Methods to Determine and Analyze the Cellular Spatial Distribution Extracted From Multiplex Immunofluorescence Data
1Department of Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX, United States.
Insights
Multiplex immunofluorescence (mIF) image analysis reveals cell distribution patterns within tumors. Understanding these spatial relationships enhances insights into the tumor microenvironment for oncology research.
Area of Science:
- Biomedical Imaging
- Computational Pathology
- Oncology Research
Background:
- Multiplex immunofluorescence (mIF) enables high-fidelity detection of multiple proteins in single tissue sections.
- mIF has revolutionized immunohistochemistry, allowing detailed characterization of individual and rare cell populations.
- This technology is crucial for translational oncology and has potential clinical applications.
Purpose of the Study:
- To review mIF image analysis methods for spatial distribution of cell populations.
- To explore how cellular distribution patterns in mIF images encode clinical information.
- To enhance understanding of the tumor microenvironment through spatial analysis.
Main Methods:
- Utilizing mIF for high-resolution protein detection in tissue samples.
- Applying spatial analysis techniques, including point pattern analysis, to mIF images.
- Calculating metrics based on cell distances and distribution patterns.
Main Results:
- mIF provides detailed cellular phenotypes and distribution information.
- Spatial analysis of cell locations reveals patterns within the tumor microenvironment.
- Identifying relationships between spatial cell distribution and established tumor patterns.
Conclusions:
- mIF image analysis offers powerful tools for dissecting tumor heterogeneity.
- Spatial analysis of cellular interactions is key to understanding tumor biology.
- This review highlights methods to extract meaningful spatial information from mIF data.
Abstract:
Image analysis using multiplex immunofluorescence (mIF) to detect different proteins in a single tissue section has revolutionized immunohistochemical methods in recent years. With mIF, individual cell phenotypes, as well as different cell subpopulations and even rare cell populations, can be identified with extraordinary fidelity according to the expression of antibodies in an mIF panel. This technology therefore has an important role in translational oncology studies and probably will be incorporated in the clinic. The expression of different biomarkers of interest can be examined at the tissue or individual cell level using mIF, providing information about cell phenotypes, distribution of cells, and cell biological processes in tumor samples. At present, the main challenge in spatial analysis is choosing the most appropriate method for extracting meaningful information about cell distribution from mIF images for analysis. Thus, knowing how the spatial interaction between cells in the tumor encodes clinical information is important. Exploratory analysis of the location of the cell phenotypes using point patterns of distribution is used to calculate metrics summarizing the distances at which cells are processed and the interpretation of those distances. Various methods can be used to analyze cellular distribution in an mIF image, and several mathematical functions can be applied to identify the most elemental relationships between the spatial analysis of cells in the image and established patterns of cellular distribution in tumor samples. The aim of this review is to describe the characteristics of mIF image analysis at different levels, including spatial distribution of cell populations and cellular distribution patterns, that can increase understanding of the tumor microenvironment.
