Year: 2021
Type: article
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 that the method is robust to acute infections and that the immune fingerprint is stable for at least three years. These results emphasize the private and personal nature of repertoire data.
Source: PLoS Genetics
Institutions: Harvard University, University of Cambridge, École Normale Supérieure - PSL, Laboratoire de Physique de l'Ecole Normale Supérieure, Centre National de la Recherche Scientifique, Sorbonne Université, Institute of Bioorganic Chemistry, Institute of Bioorganic Chemistry, Pirogov Russian National Research Medical University
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,575
Awards: G2803418680, G4507485815, G506492324
OpenAlex ID: W3120048001