key: cord-0281688-v1t51qqk
authors: Adamo, Sarah; Michler, Jan; Zurbuchen, Yves; Cervia, Carlo; Taeschler, Patrick; Raeber, Miro E.; Sain, Simona Baghai; Nilsson, Jakob; Moor, Andreas E.; Boyman, Onur
title: CD8+ T cell signature in acute SARS-CoV-2 infection identifies memory precursors
date: 2021-07-22
journal: bioRxiv
DOI: 10.1101/2021.07.22.453029
sha: bd3b630732b3b4ca74f14705a745c86e9a2b6597
doc_id: 281688
cord_uid: v1t51qqk

Immunological memory is a hallmark of adaptive immunity and facilitates an accelerated and enhanced immune response upon re-infection with the same pathogen1, 2. Since the outbreak of the ongoing coronavirus disease 19 (COVID-19) pandemic, a key question has focused on whether severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)-specific T cells stimulated during acute infection give rise to long-lived memory T cells3. Using spectral flow cytometry combined with cellular indexing of transcriptomes and T cell receptor (TCR) sequencing we longitudinally characterize individual SARS-CoV-2-specific CD8+ T cells of COVID-19 patients from acute infection to one year into recovery and find a distinct signature identifying long-lived memory CD8+ T cells. SARS-CoV-2-specific memory CD8+ T cells persisting one year after acute infection re-express CD45RA and interleukin-7 receptor α (CD127), upregulate T cell factor-1 (TCF1), and maintain low CCR7, thus resembling CD45RA+ effector-memory T (TEMRA) cells. Tracking individual clones of SARS-CoV-2-specific CD8+ T cells, we reveal that an interferon signature marks clones giving rise to long-lived cells, whereas prolonged proliferation and mammalian target of rapamycin (mTOR) signaling are associated with clone contraction and disappearance. Collectively, we identify a transcriptional signature differentiating short-from long-lived memory CD8+ T cells following an acute virus infection in humans.

Immunological memory is a hallmark of adaptive immunity and facilitates an accelerated and enhanced immune response upon re-infection with the same pathogen 1, 2 . Since the outbreak of the ongoing coronavirus disease 19 pandemic, a key question has focused on whether severe acute respiratory syndrome 25 coronavirus 2 (SARS-CoV-2)-specific T cells stimulated during acute infection give rise to long-lived memory T cells 3 The COVID-19 pandemic has taken an unprecedented toll on global health and economy, impacting billions of lives all over the world. Importantly, the ongoing vaccination efforts appear to curtail the spread of SARS-CoV-2 and prevent severe disease, even as new virus variants emerge 4, 5 . Yet, a prevailing question concerns 45 whether and how exposure to SARS-CoV-2 by infection or immunization might result in long-term protective immunity. To determine which CD8 + T cell features present during acute infection are predictive of acquisition of memory and persistence several months after infection, we profiled SARS-CoV-2-specific CD8 + T cells directly ex vivo by using spectral flow cytometry, single cell RNA sequencing (scRNAseq) and TCR sequencing during acute COVID-65 19 as well as six and 12 months after primary infection.

4

To assess the dynamics of antigen-specific T cells in COVID-19, we recruited 175 patients with real-time polymerase chain reaction (RT-PCR)-confirmed COVID-19, 70 sampled during their symptomatic acute phase and followed up six months and one

year after acute infection (Fig. 1a) . We conducted human leukocyte antigen (HLA) typing on all patients and healthy controls and selected individuals carrying the HLA-A*01:01, HLA-A*11:01 or HLA-A*24:02 alleles for this study (n = 33 patients, n = 13 healthy controls). In these individuals, SARS-CoV-2-specific CD8 + T cells were 75 detected by using HLA-A*01:01, HLA-A*11:01 and HLA-A*24:02 major histocompatibility complex class I (MHC-I) dextramers 16 , hereafter collectively termed CoV2-Dex ( Fig. 1b and Extended Data Fig. 1a) , and validated by using HLA-A*01:01 and HLA-A*11:01 MHC-I pentamers 17 , hereafter termed CoV2-Pent (Extended Data Fig. 1b,c) . SARS-CoV-2-specific CD8 + T cells were found in most 80 patients in the acute phase and six months after acute infection (Fig. 1c) , confirming previous findings using SARS-CoV-2 peptide stimulation 9-11,18-22 . Moreover, we detected SARS-CoV-2-specific CD8 + T cells one year after acute SARS-CoV-2 infection (Fig. 1b,c) . As observed for murine virus infection 23 , the frequency of CoV2-Dex + cells in the acute infection correlated with the frequency of specific cells 85 during the memory phase (Fig. 1d ).

In the acute phase, flow cytometry analysis of CoV2-Dex + cells revealed a circumscript phenotype of activated cells, dominated by very high abundance of Ki-67 and HLA-DR ( Fig. 1e and Extended Data Fig. 1e ,f). CoV2-Dex + cells also tended to express granzyme B and the terminal differentiation marker CX3CR1, whereas 90 surface CD127 was markedly downregulated on CoV2-Dex + cells (Fig. 1f) . At the six-month and one-year timepoints, the frequencies of Ki-67 + and HLA-DR + CoV2-Dex + cells declined and the frequency of CD127 + cells increased, indicating a transition from effector to memory state, while granyzme B and CX3CR1 levels remained stable (Fig. 1g, Extended Data Fig. 2) . 95

To examine the transcriptional phenotype of SARS-CoV-2-specific CD8 + T cells, we sorted CoV2-Dex + CD8 + T cells and CoV2-Dex -CD8 + T cells, mixed them at a 1:10 ratio, and performed scRNAseq on a subgroup of patients (n = 20 acute, n = 19 sixmonth timepoint). We classified sequenced cells as CoV2-Dexor CoV2-Dex + based on their dCODE dextramer unique molecular identifier (UMI) counts (see Methods 100 section and Extended Data Fig. 3a ) and positivity for a single SARS-CoV2 epitope (Extended Data Fig. 3b ). Unbiased clustering revealed 12 distinct CD8 + T cell clusters ( Fig. 2a) , none of which was dominated by a single patient (Extended Data Fig.4 ).

Some clusters showed nearly complete segregation between acute and memory phase.

Thus, cluster 12 was almost exclusive to acute COVID-19, whereas cluster 11 was 105 mainly detectable in the convalescent patients (Fig. 2b,c) . In line with our flow cytometry data ( Fig. 1e and Extended Data Fig. 5 ), CoV2-Dex + CD8 + T cells showed a more defined transcriptional makeup in the acute phase, whereas their transcriptional expression was more heterogeneous six months after infection (Fig.   2d ). Comparing the contribution of CoV2-Dex + cells to different clusters, we 110 observed that clusters 1, 2 and 12 dominated the CoV2-Dex + CD8 + T cell response in the acute phase, whereas cluster 11 contained CoV2-Dex + cells exclusively from the recovery phase ( Fig. 2e and Extended Data Fig. 6 ). While clusters 1, 2 and 12 corresponded to cytotoxic, activated, and proliferating cells, respectively, cluster 11 showed a dual signature marked by enrichment of interferon (IFN) response genes 115 and genes encoding the effector cytokines IFN-γ, tumor necrosis factor (TNF), and 6 lymphotoxin-α (LT-α) (Fig. 2f,g) . Collectively, these data show that SARS-CoV-2specific CD8 + T cells are predominantly activated and proliferating cells with a cytotoxic phenotype during acute COVID-19. However, at the six-month and oneyear timepoints after infection, activation and proliferation subside and other 120 hallmarks typical of resting memory T cells emerge, such as CD127 expression 24,25 .

While a few seminal studies have explored phenotypic differentiation trajectories of antigen-specific CD8 + T cells in vaccinated human subjects 14,15 , little is known about 125 the temporal changes of longitudinally tracked human antigen-specific CD8 + T cells following an acute infection. Thus, we aimed at identifying phenotypic differentiation trajectories of SARS-CoV-2-specific CD8 + T cells, since our longitudinal sampling allowed such analysis. In the acute phase, CoV2-Dex + cells showed mostly an effector/effector-memory (T effector /T EM ) phenotype, whereas frequencies of naïve 130 (T naïve ) and central memory (T CM ) cells were lower in CoV2-Dex + compared to CoV2-Dex -CD8 + T cells (Fig. 3a ,b, Extended Data Fig. 7a ). These data were confirmed in CoV2-Pent + cells (Extended Data Fig. 7b,c) . At the six-month and one-year timepoints after infection, we observed a progressive switch from a T effector /T EM to a T EMRA phenotype, thus one year after acute COVID-19, most CoV2Dex + cells were of 135 a T EMRA phenotype (Fig. 3c ).

Subsequently, we assessed whether the T effector /T EM and T EMRA phenotypes were associated with specific T cell markers, suggesting distinct differentiation states.

Indeed, in CoV2-Dex + cells, we observed several differences between the T effector /T EM and T EMRA populations (Fig. 3d) . CoV2-Dex + T EM cells showed higher Ki-67 and 140 HLA-DR, whereas they had lower abundance of CX3CR1 already during the acute 7 phase. Notably, we observed similar phenotypic changes in CoV2-Dex + T EM cells six months after infection (Fig. 3e ).

As T cell phenotypes are driven by specific transcription factors, we assessed the transcription factors TCF1, T-box expressed in T cells (TBET), eomesodermin 145 (EOMES), and thymocyte selection-associated high-mobility group box (TOX), which are known to play important roles in T cell differentiation 26-28 . CoV2-Dex + cells expressed increased TBET in the acute phase ( Fig. 3f and Extended Data Fig.   8a ), which was maintained for more than a year after infection. Conversely, TCF1 was downregulated during the acute phase and was progressively restored at 150 subsequent timepoints ( Fig. 3g and Extended Data Fig. 8b) . A difference in TCF1 or TBET expression between specific T effector / T EM and T EMRA was not evident, although specific T EMRA expressed significantly lower EOMES six months after infection (Extended Data Fig. 8c ). In summary, our data support a model in which transition to memory is accompanied by progressive CD45RA expression, cessation of rapid 155 proliferation, and acquisition of TCF1 26 .

To longitudinally track individual antigen-specific T cell clones, we performed TCR sequencing of CoV2-Dex + cells, which revealed several antigen-specific CD8 + T cell 160 clones for each epitope investigated (Fig. 4a ). Clones were considered antigenspecific if any of the clonal cells were CoV2-Dex + (see Supplementary Dataset 1), and clones that were CoV2-Dex + in the acute phase were considered CoV2-Dex + independently of CoV2-Dex staining at six months after infection, and vice versa. As expected, the number of clones detected during convalescence was markedly lower 165 than that detected during the acute phase of infection (Fig. 4a ). In most cases, but not all, dominant clones in the acute phase corresponded to the largest clones found in the recovery phase (Fig. 4b) . We examined the clones detected in the acute phase that were still present in the convalescent phase (persistent) or became undetectable (nonpersistent) ( Fig. 4c and Extended Data Fig. 9a ). Clone size correlated positively with 170 persistence (Fig. 4d) . Interestingly, cells of persistent clones showed a different transcriptional makeup at the acute phase when compared to cells of non-persistent clones (Fig. 4e) , which also resulted in a different distribution among the previously identified CD8 + T cell clusters (Extended Data Fig. 9b ). This effect was robustly seen in different clones and was not due to a few hyper-expanded clones (Extended Data 175 and cytotoxicity (GZMM, NKG7) genes to be enriched in persisters, together with CD45RA protein expression determined by TotalSeq TM (Fig. 4g) . Conversely, nonpersisters showed higher expression of CTLA-4, TIM-3 (HAVCR2) and Ki-67 (MKI67), as well as the pro-inflammatory cytokine interleukin-32 (Fig. 4g) . We also 185 observed differential TCR-Vβ usage between persistent and non-persistent clones (Fig. 4g ).

As hundreds of millions of people worldwide recover from COVID-19, the longevity 190 of functional T cell memory after infection with SARS-CoV-2 will remain a major 9 concern in the coming years. Here, we report for the first time, robust T cell memory one year after infection. Furthermore, this is the first description in humans of a transcriptional and phenotypical trajectory of memory CD8 + T cells differentiating from T effector /T EM to T EMRA and concomitantly upregulating TCF1 following de novo 195 infection with a natural virus. Our data is in agreement with previous reports of memory differentiation in yellow fever virus vaccinees 14,15 , revealing that despite their poor proliferative capacity upon TCR stimulation in vitro 29 , CD8 + T EMRA cells constitute the main memory phenotype in peripheral blood.

Understanding how the immune system maintains the balance between effector 200 response and memory formation could explain why some infections result in robust and long-lasting T cell memory, whereas others fail to do so. Likely, both pathogen and host factors shape the fate of memory T cells. Our study helps unravel the complexity of these processes by finding a transcriptional signature that correlates with the acquisition of long-lived memory T cells. Somewhat surprisingly, although 205 clone size during the acute infection is generally related to persistence, we find that a strongly proliferative phenotype is associated with clonal contraction and disappearance. Furthermore mTOR signaling, which is likely stimulated by TCR engagement, appears to play a role in instructing the fate of short-lived effector cells by skewing cells away from memory precursors in humans, as previously shown in 210 mice 12 . Conversely, cytokine signaling mark cells destined to become memory, in agreement with previous studies showing the importance of type I IFN signaling for memory generation 30 . Our data suggest formation of memory CD8 + T cells to be strongly dependent on a delicate balance between cytokine and TCR signaling during acute infection, which in turn influence outcomes of long-lived memory T cells. Extended Table 1 for complete list), a concentrated antibody mix was then added to the samples and cells incubated for further 20 minutes at room temperature. Frozen PBMCs were used throughout the study.

After dextramer staining, a concentrated surface staining antibody mix (see Supplementary Table 2 

We additionally generated two more sample sets: using 5000 unsorted PBMCs from each patient's sample: (5) the downstream analysis. For other dextramers, cells were considered CoV2-Dex + when the UMI count of a CoV2-Dextramer was >10 and more than five times higher than the UMI count of the negative control in the same cell. Cells that were positive for more than one dextramer according to this classification (< 0.2% of all cells with known TCR) were excluded from the analysis. A TCR clone was considered SARS-445

CoV-2-specific when at least one cell of the clone was CoV2-Dex + .

The Wilcoxon-Mann-Whitney test was used for comparisons of two independent groups. The Wilcoxon signed rank test was used for paired testing. 

The sequencing dataset generated during the current study have been deposited at zenodo.org and are available at https://doi.org/10.5281/zenodo.5119633. Flow 475 cytometry datasets are available from the corresponding author on reasonable request.

The code generated during the current study is available at https://github.com/TheMoorLab. 480 

Clone size of persisting T cell clones, compared to clones that were not detectable six months after primary infection. The p-value was calculated with a Mann-Whitney-590

Wilcoxon test. e, UMAP plot of T cells belonging to CoV2 + T cell clones colored by status (persistent vs. non persistent clones). f, Gene set enrichment analysis (GSEA) showing the enrichment of genes associated with cytokine signaling among persistent clones and mTOR signaling as well as proliferation among non-persistent clones. g, Expression of selected genes (italics) and CD45RA protein determined by Totalseq TM 595 for persistent vs. non-persistent clones. P-values were adjusted for multiple comparisons.

Systemic and mucosal antibody responses specific to SARS-CoV-2 during mild versus severe COVID-19

A distinct innate immune signature marks progression from 460 mild to severe COVID-19

Profound dysregulation of T cell homeostasisand function in patients with severe COVID 19

Gene set enrichment analysis: A knowledge-based 20 approach for interpreting genome-wide expression profiles

Fast gene set enrichment analysis

scRepertoire: An R-based toolkit for single-cell immune receptor analysis

We thank Sara Hasler for her assistance with patient recruitment and coordination, Esther Baechli, Alain Rudiger, Melina Stüssi-Helbling, Lars C. Huber, and Dominik J. Schaer for their support in patient recruitment, and the members of the Boyman 485 Laboratory for helpful discussions. Graphical representations were generated with BioRender.com.

The authors declare no competing financial interests.

Supplementary Information is available for this paper.Correspondence and requests for materials should be addressed to O.B. 515