Understanding T-cell receptor (TCR) specificity is not only essential for fundamental research, but could open up novel avenues for diagnostics, cancer immunotherapy, and the targeted treatment of autoimmune diseases. The immune system responds to challenges through groups of T-cells with similar TCR sequences. In recent years, searching for TCRs with an enrichment of similar sequences - neighbors - in a TCR repertoire has become a standard procedure for antigen-specific TCR identification. This study provides a systematic comparison of computational algorithms-ALICE, TCRNET, GLIPH2, and tcrdist3-that leverage neighborhood enrichment for antigen-specific TCR identification. Using published murine datasets from Lymphocytic choriomeningitis virus (LCMV) infection and novel datasets from Sputnik V vaccination and Mycobacterium tuberculosis (Mtb) infection, we evaluated the performance of these algorithms. To facilitate reproducible analysis, we developed TCRgrapher, an R library that integrates these pipelines into a user-friendly framework. TCRgrapher enables efficient identification of antigen-specific TCRs from single repertoire snapshots and supports flexible parameter customization. Our comparative analysis revealed that ALICE and TCRNET consistently outperformed GLIPH2 and tcrdist3 across most datasets, achieving higher area under precision-recall curve. While murine datasets provide valuable insights into algorithm performance, caution is advised when extrapolating these results to other species or different experimental conditions. TCRgrapher is freely available on GitHub (https://github.com/KseniaMIPT/tcrgrapher), offering researchers a robust tool for investigating TCR specificity and advancing immunological studies.

• Keywords
• TCR repertoire, TCR specificity, immunoinformatics, software,
• MeSH
• Algorithms * MeSH
• Antigens * immunology   MeSH
• Humans MeSH
• Mycobacterium tuberculosis immunology   MeSH
• Mice MeSH
• Receptors, Antigen, T-Cell * immunology   genetics   MeSH
• Lymphocytic choriomeningitis virus immunology   MeSH
• Computational Biology * methods   MeSH
• Animals MeSH
• Check Tag
• Humans MeSH
• Mice MeSH
• Animals MeSH
• Publication type
• Journal Article MeSH
• Names of Substances
• Antigens * MeSH
• Receptors, Antigen, T-Cell * MeSH

T-cell engagers represent a transformative approach to cancer immunotherapy leveraging bispecific and multispecific antibody constructs to redirect T-cell cytotoxicity toward malignant cells. These molecules bridge T cells and tumor cells by simultaneously binding CD3 on T cells and tumor-associated antigens on cancer cells, thereby enabling precise immune targeting even in immunologically "cold" tumors. Recent advancements include conditional T-cell engagers activated by tumor microenvironment proteases to minimize off-tumor toxicity as well as T-cell receptor-based engagers targeting intracellular antigens via MHC presentation. Clinical successes, such as Kimmtrak in metastatic uveal melanoma, underscore good potential of these modalities, while challenges persist in the management of cytokine release syndrome, neurotoxicity, and tumor resistance. Emerging multispecific engagers are aimed at enhancing efficacy via incorporation of costimulatory signals, thus offering a promising trajectory for next-generation immunotherapies. T-cell engagers are also gaining attention in the treatment of autoimmune disorders, where they can be designed to selectively modulate pathogenic immune responses. By targeting autoreactive T or B cells, T-cell engagers hold promise for restoring immune tolerance in such conditions as HLA-B*27-associated autoimmunity subtypes, multiple sclerosis, rheumatoid arthritis, and type 1 diabetes mellitus. Engineering strategies that incorporate inhibitory receptors or tissue-specific antigens may further refine T-cell engagers' therapeutic potential in autoimmunity, by minimizing systemic immunosuppression while preserving immune homeostasis.

• Keywords
• T-cell engager, autoimmunity, gene engineering, immunotherapy, soluble TCR,
• MeSH
• Immunotherapy * methods   MeSH
• Humans MeSH
• Tumor Microenvironment immunology   MeSH
• Neoplasms * immunology   therapy   MeSH
• Antibodies, Bispecific therapeutic use   immunology   MeSH
• Receptors, Antigen, T-Cell immunology   metabolism   MeSH
• T-Lymphocytes * immunology   metabolism   MeSH
• Animals MeSH
• Check Tag
• Humans MeSH
• Animals MeSH
• Publication type
• Journal Article MeSH
• Review MeSH
• Names of Substances
• Antibodies, Bispecific MeSH
• Receptors, Antigen, T-Cell MeSH

INTRODUCTION: The functional programs of CD4+ T helper (Th) cell clones play a central role in shaping immune responses to different challenges. While advances in single-cell RNA sequencing (scRNA-Seq) have significantly improved our understanding of the diversity of Th cells, the relationship between scRNA-Seq clusters and the traditionally characterized Th subsets remains ambiguous. METHODS: In this study, we introduce TCR-Track, a method leveraging immune repertoire data to map phenotypically sorted Th subsets onto scRNA-Seq profiles. RESULTS AND DISCUSSION: This approach accurately positions the Th1, Th1-17, Th17, Th22, Th2a, Th2, T follicular helper (Tfh), and regulatory T-cell (Treg) subsets, outperforming mapping based on CITE-Seq. Remarkably, the mapping is tightly focused on specific scRNA-Seq clusters, despite 4-year interval between subset sorting and the effector CD4+ scRNA-Seq experiment. These findings highlight the intrinsic program stability of Th clones circulating in peripheral blood. Repertoire overlap analysis at the scRNA-Seq level confirms that the circulating Th1, Th2, Th2a, Th17, Th22, and Treg subsets are clonally independent. However, a significant clonal overlap between the Th1 and cytotoxic CD4+ T-cell clusters suggests that cytotoxic CD4+ T cells differentiate from Th1 clones. In addition, this study resolves a longstanding ambiguity: we demonstrate that, while CCR10+ Th cells align with a specific Th22 scRNA-Seq cluster, CCR10-CCR6+CXCR3-CCR4+ cells, typically classified as Th17, represent a mixture of bona fide Th17 cells and clonally unrelated CCR10low Th22 cells. The clear distinction between the Th17 and Th22 subsets should influence the development of vaccine- and T-cell-based therapies. Furthermore, we show that severe acute SARS-CoV-2 infection induces systemic type 1 interferon (IFN) activation of naive Th cells. An increased proportion of effector IFN-induced Th cells is associated with a moderate course of the disease but remains low in critical COVID-19 cases. Using integrated scRNA-Seq, TCR-Track, and CITE-Seq data from 122 donors, we provide a comprehensive Th scRNA-Seq reference that should facilitate further investigation of Th subsets in fundamental and clinical studies.

• Keywords
• T cell memory, Th17, Th22, cytotoxic CD4+ T cells, helper T cell subsets, immune repertoires, scRNA-Seq, scTCR-seq,
• MeSH
• Single-Cell Gene Expression Analysis MeSH
• Single-Cell Analysis * methods   MeSH
• Humans MeSH
• Sequence Analysis, RNA MeSH
• RNA-Seq * methods   MeSH
• T-Lymphocyte Subsets * immunology   MeSH
• T-Lymphocytes, Helper-Inducer * immunology   MeSH
• Check Tag
• Humans MeSH
• Publication type
• Journal Article MeSH