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The Tumor Microenvironment

The Tumor Microenvironment

The Tumor Microenvironment

The Tumor Microenvironment

Every normal cell or tissue is embedded in a complex local environment called stroma, consisting of different cell types, a basal membrane, and blood vessels. As normal cells mutate and develop into cancer cells, their local environment also changes to allow cancer progression. The tumor microenvironment (TME) consists of a complex cellular matrix of stromal cells and the developing tumor. The cross-talk between cancer cells and surrounding stromal cells is critical to disrupt normal tissue...

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Related Experiment Video

Updated: Apr 16, 2026

Spatial Profiling of Protein and RNA Expression in Tissue: An Approach to Fine-Tune Virtual Microdissection

Spatial Profiling of Protein and RNA Expression in Tissue: An Approach to Fine-Tune Virtual Microdissection

Published on: July 6, 2022

Spatial omics for profiling the dynamic tumor microenvironment.

Hao Nguyen¹, Merrin Mary Eapen^2,3, Quan Nguyen^1,4

¹Queensland Institute of Medical Research, Berghofer Brisbane QLD Australia.

Clinical & Translational Immunology

|April 15, 2026

Summary

This summary is machine-generated.

Spatial transcriptomics and proteomics map tissue interactions, advancing tumor microenvironment (TME) research. Integrating AI and standardized tools is crucial for precision cancer medicine.

Keywords:

artifical intelligence cellular interactions precision cancer medicine spatial proteomics spatial transcriptomics tumor microenvironment

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Area of Science:

Biotechnology
Cancer Research
Bioinformatics

Background:

Spatial omics technologies like spatial transcriptomics (ST) and spatial proteomics (SP) enable mapping of RNA and protein distributions in tissues.
These technologies provide insights into cellular interactions in both healthy and diseased states, particularly the tumor microenvironment (TME).

Purpose of the Study:

To discuss the application of the latest ST and SP technologies, data resources, and computational pipelines for studying the TME.
To highlight advancements in spatial characterization of tumors and TME across cancer progression.
To identify persistent gaps and future directions for spatial omics in cancer research.

Main Methods:

Review of current ST and SP technologies.
Analysis of large public spatial omics data resources.
Discussion of advanced computational pipelines for TME analysis.
Exploration of artificial intelligence (AI) integration for spatial omics data.

Main Results:

ST and SP have revolutionized the study of cellular interactions within the TME.
These technologies allow for in-depth spatial characterization of tumors from initiation to metastasis.
Significant challenges remain in cross-platform integration, data standardization, and computational scalability.

Conclusions:

AI integration offers promise for biological and translational applications but requires standardized, validated, and interpretable workflows.
Development of explainable and scalable tools is essential for integrating spatial omics into precision cancer medicine.
Cross-disciplinary collaboration is key to advancing the use of spatial omics for TME analysis and cancer treatment.