Multimodal Single-Cell Omics Analysis of COVID-19 Sex Differences in Human Immune Systems

  1. Yuan Hou
  2. Yadi Zhou
  3. Michaela U. Gack
  4. Justin D. Lathia
  5. Asha Kallianpur
  6. Reena Mehra
  7. Timothy Chan
  8. Jae U. Jung
  9. Lara Jehi
  10. Charis Eng
  11. Feixiong Cheng

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Abstract

Sex differences in the risk of SARS-CoV-2 infection have been controversial and the underlying mechanisms of COVID-19 sexual dimorphism remain understudied. Here we inspected sex differences in SARS-CoV-2 positivity, hospitalization, admission to the intensive care unit (ICU), sera immune profiling, and two single-cell RNA-sequencing (snRNA-seq) profiles from nasal tissues and peripheral blood mononuclear cells (PBMCs) of COVID-19 patients with varying degrees of disease severity. Our propensity score-matching observations revealed that male individuals have a 29% increased likelihood of SARS-CoV-2 positivity, with a hazard ration (HR) 1.32 (95% confidence interval [CI] 1.18-1.48) for hospitalization and HR 1.51 (95% CI 1.24-1.84) for admission to ICU. Sera from male patients at hospital admission had decreased lymphocyte count and elevated inflammatory markers (C-reactive protein, procalcitonin, and neutrophils). We found that SARS-CoV-2 entry factors, including ACE2, TMPRSS2, FURIN and NRP1, have elevated expression in nasal squamous cells from males with moderate and severe COVID-19. Cell-cell network proximity analysis suggests possible epithelium-immune cell interactions and immune vulnerability underlying a higher mortality in males with COVID-19. Monocyte-elevated expression of Toll like receptor 7 (TLR7) and Bruton tyrosine kinase (BTK) is associated with severe outcomes in males with COVID-19. These findings provide basis for understanding immune responses underlying sex differences, and designing sex-specific targeted treatments and patient care for COVID-19.

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    SciScore for 10.1101/2020.12.01.407007: (What is this?)

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

    Table 1: Rigor

    NIH rigor criteria are not applicable to paper type.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    We collected and managed all patient data using REDCap electronic data capture tools.
    REDCap
    suggested: (REDCap, RRID:SCR_003445)
    All the cumulative hazard analyses were performed using the Survival and Survminer packages in R 3.6.0 (https://www.r-project.org).
    https://www.r-project.org
    suggested: (R Project for Statistical Computing, RRID:SCR_001905)
    Therefore, all analysis in dataset-1 were based on their cell type annotation. ii) Dataset-2 (GSE149689)39 was downloaded from the NCBI GEO database.
    NCBI GEO
    suggested: None
    All single-cell data analyses and visualizations were performed with the R package Seurat v3.1.4 40.
    Seurat
    suggested: (SEURAT, RRID:SCR_007322)
    All analyses were performed with the prerank function in GSEApy package (https://gseapy.readthedocs.io/en/master/index.html) on Python 3.7 platform.
    GSEApy
    suggested: None
    Python
    suggested: (IPython, RRID:SCR_001658)
    Functional enrichment analysis: We performed KEGG enrichment analyses to reveal the biological relevance and functional pathways.
    KEGG
    suggested: (KEGG, RRID:SCR_012773)
    7, PINA v2.048 and InnateDB49; (2) binary PPIs tested by high-throughput yeast-two-hybrid (Y2H) systems from two public available high-quality Y2H datasets50, 51 and one in-house dataset52; (3) kinase-substrate interactions by literature-derived low-throughput or high-throughput experiments from Kinome NetworkX53, Human Protein Resource Database (HPRD)54, PhosphositePlus55, PhosphoNetworks56, Phospho.ELM57 and DbPTM 3.058; (4) signaling network by literature-derived low-throughput experiments from SignaLink 2.059; and (5) protein complexes data identified by a robust affinity purification-mass spectrometry methodology collected from BioPlex v2.060.
    DbPTM
    suggested: (dbPTM: An informational repository of proteins and post-translational modifications, RRID:SCR_007619)
    SignaLink
    suggested: (SignaLink, RRID:SCR_003569)
    BioPlex
    suggested: (BioPlex, RRID:SCR_016144)
    This step was performed with the NetworkX package (https://networkx.github.io/) on Python 3.7 platform.
    https://networkx.github.io/
    suggested: (NetworkX, RRID:SCR_016864)
    Statistical analysis and network visualization: Statistical tests for assessing categorical data through χ2 was performed by SciPy 1.2.1 (https://www.scipy.org/).
    SciPy
    suggested: (SciPy, RRID:SCR_008058)
    Networks were visualized using Cytoscape.
    Cytoscape
    suggested: (Cytoscape, RRID:SCR_003032)

    Results from OddPub: Thank you for sharing your code and data.

    Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:
    We acknowledge several potential limitations of our study. Samples sizes of snRNA-seq datasets analyzed in this study are relatively small; and the smaller number of female patients compared to males may influence the findings of differential cell subpopulation analysis. Thus, the sex-biased cell types and transcriptional networks we identified should be validated further in prospective large-scale cohorts with varying degrees of COVID-19 pathology, including asymptomatic patients. In addition, we observed that the male patients aged between 30 to 80 years have a greater risk of infection by SARS-CoV-2 (Supplementary Fig 1a), but in the group of females aged 80 years or older females had a higher prevalence of confirmed SARS-COV-2 infections. Exploring the sex differences and underlying immune mechanisms in younger COVID-19 patients, including the pediatric population, may provide more actionable biomarkers and immune targets for disease prevention and vaccine development35, 36. Finally, genetic basis of sex differences should be investigated in the future using the genetic datasets from the growing COVID-19 population, such as the genome-wide association studies from COVID-19 Host Genetics Initiative37 (https://www.covid19hg.org/). Taken together, our analysis provides a comprehensive understanding of the clinical characteristics and immune mechanisms underlying sex differences in COVID-19. We found that male individuals with COVID-19 have significantly elevated rates of hos...

    Results from TrialIdentifier: No clinical trial numbers were referenced.

    Results from Barzooka: We found bar graphs of continuous data. We recommend replacing bar graphs with more informative graphics, as many different datasets can lead to the same bar graph. The actual data may suggest different conclusions from the summary statistics. For more information, please see Weissgerber et al (2015).

    Results from JetFighter: Please consider improving the rainbow (“jet”) colormap(s) used on page 43. At least one figure is not accessible to readers with colorblindness and/or is not true to the data, i.e. not perceptually uniform.

    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.

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  2. Version published to 10.1101/2020.12.01.407007v1 on bioRxiv