Multi-organ proteomic landscape of COVID-19 autopsies

  1. Xiu Nie
  2. Liujia Qian
  3. Rui Sun
  4. Bo Huang
  5. Xiaochuan Dong
  6. Qi Xiao
  7. Qiushi Zhang
  8. Tian Lu
  9. Liang Yue
  10. Shuo Chen
  11. Xiang Li
  12. Yaoting Sun
  13. Lu Li
  14. Luang Xu
  15. Yan Li
  16. Ming Yang
  17. Zhangzhi Xue
  18. Shuang Liang
  19. Xuan Ding
  20. Chunhui Yuan
  21. Li Peng
  22. Wei Liu
  23. Xiao Yi
  24. Mengge Lyu
  25. Guixiang Xiao
  26. Xia Xu
  27. Weigang Ge
  28. Jiale He
  29. Jun Fan
  30. Junhua Wu
  31. Meng Luo
  32. Xiaona Chang
  33. Huaxiong Pan
  34. Xue Cai
  35. Junjie Zhou
  36. Jing Yu
  37. Huanhuan Gao
  38. Mingxing Xie
  39. Sihua Wang
  40. Guan Ruan
  41. Hao Chen
  42. Hua Su
  43. Heng Mei
  44. Danju Luo
  45. Dashi Zhao
  46. Fei Xu
  47. Yan Li
  48. Yi Zhu
  49. Jiahong Xia
  50. Yu Hu
  51. Tiannan Guo

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  1. ScreenIT

    SciScore for 10.1101/2020.08.16.20176065: (What is this?)

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

    Table 1: Rigor

    Institutional Review Board StatementIRB: Clinical specimens and histological analysis: This study was approved by the Medical Ethics Committee of Union Hospital affiliated to Tongji Medical College of Huazhong University of Science and Technology (No.2020–0043–1) and Medical Ethical Committee of Westlake University (No.20200329GTN001).
    Consent: With the consent by the fatal COVID-19 patients’ families, ultrasound-guided percutaneous multi-point postmortem core biopsies were performed within two hours after death in a negative pressure isolation ward, from February to April 2020.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    The tissue specific pooled samples were prepared by mixing equal amount of peptides from all samples of a specific organ) were labeled with TMTpro 16plex reagent (Thermo Fisher Scientific™, San Jose, USA)(Li et al., 2020).
    Thermo Fisher Scientific™
    suggested: (Thermo Fisher Scientific, RRID:SCR_008452)
    The MS raw data were analyzed by Proteome Discoverer (Version 2.4.1.15, Thermo Fisher Scientific) using a combination of the FASTA files of 20,365 reviewed Homo sapiens proteins downloaded from UniProt website on 14 April, 2020 and the SARS-CoV-2 virus FASTA of 10 SARS-CoV-2 proteins downloaded from NCBI (version MN908947.3)
    Proteome Discoverer
    suggested: (Proteome Discoverer, RRID:SCR_014477)
    UniProt
    suggested: (UniProtKB, RRID:SCR_004426)
    NCBI
    suggested: (NCBI, RRID:SCR_006472)
    5, Z score > 0 or < 0), or Metascape web-based platform(Zhou et al., 2019) (with p-value < 0.05), or string web-based platform(Szklarczyk et al., 2019).
    Metascape
    suggested: (Metascape, RRID:SCR_016620)

    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.
    • 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 10.1016/j.cell.2021.01.004
  3. Version published to 10.1101/2020.08.16.20176065v1 on medRxiv
  4. Version published to 10.1101/2020.08.16.20176065v2 on medRxiv