Signatures of T cell immunity revealed using sequence similarity with TCRDivER algorithm.

Area of Science:

• Immunology
• Bioinformatics
• Computational Biology

Background:

• T cell receptor (TCR) repertoire changes are key indicators for disease and therapy monitoring, especially with increased immunotherapy use.
• Assessing TCR repertoire state is vital for cancer, infectious, and autoimmune diseases.
• T cell proliferation, driven by factors like immunization, significantly alters TCR repertoires.

Purpose of the Study:

• To address limitations in comparing T cell receptor repertoires by accounting for clone size and sequence similarity.
• To introduce a novel algorithm, TCRDivER, for comprehensive T cell repertoire comparison.
• To enhance the understanding of immune responses and differences in TCR repertoires across various biological contexts.

Main Methods:

• Development of the TCRDivER algorithm, a global method for T cell repertoire comparison.
• Utilizing diversity profiles sensitive to both clone size and sequence similarity.
• Application of the algorithm to distinguish spleen TCR repertoires between immunized and non-immunized mice.

Main Results:

• TCRDivER successfully distinguished between spleen TCR repertoires of immunized and non-immunized mice.
• The method demonstrated the necessity of simultaneously considering clone size and sequence similarity for accurate repertoire analysis.
• Analysis revealed biologically meaningful relationships between TCR sequence similarity and clonality.

Conclusions:

• The TCRDivER algorithm provides a robust approach for comparing T cell receptor repertoires by integrating clone size and sequence similarity.
• This method aids in understanding immune response variations and repertoire differences in diverse biological contexts.
• TCRDivER is expected to be widely adopted in clinical and research settings due to the increasing availability of sequencing data.