T-cell hyperactivation and paralysis in severe COVID-19 infection revealed by single-cell analysis

  1. Bahire Kalfaoglu
  2. José Almeida-Santos
  3. Chanidapa Adele Tye
  4. Yorifumi Satou
  5. Masahiro Ono

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Abstract

Severe COVID-19 patients can show respiratory failure, T-cell reduction, and cytokine release syndrome (CRS), which can be fatal in both young and aged patients and is a major concern of the pandemic. However, the pathogenetic mechanisms of CRS in COVID-19 are poorly understood. Here we show single cell-level mechanisms for T-cell dysregulation in severe SARS-CoV-2 infection, and thereby demonstrate the mechanisms underlying T-cell hyperactivation and paralysis in severe COVID-19 patients. By in silico sorting CD4+ T-cells from a single cell RNA-seq dataset, we found that CD4+ T-cells were highly activated and showed unique differentiation pathways in the lung of severe COVID-19 patients. Notably, those T-cells in severe COVID-19 patients highly expressed immunoregulatory receptors and CD25, whilst repressing the expression of the transcription factor FOXP3 and interestingly, both the differentiation of regulatory T-cells (Tregs) and Th17 was inhibited. Meanwhile, highly activated CD4 + T-cells express PD-1 alongside macrophages that express PD-1 ligands in severe patients, suggesting that PD-1-mediated immunoregulation was partially operating. Furthermore, we show that CD25 + hyperactivated T-cells differentiate into multiple helper T-cell lineages, showing multifaceted effector T-cells with Th1 and Th2 characteristics. Lastly, we show that CD4 + T-cells, particularly CD25-expressing hyperactivated T-cells, produce the protease Furin, which facilitates the viral entry of SARS-CoV-2. Collectively, CD4 + T-cells from severe COVID-19 patients are hyperactivated and FOXP3-mediated negative feedback mechanisms are impaired in the lung, while activated CD4 + T-cells continue to promote further viral infection through the production of Furin. Therefore, our study proposes a new model of T-cell hyperactivation and paralysis that drives pulmonary damage, systemic CRS and organ failure in severe COVID-19 patients.

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    SciScore for 10.1101/2020.05.26.115923: (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
    In silico sorting of CD4 T-cells: We used h5 files of the scRNA-seq dataset (GSE14592616) which were aligned to the human genome (GRCh38) using Cell Ranger, by importing them into the CRAN package Seurat 3.0.57 Single cells with high mitochondrial gene expression (higher than 5%) were excluded from further analyses.
    Seurat
    suggested: (SEURAT, RRID:SCR_007322)
    Pathway analysis: The enrichment of biological pathways in the gene lists was tested by the Bioconductor package clusterProfiler,58 using the Reactome database through the Bioconductor package ReactomePA, and pathways with false discovery rate < 0.01 and q-value < 0.1 were considered significant.
    Reactome
    suggested: (Reactome, RRID:SCR_003485)
    Pseudotime analysis: Trajectories were identified using the Bioconductor package slingshot, assuming that the cluster that show the highest expression of IL7R and CCR7 is the origin.
    Bioconductor
    suggested: (Bioconductor, RRID:SCR_006442)
    The CRAN package ggplot2 was used to apply a generalised additive model of the CRAN package gam to each gene expression data.
    CRAN
    suggested: (CRAN, RRID:SCR_003005)
    ggplot2
    suggested: (ggplot2, RRID:SCR_014601)

    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

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  2. Version published to 10.1101/2020.05.26.115923 on bioRxiv