Department of Pathology, Division of Neuropathology, The Ohio State University College of Medicine, Columbus, OH, USA.
Department of Biomedical Education & Anatomy, Division of Anatomy, The Ohio State University College of Medicine, Columbus, OH, USA.
J Comp Neurol. 2021 Jul 1;529(10):2464-2483. doi: 10.1002/cne.25105. Epub 2021 Mar 8.
Evaluation of reactive astrogliosis by neuroanatomical assays represents a common experimental outcome for neuroanatomists. The literature demonstrates several conflicting results as to the accuracy of such measures. We posited that the diverging results within the neuroanatomy literature were due to suboptimal analytical workflows in addition to astrocyte regional heterogeneity. We therefore generated an automated segmentation workflow to extract features of glial fibrillary acidic protein (GFAP) and aldehyde dehydrogenase family 1, member L1 (ALDH1L1) labeled astrocytes with and without neuroinflammation. We achieved this by capturing multiplexed immunofluorescent confocal images of mouse brains treated with either vehicle or lipopolysaccharide (LPS) followed by implementation of our workflows. Using classical image analysis techniques focused on pixel intensity only, we were unable to identify differences between vehicle-treated and LPS-treated animals. However, when utilizing machine learning-based algorithms, we were able to (1) accurately predict which objects were derived from GFAP or ALDH1L1-stained images indicating that GFAP and ALDH1L1 highlight distinct morphological aspects of astrocytes, (2) we could predict which neuroanatomical region the segmented GFAP or ALDH1L1 object had been derived from, indicating that morphological features of astrocytes change as a function of neuroanatomical location. (3) We discovered a statistically significant, albeit not highly accurate, prediction of which objects had come from LPS versus vehicle-treated animals, indicating that although features exist capable of distinguishing LPS-treated versus vehicle-treated GFAP and ALDH1L1-segmented objects, that significant overlap between morphologies exists. We further determined that for most classification scenarios, nonlinear models were required for improved treatment class designations. We propose that unbiased automated image analysis techniques coupled with well-validated machine learning tools represent highly useful models capable of providing insights into neuroanatomical assays.
通过神经解剖学检测评估反应性星形胶质细胞增生是神经解剖学家常用的实验结果。文献表明，这些测量方法的准确性存在一些相互矛盾的结果。我们推测，神经解剖学文献中的分歧结果除了星形胶质细胞的区域异质性外，还与分析工作流程不优有关。因此，我们生成了一个自动分割工作流程，以提取具有和不具有神经炎症的胶质纤维酸性蛋白 (GFAP) 和醛脱氢酶家族 1 成员 L1 (ALDH1L1) 标记星形胶质细胞的特征。我们通过捕获用载体或脂多糖 (LPS) 处理的小鼠大脑的多重免疫荧光共聚焦图像，然后实施我们的工作流程来实现这一目标。仅使用专注于像素强度的经典图像分析技术，我们无法识别载体处理和 LPS 处理动物之间的差异。但是，当使用基于机器学习的算法时，我们能够：(1) 准确预测哪些物体是源自 GFAP 或 ALDH1L1 染色图像的，这表明 GFAP 和 ALDH1L1 突出了星形胶质细胞的不同形态特征；(2) 我们可以预测分割的 GFAP 或 ALDH1L1 物体来自哪个神经解剖区域，这表明星形胶质细胞的形态特征会随神经解剖位置而变化。(3) 我们发现了一个具有统计学意义的、尽管不是非常准确的预测，即哪些物体来自 LPS 与载体处理的动物，这表明尽管存在能够区分 LPS 处理与载体处理的 GFAP 和 ALDH1L1 分割物体的特征，但形态之间存在显著重叠。我们还进一步确定，对于大多数分类情况，都需要使用非线性模型来改善处理类别指定。我们提出，无偏自动图像分析技术与经过充分验证的机器学习工具相结合，代表了提供神经解剖学检测见解的非常有用的模型。
