Detecting T cell receptors involved in immune responses from single repertoire snapshots

Abstract

Hypervariable T cell receptors (TCRs) play a key role in adaptive immunity, recognizing a vast diversity of pathogen-derived antigens. 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. 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 [AS]), under cancer immunotherapy, or subject to an acute infection (live yellow fever [YF] vaccine). We validate the method with independent assays. ALICE requires no longitudinal data collection nor large cohorts, and it is directly applicable to most RepSeq datasets. Its results facilitate the identification of TCR variants associated with diseases and conditions, which can be used for diagnostics and rational vaccine design.

A framework for annotation of antigen specificities in high-throughput T-cell repertoire sequencing studies

“…TCR similarity networks were constructed by allowing a single substitution (a Hamming distance of 1) in CDR3 amino acid sequences. Neighborhood size (degree) enrichment of TCR similarity network nodes was tested against VDJ rearrangement model using ALICE algorithm [4]. Minimal number of neighbours was set to 2, Q selection factor was set to 1(no thymic selection) for the analysis of sequences generated with OLGA (see Fig 2), and to 9.41 (default) for the analysis of HSCT dataset (see Fig.…”

“…Allowing for indels or more substitutions, on the other hand, leads to larger hubs at the cost of greatly increasing the number of false-positives (see [12]). These theoretic assumptions provide a basis for usage of TCR neighborhood enrichment tests implemented in ALICE and TCRNET algorithms [2], [4]. The difference between the latter two is that ALICE uses VDJ rearrangement model described by Murugan et al as a control, while TCRNET utilizes pools of healthy control samples as background (negative) set of clonotypes.…”

“…The difference between the latter two is that ALICE uses VDJ rearrangement model described by Murugan et al as a control, while TCRNET utilizes pools of healthy control samples as background (negative) set of clonotypes. Thus, the former tests against VDJ rearrangement biases and thymic selection biases defined by the Q-factor [4], while the latter implicitly tests against thymic and peripheral selection biases and common pathogen-specific responses based on the structure of the control TCR set. In a random sample of 1000 TCRs one can still observe some of those hubs, however, those are smaller and lack any TCRs enriched with neighbours according to the ALICE algorithm ( Figure 2b).…”

“…Immune repertoire profiling technology (AIRR-Seq, [1]) is an efficient technique that can be employed to study the structure and dynamics of the adaptive immune system. AIRR-Seq makes it possible to characterize the structure of both naive and antigen-experienced T-cell receptor (TCR) repertoires [2]- [4], tumor infiltrating T-cells [5], TCRs related to autoimmunity [6], leading to numerous downstream applications in both basic and applied immunological research [7]. While novel single-cell RNA sequencing methods allow coupling individual T-cell clones to their phenotype and function using their gene expression profiles [8], the actual antigen specificity (i.e.…”

“…For example, lists pathogen-and disease-associated TCRs and VDJdb database [12] features a large set of TCRs with experimentally verified epitope specificities and their MHC restrictions. Existing computational methods for TCR repertoire annotation allow both matching against a database of known antigen specificities [12], [13] and clustering of TCR sequences for de novo motif detection [4], [14]. Annotation of a large number of TCR repertoires from healthy donors [15], [16] demonstrates both high variance of frequencies of epitope-specific T-cells and the imprint of past and ongoing pathogen encounters.…”