T cell receptor sequencing identifies prior SARS-CoV-2 infection and correlates with neutralizing antibodies and disease severity

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Abstract

No abstract available

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  1. SciScore for 10.1101/2021.03.19.21251426: (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

    No key resources detected.


    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:
    We acknowledge several limitations in the present study, including smaller sample sizes at later timepoints analyzed, restriction of the cohort to symptomatic cases only, and limited ethnic diversity of the sample population. Nevertheless, these results collectively indicate that analysis of the TCR repertoire from small-volume standard blood samples may be a useful surrogate for evaluating past infection and immune protection months after COVID-19 illness that could overcome several challenges in nAb and serologic testing, including labor intensity, biohazard risks, scalability, incomplete or absent antibody signal in non-severe illness, or limited antibody durability over time. Future studies across diverse populations and longer-term follow-up are needed to better define the nature and duration of the detectable T-cell response and its utility as a biomarker for assessing both natural and vaccine-mediated immunity.

    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.