Innate lymphoid cell composition associates with COVID-19 disease severity

  1. Marina García
  2. Efthymia Kokkinou
  3. Anna Carrasco García
  4. Tiphaine Parrot
  5. Laura M. Palma Medina
  6. Kimia T. Maleki
  7. Wanda Christ
  8. Renata Varnaitė
  9. Iva Filipovic
  10. Hans-Gustaf Ljunggren
  11. Niklas K. Björkström
  12. Elin Folkesson
  13. Olav Rooyackers
  14. Lars I. Eriksson
  15. Anders Sönnerborg
  16. Soo Aleman
  17. Kristoffer Strålin
  18. Sara Gredmark-Russ
  19. Jonas Klingström
  20. Jenny Mjösberg
  21. the Karolinska KI/K COVID-19 Study Group

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Abstract

Objectives

The role of innate lymphoid cells (ILCs) in coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is unknown. Understanding the immune response in COVID-19 could contribute to unravel the pathogenesis and identification of treatment targets. To describe the phenotypic landscape of circulating ILCs in COVID-19 patients and to identify ILC phenotypes correlated to serum biomarkers, clinical markers, and laboratory parameters relevant in COVID-19.

Methods

Blood samples collected from moderately (n=11) and severely ill (n=12) COVID-19 patients as well as healthy control donors (n=16), were analyzed with 18-parameter flow cytometry. Using supervised and unsupervised approaches, we examined the ILC activation status and homing profile. Clinical and laboratory parameters were obtained from all COVID-19 patients and serum biomarkers were analyzed with multiplex immunoassays.

Results

ILCs were largely depleted from the circulation of COVID-19 patients compared with healthy controls. Remaining circulating ILCs from patients revealed increased frequencies of ILC2 in moderate COVID-19, with a concomitant decrease of ILC precursors (ILCp), as compared with controls. ILC2 and ILCp showed an activated phenotype with increased CD69 expression, whereas expression levels of the chemokine receptors CXCR3 and CCR4 were significantly altered in ILC2 and ILCp, and ILC1, respectively. The activated ILC profile of COVID-19 patients was associated with soluble inflammatory markers, while frequencies of ILC subsets were correlated with laboratory parameters that reflect the disease severity.

Conclusion

This study provides insights into the potential role of ILCs in immune responses against SARS-CoV-2, particularly linked to the severity of COVID-19.

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  1. ScreenIT

    SciScore for 10.1101/2020.10.13.20211367: (What is this?)

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

    Table 1: Rigor

    Institutional Review Board StatementConsent: All patients or next of kin and control donors provided oral and/or written informed consent in line with the ethical approval.
    Randomizationnot detected.
    BlindingUMAP analysis: To ensure unbiased manual gating, a blinded analysis was implemented, whereby all FCS3.0 files were renamed and coded by one person and blindly analyzed by another person.
    Power Analysisnot detected.
    Sex as a biological variableCOVID-19 Immune Atlas, 23 COVID-19 patients (6 females and 17 males; median age 57 years; age range 18 - 74 years) positive for SARS-CoV-2 RNA by diagnostic RT-qPCR and hospitalized at the Karolinska University Hospital (Stockholm, Sweden) were included in the present study.
    Cell Line Authenticationnot detected.

    Table 2: Resources

    Experimental Models: Cell Lines
    SentencesResources
    Mixtures were then added to Vero E6 cells and incubated at 37 °C 5% CO2 for four days.
    Vero E6
    suggested: None
    Software and Algorithms
    SentencesResources
    For unsupervised analysis, the following FlowJo plugins were used: DownSample (v.1.1), UMAP (v.2.2), Phenograph (v.2.4) and ClusterExplorer (v.1.2.2) (all FlowJo LLC).
    FlowJo
    suggested: (FlowJo, RRID:SCR_008520)
    Phenograph
    suggested: (Phenograph, RRID:SCR_016919)
    ClusterExplorer
    suggested: None
    Left-censored data from the multiplex analysis were imputed using GSimp package27 in R (v. 3.6.0)28.
    GSimp
    suggested: None
    Statistical analysis: Statistical analyses were performed using Prism version 8.4.3 (
    Prism
    suggested: (PRISM, RRID:SCR_005375)
    (GraphPad Software Inc.).
    GraphPad
    suggested: (GraphPad Prism, RRID:SCR_002798)
    Principal component analysis (PCA) was performed in R (v.4.0.2; R Core Team, 2020) using packages Factoextra (v.1.0.7)31, FactoMineR (v.2.3)32, RColorBrewer (v.1.1-2)33, and ggplot2 (v.3.3.2)34.
    FactoMineR
    suggested: (FactoMineR, RRID:SCR_014602)
    ggplot2
    suggested: (ggplot2, RRID:SCR_014601)

    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: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.

    Results from TrialIdentifier: No clinical trial numbers were referenced.

    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.
    • No protocol registration statement was detected.

    About SciScore

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  2. Version published to 10.1101/2020.10.13.20211367v1 on medRxiv
  3. Version published to 10.1002/cti2.1224