Methods to Determine and Analyze the Cellular Spatial Distribution Extracted From Multiplex Immunofluorescence Data to Understand the Tumor Microenvironment

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.

Integrated Cells and Collagen Fibers Spatial Image Analysis

“…After that, extracted information can be passed to K-nearest neighbors, N-distance, or self-organizing map algorithms for identification of neighborhoods with similar features (Roeder et al, 2012;Heindl et al, 2015;Parra, 2021).…”

Approaches to spatially resolving the tumour immune microenvironment of hepatocellular carcinoma

“… 86 This is an ever-adapting field, with many new approaches being developed for the various technologies being made available. 87 , 88 …”

Immune cellular patterns of distribution affect outcomes of patients with non-small cell lung cancer

“…We created cord plots to better visualize cell phenotypes based on the co-expression of markers used in the 5 mIF panels. Additionally, dimensional reduction clustering was applied using uniform manifold approximation and projection (UMAP) to characterize all possible cell populations in each panel (19,20).…”

“…Furthermore, using the spatial point pattern distribution of the cell phenotypes relative to malignant cells, we measured the distance from CK + malignant cells to each cell phenotype included in the panels using a matrix created with each cell's X and Y coordinates in R studio software v.3.6.1. We applied the median nearest neighbor function from CK + malignant cells to CD3 + T-cells, CD20 + B-cells, CD68 + macrophages, and CD66b + granulocytic cells (PMNs), as well as to the other cell phenotypes, to determine where these TAICs were located; speci cally, whether the TAICs were close to (equal to or less than the median distance) or far from (more than the median distance) the CK + malignant cells (19).…”