Abstract：
Neurons contain three compartments, the soma, long axon, and dendrites, which have distinct energetic and biochemical requirements. Mitochondria feature in all compartments and regulate neuronal activity and survival, including energy generation and calcium buffering alongside other roles including proapoptotic signaling and steroid synthesis. Their dynamicity allows them to undergo constant fusion and fission events in response to the changing energy and biochemical requirements. These events, termed mitochondrial dynamics, impact their morphology and a variety of three-dimensional (3D) morphologies exist within the neuronal mitochondrial network. Distortions in the morphological profile alongside mitochondrial dysfunction may begin in the neuronal soma in ageing and common neurodegenerative disorders. However, 3D morphology cannot be comprehensively examined in flat, two-dimensional (2D) images. This highlights a need to segment mitochondria within volume data to provide a representative snapshot of the processes underpinning mitochondrial dynamics and mitophagy within healthy and diseased neurons. The advent of automated high-resolution volumetric imaging methods such as Serial Block Face Scanning Electron Microscopy (SBF-SEM) as well as the range of image software packages allow this to be performed.We describe and evaluate a method for randomly sampling mitochondria and manually segmenting their whole morphologies within randomly generated regions of interest of the neuronal soma from SBF-SEM image stacks. These 3D reconstructions can then be used to generate quantitative data about mitochondrial and cellular morphologies. We further describe the use of a macro that automatically dissects the soma and localizes 3D mitochondria into the subregions created.
摘要：
神经元包含三个隔室，索马，长轴突,和树突,具有不同的能量和生化要求。线粒体的特征在所有区室和调节神经元的活动和生存，包括能量产生和钙缓冲以及其他作用，包括促凋亡信号和类固醇合成。它们的动态性使它们能够响应不断变化的能量和生化需求而经历不断的融合和裂变事件。这些事件,称为线粒体动力学，影响它们的形态和各种三维（3D）形态存在于神经元线粒体网络中。在衰老和常见的神经退行性疾病中，形态学特征的扭曲以及线粒体功能障碍可能始于神经元体细胞。然而,3D形态学不能在平面中全面检查，二维(2D)图像。这突出了需要在体积数据内分割线粒体，以提供支持健康和患病神经元内线粒体动力学和线粒体自噬的过程的代表性快照。自动高分辨率体积成像方法的出现，如串行块扫描电子显微镜（SBF-SEM）以及图像软件包的范围允许执行这一点。我们描述并评估了一种方法，该方法用于从SBF-SEM图像堆栈中随机生成的神经元体细胞感兴趣区域中随机采样线粒体并手动分割其整个形态。然后可以使用这些3D重建来生成关于线粒体和细胞形态的定量数据。我们进一步描述了宏的使用，该宏自动解剖体细胞并将3D线粒体定位到创建的子区域中。
