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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: Aug 26, 2025

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers

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Elucidating tumor heterogeneity from spatially resolved transcriptomics data by multi-view graph collaborative

Chunman Zuo^1,2, Yijian Zhang³, Chen Cao⁴

¹Institute of Artificial Intelligence, Donghua University, Shanghai, 201620, China. cmzuo@dhu.edu.cn.

Nature Communications

|October 10, 2022

Summary

This summary is machine-generated.

Spatially resolved transcriptomics (SRT) analysis is advanced by stMVC, a novel computational model. This multi-view graph learning approach integrates diverse data to reveal tissue heterogeneity and identify disease-related cell states for clinical applications.

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Area of Science:

Computational biology
Genomics
Bioinformatics

Background:

Spatially resolved transcriptomics (SRT) offers insights into tissue architecture and cell development, particularly in tumors.
Current computational methods struggle to fully exploit biological contexts and multi-view features for elucidating tissue heterogeneity.

Purpose of the Study:

To develop an advanced computational model, stMVC, for comprehensive analysis of SRT data.
To integrate histology, gene expression, spatial location, and biological contexts for a deeper understanding of tissue heterogeneity.

Main Methods:

Proposed stMVC, a multi-view graph collaborative-learning model utilizing a semi-supervised graph attention autoencoder.
Integrated histological-similarity and spatial-location graphs using attention mechanisms under biological context supervision.

Main Results:

stMVC demonstrated superior performance in detecting tissue structure, inferring trajectory relationships, and denoising on human cortex benchmark data.
Identified disease-related and transition cell states in breast cancer, validated by independent clinical data analysis.

Conclusions:

stMVC effectively integrates multi-view features and biological contexts for robust SRT data analysis.
The model shows significant potential for clinical and prognostic applications using SRT data, including disease state identification.