Interplay of Monocytes and T Lymphocytes in COVID-19 Severity

  1. Lindsey E. Padgett
  2. Huy Q. Dinh
  3. Serena J. Chee
  4. Claire E. Olingy
  5. Runpei Wu
  6. Daniel J. Araujo
  7. Pandurangan Vijayanand
  8. Christian H. Ottensmeier
  9. Catherine C. Hedrick

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Abstract

The COVID-19 pandemic represents an ongoing global crisis that has already impacted over 13 million people. The responses of specific immune cell populations to the disease remain poorly defined, which hinders improvements in treatment and care management. Here, we utilized mass cytometry (CyTOF) to thoroughly phenotype peripheral myeloid cells and T lymphocytes from 30 convalescent patients with mild, moderate, and severe cases of COVID-19. We identified 10 clusters of monocytes and dendritic cells and 17 clusters of T cells. Examination of these clusters revealed that both CD14 + CD16 + intermediate and CD14 dim CD16 + nonclassical monocytes, as well as CD4 + stem cell memory T (T SCM ) cells, correlated with COVID-19 severity, coagulation factor levels, and/or inflammatory indicators. We also identified two nonclassical monocyte subsets distinguished by expression of the sugar residue 6-Sulfo LacNac (Slan). One of these subsets (Slan lo , nMo1) was depleted in moderately and severely ill patients, while the other (Slan hi , nMo2) increased with disease severity and was linked to CD4 + T effector memory (T EM ) cell frequencies, coagulation factors, and inflammatory indicators. Intermediate monocytes tightly correlated with loss of naive T cells as well as an increased abundance of effector memory T cells expressing the exhaustion marker PD-1. Our data suggest that both intermediate and non-classical monocyte subsets shape the adaptive immune response to SARS-CoV-2. In summary, our study provides both broad and in-depth characterization of immune cell phenotypes in response to COVID-19 and suggests functional interactions between distinct cell types during the disease.

One Sentence Summary

Use of mass cytometry on peripheral blood mononuclear cells from convalescent COVID-19 patients allows correlation of distinct monocyte and T lymphocyte subsets with clinical factors.

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  1. ScreenIT

    SciScore for 10.1101/2020.07.17.209304: (What is this?)

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

    Table 1: Rigor

    Institutional Review Board StatementIRB: Patient Samples: Ethical approval for this study was received from the Berkshire Research Ethics 20/SC/0155 and the Ethics committee of the La Jolla Institute for Immunology.
    Consent: Written, informed consent was obtained for all 30 subjects
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variableWithin the mild cohort, 50% were male, and within the moderate cohort, 77% of individuals were male.

    Table 2: Resources

    Antibodies
    SentencesResources
    SARS-CoV2 was confirmed by reverse transcriptase polymerase reaction (RT-PCR) or by detection of anti-spike protein antibodies performed in the clinical virology laboratory of the hospital.
    anti-spike protein
    suggested: None
    The mild cohort comprised eight health care workers who were not hospitalized, but COVID-19 diagnosis was confirmed via RT-PCR or serological evidence of SARS-CoV2 antibodies.
    SARS-CoV2
    suggested: None
    Software and Algorithms
    SentencesResources
    CyTOF analysis - Debarcoding and batch correction: Each CyTOF sample was debarcoded from the original fcs file (with spike-in healthy PBMC samples as a control) using a deconvolution algorithm (79) implemented in the CATALYST Bioconductor package.
    CATALYST
    suggested: (CATALYST, RRID:SCR_017127)
    Then, we took advantage of the same spike-in healthy control for the batch correction, using a quantile normalization method for the pooled distribution of each batch (a pair of sample and spike-in control), as implemented in the function normalizeBatch from the CYDAR Bioconductor package (80).
    CYDAR
    suggested: None
    Bioconductor
    suggested: (Bioconductor, RRID:SCR_006442)
    Heatmaps of median protein expression were produced using the pheatmap R package (v0.2).
    pheatmap
    suggested: (pheatmap, RRID:SCR_016418)
    UMAP deduction was done using the umap R package (v0.2.3.1), a wrapper for Python package ‘umap-learn’.
    Python
    suggested: (IPython, RRID:SCR_001658)
    For T cell clustering in Figure 3, CD14loCD66b/Siglec/CD19lo CD3+CD4+ and CD3+CD8+ T cells were gated in FlowJo v10.3 prior to downstream FlowSOM clustering.
    FlowJo
    suggested: (FlowJo, RRID:SCR_008520)
    FlowSOM
    suggested: (FlowSOM, RRID:SCR_016899)

    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 10.1101/2020.07.17.209304 on bioRxiv