Impaired local intrinsic immunity to SARS-CoV-2 infection in severe COVID-19

  1. Carly G.K. Ziegler
  2. Vincent N. Miao
  3. Anna H. Owings
  4. Andrew W. Navia
  5. Ying Tang
  6. Joshua D. Bromley
  7. Peter Lotfy
  8. Meredith Sloan
  9. Hannah Laird
  10. Haley B. Williams
  11. Micayla George
  12. Riley S. Drake
  13. Taylor Christian
  14. Adam Parker
  15. Campbell B. Sindel
  16. Molly W. Burger
  17. Yilianys Pride
  18. Mohammad Hasan
  19. George E. Abraham
  20. Michal Senitko
  21. Tanya O. Robinson
  22. Alex K. Shalek
  23. Sarah C. Glover
  24. Bruce H. Horwitz
  25. Jose Ordovas-Montanes

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Abstract

No abstract available

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  1. Version published to 10.1016/j.cell.2021.07.023
  2. Rapid Reviews Infectious Diseases

    Alejandro Berrio

    Review 2: "Impaired local intrinsic immunity to SARS-CoV-2 infection in severe COVID-19"

    This preprint uses single cell RNA-seq (scRNA-seq) to reconstruct nasopharyngeal tissue reorganization in COVID-19 patients. Reviewers deemed the manuscript's main claims well-substantiated, carefully qualified, and significantly novel.

  3. Rapid Reviews Infectious Diseases

    Alessandro Marcello

    Review 1: "Impaired local intrinsic immunity to SARS-CoV-2 infection in severe COVID-19"

    This preprint uses single cell RNA-seq (scRNA-seq) to reconstruct nasopharyngeal tissue reorganization in COVID-19 patients. Reviewers deemed the manuscript's main claims well-substantiated, carefully qualified, and significantly novel.

  5. ScreenIT

    SciScore for 10.1101/2021.02.20.431155: (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

    Antibodies
    SentencesResources
    Cells were then stained with a surface marker antibody cocktail on ice for 15 mins, which contained PerCP-Cy5.5-conjugated anti-human CD45 (clone: HI30, BioLegend), Brilliant Violet 711-conjugated anti-human CD3 (clone: SK7, BioLegend), APC-Cy7-conjugated anti-human CD8 (clone: SK1, BioLegend), PE-conjugated anti-human CD4 (clone: RPA-T4, BioLegend), Brilliant Violet 786-conjugated anti-human CD326 (clone: 9C4, BioLegend), PE-Cy5-conjugated anti-human CD19 (clone: HIB19, BioLegend), PE-Cy7-conjugated anti-human CD66b (clone: G10F5, BioLegend), Brilliant Violet 650-conjugated anti-human CD11c (clone: Bu15, BioLegend), FITC-conjugated anti-human CD14 (clone: M5E2, BioLegend), and Brilliant Violet 421-conjugated anti-human CD56 (clone: 5.1H11, BioLegend).
    anti-human CD45
    suggested: None
    anti-human CD3
    suggested: (BioLegend Cat# 348805, RRID:AB_2889063)
    anti-human CD8
    suggested: None
    anti-human CD4
    suggested: None
    anti-human CD326
    suggested: None
    anti-human CD19
    suggested: (BioLegend Cat# 348805, RRID:AB_2889063)
    anti-human CD66b
    suggested: None
    anti-human CD11c
    suggested: None
    anti-human CD14
    suggested: (BioLegend Cat# 348805, RRID:AB_2889063)
    anti-human CD56
    suggested: None
    Software and Algorithms
    SentencesResources
    Data were acquired on an LSRFortessa flow cytometer (BD Biosciences) using BD FACSDiva software, and analyzed by FlowJo software (Version 10.7.1, Tree Star Inc.).
    BD FACSDiva
    suggested: (BD FACSDiva Software, RRID:SCR_001456)
    FlowJo
    suggested: (FlowJo, RRID:SCR_008520)
    Data Preprocessing and Quality Control: Pooled libraries were demultiplexed using bcl2fastq (v2.17.1.14) with default settings (mask_short_adapter_reads 10, minimum_trimmed_read_length 10, implemented using Cumulus, snapshot 4, https://cumulus.readthedocs.io/en/stable/bcl2fastq.html)122.
    bcl2fastq
    suggested: (bcl2fastq , RRID:SCR_015058)
    Libraries were aligned using STAR within the Drop-Seq Computational Protocol (https://github.com/broadinstitute/Drop-seq) and implemented on Cumulus (https://cumulus.readthedocs.io/en/latest/drop_seq.html, snapshot 9, default parameters)121.
    STAR
    suggested: (STAR, RRID:SCR_015899)
    Using cluster annotations previously assigned from iterative clustering in Seurat, cells from epithelial cell types were pre-processed according to the scVelo pipeline: genes were normalized using default parameters (pp.filter_and_normalize), principal components and nearest neighbors in PCA space were calculated (using defaults of 30 PCs, 30 nearest neighbors), and the first and second order moments of nearest neighbors were computed, which are used as inputs into velocity estimates (pp.moments).
    scVelo
    suggested: (scVelo, RRID:SCR_018168)
    Metatranscriptomic Classification of Reads from Single-Cell RNA-Seq: To identify co-detected microbial taxa present in the cell-associated or ambient RNA of nasopharyngeal swabs, we used the Kraken2 software implemented using the Broad Institute viral-ngs pipelines on Terra (https://github.com/broadinstitute/viral-pipelines/tree/master)86.
    Kraken2
    suggested: None
    Here, we employed CellBender (https://github.com/broadinstitute/CellBender), a software package built to learn the ambient RNA profile per sample and provide an ambient RNA-corrected output93.
    CellBender
    suggested: None
    DESeq2 was run with default parameters and test = “Wald”.
    DESeq2
    suggested: (DESeq, RRID:SCR_000154)
    Gene set enrichment analysis (GSEA) was completed using the R package fgsea over genes ranked by average log foldchange expression between each group, including all genes with an average expression > 0.5 UMI within each respective cell type128.
    Gene set enrichment analysis
    suggested: (Gene Set Enrichment Analysis, RRID:SCR_003199)
    Statistical Testing: All statistical tests were implemented either in R (v4.0.2) or Prism (v6) software129.
    Prism
    suggested: (PRISM, RRID:SCR_005375)
    Data and Code Availability: Prism (v6), R (v4.0.2) packages ggplot2 (v3.3.2130), Seurat (v3.2.2131), ComplexHeatmap (v2.7.3132), Circlize (0.4.11133), fgsea (v.1.16.0128), DESeq2 (v1.30.0126), and Python (v3.8.3) package scVelo (v0.3.077) were used for visualization.
    ggplot2
    suggested: (ggplot2, RRID:SCR_014601)
    ComplexHeatmap
    suggested: (ComplexHeatmap, RRID:SCR_017270)
    Circlize
    suggested: (circlize, RRID:SCR_002141)
    Python
    suggested: (IPython, RRID:SCR_001658)

    Results from OddPub: Thank you for sharing your code.

    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.

  6. Version published to 10.1101/2021.02.20.431155v1 on bioRxiv