Heterogeneous immunological recovery trajectories revealed in post-acute COVID-19

  1. Yapeng Su
  2. Dan Yuan
  3. Daniel G. Chen
  4. Kai Wang
  5. Jongchan Choi
  6. Chengzhen L. Dai
  7. Sunga Hong
  8. Rongyu Zhang
  9. Jingyi Xie
  10. Sarah Li
  11. Kelsey Scherler
  12. Ana Jimena Pavlovitch-Bedzyk
  13. Shen Dong
  14. Christopher Lausted
  15. Rachel H. Ng
  16. Inyoul Lee
  17. Shannon Fallen
  18. Sergey A. Kornilov
  19. Priyanka Baloni
  20. Venkata R. Duvvuri
  21. Kristin G. Anderson
  22. Jing Li
  23. Fan Yang
  24. Clifford Rostomily
  25. Pamela Troisch
  26. Brett Smith
  27. Jing Zhou
  28. Sean Mackay
  29. Kim Murray
  30. Rick Edmark
  31. Lesley Jones
  32. Yong Zhou
  33. Lee Rowen
  34. Rachel Liu
  35. William Chour
  36. William R. Berrington
  37. Julie A Wallick
  38. Heather A Algren
  39. the ISB-Swedish COVID19 Biobanking Unit
  40. Terri Wrin
  41. Christos J. Petropoulos
  42. Wei Wei
  43. Nathan D. Price
  44. Naeha Subramanian
  45. Jennifer Hadlock
  46. Andrew T. Magis
  47. Antoni Ribas
  48. Lewis L. Lanier
  49. Scott D. Boyd
  50. Jeffrey A. Bluestone
  51. Leroy Hood
  52. Raphael Gottardo
  53. Philip D. Greenberg
  54. Mark M. Davis
  55. Jason D. Goldman
  56. James R. Heath

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Abstract

The immunological picture of how different patients recover from COVID-19, and how those recovery trajectories are influenced by infection severity, remain unclear. We investigated 140 COVID-19 patients from diagnosis to convalescence using clinical data, viral load assessments, and multi-omic analyses of blood plasma and circulating immune cells. Immune-phenotype dynamics resolved four recovery trajectories. One trajectory signals a return to pre-infection healthy baseline, while the other three are characterized by differing fractions of persistent cytotoxic and proliferative T cells, distinct B cell maturation processes, and memory-like innate immunity. We resolve a small panel of plasma proteins that, when measured at diagnosis, can predict patient survival and recovery-trajectory commitment. Our study offers novel insights into post-acute immunological outcomes of COVID-19 that likely influence long-term adverse sequelae.

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  1. ScreenIT

    SciScore for 10.1101/2021.03.19.21254004: (What is this?)

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

    Table 1: Rigor

    Institutional Review Board StatementConsent: All enrolled patients provided written informed consent.
    IRB: Procedures for the current study were approved by the Institutional Review Board (IRB) at Providence St.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variablesubjects, WOS score and EHR data extraction: This study included data from 140 COVID-19 patients (63 males and 77 females) and 333 unexposed healthy controls.
    Cell Line Authenticationnot detected.

    Table 2: Resources

    Antibodies
    SentencesResources
    Cells were stained with cell hashtag antibodies (BioLegend, 394661, 394663, 394665, 394667, 394669, 394671, 394673, 394675, 394677, 394679) and TotalSeq-C custom human antibodies (BioLegend, 99814)
    cell hashtag antibodies ( BioLegend , 394661 , 394663 , 394665 , 394667 , 394669 , 394671 , 394673 , 394675 , 394677 , 394679
    suggested: None
    The isolated CD4+ and CD8+ T cells were seeded at a density of 1⨯105 cells/well in a 96 well-plate and stimulated for six hours with plate-bound anti-CD3 antibodies (eBioscience, 16-0037-85, pre-coated at 10 µg/ml overnight at 4°C) and 5 µg/mL of soluble anti-CD28 antibodies (eBioscience, 16-0289-85,) in complete medium at 37°C, 5% CO2.
    anti-CD3
    suggested: (Thermo Fisher Scientific Cat# 16-0037-85, RRID:AB_468855)
    anti-CD28
    suggested: (Thermo Fisher Scientific Cat# 16-0289-85, RRID:AB_468927)
    The anti-S antibody (abcam, ab273073
    anti-S
    suggested: None
    ) anti-N antibody (abcam, ab272852) at nine serial three-fold dilutions, starting from 2 µg/mL were used as positive controls.
    anti-N
    suggested: None
    dilution), IgA (Sigma, A0295, 1:5000 dilution), or IgM (Sigma, A6907, 1:1000 dilution) antibodies in blocking buffer for 1 hour at RT.
    A0295
    suggested: (Sigma-Aldrich Cat# A0295, RRID:AB_257876)
    IgM
    suggested: (Sigma-Aldrich Cat# A6907, RRID:AB_258318)
    A6907
    suggested: (ABclonal Cat# A6907, RRID:AB_2767466)
    BCR convergent sequence analysis: IGH sequences from single cells with paired productive heavy and light chains were searched against a list of known SARS-CoV-2 binding antibodies to identify convergent sequences according to the following criteria: utilization of the same IGHV and IGHJ genes; same CDR-H3 lengths; and CDR-H3 amino acid sequences that were within a Hamming distance cutoff of 15% of the length of the CDR-H3.
    CDR-H3
    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. ELISAs: Briefly, 384-well plates (ThermoFisher, 464718) were coated with 10 µL of 5 µg/mL SARS-CoV-2 RBD (Invitrogen, RP-87678)
    MCP-1
    suggested: None
    HEK-293 cells expressing ACE2 were added to the 96-well plate and incubated for additional 60-80 hours at 37°C for luminescence measurements.
    HEK-293
    suggested: None
    Software and Algorithms
    SentencesResources
    Statistical analysis: Statistical analyses were performed using Python or R software packages.
    Python
    suggested: (IPython, RRID:SCR_001658)

    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: 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.
    • Thank you for including a protocol registration statement.

    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 10.1101/2021.03.19.21254004 on medRxiv