Wernitznig Stefan, Reichmann Florian, Sele Mariella, Birkl Christoph, Haybäck Johannes, Kleinegger Florian, Birkl-Töglhofer Anna, Krassnig Stefanie, Wodlej Christina, Holzer Peter, Kummer Daniel, Bock Elisabeth, Leitinger Gerd
Department of Cell Biology, Histology and Embryology, Gottfried Schatz Research Center, Medical University of Graz.
Department of Pharmacology, Otto Loewi Research Center, Medical University of Graz.
J Vis Exp. 2019 Apr 13(146). doi: 10.3791/58745.
Investigations of the ultrastructural features of neurons and their synapses are only possible with electron microscopy. Especially for comparative studies of the changes in densities and distributions of such features, an unbiased sampling protocol is vital for reliable results. Here, we present a workflow for the image acquisition of brain samples. The workflow allows systematic uniform random sampling within a defined brain region, and the images can be analyzed using a disector. This technique is much faster than extensive examination of serial sections but still presents a feasible approach to estimate the densities and distributions of ultrastructure features. Before embedding, stained vibratome sections were used as a reference to identify the brain region under investigation, which helped speed up the overall specimen preparation process. This approach was used for comparative studies investigating the effect of an enriched-housing environment on several ultrastructural parameters in the mouse brain. Based on the successful use of the workflow, we adapted it for the purpose of elemental analysis of brain samples. We optimized the protocol in terms of the time of user-interaction. Automating all the time-consuming steps by compiling a script for the open source software SerialEM helps the user to focus on the main work of acquiring the elemental maps. As in the original workflow, we paid attention to the unbiased sampling approach to guarantee reliable results.
只有通过电子显微镜才能对神经元及其突触的超微结构特征进行研究。特别是对于此类特征的密度和分布变化的比较研究，无偏抽样方案对于获得可靠结果至关重要。在此，我们展示了一种用于脑样本图像采集的工作流程。该工作流程允许在定义的脑区内进行系统的均匀随机抽样，并且可以使用断层解剖法对图像进行分析。这种技术比广泛检查连续切片要快得多，但仍然是一种估计超微结构特征密度和分布的可行方法。在包埋之前，使用染色的振动切片作为参考来识别所研究的脑区，这有助于加快整个标本制备过程。这种方法用于比较研究丰富饲养环境对小鼠脑内几个超微结构参数的影响。基于该工作流程的成功应用，我们将其改编用于脑样本的元素分析。我们在用户交互时间方面对方案进行了优化。通过为开源软件SerialEM编写脚本，将所有耗时的步骤自动化，有助于用户专注于获取元素图谱的主要工作。与原始工作流程一样，我们注重无偏抽样方法以确保可靠的结果。
