Harmany Zachary T, Fereidouni Farzad, Levenson Richard M
Department of Pathology and Laboratory Medicine, University of California-Davis Medical Center, Sacramento, CA, USA.
Methods Mol Biol. 2017;1627:491-509. doi: 10.1007/978-1-4939-7113-8_30.
Collagen and other components in the extracellular matrix are proving of increasing importance for the understanding of complex cell and tissue interactions in a variety of settings. Detection and quantitation of these components can still prove challenging, and a number of techniques have been developed. We focus here on methods in fluorescence-based assessments, including multiplexed immunodetection and the use of simpler histochemical stains, both complemented by linear unmixing techniques. Typically, differentiating these components requires the use of a set of optical filters to isolate each fluorescent compound from each other and from often bright background autofluorescence signals. However, standard fluorescent microscopes are usually only able to separate a limited number of components. If the emission spectra of the fluorophores are spectrally distinct, but overlapping, sophisticated spectral imaging or computational methods can be used to optimize separation and quantitation. This chapter describes spectral unmixing methodology and associated open-source software tools available to analyze multispectral as well as simple color (RGB) images.
细胞外基质中的胶原蛋白和其他成分对于理解各种环境下复杂的细胞与组织相互作用愈发重要。对这些成分进行检测和定量仍具有挑战性，为此已开发出多种技术。我们在此重点介绍基于荧光评估的方法，包括多重免疫检测以及使用更简单的组织化学染色方法，这两种方法都辅以线性解混技术。通常，区分这些成分需要使用一组光学滤光片，以将每种荧光化合物彼此分离，并与通常明亮的背景自发荧光信号分离。然而，标准荧光显微镜通常只能分离有限数量的成分。如果荧光团的发射光谱在光谱上是不同的，但有重叠，可以使用复杂的光谱成像或计算方法来优化分离和定量。本章介绍光谱解混方法以及用于分析多光谱和简单彩色（RGB）图像的相关开源软件工具。
