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多重免疫荧光和免疫组织化学成像数据的统计分析

Statistical Analysis of Multiplex Immunofluorescence and Immunohistochemistry Imaging Data.

作者信息

Wrobel Julia, Harris Coleman, Vandekar Simon

机构信息

Department of Biostatistics and Informatics, University of Colorado Anschutz Medical Campus, Aurora, CO, USA.

Department of Biostatistics, Vanderbilt University Medical Center, Nashville, TN, USA.

出版信息

Methods Mol Biol. 2023;2629:141-168. doi: 10.1007/978-1-0716-2986-4_8.

Abstract

Advances in multiplexed single-cell immunofluorescence (mIF) and multiplex immunohistochemistry (mIHC) imaging technologies have enabled the analysis of cell-to-cell spatial relationships that promise to revolutionize our understanding of tissue-based diseases and autoimmune disorders. Multiplex images are collected as multichannel TIFF files; then denoised, segmented to identify cells and nuclei, normalized across slides with protein markers to correct for batch effects, and phenotyped; and then tissue composition and spatial context at the cellular level are analyzed. This chapter discusses methods and software infrastructure for image processing and statistical analysis of mIF/mIHC data.

摘要

多重单细胞免疫荧光(mIF)和多重免疫组织化学(mIHC)成像技术的进步,使得对细胞间空间关系的分析成为可能,有望彻底改变我们对基于组织的疾病和自身免疫性疾病的理解。多重图像以多通道TIFF文件形式收集;然后进行去噪、分割以识别细胞和细胞核,通过蛋白质标记在玻片间进行归一化以校正批次效应,并进行表型分析;接着在细胞水平上分析组织组成和空间背景。本章讨论了用于mIF/mIHC数据图像处理和统计分析的方法及软件基础设施。

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