A Framework for Annotation of Antigen Specificities in High-Throughput T-Cell Repertoire Sequencing Studies

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.