Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the virus that causes coronavirus disease 2019 (COVID-19), has infected millions of individuals, resulted in >4 million deaths, and greatly disrupted societies worldwide (Dong et al., 2020). Recovered individuals mostly exhibit robust humoral and cellular SARS-CoV-2 immunity (Grifoni et al., 2020; Koutsakos et al., 2021; Mathew et al., 2020; Sekine et al., 2020; Thevarajan et al., 2020) and generate immunological memory. In addition, multiple effective vaccines have been deployed to induce immune-mediated protection against SARS-CoV-2 leading to a substantial risk reduction of developing severe disease. Several studies in healthy individuals demonstrated that the mRNA vaccines, BNT162b2 (Pfizer-BioNTech) and mRNA-1273 (Moderna), successfully induce SARS-CoV-2-specific humoral (Gaebler et al., 2021; Ma et al., 2020) and cellular immunity (Collier et al., 2021; Minervina et al., 2021; Oberhardt et al., 2021; Sahin et al., 2021) and are highly efficacious (>94%) in reducing transmissibility and induction of serious disease and hospitalization of most variants (Baden et al., 2021; Lyngse et al., 2021; Polack et al., 2020; Tenforde and Patel, 2021). In addition to long-lived antibodies, the efficacy of these vaccines greatly relies on the induction of memory B and T cells.

However, about 4% of the world’s population is affected by one of over 80 different types of autoimmune disease, including common conditions such as multiple sclerosis (MS) and rheumatoid arthritis (RA) (National Stem Cell Foundation, 2021). Autoimmune diseases are often treated with immunosuppressants which successfully suppress autoreactive immune responses (Burmester and Pope, 2017; Cronstein and Aune, 2020; Edwards et al., 2004; Edwards and Cambridge, 2006; Mulero et al., 2018; Wolinsky et al., 2020), but simultaneously also affect the functionality of the adaptive immune system during infection or vaccination, depending on the immunosuppressant that is used. The severity of COVID-19 in individuals on immunosuppressants varies from mild to severe, depending on the type of immunosuppressants (Gianfrancesco et al., 2020; Möhn et al., 2020; Sparks et al., 2021; Strangfeld et al., 2021) and other underlying risk factors (Baden et al., 2021; Chaudhry et al., 2021; Hughes et al., 2021; Möhn et al., 2020; Pablos et al., 2020; Sparks et al., 2021; Strangfeld et al., 2021; Williamson et al., 2020; Zabalza et al., 2021). It is not well understood how immunosuppressants affect the formation of protective immunological B and T cell memory upon SARS-CoV-2 infection or vaccination, in part because these patient groups were excluded from many phase 3 clinical trials. Failure to induce effective immunological memory could leave these patients at risk for symptomatic re-infections. As SARS-CoV-2-specific immunity may not be very long-lived and/or can be breached by novel variants, like omicron that has mutated antibody recognition sites, it is important to evaluate if treatment with immunosuppressants allows efficient induction of SARS-CoV-2-specific B and broad-protective T cell memory upon SARS-CoV-2 exposure and subsequent reactivation of memory cells upon SARS-CoV-2 antigen recall (Apostolidis et al., 2021; Felten et al., 2022; Goel et al., 2022; Haberman et al., 2022; Mahil et al., 2021). To assess the latter, ex vivo evaluation of SARS-CoV-2 immune dynamics in patients with autoimmune diseases treated with immunosuppressants following recovery of SARS-CoV-2 infection and subsequent vaccination is required. Recent studies demonstrated that a single dose of mRNA vaccine in SARS-CoV-2 experienced healthy individuals resulted in stronger and broader immune responses than with vaccination alone, the so-called hybrid immunity (Reynolds et al., 2021; Stamatatos et al., 2021; Tauzin et al., 2021; Wang et al., 2021a). It remains to be established whether SARS-CoV-2 experienced individuals treated with immunosuppressants also induce a similar recall response upon vaccination. In this study, we aim to understand if and how immunosuppressive medication may interfere with the formation of protective immunological memory against SARS-CoV-2 to refine and/or optimize vaccine strategies to generate long-lasting, protective immunological memory in individuals using immunosuppressants.

B cell-depleting anti-CD20 monoclonal antibody (anti-CD20) therapies such as ocrelizumab (OCR) and rituximab (RTX) are successfully used to treat multiple diseases including MS (Edwards et al., 2004; Edwards and Cambridge, 2006; Mulero et al., 2018; Wolinsky et al., 2020). However, B cells play a critical role in the formation of protective humoral immunity during viral infections. With help from cognate CD4⁺ T follicular helper cells (T_fh cells), B cells differentiate into memory B cells and plasmablasts or long-lived plasma cells that secrete class-switched, high-affinity antibodies upon antigen stimulation through infection or vaccination (Elsner and Shlomchik, 2020; Nutt et al., 2015; Verstegen et al., 2021). SARS-CoV-2 infection in otherwise healthy individuals has demonstrated that both B and T_fh cells are of great importance for the formation of a protective humoral immunity (Koutsakos et al., 2021; Mathew et al., 2020; Ng et al., 2020; Thevarajan et al., 2020; Wang et al., 2021b). However, the majority of patients on anti-CD20 therapies did not generate significant SARS-CoV-2-specific antibody titers following the natural infection (Koutsakos et al., 2021; Simon et al., 2021; Zabalza et al., 2021) or vaccination (Apostolidis et al., 2021; Deepak et al., 2021; van Kempen et al., 2022). A study in SARS-CoV-2 naïve anti-CD20-treated MS patients demonstrated that low vaccination-induced SARS-CoV-2 antibody titers correlate with reduced frequencies of circulating B cells (in line with anti-CD20-mediated B cell depletion) and low T_fh cells (Apostolidis et al., 2021). Interestingly, SARS-CoV-2 naïve anti-CD20-treated MS patients which induced the lowest antibody titers displayed the highest induction of activated CD8⁺ T cells after vaccination (Apostolidis et al., 2021; Brill et al., 2021; Gadani et al., 2021; Madelon et al., 2021). CD8⁺ T cells form an important second line of protection against severe illness and mortality, as they are essential for the viral clearance (Kundu et al., 2022; Tan et al., 2021). To date, it remains to be evaluated if patients treated with anti-CD20 induce enough immunological B and T cell memory upon primary infection to be efficiently recalled upon re-exposure and/or with targeted vaccination.

Methotrexate (MTX) is another widely used immunosuppressant, which is one of the most common and effective medications to treat RA (Burmester and Pope, 2017; Cronstein and Aune, 2020). MTX has broad immunomodulating functions affecting multiple arms of the immune system and has been shown to reduce circulating leukocytes numbers (neutrophils and lymphocytes), their proliferation capacity, T cell receptor (TCR) activation, T cell lytic capacity, inhibiting pro-inflammatory pathways, neutrophil recruitment, extracellular trap formation and cytokine expression by macrophages and increasing the number of regulatory T cells (T_regs) (Cronstein and Aune, 2020). Although the immune-modulatory function of MTX effectively mitigates effects of autoimmune inflammatory reactions and potentially contributes to reduced severity of COVID-19 (Sparks et al., 2021; Strangfeld et al., 2021), it has also been reported to reduce antibody titers after SARS-CoV-2 vaccination in SARS-CoV-2 naïve autoimmune patients (Deepak et al., 2021; Furer et al., 2021; Haberman et al., 2020; Spiera et al., 2021). In contrast to anti-CD20-treated MS patients, mRNA vaccination of SARS-CoV-2 naïve MTX-treated patients with immune-mediated inflammatory disease (IMID) did not induce activated CD8⁺ T cells following mRNA vaccination (Haberman et al., 2020). Proportions of spike-specific B cells, T_fh, and activated CD4⁺ T cells were reported to be induced to similar levels as observed in healthy individuals and IMID patients without MTX treatment although induction of antibody titers was less (Haberman et al., 2020). However, it is currently unknown whether MTX affects immunological memory formation after natural SARS-CoV-2 infection and/or B and T cell reactivation upon re-infection and/or vaccination.

To understand how immunosuppressive medications like OCR and MTX affect the induction of immunological memory and subsequent recall response, the humoral and cellular immune responses in SARS-CoV-2 experienced RA-MTX and MS-OCR patients were established and compared to SARS-CoV-2 experienced healthy individuals following the first and second dose of the Moderna mRNA vaccine. The immune dynamics of the recall response were elucidated by measuring SARS-CoV-2-specific antibody responses and deep-immune profiling of B and T cell responses in fresh peripheral blood. This study shows that OCR-treated MS patients exhibit a preserved recall response of CD8⁺ T cells following first vaccination compared to healthy controls (HCs) and a normal CD4⁺ T_fh1 and T_h1 dynamics, whereas humoral and B cell responses were strongly impaired. In contrast, RA patients treated with MTX displayed a delayed induction of the humoral recall responses after the first vaccination, but antibody levels were comparable to HCs following a second vaccination. However, MTX treatment delayed and/or hampered CD4⁺ and CD8⁺ T cell reactivation as demonstrated by the absence of co-expression of multiple dynamic markers.

The formation of immunological memory following infection and/or vaccination and the subsequent recall response is of great importance to prevent severe disease when re-exposed to the same pathogen. This study aimed to unravel the SARS-CoV-2 immunological recall potential in SARS-CoV-2-experienced patients using two frequently prescribed immunosuppressive medications OCR and MTX. As an alternative to re-exposure with SARS-CoV-2, the immunological recall was investigated following SARS-CoV-2 Moderna mRNA vaccination of SARS-CoV-2 experienced RA-MTX and MS-OCR patients. Results were compared to vaccinated SARS-CoV-2 experienced HCs. Our study provides a comprehensive analysis of the SARS-CoV-2 vaccine-induced recall immune response in SARS-CoV-2 experienced RA-MTX and MS-OCR patients. Despite the small group sizes, both immunosuppressants were shown to affect the vaccine-induced immunological recall response differently.

The quality (measured by the number per µl blood) and quality (measured by the percentage of responding cells and the co-expression of dynamic markers) of the SARS-CoV-2 vaccine-induced CD8⁺ T cell responses was established. Although MS-OCR patients had a smaller CD8⁺ T_cm population compared to HC before vaccination and upon second vaccination, substantial stronger quality of the CD8⁺ T_cm cell recall response in OCR-treated MS patients was observed compared to those of SARS-CoV-2 experienced HCs following first vaccination. Even though the percentage of responding cells remained high in MS-OCR patients following the second vaccination, HCs displayed responses of slightly higher quality. In general, the overall quality of the CD4⁺ T_fh1, T_h1, and T_h1-like recall response was still intact in SARS-CoV-2 experienced OCR-treated MS patients. The dynamic CD4⁺ T_fh1 recall response profile was somewhat surprising considering the lack of B cells and significantly smaller CD4⁺ T_fh1 population before vaccination. Mice studies previously demonstrated that B cells contribute to the CD4⁺ T cell activation (Bouaziz et al., 2007). However, others also observed CD4⁺ T_fh1 cells activation following SARS-CoV-2 vaccination of unexperienced OCR-treated MS patients (Apostolidis et al., 2021). One interesting observation was the lack of CTLA-4 expression in SARS-CoV-2 experienced MS-OCR patients. CTLA-4 is known to increase on activated T cells to inhibit their immune response, which could indicate that by day 7 post vaccination the recall immune response in HCs is already declining (Chikuma, 2017). Whether failure to induce CTLA-4 expression on CD4⁺ T_fh1 cells in MS-OCR patients is the result of a less tolerant immune system as part of the autoimmune disease or a direct result of missing B cells in the germinal response which may normally be responsible for the induction of CTLA-4 on CD4⁺ T_fh1 cells as part of a negative feedback loop remains to be established. In addition, the lack of B cell immunity in MS-OCR patients may have resulted in an altered T_h response, which focused more on the activation of CD4⁺ T_h1 responses, skewing the recall immune response to a preserved CD8⁺ T cell performance. This is in accordance with other studies where low antibody responses following SARS-CoV-2 vaccination of unexperienced MS-OCR patients are also correlated to superior CD8⁺ T cell responses (Apostolidis et al., 2021; Brill et al., 2021; Gadani et al., 2021; Madelon et al., 2021). In contrast to the T cell recall and despite the surprising presence of low SARS-CoV-2 antibody titers before vaccination, anti-CD20 treatment severely impaired the antigen-specific humoral and B cell recall responses following vaccination, which coincidenced with failure to increase antibody titers and/or seroconversion. OCR treatment is known to eliminate CD20⁺ B cell populations for prolonged periods (van Lierop et al., 2022), which likely hampered the formation of SARS-CoV-2-specific B cell memory in MS patients and indeed memory B cells were undetectable before vaccination. Together, OCR treatment deprived MS patients of an effective B cell recall response, which in part could explain the failure to induce any humoral immunity upon vaccination. It remains to be established why MS-OCR patients seroconverted following the initial infection and if these antibodies have had similar neutralizing qualities as those found in healthy individuals. One possibility is that OCR treatment mainly eradicated circulating CD20⁺ B cell populations, while B cells in secondary lymphoid organs may have been less affected (Kamburova et al., 2013).

MTX treatment in RA patients resulted in delayed immunological recall responses compared to HCs. This was best observed by the antigen-specific humoral immune response following the first vaccination. Despite inducing a rise in antibody titers by day 7, similar levels to SARS-CoV-2 experienced HCs were not reached till day 42 just before the second vaccination. Previous studies observed that de novo antibody titers following SARS-CoV-2 vaccination of MTX-treated RA patients were reduced compared to HCs (Deepak et al., 2021; Furer et al., 2021; Haberman et al., 2020; Spiera et al., 2021). Here, we show that recall humoral immunity is merely delayed. MTX is known to hamper the proliferation of lymphocytes (Cronstein and Aune, 2020), which could have affected the formation of memory B cells following the initial infection in RA patients and thereby the effectiveness of the recall response, hampering the formation of plasmablasts and plasma cells, which was indeed observed in most RA-MTX patients. Whether delayed humoral recall was the result of lower pre-existing B cell memory before vaccination and/or reduced proliferation and differentiation capacity of the B cells, both observed during the course of this study, remains to be established by measuring antigen-specific B cells in these patients over time. However, the immunosuppressive effect of MTX was most striking in the CD8⁺ T cell compartment. SARS-CoV-2 experienced RA-MTX patients had a significantly smaller total CD8⁺ T cell population which was mainly driven by a smaller than expected CD8⁺ T_naïve population, whereas their CD8⁺ T_cm population seemed intact. In accordance with the suppressive activity of MTX and in contrast to MS-OCR patients, the memory CD8⁺ T cell populations in RA-MTX patients did not increase during the course of the study. In addition, responding CD8⁺ T_cm and T_naïve populations only increased following the second vaccination. Furthermore, their ability to upregulate the co-expression of multiple dynamic responding markers was severely hampered in CD8⁺ T_cm cells, suggesting that the overall quality of the response was either compromised or delayed, as was observed for antigen-specific humoral recall immunity. A similar effect, albeit less strong, was observed in CD4⁺ T_fh1 and T_h1 cells. Studies that included multiple additional time points are warranted to elucidate the possible explanations.

Of specific interest is the response of CD8⁺ T_naïve cells in MS-OCR patients and HCs following the first vaccination. This indicates that next to immunological recall a novel CD8⁺ T cell response is generated, which could result in a broader immune response. Further investigation is warranted to establish whether these cells recruit novel clonotypes directed to similar epitopes as during the initial infection or CD8⁺ T cells directed against additional epitopes. The effect of both vaccinations on the CD8⁺ T cells response in RA-MTX patients seemed limited, however, it cannot be fully excluded that responses in CD8⁺ T_naïve cells were delayed and not captured by the time points in this study.

Overall, this study demonstrated that MTX and OCR immunosuppressive therapies prevent the induction of a larger-than-expected humoral and cellular recall response that was previously observed in SARS-CoV-2 experienced HCs (Reynolds et al., 2021; Stamatatos et al., 2021; Tauzin et al., 2021; Wang et al., 2021a). Despite lacking humoral and B cell immunity, the MS-OCR patients did have a preserved reactive CD8⁺ T cell recall response. CD8⁺ T cells are known to form an important second-line defense. Although CD8⁺ T cells do not prevent infection, they have a key role in viral clearance which contributes to the resolution of symptoms (Kundu et al., 2022; Thevarajan et al., 2020) and robust CD8⁺ T cell immunity has been shown to result in overall milder symptoms in SARS-CoV-2 patients (Rydyznski Moderbacher et al., 2020; Tan et al., 2021). Furthermore, CD8⁺ T cells recognize conserved epitopes allowing for high cross-reactivity between different SARS-CoV-2 variants (Alison Tarke et al., 2021; Gao et al., 2022; GeurtsvanKessel et al., 2021; Keeton et al., 2021; Liu et al., 2022; Rowntree et al., 2022). While CD8⁺ T cells immunity will not prevent infection, high proportions of CD8⁺ T cells have been associated with clinically favorable outcomes (Kundu et al., 2022; Tan et al., 2021). Ongoing research in this area will be important, particularly in regard to immunosuppressed populations facing novel variants of concern. MTX treatment of RA patients also had impaired recall immune responses after vaccination. Despite a clear delay in the SARS-CoV-2-specific antibody response, further studies including more time points are required to confirm whether the lower response observed in the cellular arm of the immunological recall is delayed or remains impaired over time and how this affects the immune protection of individuals under MTX therapy when infected with new SARS-CoV-2 variants. Together, these findings indicate that SARS-CoV-2 experienced MS-OCR patients may benefit from vaccines that aim to induce a broad CD8⁺ T cell response. RA-MTX patients lack similar broad-protective cellular immune responses and are likely to benefit more from revaccination strategies with vaccines updated for new variants of concern to induce broad-protective antibody responses.

All raw and processed data presented in this study are available at https://flowrepository.org/id/FR-FCM-Z52K.

1. 1. Verstegen NJM
   2. Hagen R
   3. van den Dijssel J
   4. Kuijper L
   5. Kreher C
   6. Ashhurst T
   7. Kummer LYL
   8. Steenhuis M
   9. Duurland M
   10. de Jongh R
   11. de Jong N
   12. Bos AV
   13. Kedzierska K
   14. van Dam PJ
   15. Stalman EW
   16. Boekel L
   17. Wolbink GJ
   18. Tas SW
   19. Killestein J
   20. van Kempen ZLE
   21. Wieske L
   22. Kuijpers TW
   23. Eftimov F
   24. Rispens T
   25. van Ham SM
   26. ten Brinke A
   27. van der Sandt CE

   (2022) flowrepository

   ID FR-FCM-Z52K. All raw and processed flow cytometry data.

   https://flowrepository.org/id/FR-FCM-Z52K

1. Preprint

   1. Alison Tarke A
   2. Coelho CH
   3. Zhang Z
   4. Dan JM
   5. Yu ED
   6. Methot N
   7. Bloom NI
   8. Goodwin B
   9. Phillips E
   10. Mallal S
   11. Sidney J
   12. Filaci G
   13. Weiskopf D
   14. da Silva Antunes R
   15. Crotty S
   16. Grifoni A
   17. Sette A

   (2021) SARS-CoV-2 Vaccination Induces Immunological Memory Able to Cross-Recognize Variants from Alpha to Omicron

   bioRxiv.

   https://doi.org/10.1101/2021.12.28.474333

   • Google Scholar
2. 1. Apostolidis SA
   2. Kakara M
   3. Painter MM
   4. Goel RR
   5. Mathew D
   6. Lenzi K
   7. Rezk A
   8. Patterson KR
   9. Espinoza DA
   10. Kadri JC
   11. Markowitz DM
   12. E Markowitz C
   13. Mexhitaj I
   14. Jacobs D
   15. Babb A
   16. Betts MR
   17. Prak ETL
   18. Weiskopf D
   19. Grifoni A
   20. Lundgreen KA
   21. Gouma S
   22. Sette A
   23. Bates P
   24. Hensley SE
   25. Greenplate AR
   26. Wherry EJ
   27. Li R
   28. Bar-Or A

   (2021) Cellular and humoral immune responses following SARS-CoV-2 mRNA vaccination in patients with multiple sclerosis on anti-CD20 therapy

   Nature Medicine 27:1990–2001.

   https://doi.org/10.1038/s41591-021-01507-2

   • PubMed
   • Google Scholar
3. 1. Ashhurst TM
   2. Marsh-Wakefield F
   3. Putri GH
   4. Spiteri AG
   5. Shinko D
   6. Read MN
   7. Smith AL
   8. King NJC

   (2021) Integration, exploration, and analysis of high-dimensional single-cell cytometry data using Spectre

   Cytometry. Part A 101:237–253.

   https://doi.org/10.1002/cyto.a.24350

   • PubMed
   • Google Scholar
4. 1. Baden LR
   2. El Sahly HM
   3. Essink B
   4. Kotloff K
   5. Frey S
   6. Novak R
   7. Diemert D
   8. Spector SA
   9. Rouphael N
   10. Creech CB
   11. McGettigan J
   12. Khetan S
   13. Segall N
   14. Solis J
   15. Brosz A
   16. Fierro C
   17. Schwartz H
   18. Neuzil K
   19. Corey L
   20. Gilbert P
   21. Janes H
   22. Follmann D
   23. Marovich M
   24. Mascola J
   25. Polakowski L
   26. Ledgerwood J
   27. Graham BS
   28. Bennett H
   29. Pajon R
   30. Knightly C
   31. Leav B
   32. Deng W
   33. Zhou H
   34. Han S
   35. Ivarsson M
   36. Miller J
   37. Zaks T
   38. COVE Study Group

   (2021) Efficacy and Safety of the mRNA-1273 SARS-CoV-2 Vaccine

   The New England Journal of Medicine 384:403–416.

   https://doi.org/10.1056/NEJMoa2035389

   • PubMed
   • Google Scholar
5. 1. Bouaziz JD
   2. Yanaba K
   3. Venturi GM
   4. Wang Y
   5. Tisch RM
   6. Poe JC
   7. Tedder TF

   (2007) Therapeutic B cell depletion impairs adaptive and autoreactive CD4+ T cell activation in mice

   PNAS 104:20878–20883.

   https://doi.org/10.1073/pnas.0709205105

   • PubMed
   • Google Scholar
6. 1. Brill L
   2. Rechtman A
   3. Zveik O
   4. Haham N
   5. Oiknine-Djian E
   6. Wolf DG
   7. Levin N
   8. Raposo C
   9. Vaknin-Dembinsky A

   (2021) Humoral and t-cell response to sars-cov-2 vaccination in patients with multiple sclerosis treated with ocrelizumab

   JAMA Neurology 78:1510–1514.

   https://doi.org/10.1001/jamaneurol.2021.3599

   • PubMed
   • Google Scholar
7. 1. Burmester GR
   2. Pope JE

   (2017) Novel treatment strategies in rheumatoid arthritis

   Lancet 389:2338–2348.

   https://doi.org/10.1016/S0140-6736(17)31491-5

   • PubMed
   • Google Scholar
8. 1. Chaudhry F
   2. Jageka C
   3. Levy PD
   4. Cerghet M
   5. Lisak RP

   (2021) Review of the covid-19 risk in multiple sclerosis

   Journal of Cellular Immunology 3:68–77.

   https://doi.org/10.33696/immunology.3.080

   • PubMed
   • Google Scholar
9. 1. Chikuma S

   (2017) Ctla-4, an essential immune-checkpoint for t-cell activation

   Current Topics in Microbiology and Immunology 410:99–126.

   https://doi.org/10.1007/82_2017_61

   • PubMed
   • Google Scholar
10. 1. Collier DA
    2. Ferreira IATM
    3. Kotagiri P
    4. Datir RP
    5. Lim EY
    6. Touizer E
    7. Meng B
    8. Abdullahi A
    9. CITIID-NIHR BioResource COVID-19 Collaboration
    10. Elmer A
    11. Kingston N
    12. Graves B
    13. Le Gresley E
    14. Caputo D
    15. Bergamaschi L
    16. Smith KGC
    17. Bradley JR
    18. Ceron-Gutierrez L
    19. Cortes-Acevedo P
    20. Barcenas-Morales G
    21. Linterman MA
    22. McCoy LE
    23. Davis C
    24. Thomson E
    25. Lyons PA
    26. McKinney E
    27. Doffinger R
    28. Wills M
    29. Gupta RK

    (2021) Age-related immune response heterogeneity to SARS-CoV-2 vaccine BNT162b2

    Nature 596:417–422.

    https://doi.org/10.1038/s41586-021-03739-1

    • PubMed
    • Google Scholar
11. 1. Cronstein BN
    2. Aune TM

    (2020) Methotrexate and its mechanisms of action in inflammatory arthritis

    Nature Reviews. Rheumatology 16:145–154.

    https://doi.org/10.1038/s41584-020-0373-9

    • PubMed
    • Google Scholar
12. 1. Deepak P
    2. Kim W
    3. Paley MA
    4. Yang M
    5. Carvidi AB
    6. Demissie EG
    7. El-Qunni AA
    8. Haile A
    9. Huang K
    10. Kinnett B
    11. Liebeskind MJ
    12. Liu Z
    13. McMorrow LE
    14. Paez D
    15. Pawar N
    16. Perantie DC
    17. Schriefer RE
    18. Sides SE
    19. Thapa M
    20. Gergely M
    21. Abushamma S
    22. Akuse S
    23. Klebert M
    24. Mitchell L
    25. Nix D
    26. Graf J
    27. Taylor KE
    28. Chahin S
    29. Ciorba MA
    30. Katz P
    31. Matloubian M
    32. O’Halloran JA
    33. Presti RM
    34. Wu GF
    35. Whelan SPJ
    36. Buchser WJ
    37. Gensler LS
    38. Nakamura MC
    39. Ellebedy AH
    40. Kim AHJ

    (2021) Effect of immunosuppression on the immunogenicity of mrna vaccines to sars-cov-2 : A prospective cohort study

    Annals of Internal Medicine 174:1572–1585.

    https://doi.org/10.7326/M21-1757

    • PubMed
    • Google Scholar
13. 1. Deliyannis G
    2. Jackson DC
    3. Ede NJ
    4. Zeng W
    5. Hourdakis I
    6. Sakabetis E
    7. Brown LE

    (2002) Induction of long-term memory CD8(+) T cells for recall of viral clearing responses against influenza virus

    Journal of Virology 76:4212–4221.

    https://doi.org/10.1128/jvi.76.9.4212-4221.2002

    • PubMed
    • Google Scholar
14. 1. Dong E
    2. Du H
    3. Gardner L

    (2020) An interactive web-based dashboard to track COVID-19 in real time

    The Lancet. Infectious Diseases 20:533–534.

    https://doi.org/10.1016/S1473-3099(20)30120-1

    • PubMed
    • Google Scholar
15. 1. Edwards JCW
    2. Szczepanski L
    3. Szechinski J
    4. Filipowicz-Sosnowska A
    5. Emery P
    6. Close DR
    7. Stevens RM
    8. Shaw T

    (2004) Efficacy of B-cell-targeted therapy with rituximab in patients with rheumatoid arthritis

    The New England Journal of Medicine 350:2572–2581.

    https://doi.org/10.1056/NEJMoa032534

    • PubMed
    • Google Scholar
16. 1. Edwards JCW
    2. Cambridge G

    (2006) B-cell targeting in rheumatoid arthritis and other autoimmune diseases

    Nature Reviews. Immunology 6:394–403.

    https://doi.org/10.1038/nri1838

    • PubMed
    • Google Scholar
17. 1. Ellebedy AH
    2. Jackson KJL
    3. Kissick HT
    4. Nakaya HI
    5. Davis CW
    6. Roskin KM
    7. McElroy AK
    8. Oshansky CM
    9. Elbein R
    10. Thomas S
    11. Lyon GM
    12. Spiropoulou CF
    13. Mehta AK
    14. Thomas PG
    15. Boyd SD
    16. Ahmed R

    (2016) Defining antigen-specific plasmablast and memory B cell subsets in human blood after viral infection or vaccination

    Nature Immunology 17:1226–1234.

    https://doi.org/10.1038/ni.3533

    • PubMed
    • Google Scholar
18. 1. Elsner RA
    2. Shlomchik MJ

    (2020) Germinal center and extrafollicular b cell responses in vaccination, immunity, and autoimmunity

    Immunity 53:1136–1150.

    https://doi.org/10.1016/j.immuni.2020.11.006

    • PubMed
    • Google Scholar
19. 1. Felten R
    2. Gallais F
    3. Schleiss C
    4. Chatelus E
    5. Javier RM
    6. Pijnenburg L
    7. Sordet C
    8. Sibilia J
    9. Arnaud L
    10. Fafi-Kremer S
    11. Gottenberg JE

    (2022) Cellular and humoral immunity after the third dose of SARS-CoV-2 vaccine in patients treated with rituximab

    The Lancet. Rheumatology 4:e13–e16.

    https://doi.org/10.1016/S2665-9913(21)00351-9

    • PubMed
    • Google Scholar
20. 1. Furer V
    2. Eviatar T
    3. Zisman D
    4. Peleg H
    5. Paran D
    6. Levartovsky D
    7. Zisapel M
    8. Elalouf O
    9. Kaufman I
    10. Meidan R
    11. Broyde A
    12. Polachek A
    13. Wollman J
    14. Litinsky I
    15. Meridor K
    16. Nochomovitz H
    17. Silberman A
    18. Rosenberg D
    19. Feld J
    20. Haddad A
    21. Gazzit T
    22. Elias M
    23. Higazi N
    24. Kharouf F
    25. Shefer G
    26. Sharon O
    27. Pel S
    28. Nevo S
    29. Elkayam O

    (2021) Immunogenicity and safety of the BNT162b2 mRNA COVID-19 vaccine in adult patients with autoimmune inflammatory rheumatic diseases and in the general population: a multicentre study

    Annals of the Rheumatic Diseases 80:1330–1338.

    https://doi.org/10.1136/annrheumdis-2021-220647

    • PubMed
    • Google Scholar
21. 1. Gadani SP
    2. Reyes-Mantilla M
    3. Jank L
    4. Harris S
    5. Douglas M
    6. Smith MD
    7. Calabresi PA
    8. Mowry EM
    9. Fitzgerald KC
    10. Bhargava P

    (2021) Discordant humoral and T cell immune responses to SARS-CoV-2 vaccination in people with multiple sclerosis on anti-CD20 therapy

    EBioMedicine 73:103636.

    https://doi.org/10.1016/j.ebiom.2021.103636

    • PubMed
    • Google Scholar
22. Preprint

    1. Gaebler C
    2. Wang Z
    3. Lorenzi JCC
    4. Muecksch F
    5. Finkin S
    6. Tokuyama M
    7. Cho A
    8. Jankovic M
    9. Schaefer-Babajew D
    10. Oliveira TY
    11. Cipolla M
    12. Viant C
    13. Barnes CO
    14. Hurley A
    15. Turroja M
    16. Gordon K
    17. Millard KG
    18. Ramos V
    19. Schmidt F
    20. Weisblum Y
    21. Jha D
    22. Tankelevich M
    23. Yee J
    24. Shimeliovich I
    25. Robbiani DF
    26. Zhao Z
    27. Gazumyan A
    28. Hatziioannou T
    29. Bjorkman PJ
    30. Mehandru S
    31. Bieniasz PD
    32. Caskey M
    33. Nussenzweig MC
    34. Hagglof T
    35. Schwartz RE
    36. Bram Y
    37. Martinez-Delgado G
    38. Mendoza P
    39. Breton G
    40. Dizon J
    41. Unson-O’Brien C
    42. Patel R

    (2021) Evolution of antibody immunity to sars-cov-2

    bioRxiv.

    https://doi.org/10.1101/2020.11.03.367391

    • Google Scholar
23. 1. Gao Y
    2. Cai C
    3. Grifoni A
    4. Müller TR
    5. Niessl J
    6. Olofsson A
    7. Humbert M
    8. Hansson L
    9. Österborg A
    10. Bergman P
    11. Chen P
    12. Olsson A
    13. Sandberg JK
    14. Weiskopf D
    15. Price DA
    16. Ljunggren HG
    17. Karlsson AC
    18. Sette A
    19. Aleman S
    20. Buggert M

    (2022) Ancestral SARS-CoV-2-specific T cells cross-recognize the Omicron variant

    Nature Medicine 28:472–476.

    https://doi.org/10.1038/s41591-022-01700-x

    • Google Scholar
24. 1. Geers D
    2. Shamier MC
    3. Bogers S
    4. den Hartog G
    5. Gommers L
    6. Nieuwkoop NN
    7. Schmitz KS
    8. Rijsbergen LC
    9. van Osch JAT
    10. Dijkhuizen E
    11. Smits G
    12. Comvalius A
    13. van Mourik D
    14. Caniels TG
    15. van Gils MJ
    16. Sanders RW
    17. Oude Munnink BB
    18. Molenkamp R
    19. de Jager HJ
    20. Haagmans BL
    21. de Swart RL
    22. Koopmans MPG
    23. van Binnendijk RS
    24. de Vries RD
    25. GeurtsvanKessel CH

    (2021) SARS-CoV-2 variants of concern partially escape humoral but not T-cell responses in COVID-19 convalescent donors and vaccinees

    Science Immunology 6:eabj1750.

    https://doi.org/10.1126/sciimmunol.abj1750

    • PubMed
    • Google Scholar
25. Preprint

    1. GeurtsvanKessel CH
    2. Geers D
    3. Schmitz KS
    4. Mykytyn AZ
    5. Lamers MM
    6. Bogers S
    7. Gommers L
    8. Sablerolles RSG
    9. Nieuwkoop NN
    10. Rijsbergen LC
    11. van Dijk LLA
    12. de Wilde J
    13. Alblas K
    14. Breugem TI
    15. Rijnders BJA
    16. de Jager H
    17. Weiskopf D
    18. van der Kuy PHM
    19. Sette A
    20. Koopmans MPG
    21. Grifoni A
    22. Haagmans BL
    23. de Vries RD

    (2021) Divergent sars cov-2 omicron-specific t- and b-cell responses in covid-19 vaccine recipients

    medRxiv.

    https://doi.org/10.1101/2021.12.27.21268416

    • Google Scholar
26. 1. Gianfrancesco M
    2. Hyrich KL
    3. Hyrich KL
    4. Al-Adely S
    5. Al-Adely S
    6. Carmona L
    7. Danila MI
    8. Gossec L
    9. Gossec L
    10. Izadi Z
    11. Jacobsohn L
    12. Katz P
    13. Lawson-Tovey S
    14. Lawson-Tovey S
    15. Mateus EF
    16. Rush S
    17. Schmajuk G
    18. Simard J
    19. Strangfeld A
    20. Trupin L
    21. Wysham KD
    22. Bhana S
    23. Costello W
    24. Grainger R
    25. Hausmann JS
    26. Liew JW
    27. Sirotich E
    28. Sirotich E
    29. Sufka P
    30. Wallace ZS
    31. Wallace ZS
    32. Yazdany J
    33. MacHado PM
    34. MacHado PM
    35. MacHado PM
    36. Robinson PC
    37. Robinson PC

    (2020) Characteristics associated with hospitalisation for COVID-19 in people with rheumatic disease: data from the COVID-19 Global Rheumatology Alliance physician-reported registry. 79:–

    Annals of the Rheumatic Diseases 79:859–866.

    https://doi.org/10.1136/ANNRHEUMDIS-2020-217871

    • Google Scholar
27. 1. Goel RR
    2. Painter MM
    3. Lundgreen KA
    4. Apostolidis SA
    5. Baxter AE
    6. Giles JR
    7. Mathew D
    8. Pattekar A
    9. Reynaldi A
    10. Khoury DS
    11. Gouma S
    12. Hicks P
    13. Dysinger S
    14. Hicks A
    15. Sharma H
    16. Herring S
    17. Korte S
    18. Kc W
    19. Oldridge DA
    20. Erickson RI
    21. Weirick ME
    22. McAllister CM
    23. Awofolaju M
    24. Tanenbaum N
    25. Dougherty J
    26. Long S
    27. D’Andrea K
    28. Hamilton JT
    29. McLaughlin M
    30. Williams JC
    31. Adamski S
    32. Kuthuru O
    33. Drapeau EM
    34. Davenport MP
    35. Hensley SE
    36. Bates P
    37. Greenplate AR
    38. Wherry EJ

    (2022) Efficient recall of Omicron-reactive B cell memory after a third dose of SARS-CoV-2 mRNA vaccine

    Cell 185:1875–1887.

    https://doi.org/10.1016/j.cell.2022.04.009

    • PubMed
    • Google Scholar
28. 1. Grifoni A
    2. Weiskopf D
    3. Ramirez SI
    4. Mateus J
    5. Dan JM
    6. Moderbacher CR
    7. Rawlings SA
    8. Sutherland A
    9. Premkumar L
    10. Jadi RS
    11. Marrama D
    12. de Silva AM
    13. Frazier A
    14. Carlin AF
    15. Greenbaum JA
    16. Peters B
    17. Krammer F
    18. Smith DM
    19. Crotty S
    20. Sette A

    (2020) Targets of t cell responses to sars-cov-2 coronavirus in humans with covid-19 disease and unexposed individuals

    Cell 181:1489–1501.

    https://doi.org/10.1016/j.cell.2020.05.015

    • PubMed
    • Google Scholar
29. 1. Habel JR
    2. Nguyen THO
    3. van de Sandt CE
    4. Juno JA
    5. Chaurasia P
    6. Wragg K
    7. Koutsakos M
    8. Hensen L
    9. Jia X
    10. Chua B
    11. Zhang W
    12. Tan HX
    13. Flanagan KL
    14. Doolan DL
    15. Torresi J
    16. Chen W
    17. Wakim LM
    18. Cheng AC
    19. Doherty PC
    20. Petersen J
    21. Rossjohn J
    22. Wheatley AK
    23. Kent SJ
    24. Rowntree LC
    25. Kedzierska K

    (2020) Suboptimal SARS-CoV-2-specific CD8⁺ T cell response associated with the prominent HLA-A*02:01 phenotype

    PNAS 117:24384–24391.

    https://doi.org/10.1073/pnas.2015486117

    • PubMed
    • Google Scholar
30. 1. Haberman RH
    2. Herati R
    3. Simon D
    4. Samanovic M
    5. Blank RB
    6. Tuen M
    7. Koralov SB
    8. Atreya R
    9. Tascilar K
    10. Allen JR
    11. Castillo R
    12. Cornelius AR
    13. Rackoff P
    14. Solomon G
    15. Adhikari S
    16. Azar N
    17. Rosenthal P
    18. Izmirly P
    19. Samuels J
    20. Golden B
    21. Reddy SM
    22. Neurath MF
    23. Abramson SB
    24. Schett G
    25. Mulligan MJ
    26. Scher JU

    (2020) Methotrexate hampers immunogenicity to BNT162B2 mRNA COVID-19 vaccine in immune-mediated inflammatory disease

    Annals of the Rheumatic Diseases 80:1339–1344.

    https://doi.org/10.1136/annrheumdis-2021-220597

    • PubMed
    • Google Scholar
31. 1. Haberman RH
    2. Um S
    3. Axelrad JE
    4. Blank RB
    5. Uddin Z
    6. Catron S
    7. Neimann AL
    8. Mulligan MJ
    9. Herat RS
    10. Hong SJ
    11. Chang S
    12. Myrtaj A
    13. Ghiasian G
    14. Izmirly PM
    15. Saxena A
    16. Solomon G
    17. Azar N
    18. Samuels J
    19. Golden BD
    20. Rackoff P
    21. Adhikari S
    22. Hudesman DP
    23. Scher JU
    24. SAGA investigators

    (2022) Methotrexate and TNF inhibitors affect long-term immunogenicity to COVID-19 vaccination in patients with immune-mediated inflammatory disease

    The Lancet. Rheumatology 4:e384–e387.

    https://doi.org/10.1016/S2665-9913(22)00069-8

    • PubMed
    • Google Scholar
32. 1. Hao Y
    2. Hao S
    3. Andersen-Nissen E
    4. Mauck WM
    5. Zheng S
    6. Butler A
    7. Lee MJ
    8. Wilk AJ
    9. Darby C
    10. Zager M
    11. Hoffman P
    12. Stoeckius M
    13. Papalexi E
    14. Mimitou EP
    15. Jain J
    16. Srivastava A
    17. Stuart T
    18. Fleming LM
    19. Yeung B
    20. Rogers AJ
    21. McElrath JM
    22. Blish CA
    23. Gottardo R
    24. Smibert P
    25. Satija R

    (2021) Integrated analysis of multimodal single-cell data

    Cell 184:3573–3587.

    https://doi.org/10.1016/j.cell.2021.04.048

    • PubMed
    • Google Scholar
33. 1. Holm S

    (1979)

    A simple sequentially rejective multiple test procedure

    Scand J Stat 6:65–70.

    • Google Scholar
34. 1. Hughes R
    2. Whitley L
    3. Fitovski K
    4. Schneble HM
    5. Muros E
    6. Sauter A
    7. Craveiro L
    8. Dillon P
    9. Bonati U
    10. Jessop N
    11. Pedotti R
    12. Koendgen H

    (2021) COVID-19 in ocrelizumab-treated people with multiple sclerosis

    Multiple Sclerosis and Related Disorders 49:102725.

    https://doi.org/10.1016/j.msard.2020.102725

    • PubMed
    • Google Scholar
35. 1. Kamburova EG
    2. Koenen HJPM
    3. Borgman KJE
    4. ten Berge IJ
    5. Joosten I
    6. Hilbrands LB

    (2013) A single dose of rituximab does not deplete B cells in secondary lymphoid organs but alters phenotype and function

    American Journal of Transplantation 13:1503–1511.

    https://doi.org/10.1111/ajt.12220

    • PubMed
    • Google Scholar
36. Preprint

    1. Keeton R
    2. Tincho MB
    3. Ngomti A
    4. Baguma R
    5. Benede N
    6. Suzuki A
    7. Khan K
    8. Cele S
    9. Bernstein M
    10. Karim F
    11. Madzorera SV
    12. Moyo-Gwete T
    13. Mennen M
    14. Skelem S
    15. Adriaanse M
    16. Mutithu D
    17. Aremu O
    18. Stek C
    19. Bruyn E du
    20. Van Der Mescht MA
    21. de Beer Z
    22. de Villiers TR
    23. Bodenstein A
    24. van den Berg G
    25. Mendes A
    26. Strydom A
    27. Venter M
    28. Grifoni A
    29. Weiskopf D
    30. Sette A
    31. Wilkinson RJ
    32. Bekker LG
    33. Gray G
    34. Ueckermann V
    35. Rossouw T
    36. Boswell MT
    37. Bihman J
    38. Moore PL
    39. Sigal A
    40. Ntusi NAB
    41. Burgers WA
    42. Riou C

    (2021) SARS-CoV-2 Spike T Cell Responses Induced upon Vaccination or Infection Remain Robust against Omicron

    medRxiv.

    https://doi.org/10.1101/2021.12.26.21268380

    • Google Scholar
37. 1. Koutsakos M
    2. Nguyen THO
    3. Kedzierska K

    (2019) With a little help from t follicular helper friends: Humoral immunity to influenza vaccination

    Journal of Immunology 202:360–367.

    https://doi.org/10.4049/jimmunol.1800986

    • PubMed
    • Google Scholar
38. 1. Koutsakos M
    2. Rowntree LC
    3. Hensen L
    4. Chua BY
    5. van de Sandt CE
    6. Habel JR
    7. Zhang W
    8. Jia X
    9. Kedzierski L
    10. Ashhurst TM
    11. Putri GH
    12. Marsh-Wakefield F
    13. Read MN
    14. Edwards DN
    15. Clemens EB
    16. Wong CY
    17. Mordant FL
    18. Juno JA
    19. Amanat F
    20. Audsley J
    21. Holmes NE
    22. Gordon CL
    23. Smibert OC
    24. Trubiano JA
    25. Hughes CM
    26. Catton M
    27. Denholm JT
    28. Tong SYC
    29. Doolan DL
    30. Kotsimbos TC
    31. Jackson DC
    32. Krammer F
    33. Godfrey DI
    34. Chung AW
    35. King NJC
    36. Lewin SR
    37. Wheatley AK
    38. Kent SJ
    39. Subbarao K
    40. McMahon J
    41. Thevarajan I
    42. Nguyen THO
    43. Cheng AC
    44. Kedzierska K

    (2021) Integrated immune dynamics define correlates of COVID-19 severity and antibody responses

    Cell Reports. Medicine 2:100208.

    https://doi.org/10.1016/j.xcrm.2021.100208

    • PubMed
    • Google Scholar
39. 1. Kundu R
    2. Narean JS
    3. Wang L
    4. Fenn J
    5. Pillay T
    6. Fernandez ND
    7. Conibear E
    8. Koycheva A
    9. Davies M
    10. Tolosa-Wright M
    11. Hakki S
    12. Varro R
    13. McDermott E
    14. Hammett S
    15. Cutajar J
    16. Thwaites RS
    17. Parker E
    18. Rosadas C
    19. McClure M
    20. Tedder R
    21. Taylor GP
    22. Dunning J
    23. Lalvani A

    (2022) Cross-reactive memory T cells associate with protection against SARS-CoV-2 infection in COVID-19 contacts

    Nature Communications 13:80.

    https://doi.org/10.1038/s41467-021-27674-x

    • PubMed
    • Google Scholar
40. 1. Linderman GC
    2. Rachh M
    3. Hoskins JG
    4. Steinerberger S
    5. Kluger Y

    (2019) Fast interpolation-based t-SNE for improved visualization of single-cell RNA-seq data

    Nature Methods 16:243–245.

    https://doi.org/10.1038/s41592-018-0308-4

    • PubMed
    • Google Scholar
41. Preprint

    1. Liu J
    2. Chandrashekar A
    3. Sellers D
    4. Barrett J
    5. Lifton M
    6. McMahan K
    7. Sciacca M
    8. VanWyk H
    9. Wu C
    10. Yu J
    11. Collier ARY
    12. Barouch DH

    (2022) Vaccines Elicit Highly Cross-Reactive Cellular Immunity to the SARS-CoV-2 Omicron Variant

    medRxiv.

    https://doi.org/10.1101/2022.01.02.22268634

    • Google Scholar
42. Preprint

    1. Lyngse FP
    2. Mortensen LH
    3. Denwood MJ
    4. Christiansen LE
    5. Møller CH
    6. Skov RL
    7. Spiess K
    8. Fomsgaard A
    9. Lassaunière MM
    10. Rasmussen M
    11. Stegger M
    12. Nielsen C
    13. Sieber RN
    14. Cohen AS
    15. Møller FT
    16. Overvad M
    17. Mølbak K
    18. Krause TG
    19. Kirkeby CT

    (2021) SARS-CoV-2 Omicron VOC Transmission in Danish Households

    medRxiv.

    https://doi.org/10.1101/2021.12.27.21268278

    • Google Scholar
43. 1. Ma H
    2. Zeng W
    3. He H
    4. Zhao D
    5. Jiang D
    6. Zhou P
    7. Cheng L
    8. Li Y
    9. Ma X
    10. Jin T

    (2020) Serum iga, igm, and igg responses in covid-19

    Cellular & Molecular Immunology 17:773–775.

    https://doi.org/10.1038/s41423-020-0474-z

    • PubMed
    • Google Scholar
44. Preprint

    1. Madelon N
    2. Lauper K
    3. Breville G
    4. Royo IS
    5. Goldstein R
    6. Andrey DO
    7. Grifoni A
    8. Sette A
    9. Siegrist CA
    10. Finckh A
    11. Lalive PH
    12. Didierlaurent AM
    13. Eberhardt CS

    (2021) Patients Treated with Anti-CD20 Therapy Can Mount Robust T Cell Responses to MRNA-Based COVID-19 Vaccines

    medRxiv.

    https://doi.org/10.1101/2021.07.21.21260928

    • Google Scholar
45. 1. Mahil SK
    2. Bechman K
    3. Raharja A
    4. Domingo-Vila C
    5. Baudry D
    6. Brown MA
    7. Cope AP
    8. Dasandi T
    9. Graham C
    10. Lechmere T
    11. Malim MH
    12. Meynell F
    13. Pollock E
    14. Seow J
    15. Sychowska K
    16. Barker JN
    17. Norton S
    18. Galloway JB
    19. Doores KJ
    20. Tree T
    21. Smith CH

    (2021) The effect of methotrexate and targeted immunosuppression on humoral and cellular immune responses to the COVID-19 vaccine BNT162b2: a cohort study

    The Lancet. Rheumatology 3:e627–e637.

    https://doi.org/10.1016/S2665-9913(21)00212-5

    • PubMed
    • Google Scholar
46. 1. Mathew D
    2. Giles JR
    3. Baxter AE
    4. Oldridge DA
    5. Greenplate AR
    6. Wu JE
    7. Alanio C
    8. Kuri-Cervantes L
    9. Pampena MB
    10. D’Andrea K
    11. Manne S
    12. Chen Z
    13. Huang YJ
    14. Reilly JP
    15. Weisman AR
    16. Ittner CAG
    17. Kuthuru O
    18. Dougherty J
    19. Nzingha K
    20. Han N
    21. Kim J
    22. Pattekar A
    23. Goodwin EC
    24. Anderson EM
    25. Weirick ME
    26. Gouma S
    27. Arevalo CP
    28. Bolton MJ
    29. Chen F
    30. Lacey SF
    31. Ramage H
    32. Cherry S
    33. Hensley SE
    34. Apostolidis SA
    35. Huang AC
    36. Vella LA
    37. Betts MR
    38. Meyer NJ
    39. Wherry EJ
    40. UPenn COVID Processing Unit

    (2020) Deep immune profiling of COVID-19 patients reveals distinct immunotypes with therapeutic implications

    Science 369:eabc8511.

    https://doi.org/10.1126/science.abc8511

    • PubMed
    • Google Scholar
47. 1. McElroy AK
    2. Akondy RS
    3. Davis CW
    4. Ellebedy AH
    5. Mehta AK
    6. Kraft CS
    7. Lyon GM
    8. Ribner BS
    9. Varkey J
    10. Sidney J
    11. Sette A
    12. Campbell S
    13. Ströher U
    14. Damon I
    15. Nichol ST
    16. Spiropoulou CF
    17. Ahmed R

    (2015) Human Ebola virus infection results in substantial immune activation

    PNAS 112:4719–4724.

    https://doi.org/10.1073/pnas.1502619112

    • PubMed
    • Google Scholar
48. Preprint

    1. Minervina AA
    2. Pogorelyy MV
    3. Kirk AM
    4. Allen EK
    5. Allison KJ
    6. Lin CY
    7. Brice DC
    8. Zhu X
    9. Vegesana K
    10. Wu G
    11. Crawford JC
    12. Schultz-Cherry S
    13. Estepp JH
    14. McGargill MA
    15. Team SS
    16. Wolf J
    17. Thomas PG

    (2021) Convergent Epitope-Specific T Cell Responses after SARS-CoV-2 Infection and Vaccination

    medRxiv.

    https://doi.org/10.1101/2021.07.12.21260227

    • Google Scholar
49. 1. Möhn N
    2. Konen FF
    3. Pul R
    4. Kleinschnitz C
    5. Prüss H
    6. Witte T
    7. Stangel M
    8. Skripuletz T

    (2020) Experience in multiple sclerosis patients with covid-19 and disease-modifying therapies: A review of 873 published cases

    Journal of Clinical Medicine 9:1–12.

    https://doi.org/10.3390/jcm9124067

    • PubMed
    • Google Scholar
50. 1. Monaco G
    2. Chen H
    3. Poidinger M
    4. Chen J
    5. de Magalhães JP
    6. Larbi A

    (2016) flowAI: automatic and interactive anomaly discerning tools for flow cytometry data

    Bioinformatics 32:2473–2480.

    https://doi.org/10.1093/bioinformatics/btw191

    • PubMed
    • Google Scholar
51. 1. Moser T
    2. Otto F
    3. O’Sullivan C
    4. Hitzl W
    5. Pilz G
    6. Harrer A
    7. Trinka E
    8. Wipfler P

    (2022) Recall response to COVID-19 antigen is preserved in people with multiple sclerosis on anti-CD20 medications - A pilot study

    Multiple Sclerosis and Related Disorders 59:103560.

    https://doi.org/10.1016/j.msard.2022.103560

    • PubMed
    • Google Scholar
52. 1. Mudd PA
    2. Minervina AA
    3. Pogorelyy MV
    4. Turner JS
    5. Kim W
    6. Kalaidina E
    7. Petersen J
    8. Schmitz AJ
    9. Lei T
    10. Haile A
    11. Kirk AM
    12. Mettelman RC
    13. Crawford JC
    14. Nguyen THO
    15. Rowntree LC
    16. Rosati E
    17. Richards KA
    18. Sant AJ
    19. Klebert MK
    20. Suessen T
    21. Middleton WD
    22. Wolf J
    23. Teefey SA
    24. O’Halloran JA
    25. Presti RM
    26. Kedzierska K
    27. Rossjohn J
    28. Thomas PG
    29. Ellebedy AH
    30. SJTRC Study Team

    (2022) SARS-CoV-2 mRNA vaccination elicits a robust and persistent T follicular helper cell response in humans

    Cell 185:603–613.

    https://doi.org/10.1016/j.cell.2021.12.026

    • PubMed
    • Google Scholar
53. 1. Mulero P
    2. Midaglia L
    3. Montalban X

    (2018) Ocrelizumab: a new milestone in multiple sclerosis therapy

    Therapeutic Advances in Neurological Disorders 11:1756286418773025.

    https://doi.org/10.1177/1756286418773025

    • PubMed
    • Google Scholar
54. 1. National Stem Cell Foundation

    (2021)

    Autoimmune disease – national stem cell foundation

    Autoimmune Disease pp. 1–2.

    • Google Scholar
55. 1. Ng DL
    2. Goldgof GM
    3. Shy BR
    4. Levine AG
    5. Balcerek J
    6. Bapat SP
    7. Prostko J
    8. Rodgers M
    9. Coller K
    10. Pearce S
    11. Franz S
    12. Du L
    13. Stone M
    14. Pillai SK
    15. Sotomayor-Gonzalez A
    16. Servellita V
    17. Martin CSS
    18. Granados A
    19. Glasner DR
    20. Han LM
    21. Truong K
    22. Akagi N
    23. Nguyen DN
    24. Neumann NM
    25. Qazi D
    26. Hsu E
    27. Gu W
    28. Santos YA
    29. Custer B
    30. Green V
    31. Williamson P
    32. Hills NK
    33. Lu CM
    34. Whitman JD
    35. Stramer SL
    36. Wang C
    37. Reyes K
    38. Hakim JMC
    39. Sujishi K
    40. Alazzeh F
    41. Pham L
    42. Thornborrow E
    43. Oon CY
    44. Miller S
    45. Kurtz T
    46. Simmons G
    47. Hackett J
    48. Busch MP
    49. Chiu CY

    (2020) SARS-CoV-2 seroprevalence and neutralizing activity in donor and patient blood

    Nature Communications 11:1–7.

    https://doi.org/10.1038/s41467-020-18468-8

    • PubMed
    • Google Scholar
56. 1. Nguyen THO
    2. Koutsakos M
    3. van de Sandt CE
    4. Crawford JC
    5. Loh L
    6. Sant S
    7. Grzelak L
    8. Allen EK
    9. Brahm T
    10. Clemens EB
    11. Auladell M
    12. Hensen L
    13. Wang Z
    14. Nüssing S
    15. Jia X
    16. Günther P
    17. Wheatley AK
    18. Kent SJ
    19. Aban M
    20. Deng Y-M
    21. Laurie KL
    22. Hurt AC
    23. Gras S
    24. Rossjohn J
    25. Crowe J
    26. Xu J
    27. Jackson D
    28. Brown LE
    29. La Gruta N
    30. Chen W
    31. Doherty PC
    32. Turner SJ
    33. Kotsimbos TC
    34. Thomas PG
    35. Cheng AC
    36. Kedzierska K

    (2021) Immune cellular networks underlying recovery from influenza virus infection in acute hospitalized patients

    Nature Communications 12:2691.

    https://doi.org/10.1038/s41467-021-23018-x

    • PubMed
    • Google Scholar
57. 1. Nutt SL
    2. Hodgkin PD
    3. Tarlinton DM
    4. Corcoran LM

    (2015) The generation of antibody-secreting plasma cells

    Nature Reviews. Immunology 15:160–171.

    https://doi.org/10.1038/nri3795

    • PubMed
    • Google Scholar
58. 1. Oberhardt V
    2. Luxenburger H
    3. Kemming J
    4. Schulien I
    5. Ciminski K
    6. Giese S
    7. Csernalabics B
    8. Lang-Meli J
    9. Janowska I
    10. Staniek J
    11. Wild K
    12. Basho K
    13. Marinescu MS
    14. Fuchs J
    15. Topfstedt F
    16. Janda A
    17. Sogukpinar O
    18. Hilger H
    19. Stete K
    20. Emmerich F
    21. Bengsch B
    22. Waller CF
    23. Rieg S
    24. Boettler T
    25. Zoldan K
    26. Kochs G
    27. Schwemmle M
    28. Rizzi M
    29. Thimme R
    30. Neumann-Haefelin C
    31. Hofmann M

    (2021) Rapid and stable mobilization of CD8⁺ T cells by SARS-CoV-2 mRNA vaccine

    Nature 597:268–273.

    https://doi.org/10.1038/s41586-021-03841-4

    • PubMed
    • Google Scholar
59. 1. Oja AE
    2. Saris A
    3. Ghandour CA
    4. Kragten NAM
    5. Hogema BM
    6. Nossent EJ
    7. Heunks LMA
    8. Cuvalay S
    9. Slot E
    10. Linty F
    11. Swaneveld FH
    12. Vrielink H
    13. Vidarsson G
    14. Rispens T
    15. van der Schoot E
    16. van Lier RAW
    17. Ten Brinke A
    18. Hombrink P

    (2020) Divergent SARS-CoV-2-specific T- and B-cell responses in severe but not mild COVID-19 patients

    European Journal of Immunology 50:1998–2012.

    https://doi.org/10.1002/eji.202048908

    • PubMed
    • Google Scholar
60. 1. Pablos JL
    2. Galindo M
    3. Carmona L
    4. Lledó A
    5. Retuerto M
    6. Blanco R
    7. Gonzalez-Gay MA
    8. Martinez-Lopez D
    9. Castrejón I
    10. Alvaro-Gracia JM
    11. Fernández Fernández D
    12. Mera-Varela A
    13. Manrique-Arija S
    14. Mena Vázquez N
    15. Fernandez-Nebro A
    16. RIER Investigators Group
    17. RIER investigators group

    (2020) Clinical outcomes of hospitalised patients with COVID-19 and chronic inflammatory and autoimmune rheumatic diseases: a multicentric matched cohort study

    Annals of the Rheumatic Diseases 79:1544–1549.

    https://doi.org/10.1136/annrheumdis-2020-218296

    • PubMed
    • Google Scholar
61. 1. Polack FP
    2. Thomas SJ
    3. Kitchin N
    4. Absalon J
    5. Gurtman A
    6. Lockhart S
    7. Perez JL
    8. Pérez Marc G
    9. Moreira ED
    10. Zerbini C
    11. Bailey R
    12. Swanson KA
    13. Roychoudhury S
    14. Koury K
    15. Li P
    16. Kalina WV
    17. Cooper D
    18. Frenck RW Jr
    19. Hammitt LL
    20. Türeci Ö
    21. Nell H
    22. Schaefer A
    23. Ünal S
    24. Tresnan DB
    25. Mather S
    26. Dormitzer PR
    27. Şahin U
    28. Jansen KU
    29. Gruber WC
    30. C4591001 Clinical Trial Group

    (2020) Safety and efficacy of the bnt162b2 mrna covid-19 vaccine

    The New England Journal of Medicine 383:2603–2615.

    https://doi.org/10.1056/NEJMoa2034577

    • PubMed
    • Google Scholar
62. 1. Reynolds CJ
    2. Pade C
    3. Gibbons JM
    4. Butler DK
    5. Otter AD
    6. Menacho K
    7. Fontana M
    8. Smit A
    9. Sackville-West JE
    10. Cutino-Moguel T
    11. Maini MK
    12. Chain B
    13. Noursadeghi M
    14. Brooks T
    15. Semper A
    16. Manisty C
    17. Treibel TA
    18. Moon JC
    19. Valdes AM
    20. McKnight Á
    21. Altmann DM
    22. Boyton R
    23. UK COVIDsortium Immune Correlates Network
    24. UK COVIDsortium Investigators

    (2021) Prior SARS-CoV-2 infection rescues B and T cell responses to variants after first vaccine dose

    Science 1:eabh1282.

    https://doi.org/10.1126/science.abh1282

    • PubMed
    • Google Scholar
63. 1. Rha MS
    2. Jeong HW
    3. Ko JH
    4. Choi SJ
    5. Seo IH
    6. Lee JS
    7. Sa M
    8. Kim AR
    9. Joo EJ
    10. Ahn JY
    11. Kim JH
    12. Song KH
    13. Kim ES
    14. Oh DH
    15. Ahn MY
    16. Choi HK
    17. Jeon JH
    18. Choi JP
    19. Kim HB
    20. Kim YK
    21. Park SH
    22. Choi WS
    23. Choi JY
    24. Peck KR
    25. Shin EC

    (2021) PD-1-expressing SARS-CoV-2-specific CD8⁺ T cells are not exhausted, but Functional in Patients with COVID-19

    Immunity 54:44–52.

    https://doi.org/10.1016/j.immuni.2020.12.002

    • PubMed
    • Google Scholar
64. 1. Riberdy JM
    2. Christensen JP
    3. Branum K
    4. Doherty PC

    (2000) Diminished primary and secondary influenza virus-specific CD8(+) T-cell responses in CD4-depleted Ig(-/-) mice

    Journal of Virology 74:9762–9765.

    https://doi.org/10.1128/jvi.74.20.9762-9765.2000

    • PubMed
    • Google Scholar
65. 1. Rowntree LC
    2. Nguyen THO
    3. Kedzierski L
    4. Neeland MR
    5. Petersen J
    6. Crawford JC
    7. Allen LF
    8. Clemens EB
    9. Chua B
    10. McQuilten HA
    11. Minervina AA
    12. Pogorelyy MV
    13. Chaurasia P
    14. Tan HX
    15. Wheatley AK
    16. Jia X
    17. Amanat F
    18. Krammer F
    19. Allen EK
    20. Sonda S
    21. Flanagan KL
    22. Jumarang J
    23. Pannaraj PS
    24. Licciardi PV
    25. Kent SJ
    26. Bond KA
    27. Williamson DA
    28. Rossjohn J
    29. Thomas PG
    30. Tosif S
    31. Crawford NW
    32. van de Sandt CE
    33. Kedzierska K

    (2022) SARS-CoV-2-specific T cell memory with common TCRαβ motifs is established in unvaccinated children who seroconvert after infection

    Immunity 55:1299–1315.

    https://doi.org/10.1016/j.immuni.2022.06.003

    • PubMed
    • Google Scholar
66. 1. Rydyznski Moderbacher C
    2. Ramirez SI
    3. Dan JM
    4. Grifoni A
    5. Hastie KM
    6. Weiskopf D
    7. Belanger S
    8. Abbott RK
    9. Kim C
    10. Choi J
    11. Kato Y
    12. Crotty EG
    13. Kim C
    14. Rawlings SA
    15. Mateus J
    16. Tse LPV
    17. Frazier A
    18. Baric R
    19. Peters B
    20. Greenbaum J
    21. Ollmann Saphire E
    22. Smith DM
    23. Sette A
    24. Crotty S

    (2020) Antigen-specific adaptive immunity to SARS-cov-2 in acute COVID-19 and associations with age and disease severityAntigen-Specific Adaptive Immunity to SARS-CoV-2 in Acute COVID-19 and Associations with Age and Disease Severity

    Cell 183:996–1012.

    https://doi.org/10.1016/j.cell.2020.09.038

    • PubMed
    • Google Scholar
67. 1. Sahin U
    2. Muik A
    3. Vogler I
    4. Derhovanessian E
    5. Kranz LM
    6. Vormehr M
    7. Quandt J
    8. Bidmon N
    9. Ulges A
    10. Baum A
    11. Pascal KE
    12. Maurus D
    13. Brachtendorf S
    14. Lörks V
    15. Sikorski J
    16. Koch P
    17. Hilker R
    18. Becker D
    19. Eller AK
    20. Grützner J
    21. Tonigold M
    22. Boesler C
    23. Rosenbaum C
    24. Heesen L
    25. Kühnle MC
    26. Poran A
    27. Dong JZ
    28. Luxemburger U
    29. Kemmer-Brück A
    30. Langer D
    31. Bexon M
    32. Bolte S
    33. Palanche T
    34. Schultz A
    35. Baumann S
    36. Mahiny AJ
    37. Boros G
    38. Reinholz J
    39. Szabó GT
    40. Karikó K
    41. Shi PY
    42. Fontes-Garfias C
    43. Perez JL
    44. Cutler M
    45. Cooper D
    46. Kyratsous CA
    47. Dormitzer PR
    48. Jansen KU
    49. Türeci Ö

    (2021) BNT162b2 vaccine induces neutralizing antibodies and poly-specific T cells in humans

    Nature 595:572–577.

    https://doi.org/10.1038/s41586-021-03653-6

    • PubMed
    • Google Scholar
68. Preprint

    1. Samanovic MI
    2. Cornelius AR
    3. Gray-Gaillard SL
    4. Allen JR
    5. Karmacharya T
    6. Wilson JP
    7. Hyman SW
    8. Tuen M
    9. Koralov SB
    10. Mulligan MJ
    11. Herati RS

    (2021) Poor Antigen-Specific Responses to the Second BNT162b2 MRNA Vaccine Dose in SARS-CoV-2-Experienced Individuals

    medRxiv.

    https://doi.org/10.1101/2021.02.07.21251311

    • Google Scholar
69. 1. Sekine T
    2. Perez-Potti A
    3. Rivera-Ballesteros O
    4. Strålin K
    5. Gorin JB
    6. Olsson A
    7. Llewellyn-Lacey S
    8. Kamal H
    9. Bogdanovic G
    10. Muschiol S
    11. Wullimann DJ
    12. Kammann T
    13. Emgård J
    14. Parrot T
    15. Folkesson E
    16. Rooyackers O
    17. Eriksson LI
    18. Henter JI
    19. Sönnerborg A
    20. Allander T
    21. Albert J
    22. Nielsen M
    23. Klingström J
    24. Gredmark-Russ S
    25. Björkström NK
    26. Sandberg JK
    27. Price DA
    28. Ljunggren HG
    29. Aleman S
    30. Buggert M
    31. Karolinska COVID-19 Study Group

    (2020) Robust T cell immunity in convalescent individuals with asymptomatic or mild COVID-19Robust T Cell Immunity in Convalescent Individuals with Asymptomatic or Mild COVID-19

    Cell 183:158–168.

    https://doi.org/10.1016/j.cell.2020.08.017

    • PubMed
    • Google Scholar
70. 1. Simon D
    2. Tascilar K
    3. Schmidt K
    4. Manger B
    5. Weckwerth L
    6. Sokolova M
    7. Bucci L
    8. Fagni F
    9. Manger K
    10. Schuch F
    11. Ronneberger M
    12. Hueber A
    13. Steffen U
    14. Mielenz D
    15. Herrmann M
    16. Harrer T
    17. Kleyer A
    18. Krönke G
    19. Schett G

    (2021) Humoral and cellular immune responses to sars–cov‐2 infection and vaccination in autoimmune disease patients with b cell depletion

    Arthritis & Rheumatology 74:33–37.

    https://doi.org/10.1002/art.41914

    • Google Scholar
71. 1. Sparks JA
    2. Wallace ZS
    3. Seet AM
    4. Gianfrancesco MA
    5. Izadi Z
    6. Hyrich KL
    7. Strangfeld A
    8. Gossec L
    9. Carmona L
    10. Mateus EF
    11. Lawson-Tovey S
    12. Trupin L
    13. Rush S
    14. Katz P
    15. Schmajuk G
    16. Jacobsohn L
    17. Wise L
    18. Gilbert EL
    19. Duarte-García A
    20. Valenzuela-Almada MO
    21. Pons-Estel GJ
    22. Isnardi CA
    23. Berbotto GA
    24. Hsu TYT
    25. D’Silva KM
    26. Patel NJ
    27. Kearsley-Fleet L
    28. Schäfer M
    29. Ribeiro SLE
    30. Al Emadi S
    31. Tidblad L
    32. Scirè CA
    33. Raffeiner B
    34. Thomas T
    35. Flipo RM
    36. Avouac J
    37. Seror R
    38. Bernardes M
    39. Cunha MM
    40. Hasseli R
    41. Schulze-Koops H
    42. Müller-Ladner U
    43. Specker C
    44. Souza VAD
    45. Mota L
    46. Gomides APM
    47. Dieudé P
    48. Nikiphorou E
    49. Kronzer VL
    50. Singh N
    51. Ugarte-Gil MF
    52. Wallace B
    53. Akpabio A
    54. Thomas R
    55. Bhana S
    56. Costello W
    57. Grainger R
    58. Hausmann JS
    59. Liew JW
    60. Sirotich E
    61. Sufka P
    62. Robinson PC
    63. Machado PM
    64. Yazdany J
    65. COVID-19 Global Rheumatology Alliance

    (2021) Associations of baseline use of biologic or targeted synthetic DMARDs with COVID-19 severity in rheumatoid arthritis: Results from the covid-19 global rheumatology alliance physician registry

    Annals of the Rheumatic Diseases 80:1137–1146.

    https://doi.org/10.1136/annrheumdis-2021-220418

    • PubMed
    • Google Scholar
72. 1. Spiera R
    2. Jinich S
    3. Jannat-Khah D

    (2021) Rituximab, but not other antirheumatic therapies, is associated with impaired serological response to SARS- CoV-2 vaccination in patients with rheumatic diseases

    Annals of the Rheumatic Diseases 80:1357–1359.

    https://doi.org/10.1136/annrheumdis-2021-220604

    • PubMed
    • Google Scholar
73. 1. Stamatatos L
    2. Czartoski J
    3. Wan YH
    4. Homad LJ
    5. Rubin V
    6. Glantz H
    7. Neradilek M
    8. Seydoux E
    9. Jennewein MF
    10. MacCamy AJ
    11. Feng J
    12. Mize G
    13. De Rosa SC
    14. Finzi A
    15. Lemos MP
    16. Cohen KW
    17. Moodie Z
    18. McElrath MJ
    19. McGuire AT

    (2021) mRNA vaccination boosts cross-variant neutralizing antibodies elicited by SARS-CoV-2 infection

    Science 372:1413–1418.

    https://doi.org/10.1126/science.abg9175

    • PubMed
    • Google Scholar
74. 1. Steenhuis M
    2. van Mierlo G
    3. Derksen NI
    4. Ooijevaar-de Heer P
    5. Kruithof S
    6. Loeff FL
    7. Berkhout LC
    8. Linty F
    9. Reusken C
    10. Reimerink J
    11. Hogema B
    12. Zaaijer H
    13. van de Watering L
    14. Swaneveld F
    15. van Gils MJ
    16. Bosch BJ
    17. van Ham SM
    18. Ten Brinke A
    19. Vidarsson G
    20. van der Schoot EC
    21. Rispens T

    (2021) Dynamics of antibodies to SARS-CoV-2 in convalescent plasma donors

    Clinical & Translational Immunology 10:e1285.

    https://doi.org/10.1002/cti2.1285

    • PubMed
    • Google Scholar
75. 1. Strangfeld A
    2. Schäfer M
    3. Gianfrancesco MA
    4. Lawson-Tovey S
    5. Liew JW
    6. Ljung L
    7. Mateus EF
    8. Richez C
    9. Santos MJ
    10. Schmajuk G
    11. Scirè CA
    12. Sirotich E
    13. Sparks JA
    14. Sufka P
    15. Thomas T
    16. Trupin L
    17. Wallace ZS
    18. Al-Adely S
    19. Bachiller-Corral J
    20. Bhana S
    21. Cacoub P
    22. Carmona L
    23. Costello R
    24. Costello W
    25. Gossec L
    26. Grainger R
    27. Hachulla E
    28. Hasseli R
    29. Hausmann JS
    30. Hyrich KL
    31. Izadi Z
    32. Jacobsohn L
    33. Katz P
    34. Kearsley-Fleet L
    35. Robinson PC
    36. Yazdany J
    37. Machado PM
    38. COVID-19 Global Rheumatology Alliance

    (2021) Factors associated with COVID-19-related death in people with rheumatic diseases: results from the COVID-19 Global Rheumatology Alliance physician-reported registry

    Annals of the Rheumatic Diseases 80:930–942.

    https://doi.org/10.1136/annrheumdis-2020-219498

    • PubMed
    • Google Scholar
76. 1. Tan AT
    2. Linster M
    3. Tan CW
    4. Le Bert N
    5. Chia WN
    6. Kunasegaran K
    7. Zhuang Y
    8. Tham CYL
    9. Chia A
    10. Smith GJD
    11. Young B
    12. Kalimuddin S
    13. Low JGH
    14. Lye D
    15. Wang LF
    16. Bertoletti A

    (2021) Early induction of functional SARS-CoV-2-specific T cells associates with rapid viral clearance and mild disease in COVID-19 patients

    Cell Reports 34:108728.

    https://doi.org/10.1016/j.celrep.2021.108728

    • PubMed
    • Google Scholar
77. 1. Tauzin A
    2. Nayrac M
    3. Benlarbi M
    4. Gong SY
    5. Gasser R
    6. Beaudoin-Bussières G
    7. Brassard N
    8. Laumaea A
    9. Vézina D
    10. Prévost J
    11. Anand SP
    12. Bourassa C
    13. Gendron-Lepage G
    14. Medjahed H
    15. Goyette G
    16. Niessl J
    17. Tastet O
    18. Gokool L
    19. Morrisseau C
    20. Arlotto P
    21. Stamatatos L
    22. McGuire AT
    23. Larochelle C
    24. Uchil P
    25. Lu M
    26. Mothes W
    27. De Serres G
    28. Moreira S
    29. Roger M
    30. Richard J
    31. Martel-Laferrière V
    32. Duerr R
    33. Tremblay C
    34. Kaufmann DE
    35. Finzi A

    (2021) A single dose of the SARS-CoV-2 vaccine BNT162b2 elicits Fc-mediated antibody effector functions and T cell responses

    Cell Host & Microbe 29:1137–1150.

    https://doi.org/10.1016/j.chom.2021.06.001

    • PubMed
    • Google Scholar
78. 1. Tenforde MW
    2. Patel MM

    (2021) Effectiveness of SARS-cov-2 mrna vaccines for preventing covid-19 hospitalizations in the united states: Clin infect diseffectiveness of sars-cov-2 mrna vaccines for preventing covid-19 hospitalizations in the united states

    Influenza and Other Viruses in the Acutely Ill IVY Network 1:e687.

    https://doi.org/10.1093/cid/ciab687

    • Google Scholar
79. 1. Thevarajan I
    2. Nguyen THO
    3. Koutsakos M
    4. Druce J
    5. Caly L
    6. van de Sandt CE
    7. Jia X
    8. Nicholson S
    9. Catton M
    10. Cowie B
    11. Tong SYC
    12. Lewin SR
    13. Kedzierska K

    (2020) Breadth of concomitant immune responses prior to patient recovery: a case report of non-severe COVID-19

    Nature Medicine 26:453–455.

    https://doi.org/10.1038/s41591-020-0819-2

    • PubMed
    • Google Scholar
80. 1. Van Gassen S
    2. Callebaut B
    3. Van Helden MJ
    4. Lambrecht BN
    5. Demeester P
    6. Dhaene T
    7. Saeys Y

    (2015) FlowSOM: Using self-organizing maps for visualization and interpretation of cytometry data

    Cytometry. Part A 87:636–645.

    https://doi.org/10.1002/cyto.a.22625

    • PubMed
    • Google Scholar
81. 1. van Kempen ZLE
    2. Wieske L
    3. Stalman EW
    4. Kummer LYL
    5. van Dam PJ
    6. Volkers AG
    7. Boekel L
    8. Toorop AA
    9. Strijbis EMM
    10. Tas SW
    11. Wolbink GJ
    12. Löwenberg M
    13. van Sandt C
    14. Ten Brinke A
    15. Verstegen NJM
    16. Steenhuis M
    17. Kuijpers TW
    18. van Ham SM
    19. Rispens T
    20. Eftimov F
    21. Killestein J
    22. T2B! immunity against SARS-CoV-2 study group

    (2022) Longitudinal humoral response after SARS-CoV-2 vaccination in ocrelizumab treated MS patients: To wait and repopulate?

    Multiple Sclerosis and Related Disorders 57:103416.

    https://doi.org/10.1016/j.msard.2021.103416

    • PubMed
    • Google Scholar
82. 1. van Lierop ZY
    2. Wieske L
    3. Koel-Simmelink MJ
    4. Chatterjee M
    5. Dekker I
    6. Leurs CE
    7. Willemse EA
    8. Moraal B
    9. Barkhof F
    10. Eftimov F
    11. Uitdehaag BM
    12. Killestein J
    13. Teunissen CE

    (2022) Serum contactin-1 as a biomarker of long-term disease progression in natalizumab-treated multiple sclerosis

    Multiple Sclerosis 28:102–110.

    https://doi.org/10.1177/13524585211010097

    • PubMed
    • Google Scholar
83. 1. Verstegen NJM
    2. Ubels V
    3. Westerhoff HV
    4. van Ham SM
    5. Barberis M

    (2021) System-Level Scenarios for the Elucidation of T Cell-Mediated Germinal Center B Cell Differentiation

    Frontiers in Immunology 12:734282.

    https://doi.org/10.3389/fimmu.2021.734282

    • PubMed
    • Google Scholar
84. 1. Vogelzang EH
    2. Loeff FC
    3. Derksen NIL
    4. Kruithof S
    5. Ooijevaar-de Heer P
    6. van Mierlo G
    7. Linty F
    8. Mok JY
    9. van Esch W
    10. de Bruin S
    11. Vlaar APJ
    12. Seppen B
    13. Leeuw M
    14. van Oudheusden AJG
    15. Buiting AGM
    16. Jim KK
    17. Vrielink H
    18. Swaneveld F
    19. Vidarsson G
    20. van der Schoot CE
    21. Wever PC
    22. Li W
    23. van Kuppeveld F
    24. Murk JL
    25. Bosch BJ
    26. Wolbink GJ
    27. Rispens T
    28. Amsterdam University Medical Center COVID-19 Biobank Study Group

    (2020) Development of a sars-cov-2 total antibody assay and the dynamics of antibody response over time in hospitalized and nonhospitalized patients with covid-19

    Journal of Immunology 205:3491–3499.

    https://doi.org/10.4049/jimmunol.2000767

    • PubMed
    • Google Scholar
85. 1. Wang EY
    2. Mao T
    3. Klein J
    4. Dai Y
    5. Huck JD
    6. Jaycox JR
    7. Liu F
    8. Zhou T
    9. Israelow B
    10. Wong P
    11. Coppi A
    12. Lucas C
    13. Silva J
    14. Oh JE
    15. Song E
    16. Perotti ES
    17. Zheng NS
    18. Fischer S
    19. Campbell M
    20. Fournier JB
    21. Wyllie AL
    22. Vogels CBF
    23. Ott IM
    24. Kalinich CC
    25. Petrone ME
    26. Watkins AE
    27. Dela Cruz C
    28. Farhadian SF
    29. Schulz WL
    30. Ma S
    31. Grubaugh ND
    32. Ko AI
    33. Iwasaki A
    34. Ring AM
    35. Yale IMPACT Team

    (2021a) Diverse functional autoantibodies in patients with COVID-19

    Nature 595:283–288.

    https://doi.org/10.1038/s41586-021-03631-y

    • PubMed
    • Google Scholar
86. 1. Wang Z
    2. Muecksch F
    3. Schaefer-Babajew D
    4. Finkin S
    5. Viant C
    6. Gaebler C
    7. Hoffmann HH
    8. Barnes CO
    9. Cipolla M
    10. Ramos V
    11. Oliveira TY
    12. Cho A
    13. Schmidt F
    14. Da Silva J
    15. Bednarski E
    16. Aguado L
    17. Yee J
    18. Daga M
    19. Turroja M
    20. Millard KG
    21. Jankovic M
    22. Gazumyan A
    23. Zhao Z
    24. Rice CM
    25. Bieniasz PD
    26. Caskey M
    27. Hatziioannou T
    28. Nussenzweig MC

    (2021b) Naturally enhanced neutralizing breadth against SARS-CoV-2 one year after infection

    Nature 595:426–431.

    https://doi.org/10.1038/s41586-021-03696-9

    • PubMed
    • Google Scholar
87. 1. Wieske L
    2. van Dam KPJ
    3. Steenhuis M
    4. Stalman EW
    5. Kummer LYL
    6. van Kempen ZLE
    7. Killestein J
    8. Volkers AG
    9. Tas SW
    10. Boekel L
    11. Wolbink GJ
    12. van der Kooi AJ
    13. Raaphorst J
    14. Löwenberg M
    15. Takkenberg RB
    16. D’Haens GRAM
    17. Spuls PI
    18. Bekkenk MW
    19. Musters AH
    20. Post NF
    21. Bosma AL
    22. Hilhorst ML
    23. Vegting Y
    24. Bemelman FJ
    25. Voskuyl AE
    26. Broens B
    27. Sanchez AP
    28. van Els CACM
    29. de Wit J
    30. Rutgers A
    31. de Leeuw K
    32. Horváth B
    33. Verschuuren JJGM
    34. Ruiter AM
    35. van Ouwerkerk L
    36. van der Woude D
    37. Allaart RCF
    38. Teng YKO
    39. van Paassen P
    40. Busch MH
    41. Jallah PBP
    42. Brusse E
    43. van Doorn PA
    44. Baars AE
    45. Hijnen DJ
    46. Schreurs CRG
    47. van der Pol WL
    48. Goedee HS
    49. Keijzer S
    50. Keijser JBD
    51. Boogaard A
    52. Cristianawati O
    53. Ten Brinke A
    54. Verstegen NJM
    55. Zwinderman KAH
    56. van Ham SM
    57. Kuijpers TW
    58. Rispens T
    59. Eftimov F
    60. T2B! Immunity against SARS-CoV-2 study group

    (2022) Humoral responses after second and third SARS-CoV-2 vaccination in patients with immune-mediated inflammatory disorders on immunosuppressants: a cohort study

    The Lancet. Rheumatology 4:e338–e350.

    https://doi.org/10.1016/S2665-9913(22)00034-0

    • PubMed
    • Google Scholar
88. 1. Williamson EJ
    2. Walker AJ
    3. Bhaskaran K
    4. Bacon S
    5. Bates C
    6. Morton CE
    7. Curtis HJ
    8. Mehrkar A
    9. Evans D
    10. Inglesby P
    11. Cockburn J
    12. McDonald HI
    13. MacKenna B
    14. Tomlinson L
    15. Douglas IJ
    16. Rentsch CT
    17. Mathur R
    18. Wong AYS
    19. Grieve R
    20. Harrison D
    21. Forbes H
    22. Schultze A
    23. Croker R
    24. Parry J
    25. Hester F
    26. Harper S
    27. Perera R
    28. Evans SJW
    29. Smeeth L
    30. Goldacre B

    (2020) Factors associated with COVID-19-related death using OpenSAFELY

    Nature 584:430–436.

    https://doi.org/10.1038/s41586-020-2521-4

    • PubMed
    • Google Scholar
89. 1. Wolinsky JS
    2. Arnold DL
    3. Brochet B
    4. Hartung HP
    5. Montalban X
    6. Naismith RT
    7. Manfrini M
    8. Overell J
    9. Koendgen H
    10. Sauter A
    11. Bennett I
    12. Hubeaux S
    13. Kappos L
    14. Hauser SL

    (2020) Long-term follow-up from the ORATORIO trial of ocrelizumab for primary progressive multiple sclerosis: a post-hoc analysis from the ongoing open-label extension of the randomised, placebo-controlled, phase 3 trial

    The Lancet. Neurology 19:998–1009.

    https://doi.org/10.1016/S1474-4422(20)30342-2

    • PubMed
    • Google Scholar
90. 1. Xiao Y
    2. Hsiao TH
    3. Suresh U
    4. Chen HIH
    5. Wu X
    6. Wolf SE
    7. Chen Y

    (2014) A novel significance score for gene selection and ranking

    Bioinformatics 30:801–807.

    https://doi.org/10.1093/bioinformatics/btr671

    • PubMed
    • Google Scholar
91. 1. Zabalza A
    2. Cárdenas-Robledo S
    3. Tagliani P
    4. Arrambide G
    5. Otero-Romero S
    6. Carbonell-Mirabent P
    7. Rodriguez-Barranco M
    8. Rodríguez-Acevedo B
    9. Restrepo Vera JL
    10. Resina-Salles M
    11. Midaglia L
    12. Vidal-Jordana A
    13. Río J
    14. Galan I
    15. Castillo J
    16. Cobo-Calvo Á
    17. Comabella M
    18. Nos C
    19. Sastre-Garriga J
    20. Tintore M
    21. Montalban X

    (2021) COVID-19 in multiple sclerosis patients: susceptibility, severity risk factors and serological response

    European Journal of Neurology 28:3384–3395.

    https://doi.org/10.1111/ene.14690

    • PubMed
    • Google Scholar
92. 1. Zhu J
    2. Paul WE

    (2009) Heterogeneity and plasticity of T helper cells

    Cell Research 20:4–12.

    https://doi.org/10.1038/cr.2009.138

    • PubMed
    • Google Scholar

Decision letter after peer review:

Thank you for submitting your article "Immune dynamics in SARS-CoV-2 experienced immunosuppressed rheumatoid arthritis or multiple sclerosis patients vaccinated with mRNA-1273" for consideration by eLife. Your article has been reviewed by 3 peer reviewers, including Sarah Sasson as the Reviewing Editor and Reviewer #1, and the evaluation has been overseen Miles Davenport as the Senior Editor.

The reviewers have discussed their reviews with one another, and the Reviewing Editor has drafted this to help you prepare a revised submission.

Essential revisions:

1) Statistical Analysis: All 3 reviewers have concerns regarding the statistical analysis. Please ensure a Bonferroni correction has been performed where multiple comparisons have been undertaken. This will likely impact on the p-value determination and significance of some results. Please review the validity of a Wilcoxon signed-rank test in Figure 3f.

2) There is little evidence in the current literature that T cell responses alone can prevent SARS-CoV-2 infection. References to this should be modified in the text as highlighted by Reviewer 1.

3) The presented methodology is not sufficient for defining antigen-specific T cells. Contemporary methods for measuring antigen-specific T cells should be performed as highlighted by Reviewer 2 or references to antigen-specific T cells should be removed from the text.

4) The potential confounding effects of the underlying disease processes (that are divergent between groups) should be acknowledged in the Discussion.

Reviewer #1 (Recommendations for the authors):

This report is well-written, logically presented and highly focused on two patient populations: MS-OCR and RA-MTX. It provides a thorough mapping of immune responses and recalls adaptive immune responses in SARS-CoV-2 experienced patients undergoing mRNA vaccination. The Figures are clear and concise. Overall, it has the potential to contribute meaningfully to the field.

I have some concerns outlined below that should be addressed prior to publication.

1. Overall, the number of patients in the OCR and MTX is relatively small.

2. In the Abstract>Conclusion the authors state that the expanded CD8⁺ T cell population evident in OCR treated MS patients indicates that this patient group "still benefits" from vaccination. This statement needs to be interpreted in light of the fact that very little data exists to support that T cells alone can protect patients from COVID-19 in real-world clinical settings. This statement should be edited to reflect this complexity.

3. Similarly, the delayed kinetics in MTX lead the authors to state "repeat vaccination strategies should be supported", however, provide no evidence that such repeated strategies are able to overcome treatment with MTX. Indeed, other therapeutic interventions should also be considered and these may include: treatment interruptions, mixed vaccine strategies and synthetic monoclonal antibodies e.g. tixagevimab/cilgavimab.

4. Results: The differences in the CD8⁺ subset in OCR compared to HC appear overstated. In Figure 5e the proportion of CD8⁺ TCM and "naïve-like" cells appear to be incrementally different from HC (p=0.03). It is unclear if Bonferroni corrections have been made for comparisons between groups, but if not, these results are not likely to be statistically significant.

5. In regard to Point 4 the Methods>Statistics do not state a Bonferroni correction has been undertaken for multiple comparisons. This should be done for all analyses in order to determine statistical significance more accurately.

6. Discussion Line 475-479 "Although MS-OCR patients had a smaller CD8⁺ Tcm population compared to HC prior to vaccination, the quantity (measured by the number per µl blood) and quality (measured by the percentage of responding cells and the co-expression of dynamic markers) of the SARS-CoV-2 vaccine-induced CD8⁺ Tcm cell recall response in OCR-treated MS patients outperformed those of SARS-CoV-2 experienced HCs." Again, the comment regarding quantity appears overstated and this sentence should be revised.

7. Line 496-497 "skewing the recall immune response to a superior CD8⁺ T cell performance." Is overstated and not supported by the data. The data is more in keeping with preserved CD8⁺ T cell responses. This sentence should be revised.

8. Line 501 the word "remarkable" should be deleted.

9. Line 554 the word "exceptional" should be removed and replaced with "preserved" or similar.

10. Line 560 "These CD8⁺ T cells are therefore likely to provide MS patients with adequate protection from severe disease following infection with novel SARS-CoV-2 variants." Is highly speculative and not supported by the current literature. This sentence should be removed or revised.

11. Line 567 replace "could" with "may".

12. Line 554 OCR CD8⁺ response described as "exceptional". This should be revised.

13. Line 560-561 Authors postulate high CD8⁺ T cell responses will likely protect against COVID-19 but there is very little data to support this.

Reviewer #2 (Recommendations for the authors):

Hagen and colleagues used high-dimensional flow cytometry (38 markers total) to characterise the humoral and cellular immune response to prime-boost vaccination with the mRNA-1273/Moderna SARS-CoV-2 vaccine in healthy controls (HCs), and rheumatoid arthritis (RA) and multiple sclerosis (MS) patients on immunosuppressants, all of which had previously been infected with SARS-CoV-2.

These are important samples to study and this work adds to the body of literature exploring the impact of disease and/or immunosuppressant treatment on the immune response to COVID-19 vaccination. This data will be critical for identifying the best vaccine platforms for these patients, as well as appropriate vaccine regimens.

However, not all the authors' conclusions are supported by the data. For example, there is insufficient evidence to support a "superior recall response of CD8⁺ T central memory cells following first vaccination" in OCR-treated MS patients, as an invalid approach was used to define antigen-specific T cells (cells that upregulated one or more of the following markers relative to the pre-vaccination timepoint: CD38, HLA-DR, PD-1, CTLA-4, TIGIT, TIM-3, CD40L and CD137). Other conclusions need to be rephrased. For example, it is unclear whether "similar CD4⁺ T follicular helper 1 and T helper 1 dynamics" refers to the frequency or activation profile of these cells in OCR-treated MS patients, since Tfh1 cells showed a significant increase in number per μl of blood only in HCs, and not MS or RA patients, following first vaccination.

The main limitation of this paper is the small sample size. Thus, how well the findings will translate to other patients on immunosuppressants is unclear. Disease and treatment are also confounded, so the results could reflect a disease and/or treatment effect. The results indicate the frequency of different cell subsets and the expression of activation markers, but there is a lack of information on the frequency and activation state of antigen-specific T and B cells.

The authors examined the expression of eight dynamic markers (CD38, HLA-DR, PD-1, CTLA-4, TIGIT, TIM-3, CD40L and CD137) "as a surrogate to verify antigen specificity". They have then classed "responding cells" as those expressing one or more of these markers. These criteria are very lenient, and many of the identified cells will not be antigen-specific. To show antigen specificity, the authors could use tetramers or an AIM assay, for example. In Grifoni et al., Cell, 2020, they show that the frequency of SARS-CoV-2-specific CD4⁺ and CD8⁺ T cells in exposed COVID-19 individuals is generally < 1%, much lower than the frequencies of responding cells observed in this paper. The authors should perform one of the suggested assays or remove all references to antigen-specific T cells.

The authors have provided lots of detail in the text about the changes in cell frequencies and marker expression detected or not detected for each cell subset. However, this leads to the key points and the message being somewhat lost. Shortening the text and highlighting the main findings would make the paper more impactful.

The authors compare cell frequencies and dynamic marker expression at T3 (post-vaccine 2) with T0 (pre-vaccine 1), as they did not collect blood pre-vaccine 2 (T2). This makes it challenging to interpret the changes in response to the second vaccine, since cell frequencies and marker expression may differ at T2 relative to T0. The authors should be clear in the text when they are comparing T3 with T0.

In general, the effects of immunosuppressant treatment are confounded by the disease state. The authors should clarify this in the text.

The authors have defined Tfh cells as CXCR5+ CD4⁺ T cells. However, true Tfh cells are located in lymphoid tissues, and are PD-1+ ICOShi and express the transcription factor BCl^-6 (as discussed in Tangye et al., Nat Rev Immunol, 2013). The authors should rename these populations as Tfh-like or cTfh to distinguish them from true Tfh cells.

What statistical test was used to identify changes in population frequency in Figure 1h,i? Was multiple testing correction performed? Were compositional effects considered in Figure 1i?

The Wilcoxon signed-rank test is not appropriate for Figure 3f.

Reviewer #3 (Recommendations for the authors):

The authors have performed a longitudinal study of vaccine responses in 6 individuals with rheumatoid arthritis treated with methotrexate (MTX) and 7 individuals with MS treated with CD20 depletion, all of whom have recovered from COVID-19. Their responses have been compared to 15 reasonably well-matched control individuals.

Their approach is an interesting one – few studies have specifically focused on individuals who have recovered from COVID-19 with these comorbidities. They have chosen to study the Moderna mRNA vaccine, another area where fewer studies have focused. These are all strengths and would add value to the literature as hypothesis-generating for future studies.

The authors have mentioned some of the weaknesses of the study in their conclusions. Namely:

The limited number of participants (further complicated by some of whom hadn't received both vaccine doses).

The fact that they describe their findings as a "recall" response, but this has only been demonstrated using surrogate markers of activation and not in an antigen-specific manner.

To these, I would add the further limitation that:

1. In my opinion, a substantial amount of statistical analysis has been performed on data from a limited number of patients. 13 quite complex multi-panel figures have been created from 13 participants. It is not clear whether any consideration of multiple comparisons has been undertaken.

2. Within the representative flow cytometry plots describing the Tfh subsets there appears to be considerable overlap between the phenotype of some of the subsets (except Tfh1 and Tfh17 which look distinct) which presumably feeds forward into the further analysis of these small populations.

3. I may have missed this in the analysis, but the conclusion regarding CD8 responses in patients with MS – they appear to have a very activated phenotype at T0 (prior to vaccination). Are there actually dynamic differences between baseline at T1 in naive/CM?

4. The study design doesn't allow us to look at the B cell phenotype at timepoint 2 of the RA patients treated with MTX who demonstrate slower kinetics of peak antibody concentration following initial immunisation.

5. One could argue that the study may have been improved if a single disease was studied (e.g. RA where both MTX and anti-CD20 are used rather than introducing a potential confounding effect of the disease process).

Line 116 – there already is a reasonable tranche of literature on this already (both rituximab and methotrexate).

Line 267 – worth mentioning in the methods how many pre-pandemic samples the authors have used to set the threshold for positivity on the serological assay, its sensitivity and specificity.

Using a sufficiently sensitive assay >98% of healthy individuals mount an antibody response to SARS-CoV-2. I found it odd that going into the study a greater percentage of individuals with RA on MTX has a humoral response vs healthy controls. Related to this the authors conclude the patients on MTX have a slower development of antibodies. To justify this, it would also be helpful to specify the precision of the assay, for example the %CV.

Line 271 – It's interesting that some MS patients on anti-CD20 mount humoral responses. What can be said about the relationship between the timing of previous B cell depletion and the timing of that original infection?

Figure 2A and B – I don't agree with the statistical approach to the comparison of timepoints and disease groups here. It looks like a Mann-witney test has been performed that has included the data points below the threshold of the assay. By definition, as the cutoff has been set with pre-pandemic controls, these samples must be negative, as they contain no specific antibodies. They can't be included with a numerical value, even if it is "arbitrary units".

Figure 3A – there does appear to be marked overlap between the defined populations of various Tfh subsets; they don't seem terribly distinct, with the exception of Tfh1 and Thf17. Are there confidence intervals for the gating used to define the various populations here? These populations are then used for further analysis downstream. I'd be concerned about the validity of that analysis.

Figure 5F – I don't fully understand the explanation offered about the differences between the mobilisation of CD8 cells here. For MS patients, they have a usually high frequency of naive-like CD8 T cells expressing activation markers – why is this? Do these significantly increase (i.e mobilised by the vaccine)? Similarly for CD8 CM cells.

Other considerations

– RA and MS are risk factors for severe disease and death from COVID-19; is their survivor bias in the cohort at large?

– There is a wide range of time from SARS-CoV-2 infection to vaccination – is this a potential confounding variable.

– I'd avoid the frequent use of the term "trends" when discussing such a small sample size is likely to be over-interpreting the data.

[Editors' note: further revisions were suggested prior to acceptance, as described below.]

Thank you for resubmitting your work entitled "Immune dynamics in SARS-CoV-2 experienced immunosuppressed rheumatoid arthritis or multiple sclerosis patients vaccinated with mRNA-1273" for further consideration by eLife. Your revised article has been evaluated by Miles Davenport (Senior Editor) and a Reviewing Editor.

The manuscript has been substantially improved but there are 3 significant remaining issues that need to be addressed, as outlined below:

1. The role of CD8⁺ T cells in clearing SARS-CoV-2 and improving the clinical course remains overstated in light of the current evidence. Of the supporting literature, the paper by Thevarajan et al. (Nat Med 202) is in fact a case report. Additionally, the paper by Tan et all (Cell Reports 2021) demonstrates an association between CD8⁺ T cell count and milder disease course but evidence of causation cannot be drawn from these.

The Abstract conclusion "Together, these findings indicate that SARS-CoV-2 experienced MS-OCR patients still benefit from vaccination by inducing a broad CD8⁺ T cell response which can contribute to milder disease outcome." Should be edited to: "Together, these findings indicate that SARS-CoV-2 experienced MS-OCR patients may still benefit from vaccination by inducing a broad CD8⁺ T cell response which has been associated with milder disease outcome." (or similar).

Line 770-772 "So even though CD8⁺ T cells will not prevent infection, they are therefore likely to provide MS patients with adequate protection from severe disease following infection with novel SARS-CoV-2 variants." This comment remains speculative and should be edited to: "While CD8⁺ T cells immunity will not prevent infection, high proportions of CD8⁺ T cells have been associated with clinically favorable outcomes. Ongoing research in this area will be important, particularly in regard to immunosuppressed populations facing novel variants of concern." (or similar).

2. Abstract Line 74-76: The statement "A delayed dynamics of vaccine-induced immunological recall in RA-MTX patients support repeated vaccine strategies to protect against future variants of concern, especially for these patients." is not supported by the data. Instead, the authors should highlight the delayed IgG kinetics in the MTX and how this might influence healthcare provider advice to affected patients regarding shielding (mask-wearing etc) and alternative strategies such as treatment interruptions or synthetic IgG products for vulnerable patients. it is unclear how repeated vaccines would impact the kinetics and this has not been shown in this paper.

3. References to "Antigen-specific" T cells have been retained in the manuscript despite no contemporary methods for measuring antigen-specific cells being used. Multi-parameter immunophenotyping is not an adequate replacement for such assays and reporting such data in the context of "antigen specificity" is not scientifically correct in the current context. Such assays can be performed on whole blood (ie the OX40/CD137 AIM assay). Other options include the use of ELISPOT or tetramers both of which could be performed on PBMCs. This data could be retained under a section on advanced immunophenotyping but should not be reported as a measurement of "antigen-specific cells."

Essential revisions:

1) Statistical Analysis: All 3 reviewers have concerns regarding the statistical analysis. Please ensure a Bonferroni correction has been performed where multiple comparisons have been undertaken. This will likely impact on the p-value determination and significance of some results. Please review the validity of a Wilcoxon signed-rank test in Figure 3f.

We have now performed a Bonferroni-Holms correction for all our multiple comparison analysis.

2) There is little evidence in the current literature that T cell responses alone can prevent SARS-CoV-2 infection. References to this should be modified in the text as highlighted by Reviewer 1.

We like to emphasize that we do not claim that CD8⁺ T cells can prevent infection, which is the role of neutralizing antibodies. However, there is sufficient evidence that CD8⁺ T cells contribute to milder disease outcomes, as shown by several studies. We have responded in more detail to the respective questions from Reviewer 1, namely points 2 and 10.

3) The presented methodology is not sufficient for defining antigen-specific T cells. Contemporary methods for measuring antigen-specific T cells should be performed as highlighted by Reviewer 2 or references to antigen-specific T cells should be removed from the text.

We understand the concerns of Reviewer two (point 1) and hope that our answer, including multiple detailed references to previous studies which confirmed the expression of these dynamic markers on antigen-specific T cells, has reassured the Reviewer that dynamic markers were selected with the greatest of care. We have also highlighted some of the challenges of antigen-specific assays and the advantages of our current approach. Unfortunately, no material is available to study the antigen-specific T cell response in an AIMS assay. As we cannot eliminate the slight possibility of bystander activation, we have now rephrased the antigen-specific wording throughout our manuscript by the antigen-induced recall.

4) The potential confounding effects of the underlying disease processes (that are divergent between groups) should be acknowledged in the Discussion.

We have now acknowledged this potential confounding effect in the limitations section of the manuscript.

Reviewer #1 (Recommendations for the authors):

This report is well-written, logically presented and highly focused on two patient populations: MS-OCR and RA-MTX. It provides a thorough mapping of immune responses and recalls adaptive immune responses in SARS-CoV-2 experienced patients undergoing mRNA vaccination. The Figures are clear and concise. Overall, it has the potential to contribute meaningfully to the field.

We thank the reviewer for the kind summary and for recognizing the importance of the study. We have addressed the reviewer's comments in a point-by-point form.

I have some concerns outlined below that should be addressed prior to publication.

1. Overall, the number of patients in the OCR and MTX is relatively small.

We agree with the reviewer that the number of patients in the OCR and MTX groups is relatively small. Our understanding from clinicians in the field is that both MS-OCR and RA-MTX patients have been very careful prior to vaccination not to get infected, hence the low numbers of infected patients before vaccination. We have attempted to increase our cohort, but patients have been very compliant with the vaccination strategies, hence no additional infected-vaccinated patients could be included in the study. In addition, considering that this group is so rare it will become less likely that any future information on recall responses in this specific pre-infected patient group will be gathered. We review this limitation in the “Limitations” section of the manuscript, which now reads:

“We recognize that our study had several limitations. Firstly, the analysis performed was limited to the relatively small number of patients available. This particularly prevented correlation. MS and RA patients in general have been careful during the first year of the infection not to get infected, so pre-infected patient numbers were relatively low compared to the general population. Today most patients have been vaccinated before getting infected.”

2. In the Abstract>Conclusion the authors state that the expanded CD8⁺ T cell population evident in OCR treated MS patients indicates that this patient group "still benefits" from vaccination. This statement needs to be interpreted in light of the fact that very little data exists to support that T cells alone can protect patients from COVID-19 in real-world clinical settings. This statement should be edited to reflect this complexity.

We apologize for the confusion this sentence may have caused. We agree with the Reviewer that CD8⁺ T cells are unlikely to prevent infection similarly as neutralizing antibodies. Instead, CD8⁺ T cells play a key role in clearing virus-infected cells, which limits viral replication and thereby results contributes to the resolution of the symptoms. In addition, robust CD8⁺ T cells responses have also been shown to result in overall milder symptoms for the patients. This has been demonstrated by several studies including:

Thevarajan et al., 2020, Nat Med; Moderbacher et al., 2020, Cell; Tan et al., 2021, Cell Rep

We have now clarified this in the Abstract, which now reads:

“Together, these findings indicate that SARS-CoV-2 experienced MS-OCR patients still benefit from vaccination by inducing a broad CD8⁺ T cell response which can contribute to milder disease outcome.”

3. Similarly, the delayed kinetics in MTX lead the authors to state "repeat vaccination strategies should be supported", however, provide no evidence that such repeated strategies are able to overcome treatment with MTX. Indeed, other therapeutic interventions should also be considered and these may include: treatment interruptions, mixed vaccine strategies and synthetic monoclonal antibodies e.g. tixagevimab/cilgavimab.

We agree with the Reviewer that multiple therapeutic intervention strategies could be relevant to improving the overall immune response in these patients. However, our study has focussed on their ability to form immune responses upon vaccination. The delayed immune response observed in RA-MTX patients in combination with reduced activation of CD8⁺ T cells, which others have shown to be crossreactive with variants of concern (Geers 2021 Sci Immunol), in combination with their ability to induce antibody responses means that these RA-MTX patients are likely to benefit most from their humoral immunity which needs to be more frequent updated when variants of concern emerge. We have now clarified our statement in the abstract, which now reads:

“A delayed dynamics of vaccine-induced immunological recall in RA-MTX patients support repeated vaccine strategies to protect against future variants of concern, especially for these patients.”

4. Results: The differences in the CD8⁺ subset in OCR compared to HC appear overstated. In Figure 5e the proportion of CD8⁺ TCM and "naïve-like" cells appear to be incrementally different from HC (p=0.03). It is unclear if Bonferroni corrections have been made for comparisons between groups, but if not, these results are not likely to be statistically significant.

We agree with the Reviewer that in the first draft of our manuscript the multiple comparisons corrections were not included. In the current version, we have implemented the Bonferroni-Holms correction for the two comparisons of the disease groups to the healthy control.

5. In regard to Point 4 the Methods>Statistics do not state a Bonferroni correction has been undertaken for multiple comparisons. This should be done for all analyses in order to determine statistical significance more accurately.

We agree with the Reviewer, we have now implemented the Bonferroni-Holms correction throughout the manuscript according to the reviewer’s suggestion.

6. Discussion Line 475-479 "Although MS-OCR patients had a smaller CD8⁺ Tcm population compared to HC prior to vaccination, the quantity (measured by the number per µl blood) and quality (measured by the percentage of responding cells and the co-expression of dynamic markers) of the SARS-CoV-2 vaccine-induced CD8⁺ Tcm cell recall response in OCR-treated MS patients outperformed those of SARS-CoV-2 experienced HCs." Again, the comment regarding quantity appears overstated and this sentence should be revised.

We agree with the Reviewer that the statement with regards to the quantity of the response was stronger than the data would suggest, we have now rephrased this sentence, which now reads:

“The quality (measured by the number per µl blood) and quality (measured by the percentage of responding cells and the co-expression of dynamic markers) of the SARS-CoV-2 vaccine-induced CD8⁺ T cell responses were established. Although MS-OCR patients had a smaller CD8⁺ Tcm population compared to HC before vaccination and upon second vaccination, substantially stronger quality of the CD8⁺ Tcm cell recall response in OCR-treated MS patients was observed compared to those of SARS-CoV2 experienced HCs following the first vaccination.

7. Line 496-497 "skewing the recall immune response to a superior CD8⁺ T cell performance." Is overstated and not supported by the data. The data is more in keeping with preserved CD8⁺ T cell responses. This sentence should be revised.

We agree with the Reviewer and have changed the sentence according to the Reviewer’s suggestion. The sentence now reads:

“In addition, the lack of B cell immunity in MS-OCR patients may have resulted in an altered T_h response, which focused more on the activation of CD4⁺ T_h1 responses, skewing the recall immune response to a preserved CD8⁺ T cell performance.”

8. Line 501 the word "remarkable" should be deleted.

We have deleted the word remarkable from the current manuscript.

9. Line 554 the word "exceptional" should be removed and replaced with "preserved" or similar.

We have replaced the word “exceptional” in the current manuscript with “preserved”. In line with the Reviewer’s suggestions regarding the CD8⁺ T cell response in MS-OCR patients, we have also rephrased the sentence in the abstract, which now reads

“OCR-treated MS patients exhibit a preserved recall response of CD8⁺ T central memory cells following first vaccination compared to healthy controls and a similar CD4⁺ circulating T follicular helper 1 and T helper 1 dynamics, whereas humoral and B cell responses were strongly impaired resulting in absence of SARS-CoV-2 specific humoral immunity.”

10. Line 560 "These CD8⁺ T cells are therefore likely to provide MS patients with adequate protection from severe disease following infection with novel SARS-CoV-2 variants." Is highly speculative and not supported by the current literature. This sentence should be removed or revised.

We respectfully disagree with the Reviewer. We like to emphasize that we do not claim that CD8⁺ T cells can prevent infection, which is the role of neutralizing antibodies. However, multiple studies have shown the importance of CD8⁺ T cell immunity in viral clearance and have demonstrated the key role of strong CD8⁺ T cell immunity before infection and/or during the acute phase of the infection in both resolution of symptoms and milder disease outcome, not only for SARS-CoV-2 (Thevarajan et al., 2020, Nat Med; Moderbacher et al., 2020, Cell; Tan et al., 2021, Cell Rep) but also for other respiratory infections including influenza especially in the absence of neutralizing antibodies (Sridhar 2013 Nat Med; Wang 2015 Nat Comm; Hayward 2015 Am J Resp Crit Care Med). Since MS-OCR patients were unable to produce strong humoral immunity, their protection against severe disease following SARS-CoV-2 infection will greatly depend on their ability to generate other correlates of protection of which CD8⁺ T cells seem to be the most potent. We, therefore, believe that these cells will play an important role in protecting these MS-ORC patients from severe disease in the future, not from the infection itself.

We have now further clarified this by rephrasing the relevant section, which now reads:

“Despite lacking humoral and B cell immunity the MS-OCR patients did have a preserved reactive CD8⁺ T cell recall response. CD8⁺ T cells are known to form an important second-line defense. Although CD8⁺ T cells do not prevent infection, they have a key role in viral clearance which contributes to the resolution of symptoms (Kundu et al., 2022; Thevarajan et al., 2020) and robust CD8⁺ T cell immunity has been shown to result in overall milder symptoms in SARS-CoV-2 patients (Rydyznski Moderbacher et al., 2020; Tan et al., 2021). Furthermore, CD8⁺ T cells recognize conserved epitopes allowing for high crossreactivity between different SARS-CoV-2 variants (Alison Tarke et al., 2021; Gao et al., 2022; GeurtsvanKessel et al., 2021; Keeton et al., 2021; Liu et al., 2022; Rowntree et al., 2021). So even though CD8⁺ T cells will not prevent infection, they are likely to provide MS patients with adequate protection from severe disease following infection with novel SARS-CoV-2 variants.”

11. Line 567 replace "could" with "may".

We have changed the word “could” to “may”.

12. Line 554 OCR CD8⁺ response described as "exceptional". This should be revised.

We agree with the reviewer that the word exceptional may have been too strong in this line. As also touched upon in point 9, we have changed it to “preserved”. The new sentence now reads:

“Despite lacking humoral and B cell immunity the MS-OCR patients did have a preserved reactive CD8⁺ T cell recall response.”

13. Line 560-561 Authors postulate high CD8⁺ T cell responses will likely protect against COVID-19 but there is very little data to support this.

See points 2 and 10.

Reviewer #2 (Recommendations for the authors):

Hagen and colleagues used high-dimensional flow cytometry (38 markers total) to characterise the humoral and cellular immune response to prime-boost vaccination with the mRNA-1273/Moderna SARS-CoV-2 vaccine in healthy controls (HCs), and rheumatoid arthritis (RA) and multiple sclerosis (MS) patients on immunosuppressants, all of which had previously been infected with SARS-CoV-2.

These are important samples to study and this work adds to the body of literature exploring the impact of disease and/or immunosuppressant treatment on the immune response to COVID-19 vaccination. This data will be critical for identifying the best vaccine platforms for these patients, as well as appropriate vaccine regimens.

However, not all the authors' conclusions are supported by the data. For example, there is insufficient evidence to support a "superior recall response of CD8⁺ T central memory cells following first vaccination" in OCR-treated MS patients, as an invalid approach was used to define antigen-specific T cells (cells that upregulated one or more of the following markers relative to the pre-vaccination timepoint: CD38, HLA-DR, PD-1, CTLA-4, TIGIT, TIM-3, CD40L and CD137). Other conclusions need to be rephrased. For example, it is unclear whether "similar CD4⁺ T follicular helper 1 and T helper 1 dynamics" refers to the frequency or activation profile of these cells in OCR-treated MS patients, since Tfh1 cells showed a significant increase in number per μl of blood only in HCs, and not MS or RA patients, following first vaccination.

The main limitation of this paper is the small sample size. Thus, how well the findings will translate to other patients on immunosuppressants is unclear. Disease and treatment are also confounded, so the results could reflect a disease and/or treatment effect. The results indicate the frequency of different cell subsets and the expression of activation markers, but there is a lack of information on the frequency and activation state of antigen-specific T and B cells.

We thank the Reviewer for recognizing the importance and relevance of our study. We do agree with the reviewer that due to the small sample size we may have to be a bit more careful with the phrasing that we have used as to not overinterpret or data. We have responded to the Reviewer in a point-by-point form below.

The authors examined the expression of eight dynamic markers (CD38, HLA-DR, PD-1, CTLA-4, TIGIT, TIM-3, CD40L and CD137) "as a surrogate to verify antigen specificity". They have then classed "responding cells" as those expressing one or more of these markers. These criteria are very lenient, and many of the identified cells will not be antigen-specific. To show antigen specificity, the authors could use tetramers or an AIM assay, for example. In Grifoni et al., Cell, 2020, they show that the frequency of SARS-CoV-2-specific CD4⁺ and CD8⁺ T cells in exposed COVID-19 individuals is generally < 1%, much lower than the frequencies of responding cells observed in this paper. The authors should perform one of the suggested assays or remove all references to antigen-specific T cells.

We thank the Reviewer for sharing their concern and hope to reassure the Reviewer that dynamic markers were selected with greatest of care.

As indicated in the limitations section of our manuscript we wished to understand the dynamics of the overall recall response in those patients we performed deep-immune profiling on whole blood samples, allowing the detection of vulnerable antibody-secreting B cell populations, but unfortunately, this prohibited antigen-specific staining of our immune cells, as this is technically challenging for whole blood analysis. Instead, we studied a range of dynamic markers which are associated with antigen induced reactivity and activation and their co-expression is often associated with higher quality T cell responses.

The co-expression of CD38 and HLA-DR is a key phenotype of activation of CD4⁺ and CD8⁺ T cells in response to viral infections, as per reports for Ebola (McElroy 2015 PNAS), influenza including tetramer (Ellebedy 2016 Nat Immunol, Nguyen 2021 Nat Comm) and SARS-CoV-2 (Thevarajan 2020 Nat Med, Koutsakos 2021 Cell Rep Med), co-expression of CD38 and HLA-DR on CD4⁺ and CD8⁺ T cells (assed as the frequency of CD38⁺HLADR⁺ CD8⁺ T cells) rapidly increase during the acute phase of infection and decline over time following resolution of the symptoms and have been previously demonstrated to be directly linked to virus-specific tetramer⁺ CD4⁺ and CD8⁺ T cells (Nguyen 2021 Nat Comm).

The expression of PD-1 has been linked to activation of CD4⁺ T cells during SARS-CoV-2 infection (Thevarajan 2020 Nat Med, Koutsakos 2021 Cell Rep Med), influenza infection (Tetramer+CD4⁺ T cells) (Nguyen 2021 Nat Comm) and vaccination (Koutsakos 2018 Sci Trans Med, Oja 2021 Euro J Immunol) and recently PD-1 was associated with highly functional antigen-specific (Tetramer⁺) CD8⁺ T cells in COVID-19 patients (Rha 2021 Immunity, Oja 2021 Eur J Immunol).

Similar, the combination of CD40L and CD137 has been well established as activation markers of antigen-specific CD4 T cell subsets (Oja 2021 Eur J Immunol) and antigen-specific stimulation of CD8⁺ T cells also increases the expression of CD137 (van de Sandt 2015 J Virol).

These studies have now been cited in the manuscript:

“A T cell activation panel was used to define clusters of highly specialized memory CD4⁺ T cell subsets, CD8⁺ T cell phenotypes, and more in-depth analysis of dynamics markers previously associated with antigen-specific reactivity and activation (Ellebedy et al., 2016; Koutsakos et al., 2021; McElroy et al., 2015; Nguyen et al., 2021; Oja et al., 2020; Rha et al., 2021; Thevarajan et al., 2020), namely CD38, HLA-DR, PD-1, CTLA-4, TIGIT, TIM-3, CD40L and CD137 (Figure 1e and Figure 1—figure supplement 1c,d).”

It is to be expected that other dynamic activation/exhaustion markers can also play a role, however, most studies are limited by their flow cytometry in terms of the number of dynamic markers that can be included in their panels (often limited to 8-14 markers in total). Additional dynamic markers which can be associated with activation or exhaustion, include TIGIT, CTLA-4, and TIM3 which are often studied in relation to various cancer therapies but are frequently overlooked in the virus-specific immune response. This also highlights a unique aspect of our study, as the FACS Symphony allowed us to expand our panels and include these additional markers and study their expression directly ex vivo in combination with the above mentioned and well-defined activation markers without the need for any in vitro manipulation.

We hope that the Reviewer will appreciate that we have carefully selected the dynamic markers based on confirmed antigen-specific markers which were previously confirmed in other studies while exploring the potential of additional markers of interest.

With regards to the AIMS assay, this assay uses either a limited number of peptides or a set of long overlapping peptides which could affect the cleavage of small 7-10 aa peptides and their presentation on HLA-class I molecules needed for CD8⁺ T cell activation, and could therefore result in a slight underestimate of the total antigen-specific CD8⁺ T cell response. Furthermore, T cell responses detected by peptide stimulation and functional readouts could also be affected by differences in antigen presentation. In addition, a recent study (Brodin 2022 Immunity) suggests the potential for bystander activation of T lymphocytes. We have therefore now removed all mention of antigen-specific T cells from the manuscript and replaced those with the antigen-induced recall.

The authors have provided lots of detail in the text about the changes in cell frequencies and marker expression detected or not detected for each cell subset. However, this leads to the key points and the message being somewhat lost. Shortening the text and highlighting the main findings would make the paper more impactful.

We agree with the Reviewer that the level of detail could be overwhelming for the reader. Based on the suggestion from the reviewer (first minor point) we have now simplified our analysis, limited the number of panels in the main figures, and reduced the level of detail in the text by limiting the description of B and T cell population to absolute numbers only which highlight our key findings.

The authors compare cell frequencies and dynamic marker expression at T3 (post-vaccine 2) with T0 (pre-vaccine 1), as they did not collect blood pre-vaccine 2 (T2). This makes it challenging to interpret the changes in response to the second vaccine, since cell frequencies and marker expression may differ at T2 relative to T0. The authors should be clear in the text when they are comparing T3 with T0.

We apologize to the Reviewer that this was not clear in the first draft of the manuscript. In all instances that we make cellular comparisons including T3, we compare to the baseline sample T0. Unfortunately, it was logistically impossible to perform the flow cytometry data acquisition on the day of the second vaccination.

In general, the effects of immunosuppressant treatment are confounded by the disease state. The authors should clarify this in the text.

We agree with the Reviewer that the disease state may by itself also have affected the outcome of the vaccination response, this has now been acknowledged in the limitations section of the manuscript

“Thirdly, since no MS or RA patients without medication were included, we could not rule out that the disease itself may had an effect on vaccine induced immune response.”

The authors have defined Tfh cells as CXCR5+ CD4⁺ T cells. However, true Tfh cells are located in lymphoid tissues, and are PD-1+ ICOShi and express the transcription factor BCl^-6 (as discussed in Tangye et al., Nat Rev Immunol, 2013). The authors should rename these populations as Tfh-like or cTfh to distinguish them from true Tfh cells.

We agree with the Reviewer that true T follicular helper cells are located in secondary lymphoid tissues. We have renamed all instances of Tfh to cTfh.

What statistical test was used to identify changes in population frequency in Figure 1h,i? Was multiple testing correction performed? Were compositional effects considered in Figure 1i?

In the first draft of our manuscript, no multiple comparison corrections were performed. In the current version of our manuscript, we have implemented the Bonferroni-Holms correction for the comparisons of the two disease groups to the healthy control group.

The Wilcoxon signed-rank test is not appropriate for Figure 3f.

We agree with the Reviewer that Wilcoxon signed-rank test is not appropriate for figure 3f. We have deleted the test from the current manuscript. Due to the small group sizes, we have decided to just show the trends as, this has been addressed as a limitation of our study in the limitations section.

Reviewer #3 (Recommendations for the authors):

The authors have performed a longitudinal study of vaccine responses in 6 individuals with rheumatoid arthritis treated with methotrexate (MTX) and 7 individuals with MS treated with CD20 depletion, all of whom have recovered from COVID-19. Their responses have been compared to 15 reasonably well-matched control individuals.

Their approach is an interesting one – few studies have specifically focused on individuals who have recovered from COVID-19 with these comorbidities. They have chosen to study the Moderna mRNA vaccine, another area where fewer studies have focused. These are all strengths and would add value to the literature as hypothesis-generating for future studies.

The authors have mentioned some of the weaknesses of the study in their conclusions. Namely:

The limited number of participants (further complicated by some of whom hadn't received both vaccine doses).

The fact that they describe their findings as a "recall" response, but this has only been demonstrated using surrogate markers of activation and not in an antigen-specific manner.

To these, I would add the further limitation that:

We thank the Reviewer for recognizing the importance and relevance of our study. We have responded to the Reviewer in a point-by-point form below.

1. In my opinion, a substantial amount of statistical analysis has been performed on data from a limited number of patients. 13 quite complex multi-panel figures have been created from 13 participants. It is not clear whether any consideration of multiple comparisons has been undertaken.

We agree with the Reviewer that in the first draft of our manuscript the multiple comparisons corrections were not included. In the current version, we have implemented the Bonferroni-Holms correction for the comparisons of the two disease groups to the healthy control.

2. Within the representative flow cytometry plots describing the Tfh subsets there appears to be considerable overlap between the phenotype of some of the subsets (except Tfh1 and Tfh17 which look distinct) which presumably feeds forward into the further analysis of these small populations.

Although we agree with the Reviewer that looking at the representative flow cytometry plots the circulating Tfh subsets appear to have considerable overlap. However, for the annotation of the multiple Tfh cells, we have performed an unbiased clustering analysis, of which the results are depicted in figure 1e. This unbiased clustering analysis did place the multiple annotated Tfh cell subset in different meta clusters.

3. I may have missed this in the analysis, but the conclusion regarding CD8 responses in patients with MS – they appear to have a very activated phenotype at T0 (prior to vaccination). Are there actually dynamic differences between baseline at T1 in naive/CM?

The dynamic differences between baseline and 7-10 days after the first vaccination (T1) is summarized in the new figure 5d of the current manuscript. The change in the percentage of CD8⁺ T cells expressing one of the dynamic markers (CD38, HLA-DR, PD1, CTLA-4, TIGIT, TIM3, CD40L or CD137) is represented as a heatmap. All inductions are represented in red, whereas a reduced proportion would be depicted in blue. In the current figure, we significant differences are indicated with a * in this figure. What we hope the Reviewer appreciates is that there is a profound induction of dynamic marker expression in all but the effector memory CD8⁺ T cell subsets.

4. The study design doesn't allow us to look at the B cell phenotype at timepoint 2 of the RA patients treated with MTX who demonstrate slower kinetics of peak antibody concentration following initial immunisation.

Unfortunately, the study design indeed did not allow us to compare the B cell phenotype at timepoint 2 of the RA patients treated with MTX, which would, considering the results presented in the current manuscript, be an interesting time point to follow-up on. This has now been addressed in the limitation section of the manuscript:

“Finally, to understand the delayed dynamics of the cellular responses observed in RA-MTX patients further studies including multiple timepoints post vaccination are warranted.”

5. One could argue that the study may have been improved if a single disease was studied (e.g. RA where both MTX and anti-CD20 are used rather than introducing a potential confounding effect of the disease process).

Studying a single disease may have benefited the statistical analysis as it would have limited the number of multiple comparisons. However, the benefit of studying two disease-medication regiments side by side gave us the ability to highlight their unique differences in immune dynamics following vaccination.

Line 116 – there already is a reasonable tranche of literature on this already (both rituximab and methotrexate).

We have acknowledged the literature that describes this point.

Line 267 – worth mentioning in the methods how many pre-pandemic samples the authors have used to set the threshold for positivity on the serological assay, its sensitivity and specificity.

The serological assay used in this study has been used before (see reference in the text and the URL links below). We have included in the text the number (n=600) of pre-pandemic samples that have been used to set the threshold for positivity.

https://pubmed.ncbi.nlm.nih.gov/34026115/ https://pubmed.ncbi.nlm.nih.gov/33127820/

“Based on IgG titers, the majority of SARS-CoV-2 experienced RA-MTX (67%), MS-OCR patients (50%), and HC (58%) still had detectible antibody titers antibody titers above the cutoff of 4 AU/mL levels; determined using 600 pre-COVID-19 outbreak samples as published before (Steenhuis et al., 2021; Vogelzang et al., 2020) (Figure 2a and Figure 2—figure supplement 1a,b).”

Using a sufficiently sensitive assay >98% of healthy individuals mount an antibody response to SARS-CoV-2. I found it odd that going into the study a greater percentage of individuals with RA on MTX has a humoral response vs healthy controls. Related to this the authors conclude the patients on MTX have a slower development of antibodies. To justify this, it would also be helpful to specify the precision of the assay, for example the %CV.

We find that before vaccination 50% of the individuals had a strong antibody response (Figure 2—figure supplement 1a) and only slightly (not significant) higher in RA-MTX patients. The slower antibody response development observed in RA-MTX patients is mainly based on the continuous increase in Ab titers between T1 and T2 post vaccinations, reading a maximum antibody titer at T2. In contrast, HCs displayed a rapid increase in antibody titer following the first vaccination, reaching maximum titers already at T1 post-vaccination.

We can confirm that the sensitivity of our assay for IgG is >98%. To justify this, we have calculated the precision of the assay. The %CV of this assay is 6,2% using 217 samples.

Line 271 – It's interesting that some MS patients on anti-CD20 mount humoral responses. What can be said about the relationship between the timing of previous B cell depletion and the timing of that original infection?

We agree with the Reviewer that this is a very interesting question we have wondered ourselves. We have checked the timing of the previous B cell depletion and the timing of original infection in the individual patients and did not find a correlation, which may be due to the relatively small size of the patients included in the study. This is also why our group has studied this question in more detail. The paper can be found here (https://pubmed.ncbi.nlm.nih.gov/34847379/), where antibody responses following vaccination of MS patients without prior SARS-CoV-2 infection were studied with regard to the timing of their treatment.

Figure 2A and B – I don't agree with the statistical approach to the comparison of timepoints and disease groups here. It looks like a Mann-witney test has been performed that has included the data points below the threshold of the assay. By definition, as the cutoff has been set with pre-pandemic controls, these samples must be negative, as they contain no specific antibodies. They can't be included with a numerical value, even if it is "arbitrary units".

The threshold set for these assays is based on a statistical analysis of samples considered negative – in this case, pre-outbreak samples. This cut-off itself is therefore also only a statistical reality, representing a set chance (e.g. 95 or 99%) that a 'true negative' sample will yield a signal below the threshold. In other words, the assays do yield numerical values below the threshold, and as such can be included in e.g. comparison of timepoints

Figure 3A – there does appear to be marked overlap between the defined populations of various Tfh subsets; they don't seem terribly distinct, with the exception of Tfh1 and Thf17. Are there confidence intervals for the gating used to define the various populations here? These populations are then used for further analysis downstream. I'd be concerned about the validity of that analysis.

See point 2 above.

Although we agree with the Reviewer that looking at the representative flow cytometry plots the circulating Tfh subsets appear to have considerable overlap. However, for the annotation of the multiple Tfh cells, we have performed an unbiased clustering analysis, of which the results are depicted in figure 1e. This unbiased clustering analysis did place the multiple annotated Tfh cell subset in different meta clusters.

Figure 5F – I don't fully understand the explanation offered about the differences between the mobilisation of CD8 cells here. For MS patients, they have a usually high frequency of naive-like CD8 T cells expressing activation markers – why is this? Do these significantly increase (i.e mobilised by the vaccine)? Similarly for CD8 CM cells.

We are unsure if we understand correctly what the Reviewer means by “mobilization”. Does the Reviewer mean higher expression of dynamic markers on CM and Naïve-like CD8⁺ T cells in MS-OCR patients instead of HCs? We reason that the upregulation of dynamic markers on the CD8⁺ T_CM population indicates reactivation of a previously established memory population, most likely during the initial SARS-CoV-2 infection, whereas the upregulation of the T_naive-like population most likely indicates the recruitment of de novo CD8⁺ T cells which were not previously activated during natural infection. Indeed, a recent study indicates that vaccination is as a much better ability to activate spike-specific CD8⁺ T cells which are hardly activated during natural infection (Minervina 2022 Nat Immunol). The fact that the frequency of these responses is higher in MS-OCR compared to HCs indicates that they mount a more robust immune response upon vaccination.

Other considerations

– RA and MS are risk factors for severe disease and death from COVID-19; is their survivor bias in the cohort at large?

We have cited several studies in the introduction which have studied the risk for severe disease in both RA and MS patients:

“The severity of COVID-19 in individuals on immunosuppressants, varies from mild to severe, depending on the type of immunosuppressants (Gianfrancesco et al., 2020; Möhn et al., 2020; Sparks et al., 2021; Strangfeld et al., 2021) and other underlying risk factors (Baden et al., 2021; F et al., 2021; Hughes et al., 2021; Möhn et al., 2020; Pablos et al., 2020; Sparks et al., 2021; Strangfeld et al., 2021; Williamson et al., 2020; Zabalza et al., 2021).”

– There is a wide range of time from SARS-CoV-2 infection to vaccination – is this a potential confounding variable.

We agree with the Reviewer that there is a wide range between the time of infection and vaccination, however, this wide range is similar between the different groups included in the study. We have included this information to figure 1b as we agree with the Reviewer that this is valuable information to compare the nature and quality of the immune response subsequent vaccination.

– I'd avoid the frequent use of the term "trends" when discussing such a small sample size is likely to be over-interpreting the data.

We thank the reviewer for this observation. In an attempt to focus more on the key messages and less on the trends of less relevant populations we have now excluded the proportion of T and B cell populations from the main figures (data can still be found in the supplemental material) and we focus on the changes in the absolute numbers instead.

[Editors' note: further revisions were suggested prior to acceptance, as described below.]

The manuscript has been substantially improved but there are 3 significant remaining issues that need to be addressed, as outlined below:

We thank the Editor for the additional suggestions, which we have now addressed in the revised version of our manuscript.

1. The role of CD8⁺ T cells in clearing SARS-CoV-2 and improving the clinical course remains overstated in light of the current evidence. Of the supporting literature, the paper by Thevarajan et al. (Nat Med 202) is in fact a case report. Additionally, the paper by Tan et all (Cell Reports 2021) demonstrates an association between CD8⁺ T cell count and milder disease course but evidence of causation cannot be drawn from these.

The Abstract conclusion "Together, these findings indicate that SARS-CoV-2 experienced MS-OCR patients still benefit from vaccination by inducing a broad CD8⁺ T cell response which can contribute to milder disease outcome." Should be edited to: "Together, these findings indicate that SARS-CoV-2 experienced MS-OCR patients may still benefit from vaccination by inducing a broad CD8⁺ T cell response which has been associated with milder disease outcome." (or similar).

We agree and the sentence now reads “Together, these findings indicate that SARS-CoV-2 experienced MS-OCR patients may still benefit from vaccination by inducing a broad CD8⁺ T cell response which has been associated with milder disease outcome.” Following Editor’s suggestion.

Line 770-772 "So even though CD8⁺ T cells will not prevent infection, they are therefore likely to provide MS patients with adequate protection from severe disease following infection with novel SARS-CoV-2 variants." This comment remains speculative and should be edited to: "While CD8⁺ T cells immunity will not prevent infection, high proportions of CD8⁺ T cells have been associated with clinically favorable outcomes. Ongoing research in this area will be important, particularly in regard to immunosuppressed populations facing novel variants of concern." (or similar).

We agree and the sentence now reads “While CD8⁺ T cells immunity will not prevent infection, high proportions of CD8⁺ T cells have been associated with clinically favorable outcomes (Kundu et al., 2022; Tan et al., 2021). Ongoing research in this area will be important, particularly in regard to immunosuppressed populations facing novel variants of concern.” Following Editor’s suggestion.

2. Abstract Line 74-76: The statement "A delayed dynamics of vaccine-induced immunological recall in RA-MTX patients support repeated vaccine strategies to protect against future variants of concern, especially for these patients." is not supported by the data. Instead, the authors should highlight the delayed IgG kinetics in the MTX and how this might influence healthcare provider advice to affected patients regarding shielding (mask-wearing etc) and alternative strategies such as treatment interruptions or synthetic IgG products for vulnerable patients. it is unclear how repeated vaccines would impact the kinetics and this has not been shown in this paper.

We appreciate the Editors/Reviewers concerns and we have now adjusted the sentence accordingly, the new sentence now reads “The delayed vaccine-induced IgG kinetics in RA-MTX patients indicate an increased risk after the first vaccination, which might require additional shielding or alternative strategies such as treatment interruptions in vulnerable patients.”

3. References to "Antigen-specific" T cells have been retained in the manuscript despite no contemporary methods for measuring antigen-specific cells being used. Multi-parameter immunophenotyping is not an adequate replacement for such assays and reporting such data in the context of "antigen specificity" is not scientifically correct in the current context. Such assays can be performed on whole blood (ie the OX40/CD137 AIM assay). Other options include the use of ELISPOT or tetramers both of which could be performed on PBMCs. This data could be retained under a section on advanced immunophenotyping but should not be reported as a measurement of "antigen-specific cells."

We understand the Editor and Reviewers concerns regarding antigen-specificity. We have now removed all references to antigen-specific T cells from the manuscript.

Line 353-359 now reads: “A T cell activation panel was used to define clusters of memory CD4⁺ T cell subsets, CD8⁺ T cell phenotypes, and more in-depth analysis of dynamics markers previously associated with activated/responding T cells (Ellebedy et al., 2016; Koutsakos et al., 2021; McElroy et al., 2015; Nguyen et al., 2021; Oja et al., 2020; Rha et al., 2021; Thevarajan et al., 2020), namely CD38, HLA-DR, PD-1, CTLA-4, TIGIT, TIM-3, CD40L and CD137 (Figure 1e and Figure 1—figure supplements 1c-e).”

Line 486-489 now reads: “Next, the functional activation/exhaustion profile of the CD4⁺ cTfh cells upon vaccination was assessed. Expression of CD38, HLA-DR, PD-1, CTLA-4, TIGIT, TIM-3, CD40L, and CD137 (Figure 1—figure supplements 1b) on CD4⁺ cTfh subsets was used to verify activation upon vaccination.”

Line 494-496 now reads: “Analysis of the proportion of responding cells (cells that upregulated one or more dynamic markers) demonstrated that the CD4⁺ cTfh1, cTfh17 and cTfhTripPos subsets are the most responsive after the first vaccination (Figure 3e and Figure 3—figure supplements 1c).”

Line 528-529 now reads: “Next, the response profile of CD4⁺ Th subsets was analyzed using the same eight dynamic markers as described for cTfh cells (Figure 1—figure supplements 1b).”

Line 561-564 now reads: “Next, the effect of OCR and MTX treatment on the response profile of CD8⁺ T cell subsets was analyzed using the same eight dynamic markers as described for CD4⁺ cTfh and Th cells. The expression of these markers was assessed on memory and naïve CD8⁺ T cell populations in SARS-CoV-2 experienced individuals.”

We further clarified the selection of our markers in the limitations section of our manuscript which addresses the limitations of measuring antigen-specific T cell responses in our current analysis:

Line 730-738 now reads:

“Secondly, although we were able to perform deep-immune profiling on whole blood samples, allowing the detection of vulnerable antibody-secreting B cell populations, this prohibited antigen-specific staining of our immune cells. Instead, we studied a range of dynamic markers which are associated with activated/responding T cells and their co-expression is often associated with higher quality T cell responses (Koutsakos et al., 2021; Thevarajan et al., 2020). The expression of these markers on antigen-specific cells using AIM peptide stimulations and/or tetramer staining of acute timepoints have been show by others (Nguyen 2021 Nat Comm, Habel 2020 PNAS, Oja 2020, Rha 2021, Geers 2021 Sci Immunol, Mudd 2022 Cell).”

Author details

1. Niels JM Verstegen

   Department of Immunopathology, Sanquin Research and Landsteiner Laboratory, University of Amsterdam, Amsterdam, Netherlands

   Contribution

   Designed the experiments; analyzed the data; provided intellectual input into the study; wrote the manuscript

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0001-5732-4979

2. Ruth R Hagen

   1. Department of Hematopoiesis, Sanquin Research and Landsteiner Laboratory, University of Amsterdam, Amsterdam, Netherlands
   2. Department of Experimental Immunohematology, Sanquin Research and Landsteiner Laboratory, Amsterdam, Netherlands

   Contribution

   Performed the experiments

   Contributed equally with

   Jet van den Dijssel, Lisan H Kuijper, Christine Kreher and Thomas Ashhurst

   Competing interests

   No competing interests declared

3. Jet van den Dijssel

   1. Department of Hematopoiesis, Sanquin Research and Landsteiner Laboratory, University of Amsterdam, Amsterdam, Netherlands
   2. Department of Experimental Immunohematology, Sanquin Research and Landsteiner Laboratory, Amsterdam, Netherlands

   Contribution

   Performed the experiments

   Contributed equally with

   Ruth R Hagen, Lisan H Kuijper, Christine Kreher and Thomas Ashhurst

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-4849-0374

4. Lisan H Kuijper

   Department of Immunopathology, Sanquin Research and Landsteiner Laboratory, University of Amsterdam, Amsterdam, Netherlands

   Contribution

   Performed the experiments

   Contributed equally with

   Ruth R Hagen, Jet van den Dijssel, Christine Kreher and Thomas Ashhurst

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0003-3009-0943

5. Christine Kreher

   Department of Immunopathology, Sanquin Research and Landsteiner Laboratory, University of Amsterdam, Amsterdam, Netherlands

   Contribution

   Performed the experiments

   Contributed equally with

   Ruth R Hagen, Jet van den Dijssel, Lisan H Kuijper and Thomas Ashhurst

   Competing interests

   No competing interests declared

6. Thomas Ashhurst

   1. Sydney Cytometry Core Research Facility, Charles Perkins Centre, Centenary Institute, and The University of Sydney, Sydney, Australia
   2. School of Medical Sciences, Faculty of Medicine and Health, The University of Sydney, Sydney, Australia

   Contribution

   Analyzed the data; provided intellectual input into the study

   Contributed equally with

   Ruth R Hagen, Jet van den Dijssel, Lisan H Kuijper and Christine Kreher

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0001-7269-7773

7. Laura YL Kummer

   Department of Neurology and Neurophysiology, Amsterdam Neuroscience, University of Amsterdam, Amsterdam, Netherlands

   Contribution

   Managed the participant database; involved in clinical recruitment

   Competing interests

   No competing interests declared

8. Maurice Steenhuis

   Department of Immunopathology, Sanquin Research and Landsteiner Laboratory, University of Amsterdam, Amsterdam, Netherlands

   Contribution

   Performed the experiments

   Competing interests

   No competing interests declared

9. Mariel Duurland

   Department of Immunopathology, Sanquin Research and Landsteiner Laboratory, University of Amsterdam, Amsterdam, Netherlands

   Contribution

   Performed the experiments

   Competing interests

   No competing interests declared

10. Rivka de Jongh

    Department of Immunopathology, Sanquin Research and Landsteiner Laboratory, University of Amsterdam, Amsterdam, Netherlands

    Contribution

    Performed the experiments

    Competing interests

    No competing interests declared

11. Nina de Jong

    Department of Immunopathology, Sanquin Research and Landsteiner Laboratory, University of Amsterdam, Amsterdam, Netherlands

    Contribution

    Performed the experiments

    Competing interests

    No competing interests declared

12. C Ellen van der Schoot

    Department of Experimental Immunohematology, Sanquin Research and Landsteiner Laboratory, Amsterdam, Netherlands

    Contribution

    Provided intellectual input into the study

    Competing interests

    No competing interests declared

13. Amélie V Bos

    Department of Immunopathology, Sanquin Research and Landsteiner Laboratory, University of Amsterdam, Amsterdam, Netherlands

    Contribution

    Provided intellectual input into the study

    Competing interests

    No competing interests declared

14. Erik Mul

    Department of Research Facilities, Sanquin Research, Amsterdam, Netherlands

    Contribution

    Provided intellectual input into the study

    Competing interests

    No competing interests declared

15. Katherine Kedzierska

    1. Department of Microbiology and Immunology, University of Melbourne at the Peter Doherty Institute for Infection and Immunity, Melbourne, Australia
    2. Global Station for Zoonosis Control, Global Institution for Collaborative Research and Education (GI-CoRE), Hokkaido University, Sapporo, Japan

    Contribution

    Provided intellectual input into the study

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0001-6141-335X

16. Koos PJ van Dam

    Department of Neurology and Neurophysiology, Amsterdam Neuroscience, University of Amsterdam, Amsterdam, Netherlands

    Contribution

    Involved in clinical recruitment

    Competing interests

    No competing interests declared

17. Eileen W Stalman

    Department of Neurology and Neurophysiology, Amsterdam Neuroscience, University of Amsterdam, Amsterdam, Netherlands

    Contribution

    Involved in clinical recruitment

    Competing interests

    No competing interests declared

18. Laura Boekel

    Department of Rheumatology, Amsterdam Rheumatology and immunology Center, Amsterdam, Netherlands

    Contribution

    Involved in clinical recruitment

    Competing interests

    No competing interests declared

19. Gertjan Wolbink

    1. Department of Immunopathology, Sanquin Research and Landsteiner Laboratory, University of Amsterdam, Amsterdam, Netherlands
    2. Department of Rheumatology, Amsterdam Rheumatology and immunology Center, Amsterdam, Netherlands

    Contribution

    Involved in clinical recruitment

    Competing interests

    No competing interests declared

20. Sander W Tas

    Amsterdam Rheumatology and immunology Center, Department of Rheumatology and Clinical Immunology, University of Amsterdam, Amsterdam, Netherlands

    Contribution

    Involved in clinical recruitment

    Competing interests

    No competing interests declared

21. Joep Killestein

    Amsterdam UMC, Vrije Universiteit, Department of Neurology, Amsterdam, Netherlands

    Contribution

    Involved in clinical recruitment

    Competing interests

    has speaking relationships with Merck, Biogen, TEVA, Sanofi, Genzyme, Roche and Novartis. AmsterdamUMC, location VUmc, MS Center Amsterdam has received financial support for research activities from Merck, Celgene, Biogen, GlaxoSmithKline, Immunic, Roche, Teva, Sanofi, Genzyme, and Novartis

22. Zoé LE van Kempen

    Amsterdam UMC, Vrije Universiteit, Department of Neurology, Amsterdam, Netherlands

    Contribution

    Involved in clinical recruitment

    Competing interests

    No competing interests declared

23. Luuk Wieske

    1. Department of Neurology and Neurophysiology, Amsterdam Neuroscience, University of Amsterdam, Amsterdam, Netherlands
    2. Department of Clinical Neurophysiology, St Antonius Hospital, Nieuwegein, Netherlands

    Contribution

    Managed the participant database; involved in clinical recruitment

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0002-2522-4081

24. Taco W Kuijpers

    Department of Pediatric Immunology, Rheumatology and Infectious Disease, University of Amsterdam, Amsterdam, Netherlands

    Contribution

    Conceived the study; involved in clinical recruitment

    Competing interests

    No competing interests declared

25. Filip Eftimov

    Department of Neurology and Neurophysiology, Amsterdam Neuroscience, University of Amsterdam, Amsterdam, Netherlands

    Contribution

    Conceived the study; involved in clinical recruitment

    Competing interests

    No competing interests declared

26. Theo Rispens

    Department of Immunopathology, Sanquin Research and Landsteiner Laboratory, University of Amsterdam, Amsterdam, Netherlands

    Contribution

    Conceived the study

    Competing interests

    No competing interests declared

27. S Marieke van Ham

    1. Department of Immunopathology, Sanquin Research and Landsteiner Laboratory, University of Amsterdam, Amsterdam, Netherlands
    2. Swammerdam Institute for Life Sciences, University of Amsterdam, Amsterdam, Netherlands

    Contribution

    Conceived the study; led the study; supervised the study; provided intellectual input into the study; wrote the manuscript

    Contributed equally with

    Anja ten Brinke and Carolien E van de Sandt

    Competing interests

    No competing interests declared

28. Anja ten Brinke

    Department of Immunopathology, Sanquin Research and Landsteiner Laboratory, University of Amsterdam, Amsterdam, Netherlands

    Contribution

    Supervised the study; provided intellectual input into the study; wrote the manuscript

    Contributed equally with

    S Marieke van Ham and Carolien E van de Sandt

    For correspondence

    a.tenbrinke@sanquin.nl

    Competing interests

    No competing interests declared

29. Carolien E van de Sandt

    1. Department of Hematopoiesis, Sanquin Research and Landsteiner Laboratory, University of Amsterdam, Amsterdam, Netherlands
    2. Department of Microbiology and Immunology, University of Melbourne at the Peter Doherty Institute for Infection and Immunity, Melbourne, Australia

    Contribution

    Supervised the study; designed the experiments; performed the experiments; analyzed the data; provided intellectual input into the study; wrote the manuscript

    Contributed equally with

    S Marieke van Ham and Anja ten Brinke

    For correspondence

    cvandesandt@unimelb.edu.au

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0002-4155-7433

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