Diverse modes of T cell receptor sequence convergence define unique functional and cellular phenotypes

Area of Science:

• Immunology
• Bioinformatics
• Genomics

Background:

• Single-cell technologies enable simultaneous analysis of gene expression and T cell receptor (TCR) sequences.
• Understanding the relationship between TCR structure and T cell function is crucial for immunology research.

Purpose of the Study:

• To perform a large-scale meta-analysis of T cell data using the metaCoNGA tool.
• To map TCR sequence similarity across diverse human tissues and diseases.
• To develop a systematic framework for classifying unconventional T cells.

Main Methods:

• Analysis of 6 million T cells from 91 diverse studies.
• Utilizing the metaCoNGA software for TCR sequence and gene expression analysis.
• Development of a novel TCR clustering approach to identify convergent sequences.

Main Results:

• Identification of shared TCR features within specific T cell subsets linked to infection, cancer, and autoimmunity.
• A new method for classifying T cell groups based on gene expression and TCR amino acid composition.
• Discovery of thousands of convergent TCR sequence clusters potentially targeting shared antigens, exhibiting coherent gene expression patterns.

Conclusions:

• The metaCoNGA resource provides a systematic framework for classifying diverse T cells and understanding antigen-driven T cell behavior.
• This work enhances the investigation into the complex relationship between TCR sequence and T cell function in human health.
• A user-friendly tool is provided for integrating new data and identifying T cell features.