Xu Yu Kang T, Call Cody L, Sulam Jeremias, Bergles Dwight E
The Solomon H. Snyder Department of Neuroscience, Johns Hopkins University, Baltimore, MD, United States.
Kavli Neuroscience Discovery Institute, Whiting School of Engineering, Johns Hopkins University, Baltimore, MD, United States.
Front Cell Neurosci. 2021 Apr 12;15:667595. doi: 10.3389/fncel.2021.667595. eCollection 2021.
Oligodendrocytes exert a profound influence on neural circuits by accelerating action potential conduction, altering excitability, and providing metabolic support. As oligodendrogenesis continues in the adult brain and is essential for myelin repair, uncovering the factors that control their dynamics is necessary to understand the consequences of adaptive myelination and develop new strategies to enhance remyelination in diseases such as multiple sclerosis. Unfortunately, few methods exist for analysis of oligodendrocyte dynamics, and even fewer are suitable for investigation. Here, we describe the development of a fully automated cell tracking pipeline using convolutional neural networks () that provides rapid volumetric segmentation and tracking of thousands of cells over weeks . This system reliably replicated human analysis, outperformed traditional analytic approaches, and extracted injury and repair dynamics at multiple cortical depths, establishing that oligodendrogenesis after cuprizone-mediated demyelination is suppressed in deeper cortical layers. Volumetric data provided by this analysis revealed that oligodendrocyte soma size progressively decreases after their generation, and declines further prior to death, providing a means to predict cell age and eventual cell death from individual time points. This new CNN-based analysis pipeline offers a rapid, robust method to quantitatively analyze oligodendrocyte dynamics , which will aid in understanding how changes in these myelinating cells influence circuit function and recovery from injury and disease.
少突胶质细胞通过加速动作电位传导、改变兴奋性以及提供代谢支持,对神经回路产生深远影响。由于成人大脑中少突胶质细胞生成持续存在且对髓鞘修复至关重要,因此揭示控制其动态变化的因素对于理解适应性髓鞘形成的后果以及制定新策略以增强多发性硬化症等疾病中的髓鞘再生至关重要。不幸的是,用于分析少突胶质细胞动态变化的方法很少,适合研究的方法更少。在此,我们描述了一种使用卷积神经网络()开发的全自动细胞追踪流程,该流程可在数周内对数千个细胞进行快速体积分割和追踪。该系统可靠地复制了人工分析结果,优于传统分析方法,并提取了多个皮质深度的损伤和修复动态变化,证实了在铜螯合剂介导的脱髓鞘后,深层皮质层中的少突胶质细胞生成受到抑制。该分析提供的体积数据显示,少突胶质细胞胞体大小在生成后逐渐减小,并在死亡前进一步减小,这提供了一种从单个时间点预测细胞年龄和最终细胞死亡的方法。这种基于卷积神经网络的新分析流程提供了一种快速、强大的方法来定量分析少突胶质细胞动态变化,这将有助于理解这些髓鞘形成细胞的变化如何影响神经回路功能以及从损伤和疾病中恢复。