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

Severe COVID-19 is associated with alterations to the neutrophil compartment with circulating hyperactive immature neutrophils and a maintenance of CXCR2 expression.


General Statements
The reviewers raised some useful points that will improve the manuscript. Reviewer 1 questioned the novelty of our report ("This work is interesting, yet the main problem of this work is that it lacks of novelty…" In response, we would like to more clearly point out three novel contributions of our manuscript: 1) The reviewer is correct that CD10low, immature neutrophils have been linked to severe COVID-19 in multiple reports. It was precisely in response to these reports that we set out to investigate, in greater detail, the activation status of immature neutrophils. To the best of our knowledge, there are no reports showing that CD10low neutrophils are in fact engaging in elevated rates of secondary granule release (CD66b and CD177 upregulation) and elevated surface protease activity (CD62L cleavage), both of which are commonly used readouts of neutrophil activation. What has been previously reported for immature neutrophils is simply an association with severe COVID-19, however there is a lack of mechanistic understanding of how they may contribute to pathology. Our report builds on these previous publications and provides evidence that immature neutrophils display elevated degranulation rates (expose secondary granule markers on the surface), compared to mature neutrophils, in vivo. This finding is an important addition to our understanding of immature neutrophils and may help pave the way for targeting them therapeutically. We will state this more clearly in the revised manuscript.
2) Neutrophil CXCR2 surface expression has not been extensively investigated in COVID-19, despite the fact that this is the main chemotactic receptor, with obvious relevance to lung trafficking. Our report identifies a link between surface CXCR2 expression and disease severity: neutrophils from moderately ill patients dowregulate, while neutrophils from severe COVID-19 patients maintain CXCR2 levels, potentially identifying a protective mechanism in moderate COVID-19, that is of clinical and therapeutic relevance. In fact, since submission of our manuscript, a Phase II clinical trial of the CXCR2 inhibitor Reparixin reported a significant decrease in adverse clinical outcomes in COVID-19, compared to standard of care (1). This further strengthens our hypothesis that modulation of CXCR2 is central to disease progression and emphasizes the need to publish this finding. We will state this more clearly in the revised manuscript. We agree with the reviewer that the proteomics data are not central to our findings (beyond confirming that neutrophils are activated); we will move the data to the supplement. We agree with the reviewer that the term 'hyperactivation' is inappropriate when referring to the proteomics data, since there is no comparison with moderate COVID-19. We will remove this term and simply call it activation.
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?
We will include these data.
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? This is an interesting suggestion. We will measure circulating levels of IL-8, the major CXCR2 ligand, in plasma of moderate and severe patients. With respect to the comment on MFI: since MFI is relative, it is difficult to know whether the ' approximately five hundred units' that we detected in moderate patients consists of meaningful expression or is simply background. We will include an FMO control (Fluorescence minus one) to answer this.
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.
We agree with the reviewer that the mass spectrometry data is not appropriate for the mechanistic analysis. We will move the mass spec data to the supplement. We will address the mechanism of CXCR2 downregulation using stored patient samples: a) To test for transcriptional suppression, we will quantify CXCR2 mRNA abundance using qPCR in moderate and severe patients (paired with surface CXCR2 expression data). b) To test for internalisation of CXCR2, we will perform immunoblotting to detect internal CXCR in neutrophil lysates from patients where we detected reduced surface CXCR2 expression.

Revision Plan
These two approaches are expected to begin to elucidate the mechanism by which CXCR2 is specifically reduced in moderately ill patients.
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?
We will provide data on donor characteristics for healthy donors, as well as clinical data for patients. 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.

5.
We agree with the reviewer. The more appropriate term is 'activation', rather than 'hyperactivation', since proteomic analysis of moderate COVID is not available. We will change this term throughout the manuscript.

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?
We disagree with the author here. We analysed neutrophil activation status ex vivo, using flow cytometry. As explained above, we tested surface protease activity as well as degranulation, both of which are important neutrophil functional responses. To confirm our flow cytometry data and provide more evidence of elevated degranulation in patients, we measured degranulation in isolated neutrophils, treated with a strong inducer of degranulation (Calcium ionophore). We found significantly elevated release of secondary granule proteins OLFM, LYZ and LCN2, validating our flow cytometry data. We will include the data in the revised manuscript.

Revision Plan
The differing approaches are due to the different nature of the data produced and their distribution. For percentile data, where we are detecting increases from negative (CD63 >0%) or reductions from fully positive (CD62L <100%), the data will be, by definition, unlikely to be normally distributed. Indeed, multiple normality tests on this data (Anderson-Darling, D'Agostino & Pearson, Shapiro-Wilk and Kolmogorov-Smirnov) demonstrated that the data were not normally distributed and therefore non-parametric tests (Kruskal Wallis with Dunn's multiple comparisons) were performed. For MFI data, due to the continuous nature of the data (i.e. not constrained by minimal, 0%, or maximal, 100%, values) likelihood of normal distribution is far higher. Normality tests found that most of the MFI data were normally distributed, however exceptions occurred in some groups. To improve the likelihood of normal distribution of the data, we performed log transformation of MFI data and analysed the logtransformed numbers with parametric tests such as One-way ANOVA with Tukey's multiple comparisons. We will confirm our approach with a statistician prior to resubmission.
8. 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.
We agree with the reviewer that this is interesting. It was observed in patients with severe COVID-19, although we also observed gradual, continuous decreases. We have also observed complete absence of CD10 in severe malaria and we think it is a feature of very immature neutrophils. We will discuss this further in the revised manuscript.

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.
We will make this change.
10. 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.
We will carry out this analysis for both CD10 and CXCR2.
Reviewer #2 (Significance (Required)): 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.
We thank the reviewer for deeming our analysis 'meticulously executed and informative'. We would like to point out that confirmation of 'previously published data on the immature status of circulating neutrophils in COVID-19' pertains to Figure 1A only. The remainder of the flow cytometry analysis aims to determine the activation status and pro-inflammatory potential of these cells, and we believe this analysis to be the first of its kind in COVID-19.
3. Description of the revisions that have already been incorporated in the transferred manuscript

Description of analyses that authors prefer not to carry out
We cannot carry out single cell RNA sequencing of neutrophils as suggested by reviewer 1, because this is beyond the scope of this manuscript. Thank you for submitting your manuscript entitled "Hyperactive immature state and differential CXCR2 expression of neutrophils in severe COVID-19" to Life Science Alliance. We invite you to re-submit the manuscript, revised according to your Revision Plan.
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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.
We thank the reviewer for their comment. In response, we would like to more clearly point out three novel contributions of our manuscript: 1) The reviewer is correct that CD10low, immature neutrophils have been linked to severe COVID-19 in multiple reports. It was precisely in response to these reports that we set out to investigate, in greater detail, the activation status of immature neutrophils. To the best of our knowledge, there are no reports showing that CD10low neutrophils are in fact engaging in a) elevated rates of secondary granule release (CD66b and CD177 externalisation) and b) elevated surface protease activity (CD62L cleavage), both of which are commonly used readouts of neutrophil activation. What has been previously reported for immature neutrophils is simply an association with severe COVID-19, and there is currently a lack of functional characterisation of how they may contribute to pathology. Our report builds on these previous publications and provides evidence that immature neutrophils display elevated degranulation rates compared to mature neutrophils, in vivo. This finding is an important addition to our understanding of immature neutrophils and may help pave the way for targeting them therapeutically. We have stated this more clearly in the manuscript, please see 'Discussion' lines 299-305. 2) Neutrophil CXCR2 surface expression has not been extensively investigated in COVID-19, despite the fact that this is the main neutrophil chemotactic receptor, with obvious relevance to lung trafficking. Our report identifies a link between surface CXCR2 expression and disease severity: neutrophils from moderately ill patients downregulate CXCR2, while neutrophils from severe COVID-19 patients maintain higher CXCR2 expression. We are therefore proposing a potential protective/adaptive mechanism in moderate COVID-19, which may act to limit neutrophil infiltration of organs in moderate disease. This finding is of clinical and therapeutic relevance. In fact, since submission of our manuscript, a Phase II clinical trial of the CXCR2 inhibitor Reparixin reported a significant decrease in adverse clinical outcomes in COVID-19, compared to standard of care (1). This further strengthens our hypothesis that modulation of CXCR2 is central to disease progression and emphasizes the need to publish this finding. We have stated this more clearly in the revised manuscript, please see 'Discussion', lines 337-341.
3) Finally, we unexpectedly identified long-lasting, persistent changes in the neutrophil compartment, which, to our knowledge, have not been previously reported in disease (neither COVID-19 nor in other infectious disease). We agree that our analysis is limited and have pointed out these limitations in the discussion (lines 332-336); we hope that this report encourages additional investigations into long term neutrophil changes, which may have relevance for long COVID and other inflammatory diseases.

The author 's main conclusions were based on flow cytometry. However, they didn't validate the purity of neutrophiles sorted by their sorting strategy.
We thank the reviewer for pointing out that our gating strategy was unclear.
We used an extensive flow cytometry panel to detect and analyse properties of neutrophils in unsorted whole blood, with a high degree of precision. We have now included a detailed validation of our gating strategy, and microscopy evidence that it does in fact identify neutrophils. Please see Supplemental Figure 1 and lines 134-136. This is a valuable addition to the manuscript.

The statical analysis should be checked by statisticians.
As requested, we had the manuscript reviewed by a statistician, Dr. Alan Hedges (Honorary Research Fellow, University of Bristol), who found our use of statistics appropriate. The differing approaches are due to the distinctive nature of the data produced and their distributions. For percentile data, where we are detecting increases from negative (eg. CD63 >0%) or reductions from fully positive (eg. CD62L <100%), the data is, by definition, unlikely to be normally distributed. Indeed, multiple normality tests on this data (Anderson-Darling, D'Agostino & Pearson, Shapiro-Wilk and Kolmogorov-Smirnov) demonstrated that the data are not normally distributed and therefore non-parametric tests were performed (Kruskal Wallis with Dunn's multiple comparisons for more than 2 groups or Mann-Whitney tests for 2 parameters). For MFI data, due to the continuous nature of the data (i.e. not constrained by minimal, 0%, or maximal, 100%, values) the likelihood of normal distribution is far higher. Normality tests found that most of the MFI data were normally distributed, however exceptions occurred in some groups. To improve the likelihood of normal distribution of the data, we performed log transformation of MFI data prior to analysis with parametric tests (One-way ANOVA with Tukey's multiple comparisons for 3 or more groups and t-tests for comparing 2 groups). We have improved the wording of our statistical analysis in the methods section and in the figure legends throughout the manuscript.

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).
We completely agree with this comment. We replicated the association between immature neutrophils (CD10low) and disease. However, we also go one step further and demonstrate two unique functional features of immature neutrophils: a) increased exposure of CD66b and CD177, indicative of secondary granule release (1), which contain important inflammatory and immunostimulatory factors and b) decreased CD62L cleavage, which is indicative of elevated surface protease activity (2). To the best of our knowledge, this is the first demonstration of hyperactivation of immature CD10low neutrophils compared to mature CD10hi neutrophils, in COVID19. We have stated this more clearly in the text, please see lines 299-305. 2) Li Y, Brazzell J, Herrera A, Walcheck B. ADAM17 deficiency by mature neutrophils has differential effects on L-selectin shedding. Blood. 2006 Oct 1;108 (7):2275-9.

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.
The reviewer is correct that we specifically picked CD10. We were hypothesis-driven and set out to investigate the activation state of CD10low, immature neutrophils. We detected CD10 by a commercially available and well cited monoclonal flow cytometry antibody. This technique is superior to transcriptome and western blotting because it quantifies surface expression of this receptor on an individual cell-per-cell basis, i.e. allows us to quantify it in its physiological state. We further confirmed that we correctly detected immature neutrophils by use of a second surface receptor associated with neutrophil maturation (CD101).

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.
Here we again agree with the reviewer: more extensive proteomic analyses have been reported by other groups that are cited in the manuscript, and our findings have simply validated these reports. We have moved our proteome data to Supplementary Figure 4 because they are somewhat tangential to the central findings of the manuscript (hyperactivation of immature neutrophils, reduced CXCR2 in moderate disease and longlasting neutrophil phenotypic changes). This resulted in a more focused and streamlined manuscript, so we thank the reviewer for this comment.
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.
We thank the reviewer for this comment. The long term neutrophil phenotypic changes detected by UMAP are unexpected and novel but we agree with the reviewer that we did not perform an extensive analysis. We have changed the wording for this conclusion to make it more nuanced, please see lines 332-336.
As suggested we now include additional analysis pertaining to the presence of persistent immature neutrophil clusters in previously hospitalised convalescent patients. Firstly, we have additionally stratified percentage of neutrophils in cluster 3 by acute disease severity (Supplemental Fig 5H). Secondly, as suggested by the reviewers, we have correlated cluster 3 positivity against absolute neutrophil count from the acute infection or at 12 weeks post hospitalisation, when cluster 3 is detected (Supplemental Fig 5I). We additionally look for long term changes in neutrophil count by comparing absolute neutrophil count between acute infection and 12 weeks post hospitalisation in these patients (Supplemental Fig 5J). Unfortunately, none of these analyses identified a reciprocal alteration in total neutrophil counts and as discussed in lines 288-290, further investigation will be required to uncover the mechanism of long-term neutrophil alterations.

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.
We agree with the reviewer that the pathways that were detected by proteomic analysis (eg interferon response) are not neutrophil specific. However, we wish to note here that the mass spectrometry analysis was performed on isolated neutrophils, meaning all identified proteins are related to neutrophil function, but, as the reviewer points out, not exclusive to neutrophils. We agree with the reviewer that it is interesting to distinguish specific versus general activation pathways. The proteome result has been moved to the supplement (Supplemental Figure 4) and we have pointed out whether proteins are neutrophil specific or not in lines 224-225.

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.
We agree with the reviewer that flow cytometry is inherently limited because of restricted markers. However, we wish to argue that the technique is also superior to other methods because it analyses protein abundance in the correct conformation and location (cell surface) and allows one to ask targeted questions. Furthermore, the X-shift analysis was performed by comparing the expression of 10 surface markers simultaneously and categorising individual cells into automatically determined populations. Unfortunately, single cell transcriptomics or additional proteomes are beyond the scope of this manuscript, but we have included this point in the discussion (lines 371-373)

There are multiple published papers describe the immune cell subsets of COVID-19 (PMID: 32838342, PMID: 33657410), the author should compare with them.
We agree with the reviewer that there are multiple, comprehensive analyses looking at activation of immune cells in COVID-19 and that it is valuable to compare our neutrophilspecific analysis with these large-scale studies. We did this for the two reports mentioned above as well as 3 additional ones that we found. These are summarised below: PMID: 32838342; Rodrigues et al 2020: this paper only reports elevated neutrophil counts in acutely ill patients and does not specifically analyse neutrophils. We added the reference to our introduction. PMID: 33657410; Ren at al 2021: this paper performed RNA seq on the PBMC fraction, which normally excludes neutrophils and other granulocytes (except when neutrophil buoyancy is altered by activation). Interestingly, they also analysed bronchiolar lavage fluid (BALF) and found neutrophils to be enriched in the lungs of severely ill patients. The BALF neutrophils also had a strong type 1 interferon RNA signature, matching our proteomic findings. The authors also provide bioinformatic evidence that virus-RNA containing epithelial cells may be interacting with neutrophils. We have added this reference to the introduction and the discussion.