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免疫组织化学中用于分子定量的图像自动分析

Automated analysis of images for molecular quantification in immunohistochemistry.

作者信息

Guirado Ramon, Carceller Héctor, Castillo-Gómez Esther, Castrén Eero, Nacher Juan

机构信息

Neurobiology Unit, Department of Cell Biology, Interdisciplinary Research Structure for Biotechnology and Biomedicine (BIOTECMED), Universitat de Valencia, Spain.

Departament of Medicine, School of Medical Sciences, Universitat Jaume I, Spain.

出版信息

Heliyon. 2018 Jun 29;4(6):e00669. doi: 10.1016/j.heliyon.2018.e00669. eCollection 2018 Jun.

DOI:10.1016/j.heliyon.2018.e00669
PMID:30003163
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC6039854/
Abstract

The quantification of the expression of different molecules is a key question in both basic and applied sciences. While protein quantification through molecular techniques leads to the loss of spatial information and resolution, immunohistochemistry is usually associated with time-consuming image analysis and human bias. In addition, the scarce automatic software analysis is often proprietary and expensive and relies on a fixed threshold binarization. Here we describe and share a set of macros ready for automated fluorescence analysis of large batches of fixed tissue samples using FIJI/ImageJ. The quantification of the molecules of interest are based on an automatic threshold analysis of immunofluorescence images to automatically identify the top brightest structures of each image. These macros measure several parameters commonly quantified in basic neuroscience research, such as neuropil density and fluorescence intensity of synaptic puncta, perisomatic innervation and col-localization of different molecules and analysis of the neurochemical phenotype of neuronal subpopulations. In addition, these same macro functions can be easily modified to improve similar analysis of fluorescent probes in human biopsies for diagnostic purposes based on the expression patterns of several molecules.

摘要

不同分子表达的定量分析是基础科学和应用科学中的关键问题。虽然通过分子技术进行蛋白质定量会导致空间信息和分辨率的丢失,但免疫组织化学通常与耗时的图像分析和人为偏差相关。此外,稀缺的自动软件分析通常是专有的且昂贵,并且依赖于固定阈值二值化。在这里,我们描述并分享了一组宏,可用于使用FIJI/ImageJ对大量固定组织样本进行自动荧光分析。感兴趣分子的定量基于免疫荧光图像的自动阈值分析,以自动识别每个图像中最亮的结构。这些宏测量基础神经科学研究中常用的几个参数,如神经纤维密度、突触小体的荧光强度、胞体周围神经支配、不同分子的共定位以及神经元亚群的神经化学表型分析。此外,基于几种分子的表达模式,这些相同的宏功能可以很容易地修改,以改进用于诊断目的的人类活检中荧光探针的类似分析。

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