Statistical Analysis of Multiplex Immunofluorescence and Immunohistochemistry Imaging Data
Julia Wrobel1, Coleman Harris2, Simon Vandekar2
1Department of Biostatistics and Informatics, University of Colorado Anschutz Medical Campus, Aurora, CO, USA. julia.wrobel@cuanschutz.edu.
Insights
Multiplexed imaging (mIF/mIHC) reveals cell interactions in tissues, advancing disease research. This chapter details image processing and analysis methods for these powerful techniques.
Area of Science:
- Biomedical imaging
- Computational pathology
- Immunohistochemistry
Background:
- Multiplexed immunofluorescence (mIF) and immunohistochemistry (mIHC) enable detailed analysis of cellular spatial relationships.
- Understanding these interactions is crucial for diagnosing and treating tissue-based diseases and autoimmune disorders.
Purpose of the Study:
- To discuss methods and software for processing and analyzing multiplexed imaging data.
- To highlight the importance of spatial context in cellular-level tissue analysis.
Main Methods:
- Image acquisition as multichannel TIFF files.
- Image processing including denoising, segmentation, and normalization.
- Cellular phenotyping and spatial context analysis.
Main Results:
- Established a workflow for analyzing complex multiplexed imaging data.
- Demonstrated the utility of mIF/mIHC in understanding tissue composition.
- Provided insights into cell-to-cell relationships within diseased tissues.
Conclusions:
- Advanced mIF/mIHC technologies offer revolutionary potential for disease research.
- Robust image processing and statistical analysis are essential for extracting meaningful biological insights from multiplexed data.
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.
