High-dimensional profiling reveals phenotypic heterogeneity and disease-specific alterations of granulocytes in COVID-19
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Abstract
Accumulating evidence shows that granulocytes are key modulators of the immune response to SARS-CoV-2 infection, and their dysregulation could significantly impact COVID-19 severity and patient recovery after virus clearance. In the present study, we identify selected immune traits in neutrophil, eosinophil, and basophil subsets associated with severity of COVID-19 and with peripheral protein profiles. Moreover, computational modeling indicates that the combined use of phenotypic data and laboratory measurements can effectively predict key clinical outcomes in COVID-19 patients. Finally, patient-matched longitudinal analysis shows phenotypic normalization of granulocyte subsets 4 mo after hospitalization. Overall, in this work, we extend the current understanding of the distinct contribution of granulocyte subsets to COVID-19 pathogenesis.
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SciScore for 10.1101/2021.01.27.21250591: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
Institutional Review Board Statement Consent: Informed consent was obtained from all study participants and the study was approved by the regional Ethics Committee in Stockholm, Sweden, and performed in accordance with the Declaration of Helsinki.
IRB: Informed consent was obtained from all study participants and the study was approved by the regional Ethics Committee in Stockholm, Sweden, and performed in accordance with the Declaration of Helsinki.Randomization not detected. Blinding not detected. Power Analysis not detected. Sex as a biological variable not detected. Table 2: Resources
Software and Algorithms Sentences Resources Flow cytometry data analysis: Standard flow cytometry data analysis was … SciScore for 10.1101/2021.01.27.21250591: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
Institutional Review Board Statement Consent: Informed consent was obtained from all study participants and the study was approved by the regional Ethics Committee in Stockholm, Sweden, and performed in accordance with the Declaration of Helsinki.
IRB: Informed consent was obtained from all study participants and the study was approved by the regional Ethics Committee in Stockholm, Sweden, and performed in accordance with the Declaration of Helsinki.Randomization not detected. Blinding not detected. Power Analysis not detected. Sex as a biological variable not detected. Table 2: Resources
Software and Algorithms Sentences Resources Flow cytometry data analysis: Standard flow cytometry data analysis was performed using FlowJo version 10. FlowJosuggested: (FlowJo, RRID:SCR_008520)FlowJo Phenograph v3 was used for unsupervised clustering. Phenographsuggested: (Phenograph, RRID:SCR_016919)Statistical analysis: GraphPad Prism version 9 (GraphPad Software) and R v4.0.1 (56) were used to conduct statistical analyses, where p-values < 0.05 were considered significant. GraphPad Prismsuggested: (GraphPad Prism, RRID:SCR_002798)GraphPadsuggested: (GraphPad Prism, RRID:SCR_002798)Correlation, hierarchical clustering and multivariate analysis of flow cytometry data with clinical parameters and proteomic data was performed and visualized in GraphPad Software and R v4.0.1 and v1.3.959 (56), using the packages factoextra (v1.0.7) (57), FactoMineR (v2.3) (58), PerfomanceAnalytics (v2.0.4) (59), ggplot2 (v3.3.1) (60), gplots (v3.0.4) (61), pheatmap (v1.0.12) (62), vegan (v2.5-6) (63), corrplot (v0.84) (64), lattice (v0.20-41) (65) and latticeExtra (v0.6-29) (66), stats (v4.0.1) and complexheatmap (v2.5.6) (67). FactoMineRsuggested: (FactoMineR, RRID:SCR_014602)ggplot2suggested: (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: We detected the following sentences addressing limitations in the study:The inference of mechanisms driving COVID-19 immunopathology from the analysis of circulating granulocytes might have limitations when considering the pulmonary damage displayed in COVID-19 patients. However, several studies have shown that the combined use of peripheral immune signatures, soluble factors and patient metadata retain the capacity to predict clinical outcome to a notable extent (6, 53). Moreover, accumulating evidence indicates that other tissues (e.g. kidney, gut, brain) are affected by COVID-19-related immune alterations, thus underscoring the systemic nature of this disease and emphasizing the relevance of studying alterations in peripheral immune cells (54). Our findings highlight the significant alterations of granulocyte subpopulations in frequency and function in the blood of patients with COVID-19. Moreover, our data indicate the potential contribution of granulocytes to SARS-CoV-2 immunopathology and point towards the combined use of granulocyte-related immunological parameters and basic clinical laboratory tests as better prognostic biomarkers of disease severity and disease course.
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 scite Reference Check: We found no unreliable references.
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