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在帕金森病的临床前啮齿动物模型中自动量化神经元肿胀可检测到病理学的特定区域变化。

Automated quantification of neuronal swellings in a preclinical rodent model of Parkinson's disease detects region-specific changes in pathology.

机构信息

CNS Gene Therapy, Department of Experimental Medical Sciences, Lund University, Lund, Sweden.

Behavioural Neuroscience Laboratory, Department of Experimental Medical Sciences, Lund University, Lund, Sweden.

出版信息

J Neurosci Methods. 2022 Aug 1;378:109640. doi: 10.1016/j.jneumeth.2022.109640. Epub 2022 Jun 8.

DOI:10.1016/j.jneumeth.2022.109640
PMID:35690332
Abstract

BACKGROUND

The development of axonal pathology is a key characteristic of many neurodegenerative disease such as Parkinson's disease and Alzheimer's disease. With advanced disease progression, affected axons do display several signs of pathology such as swelling and fragmentation. In the AAV vector-mediated alpha-synuclein overexpression model of Parkinson's disease, large (> 20 µm) pathological swellings are prominent characteristics in cortical and subcortical structures.

NEW METHOD

This report describes a novel, macro-based workflow to quantify axonal pathology in the form of axonal swellings in the AAV vector-based alpha-synuclein overexpression model. Specifically, the approach is using background correction and thresholding before quantification of structures in 3D throughout a tissue stack.

RESULTS

The method was used to quantify TH and aSYN axonal swellings in the prefrontal cortex, striatum, and hippocampus. Regional differences in volume and number of axonal swellings were observed for both in TH and aSYN, with the striatum displaying the greatest signs of pathology.

COMPARISON WITH EXISTING METHODS

Existing methods for the quantification of axonal pathology do either rely on proprietary software or are based on manual quantification. The ImageJ workflow described here provides a method to objectively quantify axonal swellings both in volume and number.

CONCLUSION

The method described can readily assess axonal pathology in preclinical rodent models of Parkinson's disease and can be easily adapted to other model systems and/or markers.

摘要

背景

轴突病理学的发展是许多神经退行性疾病(如帕金森病和阿尔茨海默病)的一个关键特征。随着疾病的进展,受影响的轴突确实表现出几种病理学迹象,如肿胀和碎片化。在帕金森病的 AAV 载体介导的α-突触核蛋白过表达模型中,皮质和皮质下结构中存在大(>20 µm)病理性肿胀是突出的特征。

新方法

本报告描述了一种新的、基于宏观的工作流程,用于量化 AAV 载体介导的α-突触核蛋白过表达模型中以轴突肿胀形式存在的轴突病理学。具体来说,该方法在对组织堆栈中的 3D 结构进行量化之前,使用背景校正和阈值处理。

结果

该方法用于量化前额叶皮层、纹状体和海马体中 TH 和 aSYN 轴突肿胀。TH 和 aSYN 均观察到体积和轴突肿胀数量的区域差异,纹状体显示出最大的病理学迹象。

与现有方法的比较

现有的轴突病理学量化方法要么依赖于专有软件,要么基于手动量化。这里描述的 ImageJ 工作流程提供了一种客观量化轴突肿胀体积和数量的方法。

结论

该方法可用于评估帕金森病的临床前啮齿动物模型中的轴突病理学,并且可以很容易地适应其他模型系统和/或标记物。

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Automated quantification of neuronal swellings in a preclinical rodent model of Parkinson's disease detects region-specific changes in pathology.在帕金森病的临床前啮齿动物模型中自动量化神经元肿胀可检测到病理学的特定区域变化。
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A comparison of machine learning approaches for the quantification of microglial cells in the brain of mice, rats and non-human primates.机器学习方法在量化小鼠、大鼠和非人灵长类动物大脑中小胶质细胞中的比较。
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Characterisation of functional deficits induced by AAV overexpression of alpha-synuclein in rats.
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Curr Res Neurobiol. 2022 Dec 16;4:100065. doi: 10.1016/j.crneur.2022.100065. eCollection 2023.