Immune fingerprinting through repertoire similarity

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