Statistical Analysis of Multiplex Immunofluorescence and Immunohistochemistry Imaging Data

Julia Wrobel; Coleman Harris; Simon Vandekar

doi:10.1007/978-1-0716-2986-4_8

Statistical Analysis of Multiplex Immunofluorescence and Immunohistochemistry Imaging Data

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

Authors

Julia Wrobel¹, Coleman Harris², Simon Vandekar²

Affiliations

¹ Department of Biostatistics and Informatics, University of Colorado Anschutz Medical Campus, Aurora, CO, USA. julia.wrobel@cuanschutz.edu.
² Department of Biostatistics, Vanderbilt University Medical Center, Nashville, TN, USA.

PMID: 36929077
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.

Keywords: Bioimage analysis; Cell phenotyping; Image normalization; Multiplex imaging; Spatial statistics.

Publication types

Review
Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't

MeSH terms

Datasets as Topic
Fluorescent Antibody Technique* / methods
Humans
Image Processing, Computer-Assisted* / methods
Immunohistochemistry* / methods
Lung Neoplasms / pathology
Models, Statistical*
Ovarian Neoplasms / pathology
Phenotype
Single-Cell Analysis* / methods
Software

Grants and funding

U2C CA233291/CA/NCI NIH HHS/United States