Immune Fingerprinting through Repertoire Similarity

TL;DR

This paper introduces 'Immprint', a novel classifier that uses repertoire similarity to accurately identify individuals from their immune receptor data, highlighting the personal nature of immune repertoires.

Contribution

The paper presents a new information-theoretic method for individual identification based on immune repertoire similarity, achieving high accuracy even among identical twins.

Findings

Identification accuracy with false positive/negative rates < 10^{-6}

Robustness to infections and time passage demonstrated

Effective on published T-cell receptor datasets

Abstract

Immune repertoires provide a unique fingerprint reflecting the immune history of individuals, with potential applications in precision medicine. However, the question of how personal that information is and how it can be used to identify individuals has not been explored. Here, we show that individuals can be uniquely identified from repertoires of just a few thousands lymphocytes. We present "Immprint," a classifier using an information-theoretic measure of repertoire similarity to distinguish pairs of repertoire samples coming from the same versus different individuals. Using published T-cell receptor repertoires and statistical modeling, we tested its ability to identify individuals with great accuracy, including identical twins, by computing false positive and false negative rates $< 10^{-6}$ from samples composed of 10,000 T-cells. We verified through longitudinal datasets and simulations that the method is robust to acute infections and the passage of time. These results emphasize the private and personal nature of repertoire data.