A modular framework for the development of targeted Covid-19 blood transcript profiling panels

  1. Darawan Rinchai
  2. Basirudeen Syed Ahamed Kabeer
  3. Mohammed Toufiq
  4. Zohreh Tatari-Calderone
  5. Sara Deola
  6. Tobias Brummaier
  7. Mathieu Garand
  8. Ricardo Branco
  9. Nicole Baldwin
  10. Mohamed Alfaki
  11. Matthew C. Altman
  12. Alberto Ballestrero
  13. Matteo Bassetti
  14. Gabriele Zoppoli
  15. Andrea De Maria
  16. Benjamin Tang
  17. Davide Bedognetti
  18. Damien Chaussabel

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Abstract

Background

Covid-19 morbidity and mortality are associated with a dysregulated immune response. Tools are needed to enhance existing immune profiling capabilities in affected patients. Here we aimed to develop an approach to support the design of targeted blood transcriptome panels for profiling the immune response to SARS-CoV-2 infection.

Methods

We designed a pool of candidates based on a pre-existing and well-characterized repertoire of blood transcriptional modules. Available Covid-19 blood transcriptome data was also used to guide this process. Further selection steps relied on expert curation. Additionally, we developed several custom web applications to support the evaluation of candidates.

Results

As a proof of principle, we designed three targeted blood transcript panels, each with a different translational connotation: immunological relevance, therapeutic development relevance and SARS biology relevance.

Conclusion

Altogether the work presented here may contribute to the future expansion of immune profiling capabilities via targeted profiling of blood transcript abundance in Covid-19 patients.

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  1. Version published to 10.1186/s12967-020-02456-z
  2. ScreenIT

    SciScore for 10.1101/2020.05.20.107243: (What is this?)

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

    Table 1: Rigor

    NIH rigor criteria are not applicable to paper type.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    FASTQ passed QC and were aligned to reference genome GRChg38/hg19 using Hisat2 (v2.05).
    Hisat2
    suggested: (HISAT2, RRID:SCR_015530)
    Raw expression data was corrected for within lane and between lane effects using R package EDASeq (v2.12.0) and quantile normalized using preprocessCore (v1.36.0).
    EDASeq
    suggested: (EDASeq, RRID:SCR_006751)
    A detailed description and source code will be available as part of a separate publication BioRxiv deposition on GitHub and BioRxiv (in preparation).
    BioRxiv
    suggested: (bioRxiv, RRID:SCR_003933)

    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.

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  3. Version published to 10.21203/rs.3.rs-33274/v1 on Research Square
  4. Version published to 10.1101/2020.05.20.107243v1 on bioRxiv