RACER-m leverages structural features for sparse T cell specificity prediction.

Reliable prediction of T cell specificity against antigenic signatures is a formidable task, complicated by the immense diversity of T cell receptor and antigen sequence space and the resulting limited availability of training sets for inferential models. Recent modeling efforts have demonstrated the advantage of incorporating structural information to overcome the need for extensive training sequence data, yet disentangling the heterogeneous TCR-antigen interface to accurately predict MHC-allele-restricted TCR-peptide interactions has remained challenging. Here, we present RACER-m, a coarse-grained structural model leveraging key biophysical information from the diversity of publicly available TCR-antigen crystal structures. Explicit inclusion of structural content substantially reduces the required number of training examples and maintains reliable predictions of TCR-recognition specificity and sensitivity across diverse biological contexts. Our model capably identifies biophysically meaningful point-mutant peptides that affect binding affinity, distinguishing its ability in predicting TCR specificity of point-mutants from alternative sequence-based methods. Its application is broadly applicable to studies involving both closely related and structurally diverse TCR-peptide pairs.

Enhanced T cell receptor specificity through framework engineering.

Development of T cell receptors (TCRs) as immunotherapeutics is hindered by inherent TCR cross-reactivity. Engineering more specific TCRs has proven challenging, as unlike antibodies, improving TCR affinity does not usually improve specificity. Although various protein design approaches have been explored to surmount this, mutations in TCR binding interfaces risk broadening specificity or introducing new reactivities. Here we explored if TCR specificity could alternatively be tuned through framework mutations distant from the interface. Studying the 868 TCR specific for the HIV SL9 epitope presented by HLA-A2, we used deep mutational scanning to identify a framework mutation above the mobile CDR3ß loop. This glycine to proline mutation had no discernable impact on binding affinity or functional avidity towards the SL9 epitope but weakened recognition of SL9 escape variants and led to fewer responses in a SL9-derived positional scanning library. In contrast, an interfacial mutation near the tip of CDR3α that also did not impact affinity or functional avidity towards SL9 weakened specificity. Simulations indicated that the specificity-enhancing mutation functions by reducing the range of loop motions, limiting the ability of the TCR to adjust to different ligands. Although our results are likely to be TCR dependent, using framework engineering to control TCR loop motions may be a viable strategy for improving the specificity of TCR-based immunotherapies.

In-depth analysis of human virus-specific CD8+ T cells delineates unique phenotypic signatures for T cell specificity prediction.

Following viral infection, the human immune system generates CD8+ T cell responses to virus antigens that differ in specificity, abundance, and phenotype. A characterization of virus-specific T cell responses allows one to assess infection history and to understand its contribution to protective immunity. Here, we perform in-depth profiling of CD8+ T cells binding to CMV-, EBV-, influenza-, and SARS-CoV-2-derived antigens in peripheral blood samples from 114 healthy donors and 55 cancer patients using high-dimensional mass cytometry and single-cell RNA sequencing. We analyze over 500 antigen-specific T cell responses across six different HLA alleles and observed unique phenotypes of T cells specific for antigens from different virus categories. Using machine learning, we extract phenotypic signatures of antigen-specific T cells, predict virus specificity for bulk CD8+ T cells, and validate these predictions, suggesting that machine learning can be used to accurately predict antigen specificity from T cell phenotypes.

A transfer-learning approach to predict antigen immunogenicity and T-cell receptor specificity.

Antigen immunogenicity and the specificity of binding of T-cell receptors to antigens are key properties underlying effective immune responses. Here we propose diffRBM, an approach based on transfer learning and Restricted Boltzmann Machines, to build sequence-based predictive models of these properties. DiffRBM is designed to learn the distinctive patterns in amino-acid composition that, on the one hand, underlie the antigen's probability of triggering a response, and on the other hand the T-cell receptor's ability to bind to a given antigen. We show that the patterns learnt by diffRBM allow us to predict putative contact sites of the antigen-receptor complex. We also discriminate immunogenic and non-immunogenic antigens, antigen-specific and generic receptors, reaching performances that compare favorably to existing sequence-based predictors of antigen immunogenicity and T-cell receptor specificity.

Large-scale template-based structural modeling of T-cell receptors with known antigen specificity reveals complementarity features.

Introduction: T-cell receptor (TCR) recognition of foreign peptides presented by the major histocompatibility complex (MHC) initiates the adaptive immune response against pathogens. While a large number of TCR sequences specific to different antigenic peptides are known to date, the structural data describing the conformation and contacting residues for TCR-peptide-MHC complexes is relatively limited. In the present study we aim to extend and analyze the set of available structures by performing highly accurate template-based modeling of these complexes using TCR sequences with known specificity. Methods: Identification of CDR3 sequences and their further clustering, based on available spatial structures, V- and J-genes of corresponding T-cell receptors, and epitopes, was performed using the VDJdb database. Modeling of the selected CDR3 loops was conducted using a stepwise introduction of single amino acid substitutions to the template PDB structures, followed by optimization of the TCR-peptide-MHC contacting interface using the Rosetta package applications. Statistical analysis and recursive feature elimination procedures were carried out on computed energy values and properties of contacting amino acid residues between CDR3 loops and peptides, using R. Results: Using the set of 29 complex templates (including a template with SARS-CoV-2 antigen) and 732 specificity records, we built a database of 1585 model structures carrying substitutions in either TCRα or TCRß chains with some models representing the result of different mutation pathways for the same final structure. This database allowed us to analyze features of amino acid contacts in TCR - peptide interfaces that govern antigen recognition preferences and interpret these interactions in terms of physicochemical properties of interacting residues. Conclusion: Our results provide a methodology for creating high-quality TCR-peptide-MHC models for antigens of interest that can be utilized to predict TCR specificity.

Can we predict T cell specificity with digital biology and machine learning?

Recent advances in machine learning and experimental biology have offered breakthrough solutions to problems such as protein structure prediction that were long thought to be intractable. However, despite the pivotal role of the T cell receptor (TCR) in orchestrating cellular immunity in health and disease, computational reconstruction of a reliable map from a TCR to its cognate antigens remains a holy grail of systems immunology. Current data sets are limited to a negligible fraction of the universe of possible TCR-ligand pairs, and performance of state-of-the-art predictive models wanes when applied beyond these known binders. In this Perspective article, we make the case for renewed and coordinated interdisciplinary effort to tackle the problem of predicting TCR-antigen specificity. We set out the general requirements of predictive models of antigen binding, highlight critical challenges and discuss how recent advances in digital biology such as single-cell technology and machine learning may provide possible solutions. Finally, we describe how predicting TCR specificity might contribute to our understanding of the broader puzzle of antigen immunogenicity.

Discrete LAT condensates encode antigen information from single pMHC:TCR binding events.

LAT assembly into a two-dimensional protein condensate is a prominent feature of antigen discrimination by T cells. Here, we use single-molecule imaging techniques to resolve the spatial position and temporal duration of each pMHC:TCR molecular binding event while simultaneously monitoring LAT condensation at the membrane. An individual binding event is sufficient to trigger a LAT condensate, which is self-limiting, and neither its size nor lifetime is correlated with the duration of the originating pMHC:TCR binding event. Only the probability of the LAT condensate forming is related to the pMHC:TCR binding dwell time. LAT condenses abruptly, but after an extended delay from the originating binding event. A LAT mutation that facilitates phosphorylation at the PLC-γ1 recruitment site shortens the delay time to LAT condensation and alters T cell antigen specificity. These results identify a function for the LAT protein condensation phase transition in setting antigen discrimination thresholds in T cells.

CD44v6 chimeric antigen receptor T cell specificity towards AML with FLT3 or DNMT3A mutations.

BACKGROUND: Chimeric antigen receptor T-cell (CAR-T) therapy for acute myeloid leukaemia (AML) has thus far been elusive, in part due to target restriction and phenotypic heterogeneity of AML cells. Mutations of the FMS-like tyrosine kinase 3 (FLT3) and DNA methyltransferase 3A (DNMT3A) genes are common driver mutations that present with a poor prognosis in AML patients. We found that AML patients with FLT3 or DNMT3A mutations had higher expression of CD44 isoform 6 (CD44v6) compared to normal specimens. Therefore, we intended to demonstrate CD44v6 could be a specific option for AML with FLT3 or DNMT3A mutations. METHODS: Internal tandem duplication (ITD) mutations of FLT3 (FLT3/ITD) knock-in clone and DNMT3A-R882H mutant clones of SKM-1 cells were generated using CRISPR/Cas9 and lentiviral transfection, respectively. CD44v6 CAR-T cells were constructed by transfecting T cells with lentivirus containing CD44v6 CAR. CD44v6 expression in AML cell lines, AML patients and healthy donors was evaluated by flow cytometry. DNA methylation assays were used to analyse the mechanisms of FLT3 and DNMT3A mutations affecting CD44v6 expression. RESULTS: Aberrant overexpression of CD44v6 was observed in AML cell lines with FLT3 or DNMT3A mutations compared to the wild-type SKM-1 or K562 cells. AML patients with FLT3 or DNMT3A mutations had higher expression of CD44v6 compared to normal specimens. Then we constructed CD44v6 CAR-T cells and found that CD44v6 CAR-T specifically lysed CD44v6+ cells, accompanied by cytokines release. No significant killing effect was observed from CD44v6- AML cells and normal cells after co-culture with CD44v6 CAR-T. These results were also observed in vivo. Furthermore, we found that FLT3 or DNMT3A mutations induced CD44v6 overexpression by downregulating the CpG methylation of CD44 promoter. CONCLUSIONS: Collectively, CD44v6 is a promising target of CAR-T for AML patients with FLT3 or DNMT3A mutations.

Resolving SARS-CoV-2 CD4+ T cell specificity via reverse epitope discovery.

The current strategy to detect immunodominant T cell responses focuses on the antigen, employing large peptide pools to screen for functional cell activation. However, these approaches are labor and sample intensive and scale poorly with increasing size of the pathogen peptidome. T cell receptors (TCRs) recognizing the same epitope frequently have highly similar sequences, and thus, the presence of large sequence similarity clusters in the TCR repertoire likely identify the most public and immunodominant responses. Here, we perform a meta-analysis of large, publicly available single-cell and bulk TCR datasets from severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)-infected individuals to identify public CD4+ responses. We report more than 1,200 αßTCRs forming six prominent similarity clusters and validate histocompatibility leukocyte antigen (HLA) restriction and epitope specificity predictions for five clusters using transgenic T cell lines. Collectively, these data provide information on immunodominant CD4+ T cell responses to SARS-CoV-2 and demonstrate the utility of the reverse epitope discovery approach.

Structural basis of T cell receptor specificity and cross-reactivity of two HLA-DQ2.5-restricted gluten epitopes in celiac disease.

Celiac disease is a T cell-mediated chronic inflammatory condition often characterized by human leukocyte antigen (HLA)-DQ2.5 molecules presenting gluten epitopes derived from wheat, barley, and rye. Although some T cells exhibit cross-reactivity toward distinct gluten epitopes, the structural basis underpinning such cross-reactivity is unclear. Here, we investigated the T-cell receptor specificity and cross-reactivity of two immunodominant wheat gluten epitopes, DQ2.5-glia-α1a (PFPQPELPY) and DQ2.5-glia-ω1 (PFPQPEQPF). We show by surface plasmon resonance that a T-cell receptor alpha variable (TRAV) 4+-T-cell receptor beta variable (TRBV) 29-1+ TCR bound to HLA-DQ2.5-glia-α1a and HLA-DQ2.5-glia-ω1 with similar affinity, whereas a TRAV4- (TRAV9-2+) TCR recognized HLA-DQ2.5-glia-ω1 only. We further determined the crystal structures of the TRAV4+-TRBV29-1+ TCR bound to HLA-DQ2.5-glia-α1a and HLA-DQ2.5-glia-ω1, as well as the structure of an epitope-specific TRAV9-2+-TRBV7-3+ TCR-HLA-DQ2.5-glia-ω1 complex. We found that position 7 (p7) of the DQ2.5-glia-α1a and DQ2.5-glia-ω1 epitopes made very limited contacts with the TRAV4+ TCR, thereby explaining the TCR cross-reactivity across these two epitopes. In contrast, within the TRAV9-2+ TCR-HLA-DQ2.5-glia-ω1 ternary complex, the p7-Gln was situated in an electrostatic pocket formed by the hypervariable CDR3ß loop of the TCR and Arg70ß from HLA-DQ2.5, a polar network which would not be supported by the p7-Leu residue of DQ2.5-glia-α1a. In conclusion, we provide additional insights into the molecular determinants of TCR specificity and cross-reactivity to two closely-related epitopes in celiac disease.

A blend of broadly-reactive and pathogen-selected Vγ4 Vδ1 T cell receptors confer broad bacterial reactivity of resident memory γδ T cells.

Although murine γδ T cells are largely considered innate immune cells, they have recently been reported to form long-lived memory populations. Much remains unknown about the biology and specificity of memory γδ T cells. Here, we interrogated intestinal memory Vγ4 Vδ1 T cells generated after foodborne Listeria monocytogenes (Lm) infection to uncover an unanticipated complexity in the specificity of these cells. Deep TCR sequencing revealed that a subset of non-canonical Vδ1 clones are selected by Lm infection, consistent with antigen-specific clonal expansion. Ex vivo stimulations and in vivo heterologous challenge infections with diverse pathogenic bacteria revealed that Lm-elicited memory Vγ4 Vδ1 T cells are broadly reactive. The Vγ4 Vδ1 T cell recall response to Lm, Salmonella enterica serovar Typhimurium (STm) and Citrobacter rodentium was largely mediated by the γδTCR as internalizing the γδTCR prevented T cell expansion. Both broadly-reactive canonical and pathogen-selected non-canonical Vδ1 clones contributed to memory responses to Lm and STm. Interestingly, some non-canonical γδ T cell clones selected by Lm infection also responded after STm infection, suggesting some level of cross-reactivity. These findings underscore the promiscuous nature of memory γδ T cells and suggest that pathogen-elicited memory γδ T cells are potential targets for broad-spectrum anti-infective vaccines.

Failures in thymus medulla regeneration during immune recovery cause tolerance loss and prime recipients for auto-GVHD.

Bone marrow transplantation (BMT) is a widely used therapy for blood cancers and primary immunodeficiency. Following transplant, the thymus plays a key role in immune reconstitution by generating a naive αßT cell pool from transplant-derived progenitors. While donor-derived thymopoiesis during the early post-transplant period is well studied, the ability of the thymus to synchronize T cell development with essential tolerance mechanisms is poorly understood. Using a syngeneic mouse transplant model, we analyzed T cell recovery alongside the regeneration and function of intrathymic microenvironments. We report a specific and prolonged failure in the post-transplant recovery of medullary thymic epithelial cells (mTECs). This manifests as loss of medulla-dependent tolerance mechanisms, including failures in Foxp3+ regulatory T cell development and formation of the intrathymic dendritic cell pool. In addition, defective negative selection enables escape of self-reactive conventional αßT cells that promote autoimmunity. Collectively, we show that post-transplant T cell recovery involves an uncoupling of thymopoiesis from thymic tolerance, which results in autoimmune reconstitution caused by failures in thymic medulla regeneration.

CD8+ T Cells Expressing an HLA-DR1 Chimeric Antigen Receptor Target Autoimmune CD4+ T Cells in an Antigen-Specific Manner and Inhibit the Development of Autoimmune Arthritis.

Ag-specific immunotherapy is a long-term goal for the treatment of autoimmune diseases; however developing a means of therapeutically targeting autoimmune T cells in an Ag-specific manner has been difficult. Through the engineering of an HLA-DR1 chimeric Ag receptor (CAR), we have produced CD8+ CAR T cells that target CD4+ T cells in an Ag-specific manner and tested their ability to inhibit the development of autoimmune arthritis in a mouse model. The DR1 CAR molecule was engineered to contain CD3ζ activation and CD28 signaling domains and a covalently linked autoantigenic peptide from type II collagen (CII; DR1-CII) to provide specificity for targeting the autoimmune T cells. Stimulation of the DR1-CII CAR T cells by an anti-DR Ab induced cytokine production, indicating that the DR1-CAR functions as a chimeric molecule. In vitro CTL assays using cloned CD4+ T cells as target cells demonstrated that the DR1-CII CAR T cells efficiently recognize and kill CD4+ T cells that are specific for the CII autoantigen. The CTL function was highly specific, as no killing was observed using DR1-restricted CD4+ T cells that recognize other Ags. When B6.DR1 mice, in which autoimmune arthritis had been induced, were treated with the DR1-CII CAR T cells, the CII-specific autoimmune CD4+ T cell response was significantly decreased, autoantibody production was suppressed, and the incidence and severity of the autoimmune arthritis was diminished. These data demonstrate that HLA-DR CAR T cells have the potential to provide a highly specific therapeutic approach for the treatment of autoimmune disease.

TCR signal strength defines distinct mechanisms of T cell dysfunction and cancer evasion.

T cell receptor (TCR) signal strength is a key determinant of T cell responses. We developed a cancer mouse model in which tumor-specific CD8 T cells (TST cells) encounter tumor antigens with varying TCR signal strength. High-signal-strength interactions caused TST cells to up-regulate inhibitory receptors (IRs), lose effector function, and establish a dysfunction-associated molecular program. TST cells undergoing low-signal-strength interactions also up-regulated IRs, including PD1, but retained a cell-intrinsic functional state. Surprisingly, neither high- nor low-signal-strength interactions led to tumor control in vivo, revealing two distinct mechanisms by which PD1hi TST cells permit tumor escape; high signal strength drives dysfunction, while low signal strength results in functional inertness, where the signal strength is too low to mediate effective cancer cell killing by functional TST cells. CRISPR-Cas9-mediated fine-tuning of signal strength to an intermediate range improved anti-tumor activity in vivo. Our study defines the role of TCR signal strength in TST cell function, with important implications for T cell-based cancer immunotherapies.

Engineering Cancer Antigen-Specific T Cells to Overcome the Immunosuppressive Effects of TGF-ß.

Adoptive T cell therapy with T cells expressing affinity-enhanced TCRs has shown promising results in phase 1/2 clinical trials for solid and hematological tumors. However, depth and durability of responses to adoptive T cell therapy can suffer from an inhibitory tumor microenvironment. A common immune-suppressive agent is TGF-ß, which is secreted by tumor cells and cells recruited to the tumor. We investigated whether human T cells could be engineered to be resistant to inhibition by TGF-ß. Truncating the intracellular signaling domain from TGF-ß receptor (TGFßR) II produces a dominant-negative receptor (dnTGFßRII) that dimerizes with endogenous TGFßRI to form a receptor that can bind TGF-ß but cannot signal. We previously generated specific peptide enhanced affinity receptor TCRs recognizing the HLA-A*02-restricted peptides New York esophageal squamous cell carcinoma 1 (NY-ESO-1)157-165/l-Ag family member-1A (TCR: GSK3377794, formerly NY-ESO-1c259) and melanoma Ag gene A10254-262 (TCR: ADP-A2M10, formerly melanoma Ag gene A10c796). In this article, we show that exogenous TGF-ß inhibited in vitro proliferation and effector functions of human T cells expressing these first-generation high-affinity TCRs, whereas inhibition was reduced or abolished in the case of second-generation TCRs coexpressed with dnTGFßRII (e.g., GSK3845097). TGF-ß isoforms and a panel of TGF-ß-associated genes are overexpressed in a range of cancer indications in which NY-ESO-1 is commonly expressed, particularly in synovial sarcoma. As an example, immunohistochemistry/RNAscope identified TGF-ß-positive cells close to T cells in tumor nests and stroma, which had low frequencies of cells expressing IFN-γ in a non-small cell lung cancer setting. Coexpression of dnTGFßRII may therefore improve the efficacy of TCR-transduced T cells.

Combination Immune Checkpoint Blockade Enhances IL-2 and CD107a Production from HIV-Specific T Cells Ex Vivo in People Living with HIV on Antiretroviral Therapy.

In people with HIV (PWH) on antiretroviral therapy (ART), immune dysfunction persists, including elevated expression of immune checkpoint (IC) proteins on total and HIV-specific T cells. Reversing immune exhaustion is one strategy to enhance the elimination of HIV-infected cells that persist in PWH on ART. We aimed to evaluate whether blocking CTL-associated protein 4 (CTLA-4), programmed cell death protein 1 (PD-1), T cell Ig domain and mucin domain 3 (TIM-3), T cell Ig and ITIM domain (TIGIT) and lymphocyte activation gene-3 (LAG-3) alone or in combination would enhance HIV-specific CD4+ and CD8+ T cell function ex vivo. Intracellular cytokine staining was performed using human PBMCs from PWH on ART (n = 11) and expression of CD107a, IFN-γ, TNF-α, and IL-2 was quantified with HIV peptides and Abs to IC. We found the following: 1) IC blockade enhanced the induction of CD107a and IL-2 but not IFN-γ and TNF-α in response to Gag and Nef peptides; 2) the induction of CD107a and IL-2 was greatest with multiple combinations of two Abs; and 3) Abs to LAG-3, CTLA-4, and TIGIT in combinations showed synergistic induction of IL-2 in HIV-specific CD8+ and CD107a and IL-2 production in HIV-specific CD4+ and CD8+ T cells. These results demonstrate that the combination of Abs to LAG-3, CTLA-4, or TIGIT can increase the frequency of cells expressing CD107a and IL-2 that associated with cytotoxicity and survival of HIV-specific CD4+ and CD8+ T cells in PWH on ART. These combinations should be further explored for an HIV cure.

Conduit CAR: Redirecting CAR T-Cell Specificity with A Universal and Adaptable Bispecific Antibody Platform.

The success of chimeric antigen receptor (CAR) T-cell therapy against hematologic malignancies has altered the treatment paradigm for patients with these diseases. Nevertheless, the occurrence of relapse due to antigen escape or heterogeneous antigen expression on tumors remains a challenge for first-generation CAR T-cell therapies as only a single tumor antigen can be targeted. To address this limitation and to add a further level of tunability and control to CAR T-cell therapies, adapter or universal CAR T-cell approaches use a soluble mediator to bridge CAR T cells with tumor cells. Adapter CARs allow simultaneous or sequential targeting of multiple tumor antigens, control of immune synapse geometry, dose control, and the potential for improved safety. Herein, we described a novel CAR T-cell adapter platform that relies on a bispecific antibody (BsAb) targeting both a tumor antigen and the GGGGS (G4S) linker commonly used in single-chain Fv (ScFv) domains expressed on CAR T-cell surfaces. We demonstrated that the BsAb can bridge CAR T cells to tumor cells and potentiate CAR T-cell activation, proliferation, and tumor cell cytolysis. The cytolytic activity of CAR T-cells was redirected to different tumor antigens by changing the BsAb in a dose-dependent manner. This study highlights the potential of G4S-displaying CAR T cells to be redirected to engage alternative tumor-associated antigens (TAA). Significance: New approaches are needed to address relapsed/refractory disease and manage potential toxicities associated with CAR T-cell therapy. We describe an adapter CAR approach to redirect CAR T cells to engage novel TAA-expressing cells via a BsAb targeting a linker present on many clinical CAR T-cell therapeutics. We anticipate the use of such adapters could increase CAR T-cell efficacy and reduce potential CAR-associated toxicities.

HIV-1-Specific CD11c+ CD8+ T Cells Display Low PD-1 Expression and Strong Anti-HIV-1 Activity.

Exhaustion of HIV-1-specific CD8+ T cells prevents optimal control of HIV-1 infection. Identifying unconventional CD8+ T cell subsets to effectively control HIV-1 replication is vital. In this study, the role of CD11c+ CD8+ T cells during HIV-1 infection was evaluated. The frequencies of CD11c+ CD8+ T cells significantly increased and were negatively correlated with viral load in HIV-1-infected treatment-naïve patients. HIV-1-specific cells were enriched more in CD11c+ CD8+ T cells than in CD11c- CD8+ T cells, which could be induced by HIV-1-derived overlapping peptides, marking an HIV-1-specific CD8+ T cell population. This subset expressed higher levels of activating markers (CD38 and HLA-DR), cytotoxic markers (granzyme B, perforin, and CD107a), and cytokines (IL-2 and TNF-α), with lower levels of PD-1 compared to the CD11c- CD8+ T cell subset. In vitro analysis verified that CD11c+ CD8+ T cells displayed a stronger HIV-1-specific killing capacity than the CD11c- counterparts. These findings indicate that CD11c+ CD8+ T cells have potent immunotherapeutic efficacy in controlling HIV-1 infection.

Single-Cell Analysis of Antigen-Specific CD8+ T-Cell Transcripts Reveals Profiles Specific to mRNA or Adjuvanted Protein Vaccines.

CD8+ T cells play a key role in mediating protective immunity after immune challenges such as infection or vaccination. Several subsets of differentiated CD8+ T cells have been identified, however, a deeper understanding of the molecular mechanism that underlies T-cell differentiation is lacking. Conventional approaches to the study of immune responses are typically limited to the analysis of bulk groups of cells that mask the cells' heterogeneity (RNA-seq, microarray) and to the assessment of a relatively limited number of biomarkers that can be evaluated simultaneously at the population level (flow and mass cytometry). Single-cell analysis, on the other hand, represents a possible alternative that enables a deeper characterization of the underlying cellular heterogeneity. In this study, a murine model was used to characterize immunodominant hemagglutinin (HA533-541)-specific CD8+ T-cell responses to nucleic- and protein-based influenza vaccine candidates, using single-cell sorting followed by transcriptomic analysis. Investigation of single-cell gene expression profiles enabled the discovery of unique subsets of CD8+ T cells that co-expressed cytotoxic genes after vaccination. Moreover, this method enabled the characterization of antigen specific CD8+ T cells that were previously undetected. Single-cell transcriptome profiling has the potential to allow for qualitative discrimination of cells, which could lead to novel insights on biological pathways involved in cellular responses. This approach could be further validated and allow for more informed decision making in preclinical and clinical settings.