Identification and characterization of a SARS-CoV-2 specific CD8+ T cell response with immunodominant features

The COVID-19 pandemic caused by SARS-CoV-2 is a continuous challenge worldwide, and there is an urgent need to map the landscape of immunogenic and immunodominant epitopes recognized by CD8+ T cells. Here, we analyze samples from 31 patients with COVID-19 for CD8+ T cell recognition of 500 peptide-HLA class I complexes, restricted by 10 common HLA alleles. We identify 18 CD8+ T cell recognized SARS-CoV-2 epitopes, including an epitope with immunodominant features derived from ORF1ab and restricted by HLA-A*01:01. In-depth characterization of SARS-CoV-2-specific CD8+ T cell responses of patients with acute critical and severe disease reveals high expression of NKG2A, lack of cytokine production and a gene expression profile inhibiting T cell re-activation and migration while sustaining survival. SARS-CoV-2-specific CD8+ T cell responses are detectable up to 5 months after recovery from critical and severe disease, and these responses convert from dysfunctional effector to functional memory CD8+ T cells during convalescence.

T he coronavirus disease 2019  pandemic, caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is an ongoing global emergency. First cases of COVID-19 were reported in December 2019, and as of February 4, 2021, there are more than 104,400,000 confirmed cases and 2,264,000 deaths worldwide 1 . Due to the measures necessary to contain the rapid spread of the infection, this pandemic is having tremendous health and socioeconomic consequences, and there is an urgent need for a better understanding of the natural adaptive immune response.
Several studies have characterized the composition of immune cell lineages in COVID-19 patients, which showed that bulk T cells have impaired effector functions (i.e., cytokine production) and higher expression levels of inhibitory receptors compared to bulk T cells from healthy individuals, which is worsening with disease stage [21][22][23] . However, in-depth characterization of the SARS-CoV-2-specific CD8 T cell response during acute COVID-19 disease directly ex vivo is currently lacking. Such information is essential to improve our understanding about the role of CD8 T cells in the host defense against SARS-CoV-2 and during COVID-19 disease development.
In this work, we probe for CD8 T cell recognition toward 500 SARS-CoV-2 epitope human leukocyte antigen (HLA) complexes, restricted by 10 common HLA class I alleles. A substantial fraction of the identified CD8 T cell responses is directed towards epitopes derived from the open reading frame 1ab polyprotein (ORF1ab), including an epitope with immunodominant characteristics restricted by HLA-A*01:01. In-depth characterization of identified SARS-CoV-2-specific CD8 T cell responses reveals high expression of NKG2A, lack of cytokine production, and a gene expression profile of constrained T cell re-activation. In addition, we show that SARS-CoV-2-specific CD8 T cell responses convert from dysfunctional effector CD8 T cells to functional memory CD8 T cells during convalescence and persist for up to 5 months post COVID-19 disease.
CD8 T cell recognized SARS-CoV-2 epitopes. We identified 18 different SARS-CoV-2 epitopes that were recognized by CD8 T cells from COVID-19 patients across 6 of the 10 included HLA alleles (Fig. 1f). Eight of these epitopes were specific for SARS-CoV-2, while 10 epitopes were shared with SARS-CoV-1 and 1 of these epitopes was shared with 3 of the "common cold" coronaviruses (Supplementary Table 3). The origin of these epitopes was diverse, including epitopes derived from the ORF1ab (n = 8), spike protein (n = 6), membrane protein (n = 2), nucleoprotein (n = 1), and ORF3a (n = 1). Of note, although a high fraction of the selected epitopes was derived from the ORF1ab (driven by the large size of the ORF1ab within the SARS-CoV-2 proteome) we observed an enrichment for CD8 T cell-recognized epitopes derived from the spike protein (Fig. 1g).
The ORF1ab encodes an epitope with immunodominant features. Strikingly, CD8 T cell responses specific for the TTDPSFLGRY (TTD) epitope were detected in all HLA-A*01:01 + patients (8/8) with acute COVID-19 disease (Supplementary Table 3). The magnitude of these CD8 T cell responses was remarkably high: on average >7% of total CD8 + cells (range: 0.074-19%, Fig. 2a and Supplementary Fig. 2). Furthermore, these CD8 T cell responses were of significantly higher magnitude compared to other detected SARS-CoV-2-specific CD8 T cell responses in all COVID-19 patients with acute COVID-19 disease (Fig. 2a, P = 0.0003, two-tailed Mann-Whitney U test). Additional SARS-CoV-2-specific CD8 T cell responses were identified for 5 of the 8 HLA-A*01:01 + COVID-19 patients, and the highest magnitude of these responses was on average 70-fold (range: 1.2-216) lower compared to the TTD-specific CD8 T cell response (Fig. 2b, c, P = 0.0136, two-tailed Mann-Whitney U test). The immunodominance hierarchy is partially determined by the naive precursor frequency 32,33 . To further investigate whether more than one T cell clone gave rise to the TTD-specific CD8 T cell response, TCR sequencing data on 542 TTD-specific CD8 T cells obtained from 5 patients was analyzed. In line with the immunodominance pattern based on the presence and the high magnitude of the TTD-specific CD8 T cell responses in all HLA-A*01:01 + patients, we observed a high level of TCR diversity with 150 unique TCR beta-complementarity-determining region 3 (TRB-CDR3) sequences that were expressed in only 1 cell vs. 75 unique TRB-CDR3 sequences that were expressed in ≥2 cells (information for TRB-CDR3 sequences was available for 431/542 cells, Fig. 2d). In addition, we observed an enrichment of TCRs with the TCR beta chain V27 segment within the TTD-specific TCR sequences compared to the TCR repertoire of bulk CD8 T cells (Fig. 2e) suggesting that TCRs specific for the TTD epitope may more likely to be rearranged during T cell development. Together, these data strongly indicate that (depending on the composition of the entire set of HLA alleles) TTDPSFGLGRY is an immunodominant epitope for the subgroup of HLA-A*01:01 + patients, which is present in approximately 30% of the European population (http://www.allelefrequencies.net).  Supplementary Fig. 1. c Heatmap of detected SARS-CoV-2-specific CD8 T cell responses (n = 35) including information about CD8 T cell-recognized SARS-CoV-2 epitopes (n = 18) and their viral protein origin, the magnitude of the response (pHLA + cells of total CD8 + cells), and HLA coverage as well as the disease status for each COVID-19 patient (n = 31) and healthy donor (n = 7). Confirmation of identified responses was done for patients COVID-004, 087, 096, 121, 127, 143, and 153 with similar results. d Overview of the magnitude, target epitope origin, and number of detected SARS-CoV-2-specific CD8 T cell responses (n = 35) across the included HLA alleles. Source data are provided as a Source data file. e Difference in the magnitude of SARS-CoV-2-specific CD8 T cell responses targeting epitopes derived from ORF1ab (n = 20) compared to CD8 T cell responses targeting epitopes derived from all other SARS-CoV-2 proteins (n = 15) combined. All detected responses are included. Box plots indicate the median (line), 25th and 75th percentile (box), min and max (whiskers), and all data points (single circles). Statistical significance was tested with a two-tailed Mann-Whitney U test, P = 0.0309. Source data are provided as a Source data file. f Number of SARS-CoV-2 epitopes (n = 18) that were recognized by CD8 T cells across different HLA alleles. g Bar graphs illustrating the contribution of each ORF to the SARS-CoV-2 proteome (left) in comparison to the fraction of the selected epitopes per ORF based on our predictions (middle) and the contribution of each ORF to the CD8 T cell-recognized epitopes (right). ORF open reading frame, S spike, N nucleoprotein, M membrane, pHLA peptide human leukocyte antigen, PTD PTDNYITTY, TTD TTDPSFLGRY.    Accumulated frequency (%) epitope, and CD8 T cells reactive toward this epitope were found to be functional ( Supplementary Fig. 3d, e). Of note, the technical control using phorbol 12-myristate 13-acetate and Ionomycin led to the production of IFNγ, TNF, and IL-2 ( Supplementary  Fig. 3f). Importantly, these results demonstrate that detection of these responses with a functional readout failed or vastly underestimated the magnitude of the TTD-specific CD8 T cell responses during acute disease. To further examine the SARS-CoV-2-specific CD8 T cells, we measured the expression levels (ex vivo) of the inhibitory receptors NKG2A and programmed cell death protein 1 (PD-1) on SARS-CoV-2-specific CD8 T cells compared to bulk CD8 T cells from COVID-19 patients with acute disease. The fraction of NKG2A + cells was significantly higher for SARS-CoV-2-specific CD8 T cells compared to bulk CD8 T cells but not for PD-1 (Fig. 3c, d, NKG2A: P = 0.0427, PD-1: P = 0.7651, two-tailed Mann-Whitney U test). In addition, we observed a positive correlation between the magnitude of the response and the expression levels (mean fluorescence intensity (MFI)) of NKG2A and PD-1 (Fig. 3e, NKG2A: r = 0.5, P = 0.0199, PD-1: r = 0.5, P = 0.0361, Spearman correlation analysis). Together, these data suggest that the identified TTD-specific CD8 T cell responses in patients with ongoing severe and critical COVID-19 disease were highly dysfunctional and that SARS-CoV-2-specific CD8 T cell responses displayed a high level of activation/inhibitory receptors.
Transcriptomics show constrained T cell re-activation profile. To further characterize the state of the TTD-specific CD8 T cell responses during acute COVID-19 disease, we performed singlecell RNA sequencing on bulk and TTD-specific CD8 T cells from COVID-19 patients with acute severe and critical disease using the 10× Genomics Chromium system ( Supplementary Fig. 4a). We obtained data on a total of 3064 CD8 T cells from 6 patients with acute COVID-19 disease ( Table 2). The obtained cells were sorted and sequenced in two independent experiments (batch I and batch II). In the first experiment, we obtained data on bulk CD8 T cells from 5 patients (batch I: COVID-087, 096, 117, 143, 153, Supplementary Fig. 4b) and on TTD-specific CD8 T cells from 4 of these patients (COVID-087 was negative for HLA-A*01:01 and did therefore not harbor TTD-specific CD8 T cells). In the second experiment, we obtained data from 1 additional patient including bulk and TTD-specific CD8 T cells (batch II: COVID-131, Supplementary Fig. 4b). Louvain clustering based on highly variable genes resulted in six and eight clusters in batch I and batch II, respectively ( Fig. 4a and Supplementary Data 2). We compiled a consensus list of well-established T cell differentiation gene markers (Supplementary Data 3), which allowed us to discriminate naive (batch I and batch II: C1) from effector (batch I: C2-C6 and batch II: C2-C7) and memory CD8 T cells (C8 only clearly separated in batch II, Fig. 4b). Data on 542 TTDspecific CD8 T cells was obtained for 5 of the 6 patients (Table 2), with the vast majority of cells derived from 2 patients (batch I: COVID-096 and batch II: COVID-131). TTD-specific CD8 T cells were predominantly present in cluster C2, C4, and C6 in batch I, while TTD-specific CD8 T cells in batch II formed a separate cluster (C2, Fig. 4c). This difference in clustering is likely based on the difference in numbers of TTD-specific CD8 T cells (batch I: n = 48, batch II: n = 494). Subsequently, we performed differential gene expression analysis between TTD-specific CD8 T cells and bulk CD8 T cells from the naive cluster (C1). The analysis resulted in 175 and 2594 differentially expressed genes in TTD-specific CD8 T cells in batch I and batch II, respectively (Supplementary Fig. 4c and Supplementary Data 4). Gene ontology analysis revealed the presence of several immune response-related processes, including T cell activation, regulation of T cell activation, adhesion, and proliferation in the TTDspecific CD8 T cells compared to the naive cells (Supplementary Data 4). Next, we assessed the difference in gene expression between TTD-specific CD8 T cells and the effector/memory bulk CD8 T cells (C2-C8). We focused our analysis on genes that were identified in both data sets to be either upregulated (n = 12) or downregulated (n = 209) in TTD-specific CD8 T cells compared to bulk effector/memory CD8 T cells (Supplementary Fig. 4d and Supplementary Data 5). Upregulated genes were associated with T cell survival (e.g., CD27) and TCR signaling (e.g., PAG1 and COTL1) while downregulated genes were associated with T cell activation (e.g., TNFRSF9 [CD137] and LMNA), migration to epithelial tissue and site of inflammation (e.g., CXCR2, CCL3), proliferation (e.g., MYBL1, CDK2, and USP37), and effector function (e.g., GNLY). Together, these results suggest that activated TTD-specific CD8 T cells display a gene expression program of maintained cell survival but restricted T cell (re) activation, proliferation, and migration, which is in line with the lack of ability to produce cytokines in response to peptide stimulation.
SARS-CoV-2-specific CD8 T cells convert to functional memory cells. To investigate whether SARS-CoV-2-specific CD8 T cell responses persist after recovery from acute COVID-19 disease, we first analyzed the kinetics of the identified CD8 T cell responses from 3 COVID-19 patients during acute disease and convalescence (4-5 months post hospital discharge). All of the six previously identified SARS-CoV-2-specific CD8 T cell responses were detected during convalescence (Fig. 5a, b). The magnitude of the SARS-CoV-2-specific CD8 T cell responses (n = 5) detected in COVID-96 and -143 was on average 11-fold lower during convalescence compared to acute COVID-19 disease. Nevertheless, the magnitude of the TTD-specific CD8 T cell response in Fig. 2 Characterization of the TTD-specific CD8 T cell response. a Difference in the magnitude of SARS-CoV-2-specific CD8 T cell responses specific for the TTD epitope (n = 8) compared to CD8 T cell responses specific for all other epitopes (n = 27). Responses included in this analysis were detected in COVID-19 patients with acute disease. Box plots indicate the median (line), 25th and 75th percentile (box), min and max (whiskers), and all data points (single circles). Statistical significance was tested with a two-tailed Mann-Whitney U test, P = 0.0003. Source data are provided as a Source data file. b SARS-CoV-2-specific CD8 T cell responses specific for the TTD epitope (green) and other epitopes (white) detected in HLA-A*01:01 + patients with acute critical and severe disease (n = 8). The fold difference in magnitude between the TTD-specific CD8 T cell response and the second-highest detected SARS-CoV-2-specific CD8 T cell response is shown if present. c Difference in the magnitude of TTD-specific CD8 T cell responses (n = 5) compared to responses specific for other HLA-A*01:01-restricted epitopes (n = 12) that were detected in HLA-A*01:01 + patients. Box plots indicate the median (line), 25th and 75th percentile (box), min and max (whiskers), and all data points (single circles). Statistical significance was tested with a two-tailed Mann-Whitney U test, P = 0.0136. Source data are provided as a Source data file. d Quantity of unique TRB-CDR3 chains present in TTD-specific CD8 T cells (n = 431) of COVID-19 patients (n = 5). The number of single cells expressing unique TRB-CDR3 chains is indicated on the x-axis. TRB-CDR3 T cell receptor beta-complementarity-determining region 3. e Frequency of cells expressing different TRBV segments that were found in bulk CD8 T cells (left panel, n = 1860) compared to TTD-specific CD8 T cells (right panel, n = 431) of COVID-19 patients (n = 5). Accumulated (stacked) frequencies for each individual patient are shown. TTD TTDPSFLGRY, pHLA peptide human leukocyte antigen. *** P < 0.001, * P < 0.05.
COVID-143 remained considerably high (>1% of total CD8 T cells) during convalescence. For patient COVID-117, the magnitude of the TTD-specific CD8 T cell response was unaltered during convalescence. Phenotypic characterization of these responses by flow cytometry revealed that the percentage of SARS-CoV-2-specific CD8 T cells expressing activation and effector T cell markers (HLA-DR, CD95, and CXCR3) was significantly decreased. In contrast, the percentage of SARS-CoV-2specific CD8 T cells expressing the T cell differentiation markers CD45RA and CCR7 (albeit at a lower level compared to bulk naive T cells) was significantly increased during convalescence (Fig. 5c, d and Supplementary Fig. 5c). In addition, SARS-CoV-2specific CD8 T cells displayed lower expression levels (MFI) of PD-1 during convalescence compared to acute disease (Fig. 5e, f). Lastly, we assessed the functionality of SARS-CoV-2-specific CD8 T cell responses during acute disease and convalescence. Sufficient material for functional assessment was available for two of the three patients. In contrast to absent cytokine production   during acute disease, TTD-specific CD8 T cells from both patients gained the capacity to secrete IFNγ and TNF during convalescence (Fig. 5g, h). Together, these results demonstrate that SARS-CoV-2-specific CD8 T cell responses can be detected up to 5 months post recovery from acute severe and critical COVID-19 disease and that these cells convert from dysfunctional effector CD8 T cells to functional memory CD8 T cells during convalescence. Table 2 Number of CD8 T cells and TTD-specific CD8 T cells obtained from single-cell RNA-sequencing per patient.

Discussion
We employed a systematic effort to identify CD8 T cellrecognized SARS-CoV-2 epitopes by covering 10 common HLA alleles and a selection of in silico predicted peptides from the entire SARS-CoV-2 proteome resulting in 500 unique pHLA complexes. Our analyses resulted in the identification of 18 SARS-CoV-2-derived CD8 T cell epitopes. The origin of these epitopes was diverse, including the ORF1ab, spike protein, membrane protein, nucleoprotein, and the ORF3a. Eight of the 18 identified epitopes were derived from the ORF1ab including one epitope (TTDPSFLGRY), which displayed strong immunodominant characteristics. First, CD8 T cell responses specific for this epitope were detected in all HLA-A*01:01 + patients with acute COVID-19 disease. Second, the magnitude of TTD-specific CD8 T cell responses was remarkably high (average: 7%, range: 0.074-19%) and substantially higher compared to the other SARS-CoV-2-specific CD8 T cell responses across and within most patients. Notably, such remarkably high magnitude of SARS-CoV-2-specific CD8 T cell responses has not been reported in previous studies 2,4,[9][10][11][12][13][14][16][17][18]34 . Third, TCR sequencing data of the TTD-specific CD8 T cell responses revealed a high level of clonal diversity based on TRB-CDR3 regions, together with an enrichment for the TRBV27 segment suggesting that these TCR rearrangements may occur more frequently similar to what is known for, e.g., TCRs specific for the immunodominant CMV epitope NLVPMVATV 35,36 . These data provide the first TCR sequences specific for an immunodominant SARS-CoV-2 epitope. Such information can pave the way for T cell therapies in COVID-19 patients unable to mount protective T cell immunity in response to, e.g., vaccination. Although our analysis was limited by the selection of HLA alleles and the number of epitopes, as well as imperfect in silico epitope predictions, accumulating evidence from other groups 8,12,18 and our data demonstrate that epitopes derived from other parts of the SARS-CoV-2 proteome than the spike protein can induce high-magnitude CD8 T cell responses. It is currently not known whether any of the epitopes derived from the spike protein have immunodominant properties in the absence of, e.g., the ORF1ab, and it remains to be established whether CD8 T cell responses raised against the spike protein alone are sufficient to induce long-lasting protective T cell immunity. The spike protein is the focus of the currently approved vaccines against SARS-CoV-2 37,38 , and future studies assessing vaccine-induced T cell responses will be able to answer such outstanding questions.
A number of recent studies provide evidence suggesting that individual genetic variation in HLA type may predict COVID-19 outcome across a population [39][40][41] . Such a scenario is well described for, e.g., HLA-B*57:01 + HIV patients who are more likely to be long-term non-progressors 42 . One study on COVID-19 patients showed that HLA-A*01:01 and HLA-A*02:01 were associated with higher risk for severe disease in Italy; however, this analysis was not based on separate HLA alleles but rather combinations 40 . Based on our data, it appears that there may be a bias for HLA alleles capable of presenting epitopes, which can induce CD8 T cell responses of profound magnitude. We detected 27 SARS-CoV-2-specific CD8 T cell responses in samples collected during acute COVID-19 disease. The average magnitude of the 20 HLA-A*01:01-restricted CD8 T cell responses that were detected in patients with acute COVID-19 disease was 2.97% (range: 0.017-19%) of total CD8 T cells. In contrast, the average magnitude of the 7 detected CD8 T cell responses restricted by other HLA alleles was 0.084% (range: 0.006-0.22%) of total CD8 T cells. This difference in magnitude was not only driven by CD8 T cell responses specific for the TTD epitope, as the average magnitude of the 12 other (not TTD-specific) HLA-A*01:01restricted CD8 T cell responses was 2-fold higher compared to CD8 T cell responses restricted by other HLA alleles (0.182 vs. 0.082%). This observation suggests that patients with a specific (set of) HLA allele(s) may be able to raise a substantial CD8 T cell response toward SARS-CoV-2. However, given the limited size of patients in our and other studies [39][40][41] , this hypothesis needs to be addressed in larger patient cohorts in the future.
Interestingly, in-depth characterization of SARS-CoV-2specific CD8 T cells based on a stimulation assay, ex vivo flow cytometry, and transcriptome analysis revealed a regulated activation program that maintains CD8 T cell survival while limiting their effector function during acute COVID-19 disease. First, we observed a lack or strong reduction of cytokine production (IFNγ, TNF, IL-2, and IL-17) upon stimulation with the TTD epitope demonstrating a highly dysfunctional state of SARS-CoV-2specific CD8 T cells during acute severe and critical COVID-19 disease. These data are in line with other studies demonstrating that the SARS-CoV-2-specific CD8 T cell response is functionally impaired in COVID-19 patients with severe disease in comparison to patients with acute mild and moderate disease 9,11,43,44 . Second, the percentage of SARS-CoV-2-specific CD8 T cells expressing NKG2A was significantly higher compared to bulk CD8 T cells, and the magnitude of SARS-CoV-2-specific CD8 T cell responses correlated with increased expression of NKG2A and PD-1. The expression of the NKG2A receptor, in particular, may in part explain the inability of SARS-CoV-2-specific CD8 T cells to produce cytokines. Expression of NKG2A can limit immunopathology in influenza by inhibiting effector function 45 . This receptor may have a similar role in COVID-19 patients who frequently present with abnormally high levels of IL-6 21,46-48 , which drives the expression of the NKG2A ligand HLA-E on T cells, B cells, and macrophages 49,50 . Third, several studies showed that bulk T cells have impaired effector functions (i.e., cytokine production) and higher expression levels of inhibitory receptors compared to bulk T cells from healthy individuals, which is worsening with disease stage [21][22][23] . In line with the functional data, our results demonstrate that identified SARS-CoV-2-specific CD8 T cell responses harbor a regulated activation program that limits their functional capacity and migration in patients with acute severe and critical COVID-19 disease. These data may be a cautious suggestion that SARS-CoV-2specific CD8 T cells-unlike CD4 T cells 51 -are less likely to contribute to the immunopathology observed in patients with severe and critical COVID-19 52 but rather have a protective role, which is in line with findings from other studies 43,44 .
Our longitudinal data analysis demonstrates that SARS-CoV-2specific CD8 T cell responses persist up to 5 months post hospital discharge (as expected at a lower magnitude compared to acute disease for most responses) and that these responses convert to functional memory T cells. These findings are in line with previous studies demonstrating functional memory SARS-CoV-2-specific CD8 T cell responses at the time of convalescence 8,10,12,14,18 . Such memory T cell responses can persist up to 6 years in recovered SARS-CoV-1 patients 53 , and it is of key importance to understand if Fig. 5 Characterization of SARS-CoV-2-specific CD8 T cells during acute disease and convalescence. a Representative dot plots of the TTD-specific CD8 T cell response detected in patient COVID-143. Magnitude of the response (percentage of double-positive pHLA + cells [green] of total CD8 + cells [gray]) is indicated. Representative gating strategy is shown in Supplementary Fig. 5. b Magnitude of SARS-CoV-2-specific CD8 T cell responses (n = 6) detected in COVID-19 patients (n = 3). Source data are provided as a Source data file. c Representative gating strategy used to quantify the fraction of CD95 + , CXCR3 + , HLA-DR + , CD45RA + , and CCR7 + TTD-specific CD8 T cells in COVID-096. Gates were set based on bulk CD8 T cells (representative gating strategy is shown in Supplementary Fig. 5). d Fraction of CD95 + , CXCR3 + , HLA-DR + , CD45RA + , and CCR7 + SARS-CoV-2-specific CD8 T cell responses (n = 6) detected in COVID-19 patients (n = 3). Individual points are annotated according to CD8 T cell specificity for individual patients as shown in Fig. 5b. Statistical significance was tested with a two-tailed Wilcoxon signed-rank test, P = 0.0312. Source data are provided as a Source data file. e Representative histogram of the expression levels (MFI) of PD-1 on SARS-CoV-2-specific CD8 T cells in COVID-096. f Expression levels (MFI) of PD-1 on SARS-CoV-2-specific CD8 T cell responses (n = 6) detected in COVID-19 patients (n = 3). Individual points are annotated according to CD8 T cell specificity for individual patients as shown in Fig. 5b. Statistical significance was tested with a two-tailed Wilcoxon signed-rank test, P = 0.0312. Source data are provided as a Source data file. g Representative gating used to assess the expression of IFNγ, TNF, IL-2, and IL-17 in CD8 T cells in patient COVID-143 after 12-h peptide stimulation with TTD. Percentages of cytokine-producing cells are indicated. Full gating strategy is shown in Supplementary Fig. 3a-c. h Functionality of CD8 T cells (n = 2) after 12-h peptide stimulation with TTD. DMSO control-subtracted percentages are shown. Two independent experiments were performed for patient COVID-143 with similar results. AD acute disease, CON convalescence, TTD TTDPSFLGRY, PTD PTDNYITTY, DTD DTDFVNEFY, pHLA peptide human leukocyte antigen. * P < 0.05. this will also be the case for the SARS-CoV-2-specific T cell response.
To summarize, we have identified a SARS-CoV-2 epitope with immunodominant features, which is encoded by the ORF1ab and restricted by a common HLA allele. Interestingly, our data provide evidence for a dysfunctional state of SARS-CoV-2-specific CD8 T cell responses during acute infection and that these responses convert to functional memory T cells during convalescence.

Methods
Patient material. Peripheral blood samples of COVID-19 patients and HDs were collected in accordance with the Declaration of Helsinki after approval by the institutional review boards (Ethical Committee of Area Vasta Emilia Romagna, protocol number 177/2020, March 10, 2020, and subsequent amendments). Each participant gave informed consent. All COVID-19 patients were tested positive for SARS-CoV-2 using reverse transcriptase PCR from an upper respiratory tract (nose/throat) swab test in accredited laboratories. Patients were assigned in groups based on the COVID-19 disease status during sample collection according to the World Health Organization guidelines 54 . Samples were collected as follows: during hospitalization/acute disease for COVID-19 patients with moderate, severe, or critical disease, 3 months post positive PCR test for asymptomatic COVID-19 patients, before November 2019 for HDs, and 4-5 months post hospital discharge, and with a negative SARS-CoV-2 PCR test, for convalescent patients who recovered from severe or critical COVID-19 disease (Table 1 and Supplementary  Table 1). Peripheral blood was collected in ethylenediaminetetraacetic acid tubes following subsequent isolation of PBMCs using Ficoll-Paque density centrifugation according to standard protocol. PBMCs were suspended in fetal bovine serum (FBS, Sigma, F7524) with dimethyl sulfoxide (DMSO, Sigma, D4540, 10% v/v) and stored in liquid nitrogen.
SARS-CoV-2 epitopes. The entire SARS-CoV-2 proteome obtained from UniProt (Proteome ID: UP000464024) was considered for predicting SARS-CoV-2 epitopes using R version (v.3.6.3) and data.table (v.12.6). List of selected SARS-CoV-2epitopes is provided in Supplementary Data 1. Potential SARS-CoV-2 epitopes were first selected based on a predicted NetChop-3.1 24 proteasomal processing score >0.5, followed by a final selection step based on the highest predicted binding affinity to major histocompatibility complex (MHC) according to NetMHCpan-4.0 25 . In addition, SARS-CoV-2 epitopes that were predicted to be most immunogenic by the science community were included for analysis 34,[55][56][57] . Predicted 9-11mer epitopes from the following ORFs of the SARS-CoV-2 proteome were included in the final analysis: ORF 1ab, 3a, 6, 7a, 7b, 8, 9b, 10, 14, envelope, membrane, nucleoprotein, and spike protein. Fifty SARS-CoV-2 epitope-HLA combinations were selected for each of the top 10 most prevalent HLA alleles in Italy, which resulted in a total of 438 unique peptides that were synthesized by the Chemical Biology group, Leiden University Medical Centre. Predicted scores for proteasomal processing (NetChop-3.1) and binding affinity (NetMHCpan-4.0) of the 50 selected and 18 CD8 T cell-recognized epitopes for each allele are shown in Supplementary Fig. 6. The COVID-19 CoV Genomics tool (v.1.6.0, data cut-off: 28-01-2021) 28 , enabled by the GISAID initiative, was used to determine whether CD8 T cell-recognized epitopes were located in positions of the SARS-CoV-2 proteome with a high level of SNVs.  Table 4) followed by gel filtration, biotinylation, and final purification by high-performance liquid chromatography 59 .
Generation of fluorescent pHLA multimers. MHC complexes were loaded with the selected SARS-CoV-2 peptides via UV-induced ligand exchange 60,61 . In brief, pHLA complexes with UV-sensitive peptide were subjected to 254/366 nM UV light for 1 h at 4°C in the presence of a rescue peptide. Fourteen fluorochrome-streptavidin reagents (Supplementary Table 5) were conjugated to pHLA monomers (100 μg/mL). For each pHLA monomer, conjugation was performed with 2 fluorochrome-streptavidin reagents resulting in dual fluorescent color codes for up to 75 epitopes. Subsequently, milk (Sigma, LP0031, 1% w/v) was added to block and capture unspecific peptide-binding residues, and fluorescently labeled pHLA multimers were incubated for 30 min on ice. Finally, D-biotin (Sigma, B4501, 26.3 mM) and NaN 3 (0.02% w/v) in phosphate-buffered saline (PBS) was added to block residual binding sites.
Analysis of SARS-CoV-2-specific CD8 T cell responses. The following gating strategy shown in Supplementary Fig. 1 was applied to identify CD8 + T cells: (i) selection of live (IRDye low-dim) single-cell lymphocytes [forward scatter (FSC)-W/H low, side scatter (SSC)-W/H low, FSC/SSC-A], (ii) selection of anti-CD8 + and "dump" (anti-CD4, anti-CD14, anti-CD16, anti-CD19) negative cells. Antigenspecific CD8 T cell responses that were positive for only two and none of the other fluorescent pHLA multimers were identified using Boolean gating. Full gating strategy is shown. A minimum of 1000 CD8 T cells was required for further analysis. Cut-off values for the definition of positive antigen-specific CD8 T cell responses were ≥5 events and ≥0.005% of total CD8 T cells. To avoid experimental bias, analysis was carried out without prior knowledge about clinical patient characteristics, and to reduce researcher bias caused by manual gating, only positive responses that were confirmed by three independent researchers were defined as real. Data were analyzed using either the BD FACSDiva (v.8.0.1) or the FlowJo (v.10.6.2/10.7) software. To monitor the reproducibility of the assay system, reference samples with up to 10 CD8 T cell responses present at varying frequencies were included in each analysis.
Peptide stimulation assay. PBMCs were thawed, washed, and incubated at 37°C for 30 min or at 4°C for 60 min (if cells were simultaneously used for single-cell RNA sequencing) in RPMI 1640 (Life Technologies, 21875-034) supplemented with human serum (Sigma, H3667, 10% v/v), penicillin-streptomycin (Life Technologies, 15140-122, 1% v/v), and benzonase nuclease (Merck-Millipore, 2500 U/mL). After washing, equal amounts of PBMCs (≥1 × 10 5 cells per condition) were cultured for 12 h at 37°C in the presence of GolgiPlug (BD, 555029, 1/1000) and either the TTDPDFLGRY peptide (2 μg/mL) or equimolar amounts of DMSO (negative control). Phorbol 12-myristate 13-acetate (50 ng/mL) and Ionomycin (1 μg/mL) were used as technical control. Cells were washed and stained for 20 min on ice with surface marker antibodies (Supplementary Table 6). After washing, cells were stained for 10 min on ice with the LIVE/DEAD Fixable IR Dead Cell Stain Kit (Invitrogen, L10119, 1/400). Subsequently, cells were washed, fixed, and permeabilized using the Foxp3 Transcription Factor Staining Buffer Set (eBioscience, 00-5523-00) according to the manufacturer's protocol. Intracellular cytokines were stained for 20 min on ice with antibodies (Supplementary Table 6). Cells were washed twice before acquisition. The following gating strategy shown in Supplementary Fig. 3a-c was applied to identify CD8 + T cells using FlowJo (v.10.6.2/10.7): (i) selection of live (IRDye low-dim) single-cell lymphocytes [FSC-W/H low, SSC-W/H low, FSC/SSC-A], (ii) selection of anti-CD8 + and "dump" (anti-CD4, anti-CD14, anti-CD16, anti-CD19) negative cells. For the final analysis, acquired CD8 T cell counts of the DMSO control were normalized to the SARS-CoV-2 peptide condition. To quantify the frequency of TNF + , IFNγ + , IL-2 + , and IL-17 + CD8 + cells, gates were set based on the DMSO control. Cut-off values for the definition of cytokine-producing CD8 T cell responses stimulated with the cognate SARS-CoV-2 peptide were ≥5 events and a ≥2-fold difference in the magnitude of TNF + , IFNγ + , IL-2 + , or IL-17 + CD8 + cells compared to the DMSO control. Single-cell RNA and TCR sequencing data analysis. The Cell Ranger Software Suite (v.3.1.0) was used to perform sample de-multiplexing, barcode processing, and single-cell 5' unique molecular identifier (UMI) counting. Single-cell RNAsequencing data analysis was performed with Scanpy (v.1.5.1) 62 . Data were analyzed with Python (v.3.7.6) 63 , pandas (v.1.0.1) 64 and NumPy (v.1.18.1) 65 were used for data manipulation, and Seaborn (v.0.10.0) 66 and Matplotlib (v.3.1.3) 67 were used for plotting. The single-cell transcriptome and TCR sequencing data were analyzed separately for batch I (n = 5, COVID-087, -096, -117, -143, and -153) and batch II (n = 1, COVID-131) to avoid the batch effect introduced by the use of the different Chromium Single Cell Chip. The following criteria were applied to each cell in batch I and batch II: gene count between 200 and 2500, mitochondrial gene percentage <0.25, and ribosomal gene percentage >0.2. Data were then normalized to depth 10,000, and ln(1 + x) was calculated. After filtering and normalization, the number of counts per cell and percentage of mitochondrial genes were regressed out from the data using scanpy.pp.regress_out. Data were subsequently scaled with scanpy.pp.scale using default parameters. Principal component analysis was computed on highly variable genes. A neighborhood graph of observations was computed with 50 principal components and n_neighbors = 10. UMAP plots were plotted using scaled data. Louvain clustering was performed with scanpy.tl.louvain with default parameters. Marker genes were found using scanpy.tl.rank_gen-es_groups on the non-scaled data (use_raw = True) with t test. Differentially expressed genes were filtered based on a minimum ln fold change of >1 or <−1 and Benjamini-Hochberg false discovery rate value of <0.05. Differentially expressed genes that were upregulated or downregulated were used for gene ontology analysis using DAVID Bioinformatics Resources (v.6.8) (https://david.ncifcrf.gov/home.jsp) using default settings. TCR sequences for each single T cell were assembled by Cell Ranger vdj pipeline (v.3.1.0), leading to the identification of CDR3 sequences and the re-arranged TCR gene. TCR repertoire analysis was performed with Scirpy (v.0.3) 68 . TCR diversity and TCR clonal size were estimated using scirpy.tl. alpha_diversity and ir.pl.clonal_expansion (performing the normalization), respectively. V(D)J gene usage was estimated with scirpy.pl.vdj_usage. Abundance of particular TRB V segments was estimated with scirpy.pl.group_abundance, performing the normalization.
Reporting summary. Further information on research design is available in the Nature Research Reporting Summary linked to this article.

Data availability
Single-cell RNA-sequencing data generated in this study are deposited in the Gene Expression Omnibus (GEO) repository under the accession code GSE169503. Flow cytometry data generated in this study are deposited in FlowRepository with the identifier FR-FCM-Z3KA. Source data are provided with this paper.