Massive image-based single-cell profiling reveals high levels of circulating platelet aggregates in patients with COVID-19

  1. Masako Nishikawa
  2. Hiroshi Kanno
  3. Yuqi Zhou
  4. Ting-Hui Xiao
  5. Takuma Suzuki
  6. Yuma Ibayashi
  7. Jeffrey Harmon
  8. Shigekazu Takizawa
  9. Kotaro Hiramatsu
  10. Nao Nitta
  11. Risako Kameyama
  12. Walker Peterson
  13. Jun Takiguchi
  14. Mohammad Shifat-E-Rabbi
  15. Yan Zhuang
  16. Xuwang Yin
  17. Abu Hasnat Mohammad Rubaiyat
  18. Yunjie Deng
  19. Hongqian Zhang
  20. Shigeki Miyata
  21. Gustavo K. Rohde
  22. Wataru Iwasaki
  23. Yutaka Yatomi
  24. Keisuke Goda

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Abstract

A characteristic clinical feature of COVID-19 is the frequent incidence of microvascular thrombosis. In fact, COVID-19 autopsy reports have shown widespread thrombotic microangiopathy characterized by extensive diffuse microthrombi within peripheral capillaries and arterioles in lungs, hearts, and other organs, resulting in multiorgan failure. However, the underlying process of COVID-19-associated microvascular thrombosis remains elusive due to the lack of tools to statistically examine platelet aggregation (i.e., the initiation of microthrombus formation) in detail. Here we report the landscape of circulating platelet aggregates in COVID-19 obtained by massive single-cell image-based profiling and temporal monitoring of the blood of COVID-19 patients ( n  = 110). Surprisingly, our analysis of the big image data shows the anomalous presence of excessive platelet aggregates in nearly 90% of all COVID-19 patients. Furthermore, results indicate strong links between the concentration of platelet aggregates and the severity, mortality, respiratory condition, and vascular endothelial dysfunction level of COVID-19 patients.

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  1. Version published to 10.1038/s41467-021-27378-2
  2. Version published to 10.1101/2021.04.29.21256354v2 on medRxiv
  3. ScreenIT

    SciScore for 10.1101/2021.04.29.21256354: (What is this?)

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

    Table 1: Rigor

    EthicsIRB: This study was conducted with the approval of the Institutional Ethics Committee in the School of Medicine at the University of Tokyo [no. 11049, no. 11344] in compliance with the relevant guidelines and regulations.
    Consent: Written informed consent for participation in the study was obtained from the patients using an opt-out process on the webpage of the University of Tokyo Hospital.
    Sex as a biological variablenot detected.
    RandomizationThe training and validation set was randomly selected with an 80/20 ratio.
    Blindingnot detected.
    Power Analysisnot detected.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    The image-encoded time-domain signal was detected with a single-pixel photodetector, converted into a serial digital data stream by a digitizer, and recovered as bright-field images by a home-made LabVIEW program.
    LabVIEW
    suggested: (LabView , RRID:SCR_014325)
    Then, the outlines of the object regions were detected by using the edge detection function with the Canny method in MATLAB.
    MATLAB
    suggested: (MATLAB, RRID:SCR_001622)

    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.

    Results from scite Reference Check: We found no unreliable references.

    About SciScore

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  4. Version published to 10.1101/2021.04.29.21256354v1 on medRxiv