Binding of phosphatidylserine‐positive microparticles by PBMCs classifies disease severity in COVID‐19 patients

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Abstract

Infection with SARS‐CoV‐2 is associated with thromboinflammation, involving thrombotic and inflammatory responses, in many COVID‐19 patients. In addition, immune dysfunction occurs in patients characterised by T cell exhaustion and severe lymphopenia. We investigated the distribution of phosphatidylserine (PS), a marker of dying cells, activated platelets and platelet‐derived microparticles (PMP), during the clinical course of COVID‐19. We found an unexpectedly high amount of blood cells loaded with PS + PMPs for weeks after the initial COVID‐19 diagnosis. Elevated frequencies of PS + PMP + PBMCs correlated strongly with increasing disease severity. As a marker, PS outperformed established laboratory markers for inflammation, leucocyte composition and coagulation, currently used for COVID‐19 clinical scoring. PS + PMPs preferentially bound to CD8 + T cells with gene expression signatures of proliferating effector rather than memory T cells. As PS + PMPs carried programmed death‐ligand 1 (PD‐L1), they may affect T cell expansion or function. Our data provide a novel marker for disease severity and show that PS, which can trigger the blood coagulation cascade, the complement system, and inflammation, resides on activated immune cells. Therefore, PS may serve as a beacon to attract thromboinflammatory processes towards lymphocytes and cause immune dysfunction in COVID‐19.

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  1. SciScore for 10.1101/2021.06.18.448935: (What is this?)

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

    Table 1: Rigor

    EthicsIRB: The Ethics Committee approved the study of the LMU Munich (No: 20-308; 18-415), and patients included (≥ 18 years, mean age
    Sex as a biological variablenot detected.
    RandomizationFirst-strand cDNA synthesis was primed with a N6 randomized primer.
    Blindingnot detected.
    Power Analysisnot detected.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    RNAseq analysis: Sequencing reads were aligned to the human reference genome (version GRCH38.100) with STAR (version 2.7.3).
    STAR
    suggested: (STAR, RRID:SCR_004463)
    Expression values (TPM) were calculated with RSEM (version 1.3.3).
    RSEM
    suggested: (RSEM, RRID:SCR_013027)
    Gene set enrichment analyses were conducted with ‘clusterProfiler’ (version 3.18.1) on the statistic reported by DEseq2.
    DEseq2
    suggested: (DESeq2, RRID:SCR_015687)
    Then FCS files from all cells, only apoptotic or only EV+ cells, were exported and analyzed using FlowJo Version 10.7.1
    FlowJo
    suggested: (FlowJo, RRID:SCR_008520)
    Statistical analysis: For statistical analysis, the PRISM software (GraphPad Software, La Jolla, CA, USA) was used.
    PRISM
    suggested: (PRISM, RRID:SCR_005375)
    GraphPad
    suggested: (GraphPad Prism, RRID:SCR_002798)

    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.

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


    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.