Hohmann Urszula, Dehghani Faramarz, Hohmann Tim
Department of Anatomy and Cell Biology, Martin Luther University Halle-Wittenberg, Halle (Saale), Germany.
Front Neurosci. 2021 Oct 8;15:740178. doi: 10.3389/fnins.2021.740178. eCollection 2021.
Neuronal damage presents a major health issue necessitating extensive research to identify mechanisms of neuronal cell death and potential therapeutic targets. Commonly used models are slice cultures out of different brain regions extracted from mice or rats, excitotoxically, ischemic, or traumatically lesioned and subsequently treated with potential neuroprotective agents. Thereby cell death is regularly assessed by measuring the propidium iodide (PI) uptake or counting of PI-positive nuclei. The applied methods have a limited applicability, either in terms of objectivity and time consumption or regarding its applicability. Consequently, new tools for analysis are needed. Here, we present a framework to mimic manual counting using machine learning algorithms as tools for semantic segmentation of PI-positive dead cells in hippocampal slice cultures. Therefore, we trained a support vector machine (SVM) to classify images into either "high" or "low" neuronal damage and used naïve Bayes, discriminant analysis, random forest, and a multilayer perceptron (MLP) as classifiers for segmentation of dead cells. In our final models, pixel-wise accuracies of up to 0.97 were achieved using the MLP classifier. Furthermore, a SVM-based post-processing step was introduced to differentiate between false-positive and false-negative detections using morphological features. As only very few false-positive objects and thus training data remained when using the final model, this approach only mildly improved the results. A final object splitting step using Hough transformations was used to account for overlap, leading to a recall of up to 97.6% of the manually assigned PI-positive dead cells. Taken together, we present an analysis tool that can help to objectively and reproducibly analyze neuronal damage in brain-derived slice cultures, taking advantage of the morphology of pycnotic cells for segmentation, object splitting, and identification of false positives.
神经元损伤是一个重大的健康问题，需要进行广泛的研究以确定神经元细胞死亡的机制和潜在的治疗靶点。常用的模型是从小鼠或大鼠提取的不同脑区的脑片培养物，通过兴奋性毒性、缺血或创伤性损伤，随后用潜在的神经保护剂进行处理。通过测量碘化丙啶（PI）摄取或计数PI阳性细胞核来定期评估细胞死亡情况。所应用的方法在客观性和时间消耗方面，或在其适用性方面，适用性有限。因此，需要新的分析工具。在此，我们提出一个框架，利用机器学习算法作为海马脑片培养物中PI阳性死亡细胞语义分割的工具，来模拟人工计数。因此，我们训练了一个支持向量机（SVM）将图像分类为“高”或“低”神经元损伤，并使用朴素贝叶斯、判别分析、随机森林和多层感知器（MLP）作为死亡细胞分割的分类器。在我们的最终模型中，使用MLP分类器实现了高达0.97的逐像素准确率。此外，引入了一个基于SVM的后处理步骤，利用形态学特征区分假阳性和假阴性检测。由于使用最终模型时只有极少数假阳性对象，因此训练数据很少，这种方法对结果的改善很小。使用霍夫变换的最终对象分割步骤用于解决重叠问题，召回率高达手动指定的PI阳性死亡细胞的97.6%。综上所述，我们提出了一种分析工具，该工具可以利用固缩细胞的形态进行分割、对象分割和假阳性识别，有助于客观且可重复地分析脑源性脑片培养物中的神经元损伤。
