Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, United States.
Harvard Medical School, Boston, United States.
Elife. 2023 Jan 19;12:e84112. doi: 10.7554/eLife.84112.
Difficulty achieving complete, specific, and homogenous staining is a major bottleneck preventing the widespread use of tissue clearing techniques to image large volumes of human tissue. In this manuscript, we describe a procedure to rapidly design immunostaining protocols for antibody labeling of cleared brain tissue. We prepared libraries of 0.5-1.0 mm thick tissue sections that are fixed, pre-treated, and cleared via similar, but different procedures to optimize staining conditions for a panel of antibodies. Results from a library of mouse tissue correlate well with results from a similarly prepared library of human brain tissue, suggesting mouse tissue is an adequate substitute for protocol optimization. These data show that procedural differences do not influence every antibody-antigen pair in the same way, and minor changes can have deleterious effects, therefore, optimization should be conducted for each target. The approach outlined here will help guide researchers to successfully label a variety of targets, thus removing a major hurdle to accessing the rich 3D information available in large, cleared human tissue volumes.
难以实现完全、具体和均匀的染色是阻止组织透明化技术广泛应用于成像大量人体组织的主要瓶颈。在本文中，我们描述了一种快速设计免疫染色方案的方法，用于抗体标记透明化脑组织。我们制备了一系列 0.5-1.0 毫米厚的组织切片库，这些切片经过相似但不同的固定、预处理和透明化处理，以优化一组抗体的染色条件。来自小鼠组织文库的结果与经过类似处理的人脑组织文库的结果很好地相关，这表明小鼠组织是优化方案的合适替代品。这些数据表明，程序差异不会以相同的方式影响每一对抗体-抗原，微小的变化可能会产生有害的影响，因此，应该针对每个目标进行优化。这里概述的方法将有助于指导研究人员成功标记各种目标，从而消除在大型透明化人体组织体积中获取丰富的 3D 信息的主要障碍。
