Machine learning analysis of the T cell receptor repertoire identifies sequence features of self-reactivity

APMAT analysis reveals the association between CD8 T cell receptors, cognate antigen, and T cell phenotype and persistence

Elucidating the relationships between a class I peptide antigen, a CD8 T cell receptor (TCR) specific to that antigen, and the T cell phenotype that emerges following antigen stimulation, remains a mostly unsolved problem, largely due to the lack of large data sets that can be mined to resolve such relationships. Here, we describe Antigen-TCR Pairing and Multiomic Analysis of T-cells (APMAT), an integrated experimental-computational framework designed for the high-throughput capture and analysis of CD8 T cells, with paired antigen, TCR sequence, and single-cell transcriptome. Starting with 951 putative antigens representing a comprehensive survey of the SARS-CoV-2 viral proteome, we utilize APMAT for the capture and single cell analysis of CD8 T cells from 62 HLA A*02:01 COVID-19 participants. We leverage this comprehensive dataset to integrate with peptide antigen properties, TCR CDR3 sequences, and T cell phenotypes to show that distinct physicochemical features of the antigen-TCR pairs strongly associate with both T cell phenotype and T cell persistence. This analysis suggests that CD8 T cell phenotype following antigen stimulation is at least partially deterministic, rather than the result of stochastic biological properties.

APMAT analysis reveals the association between CD8 T cell receptors, cognate antigen, and T cell phenotype and persistence

Elucidating the relationships between a class I peptide antigen, a CD8 T cell receptor (TCR) specific to that antigen, and the T cell phenotype that emerges following antigen stimulation, remains a mostly unsolved problem, largely due to the lack of large data sets that can be mined to resolve such relationships. Here, we describe Antigen-TCR Pairing and Multiomic Analysis of T-cells (APMAT), an integrated experimental-computational framework designed for the high-throughput capture and analysis of CD8 T cells, with paired antigen, TCR sequence, and single-cell transcriptome. Starting with 951 putative antigens representing a comprehensive survey of the SARS-CoV-2 viral proteome, we utilize APMAT for the capture and single cell analysis of CD8 T cells from 62 HLA A*02:01 COVID-19 participants. We leverage this unique, comprehensive dataset to integrate with peptide antigen properties, TCR CDR3 sequences, and T cell phenotypes to show that distinct physicochemical features of the antigen-TCR pairs strongly associate with both T cell phenotype and T cell persistence. This analysis suggests that CD8+ T cell phenotype following antigen stimulation is at least partially deterministic, rather than the result of stochastic biological properties.

Probing TCR Specificity Using Artificial In Vivo Diversification of CDR3 Regions

The T-cell receptor sequences expressed on cells recognizing a specific peptide in the context of a given MHC molecule can be explored for common features that might explain their antigen specificity. However, despite the development of numerous experimental and bioinformatic strategies, the specificity problem remains unresolved. To address the need for additional experimental paradigms, we report here on an in vivo experimental strategy designed to artificially diversify a transgenic TCR by CRISPR/Cas9-mediated mutagenesis of Tcra and Tcrb chain genes. In this system, an initially monoclonal repertoire of known specificity is converted into an oligoclonal pool of TCRs of altered antigen reactivity. Tracking the fate of individual clonotypes during the intrathymic differentiation process illuminates the strong selective pressures that shape the repertoire of naïve T cells. Sequence analyses of the artificially diversified repertoires identify key amino acid residues in the CDR3 regions required for antigen recognition, indicating that artificial diversification of well-characterized TCR transgene sequences helps to reduce the complexities of learning the rules of antigen recognition.

The Evolving T Cell Receptor Recognition Code: The Rules Are More Like Guidelines

αβ T cell receptor (TCR) recognition of peptide-MHC complexes lies at the core of adaptive immunity, balancing specificity and cross-reactivity to facilitate effective antigen discrimination. Early structural studies established basic frameworks helpful for understanding and contextualizing TCR recognition and features such as peptide specificity and MHC restriction. However, the growing TCR structural database and studies launched from structural work continue to reveal exceptions to common assumptions and simplifications derived from earlier work. Here we explore our evolving understanding of TCR recognition, illustrating how structural and biophysical investigations regularly uncover complex phenomena that push against paradigms and expand our understanding of how TCRs bind to and discriminate between peptide/MHC complexes. We discuss the implications of these findings for basic, translational, and predictive immunology, including the challenges in accounting for the inherent adaptability, flexibility, and occasional biophysical sloppiness that characterize TCR recognition.

Limits on inferring T cell specificity from partial information

A key challenge in molecular biology is to decipher the mapping of protein sequence to function. To perform this mapping requires the identification of sequence features most informative about function. Here, we quantify the amount of information (in bits) that T cell receptor (TCR) sequence features provide about antigen specificity. We identify informative features by their degree of conservation among antigen-specific receptors relative to null expectations. We find that TCR specificity synergistically depends on the hypervariable regions of both receptor chains, with a degree of synergy that strongly depends on the ligand. Using a coincidence-based approach to measuring information enables us to directly bound the accuracy with which TCR specificity can be predicted from partial matches to reference sequences. We anticipate that our statistical framework will be of use for developing machine learning models for TCR specificity prediction and for optimizing TCRs for cell therapies. The proposed coincidence-based information measures might find further applications in bounding the performance of pairwise classifiers in other fields.

An amphiregulin reporter mouse enables transcriptional and clonal expansion analysis of reparative lung Treg cells

Regulatory T (Treg) cells are known to play critical roles in tissue repair via provision of growth factors such as amphiregulin (Areg). Areg-producing Treg cells have previously been difficult to study because of an inability to isolate live Areg-producing cells. In this report, we created a novel reporter mouse to detect Areg expression in live cells ( Areg Thy1.1 ). We employed influenza A and bleomycin models of lung damage to sort Areg-producing and -non-producing Treg cells for transcriptomic analyses. Single cell RNA-seq revealed distinct subpopulations of Treg cells and allowed transcriptomic comparisons of damage-induced populations. Single cell TCR sequencing showed that Treg cell clonal expansion is biased towards Areg-producing Treg cells, and largely occurs within damage-induced subgroups. Gene module analysis revealed functional divergence of Treg cells into immunosuppression-oriented and tissue repair-oriented groups, leading to identification of candidate receptors for induction of repair activity in Treg cells. We tested these using an ex vivo assay for Treg cell-mediated tissue repair, identifying 4-1BB agonism as a novel mechanism for reparative activity induction. Overall, we demonstrate that the Areg Thy1.1 mouse is a promising tool for investigating tissue repair activity in leukocytes.

Chronic infection control relies on T cells with lower foreign antigen binding strength generated by N-nucleotide diversity

The breadth of pathogens to which T cells can respond is determined by the T cell receptors (TCRs) present in an individual's repertoire. Although more than 90% of the sequence diversity among TCRs is generated by terminal deoxynucleotidyl transferase (TdT)-mediated N-nucleotide addition during V(D)J recombination, the benefit of TdT-altered TCRs remains unclear. Here, we computationally and experimentally investigated whether TCRs with higher N-nucleotide diversity via TdT make distinct contributions to acute or chronic pathogen control specifically through the inclusion of TCRs with lower antigen binding strengths (i.e., lower reactivity to peptide-major histocompatibility complex (pMHC)). When T cells with high pMHC reactivity have a greater propensity to become functionally exhausted than those of low pMHC reactivity, our computational model predicts a shift toward T cells with low pMHC reactivity over time during chronic, but not acute, infections. This TCR-affinity shift is critical, as the elimination of T cells with lower pMHC reactivity in silico substantially increased the time to clear a chronic infection, while acute infection control remained largely unchanged. Corroborating an affinity-centric benefit for TCR diversification via TdT, we found evidence that TdT-deficient TCR repertoires possess fewer T cells with weaker pMHC binding strengths in vivo and showed that TdT-deficient mice infected with a chronic, but not an acute, viral pathogen led to protracted viral clearance. In contrast, in the case of a chronic fungal pathogen where T cells fail to clear the infection, both our computational model and experimental data showed that TdT-diversified TCR repertoires conferred no additional protection to the hosts. Taken together, our in silico and in vivo data suggest that TdT-mediated TCR diversity is of particular benefit for the eventual resolution of prolonged pathogen replication through the inclusion of TCRs with lower foreign antigen binding strengths.