Detecting T-cell receptors involved in immune responses from single repertoire snapshots
Mikhail V Pogorelyy, Anastasia A Minervina, Mikhail Shugay, Dmitriy M, Chudakov, Yuri B Lebedev, Thierry Mora, Aleksandra M Walczak

TL;DR
This paper introduces ALICE, a statistical method that identifies T-cell receptor sequences involved in immune responses from a single repertoire snapshot, aiding disease diagnosis and immune system analysis.
Contribution
The paper presents ALICE, a novel approach that detects active TCR sequences from single RepSeq samples without needing longitudinal data or large cohorts.
Findings
ALICE accurately identifies disease-associated TCRs.
The method works across autoimmune, cancer, and infectious diseases.
Predictions align with independent assays and distinguish memory T cells.
Abstract
Hypervariable T-cell receptors (TCR) play a key role in adaptive immunity, recognising a vast diversity of pathogen-derived antigens. High throughput sequencing of TCR repertoires (RepSeq) produces huge datasets of T-cell receptor sequences from blood and tissue samples. However, our ability to extract clinically relevant information from RepSeq data is limited, mainly because little is known about TCR-disease associations. Here we present a statistical approach called ALICE (Antigen-specific Lymphocyte Identification by Clustering of Expanded sequences) that identifies TCR sequences that are actively involved in the current immune response from a single RepSeq sample, and apply it to repertoires of patients with a variety of disorders - autoimmune disease (ankylosing spondylitis), patients under cancer immunotherapy, or subject to an acute infection (live yellow fever vaccine). The…
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