Cell-cell communication inference and analysis in the tumour microenvironments from single-cell transcriptomics: data...

Area of Science:

• Oncology
• Computational Biology
• Immunology

Background:

• Carcinomas are complex ecosystems involving cancer, stromal, and immune cells.
• Intercellular communication within the tumor microenvironment drives cancer progression and therapy resistance.
• Quantifying this crosstalk is crucial for developing effective cancer treatments.

Purpose of the Study:

• To introduce a pipeline for estimating ligand-receptor-mediated intercellular communication from single-cell transcriptomics.
• To review and demonstrate various intercellular communication scoring strategies and inference methods.
• To summarize evaluation methods, challenges, and future directions for analyzing cell-cell communication in tumors.

Main Methods:

• Development of a pipeline for ligand-receptor interaction (LRI) based intercellular communication estimation.
• Demonstration of seven intercellular communication scoring strategies.
• Highlighting four types of inference methods: network-based, machine learning-based, spatial information-based, and others.
• Summarizing evaluation and validation approaches for these methods.

Main Results:

• A comprehensive pipeline for analyzing intercellular communication from single-cell transcriptomics is presented.
• Various scoring strategies and four major categories of inference methods are detailed.
• Advantages, limitations, and validation avenues for different communication inference methods are analyzed.
• Key challenges and future research directions in tumor microenvironment communication are discussed.

Conclusions:

• Accurate quantification of intercellular crosstalk is essential for advancing tumor-targeted therapies.
• The presented work provides a framework for understanding cell-cell communication in cancer.
• Further development of robust estimation tools can significantly improve cancer treatment strategies.