Longitudinal proteomic analysis of severe COVID-19 reveals survival-associated signatures, tissue-specific cell death, and cell-cell interactions

  1. Michael R. Filbin
  2. Arnav Mehta
  3. Alexis M. Schneider
  4. Kyle R. Kays
  5. Jamey R. Guess
  6. Matteo Gentili
  7. Bánk G. Fenyves
  8. Nicole C. Charland
  9. Anna L.K. Gonye
  10. Irena Gushterova
  11. Hargun K. Khanna
  12. Thomas J. LaSalle
  13. Kendall M. Lavin-Parsons
  14. Brendan M. Lilley
  15. Carl L. Lodenstein
  16. Kasidet Manakongtreecheep
  17. Justin D. Margolin
  18. Brenna N. McKaig
  19. Maricarmen Rojas-Lopez
  20. Brian C. Russo
  21. Nihaarika Sharma
  22. Jessica Tantivit
  23. Molly F. Thomas
  24. Robert E. Gerszten
  25. Graham S. Heimberg
  26. Paul J. Hoover
  27. David J. Lieb
  28. Brian Lin
  29. Debby Ngo
  30. Karin Pelka
  31. Miguel Reyes
  32. Christopher S. Smillie
  33. Avinash Waghray
  34. Thomas E. Wood
  35. Amanda S. Zajac
  36. Lori L. Jennings
  37. Ida Grundberg
  38. Roby P. Bhattacharyya
  39. Blair Alden Parry
  40. Alexandra-Chloé Villani
  41. Moshe Sade-Feldman
  42. Nir Hacohen
  43. Marcia B. Goldberg

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Abstract

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  1. Version published to 10.1016/j.xcrm.2021.100287
  2. ScreenIT

    SciScore for 10.1101/2020.11.02.365536: (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

    Software and Algorithms
    SentencesResources
    All plots were generated using the ggplot2 package in R with the exception that the correlation plots were generated using the corrplot() function in R.
    ggplot2
    suggested: (ggplot2, RRID:SCR_014601)
    Organ signatures were split based on localization (intracellular versus membrane/secreted) using UniProt and literature annotations.
    UniProt
    suggested: (UniProtKB, RRID:SCR_004426)
    Python 3.8 was used to run the python package Cellphonedb v2.1.4 with the following parameters: database v2.0.0, statistical method analysis, 1000 iterations, 6000 cell subsampling.
    Python
    suggested: (IPython, RRID:SCR_001658)
    Cellphonedb
    suggested: (CellPhoneDB, RRID:SCR_017054)

    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.

  3. Version published to 10.1101/2020.11.02.365536v2 on bioRxiv
  4. Version published to 10.1101/2020.11.02.365536v1 on bioRxiv