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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: Jul 5, 2025

Enrichment and Characterization of the Tumor Immune and Non-immune Microenvironments in Established Subcutaneous Murine Tumors

Enrichment and Characterization of the Tumor Immune and Non-immune Microenvironments in Established Subcutaneous Murine Tumors

Published on: June 7, 2018

scCancer2: data-driven in-depth annotations of the tumor microenvironment at single-level resolution.

Zeyu Chen¹, Yuxin Miao¹, Zhiyuan Tan²

¹MOE Key Laboratory of Bioinformatics, BNRIST Bioinformatics Division, Institute for Precision Medicine & Department of Automation, Tsinghua University, Beijing 100084, China.

Bioinformatics (Oxford, England)

|January 20, 2024

Summary

scCancer2 enables accurate cell type annotation in tumor microenvironments using single-cell RNA sequencing (scRNA-seq) data. It integrates multiple datasets to map cell subtype similarities and improve malignant cell identification.

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Last Updated: Jul 5, 2025

Enrichment and Characterization of the Tumor Immune and Non-immune Microenvironments in Established Subcutaneous Murine Tumors

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Published on: June 7, 2018

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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

Area of Science:

Computational Biology
Genomics
Cancer Research

Background:

Single-cell RNA sequencing (scRNA-seq) is crucial for understanding tumor microenvironment (TME) complexity.
Existing scRNA-seq studies define diverse cell subtypes, necessitating computational methods for cross-dataset label transfer.
Inconsistencies in cell subtype definitions across studies require careful evaluation of subtype relationships.

Purpose of the Study:

To develop a robust machine learning framework for annotating TME cells in scRNA-seq data.
To quantitatively map similarities between cell subtypes defined in different reference datasets.
To enhance the identification of malignant cells by integrating bulk and single-cell transcriptomic data.

Main Methods:

Developed a supervised machine learning framework for TME cell annotation using 15 scRNA-seq datasets (594 samples).
Constructed cell subtype similarity maps by testing trained classifiers across all datasets.
Integrated TCGA bulk gene expression and scRNA-seq data (10 cancer types, 175 samples, 663,857 cells) for a pan-cancer malignant cell classifier.
Incorporated a module for processing and analyzing spatial transcriptomic data.

Main Results:

The scCancer2 package provides accurate annotation of TME cell subtypes across multiple scRNA-seq datasets.
Quantitative similarity maps reveal relationships between cell subtypes from different studies.
The integrated classifier demonstrates robust performance in identifying malignant cells, even without internal reference data.
scCancer2 supports the analysis of spatial transcriptomic data for TME feature exploration.

Conclusions:

scCancer2 offers a powerful, updated solution for integrative tumor scRNA-seq data analysis.
The package facilitates cross-study cell subtype comparison and improves malignant cell detection.
scCancer2 enhances the utility of scRNA-seq and spatial transcriptomics for TME research.