McQ – An open-source multiplexed SARS-CoV-2 quantification platform

  1. Sibylle C Vonesch
  2. Danila Bredikhin
  3. Nikolay Dobrev
  4. Laura Villacorta
  5. Rozemarijn Kleinendorst
  6. Elisabetta Cacace
  7. Julia Flock
  8. Max Frank
  9. Ferris Jung
  10. Julia Kornienko
  11. Karin Mitosch
  12. Mireia Osuna-López
  13. Jürgen Zimmermann
  14. Stephan Göttig
  15. Axel Hamprecht
  16. Hans-Georg Kräusslich
  17. Michael Knop
  18. Athanasios Typas
  19. Lars M Steinmetz
  20. Vladimir Benes
  21. Kim Remans
  22. Arnaud R Krebs

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Abstract

McQ is a SARS-CoV-2 quantification assay that couples early-stage barcoding with high-throughput sequencing to enable multiplexed processing of thousands of samples. McQ is based on homemade enzymes to enable low-cost testing of large sample pools, circumventing supply chain shortages.

Implementation of cost-efficient high-throughput methods for detection of RNA viruses such as SARS-CoV-2 is a potent strategy to curb ongoing and future pandemics. Here we describe Multiplexed SARS-CoV-2 Quantification platform (McQ), an in-expensive scalable framework for SARS-CoV-2 quantification in saliva samples. McQ is based on the parallel sequencing of barcoded amplicons generated from SARS- CoV-2 genomic RNA. McQ uses indexed, target-specific reverse transcription (RT) to generate barcoded cDNA for amplifying viral- and human-specific regions. The barcoding system enables early sample pooling to reduce hands-on time and makes the ap-proach scalable to thousands of samples per sequencing run. Robust and accurate quantification of viral load is achieved by measuring the abundance of Unique Molecular Identifiers (UMIs) introduced during reverse transcription. The use of homemade reverse transcriptase and polymerase enzymes and non-proprietary buffers reduces RNA to library reagent costs to 92 cents/sample and circumvents potential supply chain short-ages. We demonstrate the ability of McQ to robustly quantify various levels of viral RNA in 838 clinical samples and accu-rately diagnose positive and negative control samples in a test-ing workflow entailing self-sampling and automated RNA ex-traction from saliva. The implementation of McQ is modular, scalable and could be extended to other pathogenic targets in future.

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  1. ScreenIT

    SciScore for 10.1101/2020.12.02.20242628: (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.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    For downsampled data, we sampled a fraction of reads per plate at random with sam-tools 59 command samtools view -s.
    samtools
    suggested: (SAMTOOLS, RRID:SCR_002105)
    Using the EMBL cluster (run on Intel Xeon Gold 6136 CPU and 32 Gb of RAM), this pipeline allows generating count matrices in under 10 minutes for a sequencing run.
    EMBL
    suggested: (ChEMBL, RRID:SCR_014042)

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

  2. Version published to 10.1101/2020.12.02.20242628v1 on medRxiv