Alveolitis in severe SARS-CoV-2 pneumonia is driven by self-sustaining circuits between infected alveolar macrophages and T cells

  1. Rogan A. Grant
  2. Luisa Morales-Nebreda
  3. Nikolay S. Markov
  4. Suchitra Swaminathan
  5. Estefany R. Guzman
  6. Darryl A. Abbott
  7. Helen K. Donnelly
  8. Alvaro Donayre
  9. Isaac A. Goldberg
  10. Zasu M. Klug
  11. Nicole Borkowski
  12. Ziyan Lu
  13. Hermon Kihshen
  14. Yuliya Politanska
  15. Lango Sichizya
  16. Mengjia Kang
  17. Ali Shilatifard
  18. Chao Qi
  19. A. Christine Argento
  20. Jacqueline M. Kruser
  21. Elizabeth S. Malsin
  22. Chiagozie O. Pickens
  23. Sean Smith
  24. James M. Walter
  25. Anna E. Pawlowski
  26. Daniel Schneider
  27. Prasanth Nannapaneni
  28. Hiam Abdala-Valencia
  29. Ankit Bharat
  30. Cara J. Gottardi
  31. GR Scott Budinger
  32. Alexander V. Misharin
  33. Benjamin D. Singer
  34. Richard G. Wunderink
  35. for The NU SCRIPT Study Investigators

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Abstract

Some patients infected with Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2) develop severe pneumonia and the acute respiratory distress syndrome (ARDS) [1]. Distinct clinical features in these patients have led to speculation that the immune response to virus in the SARS-CoV-2-infected alveolus differs from other types of pneumonia [2]. We collected bronchoalveolar lavage fluid samples from 86 patients with SARS-CoV-2-induced respiratory failure and 252 patients with known or suspected pneumonia from other pathogens and subjected them to flow cytometry and bulk transcriptomic profiling. We performed single cell RNA-Seq in 5 bronchoalveolar lavage fluid samples collected from patients with severe COVID-19 within 48 hours of intubation. In the majority of patients with SARS-CoV-2 infection at the onset of mechanical ventilation, the alveolar space is persistently enriched in alveolar macrophages and T cells without neutrophilia. Bulk and single cell transcriptomic profiling suggest SARS-CoV-2 infects alveolar macrophages that respond by recruiting T cells. These T cells release interferon-gamma to induce inflammatory cytokine release from alveolar macrophages and further promote T cell recruitment. Our results suggest SARS-CoV-2 causes a slowly unfolding, spatially-limited alveolitis in which alveolar macrophages harboring SARS-CoV-2 transcripts and T cells form a positive feedback loop that drives progressive alveolar inflammation.

This manuscript is accompanied by an online resource: https://www.nupulmonary.org/covid-19/

One sentence summary

SARS-CoV-2-infected alveolar macrophages form positive feedback loops with T cells in patients with severe COVID-19.

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  1. ScreenIT

    SciScore for 10.1101/2020.08.05.238188: (What is this?)

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

    Table 1: Rigor

    Institutional Review Board StatementIRB: Human subjects: All human subjects research was approved by the Northwestern University Institutional Review Board.
    Consent: All subjects or their surrogates provided informed consent.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    FASTQ files were generated using bcl2fastq (Illumina).
    bcl2fastq
    suggested: (bcl2fastq , RRID:SCR_015058)
    0.6.4 and aligned to the hybrid genome described above using STAR 2.6.1d [47]
    STAR
    suggested: (STAR, RRID:SCR_015899)
    Gene-level assignment was then performed using featureCounts 1.6.4 [48]
    featureCounts
    suggested: (featureCounts, RRID:SCR_012919)
    Data was processed using Scanpy v1.5.1 [52], doublets were detected with scrublet v0.2.1 [53] and removed, ribosomal genes were removed and multisample integration was performed with BBKNN v1.3.12 [54]
    BBKNN
    suggested: None
    Computations were automated with snakemake v5.5.4 [56].
    snakemake
    suggested: (Snakemake, RRID:SCR_003475)
    Deconvolution of bulk RNA-seq alveolar macrophage signatures was performed using AutoGeneS v1.0.3[57] and signatures derived from integrated single cell RNA-Seq object.
    AutoGeneS
    suggested: None
    Visualization: All plotting was performed using ggplot2 version 3.3.1, with the exception of heatmaps, which were generated using pheatmap version 1.0.12.
    ggplot2
    suggested: (ggplot2, RRID:SCR_014601)
    pheatmap
    suggested: (pheatmap, RRID:SCR_016418)
    Figure layouts were generated using patchwork version 1.01 and edited in Adobe Illustrator 2020.
    Adobe Illustrator
    suggested: (Adobe Illustrator, RRID:SCR_010279)

    Results from OddPub: Thank you for sharing your data.

    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: 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.

    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 10.1101/2020.08.05.238188 on bioRxiv