Detecting T-cell clonal expansions and quantifying clone survival using deep profiling of immune repertoires.

An individual's T-cell repertoire constantly changes under the influence of external and internal factors. Cells that do not receive a stimulatory signal die, while those that encounter and recognize a pathogen or receive a co-stimulatory signal divide, resulting in clonal expansions. T-cell clones can be traced by monitoring the presence of their unique T-cell receptor (TCR) sequence, which is assembled de novo through a process known as V(D)J rearrangement. Tracking T cells can provide valuable insights into the survival of cells after hematopoietic stem cell transplantation (HSCT) or cancer treatment response and can indicate the induction of protective immunity by vaccination. In this study, we report a bioinformatic method for quantifying the T-cell repertoire dynamics from TCR sequencing data. We demonstrate its utility by measuring the T-cell repertoire stability in healthy donors, by quantifying the effect of donor lymphocyte infusion (DLI), and by tracking the fate of the different T-cell subsets in HSCT patients and the expansion of pathogen-specific clones in vaccinated individuals.

Convergence, plasticity, and tissue residence of regulatory T cell response via TCR repertoire prism.

Suppressive function of regulatory T cells (Treg) is dependent on signaling of their antigen receptors triggered by cognate self, dietary, or microbial peptides presented on MHC II. However, it remains largely unknown whether distinct or shared repertoires of Treg TCRs are mobilized in response to different challenges in the same tissue or the same challenge in different tissues. Here we use a fixed TCRß chain FoxP3-GFP mouse model to analyze conventional (eCD4) and regulatory (eTreg) effector TCRα repertoires in response to six distinct antigenic challenges to the lung and skin. This model shows highly 'digital' repertoire behavior with easy-to-track challenge-specific TCRα CDR3 clusters. For both eCD4 and eTreg subsets, we observe challenge-specific clonal expansions yielding homologous TCRα clusters within and across animals and exposure sites, which are also reflected in the draining lymph nodes but not systemically. Some CDR3 clusters are shared across cancer challenges, suggesting a response to common tumor-associated antigens. For most challenges, eCD4 and eTreg clonal response does not overlap. Such overlap is exclusively observed at the sites of certain tumor challenges, and not systematically, suggesting transient and local tumor-induced eCD4=>eTreg plasticity. This transition includes a dominant tumor-responding eCD4 CDR3 motif, as well as characteristic iNKT TCRα CDR3. In addition, we examine the homeostatic tissue residency of clonal eTreg populations by excluding the site of challenge from our analysis. We demonstrate that distinct CDR3 motifs are characteristic of eTreg cells residing in particular lymphatic tissues, regardless of the challenge. This observation reveals the tissue-resident, antigen-specific clonal Treg populations.

Structure-based prediction of T cell receptor recognition of unseen epitopes using TCRen.

T cell receptor (TCR) recognition of foreign peptides presented by major histocompatibility complex protein is a major event in triggering the adaptive immune response to pathogens or cancer. The prediction of TCR-peptide interactions has great importance for therapy of cancer as well as infectious and autoimmune diseases but remains a major challenge, particularly for novel (unseen) peptide epitopes. Here we present TCRen, a structure-based method for ranking candidate unseen epitopes for a given TCR. The first stage of the TCRen pipeline is modeling of the TCR-peptide-major histocompatibility complex structure. Then a TCR-peptide residue contact map is extracted from this structure and used to rank all candidate epitopes on the basis of an interaction score with the target TCR. Scoring is performed using an energy potential derived from the statistics of TCR-peptide contact preferences in existing crystal structures. We show that TCRen has high performance in discriminating cognate versus unrelated peptides and can facilitate the identification of cancer neoepitopes recognized by tumor-infiltrating lymphocytes.