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首页> 外文期刊>Experimental Eye Research >A freely available semi-automated method for quantifying retinal ganglion cells in entire retinal flatmounts
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A freely available semi-automated method for quantifying retinal ganglion cells in entire retinal flatmounts

机译：一种可免费获得的半自动化方法，用于量化整个视网膜平板中的视网膜神经节细胞

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摘要

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.
机译：青光眼性视神经病变的特征是视网膜神经节细胞（RGC）（将眼睛与大脑连接的神经元）逐渐丧失。这些RGC的定量是实验性视神经病变研究的基石，通常是通过人工定量视网膜的一部分来进行的。但是，这是一个耗时的过程，受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的lmageJ脚本，可以对RGC特异性转录因子Brn3a进行免疫染色后，对整个视网膜平底中的RGC进行半自动定量。 Brn3a免疫阳性RGC的斑点样信号通过Hessian矩阵的特征值增强，并且将产生的局部最大值计为RGC。在用户勾勒出视网膜平坦区域的轮廓后，将报告总RGC数量和视网膜面积，并创建等渗线图，以显示整个视网膜的RGC密度分布。半自动定量显示与广角和共焦图像的手动计数之间具有很强的相关性（Pearson 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性视神经病变模型中的应用，例如N-甲基-D-天冬氨酸引起的兴奋性毒性，视神经挤压和激光诱发的高眼压显示，RGC丢失符合文献报道。与手动计数相比，所描述的自动定量方法更快，并且显示出用户无关的一致性。此外，随着脚本检测整个视网膜平底中的RGC数量，该方法允许检测RGC密度的区域差异。这样，它可以帮助推进研究青光眼性视神经病变的退化机制和新的神经保护疗法的有效性的研究。由于该脚本具有灵活性，并且由于关键参数数量少而易于优化，因此可以与其他组织或其他标记方案结合使用。 （C）2016 Elsevier Ltd.保留所有权利。

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