A Robust, Highly Multiplexed Mass Spectrometry Assay to Identify SARS-CoV-2 Variants

  1. Matthew M. Hernandez
  2. Radhika Banu
  3. Paras Shrestha
  4. Ana S. Gonzalez-Reiche
  5. Adriana van de Guchte
  6. Keith Farrugia
  7. Robert Sebra
  8. Mount Sinai PSP Study Group
  9. Melissa R. Gitman
  10. Michael D. Nowak
  11. Carlos Cordon-Cardo
  12. Viviana Simon
  13. Harm van Bakel
  14. Emilia Mia Sordillo
  15. Nicolas Luna
  16. Angie Ramirez
  17. Sergio Andres Castañeda
  18. Luz Helena Patiño
  19. Nathalia Ballesteros
  20. Marina Muñoz
  21. Juan David Ramírez
  22. Alberto E. Paniz-Mondolfi
  23. Hala Alshammary
  24. Angela A. Amoako
  25. Mahmoud H. Awawda
  26. Christian Cognigni
  27. Aria Rooker
  28. Jose Polanco
  29. Levy A. Sominsky
  30. Komal Srivastava
  31. Huanzhi Shi
  32. Liyong Cao
  33. Feng Chen
  34. Jian Zhang
  35. Zain Khalil

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Abstract

The continued circulation of SARS-CoV-2 amid limited surveillance efforts and inconsistent vaccination of populations has resulted in the emergence of variants that uniquely impact public health systems. Thus, in conjunction with functional and clinical studies, continuous detection and identification are quintessential to informing diagnostic and public health measures.

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  1. Version published to 10.1128/spectrum.01736-22
  2. ScreenIT

    SciScore for 10.1101/2022.05.28.22275691: (What is this?)

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

    Table 1: Rigor

    EthicsIRB: For specimens from Colombia, the study was reviewed and approved by the ethics Committee from Universidad del Rosario in Bogotá, Colombia (Act number DVO005 1550-CV1499).
    Field Sample Permit: SARS-CoV-2 specimen collection and testing: Residual viral RNA from a total of 391 specimens that were previously collected from September 2, 2020 – March 2, 2022 for routine diagnostic testing were utilized for this study.
    Sex as a biological variablenot detected.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Cell Line AuthenticationAuthentication: viral RNA from MSHS underwent RT-PCR and next-generation sequencing followed by genome assembly and lineage assignment using a phylogenetic-based nomenclature as described by Rambaut et al. (36) using the Pangolin v4.0.6 tool and PANGO-v1.2.81 nomenclature scheme (https://github.com/cov-lineages/pangolin) as previously described (4, 37)

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    Ethics statement: For specimens obtained through routine testing at MSHS, the Mount Sinai Pathogen Surveillance Program was reviewed and approved by the Human Research Protection Program at the Icahn School of Medicine at Mount Sinai (ISMMS) (HS#13-00981).
    Human Research Protection Program
    suggested: None
    SARS-CoV-2 sequencing, assembly, and phylogenetics: As part of the ongoing Mount Sinai Pathogen Surveillance Program,
    Pathogen Surveillance Program
    suggested: None
    Briefly, long-read Oxford Nanopore MinION sequencing was conducted by the MinKNOW application (v1.5.5).
    MinKNOW
    suggested: None
    Reads were filtered to remove possible chimeric reads, and genome assemblies were obtained following the MinION pipeline described in the ARTIC bioinformatics pipeline (https://artic.network/ncov-2019/ncov2019-bioinformatics-sop.html accessed on 1 February 2021).
    MinION
    suggested: (MinION, RRID:SCR_017985)
    To measure the level of agreement between WGS and the variant panel, we performed agreement analyses with kappa (κ) results and 95% confidence intervals (95% CI) using the publicly-available GraphPad Prism web calculator (https://graphpad.com/quickcalcs/kappa2/, last accessed April 20, 2022).
    WGS
    suggested: None
    GraphPad
    suggested: (GraphPad Prism, RRID:SCR_002798)
    Display Items: All figures are original and were generated using the GraphPad Prism software, Microsoft Excel v16.60, and finished in Adobe Illustrator (v.26.1).
    GraphPad Prism
    suggested: (GraphPad Prism, RRID:SCR_002798)
    Microsoft Excel
    suggested: (Microsoft Excel, RRID:SCR_016137)
    Adobe Illustrator
    suggested: (Adobe Illustrator, RRID:SCR_010279)
    Fig. 1A was created in BioRender.com and finished in Adobe Illustrator.
    BioRender
    suggested: (Biorender, RRID:SCR_018361)

    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: We detected the following sentences addressing limitations in the study:
    Our study does present some limitations particularly with respect to limited sampling. While the panel has defined target signatures for 16 different variants, we were only able to recover clinical specimens that corresponded to 11 of these variants for testing. Indeed, variants with the lowest level of agreement and diagnostic performance metrics were those with some of the fewest specimens recovered and tested (e.g., Zeta (n = 1), Beta (n = 4), Eta (n = 7). We also did not include specimens from the early phase of the pandemic including D614 viruses (45, 46) which limited diagnostic analyses of the D614G variant and individual target. It is important to note, however, that the D614G polymorphism has undergone positive selection to eventuate emergent variants (47), and these older viruses have largely been replaced by the emergent Omicron lineage(s) (6, 48). We also recognize that we did not conduct this study at the extraction step of clinical specimens given limited availability of remnant upper respiratory or saliva specimens. A unique benefit of a highly multiplexed molecular assay is its adaptability to the natural evolution of the pathogen at hand which confers the ability to identify changes in circulating viruses that manifest as distinct target result signatures. To assess this potential, we included undefined variants to determine if the discrete assay target result patterns could elucidate a variant’s identity without necessarily providing a defined result as the ...

    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.

  3. Version published to 10.1101/2022.05.28.22275691v1 on medRxiv