Single-cell multi-omics reveals dyssynchrony of the innate and adaptive immune system in progressive COVID-19

  1. Avraham Unterman
  2. Tomokazu S. Sumida
  3. Nima Nouri
  4. Xiting Yan
  5. Amy Y. Zhao
  6. Victor Gasque
  7. Jonas C. Schupp
  8. Hiromitsu Asashima
  9. Yunqing Liu
  10. Carlos Cosme
  11. Wenxuan Deng
  12. Ming Chen
  13. Micha Sam Brickman Raredon
  14. Kenneth B. Hoehn
  15. Guilin Wang
  16. Zuoheng Wang
  17. Giuseppe DeIuliis
  18. Neal G. Ravindra
  19. Ningshan Li
  20. Christopher Castaldi
  21. Patrick Wong
  22. John Fournier
  23. Santos Bermejo
  24. Lokesh Sharma
  25. Arnau Casanovas-Massana
  26. Chantal B. F. Vogels
  27. Anne L. Wyllie
  28. Nathan D. Grubaugh
  29. Anthony Melillo
  30. Hailong Meng
  31. Yan Stein
  32. Maksym Minasyan
  33. Subhasis Mohanty
  34. William E. Ruff
  35. Inessa Cohen
  36. Khadir Raddassi
  37. The Yale IMPACT Research Team
  38. Allison Nelson
  39. Denise Shepard
  40. Michael Rainone
  41. Xiaohua Peng
  42. Laura E. Niklason
  43. Albert I. Ko
  44. Ruth R. Montgomery
  45. Shelli F. Farhadian
  46. Akiko Iwasaki
  47. Albert C. Shaw
  48. David van Dijk
  49. Hongyu Zhao
  50. Steven H. Kleinstein
  51. David A. Hafler
  52. Naftali Kaminski
  53. Charles S. Dela Cruz

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Abstract

Dysregulated immune responses against the SARS-CoV-2 virus are instrumental in severe COVID-19. However, the immune signatures associated with immunopathology are poorly understood. Here we use multi-omics single-cell analysis to probe the dynamic immune responses in hospitalized patients with stable or progressive course of COVID-19, explore V(D)J repertoires, and assess the cellular effects of tocilizumab. Coordinated profiling of gene expression and cell lineage protein markers shows that S100A hi /HLA-DR lo classical monocytes and activated LAG-3 hi T cells are hallmarks of progressive disease and highlights the abnormal MHC-II/LAG-3 interaction on myeloid and T cells, respectively. We also find skewed T cell receptor repertories in expanded effector CD8 + clones, unmutated IGHG + B cell clones, and mutated B cell clones with stable somatic hypermutation frequency over time. In conclusion, our in-depth immune profiling reveals dyssynchrony of the innate and adaptive immune interaction in progressive COVID-19.

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  1. Version published to 10.1038/s41467-021-27716-4
  2. ScreenIT

    SciScore for 10.1101/2020.07.16.20153437: (What is this?)

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

    Table 1: Rigor

    Institutional Review Board Statementnot detected.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.

    Table 2: Resources

    No key resources detected.

    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 relatively small sample size (18 COVID-19 samples and 13 controls) is a limitation of this study, especially for the analysis of certain subgroups (e.g. only four samples from patients who did not receive tocilizumab). However, it is larger than most COVID-19 scRNAseq studies published to-date17,83,84,96, and the similarity of baseline characteristics between stable and progressive patients and in comparison, to controls (Table 1) helps increase confidence in our results. Although the timing of blood draw A (time-point A) relative to hospitalization was consistent across subjects, the timing of blood draw B (time-point B) was variable. We mitigated that by taking into account the variable time span between the two blood draws in some of the analyses, e.g. for the analysis of IFN score changes over time shown in Fig 3A & B. This unique exploration of gene expression changes over time adds an essential dynamic layer that is critical to understand the biology of an acute viral disease. Lastly, our analysis mostly relied on RNA-based analyses including gene expression and TCR/BCR repertoire analysis, with some protein-level validation by CITE-seq and flow cytometry. Additional mechanistic validation, while beyond the scope of this study, is warranted in future studies. In conclusion, our in-depth multi-omics assessment of peripheral immune cells at single-cell resolution across patient severities and time highlights the desynchronized adaptive and innate immune response in pr...

    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.

  3. ScreenIT

    SciScore for 10.1101/2020.07.16.20153437: (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
    Here, we utilize multiomics single-cell analysis to probe dynamic immune responses in patients with stable or progressive manifestations of COVID-19, and assess the effects of tocilizumab, an antiIL-6 receptor monoclonal antibody.
    antiIL-6 receptor
    suggested: None
    The signaling pathways driven by IL-1β, TNF-α, and IL-6 have been implicated in the pathogenesis of COVID-1910 and antibodies against IL-6 receptor have shown early promise9,11-13, including our own experience14; however, large-scale randomized trials are needed to adequately evaluate their efficacy.
    TNF-α
    suggested: None
          <div style="margin-bottom:8px">
        <div><b>antibodies against IL-6</b></div>
        <div>suggested: None</div>
      </div>
    </td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Here, we employed a single-cell multi-omics approach in order to study the dynamics of the innate and adaptive immune system responses in COVID-19, explore the molecular mechanisms that contribute to the progression of the diseases, and assess the effects of tocilizumab, a humanized anti-IL-6 receptor monoclonal antibody.</td><td style="min-width:100px;border-bottom:1px solid lightgray">
      <div style="margin-bottom:8px">
        <div><b>anti-IL-6</b></div>
        <div>suggested: None</div>
      </div>
    </td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Tocilizumab effects differ across cell types and are associated with the levels of expression of IL6R and IL6ST Eight of ten COVID-19 patients in our study were treated with tocilizumab, an antiIL6 receptor (IL6R) antibody.</td><td style="min-width:100px;border-bottom:1px solid lightgray">
      <div style="margin-bottom:8px">
        <div><b>IL6ST</b></div>
        <div>suggested: (MBL International Cat# D023-3, <a href="https://scicrunch.org/resources/Any/search?q=AB_591799">AB_591799</a>)</div>
      </div>
    
      <div style="margin-bottom:8px">
        <div><b>antiIL6</b></div>
        <div>suggested: None</div>
      </div>
    
      <div style="margin-bottom:8px">
        <div><b>IL6R</b></div>
        <div>suggested: None</div>
      </div>
    </td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">To better identify cellular multiplets and enable us to superload the cells onto the 10x platform, we used Cell Hashing technique and multiplexed 56 samples in each 10x reaction by using six hashing antibodies61.</td><td style="min-width:100px;border-bottom:1px solid lightgray">
      <div style="margin-bottom:8px">
        <div><b>antibodies61</b></div>
        <div>suggested: None</div>
      </div>
    </td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Antibody-derived tag (ADT) and Hashtag oligonucleotide (HTO) sequencing libraries were generated.</td><td style="min-width:100px;border-bottom:1px solid lightgray">
      <div style="margin-bottom:8px">
        <div><b>Antibody-derived tag ( ADT )</b></div>
        <div>suggested: None</div>
      </div>
    </td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Following unsupervised clustering, annotation for CITE-seq cells was performed with both gene expression and antibody-derived counts (ADT) by using a manually curated marker gene list (Supp Table ST8).</td><td style="min-width:100px;border-bottom:1px solid lightgray">
      <div style="margin-bottom:8px">
        <div><b>antibody-derived counts ( ADT</b></div>
        <div>suggested: None</div>
      </div>
    </td></tr><tr><td style="min-width:100px;text-align:center; padding-top:4px;" colspan="2"><b>Software and Algorithms</b></td></tr><tr><td style="min-width:100px;text=align:center"><i>Sentences</i></td><td style="min-width:100px;text-align:center"><i>Resources</i></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Yale Center for Genome Analysis/Keck Biotechnology Resource Laboratory, Department of Molecular Biophysics and Biochemistry, Yale School of Medicine, New Haven, CT, USA. 14. SJTU-Yale Joint Center for Biostatistics and Data Science, Department of Bioinformatics and Biostatistics, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China. 15</td><td style="min-width:100px;border-bottom:1px solid lightgray">
      <div style="margin-bottom:8px">
        <div><b>Genome Analysis/Keck Biotechnology Resource Laboratory</b></div>
        <div>suggested: None</div>
      </div>
    
      <div style="margin-bottom:8px">
        <div><b>Biostatistics</b></div>
        <div>suggested: (BWH Biostatistics Center, <a href="https://scicrunch.org/resources/Any/search?q=SCR_009680">SCR_009680</a>)</div>
      </div>
    </td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">D.A.H. has received research funding from Bristol-Myers Squibb, Novartis, Sanofi, and Genentech.</td><td style="min-width:100px;border-bottom:1px solid lightgray">
      <div style="margin-bottom:8px">
        <div><b>Genentech</b></div>
        <div>suggested: (Genentech, <a href="https://scicrunch.org/resources/Any/search?q=SCR_003997">SCR_003997</a>)</div>
      </div>
    </td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Automated annotation using SingleR package22</td><td style="min-width:100px;border-bottom:1px solid lightgray">
      <div style="margin-bottom:8px">
        <div><b>SingleR</b></div>
        <div>suggested: None</div>
      </div>
    </td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Gene set enrichment analysis (GSEA) further demonstrated that dividing T cells in the progressive COVID-19 patients exhibited more terminally exhausted T cell signature and type 1 IFN response signature than those in stable patients (Fig 4J, Supp Table ST9).</td><td style="min-width:100px;border-bottom:1px solid lightgray">
      <div style="margin-bottom:8px">
        <div><b>Gene set enrichment analysis</b></div>
        <div>suggested: (Gene Set Enrichment Analysis, <a href="https://scicrunch.org/resources/Any/search?q=SCR_003199">SCR_003199</a>)</div>
      </div>
    </td></tr></table>

    Data from additional tools added to each annotation on a weekly basis.

    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 is not a substitute for expert review. SciScore checks for the presence and correctness of RRIDs (research resource identifiers) in the manuscript, and detects sentences that appear to be missing RRIDs. SciScore also checks to make sure that rigor criteria are addressed by authors. It does this by detecting sentences that discuss criteria such as blinding or power analysis. SciScore does not guarantee that the rigor criteria that it detects are appropriate for the particular study. Instead it assists authors, editors, and reviewers by drawing attention to sections of the manuscript that contain or should contain various rigor criteria and key resources. For details on the results shown here, including references cited, please follow this link.

  4. Version published to 10.1101/2020.07.16.20153437v1 on medRxiv