Immunodominant NP105-113-B*0702 cytotoxic T cell response controls viral replication and is associated with less severe COVID-19 disease

doi:10.21203/rs.3.rs-734011/v1

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Immunodominant NP105-113-B*0702 cytotoxic T cell response controls viral replication and is associated with less severe COVID-19 disease

https://doi.org/10.21203/rs.3.rs-734011/v1

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published 01 Dec, 2021

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NP_105-113-B*07:02 specific CD8⁺T-cell responses are considered among the most dominant in SARS-CoV-2-infected individuals. We found strong association of this response with mild disease. Analysis of NP_105-113-B*07:02 specific T-cell clones and single cell sequencing were performed concurrently, with functional avidity and anti-viral efficacy assessed using an in vitro SARS-CoV-2 infection system, and were correlated with TCR usage, transcriptome signature, and disease severity (acute N=77, convalescent N=52). We demonstrated a beneficial association of NP_105-113-B*07:02specific T-cells in COVID-19 disease progression, linked with expansion of T-cell precursors, high functional avidity and anti-viral effector function. Broad immune memory pools were narrowed post-infection but NP_105-113-B*07:02 specific T-cells were maintained 6 months after infection with preserved anti-viral efficacy to the SARS-CoV-2 Victoria strain, as well as new Alpha, Beta and Gamma variants. Our data shows that NP_105-113-B*07:02 specific T-cell responses associate with mild disease and high anti-viral efficacy, pointing to inclusion for future vaccine design.

Virology

Immunology

Infectious Diseases

SARS-CoV-2

COVID-19

immunology

vaccine development

CD8⁺ T cells play a well-documented role in clearing viral infections. Immunodominance is a central feature of CD8⁺ T cell responses in viral infections and understanding the nature of this response for a given infection where they are shown to be protective will be critical for the design of vaccines aiming to elicit optimal CD8⁺ T cell responses ^{1, 2}.

The role of the immunodominant cytotoxic T cell immune response in protection and potential disease pathogenesis of SARS-CoV-2 infection is currently poorly defined. We and others have identified immunodominant T cell epitopes restricted by common HLA types ^{3, 4, 5, 6}; in particular, we found multiple dominant epitopes in NP (nucleoprotein) restricted by HLA-B*07:02, B*27:05, B*40:01, A*03:01 and A*11:01. We also found that multi-functional NP and M (membrane) CD8⁺ T cell responses are associated with mild disease and NP is one of the most common targets for CD8⁺ dominant T cell responses in SARS-CoV-2 infection ³.

Among the dominant epitopes identified to date, NP_105-113-B*07:02 appears to be among the most dominant ^{3, 4, 6, 7}; notably, no variants are found within this epitope from over 300k sequences in COG-UK global sequence data alignment ⁸. This suggests that this epitope would be a good target for inclusion within an improved vaccine design, expanded to stimulate effective CD8⁺ T cell responses as well as neutralising antibodies, in order to protect against newly emergent viral strains that escape antibody responses to spike in some cases. ⁹.

Recently, Lineburg and colleagues showed a biased TRBV27 gene usage, with long CDR3β loops preferentially expressed in NP_105-113-B*07:02 specific TCRs in both unexposed and COVID-19 recovered individuals ¹⁰. This study suggested a role for cross-reactive responses in COVID-19 based on pre-existing immunity to seasonal coronaviruses or other pathogens. However, a subsequent study by Nguyen and colleagues suggested that the immunodominant NP_105-113-B*07:02 CD8⁺ T cell responses are unlikely to arise from pre-existing cross-reactive memory pools, but rather represent a high frequency of naive T cell precursors found across HLA-B*0702-expressing individuals ⁷.

In this study, we present an in-depth analysis to explore correlations between NP_105-113-B*07:02 specific T cell responses, T cell receptor (TCR) repertoires and disease severity. We saw stronger overall T cell responses in individuals recovered from severe COVID, which may be explained by high exposure to viral protein; however, we found an immunodominant epitope response (HLA-B*0702 NP_105-113 specific CD8⁺) which significantly associated with mild cases. Importantly, this epitope is one of the most dominant CD8⁺ T cell epitopes reported so far by us and others. We examined potential mechanisms of protection using single cell transcriptome analysis, and functional evaluation of expanded T cell clones bearing the same TCRs as those identified in single cell analysis. We also assessed the ability of T cell lines and clones to mount effective effector function against cells infected with live SARS-CoV-2 virus and Vaccinia virus expressing SARS-CoV-2 proteins. We found that NP_105-113-B*07:02 is the dominant NP response in HLA-B*07:02 positive patients with mild symptoms, with high frequency and higher magnitude when compared to severe cases. Single cell analysis revealed that preserved beneficial functional phenotypes are associated with protection from severe illness and have better overall anti-viral function. In addition, NP_105-113-B*07:02 specific T cells can recognise the naturally processed epitope in live virus and recombinant Vaccinia virus infected cells, which correlates with anti-viral efficacy.

NP_105-113-B*07:02 specific T cell responses are stronger in patients who have recovered from mild COVID-19 infection

In our previous study, we identified five dominant CD8⁺ epitopes targeting NP, including the most dominant epitope NP_105-113 (amino acid sequence SPRWYFYYL) restricted by HLA-B*07:02 ³. 52 individuals who recovered from COVID-19 were included in this current study, comprising 30 mild cases and 22 severe cases (including 4 with critical illness, STAR Methods). All the patients were HLA typed and 19 (36.5%) were HLA-B*07:02 positive (10 mild and 9 severe cases). We proceeded to carry out ex vivo IFN-γ ELISpot assays using HLA-B*0702 positive convalescent samples 1-3 months post infection (STAR Methods). 79% (15/19) of HLA-B*07:02 individuals showed responses to this epitope (Figure 1A), further confirming the dominance of this T cell response in our cohort. Among these responders, we found a higher proportion (60%) had recovered from mild infection (Figure 1B) and made significantly stronger responses to this epitope, compared to those who had recovered from severe disease (Figure 1C, P=0.04). We also observed that this NP_105-113-B*07:02-specific response is dominant in mild cases and makes up 60% of overall NP responses of each individual, whereas in severe cases, the proportion is substantially lower, with an average of 19.5% (Figure 1D, P=0.015). In addition, we did not find HLA-B*0702 association with disease outcome in our study cohorts (Figure 1E, 77 acute and 52 convalescent patients). Our data highlights the association of this dominant epitope-induced T cell response with mild disease outcome and provides evidence that this link is epitope specific rather than a wider allelic association with HLA-B*07:02.

Strong cytotoxicity and inhibitory receptor expression are associated with disease severity

To explore the mechanisms underlining this association, we sorted NP_105-113-B*07:02-specific T cells at a single cell level with peptide MHC-class I pentamers using flow cytometry. We performed single cell analysis using SmartSeq2 (STAR Methods) for PBMC samples from four convalescent patients, including two who recovered from mild COVID-19 infection (C-COV19-005 and C-COV19-046) and two who recovered from severe disease in early infection (C-COV19-038 and C-COV19-045). TCR sequences and transcriptomic profiles of each single cell were analysed.

We compared gene expression of 348 CD8⁺ NP-specificsorted single cells isolated from mild (N=208 from two patients) and severe cases (N=140 from two patients) by scoring expression levels of manually defined gene sets (Supplementary Table 1). Gene signatures associated with T cell cytotoxicity and inhibitory receptors were analysed and compared between severity groups (STAR Methods). We found that cells from patients who had recovered from severe COVID-19 have significantly higher cytotoxicity gene expression scores (Figure 2A, P=0.00032), with upregulation of GZMK (P=3.02E^-05) and GNLY (P=1.41E^-09) (encoding granzyme K and granulysin respectively, Figure 2B). These cells also displayed increased inhibitory receptor expression (Figure 2C, P=0.00072), such as TIGIT, CTLA4 and HAVCR2 (TIM3). This supports findings published by us and others ^{3, 11} where patients with severe COVID-19 disease are exposed to higher antigen loads, and that these cells are still present at 1 - 3 months convalescence, rather than CD8⁺ central memory T cells.

NP_105-113-B*07:02-specific T cells have a highly diverse TCR repertoire

Consistent with findings by other studies ⁷, we found that NP_105-113-B*07:02-specific T cells from our cohort show very broad TCR repertoires. The circos plots in Figure 3A show paired TCR α and β chains (V and J gene usage) from the four individuals analysed with SmartSeq2 single cell RNA-Seq. Figure 3B shows the combined TCR repertoire of all four patients represented by TCR clonotype (defined separately for each patient combining V gene and CDR3 amino acid sequence). Although the NP_105-113-specific TCR repertoire is diverse with unique pairings of Vα and Vβ genes, we observed that 15/45 (33.3%) of unique Vβ clonotypes were paired with several distinct Vα clonotypes. In contrast, there is only 1/55 (1.8%) Vα clonotype that pairs to multiple Vβ clonotypes; this highlights the importance of studying Vβ in the TCR repertoire. Further detailed TCR information can be found in Supplementary Table 2.

CDR3β sequences of HLA-B*07:02 NP_105-113-specific T cells from patients with mild COVID-19 display higher similarity to naïve precursors found in pre-pandemic individuals

Several studies have reported that pre-existing cross-reactive T cells to SARS-CoV-2 can be detected in unexposed individuals, and these T cells may have resulted from previous human seasonal coronavirus infection ^{7, 10, 12, 13}. Both studies found TCRs specific to NP_105-113-B*07:02 in SARS-CoV-2 unexposed and infected individuals. Nguyen et al. revealed that these cells are likely to be naïve. This is very different from the central/effector memory phenotype of SARS-CoV-2 specific T cells reported by ourselves and others. Overall, our data supported the notion that T cells bearing TCRs specific to NP_105-113-HLA-B*07:02 in SARS-CoV-2 unexposed individuals are thought unlikely to have resulted from previous seasonal coronavirus infection ⁷. To investigate this further, we sought to determine what role these T cells might play in the early stages of SARS-CoV-2 infection and COVID-19 disease, and if these cells contribute to the association of mild disease due to their specificity for this NP dominant epitope.

To take advantage of the results from our SmartSeq2 single cell RNA-Seq, we first compared TCR sequences from our four convalescent COVID-19 patients to pre-pandemic TCR sequences from healthy donors published by Lineburg, Nguyen and another study cohort, COMBAT ¹⁴. The COMBAT dataset represents a comprehensive multi-omic blood atlas encompassing acute patients with varying COVID-19 severity (41 mild and 36 severe), and 10 healthy volunteers (pre-pandemic), using bulk TCR sequencing and CITE-Seq, which combines single cell gene expression and cell surface protein expression. TCR sequences from the Lineburg and Nguyen datasets have been experimentally validated to be specific for the NP_105-113epitope, however for the COMBAT dataset, we used GLIPH2 analysis ¹⁵ to extract TCRs with predicted specificity to this epitope based on convergence with known NP_105-113-specific TCRs (STAR Methods).

We calculated similarity scores for CDR3β amino acid sequences between pairwise combinations of SmartSeq2 TCRs and pre-pandemic/healthy TCRs (STAR Methods). A similarity score of 1 indicates that the pair of CDR3β sequences are identical, while a score of 0 indicates complete dissimilarity. In our convalescent patient cohort, CDR3β from patients with mild disease are more similar to TCRs from pre-pandemic/healthy individuals, than those from severe patients (Figure 4A, P<2.20E^-16). To further support our findings, we looked at the proportion of TCR sequences from mild and severe patients (both acute and convalescent) that can be found in the same convergence groups as sequences from healthy donors, indicating high CDR3β similarity. Convergence groups containing TCRs from healthy donors appear to contain higher proportions of TCRs from mild cases rather than severe, signifying greater similarity between TCRs from pre-pandemic individuals and patients with mild disease (Figure 4B, P<2.2E^-16).

We were able to link predicted NP_105-113-B*07:02 TCRs with their corresponding single cell data from the COMBAT dataset (healthy and acute SARS-CoV-2 infected patients). In this way, we could extract single cell CITE-Seq information from the COMBAT dataset, subsetted specifically to cells with predicted NP_105-113specificity. Cellular subtyping of these CD8⁺ NP_105-113-B*07:02 T cells show a higher proportion of naïve T cells in one HLA-B*07:02 healthy individual compared to predominantly T effector memory subtypes in acute COVID-19 patients (N=17, Figure 4C). This reinforces the finding that only NP_105-113-B*07:02 specific T cells from acute HLA-B*07:02 positive patients are exposed to antigen and undergo T cell differentiation, whereas NP_105-113-specific T cells in pre-pandemic individuals are naïve precursors rather than memory cells from prior cross-reactive infection.

NP_105-113-B*07:02-specific T cells have a broad range of functional avidity and high functional avidity is associated with clonotype expansion in mild disease

In parallel with single cell sorting for SmartSeq2, we also sorted, cloned and expanded NP_105-113-B*07:02-specific T cells from the same convalescent COVID-19 patients in vitro ^{16, 17} to obtain pure clonal T cell populations ^{16, 17}. We sequenced TCRs from each T cell clone with paired TCR α chain and β chain of each clone listed in Supplementary Table 3. When comparing the TCR sequences between T cell clones and ex vivo single cells, in vitro expanded T cell clones are a good representation for the T cells isolated for ex vivo single cell analysis, with expanded TCRs from ex vivo single cells present as dominant TCRs from the T cell clones (Figure S1A).

To provide a link between T cell clones and single cell data by their respective TCR sequences, we divided all the T cells, including T cell clones and single cells from SmartSeq2, into 18 groups according to their unique TRBVβ gene usage and CDR3β sequence (Table 1). T cell functional avidity was measured by IFN-γ ELISPOT and calculated by EC50 (Figure S1B, Supplementary Table 4). We found evidence for low and high functional avidity groups (Figure 5A) based on the EC50 of T cell clones, with EC50 < 0.11 considered as high avidity, and those with EC50 > 0.11 are low avidity T cells (STAR Methods). We then aggregated RNA counts from single cells (pseudobulk) to compare differences in gene expression between the two avidity groups. Although there were only 7 significantly differentially expressed genes (Figure 5B), possibly as a result of small samples sizes and patient variation, differentially expressed genes of note upregulated in high functional avidity cells include IL10RA, PARK7 and LTA4H. The interaction of IL-10 with IL10RA expressed on CD8⁺ T cells has been reported to directly decrease CD8⁺ T cell antigen sensitivity in patients with chronic hepatitis C (HCV) infection ¹⁸, while PARK7 is promotes survival and maintains cellular homeostasis in the setting of intracellular stress ¹⁹. LTA4H is an enzyme with known potent anti-inflammatory activity, and functions as an aminopeptidase to degrade a neutrophil chemoattractant Pro-Gly-Pro (PGP) to facilitate the resolution of neutrophilic inflammation and prevent prolonged inflammation with exacerbated pathology and illness ²⁰. This supports the idea that high functional avidity T cells undergo stronger antigen stimulation and would therefore start expressing immune dampening molecules. We further found that patients with mild disease show an increased proportion of high functional avidity TCR clonotypes which are also more expanded than low functional avidity TCR clonotypes (Figure 5C), whereas TCR clonotypes from patients with severe disease show equal expansion between high and low functional avidity TCRs. Therefore, the preferential expansion of high functional avidity TCR clonotypes may contribute to mild disease after SARS-CoV-2 infection.

The strength of NP_105-113-B*07:02-specific T cells responding to naturally processed epitope is correlated with their functional avidity

Numerous studies including our own have shown the importance of antigen-processing and presentation to T cell recognition of its antigen ^{21, 22}. Some T cell epitopes may not be processed and presented as efficiently as others, which will subsequently diminish the T cell response to the epitope. To investigate T cell responses to naturally processed and presented viral epitopes, we made Vaccinia virus expressing SARS-CoV-2 viral proteins. We infected autologous Epstein-Barr virus (EBV) transformed B cells with Vaccinia virus expressing NP and co-cultured with NP_105-113-B*07:02-specific T cell clones. T cell degranulation and cytokine production was then assessed by intracellular staining after six hours of incubation (STAR Methods). Figure 6A shows an example of CD107a expression and MIP1β chemokine production from a representative T cell clone. Gating for CD107a and/or MIP1β producing cells were based on corresponding negative controls (Figure S2A). When compared to the peptide-loaded targets, we found that the response to Vaccinia virus infected BCLs was much weaker, consistent with lower antigen loads. As shown in Figure S2B, the load of this naturally processed and presented epitope was equivalent to no more than 3nM peptide. Nevertheless, NP Vaccinia virus-incubated clones with high CD107a expression showed a negative correlation with their individual EC50 values (Figure 6B, R=-0.6176, P=0.0212), consistent with higher functional avidity resulting in more effective T cell killing. A similar negative correlation was also observed with MIP1β producing cells (Figure 6C, R=-0.6879, P=0.0082).

To further investigate the anti-viral activity of NP_105-113-B*07:02specific T cells, we established an in vitro SARS-CoV-2 infection system (STAR Methods). Briefly, the ACE2 gene was delivered into autologous EBV-transformed BCLs by lentiviral transduction to enable SARS-CoV-2 infection via ACE2 protein expressed on the cell surface. ACE2⁺ BCLs were purified by FACS sorting and maintained by antibiotic selection, after which cells were subsequently used for SARS-CoV-2 virus infection. After 48 hours incubation, intracellular viral copies were quantified by quantitative PCR, where the reduction of virus replication is calculated as percentage of virus suppression by T cells. Figure 6D shows the suppression of virus replication by each individual T cell clone (SARS-CoV-2 Victoria strain). We found that the percentage of virus suppression was strongly correlated with their functional avidity; therefore, high avidity T cells can efficiently inhibit viral replication (Figure 6E, R=-0.7699, P=0.0075).

NP_105-113-B*07:02specific T cells are maintained six months after infection with proportionally narrowed TCR repertoire and preserved anti-viral efficacy

In order to examine whether the memory T cells established post-natural infection could provide sufficient protection against secondary viral infection, we collected PBMCs from three patients (C-COV19-005, C-COV19-045, C-COV19-046) six months after infection and sequenced sorted CD8⁺ NP_105-113-B*07:02-specific T cells. We discovered that six months after infection, the TCR repertoire of NP_105-113-B*07:02-specific T cells narrows (independent of cell numbers), and the T cell memory pool contains both high and low functional avidity T cells (Figure 7A). We then isolated and expanded further NP_105-113-B*07:02-specific T cell bulk lines from PBMC samples taken six months after infection. We assessed the anti-viral efficacy of these bulk T cell lines in our in vitro SARS-CoV-2 infection assays: all three T cell lines showed increased MIP1β and CD107a protein expression after incubation with NP-expressing Vaccinia virus (Figure S3) and SARS-CoV-2 infected BCLs (Figure 7B), and are capable of suppressing SARS-CoV-2 replication (Figure 7C). We further found that these antigen-specific bulk cell lines showed strong inhibition against current variants of concern, including the recently emerged Alpha, Beta and Gamma SARS-CoV-2 variants (Figure 7D). This is consistent with the evidence of conservation of this NP_105-113-B*07:02epitope, and indicates the protective role of NP_105-113-specific T cells in secondary infection against different SARS-CoV-2 variants.

Our observation of strong and dominant NP_105-113-B*07:02 specific T cell responses in mild cases highlights the possible protective role of this unique and most dominant response found so far in SARS-CoV-2 infection ^{3, 4, 5, 6}. We found high similarity of TCRs in both HLA-B*07:02 positive and HLA-B*07:02 negative COVID-19 recovered individuals, with naive precursors identified in pre-pandemic samples supporting previous reports ^{7, 10}. In addition, T cells from convalescent patients with mild disease show higher functional avidity as well as better effector and anti-viral function compared with convalescent severe COVID-19. Interestingly, the immune memory pools post-infection (six months convalescence) are narrowed but remain proportional; we found no bias towards high or low functional avidity TCRs during immune memory contraction. Moreover, this dominant NP_105-113- specific response restricted by HLA-B*07:02 is associated with protection against severe disease but does not associate with HLA-B*0702 when analysed alone.

The highly diverse T cell receptor repertoire of NP_105-113-B*07:02 specific T cells in recovered individuals is of particular interest; whether this is a common phenomenon of acute primary virus infection, or if these responses are unique, with high frequency and broader choice of TCR precursors available would merit future investigation. The latter is supported by our finding that TCRs in COVID-19 recovered individuals can be similar to those found in pre-pandemic individuals, in particular patients with mild symptoms. We hypothesise that NP_105-113-B*07:02 specific T cell responses play an important role in protecting individuals from severe illness, which is likely due to early priming and expansion of high frequency naive TCRs specific to this epitope.

We further provided evidence to support our hypothesis by studying a cohort of patients with acute SARS-CoV-2 infection, by analysing the TCR repertoire in HLA-B*07:02 positive patients. We first found high frequencies of TCR precursors with naïve phenotype in HLA-B*07:02 positive healthy donors; this further supports the recent findings from Nguyen et al., that these T cell precursors bearing NP-specific TCRs are not due to pre-existing memory from seasonal coronaviruses. We observed that strong cytotoxicity and inhibitory receptor expression is associated with disease severity, where NP_105-113-B*07:02 specific T cells are more activated and well differentiated in individuals recovered from severe illness. This is likely as the result of stronger antigen stimulation and expansion during the acute phase of viral infection.

We found overall high functional avidity T cell expansion in mild cases, and that high functional avidity is associated with expression of immune damping molecules such as IL10RA, PARK7 and LTA4H, which could potentially act to prevent prolonged inflammation with exacerbated pathology and illness ^{18, 20, 23, 24} In particular, LTA4H has a known function as an aminopeptidase to degrade a neutrophil chemoattractant PGP, facilitating the resolution of neutrophilic inflammation, which is known to be associated with immunopathology in respiratory virus infections such as COVID-19 ²⁵. This further provides evidence that expansion of high avidity precursors in mild cases contributes to the overall protective immunity from severe illness.

We show that NP_105-113-B*07:02 specific T cells can respond to cells infected with live SARS-CoV-2 virus as well as emerging viral variants, and most importantly suppress virus replication in infected cells. The magnitude and strength of response to naturally processed epitopes presented by infected cells is correlated with their functional avidity. The proportional expansion with both high and low functional avidity T cells was maintained in CD8⁺ T cell memory pools after immune memory contraction (at six months post-infection), and these cells could suppress virus replication efficiently for all viral variant strains. This is not surprising due to the conservation of this epitope across viral strains, and provides some reassurance that memory T cells generated from natural infection could respond to newly emerged variants and still provide protective immunity.

Taken together, we have demonstrated that, firstly, high frequency naive T cell precursors recognising NP_105-113-B*07:02 that are observed in pre-pandemic individuals are HLA-B*07:02 independent; secondly, the protective effect of NP_105-113-HLA-B*07:02 specific TCRs from severe illness may be due to early expansion of high frequency naïve T cell precursors bearing these TCRs. Moreover, we found that the TCR repertoire is not disturbed following virus infection and immune memory contraction, and that these memory T cells are able to suppress the original SARS-CoV-2 viral strain (contracted by the patient) as well as newly emerged viral strains.

Limitations of the study

We recognise that the number of convalescent patients analysed by single cell gene expression and TCR sequencing (N=4) is small. Also, the number of NP_105-113-B*07:02-specific cells from pre-pandemic donors and acute COVID-19 patients is low; this is partly because these cells were not pentamer-sorted before analysis. In this study we focus on CD8⁺T cell responses to a single epitope, however it may be useful in the future to see if there are any shared or distinct features with other dominant responses. The specifics of antigen loading of this particular epitope compared with other NP epitopes, as well as variation in levels of protein expression and localisation is also unknown, and warrants further investigation.

Study participants

Patients were recruited from the John Radcliffe Hospital in Oxford, UK, between March 2020 and April 2021 by identification of patients hospitalised during the SARS-COV-2 pandemic. Patients were recruited into the Sepsis Immunomics study and had samples collected during their convalescence as well as during acute disease. Patients were sampled at least 28 days from the start of their symptoms. Written informed consent was obtained from all patients. Ethical approval was given by the South Central-Oxford C Research Ethics Committee in England (Ref 13/SC/0149).

Clinical definitions

All patients were confirmed to have a positive test for SARS-CoV-2 using reverse transcriptase polymerase chain reaction (RT-PCR) from an upper respiratory tract (nose/throat) swab tested in accredited laboratories. The degree of severity was identified as mild, severe or critical infection according to recommendations from the World Health Organisation. Patients were classified as ‘mild’ if they did not require oxygen (that is, their oxygen saturations were greater than 93% on ambient air) or if their symptoms were managed at home. A large proportion of our mild cases were admitted to hospital for public health reasons during the early phase of the pandemic even though they had no medical reason to be admitted to hospital. Severe infection was defined as COVID-19 confirmed patients with one of the following conditions: respiratory distress with RR>30/min; blood oxygen saturation<93%; arterial oxygen partial pressure (PaO2) / fraction of inspired O2 (FiO2) <300mmHg; and critical infection was defined as respiratory failure requiring mechanical ventilation or shock; or other organ failures requiring admission to ICU. Since the Severe classification could potentially include individuals spanning a wide spectrum of disease severity ranging from patients receiving oxygen through a nasal cannula through to non-invasive ventilation, we also calculated the SaO2/FiO2 ratio at the height of patient illness as a quantitative marker of lung damage. This was calculated by dividing the oxygen saturation (as determined using a bedside pulse oximeter) by the fraction of inspired oxygen (21% for ambient air, 24% for nasal cannula, 28% for simple face masks and 28, 35, 40 or 60% for Venturi face masks or precise measurements for non-invasive or invasive ventilation settings). Patients not requiring oxygen with oxygen saturations (if measured) greater than 93% on ambient air, or managed at home were classified as mild disease. Viral swab Ct values were not available for all patients. In addition, we have standardised all of our analyses to the days since symptom onset.

Generating ACE2 transduced Epstein-Barr virus (EBV)-transformed B cell lines.

Epstein-Barr virus (EBV)-transformed B cell lines (BCLs) were generated as described previously ²⁶. The cDNA for the human Angiotensin Converting Enzyme 2 (ACE2) gene (ENSG00000130234) was cloned into a lentiviral vector that allows co-expression of eGFP and a Puromycin resistance marker (Addgene, Plasmid 17488). The plasmids were co-transfected with the packaging plasmids pMD2.G and psPAX2 into HEK293-TLA using PEIpro (Polyplus). Lentiviral supernatant was collected 48h and 72h post transfection and concentrated by ultracentrifugation. EBV-transformed BCLs were infected by ACE2-lentivirus at MOI 0.1 with the addition of 8µg/ml polybrene (Sigma-Aldrich, Cat. # TR-1003-G) overnight, then washed, and further cultured for 3 - 5 days. ACE2-expressing B cells were stained using a primary goat anti-human ACE2 antibody (R&D, AF933) and a donkey anti-goat AF647 secondary antibody (Abcam, Cat. # ab150135) followed by cell sorting by flow cytometry. B cells with stable expression of ACE2 were maintained with 0.5µg/ml puromycin (ThermoFisher, Cat. # A1113803). Mycoplasma test was caried out every four weeks with all the cell lines using Lonza MycoAlert detection kit (Lonza,

Generating T cell lines and clones

Short-term SARS-CoV-2-specific T cell lines were established as previously described ¹⁷. Briefly, 3 × 10⁶ to 5 × 10⁶ PBMCs were pulsed as a pellet for 1 h at 37°C with 10μM of peptides containing T cell epitope regions and cultured in R10 (RPMI 1640 medium with 10% foetal calf serum (FCS), 2mM glutamine and 100mg/ml pen/strep) at 2 × 10⁶ cells per well in a 24-well Costar plate. IL-2 was added to a final concentration of 100U/mL on day 3 and cultured for a further 10 - 14 days. T cell clones were generated by sorting HLA-B*07:02 NP_105-113 Pentamer⁺ CD8⁺ T cells at a single cell level from thawed PBMCs or short-term cell lines. T cell clones were then expanded and maintained as described previously ²⁷.

Generating Vaccinia virus expressing SARS-CoV-2 NP

SARS-CoV-2 nucleocapsid (NP) expression vectors (gifts from Dr. Peihui Wang, Shandong University, Shandong, China ²⁸) were first digested with KpnI and SacII. The resulting fragment was then cloned into VACV expression vector pSC11, which was inserted with a DNA segment encoding KpnI and SacII digestion sites (GGTACCGCGGCCGCCCGCGG). The SARS-CoV-2 NP-expressing recombinant Vaccinia virus (rVACV) was produced as described previously. ^{29, 30, 31}. In brief, HEK293T cells (ATCC, CRL-11268) were transfected with 3μg of pSC11 containing NP in the presence of polyethylenimine. At 24h post transfection, cells were infected with the Lister strain of VACV at MOI 1 for 48h. The infected cells were collected for recombinant virus purification using TK143B cells (ATCC, CRL-8303) in the presence of 25μg/ml bromodeoxyuridine. The NP-expressing rVACV was selected through β-galactosidase staining by supplementing 25μg/ml X-gal to an agarose overlay. Master stocks of rVACV were prepared by infection on rabbit RK13 (ATCC, CCL37) and titrated on African green monkey BS-C-1 (ATCC, CCL26) cells.

SARS-CoV-2 live virus propagation and titration.

SARS-CoV-2 Victoria 01/20 strain (BVIC01), and variants of concerns: Alpha (Lineage B1.1.7, 20I/501Y.V1.HMPP1) and Beta (Lineage B1.351, 201/501.V2.HV001) were originally from Public Health England and provided by Prof. Jane McKeating ³². SARS-CoV-2 Gamma (Lineage P.1) was provided by Prof. Gavin Screaton ³³. Virus was propagated and titrated as previously described. ^{32, 33}. In brief, Victoria 01/20, Alpha and Beta were propagated with Vero E6 cells, whereas SARS-CoV-2 Gamma was propagated with Vero E6/TMPRSS2 (kindly provided by Alain Townsend). Naïve Vero E6 or Vero E6/TMPRSS2 cells were plated one night before and infected with SARS-CoV-2 at MOI of 0.003. The cultures were harvested when visible cytopathic effect was observed 48 - 72h after infection and residual cell debris was removed by centrifugation. The virus-containing supernatant was aliquoted and stored at −80°C. Viral titre was determined by plaque assay with Vero E6 or Vero E6/TMPRSS2 as previously described, and plaque-forming units (PFU) per mL was used to calculate MOI.

IFN-γ ELISpot assay

Ex vivo IFN-γ ELISpot assays were performed using either freshly isolated, cryopreserved PBMCs or antigen-specific T cell clones as described previously ³. For ex vivo ELISpots, peptides were added to 200,000 PBMCs per test at the final concentration of 2μg/mL for 16–18 h. When using T cell clones, autologous EBV-transformed B cell lines were first loaded with peptides at 3-fold titrated concentrations and were subsequently co-cultured with T cells at an effector: target (E:T) ratio of 1:50 for at least 6h. To quantify antigen-specific responses, mean spots of the control wells were subtracted from the positive wells (PHA stimulation), and the results expressed as spot forming units (SFU)/10⁶ PBMCs. Responses were considered positive if results were at least three times the mean of the negative control wells and >25SFU/10⁶PBMCs. If negative control wells had >30SFU/10⁶ PBMCs or positive control wells were negative, the results were excluded from further analysis.

Flow cytometric sorting of NP_105-113-B*07:02 -speciﬁc CD8⁺ T cells

NP_105-113-B*07:02 speciﬁc CD8⁺ T cells were stained with PE-conjugated HLA-B7 NP_105-113 Pentamer (ProImmune, Oxford, UK). Live/Dead ﬁxable Aqua dye (Invitrogen) was used to exclude non-viable cells from the analysis. Subsequently, cells were washed and stained with the following surface antibodies: CD3-FITC (BD Biosciences), CD8-PercP-Cy5.5, CD14-BV510, CD19-BV510 and CD16-BV510 (Biolegend). After the ﬁnal wash, cells were resuspended in 500μl of PBS, 2 mM EDTA and 0.5% BSA (Sigma-Aldrich) solution and kept in dark at 4°C until flow cytometric acquisition. After exclusion of non-viable/CD19+/CD14+/CD16+ cells, CD3⁺ CD8⁺ Pentamer⁺ cells were sorted directly into 96-well PCR plates (Thermo Fisher, UK) using a BD Fusion sorter or BD FACS Aria III (BD Biosciences) and stored at − 80 °C for subsequent analysis.

Single cell RNA Sequencing (RNA-Seq)

Single cell RNA-Seq with ex vivo sorted CD8⁺Pentamer⁺ T cells was performed using SmartSeq2 ³⁴. with following modifications. Reverse-transcription and PCR ampliﬁcation were performed as described ³⁴ with the exception of using ISPCR primer with biotin-tagged at 5’ and increasing the number of cycles to 25. Sequencing libraries were prepared using the Nextera XT Library Preparation Kit (Illumina) and sequencing was performed on Illumina NextSeq sequencing platform.

Deep sequencing of T cell receptor (TCR) repertoire of T cell clones

100,000 cells from each T cell clone were harvested and washed three times with PBS. Total RNA was extracted using RNeasy Plus Micro Kit (Qiagen, Cat. # 74034). 100ng of total RNA from each of T cell clone was used to generate full length of TCR repertoire libraries for Illumina Sequencing using SMARTer Human TCR a/b Profiling Kit (Takara, Cat. # 635016) following supplier’s instructions. In brief, first-strand cDNA was synthesised by reverse transcription using TRBC reverse primers and further extended with a template-switched SMART-Seq®v4 Oligonucleotide. Following reverse transcription, two rounds of PCR were performed in succession to amplify cDNA sequences corresponding to variable regions of TCR-α and/or TCR-β transcripts with primers including Illumina indexes allowing for sample barcoding. PCR products were then purified using AMPure beads (Beckman Coulter). The quantity and quality of cDNA libraries were checked on Agilent 2100 Bioanalyzer system. Sequencing was performed with MiSeq reagent Kit v3 (600 cycles) on MiSeq (Illumina).

Intracellular cytokine staining (ICS)

Intracellular cytokine staining was performed as described previously ³. Briefly, T cells were co-cultured with peptide-loaded or virus-infected BCLs at an appropriate E:T ratio for a 6h incubation with GolgiPlug and GolgiStop, and surface stained with PE-anti-CD107a. Dead cells were labelled using LIVE/DEAD™ Fixable Aqua dye (Invitrogen); after staining with BV421-anti-CD8, cells were then washed, fixed with Cytofix/Cytoperm^TM and stained with AF488-anti-IFNγ, APC-anti-TNFα (eBioscience) and APC-H7-anti-MIP1ß. Negative controls without peptide-stimulation or virus infection were run for each sample. All reagents were from BD Bioscience unless otherwise stated. All samples were acquired on Attune™ NxT Flow Cytometer and analyzed using FlowJo^TM v.10 software (FlowJo LLC).

Evaluation of T cell response to Vaccinia virus infection

EBV transformed B cell lines were infected with Lister strain Vaccinia virus at a MOI 3 for 90-120 mins at 37°C. Cells were then washed three times to remove virus and incubated overnight in R10 at 37°C. The next day, cells were counted and co-cultured with T cells at an E:T ratio of 1:1. Degranulation (CD107a expression) and cytokine production of T cells were then evaluated by ICS as described above.

Evaluation of T cell response to live virus infection

EBV transformed BCLs expressing ACE2 were infected SARS-CoV-2 viruses at a MOI 3 for 120 mins at 37°C. Cells were then washed three times and incubated in R10 at 37°C. After 48h, cells were then counted and co-cultured with T cells at an E:T ratio of 1:1. Degranulation (CD107a expression) and cytokine production of T cells were then evaluated by ICS as described above.

Live Virus Suppression Assay (LVSA)

EBV transformed BCLs expressing ACE2 were infected with SARS-CoV-2 viruses at MOI 0.1 for 120 mins at 37°C. Cells were then washed three times and co-cultured with T cells at an E:T ratio of 4:1. Control wells containing virus-infected targets without T cells were also included. After 48hrs incubation, the cells were washed with PBS and lysed with Buffer RLT (Qiagen). RNA was extracted using RNeasy 96 kit (Qiagen, Cat. # 74181). Virus copies were then quantified with Takyon^TM Dry one-step RT-qPCR (Eurogentec, Cat. # UFD-NPRT-C0101) using SARS-CoV-2 (2019-nCoV) CDC qPCR Probe Assay (IDT, ISO 13485:2016) and human B2M (Beta-2-Microglobulin) as an endogenous control (Applied Biosystems™). The suppression rate was calculated by the percentage of reduction of virus replication by T cells.

SmartSeq2 single cell data processing

BCL files were converted to FASTQ format using bcl2fastq version 2.20.0.422 (Illumina). FASTQ files were aligned to human genome hg19 using STAR version 2.6.1d ³⁵. Reads were counted using featureCounts (part of subread version 2.0.0, ³⁶). The resulting counts matrix was analysed in R version 4.0.1 using Seurat version 3.9.9.9010 ³⁷.

SmartSeq2 single cell RNA sequencing analysis

After creating a Seurat object from the counts matrix, cells were filtered using the following criteria: minimum number of cells expressing specific gene = 3, minimum number of genes expressed by cell = 200 and maximum number of genes expressed by cell = 4000. Cells were also excluded if they expressed more than 5% mitochondrial genes. Patient-specific cells were integrated using Harmony version 1.0 to remove batch effects (differences between patients) but to still retain meaningful biological variation. The AddModuleScore function from the Seurat package was used to look at expression of specific gene sets (Supplementary Table). The average expression of a gene set was calculated, and the average expression levels of control gene sets were subtracted to generate a score for each cell relating to that particular gene set. Higher scores indicate that that specific signature is expressed more highly in a particular cell compared to the rest of the population. Module scores were plotted using ggplot2 version 3.3.2 ³⁸.

SmartSeq2 TCR repertoire analysis

TCR sequences were reconstructed from RNA sequencing FASTQ files using MiXCR version 3.0.13 ^{39, 40} and the command mixcr analyze shotgun to produce separate TRA and TRB output files for analysis. The output text files were parsed into R using tcR version 2.3.2. For paired αβ TCRs, cells were filtered to retain only 1α1β or 2α1β cells. Circos plots showing paired αβ TCRs were created using circlize version 0.4.12 ⁴¹. Separately, lists were generated for all 1β cells (regardless of number of α) to use for downstream analysis.

Clustering

The input data for clustering was all 1β from SmartSeq2 single cells and 1β from bulk sequencing T cell clones. Single cells and clones were grouped by Vβ usage first; TCRs from either single cells or clones which had unique Vβ gene usage were excluded. Each Vβ group was broken down into subgroups based on CDR3β sequence; any TCRs from either single cells or clones that contained unique CDR3β sequences were excluded. Only TRBV27, TRBV28, TRBV5-1 showed multiple CDR3β sequences with the same gene usage. After plotting the EC50 values of the T cell clones, groups were classified as low or high functional avidity based on a manually defined cut-off (EC50 0.11). This led to a list of 18 groups with unique Vβ gene usage and CDR3β sequences shared among the TCRs from single cell sequencing and bulk T cell clone sequencing.

In order to group as many of the SmartSeq2 single cells into one of these 18 groups, the stringsim function was used from the stringdist package version 0.9.6 ⁴² to compare the similarity between all SmartSeq2 CDR3β sequences and each of the 18 CDR3β from the single cell/clone grouping. A minimum similarity score of 0.7 was used to decide if a TCR from a SmartSeq2 single cell should belong to one of the 18 groups. Once allocated, the single cell was annotated as being high or low functional avidity based on its group number.

TCR sequencing from T cell clones (bulk sequencing)

BCL files converted to FASTQ files as before. TCRs reconstructed using MiXCR command mixcr analyze amplicon, and the resulting output files (TRA and TRB) were parsed into R using tcR as before. TCRs were filtered to retain only 1α1β for each clone. TCR clonotypes (defined as Vβ gene usage and CDR3β sequence) were compared between single TCR and bulk TCR sequencing using using ggalluvial version 0.12.2 ⁴³. The predicted functional avidity annotation was overlayed onto the plots using the stringsim function as previously described to classify TCRs into high or low functional avidity groups (minimum score 0.5).

10X VDJ sequencing

Raw files were processed using 10x Genomics Cellranger version 5.0.0 ⁴⁴. BCL files were converted to FASTQ format using cellranger mkfastq and TCRs were reconstructed using cellranger vdj. To carry out donor deconvolution from multiplexed single cell data, cellSNP version 0.3.2 ⁴⁵ was first used to generate a list of SNPs from cellranger output (BAM file). Subsequently Vireo version 0.5.6 ⁴⁶ was used to demultiplex the sequencing data into individual patients from the pooled sequenced libraries, based on the previously generated SNPs list. The filtered_contig_annotations.csv file was annotated with patient information and used for subsequent downstream analysis. TCRs from 10X sequencing represent six months convalescence and were compared to one month convalescence TCRs (SmartSeq2) from the same patient using ggalluvial. The predicted functional avidity annotation was overlayed onto the plots using the stringsim function as previously described to classify TCRs into high or low functional avidity groups (minimum score 0.5).

Gene expression analysis and cell subtyping from acute COVID-19 dataset

Normalised single cell gene expression data for T cells from the COMBAT dataset (level 2 subsets a and b) ¹⁴ was annotated with specific T cell subtypes according to COMBAT multimodal analysis (/CBD-10X-00010/multimodal.annotation.release.1.0.tsv); COMBAT TCR chain information (CBD-10X-00008/tcr_chain_information.tsv.gz); and patient metadata (/CBD-CLINNORM-00006/COMBAT_clinical_basic_data_freeze_160221.txt). Any cells without both a CD8⁺ multimodal major cell type classification and TCR chain information were excluded from further analysis. A simplified severity grouping based on the WHO ordinal scale which ranges from 0 to 8 (https://www.who.int/blueprint/priority-diseases/key-action/COVID-19_Treatment_Trial_Design_Master_Protocol_synopsis_Final_18022020.pdf) was used to classify participants into either Uninfected (0), Mild (1-4), Severe (5-7) or Death (8).

GLIPLH2 analysis

A GLIPH2 CD8⁺ TCR input file was created from the following datasets: COMBAT 10x paired chain single cell and bulk TCR from all available participants ¹⁴; pentamer sorted NP_105-113-B*07:02 specific TCR sequences and clonally expanded cells used to test functional avidity processed using MiXCR (as described previously); and NP_105-113-B*07:02 specific TCR sequences from Lineburg and Nguyen datasets ^{7, 10}. Clonotypes were defined as having a unique combination of CDR3β amino acid sequence, TRBV gene, TRBJ gene and CDR3α amino acid sequence. Where no or multiple CDR3α sequences were available for a cell, an NA value was used for the CDR3α field in accordance with GLIPH2 input guidelines. For each clonotype, additional information indicating dataset origin was appended as part of the "condition" field. For the 10x COMBAT dataset, CD8⁺ clonotypes were distinguished from CD4⁺ clonotypes based on the multimodal classification of cells within each clone.

A matching GLIPH2 participant HLA input file was created using COMBAT formal HLA typing data and where no formal typing was available from imputed HLA typing ^{3, 14}, in addition to published HLA data relating to the Lineburg and Nguyen datasets ^{7, 10}.

The GLIPH2 irtools.centos version 0.01 ¹⁵ was run on a CentOS Linux platform (release 8.3.2011) using the CD8⁺ TCR and HLA input files above, together with CD8⁺ specific V-gene usage, CDR3 length and TCR reference files from the GLIPH2 repository [http://50.255.35.37:8080/] and using the following parameters: local_min_pvalue=0.001; p_depth = 1000; global_convergence_cutoff = 1; simulation_depth=1000; kmer_min_depth=3; local_min_OVE=10; algorithm=GLIPH2; all_aa_interchangeable=1; number_of_hla_field=1; hla_association_cutoff=0.050000. A GLIPH score summary file was then programmatically curated, identifying convergence groups containing TCRs known to be NP_105-113-B*07:02 specific as described previously, with associated GLIPH2 scoring and HLA prediction.

Convergence groups from this file were further categorized as being associated with or lacking association with HLA-B*07:02 based on having a GLIPH2 HLA score of <0.05 or >=0.05 respectively. Only clonotypes belonging to a HLA-B*07 associated convergence group which were from participants known to have a HLA-B*07:02 allele were deemed HLA-B*07:02 positive TCRs. Any clonotypes from convergence groups lacking HLA-B*07:02 association but belonging to patients having a HLA-B*07:02 allele were deemed ambiguous and excluded from the HLA-B*07:02 negative clonotype set.

Similarity between pre-pandemic and convalescent COVID-19 TCRs

NP_105-113specific TCRs from pre-pandemic individuals (predicted from COMBAT dataset or experimentally defined from Lineburg et al. and Nguyen et al.) were compiled to form a single list of sequences (237 TCRs). The function stringsim was used to generate similarity scores from pairwise comparisons between each CDR3β sequence from the pre-pandemic/healthy list and each CDR3β sequence from 85 unique clonotypes from four convalescent COVID-19 patients (clonotype defined per patient, TRBV gene usage and CDR3β sequence). A score of 1 indicates total similarity while a score of 0 is total dissimilarity. Each score was plotted on a box plot using ggplot2 version 3.3.2.

Pseudobulk and differential gene expression

RNA counts from SmartSeq2 single cells were aggregated into groups based on patient origin and high/low functional avidity, and converted to a Single Cell Experiment object version 1.10.1 ⁴⁷. Differential gene expression was conducted using DESeq2 version 1.28.1 on aggregated (pseudobulk) counts. Significant genes were visualised on a heatmap using pheatmap version 1.0.12.

Statistics

Mann Whitney non-parametric test to compare two groups (R Studio); other statistical tests carried out using GraphPad Prism. Nonlinear regression with variable slope (four parameters) in dose-response-stimulation model was used for calculating the EC50 of T cell clones. ns not significant, * P < 0.05, ** P < 0.01, *** P < 0.001, **** P < 0.0001.

Acknowledgments: We are grateful to all the participants for donating their samples and data for these analyses, and the research teams involved in the consenting, recruitment, and sampling of these participants. Thank Kevin Clark and Sally-Ann Clark from WIMM flow cytometry facility for their help with cell sorting.

This work is supported by UK Medical Research Council (T.D, G.O, Y.P, J.C); Chinese Academy of Medical Sciences (CAMS) Innovation Fund for Medical Sciences (CIFMS), China (grant number: 2018-I2M-2-002) (T.D, Y.P, X.Y, G.L, D.D, J.K.K, G.O, G.S, J.M, A.M, A.T); National Institutes of Health, National Key R&D Program of China (2020YFE0202400) (T.D); China Scholarship Council (Z.Y, G.L, C.J, C.L). The study is also funded by the NIHR Oxford Biomedical Research Centre (L.P.H, G.O, J.C.K, A.J.M), Senior Investigator Award (G.O) and Clinical Research Network (G.O); Schmidt Futures (G.R.S); NIHR (SF and Cov19-RECPLAS) and WT109965MA (PK). The McKeating laboratory is funded by a Wellcome Investigator Award (IA) 200838/Z/16/Z, UK Medical Research Council (MRC) project grant MR/R022011/1 and Chinese Academy of Medical Sciences (CAMS) Innovation Fund for Medical Science (CIFMS), China (grant number: 2018-I2M-2-002). Initial funding for the VSF was provided by the Oxford Biomedical Research Centre and Cancer Research UK.

This work uses data provided by patients and collected by the NHS as part of their care and support #DataSavesLives.

Author contributions:

T.D and J.C.K conceptualized the project; T.D and Y.P designed and supervised T cell experiments; Y.P, D.D, X.Y, G.L, Z.Y, and J.C performed all T cell experiments; Y.L and G.S provided Vaccinia virus; P.A.C.W and X.Z assisted with virus infection; T.R performed HLA typing and next generation sequencing; N.A prepared cDNA libraries for sequencing; P.H, R.B and T.K.T made the ACE2 constructs and lentivirus; J.W.F provided MHC Class I pentamers; J.C.K, A.J.M, L.P.H, A.F established clinical cohorts and collected clinical samples and data; D.W, D.D-F, C.J, W.W, M.A.H, B.W, C.D, W.D processed clinical samples; R.A.F, C.W, P.S, A.K, C.R-G, R.B-R, I.N, R.A.W, O.T, C.A.T, P.K.S, F.C, S.R, L.C.G, K.J, R.C.F, M.A, R.A.R, C.D, S.N.S, B.F, J.A.M, W.J, A.T, G.R.S, and J.M provided technical assistance and critical reagents; Y.P, S.L.F, F.P and G.L analysed data; T.D, J.C.K and Y.P supervised data analysis, T.D , Y.P, S.L.F wrote the original draft. J.C.K, G.O, A.M, G.L.S, H.S, P.K, B.C, P.B and S.L.F reviewed and edited the manuscript and figures.

Competing interests: Authors declare that they have no competing interests.

Data and materials availability: All data are available in the main text or the supplementary materials.

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Table 1. Groups defined by shared TRBV gene usage and CDR3β sequence between bulk TCR sequencing from T cell clones and single cell TCR sequencing from ex vivo T cells.

Group	CDR3β	TRBV	TRBJ	Functional avidity
1	CAISEPGTSGGAILDTQYF	TRBV10-3	TRBJ2-3	Low
2	CASGPATSAEQETQYF	TRBV12-5	TRBJ2-5	High
3	CASSILQGLGGSNQPQHF	TRBV19	TRBJ1-5	Low
4	CASSVLPGPPRGEQFF	TRBV2	TRBJ2-1	High
5	CSAQVGGNYNSPLHF	TRBV20-1	TRBJ1-6	High
6	CATSDLVTSGDEQFF	TRBV24-1	TRBJ2-1	Low
7	CASSGLTSLADTQYF	TRBV25-1	TRBJ2-3	High
8	CASSLITGGAKNIQYF	TRBV27	TRBJ2-4	Low
9	CASSPIAGGRKNIQYF	TRBV27	TRBJ2-4	Low
10	CASSPLTGSAERKETQYF	TRBV27	TRBJ2-5	High
11	CASSPLVGERFRKETQYF	TRBV27	TRBJ2-5	Low
12	CASSSLLAGGFYEQFF	TRBV27	TRBJ2-1	Low
13	CASSPIETAKNIQYF	TRBV28	TRBJ2-4	Low
14	CASSSITTTGAKDGYTF	TRBV28	TRBJ1-2	High
15	CASSLAGAEAFF	TRBV5-1	TRBJ1-1	High
16	CASSLAGGPLHEQFF	TRBV5-1	TRBJ2-1	Low
17	CASSSYPGLAPVQETQYF	TRBV5-1	TRBJ2-5	High
18	CASSYLPAGSSYNSPLHF	TRBV6-3	TRBJ1-6	High

There is NO Competing Interest.

NIA32469Asuppfigs.docx
Fig. S1. TCR clonotypes for single cells and T cell clones and EC50 derivation. Fig. S2. FACS gating strategy and peptide titration. Fig. S3. T cell response six months post infection to NP Vaccinia virus. Table S1. Genes used for gene module scoring in single cell expression analysis.
NIA32469Asupptable2.xlsx
Table S2. αβ VJ gene usage and CDR3 sequences for ex vivo single cell TCR sequencing.
NIA32469Asupptable3.xlsx
Table S3. αβ VJ gene usage and CDR3 sequences from bulk TCR sequencing of T cell clones.
NIA32469Asupptable4.xlsx
Table S4. EC50 values from selected T cell clones.
NIA32469Asupptable5.xlsx
Table S5. COvid-19 Multi-omics Blood ATlas (COMBAT) Consortium.

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Immunodominant NP105-113-B*0702 cytotoxic T cell response controls viral replication and is associated with less severe COVID-19 disease

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