Shift of lung macrophage composition is associated with COVID-19 disease severity and recovery

  1. Steven T. Chen
  2. Matthew D. Park
  3. Diane Marie Del Valle
  4. Mark Buckup
  5. Alexandra Tabachnikova
  6. Nicole W. Simons
  7. Konstantinos Mouskas
  8. Brian Lee
  9. Daniel Geanon
  10. Darwin D’Souza
  11. Travis Dawson
  12. Robert Marvin
  13. Kai Nie
  14. Ryan C. Thompson
  15. Zhen Zhao
  16. Jessica LeBerichel
  17. Christie Chang
  18. Hajra Jamal
  19. Udit Chaddha
  20. Kusum Mathews
  21. Samuel Acquah
  22. Stacey-Ann Brown
  23. Michelle Reiss
  24. Timothy Harkin
  25. Marc Feldmann
  26. Charles A. Powell
  27. Jaime L. Hook
  28. Seunghee Kim-Schulze
  29. Adeeb H. Rahman
  30. Brian D. Brown
  31. The Mount Sinai COVID-19 Biobank Team
  32. Noam D. Beckmann
  33. Sacha Gnjatic
  34. Ephraim Kenigsberg
  35. Alexander W. Charney
  36. Miriam Merad

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Abstract

Though it has been 2 years since the start of the Coronavirus Disease 19 (COVID-19) pandemic, COVID-19 continues to be a worldwide health crisis. Despite the development of preventive vaccines, very little progress has been made to identify curative therapies to treat COVID-19 and other inflammatory diseases which remain a major unmet need in medicine. Our study sought to identify drivers of disease severity and death to develop tailored immunotherapy strategies to halt disease progression. Here we assembled the Mount Sinai COVID-19 Biobank which was comprised of ~600 hospitalized patients followed longitudinally during the peak of the pandemic. Moderate disease and survival were associated with a stronger antigen (Ag) presentation and effector T cell signature, while severe disease and death were associated with an altered Ag presentation signature, increased numbers of circulating inflammatory, immature myeloid cells, and extrafollicular activated B cells associated with autoantibody formation. Strikingly, we found that in severe COVID-19 patients, lung tissue resident alveolar macrophages (AM) were not only severely depleted, but also had an altered Ag presentation signature, and were replaced by inflammatory monocytes and monocyte-derived macrophages (MoMΦ). Notably, the size of the AM pool correlated with recovery or death, while AM loss and functionality were restored in patients that recovered. These data therefore suggest that local and systemic myeloid cell dysregulation is a driver of COVID-19 severity and that modulation of AM numbers and functionality in the lung may be a viable therapeutic strategy for the treatment of critical lung inflammatory illnesses.

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    SciScore for 10.1101/2022.01.11.475918: (What is this?)

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

    Table 1: Rigor

    EthicsConsent: Due to the difficulty of obtaining direct informed consent during the pandemic, the Institutional Review Board (IRB) approved sample collection from patients before consent was obtained.
    IRB: Due to the difficulty of obtaining direct informed consent during the pandemic, the Institutional Review Board (IRB) approved sample collection from patients before consent was obtained.
    Field Sample Permit: BAL fluid processing: BAL samples were processed within 30 mins of sample collection.
    Euthanasia Agents: The hematoxylin channel was then registered with an affine registration (which accounts for shear, scale, rotation, and translational dislocation) and a “b-spline” elastic warping to account for any local tissue warping or tissue damage (https://simpleelastix.readthedocs.io/NonRigidRegistration.html) The vector field transformation matrix produced from the high-resolution affine and b-spline registrations was then applied to the raw RGB tile.
    Sex as a biological variablenot detected.
    RandomizationRegistered RGB tiles were analyzed in parallel across the multiple cores of the AWS supercomputer, trimmed to eliminate overlap, and concatenated to produce one final elastically registered RGB image per marker. 100 ROIs of about 500×500 μm were randomly chosen in the image based on where tissue resided.
    Blindingnot detected.
    Power Analysisnot detected.

    Table 2: Resources

    Antibodies
    SentencesResources
    After the first staining cycle, Fab fragments (AffiniPure Fab Fragment Donkey anti-mouse (715-007-003) or anti-rabbit IgG (711-007-003)) against that primary antibody species were used to block carryover staining whenever there was a repeat of same primary antibody species.
    anti-mouse
    suggested: (Jackson ImmunoResearch Labs Cat# 715-007-003, RRID:AB_2307338)
    anti-rabbit IgG
    suggested: (Jackson ImmunoResearch Labs Cat# 711-007-003, RRID:AB_2340587)
    Software and Algorithms
    SentencesResources
    Following this, we integrated our seromics data with scRNAseq to identify 4 distinct immune responses to COVID-19.
    scRNAseq
    suggested: None
    scRNAseq immune cell cluster frequency correlations, and integrated scRNAseq cell frequencies and Olink proteomics were calculated using the corrplot package (v0.88) and visualized using the pheatmap package (v1.0.12) in R. Lung Autopsy Tissue Section Preparation: Lung autopsy samples were collected within 24 hours of death (average 10.1±6.2 hours) and fixed in 10% neutral-buffered formalin for 24 hours before transfer to 70% Ethanol (EtOH).
    pheatmap
    suggested: (pheatmap, RRID:SCR_016418)
    Images of different immunostains belonging to the same ROI were transferred to Fiji-ImageJ and co-registered using the TrakEM2 plug-in(64, 65).
    TrakEM2
    suggested: (TrakEM2, RRID:SCR_008954)
    AEC channels representing staining of each marker were assigned to different colors by using the lookup tables (LUTs) function of Fiji while hematoxylin channel was assigned to blue color to mimic fluorescent DAPI staining.
    Fiji
    suggested: (Fiji, RRID:SCR_002285)
    This registration used a third party SimpleElastic package for Python (https://simpleelastix.readthedocs.io/RigidRegistration.html).
    Python
    suggested: (IPython, RRID:SCR_001658)
    Statistical analysis: Data analysis was performed using Prism version 9.2.0 (GraphPad software) or in R version 3.6.3 and presented as stated in figure legends.
    Prism
    suggested: (PRISM, RRID:SCR_005375)
    GraphPad
    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: 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: We found the following clinical trial numbers in your paper:

    IdentifierStatusTitle
    NCT04355494No longer availableSOLIRIS® (Eculizumab) Treatment of Participants With COVID-1…

    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.

  2. Version published to 10.1101/2022.01.11.475918v1 on bioRxiv