dimensional Dendritic Spine Morphology Datasets With SpineTool Software

Dendritic spine morphology is associated with the current state of the synapse and neuron, and changes during synaptic plasticity in response to stimulus. At the same time, dendritic spine alterations are reported during various neurodegenerative and neurodevelopmental disorders and other brain states. Accurate and informative analysis of spine shape has an urgent need for studying the synaptic processes and molecular pathways in normal and pathological conditions, and for testing synapto-protective strategies during preclinical studies. Primary neuronal cultures enable high quality imaging of dendritic spines and offer a wide spectrum of accessible experimental manipulations. This article outlines the protocol for isolating, culturing, fluorescent labeling, and imaging of mouse primary hippocampal neurons by three-dimensional (3D) confocal microscopy in a normal state and in conditions of low amyloid toxicity—an in vitro model of Alzheimer's disease. An alternate protocol describes the neuronal morphology analysis using the EGFP expressing neurons in line-M transgenic mouse brain slices. Since the dendritic spines are relatively small structures lying close to the confocal microscope resolution limit, their proper segmentation on the images is challenging. This protocol highlights the image-preprocessing steps, including generation of theoretical point spread function and deconvolution, which enhances resolution and removes noise, thereby enhancing the 3D spine reconstruction results. SpineTool, an open source Python–based script, enables 3D segmentation of dendrites and spines and numerical metric calculation, including key measures, such as spine length, volume, and surface area, with a new feature, the chord length distribution histogram, improving clustering results. SpineTool supports both manual and machine learning spine classification (i.e., mushroom, thin, stubby, filopodia) and automated clustering using k-means and DBSCAN methods. This protocol provides detailed instructions for using SpineTool to analyze and classify dendritic spines in control and experimental groups, enhancing our understanding of spine morphology across different experimental conditions. © 2024 Wiley Periodicals LLC.

Basic Protocol 1: Obtaining 3D confocal dendritic spine images of hippocampal neuronal culture in normal state and conditions of low amyloid toxicity

Alternate Protocol: Obtaining confocal dendritic spine images of mice hippocampal neurons from fixed brain slices

Support Protocol: Post-processing deconvolution of confocal images

Basic Protocol 2: Segmentation of dendritic spines with SpineTool

Basic Protocol 3: Spine dataset preparation using SpineTool

Basic Protocol 4: Clustering of dendritic spines with SpineTool

Basic Protocol 5: Machine classification of dendritic spines with SpineTool

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