A streamlined whole blood CyTOF workflow defines a circulating immune cell signature of COVID ‐19

  1. Daniel Geanon
  2. Brian Lee
  3. Edgar Gonzalez‐Kozlova
  4. Geoffrey Kelly
  5. Diana Handler
  6. Bhaskar Upadhyaya
  7. John Leech
  8. Ronaldo M. De Real
  9. Manon Herbinet
  10. Assaf Magen
  11. Diane Del Valle
  12. Alexander Charney
  13. Seunghee Kim‐Schulze
  14. Sacha Gnjatic
  15. Miriam Merad
  16. Adeeb H. Rahman

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Abstract

Mass cytometry (CyTOF) represents one of the most powerful tools in immune phenotyping, allowing high throughput quantification of over 40 parameters at single‐cell resolution. However, wide deployment of CyTOF‐based immune phenotyping studies are limited by complex experimental workflows and the need for specialized CyTOF equipment and technical expertise. Furthermore, differences in cell isolation and enrichment protocols, antibody reagent preparation, sample staining, and data acquisition protocols can all introduce technical variation that can confound integrative analyses of large data‐sets of samples processed across multiple labs. Here, we present a streamlined whole blood CyTOF workflow which addresses many of these sources of experimental variation and facilitates wider adoption of CyTOF immune monitoring across sites with limited technical expertise or sample‐processing resources or equipment. Our workflow utilizes commercially available reagents including the Fluidigm MaxPar Direct Immune Profiling Assay (MDIPA), a dry tube 30‐marker immunophenotyping panel, and SmartTube Proteomic Stabilizer, which allows for simple and reliable fixation and cryopreservation of whole blood samples. We validate a workflow that allows for streamlined staining of whole blood samples with minimal processing requirements or expertise at the site of sample collection, followed by shipment to a central CyTOF core facility for batched downstream processing and data acquisition. We apply this workflow to characterize 184 whole blood samples collected longitudinally from a cohort of 72 hospitalized COVID‐19 patients and healthy controls, highlighting dynamic disease‐associated changes in circulating immune cell frequency and phenotype.

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  1. Version published to 10.1002/cyto.a.24317
  2. ScreenIT

    SciScore for 10.1101/2020.06.26.20141341: (What is this?)

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

    Table 1: Rigor

    Institutional Review Board StatementIRB: Samples: Samples were collected from consented healthy donors and hospitalized COVID-19 patients under protocols approved by the Mount Sinai Institutional Review Board.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    Data analysis: Debarcoded files were uploaded to Cytobank for analysis.
    Cytobank
    suggested: (Cytobank, RRID:SCR_014043)
    In some cases, immune cell populations were also identified using automated approaches including MaxPar PathSetter and Astrolabe, the result of which largely correlated well with our manual gating approaches.
    MaxPar PathSetter
    suggested: None

    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.

    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.

  3. Version published to 10.1101/2020.06.26.20141341v2 on medRxiv
  4. Version published to 10.1101/2020.06.26.20141341v1 on medRxiv