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对脑彩虹样本中的神经结构进行成像。

Imaging Neural Architecture in Brainbow Samples.

作者信息

Roossien Douglas H, Cai Dawen

机构信息

Cell and Developmental Biology Department, University of Michigan, Ann Arbor, MI, 48109, USA.

出版信息

Methods Mol Biol. 2017;1642:211-228. doi: 10.1007/978-1-4939-7169-5_14.

Abstract

The fluorescent protein revolution has made the light microscope the most widely used tool for studying biological structure from the single-molecule to whole organism scales. However, traditional approaches are limited in their ability to resolve components in highly complex structures, such as the brain. In recent years, this limitation has been circumvented by the development of multicolor labeling methods, termed Brainbow. Brainbow tools rely on site-specific recombinases to make stochastic "choices" between different combinations of fluorescent proteins so that structures in close proximity to one another can be resolved based on their color profile. These new approaches, however, call for more refined methods of sample preparation and imaging optimized for multispectral imaging, which are presented here. The most robust approach for generating useful Brainbow data combines immunohistology with multispectral laser scanning confocal microscopy. This chapter, therefore, focuses on this particular technique, though the imaging principle discussed here is applicable to other Brainbow approaches as well.

摘要

荧光蛋白革命使光学显微镜成为从单分子到整个生物体尺度研究生物结构最广泛使用的工具。然而,传统方法在解析高度复杂结构(如大脑)中的成分时能力有限。近年来,通过开发称为Brainbow的多色标记方法,这一局限性已被克服。Brainbow工具依靠位点特异性重组酶在荧光蛋白的不同组合之间进行随机“选择”,以便根据其颜色特征分辨彼此紧邻的结构。然而,这些新方法需要更精细的样品制备方法和针对多光谱成像优化的成像方法,本文将对此进行介绍。生成有用的Brainbow数据的最可靠方法是将免疫组织学与多光谱激光扫描共聚焦显微镜相结合。因此,本章重点介绍这一特定技术,尽管这里讨论的成像原理也适用于其他Brainbow方法。

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