Heterogenous humoral and cellular immune responses with distinct trajectories post-SARS-CoV-2 infection in a population-based cohort

  1. Dominik Menges
  2. Kyra D. Zens
  3. Tala Ballouz
  4. Nicole Caduff
  5. Daniel Llanas-Cornejo
  6. Hélène E. Aschmann
  7. Anja Domenghino
  8. Céline Pellaton
  9. Matthieu Perreau
  10. Craig Fenwick
  11. Giuseppe Pantaleo
  12. Christian R. Kahlert
  13. Christian Münz
  14. Milo A. Puhan
  15. Jan S. Fehr

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Abstract

To better understand the development of SARS-CoV-2-specific immunity over time, a detailed evaluation of humoral and cellular responses is required. Here, we characterize anti-Spike (S) IgA and IgG in a representative population-based cohort of 431 SARS-CoV-2-infected individuals up to 217 days after diagnosis, demonstrating that 85% develop and maintain anti-S responses. In a subsample of 64 participants, we further assess anti-Nucleocapsid (N) IgG, neutralizing antibody activity, and T cell responses to Membrane (M), N, and S proteins. In contrast to S-specific antibody responses, anti-N IgG levels decline substantially over time and neutralizing activity toward Delta and Omicron variants is low to non-existent within just weeks of Wildtype SARS-CoV-2 infection. Virus-specific T cells are detectable in most participants, albeit more variable than antibody responses. Cluster analyses of the co-evolution of antibody and T cell responses within individuals identify five distinct trajectories characterized by specific immune patterns and clinical factors. These findings demonstrate the relevant heterogeneity in humoral and cellular immunity to SARS-CoV-2 while also identifying consistent patterns where antibody and T cell responses may work in a compensatory manner to provide protection.

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  1. Version published to 10.1038/s41467-022-32573-w
  2. ScreenIT

    SciScore for 10.1101/2021.12.15.21267776: (What is this?)

    Please note, not all rigor criteria are appropriate for all manuscripts.

    Table 1: Rigor

    EthicsConsent: We obtained written informed consent from all participants upon study enrollment.
    IRB: The study protocol was approved by the Cantonal Ethics Committee of Zurich (BASEC Registration No. 2020-01739) and prospectively registered (ISRCTN 14990068) (58).
    Sex as a biological variablenot detected.
    RandomizationStudy Design and Participants: We recruited a population-based, age-stratified, random sample of 431 individuals diagnosed with SARS-CoV-2 infection between 6 August 2020 and 19 January 2021 in the Canton of Zurich, Switzerland.
    Blindingnot detected.
    Power Analysisnot detected.

    Table 2: Resources

    Antibodies
    SentencesResources
    Plasma aliquots were stored at -20°C prior to IgA and IgG antibody titer analyses.
    IgG
    suggested: None
    As positive controls, 2.5e5 cells per well were stimulated with anti-CD3 antibody (OKT3; Miltenyi Biotec).
    anti-CD3
    suggested: None
    After 24 hours, cells were washed in staining buffer (PBS, 0.02% NaN3, 2mM EDTA, 1% bovine serum albumin), blocked for 10 minutes with Human TruStain FcX (Biolegend) on ice and stained for 30 minutes at 4°C with the following antibodies in buffer supplemented with Super Bright Complete Staining Buffer (eBioscience): BUV395 anti-CD45RA (Clone: HI100, BD Bioscience, RRID:AB_2740037),
    anti-CD45RA
    detected: (BD Biosciences Cat# 740298, RRID:AB_2740037)
    BUV496 anti-CD8 (Clone: RPA-T8, BD Bioscience, RRID:AB_2870223),
    anti-CD8
    detected: (BD Biosciences Cat# 612942, RRID:AB_2870223)
    BUV563 anti-CD56 (Clone: NCAM16.2, BD Bioscience, RRID:AB_2870213),
    BUV563 anti-CD56
    detected: (BD Biosciences Cat# 612928, RRID:AB_2870213)
    BUV661 anti-CD14 (Clone: M5E2, BD Bioscience, RRID:AB_2871011), BUV737 anti-CD16 (Clone: 3G8, BD Bioscience, RRID:AB_2869578),
    BUV661 anti-CD14
    detected: (BD Biosciences Cat# 741603, RRID:AB_2871011)
    BUV737
    detected: (BD Biosciences Cat# 564434, RRID:AB_2869578)
    BUV805 anti-CD19 (Clone: SJ25C1, BD Bioscience, RRID:AB_2873553),
    anti-CD19
    detected: (BD Biosciences Cat# 749173, RRID:AB_2873553)
    BV421 anti-CD27 (Clone: O323, Biolegend, RRID:AB_11150782),
    anti-CD27
    detected: (BioLegend Cat# 302824, RRID:AB_11150782)
    BV510 anti-CD4 (Clone: OKt4, Biolegend, RRID:AB_2561866), BV650 anti-CD38 (Clone: HB-7, Biolegend, RRID:AB_2566233), BV786 anti-CD3 (Clone: OKt3, Biolegend, RRID:AB_2563507), PE anti-IgD (Clone: IA6-2, Biolegend, RRID:AB_10553900), PE/Dazzle594 anti-CCR7 (Clone: G043H7, Biolegend, RRID:AB_2563641)
    anti-CD4
    detected: (BioLegend Cat# 317444, RRID:AB_2561866)
    anti-CD38
    detected: (BioLegend Cat# 356620, RRID:AB_2566233)
    anti-CD3
    detected: (BioLegend Cat# 317330, RRID:AB_2563507)
    PE anti-IgD
    detected: (BioLegend Cat# 348204, RRID:AB_10553900)
    PE/Dazzle594
    detected: (BioLegend Cat# 353236, RRID:AB_2563641)
    , FITC anti-HLA-DR (Clone: L243, Biolegend, RRID:AB_314682)
    FITC
    detected: (BioLegend Cat# 307604, RRID:AB_314682)
    PE-Cy7 anti-CD137 (Clone: 4B4-1, Biolegend, RRID:AB_2207741), BB700 anti-CD134/OX40 (Clone: ACT35, BD Bioscience, RRID:AB_2743451)
    anti-CD137
    detected: (BioLegend Cat# 309818, RRID:AB_2207741)
    anti-CD134/OX40
    detected: (BD Biosciences Cat# 746071, RRID:AB_2743451)
    , APC anti-CD69 (Clone: FN50, Biolegend, RRID:AB_314845)
    APC
    detected: (BioLegend Cat# 310910, RRID:AB_314845)
    We excluded data from individuals that were never tested positive for the respective antibody (i.e., anti-S-IgA or -IgG, or anti-N-IgG).
    anti-N-IgG
    suggested: None
    Overall antibody and T cell positivity across timepoints and estimation of T cell decay kinetics. Fig. S2.
    S2
    suggested: None
    Anti-S-IgA and -IgG antibody responses in the overall study population over time.
    Anti-S-IgA
    suggested: None
    Association between demographic and clinical factors and anti-S-IgG antibody positivity at two weeks and six months.
    anti-S-IgG
    suggested: None
    Software and Algorithms
    SentencesResources
    (version 3.0.1) and FlowJo software (version 10, TreeStar Inc).
    FlowJo
    suggested: (FlowJo, RRID:SCR_008520)
    All analyses were performed using R (v4.1.1) (62), using the Hmisc (v4.5-0), lme4 (v1.1-27.1), lmerTest (v3.1-3) and KmL3D (v2.4.2) packages, and results were visualized using the ggplot2 (v3.3.5), ggpubr (v0.4.0) and pheatmap (v1.0.12) packages.
    ggplot2
    suggested: (ggplot2, RRID:SCR_014601)
    pheatmap
    suggested: (pheatmap, RRID:SCR_016418)

    Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

    Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:
    Limitations: Our cohort is one of few population-based and longitudinal studies assessing various components of the immune system in a sample of patients that is representative of the full spectrum of COVID-19. However, some limitations should be considered when interpreting our findings. We used single assays to measure antibodies or T cells in our study. The accuracy and detection levels may differ between tests and thus individuals who are negative in one assay may not be so in another. Nevertheless, the Luminex assay that we used for antibody detection has been extensively validated and was shown to be highly sensitive and specific (45). Second, we did not measure the neutralizing capacity of antibody responses. However, other studies have shown that neutralizing capacity correlates strongly with measured levels of binding antibodies (3, 25, 56). Third, we limited our T cell analysis to the three dominant antigens for cellular immune responses (S, M and N) (2, 8, 57). However, we cannot exclude that in some of the participants, subdominant T cell responses against other viral antigens play important roles, which may have led to an underestimation of the proportion of individuals with T cell responses. Fourth, the cluster analysis bears the limitations that are inherent to the methodology. Using a clustering algorithm that separates the study population into distinct clusters may not be necessarily reflective of clinically meaningful differences. However, we identified dis...

    Results from TrialIdentifier: We found the following clinical trial numbers in your paper:

    IdentifierStatusTitle
    ISRCTN14990068NANA

    Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

    Results from JetFighter: We did not find any issues relating to colormaps.

    Results from rtransparent:
    • Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
    • Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
    • Thank you for including a protocol registration statement.

    Results from scite Reference Check: We found no unreliable references.

    About SciScore

    SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

  3. Version published to 10.1101/2021.12.15.21267776 on medRxiv