Bifurcated monocyte states are predictive of mortality in severe COVID-19

  1. Anthony R. Cillo
  2. Ashwin Somasundaram
  3. Feng Shan
  4. Carly Cardello
  5. Creg J. Workman
  6. Georgios D. Kitsios
  7. Ayana Ruffin
  8. Sheryl Kunning
  9. Caleb Lampenfeld
  10. Sayali Onkar
  11. Stephanie Grebinoski
  12. Gaurav Deshmukh
  13. Barbara Methe
  14. Chang Liu
  15. Sham Nambulli
  16. Lawrence Andrews
  17. W. Paul Duprex
  18. Alok V. Joglekar
  19. Panayiotis V. Benos
  20. Prabir Ray
  21. Anuradha Ray
  22. Bryan J. McVerry
  23. Yingze Zhang
  24. Janet S. Lee
  25. Jishnu Das
  26. Harinder Singh
  27. Alison Morris
  28. Tullia C. Bruno
  29. Dario A.A. Vignali

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Abstract

Coronavirus disease 2019 (COVID-19) caused by SARS-CoV-2 infection presents with varied clinical manifestations 1 , ranging from mild symptoms to acute respiratory distress syndrome (ARDS) with high mortality 2,3 . Despite extensive analyses, there remains an urgent need to delineate immune cell states that contribute to mortality in severe COVID-19. We performed high-dimensional cellular and molecular profiling of blood and respiratory samples from critically ill COVID-19 patients to define immune cell genomic states that are predictive of outcome in severe COVID-19 disease. Critically ill patients admitted to the intensive care unit (ICU) manifested increased frequencies of inflammatory monocytes and plasmablasts that were also associated with ARDS not due to COVID-19. Single-cell RNAseq (scRNAseq)-based deconvolution of genomic states of peripheral immune cells revealed distinct gene modules that were associated with COVID-19 outcome. Notably, monocytes exhibited bifurcated genomic states, with expression of a cytokine gene module exemplified by CCL4 (MIP-1β) associated with survival and an interferon signaling module associated with death. These gene modules were correlated with higher levels of MIP-1β and CXCL10 levels in plasma, respectively. Monocytes expressing genes reflective of these divergent modules were also detectable in endotracheal aspirates. Machine learning algorithms identified the distinctive monocyte modules as part of a multivariate peripheral immune system state that was predictive of COVID-19 mortality. Follow-up analysis of the monocyte modules on ICU day 5 was consistent with bifurcated states that correlated with distinct inflammatory cytokines. Our data suggests a pivotal role for monocytes and their specific inflammatory genomic states in contributing to mortality in life-threatening COVID-19 disease and may facilitate discovery of new diagnostics and therapeutics.

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  1. ScreenIT

    SciScore for 10.1101/2021.02.10.430499: (What is this?)

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

    Table 1: Rigor

    Institutional Review Board StatementConsent: Clinical cohort: Following acquisition of written informed consent from patients or their legally authorized representatives, we enrolled 41 consecutive critically ill patients with acute hypoxemic respiratory failure and symptoms/signs suggestive of COVID-19 in a prospective, observational cohort study (University of Pittsburgh Institutional Review Board study number 20040036).
    IRB: Clinical cohort: Following acquisition of written informed consent from patients or their legally authorized representatives, we enrolled 41 consecutive critically ill patients with acute hypoxemic respiratory failure and symptoms/signs suggestive of COVID-19 in a prospective, observational cohort study (University of Pittsburgh Institutional Review Board study number 20040036).
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.
    Cell Line Authenticationnot detected.

    Table 2: Resources

    Antibodies
    SentencesResources
    We used the following 28 antibodies::fluorophore conjugates and clones to enumerate major immune lineages using the Cytek Aurora: HLA-DR::BUV395 (BD; G46-6), CD8::BUV496 (BD; RPA-T8), CD4::BUV563 (BD; RPA-T4), CD103::BUV615 (BD; Ber-ACT8), CD45::BUV661 (BD; HI30), CD14::BUV737 (BD; M5E2), CD19::BUV805 (BD; HIB19), Ki67::BV421 (Biolegend; KI-67), FoxP3::eFluor450 (ThermoFisher; PCH101), CD38::BV480 (BD; HIT2), CD1c::PE-Cy5
    Ki67::BV421
    suggested: None
    (conjugated in-house; antibody from ThermoFisher, clone L161), CD45RA (Biolegend; HI100), CD62L::BV605 (Biolegend; DREG-56), CD15::BV650 (Biolegend; SSEA-1), CD25::BV711 (Biolegend, BC96), CD20::BV750 (BD; 2H7), CD141::BV785 (BD; M80), CD36::FITC (Biolegend, 5-271), CD3::Spark Blue 550 (Biolegend; SK7), CD11b::PerCP-Cy5.5 (Biolegend; LM2), CD56::PerCP-EF710 (Invitrogen, CMSSB), ACE2::PE (R&D Systems; 171606), CD16::PE-TexasRed (Biolegend; 3G8), CD27::BV510 (Biolegend; O323), CD138::APC (Biolegend; MI15), CD11c::Alexa700 (Biolegend; 3.9), and CCR2::APC-Cy7 (Biolegend::K036C2).
    CD45RA
    suggested: (BioLegend Cat# 304139, RRID:AB_2561369)
    CD62L::BV605
    suggested: None
    CD25::BV711
    suggested: None
    CD36::FITC
    suggested: None
    CD11b::PerCP-Cy5.5
    suggested: None
    CD56::PerCP-EF710
    suggested: None
    CD27::BV510
    suggested: None
    CCR2::APC-Cy7
    suggested: None
    For our monocyte specific Fortessa II panel, we used the following 14 antibodies::fluorophore conjugates and clones: HLA-DR::BUV395 (BD; G46-6), CD14::BUV737 (BD; M5E2), CD16::BV412 (Biolegend; 3G8), CCR5::BV510 (Biolegend; J418F1), CD3::BV650 (Biolegend; UCHT1), CD19::BV650 (Biolegend; UCHT), CD20::BV650 (Biolegend; 2H7), CD56::BV650 (Biolegend; 5.1H11), CD163::BV711 (Biolegend; GHI/61), CCR2::BV785 (Biolegend; K036C2), ACE2::APC (R&D Systems; 171606), CD11c::Alexa700 (Biolegend; 3.9), NRP1::PE (Biolegend 12C2), and CD36::PE-Cy7 (Biolegend; 5-271).
    NRP1::PE
    suggested: None
    CD36::PE-Cy7
    suggested: None
    Individual samples were identified by unique expression of the anticipated TotalSeq-C antibody and the absence of alternative TotalSeq-C antibodies.
    anticipated TotalSeq-C
    suggested: None
    Cells with expression levels above the cut-offs for both TotalSeq-C antibodies were considered doublets, and were excluded from downstream analysis.
    TotalSeq-C
    suggested: None
    Experimental Models: Cell Lines
    SentencesResources
    IL-17B, IL-17C, IL-17D, IL-21, IL-22, IL-23, IL-27, IL-31, IP-10, MCP-1, MCP-4, MDC, MIP-1α,
    MCP-1
    suggested: None
    Then, modules were scaled and PCA was performed for dimensionality reduction in each fold, and PC1 and PC2 were used as predictive variables.
    PC2
    suggested: RRID:CVCL_0483)
    Software and Algorithms
    SentencesResources
    For our monocyte specific Fortessa II panel, we used the following 14 antibodies::fluorophore conjugates and clones: HLA-DR::BUV395 (BD; G46-6), CD14::BUV737 (BD; M5E2), CD16::BV412 (Biolegend; 3G8), CCR5::BV510 (Biolegend; J418F1), CD3::BV650 (Biolegend; UCHT1), CD19::BV650 (Biolegend; UCHT), CD20::BV650 (Biolegend; 2H7), CD56::BV650 (Biolegend; 5.1H11), CD163::BV711 (Biolegend; GHI/61), CCR2::BV785 (Biolegend; K036C2), ACE2::APC (R&D Systems; 171606), CD11c::Alexa700 (Biolegend; 3.9), NRP1::PE (Biolegend 12C2), and CD36::PE-Cy7 (Biolegend; 5-271).
    Biolegend
    suggested: (BioLegend, RRID:SCR_001134)
    Sample multiplexing was performed using CITEseq.
    CITEseq
    suggested: None
    Samples were then demultiplex using bcl2fastq (Illumina), using a base mask of Y28, I8, Y91 and setting the stringency to allow no barcode mismatches.
    bcl2fastq
    suggested: (bcl2fastq , RRID:SCR_015058)
    Following demultiplexing, gene expression reads were then aligned to the reference genome using CellRanger v3.1.0, and feature barcode matrices were created for each sample.
    CellRanger
    suggested: None
    Cell hashing libraries were aligned to TotalSeq-C barcodes using CITE-seq-Count v1.4.355, using a sample-specific cell barcode whitelist (i.e., only cell barcodes from cells identified by CellRanger from each sample were included in the whitelist).
    CITE-seq-Count
    suggested: (CITE-seq-Count, RRID:SCR_019239)
    Identification of individual samples from cell hashing: After generation of gene expression and feature barcode matrices, downstream analysis was performed using Seurat v3.1.456 in R 3.6.0.
    Seurat
    suggested: (SEURAT, RRID:SCR_007322)
    Filtered expression matrices and a list of expressed transcription factors were then exported to be used in GRNBoost2 from the Aboreto package in Python.
    Python
    suggested: (IPython, RRID:SCR_001658)

    Results from OddPub: Thank you for sharing your code and data.

    Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:
    Limitations of our study include the relatively small sample size of our COVID-19 patient cohort, precluding in depth analysis of relationships between flow cytometry measurements, transcriptional states of immune cells and cytokines with clinical covariates such as diabetes, age and biological sex. These covariates are important contributors to outcome and need to be more fully integrated with the high-dimensional immune system analyses in larger studies. Furthermore, emergent datasets consisting of critically ill patients that have been phenotyped as densely as our patient cohort will facilitate external validation of the findings presented here. Nevertheless, our study of severely ill COVID-19 patients admitted to the ICU has uncovered a striking bifurcation in inflammatory monocyte genomic states that is predictive of mortality outcome. These bifurcated monocytic states manifest differing temporal dynamics and are in turn linked to distinct inflammatory cytokines. Taken together, our findings may facilitate discovery of new diagnostics and therapeutics to improve outcome in severe COVID-19.

    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 scite Reference Check: We found one citation with an erratum. We recommend checking the erratum to confirm that it does not impact the accuracy of your citation.

    DOIStatusTitle
    10.1038/s41392-020-0148-4Has correctionLymphopenia predicts disease severity of COVID-19: a descrip…

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  2. Version published to 10.1101/2021.02.10.430499 on bioRxiv