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Review
. 2024 May 31;12(5):e008589.
doi: 10.1136/jitc-2023-008589.

Spatially resolved tissue imaging to analyze the tumor immune microenvironment: beyond cell-type densities

Alvaro Lopez Janeiro  1   2 , Eduardo Miraval Wong  2 , Daniel Jiménez-Sánchez  3 , Carlos Ortiz de Solorzano  4 , Maria D Lozano  1   2   5 , Alvaro Teijeira  1   5   6 , Kurt A Schalper  7 , Ignacio Melero  1   5   6   8 , Carlos E De Andrea  9   2   5   10
Affiliations
Review

Spatially resolved tissue imaging to analyze the tumor immune microenvironment: beyond cell-type densities

Alvaro Lopez Janeiro et al. J Immunother Cancer. .

Abstract

Introduction: The tissue immune microenvironment is associated with key aspects of tumor biology. The interaction between the immune system and cancer cells has predictive and prognostic potential across different tumor types. Spatially resolved tissue-based technologies allowed researchers to simultaneously quantify different immune populations in tumor samples. However, bare quantification fails to harness the spatial nature of tissue-based technologies. Tumor-immune interactions are associated with specific spatial patterns that can be measured. In recent years, several computational tools have been developed to increase our understanding of these spatial patterns.

Topics covered: In this review, we cover standard techniques as well as new advances in the field of spatial analysis of the immune microenvironment. We focused on marker quantification, spatial intratumor heterogeneity analysis, cell‒cell spatial interaction studies and neighborhood analyses.

Keywords: Biomarker; Immune Checkpoint Inhibitor; Tumor microenvironment - TME.

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Conflict of interest statement

Competing interests: No, there are no competing interests.

Figures

Figure 1
Figure 1
Top: Overview of the design, performance and analysis of a tissue imaging experiment to analyze the tissue immune microenvironment (TIME). Bottom: Table summarizing the main technologies used to perform spatial TIME analyses. Figure partially created with BioRender.com. The costs are approximately defined as follows: lowUS$1500/sample. FFPE, formalin-fixed paraffin-embedded; FFT, fresh frozen tissue; IF, immunofluorescence; QC, quality control.
Figure 2
Figure 2
Overview of the two main strategies (pixel-based and cell-based) used to quantify immunofluorescence markers. Top: Flow chart comparing the analytical pipelines. Bottom: Table displaying the feature comparison between methods.
Figure 3
Figure 3
Spatial intratumor heterogeneity analysis (s-ITH). Top: (A) Spatial heterogeneity can display different patterns. Some patterns are associated with high global heterogeneity while others are not. (B) Flow chart with the main outline of an s-ITH analytical pipeline. Bottom: Table describing different commonly used s-ITH methods. Figure partially created with BioRender.com.
Figure 4
Figure 4
Simple cell spatial interaction analysis and complex (neighborhood) interaction analysis. Top left: Table showing the different methods used to analyze cell-to-cell spatial interactions. Top right: Explanatory diagrams of simple cell-to-cell spatial interaction analysis. Bottom: Explanatory diagram of closest neighbors and UTAG neighborhood analysis approaches. UTAG, unsupervised discovery of tissue architecture with graphs.
Figure 5
Figure 5
Table summarizing the open-source analytical pipelines used in spatial data analysis. UTAG, unsupervised discovery of tissue architecture with graphs.
See this image and copyright information in PMC

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