Reference-based comparison of adaptive immune receptor repertoires

Area of Science:

• Immunology
• Bioinformatics
• Systems Biology

Background:

• Adaptive immune repertoires, including B cell receptor (BCR) and T cell receptor (TCR) repertoires, serve as a record of an individual's immune history.
• Current methods for analyzing immune repertoires often rely on limited parameters, hindering a comprehensive understanding of immune variation across different health states.
• Discriminating between healthy and diseased states using repertoire analysis is a key challenge in immunology.

Purpose of the Study:

• To introduce immuneREF, a novel quantitative, multidimensional measure for assessing adaptive immune repertoire and transcriptome similarity.
• To enable a more nuanced interpretation of immune repertoire variation by integrating repertoire features with cross-referenced simulated and experimental data.
• To explore immune repertoire similarity landscapes across diverse immune states, including healthy, autoimmune disease, and infection.

Main Methods:

• Development and application of immuneREF, a quantitative multidimensional measure for immune repertoire similarity.
• Analysis of over 2,400 datasets encompassing healthy individuals and those with autoimmune diseases or infections.
• Cross-referencing of simulated and experimental datasets to interpret immune repertoire variation.

Main Results:

• Discovery that blood-derived immune repertoires of healthy and diseased individuals exhibit high similarity in certain immune states.
• Challenging the prevailing notion that immune perturbations lead to significant, easily detectable repertoire changes.
• Demonstration of conserved immune repertoire features across different health and disease conditions.

Conclusions:

• immuneREF provides a powerful tool for population-wide studies of adaptive immune response similarity.
• Immune repertoire changes in response to perturbations may be less pronounced than previously assumed, particularly in blood samples.
• This quantitative approach facilitates a deeper understanding of immune system dynamics and individual immune histories.