Graduate Program in Neuroscience, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil.
Department of Basic Sciences/Physiology, Universidade Federal de Ciências da Saúde de Porto Alegre, Porto Alegre, Brazil.
J Comp Neurol. 2023 Feb;531(3):344-365. doi: 10.1002/cne.25430. Epub 2022 Nov 10.
Visualizing nerve cells has been fundamental for the systematic description of brain structure and function in humans and other species. Different approaches aimed to unravel the morphological features of neuron types and diversity. The inherent complexity of the human nervous tissue and the need for proper histological processing have made studying human dendrites and spines challenging in postmortem samples. In this study, we used Golgi data and open-source software for 3D image reconstruction of human neurons from the cortical amygdaloid nucleus to show different dendrites and pleomorphic spines at different angles. Procedures required minimal equipment and generated high-quality images for differently shaped cells. We used the "single-section" Golgi method adapted for the human brain to engender 3D reconstructed images of the neuronal cell body and the dendritic ramification by adopting a neuronal tracing procedure. In addition, we elaborated 3D reconstructions to visualize heterogeneous dendritic spines using a supervised machine learning-based algorithm for image segmentation. These tools provided an additional upgrade and enhanced visual display of information related to the spatial orientation of dendritic branches and for dendritic spines of varied sizes and shapes in these human subcortical neurons. This same approach can be adapted for other techniques, areas of the central or peripheral nervous system, and comparative analysis between species.
可视化神经细胞对于系统描述人类和其他物种的大脑结构和功能至关重要。不同的方法旨在揭示神经元类型和多样性的形态特征。人类神经组织的固有复杂性以及对适当组织学处理的需求，使得研究死后样本中的人类树突和棘突具有挑战性。在这项研究中，我们使用高尔基数据和开源软件，对来自皮质杏仁核核的人类神经元进行 3D 图像重建，以显示不同角度的不同树突和多形棘突。该程序所需的设备最少，可针对不同形状的细胞生成高质量的图像。我们使用适用于人脑的“单切片”高尔基法，通过采用神经元追踪程序，生成神经元细胞体和树突分支的 3D 重建图像。此外，我们还详细阐述了 3D 重建，以使用基于监督机器学习的图像分割算法可视化异质树突棘。这些工具提供了额外的升级，并增强了与树突分支的空间方向以及这些人类皮质下神经元中不同大小和形状的树突棘相关的信息的可视化显示。这种方法可以适应其他技术、中枢或周围神经系统的不同区域以及物种间的比较分析。
