Hyperactive immature state and differential CXCR2 expression of neutrophils in severe COVID-19

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Neutrophils are vital in defence against pathogens but excessive neutrophil activity can lead to tissue damage and promote acute respiratory distress syndrome (ARDS). COVID-19 is associated with systemic expansion of immature neutrophils but the functional consequences of this shift to immaturity are not understood. We used flow cytometry to investigate activity and phenotypic diversity of circulating neutrophils in acute and convalescent COVID-19 patients. First, we demonstrate hyperactivation of immature CD10 subpopulations in severe disease, with elevated markers of secondary granule release. Partially activated immature neutrophils were detectable three months post symptom onset, indication long term myeloid dysregulation in convalescent COVID-19 patients. Second, we demonstrate that neutrophils from moderately ill patients downregulate the chemokine receptor CXCR2, while neutrophils from severely ill individuals failed to do so, suggesting altered ability for organ trafficking and a potential mechanism for induction of disease tolerance. CD10 and CXCR2 hi neutrophil subpopulations were enriched in severe disease and may represent prognostic biomarkers for identification of individuals at high risk of progressing to severe COVID-19.

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  1. Note: This rebuttal was posted by the corresponding author to Review Commons. Content has not been altered except for formatting.

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    The authors do not wish to provide a response at this time.

  2. Note: This preprint has been reviewed by subject experts for Review Commons. Content has not been altered except for formatting.

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    Referee #2

    Evidence, reproducibility and clarity

    The authors found that the expression of CXCR2 is decreased in patients with moderate COVID-19. However, the mechanisms were not explored. The hyperactivation status of neutrophils is not well defined, and proteomics data are not validated. The rationale for comparing healthy controls and severe COVID-19 patients is unclear. The manuscript in its current form raised more questions than answers.

    Major concerns:

    1. No information is available on the healthy control group. How do they compare to the COVID-19 group? Age-, sex-differences? Comorbidities?
    2. Figure 1E. While the decrease in the level of CXCR2 expression in the moderate group is statistically significant, the functional significance of this finding is unclear. The MFI mean value of approximately five hundred units is still high. Whether it would it be translated into decreased neutrophil migratory activity and tissue recruitment is unknown. As with any G-protein coupled receptor, the ligand-dependent stimulation of CXCR2 would induce its internalization. Do the authors consider the possibility of increased levels of CXCR2 ligands causing lower cell surface levels of CXCR2 in patients with moderate illness?
    3. The proteomic analysis would be helpful in the identification of potential mechanisms involved in the reduced level of CXCR2 in the moderate group. However, the authors have decided to perform this analysis on healthy controls and patients with severe COVID-19 illness, two groups with a similar level of CXCR2 expression.
    4. Figure 2. No information is available on the selection criteria for the samples used in proteomic analysis. How representative were those four healthy controls and three COVID-19 patients for their respective groups?
    5. Figure 2. It is unclear why the authors believe that the changes identified in proteomic analysis indicate the hyperactivation status of neutrophils. The analysis is performed by comparing neutrophils from the severe COVID-19 group against healthy control subjects. Would it be different for mild or moderate illness groups if compared to patients with severe illness or healthy subjects? Without these data, it is hard to understand if reported changes indicate hyperactivation.
    6. The authors' statement on neutrophil activation is not confirmed by any measurements in vitro or in vivo. It is unclear if these neutrophils produce more proinflammatory cytokines or reactive oxygen species? Are they more prone to undergo NETosis?


    1. It is unclear why the statistical approach in Figures 1A and B is different from the approach used in Figures 1C, D, and E.
    2. Figure 1A, flow cytometric dot plot: It is interesting to see that the immature neutrophils are represented by a distinct subset of CD10- cells. In other studies, including those cited by the authors, immature neutrophils are characterized by gradually decreased expression of CD10, not distinctly separated from mature neutrophils.
    3. In Supplemental Figure 1 - the gating strategy for singlets is mislabeled; should be FSC-A vs. FSC-H, but listed as FSC-A vs. SSC-A.
    4. It may increase the translational value of the study if the authors perform an analysis of immune markers against clinical parameters demonstrating the severity of illness, e.g., hospital length of stay or hospital-free days, patients in an intensive care unit (ICU) versus non-ICU, and lab tests, serum CRP, WBC, NLR.


    In the current study, Rice et al. investigated the subpopulation of peripheral blood neutrophils obtained from patients with COVID-19 and healthy controls. The authors performed flow cytometric and proteomic analyses to determine the association between immunophenotype and activation of neutrophils and the severity of COVID-19 illness. The flow cytometric analysis is meticulously executed and informative and confirms previously published data on the immature status of circulating neutrophils in COVID-19.

  3. Note: This preprint has been reviewed by subject experts for Review Commons. Content has not been altered except for formatting.

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    Referee #1

    Evidence, reproducibility and clarity


    In this manuscript, the authors used flow cytometry to investigate activity and phenotypic diversity of circulating neutrophils in acute and convalescent COVID-19 patients (acute COVID-19 patients: 34; healthy controls: 20). Further analysis indicated that hyperactivation of immature CD10- subpopulations in severe disease. Additionally, the authors found CXCR2 was down-regulated in moderately ill patients, and CD10- and CXCR2hi neutrophil subpopulations were enriched in severe disease. This work is interesting, yet the main problem of this work is that it lacks of novelty, and the conclusion was proposed without solid evidence.

    Major points:

    1. The author 's main conclusions were based on flow cytometry. However, they didn't validate the purity of neutrophiles sorted by their sorting strategy.
    2. The statical analysis should be checked by statisticians.
    3. The author indicated they detected decreased expression of CD10 from moderate and severe COVID-19 patients, and concluded the potential of its prognostic utility. However, this conclusion is not novel, previous research performed by Silvin et al. and others have presented the immunosuppressive profile of CD10lowCD101-CXCR4+/- neutrophils in severe form of COVID-19 (PMID: 32810439, PMID: 33968405).
    4. It seems that the author specifically picked CD10 to present its difference between patients and heathy controls, yet, for one thing the author didn't show how they detect the expression of CD10, did they perform western blotting, transcriptome or proteome? For another, the author did not show explain if CD10 is the only proteins or the top-ranked protein that show prognostic value.
    5. To further explore the neutrophil activation and chemotactic capacity, the author compared the proteomes of circulating neutrophils from severe and healthy controls. However, comparing to the published work, the sample numbers were too small, for there are only three severe patients enrolled, the author should include more samples for analysis.
    6. The author performed UMAP analysis, and conclude long term perturbations to the myeloid compartments of convalescent patients. This conclusion is too rash, the author should include clinical index, such as absolute neutrophil counts, neutrophil percentage for integrative analysis.
    7. The proteins that the author indicated to be neutrophil functional related are more likely to be functional universal. The author should include neutrophil specific datasets and screen out neutrophil specific markers for further analysis.
    8. The author utilized X-Shift analysis to analyze the distinct neutrophil phenotypes in different disease states, yet, only one or two markers can hardly describe the whole picture. The author should conduct single cell transcriptome or proteome to systematically depict the diverse neutrophile phenotypes in different disease status.
    9. There are multiple published papers describe the immune cell subsets of COVID-19 (PMID: 32838342, PMID: 33657410), the author should compare with them.

    Minor point:

    1. In table 1, the authors did not provide the p value among Mild, Moderate, and Severe groups.
    2. In Sup Fig 1B, Sup Fig 1C, Sup Fig 2E-G, I-K, Sup Fig 3D, the authors did not provide p value.
    3. The author assumed "Principle component analysis (PCA) demonstrated heterogeneity amongst the severe patients, which was explained by patient outcome (Fig 2C)." Again, too small sample numbers, can hardly show the diversity.
    4. In Fig2G, the authors descripted patient neutrophils, and not descripted which type of patients.
    5. The authors mentioned Fig1G in the sentence "Ingenuity pathway analysis (IPA) identified pathways related to chemotaxis, such as 'Signalling by Rho family GTPases', 'RhoA signalling' and 'Regulation of Actin-based Motility by Rho' as significantly enriched in patient neutrophils (Fig 2G), which aligns with maintained expression of CXCR2 (Fig 1G)", however we did not see the corresponding Fig1G.


    The paper lacks arguments regarding the novelty of the findings, as well as context with the current literature available for COVID-19 (several examples of the available literature references are provided) including comparison to published single cell dataset of COVID-19 (PMID: 32838342, PMID: 33657410, PMID: 32810439, PMID: 33968405). The paper focused more on known example, which are indeed useful to assess their strategy, but failed to detail their findings about unknown protein candidate which would bring more value to the manuscript.

  4. SciScore for 10.1101/2022.03.23.22272828: (What is this?)

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

    Table 1: Rigor

    EthicsConsent: Human subjects and samples: Written informed consent was obtained from all patient and healthy donors, or from patients’ family if patients were too unwell to consent.
    Sex as a biological variablenot detected.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.

    Table 2: Resources

    Software and Algorithms
    X-Shift clustering was generated using the flowjo platform and used the same markers as UMAP, with the following settings: number of nearest neighbours (k): 20, Distance Metric: Angular, sampling limit 94000.
    suggested: (FlowJo, RRID:SCR_008520)
    All spectra were acquired using an Orbitrap Fusion Tribrid mass spectrometer controlled by Xcalibur 2.1 software (Thermo Scientific) and operated in data-dependent acquisition mode using an SPS-MS3 workflow.
    suggested: (Thermo Xcalibur, RRID:SCR_014593)
    (Thermo Scientific) and searched against the UniProt Human database (downloaded January 2021) using the SEQUEST HT algorithm.
    suggested: (UniProtKB, RRID:SCR_004426)
    Bioinformatics analysis of Proteomics: Following analysis in Proteome Discoverer 2.1, the proteomics data were processed and further analysed in the R statistical computing environment.
    Proteome Discoverer
    suggested: (Proteome Discoverer, RRID:SCR_014477)
    PCAs were calculated using the PCA function in the FactoMineR package, and plotted using either ggplot (2D), or Plotly (3D).
    suggested: (FactoMineR, RRID:SCR_014602)
    Statistical analysis: Statistical analysis was performed using Graphpad Prism 8.
    Graphpad Prism
    suggested: (GraphPad Prism, RRID:SCR_002798)

    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:
    A limitation of our study was the sample size where, particularly for proteomics, analysis of data following stratification by patient outcome was unfortunately not possible or underpowered. This risks the reduced detection of significant alterations to protein content due to the low number of patient samples. In summary, we show that severe COVID-19 is associated with hyperactive immature neutrophils, maintenance of CXCR2 expression and increased translational activity. Our study identifies neutrophil subpopulations as prognostic biomarkers of disease severity and supports a role for neutrophils in COVID-19 immunopathogenesis. CXCR2 downregulation on neutrophils may act to prevent progression to severe disease. Finally, as proposed by others, the IL-8 signalling pathway, and in particular CXCR2, may represent therapeutic targets for severe COVID-19 (83).

    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.

    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.