Abstract
This study investigates the longitudinal dynamic changes in immune cells in COVID-19 patients over an extended period after recovery, as well as the interplay between immune cells and antibodies. Leveraging single-cell mass spectrometry, we selected six COVID-19 patients and four healthy controls, dissecting the evolving landscape within six months post-viral RNA clearance, alongside the levels of anti-spike protein antibodies. The T cell immunophenotype ascertained via single-cell mass spectrometry underwent validation through flow cytometry in 37 samples. Our findings illuminate that CD8 + T cells, gamma-delta (gd) T cells, and NK cells witnessed an increase, in contrast to the reduction observed in monocytes, B cells, and double-negative T (DNT) cells over time. The proportion of monocytes remained significantly elevated in COVID-19 patients compared to controls even after six-month. Subpopulation-wise, an upsurge manifested within various T effector memory subsets, CD45RA + T effector memory, gdT, and NK cells, whereas declines marked the populations of DNT, naive and memory B cells, and classical as well as non-classical monocytes. Noteworthy associations surfaced between DNT, gdT, CD4 + T, NK cells, and the anti-S antibody titer. This study reveals the changes in peripheral blood mononuclear cells of COVID-19 patients within 6 months after viral RNA clearance and sheds light on the interactions between immune cells and antibodies. The findings from this research contribute to a better understanding of immune transformations during the recovery from COVID-19 and offer guidance for protective measures against reinfection in the context of viral variants.
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Introduction
The global COVID-19 pandemic, which was caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has left an indelible mark, it poses a grave threat to both public health and daily routines, leading to a substantial surge in infections and fatalities on a global scale [1]. Since the discovery of SARS-CoV-2, the number of patients experiencing reinfection has been continuously rising. There is evidence to suggest that reinfection is associated with an increase in risks of death and hospitalization during the acute phase and after, as well as the occurrence of multiple organ sequelae [38]. DNT cells, constituting 1–3% of peripheral T cells, demonstrate immunoregulatory potential by eliminating B cells during chronic graft-versus-host disease [39]. In addition, DNT cells with some CD4 + T cell functions were shown to be associated with a nonpathogenic outcome following HIV infection. The role of DNT cell shifts in COVID-19 and their implications for the disease warrant further investigation. In the context of B cells, De Biasi et al. reported a decrease in the numbers of total and naïve B cells, as well as reduced proportions and numbers of memory switched and unswitched B cells in COVID-19 patients [40]. Interestingly, our study revealed a unique pattern in the behavior of B cell subsets. Within the initial 7 days after viral RNA clearance, both naïve and memory B cell numbers were notably higher. However, by the 3-month mark, these numbers exhibited a decline. This phenomenon might be attributed to the activation of B cells during the virus clearance phase, prompting proliferation, and subsequently, a gradual return to baseline levels during the recovery period. This nuanced dynamic of B cell populations sheds light on their role in the immune response during and after COVID-19 recovery. Additionally, Maucourant et al. have reported on distinct NK cell immunotypes characterized by the expression of perforin, NKG2C, and Ksp37. These immunotypes have demonstrated a correlation with COVID-19 disease severity [41]. The results indicated that although NK cells decreased in number, they were activated and functioned against several viral infections. CD14 + CD16 − monocytes are a classical monocyte subset, which was reported to be decreased in severe COVID-19 cases [42,43,44]. However, as patients transition toward recovery, the number of these cells gradually rises, indicating rebuilding of the immune system and stronger innate immunity and pathogen clearance capacity.
It is established that antibody levels targeting the SARS-CoV-2 S antigen wane post-viral RNA clearance or vaccination [13, 14, 45]. Similarly, we observed a decrease in both anti-S IgM and IgG antibody levels within 6 months after viral RNA clearance. We found the antibody levels correlated with CD4 + T cells, gdT cells, DNT cells, and NK cells. After SARS-CoV-2 infection, SARS-CoV-2 spike-specific memory B cells can rely on CD4 + T cells to produce high-affinity antibodies [46], the positive correlation between CD4 + T cells and anti-S IgG levels is understandable in our study. NK cells can secrete various cytokines, such as IFN-γ, TNF-α, etc. Kaneko et al. found that excessive TNF-α inhibited the formation of germinal center reactions, suppressed the generation of long-lasting antibody responses originating from germinal centers, resulting in the low and transient antibody responses in COVID-19 patients [47]. This may be one of the reasons for the negative correlation between NK cells and IgG antibody levels. Notably, DNT cells exhibited positive correlations with both IgM and IgG levels, which had not been reported previously. We speculated that it might be related to the limited long-term persistence of antibodies in COVID-19 patients. DNT cells have the ability of promoting B cell apoptosis, inhibiting B cell proliferatxion and plasma cell formation [48]. We hypothesized that after SARS-CoV-2 infection, when B cells produced antibodies, DNT cells might also continuously promote the apoptosis of B cells. This resulted in the situation that antibodies could not exist for a long time and could not provide long-term immune protection. However, this assumption still requires subsequent large-sample, dynamic exploration.
In T cells, except for naïve CD4 + T cells, which showed a decrease at 6 months after recovery compared to 7 days, other CD4 + T cells and CD8 + T cells maintained higher levels at 6 months after COVID-19 recovery. Similarly, NK cells also maintained higher levels at 6 months compared to 7 days after recovery. This is consistent with the widely recognized notion that there is relatively strong immune protection against COVID-19 during the first 3–6 months after the initial infection.While higher levels of T cells and NK cells provide protection against reinfection with the SARS-CoV-2 virus, it’s worth noting that currently, SARS-CoV-2 subvariants BQ.1 (a subvariant of BA.5) and XBB (a subvariant of BA.2) have globally replaced the previously dominant Omicron variant strains (including BA.5) [49]. Viral mutations resulting in immune escape increase the probability of reinfection. After infection, the increase in the number of naïve and memory B cells is sustained for a relatively short period before declining, followed by a gradual return to levels close to those in uninfected individuals by 6 months after viral RNA clearance, suggests that their protective role against reinfection in the event of a new COVID-19 exposure may be relatively limited.
This is the first study to examine the changes in PBMCs in patients with COVID-19 within 6 months after viral RNA clearance. Although, the small number of patients enrolled in this study partially limits the reliability of the experimental results, longitudinal dynamic observations revealed consistent changes across different samples. Another limitation of this study is that the biological functions of the most altered subsets of PBMCs were not investigated in detail, and further studies are required to determine the significance of these changes to the host immune status.Furthermore, selecting 6 months as the observation endpoint, longer-term changes in immune cell profiles after clearance of COVID-19 RNA are also worth investigating.
In conclusion, we described longitudinal dynamic changes in the PBMCs of COVID-19 patients within 6 months after viral RNA clearance. Our results identified distinct changes in the subpopulations of T cells, B cells, NK cells, and monocytes. In addition, we described possible relationships between anti-S antibodies and several subsets of immune cells, which could help elucidate the immune changes that occur during COVID-19 recovery. Our study provides a strategy for guiding prevention against reinfection in the current scenario of continuously changing COVID-19 virus strains.
Data availability
The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author/s.
References
Chow EJ, Uyeki TM, Chu HY. The effects of the COVID-19 pandemic on community respiratory virus activity. Nat Rev Microbiol. 2023;21(3):195–210.
Bowe B, **e Y, Al-Aly Z. Acute and postacute sequelae associated with SARS-CoV-2 reinfection. Nat Med. 2022;28(11):2398–405.
Wiersinga WJ, Rhodes A, Cheng AC, Peacock SJ, Prescott HC. Pathophysiology, transmission, diagnosis, and treatment of Coronavirus Disease 2019 (COVID-19): a review. JAMA. 2020;324:782–93.
Hirano T, Murakami M. COVID-19: a New Virus, but a familiar receptor and cytokine release syndrome. Immunity. 2020;52:731–3.
Tang N, Li D, Wang X, Sun Z. Abnormal coagulation parameters are associated with poor prognosis in patients with novel coronavirus pneumonia. J Thromb Haemost. 2020;18:844–7.
Wen W, Su W, Tang H, Le W, Zhang X, Zheng Y et al. Immune cell profiling of COVID-19 patients in the recovery stage by single-cell sequencing. Cell Discovery. 2020; 6.
T K, M K, RU KH. T, Y Y, FD C, Conversion of terminally committed hepatocytes to Culturable Bipotent Progenitor cells with regenerative capacity. 2017; 20: 41–55.
Paul S, Shilpi, Lal G. Role of gamma-delta (gammadelta) T cells in autoimmunity. J Leukoc Biol. 2015;97:259–71.
Brandt D, Hedrich CM. TCRalphabeta(+)CD3(+)CD4(-)CD8(-) (double negative) T cells in autoimmunity. Autoimmun Rev. 2018;17:422–30.
Blanco E, Pérez-Andrés M, Arriba-Méndez S, Contreras-Sanfeliciano T, Criado I, Pelak O, et al. Age-associated distribution of normal B-cell and plasma cell subsets in peripheral blood. J Allergy Clin Immunol. 2018;141:2208–e1916.
Wilk AJ, Rustagi A, Zhao NQ, Roque J, Martinez-Colon GJ, McKechnie JL, et al. A single-cell atlas of the peripheral immune response in patients with severe COVID-19. Nat Med. 2020;26:1070–6.
Guilliams M, Mildner A, Yona S. Developmental and Functional Heterogeneity of monocytes. Immunity. 2018;49:595–613.
Zhou W, Xu X, Chang Z, Wang H, Zhong X, Tong X, et al. The dynamic changes of serum IgM and IgG against SARS-CoV‐2 in patients with COVID‐19. J Med Virol. 2020;93:924–33.
Li K, Huang B, Wu M, Zhong A, Li L, Cai Y, et al. Dynamic changes in anti-SARS-CoV-2 antibodies during SARS-CoV-2 infection and recovery from COVID-19. Nat Commun. 2020;11:6044.
Guan WJ, Ni ZY, Hu Y, Liang WH, Ou CQ, He JX, et al. Clinical characteristics of Coronavirus Disease 2019 in China. N Engl J Med. 2020;382:1708–20.
Arentz M, Yim E, Klaff L, Lokhandwala S, Riedo FX, Chong M, et al. Characteristics and outcomes of 21 critically ill patients with COVID-19 in Washington State. JAMA. 2020;323:1612–4.
Fan BE, Chong VCL, Chan SSW, Lim GH, Lim KGE, Tan GB, et al. Hematologic parameters in patients with COVID-19 infection. Am J Hematol. 2020;95:E131–4.
Huang W, Berube J, McNamara M, Saksena S, Hartman M, Arshad T, et al. Lymphocyte subset counts in COVID-19 patients: a Meta-analysis. Cytometry A. 2020;97:772–6.
Yap JKY, Moriyama M, Iwasaki A. Inflammasomes and Pyroptosis as therapeutic targets for COVID-19. J Immunol. 2020;205:307–12.
Karki R, Sharma BR, Tuladhar S, Williams EP, Zalduondo L, Samir P et al. Synergism of TNF-alpha and IFN-gamma triggers inflammatory cell death, tissue damage, and Mortality in SARS-CoV-2 infection and cytokine shock syndromes. Cell. 2021; 184: 149 – 68 e17.
Chan JF, Zhang AJ, Yuan S, Poon VK, Chan CC, Lee AC, et al. Simulation of the clinical and pathological manifestations of Coronavirus Disease 2019 (COVID-19) in a golden Syrian Hamster model: implications for Disease Pathogenesis and Transmissibility. Clin Infect Dis. 2020;71:2428–46.
Azkur AK, Akdis M, Azkur D, Sokolowska M, van de Veen W, Bruggen MC, et al. Immune response to SARS-CoV-2 and mechanisms of immunopathological changes in COVID-19. Allergy. 2020;75:1564–81.
You M, Chen L, Zhang D, Zhao P, Chen Z, Qin EQ, et al. Single-cell epigenomic landscape of peripheral immune cells reveals establishment of trained immunity in individuals convalescing from COVID-19. Nat Cell Biol. 2021;23:620–30.
Taeschler P, Adamo S, Deng Y, et al. T-cell recovery and evidence of persistent immune activation 12 months after severe COVID-19. Allergy. 2022;77(8):2468–81.
Zuo J, Dowell AC, Pearce H, Verma K, Long HM, Begum J, Aiano F, Amin-Chowdhury Z, Hoschler K, Brooks T, Taylor S, Hewson J, Hallis B, Stapley L, Borrow R, Linley E, Ahmad S, Parker B, Horsley A, Amirthalingam G, Brown K, Ramsay ME, Ladhani S, Moss P. Robust SARS-CoV-2-specific T cell immunity is maintained at 6 months following primary infection. Nat Immunol. 2021;22(5):620–6.
Gaebler C, Wang Z, Lorenzi JCC, Muecksch F, Finkin S, Tokuyama M, Cho A, Jankovic M, Schaefer-Babajew D, Oliveira TY, Cipolla M, Viant C, Barnes CO, Bram Y, Breton G, Hägglöf T, Mendoza P, Hurley A, Turroja M, Gordon K, Millard KG, Ramos V, Schmidt F, Weisblum Y, Jha D, Tankelevich M, Martinez-Delgado G, Yee J, Patel R, Dizon J, Unson-O’Brien C, Shimeliovich I, Robbiani DF, Zhao Z, Gazumyan A, Schwartz RE, Hatziioannou T, Bjorkman PJ, Mehandru S, Bieniasz PD, Caskey M, Nussenzweig MC. Evolution of antibody immunity to SARS-CoV-2. Nature. 2021;591(7851):639–44.
Miron M, Meng W, Rosenfeld AM, Dvorkin S, Poon MML, Lam N, Kumar BV, Louzoun Y, Luning Prak ET, Farber DL. Maintenance of the human memory T cell repertoire by subset and tissue site. Genome Med. 2021;13(1):100.
Wiech M, Chroscicki P, Swatler J, Stepnik D, De Biasi S, Hampel M, Brewinska-Olchowik M, Maliszewska A, Sklinda K, Durlik M, Wierzba W, Cossarizza A, Piwocka K. Remodeling of T Cell Dynamics during Long COVID is dependent on severity of SARS-CoV-2 infection. Front Immunol. 2022 Jun.
Gong F, Dai Y, Zheng T, Cheng L, Zhao D, Wang H, Liu M, Pei H, ** T, Yu D, Zhou P. Peripheral CD4 + T cell subsets and antibody response in COVID-19 convalescent individuals. J Clin Invest. 2020;130(12):6588–99.
Paniskaki K, Konik MJ, Anft M, Heidecke H, Meister TL, Pfaender S, Krawczyk A, Zettler M, Jäger J, Gaeckler A, Dolff S, Westhoff TH, Rohn H, Stervbo U, Scheibenbogen C, Witzke O, Babel N. Low avidity circulating SARS-CoV-2 reactive CD8 + T cells with proinflammatory TEMRA phenotype are associated with post-acute sequelae of COVID-19. Front Microbiol. 2023;14:1196721.
Adamo S, Michler J, Zurbuchen Y, et al. Signature of long-lived memory CD8 T cells in acute SARS-CoV-2 infection. Nature. 2022;602(7895):148–55.
Goronzy JJ, Weyand CM. Mechanisms underlying T cell ageing. Nat Rev Immunol. 2019;19(9):573–83.
Hasan A, Al-Ozairi E, Al-Baqsumi Z, Ahmad R, Al-Mulla F. Cellular and Humoral Immune responses in Covid-19 and immunotherapeutic approaches. Immunotargets Ther. 2021;10:63–85.
Du B, Guo Y, Li G, Zhu Y, Wang Y, ** X. Non-structure protein ORF1ab (NSP8) in SARS-CoV-2 contains potential γδT cell epitopes. Front Microbiol. 2022;13:936272.
von Massow G, Oh S, Lam A, Gustafsson K. Gamma Delta T Cells and their involvement in COVID-19 Virus infections. Front Immunol. 2021;12:741218.
Kared H, Martelli S, Ng TP, Pender SL, Larbi A. CD57 in human natural killer cells and T-lymphocytes. Cancer Immunol Immunother. 2016;65(4):441–52.
Lim J, Puan KJ, Wang LW, et al. Data-Driven analysis of COVID-19 reveals persistent Immune abnormalities in convalescent severe individuals. Front Immunol. 2021;12:710217.
Carissimo G, Xu W, Kwok I, et al. Whole blood immunophenoty** uncovers immature neutrophil-to-VD2 T-cell ratio as an early marker for severe COVID-19. Nat Commun. 2020;11(1):5243.
Hillhouse EE, Thiant S, Moutuou MM, Lombard-Vadnais F, Parat R, Delisle JS, et al. Double-negative T cell levels correlate with chronic graft-versus-host Disease Severity. Biol Blood Marrow Transpl. 2019;25:19–25.
De Biasi S, Lo Tartaro D, Meschiari M, Gibellini L, Bellinazzi C, Borella R, et al. Expansion of plasmablasts and loss of memory B cells in peripheral blood from COVID-19 patients with pneumonia. Eur J Immunol. 2020;50:1283–94.
Maucourant C, Filipovic I, Ponzetta A, Aleman S, Cornillet M, Hertwig L et al. Natural killer cell immunotypes related to COVID-19 disease severity. Sci Immunol. 2020; 5.
Silvin A, Chapuis N, Dunsmore G, Goubet AG, Dubuisson A, Derosa L, et al. Elevated calprotectin and abnormal myeloid cell subsets discriminate severe from mild COVID-19. Cell. 2020;182:1401–e1818.
Alzaid F, Julla JB, Diedisheim M, Potier C, Potier L, Velho G, et al. Monocytopenia, monocyte morphological anomalies and hyperinflammation characterise severe COVID-19 in type 2 diabetes. EMBO Mol Med. 2020;12:e13038.
Mann ER, Menon M, Knight SB, Konkel JE, Jagger C, Shaw TN et al. Longitudinal immune profiling reveals key myeloid signatures associated with COVID-19. Sci Immunol. 2020; 5.
Lu L, Zhang H, Zhan M, Jiang J, Yin H, Dauphars DJ, et al. Antibody response and therapy in COVID-19 patients: what can be learned for vaccine development? Sci China Life Sci. 2020;63:1833–49.
Pušnik J, Richter E, Schulte B, et al. Memory B cells targeting SARS-CoV-2 spike protein and their dependence on CD4 + T cell help. Cell Rep. 2021;35(13):109320.
Cañete PF, Vinuesa CG. COVID-19 makes B cells forget, but T cells remember. Cell. 2020;183(1):13–5.
Hu SH, Zhang LH, Gao J, et al. NKG2D enhances double-negative T cell regulation of B cells. Front Immunol. 2021;12:650788.
Uraki R, Ito M, Kiso M, Yamayoshi S, Iwatsuki-Horimoto K, Furusawa Y, Sakai-Tagawa Y, Imai M, Koga M, Yamamoto S, Adachi E, Saito M, Tsutsumi T, Otani A, Kikuchi T, Yotsuyanagi H, Halfmann PJ, Pekosz A, Kawaoka Y. Antiviral and bivalent vaccine efficacy against an omicron XBB.1.5 isolate. Lancet Infect Dis. 2023;23(4):402–3.
Funding
This study was supported by National Key R&D Program of China (2020YFE0204300), Fundamental Research Funds for the Central Universities (2022ZFJH003).
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Study concept and design: Z.Y.X, X.W.X and D.W.X.Z. Data acquisition: S.Z, D.W.X.Z, L.J.Z, L.L.X, Q.H.L, S.C.C, L.Y, H.F.Z, J.D.J, M.X and X.Y.W. Experiment implementation: D.W.X.Z and Z.Y.X. Data analysis and interpretation: Z.Y.X, D.W.X.Z, Z.Y.P, F.Z and K.T.H. Critical revision of the manuscript for important intellectual content: X.W.X and D.H.Z. Statistical analysis: Z.Y.X, D.W.X.Z, X.H.Q, X.X.W, Y.F.S and Q.H.L. Obtained funding: D.H.Z, Z.Y.X and X.W.X. All authors reviewed and approved the final manuscript.
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The study was approved by the Research Ethics Committee of the First Affiliated Hospital, Zhejiang University School of Medicine, and all subjects provided written informed consent.
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The original online version of this article was revised:Following publication of the original article, we have been notified that the first affiliation needs to be corrected to: State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, National Clinical Research Center for Infectious Diseases, National Medical Center for Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, The First Affiliated Hospital, Zhejiang University School of Medicine, 79 Qingchun Rd., Hangzhou City 310003, China. Originally published affiliation: State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, National Clinical Research Center for Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, College of Medicine, The First Affiliated Hospital, Zhejiang University, Hangzhou, China.
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Zhou, D., Zhao, S., He, K. et al. Longitudinal dynamic single-cell mass cytometry analysis of peripheral blood mononuclear cells in COVID-19 patients within 6 months after viral RNA clearance. BMC Infect Dis 24, 567 (2024). https://doi.org/10.1186/s12879-024-09464-0
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DOI: https://doi.org/10.1186/s12879-024-09464-0