![]() This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.ĭata Availability: The following references refer to the references in the manuscript. Received: MaAccepted: Published: June 13, 2019Ĭopyright: © 2019 Pogorelyy et al. Freeman, University of Edinburgh, UNITED KINGDOM PLoS Biol 17(6):Īcademic Editor: Thomas C. (2019) Detecting T cell receptors involved in immune responses from single repertoire snapshots. Its results facilitate the identification of TCR variants associated with diseases and conditions, which can be used for diagnostics and rational vaccine design.Ĭitation: Pogorelyy MV, Minervina AA, Shugay M, Chudakov DM, Lebedev YB, Mora T, et al. ALICE requires no longitudinal data collection nor large cohorts, and it is directly applicable to most RepSeq datasets. We validate the method with independent assays. We present Antigen-specific Lymphocyte Identification by Clustering of Expanded sequences (ALICE), a statistical approach that identifies TCR sequences actively involved in current immune responses from a single RepSeq sample and apply it to repertoires of patients with a variety of disorders - patients with autoimmune disease (ankylosing spondylitis ), under cancer immunotherapy, or subject to an acute infection (live yellow fever vaccine). Our ability to extract clinically relevant information from large high-throughput sequencing of TCR repertoires (RepSeq) data is limited, because little is known about TCR–disease associations. Hypervariable T cell receptors (TCRs) play a key role in adaptive immunity, recognizing a vast diversity of pathogen-derived antigens.
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