Estimating the ratio of CD4+ to CD8+ T cells using high-throughput sequence data

Area of Science:

• Immunology
• Computational Biology
• Genomics

Background:

• Mature T cells differentiate into CD4+ helper T cells and CD8+ killer T cells, crucial for adaptive immunity.
• Current methods for determining CD4:CD8 ratios, such as flow cytometry, are resource-intensive.
• T cell receptor (TCR) gene rearrangement during maturation creates unique CDR3 sequences, potentially distinguishing T cell subsets.

Purpose of the Study:

• To develop and validate a computational method for estimating CD4+ and CD8+ T cell proportions from high-throughput TCR sequencing data.
• To identify sequence features within the TCR's CDR3 region that differentiate CD4+ from CD8+ T cells.
• To assess the method's accuracy and applicability across healthy and autoimmune disease cohorts.

Main Methods:

• Applied high-throughput next-generation sequencing to TCR genes from healthy and multiple sclerosis patient cohorts.
• Analyzed TCR sequence features, including Variable gene usage and CDR3 region length, to distinguish CD4+ and CD8+ T cells.
• Developed a likelihood model to estimate CD4:CD8 ratios from sequence data and validated it with in vitro mixtures and flow cytometry.

Main Results:

• Identified distinct sequence features in the TCR CDR3 region that differentiate CD4+ and CD8+ T cells.
• The developed computational model accurately estimated CD4:CD8 T cell proportions in both healthy and diseased samples.
• The model demonstrated efficacy with as few as 1000 TCR sequences and was validated against flow cytometry.

Conclusions:

• A novel computational method enables accurate CD4:CD8 T cell ratio determination from TCR sequencing data.
• This method offers a valuable tool for analyzing immune cell populations in various clinical and research settings.
• The approach has potential applications in analyzing T cell populations in solid tumors and other sample types undergoing TCR sequencing.