A freely available semi-automated method for quantifying retinal ganglion cells in entire retinal flatmounts
A freely available semi-automated method for quantifying retinal ganglion cells in entire retinal flatmounts
复制标题
DOI：
10.1016/j.exer.2016.04.010
复制
发表时间：
2016-06-01
影响因子：
3.4
通讯作者：
Moons, L.
中科院分区：
文献类型：
--
作者：
Geeraerts, E.;Dekeyster, E.;Moons, L.
青光眼性视神经病变的特征在于视网膜神经节细胞（RGC）的进行性丧失，视网膜神经节细胞是连接眼睛和大脑的神经元。这些RGC的定量是实验性视神经病变研究的基石，并且通常通过手动定量视网膜的部分来进行。然而，这是一个耗时的过程，会受到观察者之间和观察者内部差异的影响。在这里，我们提出了一个免费提供的imageJ脚本，半自动定量RGC在整个视网膜flatmounts后，对RGC特异性转录因子Brn 3a的免疫染色。Brn 3a免疫阳性RGC的斑点样信号通过Hessian矩阵的特征值增强，并且所得局部最大值被计数为RGC。在用户勾画出视网膜平片区域之后，报告总RGC数量和视网膜面积，并创建显示整个视网膜的RGC密度分布的等密度图。半自动定量显示出与宽视野和共焦图像的手动计数的非常强的相关性（Pearson's r > 0.99），从而验证了通过开发的脚本生成的数据。此外，该方法在已建立的青光眼视神经病变模型中的应用，如N-甲基-D-天冬氨酸诱导的兴奋性毒性，视神经挤压和激光诱导的高眼压，显示RGC损失符合文献。与手动计数相比，所描述的自动定量方法更快，并且显示出与用户无关的一致性。此外，由于脚本检测整个视网膜平片中的RGC数量，因此该方法允许检测RGC密度的区域差异。因此，它可以帮助推进研究昏迷性视神经病变的退行性机制和新的神经保护治疗的有效性。由于脚本是灵活的，易于优化，由于关键参数的数量少，它可以潜在地与其他组织或替代标记协议的组合应用。(C)2016爱思唯尔有限公司版权所有
Glaucomatous optic neuropathies are characterized by progressive loss of retinal ganglion cells (RGCs), the neurons that connect the eye to the brain. Quantification of these RGCs is a cornerstone in experimental optic neuropathy research and commonly performed via manually quantifying parts of the retina. However, this is a time-consuming process subject to inter- and intra-observer variability. Here we present a freely available lmageJ script to semi-automatically quantify RGCs in entire retinal flatmounts _after immunostaining for the RGC-specific transcription factor Brn3a. The blob-like signal of Brn3aimmunopositive RGCs is enhanced via eigenvalues of the Hessian matrix and the resulting local maxima are counted as RGCs. After the user has outlined the retinal flatmount area, the total RGC number and retinal area are reported and an isodensity map, showing the RGC density distribution across the retina, is created. The semi-automated quantification shows a very strong correlation (Pearson's r > 0.99) with manual counts for both widefield and confocal images, thereby validating the data generated via the developed script. Moreover, application of this method in established glaucomatous optic neuropathy models such as N-methyl-D-aspartate-induced excitotoxicity, optic nerve crush and laser-induced ocular hypertension revealed RGC loss conform with literature. Compared to manual counting, the described automated quantification method is faster and shows user-independent consistency. Furthermore, as the script detects the RGC number in entire retinal flatmounts, the method allows detection of regional differences in RGC density. As such, it can help advance research investigating the degenerative mechanisms of glaucomatous optic neuropathies and the effectiveness of new neuroprotective treatments. Because the script is flexible and easy to optimize due to a low number of critical parameters, it can potentially be applied in combination with other tissues or alternative labeling protocols. (C) 2016 Elsevier Ltd. All rights reserved.