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通过神经突方向分布分析(NDDA)对高密度神经突图像中的神经突束状化进行半自动定量分析。

Semi-automatic quantification of neurite fasciculation in high-density neurite images by the neurite directional distribution analysis (NDDA).

作者信息

Hopkins Amy M, Wheeler Brandon, Staii Cristian, Kaplan David L, Atherton Timothy J

机构信息

Department of Biomedical Engineering, Tufts University Science & Technology Center, 4 Colby Street, Medford, MA 02155, USA.

Department of Physics and Astronomy, and Center for Nanoscopic Physics, Tufts University Science & Technology Center, 4 Colby Street, Medford, MA 02155, USA.

出版信息

J Neurosci Methods. 2014 May 15;228:100-9. doi: 10.1016/j.jneumeth.2014.03.006. Epub 2014 Mar 25.

Abstract

BACKGROUND

Bundling of neurite extensions occur during nerve development and regeneration. Understanding the factors that drive neurite bundling is important for designing biomaterials for nerve regeneration toward the innervation target and preventing nociceptive collateral sprouting. High-density neuron cultures including dorsal root ganglia explants are employed for in vitro screening of biomaterials designed to control directional outgrowth. Although some semi-automated image processing methods exist for quantification of neurite outgrowth, methods to quantify axonal fasciculation in terms of direction of neurite outgrowth are lacking.

NEW METHOD

This work presents a semi-automated program to analyze micrographs of high-density neurites; the program aims to quantify axonal fasciculation by determining the orientational distribution function of the tangent vectors of the neurites and calculating its Fourier series coefficients ('c' values).

RESULTS

We found that neurite directional distribution analysis (NDDA) of fasciculated neurites yielded 'c' values of ≥∼0.25 whereas branched outgrowth led to statistically significant lesser values of <∼0.2. The 'c' values correlated directly to the width of neurite bundles and indirectly to the number of branching points.

COMPARISON WITH EXISTING METHODS

Information about the directional distribution of outgrowth is lost in simple counting methods or achieved laboriously through manual analysis. The NDDA supplements previous quantitative analyses of axonal bundling using a vector-based approach that captures new information about the directionality of outgrowth.

CONCLUSION

The NDDA is a valuable addition to open source image processing tools available to biomedical researchers offering a robust, precise approach to quantification of imaged features important in tissue development, disease, and repair.

摘要

背景

神经突延伸的成束现象发生在神经发育和再生过程中。了解驱动神经突成束的因素对于设计用于向神经支配靶点进行神经再生的生物材料以及防止伤害性侧支发芽至关重要。包括背根神经节外植体在内的高密度神经元培养物用于体外筛选旨在控制定向生长的生物材料。尽管存在一些用于定量神经突生长的半自动图像处理方法,但缺乏根据神经突生长方向来定量轴突束状化的方法。

新方法

这项工作提出了一个半自动程序来分析高密度神经突的显微照片;该程序旨在通过确定神经突切线向量的方向分布函数并计算其傅里叶级数系数(“c”值)来定量轴突束状化。

结果

我们发现,对成束神经突进行神经突方向分布分析(NDDA)得到的“c”值≥0.25,而分支生长导致统计学上显著较小的值<0.2。“c”值与神经突束的宽度直接相关,与分支点的数量间接相关。

与现有方法的比较

在简单计数方法中会丢失关于生长方向分布的信息,或者通过手动分析费力地获得。NDDA补充了以前使用基于向量的方法对轴突束状化进行的定量分析,该方法捕获了关于生长方向性的新信息。

结论

NDDA是生物医学研究人员可用的开源图像处理工具的一个有价值的补充,它为定量组织发育、疾病和修复中重要的成像特征提供了一种强大、精确的方法。

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