Abstract
Recently developed molecular methods allow large-scale profiling of T-cell receptor (TCR) sequences that encode for antigen specificity and immunological memory of these cells. However, it is well-known that the even unperturbed TCR repertoire structure is extremely complex due to the high diversity of TCR rearrangements and multiple biases imprinted by VDJ rearrangement process. The latter gives rise to the phenomenon of “public” TCR clonotypes that can be shared across multiple individuals and non-trivial structure of the TCR similarity network. Here, we outline a framework for TCR sequencing data analysis that can control for these biases in order to infer TCRs that are involved in response to antigens of interest. We apply two previously published methods, ALICE and TCRNET, to detect groups of homologous TCRs that are enriched in samples of interest. Using an example dataset of donors with known HLA haplotype and CMV status, we demonstrate that by applying HLA restriction rules and matching against a database of TCRs with known antigen specificity, it is possible to robustly detect motifs of epitope-specific responses in individual repertoires. We also highlight potential shortcomings of TCR clustering methods and demonstrate that highly expanded TCRs should be individually assessed to get the full picture of antigen-specific response.
Highlights
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
Using an example dataset of individual human T-cell receptor (TCR) repertoires, we demonstrate the capability of the framework to infer HLA-restricted antigenspecific responses, discuss possible modifications of the proposed method, and expose potential shortcomings of the existing methodology that should be taken into account when running antigen-specific TCR inference
HLA typing is a prerequisite for any AIRR-Seq study that aims at detecting TCR motifs, and both test and control cohorts should be carefully balanced according to HLA frequency
Summary
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. 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 epitopespecific T-cells and the imprint of past and ongoing pathogen encounters. De novo discovery of T-cells associated with antigens of interest or certain disease appears to be a hard problem, complicated by the biases in the structure of the naive (unperturbed) TCR repertoire [17], the presence of existing clonal expansions specific to unrelated pathogens, and the high number of false positives that result from the extremely high diversity of the TCR repertoire
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