Targeted proteomics as a tool to detect SARS-CoV-2 proteins in clinical specimens

  1. Karel Bezstarosti
  2. Mart M. Lamers
  3. Wouter A. S. Doff
  4. Peter C. Wever
  5. Khoa T. D. Thai
  6. Jeroen J. A. van Kampen
  7. Bart L. Haagmans
  8. Jeroen A. A. Demmers

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Abstract

The rapid, sensitive and specific detection of SARS-CoV-2 is critical in responding to the current COVID-19 outbreak. In this proof-of-concept study, we explored the potential of targeted mass spectrometry (MS) based proteomics for the detection of SARS-CoV-2 proteins in both research samples and clinical specimens. First, we assessed the limit of detection for several SARS-CoV-2 proteins by parallel reaction monitoring (PRM) MS in infected Vero E6 cells. For tryptic peptides of Nucleocapsid protein, the limit of detection was estimated to be in the mid-attomole range (9E-13 g). Next, this PRM methodology was applied to the detection of viral proteins in various COVID-19 patient clinical specimens, such as sputum and nasopharyngeal swabs. SARS-CoV-2 proteins were detected in these samples with high sensitivity in all specimens with PCR Ct values <24 and in several samples with higher CT values. A clear relationship was observed between summed MS peak intensities for SARS-CoV-2 proteins and Ct values reflecting the abundance of viral RNA. Taken together, these results suggest that targeted MS based proteomics may have the potential to be used as an additional tool in COVID-19 diagnostics.

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  1. PREreview

    This Zenodo record is a permanently preserved version of a PREreview. You can view the complete PREreview at https://prereview.org/reviews/7708190.

    Targeted proteomics as a tool to detect SARS-CoV-2 proteins in clinical specimens

    by Karel Bezstarosti, Mart M. Lamers, Wouter A.S. Doff, Peter Wever, Khoa Thai, Jeroen J. A. van Kampen, Bart L. Haagmans, and Jeroen A. A. Demmers

    In this study, Karel Bezstarosti et al tried to develop a diagnostic tool for the detection of SARS-CoV-2 infection using clinical specimens such as nasopharyngeal swabs, mucus, and sputum. This method is able to detect various viral peptide fragments with high sensitivity. There are a few points that I would like to highlight in this study:

    1. This technique detects multiple peptide fragments of viral proteins, which makes it reliable in the case of mutation and evolution in strains. However, this requires constant updates of the protein database if a single peptide is used for diagnosis.

    2. They have mentioned that further optimization is required with a larger sample size, but with the given samples, specifically cohort 1, that had SARS-CoV-2 positive samples, they could not detect viral peptides in all the samples. This indicates that even the current method needs further optimization.

    3. The sample transport, neutralization (to get clearance from BSL 3), and processing for proteomics need to be further streamlined as different collection kits have introduced variability in the given study (cohort 2).

    4. Considering the pandemic scenario, the sample size, and the heterogeneity of the samples, the reliability of this method will immediately raise questions.

    5. Compared to the traditional RT-PCR based method, the results are inconsistent even with a small sample size. There were a considerable number of false negatives reported from the test samples.

    6. The author has not provided sufficient explanation and strategies that could be used to improve this method.

    7. There was no explanation in a case where the AQUA internal standard could not be detected despite having the same extraction protocol.

    8. For the data presentation they could have plotted the AUC in log scale so that in case of low intensity signal the data visualisation could have been easier for the reader.

    9. In the discussion section, paragraph 4, the point where it is mentioned that RNA could be present without the capsule protein and are present even after infection needs to be supported by literature, as we know proteins are generally more stable and tend to remain in the circulation for longer than single-stranded RNA.

    In terms of logistics, this method seems to be less cost-efficient and the investment required to setup testing centers with such facilities is beyond scope for many third-world countries. Otherwise the method seems to have a promising prospect. It does provide avenues for covid related studies and for the study and detection of rare diseases in the population. These sorts of ideas do add a new dimension when we consider the trend in public health.

    Competing interests

    The author declares that they have no competing interests.

  2. Version published to 10.1371/journal.pone.0259165
  3. Version published to 10.1101/2020.04.23.057810v3 on bioRxiv
  4. Version published to 10.1101/2020.04.23.057810v2 on bioRxiv
  5. ScreenIT

    SciScore for 10.1101/2020.04.23.057810: (What is this?)

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

    Table 1: Rigor

    Institutional Review Board Statementnot detected.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.
    Cell Line Authenticationnot detected.

    Table 2: Resources

    Experimental Models: Cell Lines
    SentencesResources
    Virus and cells: Vero E6 cells were maintained in Dulbecco’s modified Eagle’s medium (DMEM, Gibco) supplemented with 10 % fetal calf serum (FCS), HEPES, sodium bicabonate, penicillin (final concentration 100 IU/mL) and streptomycin (final concentration 100 IU/mL) at 37 °C in a humidified CO2 incubator.
    Vero E6
    suggested: None
    Software and Algorithms
    SentencesResources
    A slurry of 10 μg of Sera-Mag speedbeads (GE Healtcare) in 20 μl milliQ/ethanol (1:1, vol/vol) was added to the solution and mixed for 10 min at RT.
    GE Healtcare
    suggested: None
    Data analysis: Mass spectrometry data were analyzed using Mascot v 2.6.2 within the Proteome Discoverer v 2.3 (PD, ThermoFisher Scientific) framework or with MaxQuant v 1.6.10.43 (www.maxquant.org), all with standard settings (note: fragment tolerance set to 20 ppm).
    Mascot
    suggested: (Mascot, RRID:SCR_014322)
    Proteome Discoverer
    suggested: (Proteome Discoverer, RRID:SCR_014477)
    MaxQuant
    suggested: (MaxQuant, RRID:SCR_014485)
    PRM data were analyzed with Skyline (skyline.ms).
    Skyline
    suggested: (Skyline, RRID:SCR_014080)
    For global proteome analyses the UniprotKB SARS2 database (https://covid-19.uniprot.org/; 14 entries; May 2020) was concatenated with the UniprotKB database, taxonomy Chlorocebus (African green monkey) or taxonomy Homo sapiens (version Oct 2019).
    UniprotKB
    suggested: (UniProtKB, RRID:SCR_004426)

    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.
    • No funding statement was detected.
    • 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.

  6. Version published to 10.1101/2020.04.23.057810v1 on bioRxiv