Multiomic Immunophenotyping of COVID-19 Patients Reveals Early Infection Trajectories

  1. Yapeng Su
  2. Daniel Chen
  3. Christopher Lausted
  4. Dan Yuan
  5. Jongchan Choi
  6. Cheng Dai
  7. Valentin Voillet
  8. Kelsey Scherler
  9. Pamela Troisch
  10. Venkata R. Duvvuri
  11. Priyanka Baloni
  12. Guangrong Qin
  13. Brett Smith
  14. Sergey Kornilov
  15. Clifford Rostomily
  16. Alex Xu
  17. Jing Li
  18. Shen Dong
  19. Alissa Rothchild
  20. Jing Zhou
  21. Kim Murray
  22. Rick Edmark
  23. Sunga Hong
  24. Lesley Jones
  25. Yong Zhou
  26. Ryan Roper
  27. Sean Mackay
  28. D. Shane O’Mahony
  29. Christopher R Dale
  30. Julie A Wallick
  31. Heather A Algren
  32. Zager A Michael
  33. Andrew Magis
  34. Wei Wei
  35. Nathan D. Price
  36. Sui Huang
  37. Naeha Subramanian
  38. Kai Wang
  39. Jennifer Hadlock
  40. Leroy Hood
  41. Alan Aderem
  42. Jeffrey A. Bluestone
  43. Lewis L. Lanier
  44. Phil Greenberg
  45. Raphael Gottardo
  46. Mark M. Davis
  47. Jason D. Goldman
  48. James R. Heath
  49. the ISB-Swedish COVID19 Biobanking Unit

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Abstract

Host immune responses play central roles in controlling SARS-CoV2 infection, yet remain incompletely characterized and understood. Here, we present a comprehensive immune response map spanning 454 proteins and 847 metabolites in plasma integrated with single-cell multi-omic assays of PBMCs in which whole transcriptome, 192 surface proteins, and T and B cell receptor sequence were co-analyzed within the context of clinical measures from 50 COVID19 patient samples. Our study reveals novel cellular subpopulations, such as proliferative exhausted CD8 + and CD4 + T cells, and cytotoxic CD4 + T cells, that may be features of severe COVID-19 infection. We condensed over 1 million immune features into a single immune response axis that independently aligns with many clinical features and is also strongly associated with disease severity. Our study represents an important resource towards understanding the heterogeneous immune responses of COVID-19 patients and may provide key information for informing therapeutic development.

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  1. ScreenIT

    SciScore for 10.1101/2020.07.27.224063: (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.
    Cell Line Authenticationnot detected.

    Table 2: Resources

    Antibodies
    SentencesResources
    The enriched CD4+ and CD8+ T cells (100,000 cells/well in a 96 well-plate) were stimulated for 6 hours with plate-bound anti-CD3 antibodies (pre-coated at 10 μg/mL overnight at 4°C) and soluble anti-CD28 antibodies (5 μg/mL) in complete RPMI-1640 at 37 °C, 5% CO2.
    anti-CD3
    suggested: None
    anti-CD28
    suggested: None
    Experimental Models: Cell Lines
    SentencesResources
    IL-1β, IL-6, IL-12-p40, IL-12, IL-17A, IL-17F, MCP-1, MCP-4, MIF; Growth Factors: EGF, PDGF-BB, VEGF.
    MCP-1
    suggested: None
    Software and Algorithms
    SentencesResources
    Gene set enrichment analysis (GSEA) was performed using the R package MAST with the bootVcov1 R function.
    Gene set enrichment analysis
    suggested: (Gene Set Enrichment Analysis, RRID:SCR_003199)
    MAST
    suggested: (MAST, RRID:SCR_016340)
    KEGG, Hallmark, and blood transcription modules (Li et al., 2014) were used as gene sets.
    KEGG
    suggested: (KEGG, RRID:SCR_012773)

    Results from OddPub: Thank you for sharing your data.

    Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:
    However, a limitation is that the 50-sample, 26-patient cohort studied here is relatively modest, the numbers of co-morbidities present in this population is large, and the time-trajectories that are studied are relatively short, and so don’t capture the resolution of the infection. Nevertheless, our dataset establishes an unprecedented level of detail on the impacts of COVID-19 on the immune systems. We show that the collected single cell, bulk plasma, and clinical data sets can be synergized to reveal clear interpretable biological trends that can be associated with COVID-19 patient outcomes and may be suggestive of treatment strategies. This broad systems immunology approach should be applicable towards understanding immune responses in a host of other infectious diseases.

    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: Please consider improving the rainbow (“jet”) colormap(s) used on page 42. 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.

    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 10.1101/2020.07.27.224063v2 on bioRxiv
  3. ScreenIT

    SciScore for 10.1101/2020.07.27.224063: (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:

    However, a limitation is that the 50-sample, 26-patient cohort studied here is relatively modest, the numbers of co-morbidities present in this population is large, and the time-trajectories that are studied are relatively short, and so don’t capture the resolution of the infection. Nevertheless, our dataset establishes an unprecedented level of detail on the impacts of COVID-19 on the immune systems. We show that the collected single cell, bulk plasma, and clinical data sets can be synergized to reveal clear interpretable biological trends that can be associated with COVID-19 patient outcomes and may be suggestive of treatment strategies. This broad systems immunology approach should be applicable towards understanding immune responses in a host of other infectious diseases. ACKNOWLEGEMENT We are grateful to all participants and blood donors in this study. We are extremely grateful to the medical teams at Swedish Medical Center for their support of this research and for their assuming personal risk in their tireless care for patients suffering from COVID-19. We thank the Northwest Genomic Center, especially, Debbie Nickerson, Erica Ryke, and Peter Anderson for the help with sequencing services. We thank the insightful discussion from Prof. David Baltimore, Prof. Ilya Shmulevich and the ISB COVID19 Study Group: Inyoul Lee, Kay Chinn, Scott Bloom, Guenther Kahlert, David Baxter, Zac Simon, Matt Idso, Rachel Calder, William Chour, Alphonsus Ng, Jimmi Hopkins, John Heath, Jingyi Xie, John Heath, Jessica Yee, Kari Tetrault, Lee Rowen, Rachel Liu, Rongyu Zhang, Shannon Fallen, Simran Sidhu, Yue Lu, David Gibbs, Michael Strasser; and the Swedish COVID-19 Research Steering Committee: John Pauk, Bill Berrington, Michael Bolton, Mary Micikas, Sonam Nyatsatsang, Cynthia Maree, Shane O’Mahony, Kelly Sweerus, Anne Lipke, George Pappas, Mark Sullivan, Karen Koo, Chris Dale, George Lopez, Naomi Diggs, Hank Kaplan, Krish Patel, Livia Hegerova, Vanessa Dunleavy, Phil Gold, John Pagel, Joshua Mark, Doug Kieper, Jim Scanlan, Evonne Lackey, Jodie Davila, Justin Rueda, Julie Wallick, Heather Algren, Jennifer Hansberry and Elizabeth Wako. The ISB-Swedish COVID19 Biobanking Unit: Rick Edmark, John Heath, Simran Sidu, Jessica Yee, Cara McCoy, Jeremy Johnson, Renee Duprel, Audri Hubbard, Theresa Davis, Julie Thatcher and Zuraya Aziz, Thea Swanson, Yong Zhou, Lesley Jones, Sarah Li, Paula Manner, Andrea Drouhard, Stephanie Johnson, Julia Karr, Clementine Chalal, Mattie Sader, Margo Badman, Allison Everett, Adel Islam, Jodie Davila and Julie Wallick. We gratefully acknowledge funding support from the Wilke Family Foundation (J.R.H.), the Murdock Trust (J.R.H.), the Swedish Medical Center Foundation (J.D.G.), the Parker Institute for Cancer Immunotherapy (J.R.H., M.M.D., P.G., L.L.L., J.A.B.), Merck and the Biomedical Advanced Research and Development Authority under Contract HHSO10201600031C (J.R.H.). K.W. was funded by DOD W911NF-17-2-0086, NIH R01 DA040395, and NIH UG3TR002884. J.H. was funded by National Center For Advancing Translational Sciences of the National Institutes of Health under Award Number OT2 TR003443. R.G. was funded by the NIH Human Immunology Project Consortium (U19AI128914) and the Vaccine and Immunology Statistical Center (Bill and Melinda Gates Foundation grant no. OPP1032317). Further funding by NIH AI068129 (L.L.L.), and NIH R21 AI138258 (N.S.).

    Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

    Results from JetFighter: Please consider improving the rainbow (“jet”) colormap used on page 37. At least one figure is not accessible to readers with colorblindness and/or is not true to the data, i.e. not perceptually uniform.

    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. ScreenIT

    SciScore for 10.1101/2020.07.27.224063: (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:

    However, a limitation is that the 50-sample, 26-patient cohort studied here is relatively modest, the numbers of co-morbidities present in this population is large, and the time-trajectories that are studied are relatively short, and so don’t capture the resolution of the infection. Nevertheless, our dataset establishes an unprecedented level of detail on the impacts of COVID-19 on the immune systems. We show that the collected single cell, bulk plasma, and clinical data sets can be synergized to reveal clear interpretable biological trends that can be associated with COVID-19 patient outcomes and may be suggestive of treatment strategies. This broad systems immunology approach should be applicable towards understanding immune responses in a host of other infectious diseases. ACKNOWLEGEMENT We are grateful to all participants and blood donors in this study. We are extremely grateful to the medical teams at Swedish Medical Center for their support of this research and for their assuming personal risk in their tireless care for patients suffering from COVID-19. We thank the Northwest Genomic Center, especially, Debbie Nickerson, Erica Ryke, and Peter Anderson for the help with sequencing services. We thank the insightful discussion from Prof. David Baltimore, Prof. Ilya Shmulevich and the ISB COVID19 Study Group: Inyoul Lee, Kay Chinn, Scott Bloom, Guenther Kahlert, David Baxter, Zac Simon, Matt Idso, Rachel Calder, William Chour, Alphonsus Ng, Jimmi Hopkins, John Heath, Jingyi Xie, John Heath, Jessica Yee, Kari Tetrault, Lee Rowen, Rachel Liu, Rongyu Zhang, Shannon Fallen, Simran Sidhu, Yue Lu, David Gibbs, Michael Strasser; and the Swedish COVID-19 Research Steering Committee: John Pauk, Bill Berrington, Michael Bolton, Mary Micikas, Sonam Nyatsatsang, Cynthia Maree, Shane O’Mahony, Kelly Sweerus, Anne Lipke, George Pappas, Mark Sullivan, Karen Koo, Chris Dale, George Lopez, Naomi Diggs, Hank Kaplan, Krish Patel, Livia Hegerova, Vanessa Dunleavy, Phil Gold, John Pagel, Joshua Mark, Doug Kieper, Jim Scanlan, Evonne Lackey, Jodie Davila, Justin Rueda, Julie Wallick, Heather Algren, Jennifer Hansberry and Elizabeth Wako. The ISB-Swedish COVID19 Biobanking Unit: Rick Edmark, John Heath, Simran Sidu, Jessica Yee, Cara McCoy, Jeremy Johnson, Renee Duprel, Audri Hubbard, Theresa Davis, Julie Thatcher and Zuraya Aziz, Thea Swanson, Yong Zhou, Lesley Jones, Sarah Li, Paula Manner, Andrea Drouhard, Stephanie Johnson, Julia Karr, Clementine Chalal, Mattie Sader, Margo Badman, Allison Everett, Adel Islam, Jodie Davila and Julie Wallick. We gratefully acknowledge funding support from the Wilke Family Foundation (J.R.H.), the Murdock Trust (J.R.H.), the Swedish Medical Center Foundation (J.D.G.), the Parker Institute for Cancer Immunotherapy (J.R.H., M.M.D., P.G., L.L.L., J.A.B.), Merck and the Biomedical Advanced Research and Development Authority under Contract HHSO10201600031C (J.R.H.). K.W. was funded by DOD W911NF-17-2-0086, NIH R01 DA040395, and NIH UG3TR002884. J.H. was funded by National Center For Advancing Translational Sciences of the National Institutes of Health under Award Number OT2 TR003443. R.G. was funded by the NIH Human Immunology Project Consortium (U19AI128914) and the Vaccine and Immunology Statistical Center (Bill and Melinda Gates Foundation grant no. OPP1032317). Further funding by NIH AI068129 (L.L.L.), and NIH R21 AI138258 (N.S.).

    Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

    Results from JetFighter: Please consider improving the rainbow (“jet”) colormap used on page 37. At least one figure is not accessible to readers with colorblindness and/or is not true to the data, i.e. not perceptually uniform.

    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.

  5. ScreenIT

    SciScore for 10.1101/2020.07.27.224063: (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

    Antibodies
    SentencesResources
    Transcripts of antibody-secreting cell (ASC)-related genes and surface markers, including CD138, XBP1, SPCS3, and IGHG4, were over-represented in COVID-19 patient samples (Figures 4H and S4C).
    CD138
    suggested: None
          <div style="margin-bottom:8px">
        <div><b>XBP1</b></div>
        <div>suggested: None</div>
      </div>
    
      <div style="margin-bottom:8px">
        <div><b>SPCS3</b></div>
        <div>suggested: None</div>
      </div>
    
      <div style="margin-bottom:8px">
        <div><b>IGHG4</b></div>
        <div>suggested: None</div>
      </div>
    
      <div style="margin-bottom:8px">
        <div><b>S4C</b></div>
        <div>suggested: None</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.

  6. Version published to 10.1101/2020.07.27.224063v1 on bioRxiv