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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: Jan 7, 2026

Visualization, Quantification, and Mapping of Immune Cell Populations in the Tumor Microenvironment

Visualization, Quantification, and Mapping of Immune Cell Populations in the Tumor Microenvironment

Published on: March 25, 2020

Unveiling Tissue Structure and Tumor Microenvironment from Spatial Omics by Hypergraph Learning.

Yi Liao¹, Chong Zhang¹, Zhikang Wang^2,3

¹School of Computer Science and Engineering, South China University of Technology, Guangzhou 510006, China.

Genomics, Proteomics & Bioinformatics

|December 26, 2025

Summary

This summary is machine-generated.

HyperSTAR, a new hypergraph method, precisely identifies spatial domains across diverse resolutions in spatial omics data. This tool enhances understanding of tissue structures and cancer biology by capturing complex cellular relationships.

Keywords:

Higher-order relationship Hypergraph learning Resolution Spatial domain Spatial omics

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Area of Science:

Life Sciences
Computational Biology
Genomics

Background:

Spatial omics technologies provide simultaneous biomolecular and spatial data, crucial for understanding organ development and tumor microenvironments.
Diverse spatial omics resolutions present challenges in accurately characterizing spatial domains at finer scales.

Purpose of the Study:

To develop a novel method, HyperSTAR, for precise identification of spatial domains across varying resolutions in spatial omics data.
To leverage higher-order relationships among spatially adjacent tissue programs for improved domain delineation.

Main Methods:

Proposed HyperSTAR, a hypergraph-based method utilizing a gene expression-guided hyperedge decomposition module.
Developed a hypergraph attention convolutional neural network to learn hyperedge importance and capture complex relationships in spatially neighboring multi-spots/cells.

Main Results:

HyperSTAR outperforms existing graph neural network models in identifying tissue substructures, inferring spatiotemporal patterns, and denoising gene expression data.
Successfully revealed spatial heterogeneity in breast cancer sections, with findings validated by clinical data.
Demonstrated effective handling of diverse spatial omics data types and scalability to large datasets.

Conclusions:

HyperSTAR is a significant advancement in spatial omics analysis, offering a robust tool for exploring complex spatial patterns across resolutions and data types.
The method's ability to capture intricate higher-order relationships makes it invaluable for cancer and developmental biology research.