» Articles » PMID: 38821717

Spatially Resolved Tissue Imaging to Analyze the Tumor Immune Microenvironment: Beyond Cell-type Densities

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.

Citing Articles

Spatially-resolved analyses of muscle invasive bladder cancer microenvironment unveil a distinct fibroblast cluster associated with prognosis.

Feng C, Wang Y, Song W, Liu T, Mo H, Liu H Front Immunol. 2025; 15:1522582.

PMID: 39759522 PMC: 11695344. DOI: 10.3389/fimmu.2024.1522582.

References
1.
Park J, Kim J, Lewy T, Rice C, Elemento O, Rendeiro A . Spatial omics technologies at multimodal and single cell/subcellular level. Genome Biol. 2022; 23(1):256. PMC: 9746133. DOI: 10.1186/s13059-022-02824-6. View

2.
Agrawal L, Engel K, Greytak S, Moore H . Understanding preanalytical variables and their effects on clinical biomarkers of oncology and immunotherapy. Semin Cancer Biol. 2017; 52(Pt 2):26-38. PMC: 6004232. DOI: 10.1016/j.semcancer.2017.12.008. View

3.
MORAN P . Notes on continuous stochastic phenomena. Biometrika. 1950; 37(1-2):17-23. View

4.
Bankhead P, Loughrey M, Fernandez J, Dombrowski Y, McArt D, Dunne P . QuPath: Open source software for digital pathology image analysis. Sci Rep. 2017; 7(1):16878. PMC: 5715110. DOI: 10.1038/s41598-017-17204-5. View

5.
Greenwald N, Miller G, Moen E, Kong A, Kagel A, Dougherty T . Whole-cell segmentation of tissue images with human-level performance using large-scale data annotation and deep learning. Nat Biotechnol. 2021; 40(4):555-565. PMC: 9010346. DOI: 10.1038/s41587-021-01094-0. View