1. SciScore for 10.1101/2021.07.30.21261234: (What is this?)

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

    Table 1: Rigor

    EthicsIRB: The protocol was approved CALHN Human Research Ethics Committee, Adelaide,
    Consent: Inclusion criteria were PCR-confirmed SARS-CoV-2 infection from nasopharyngeal swabs, the ability to attend study follow up visits, and voluntary informed consent.
    Sex as a biological variableA total of 69 COVID-19 convalescent individuals (35 male, 36 female) representing a range of prior mild, moderate, severe, critical COVID-19 cases were recruited (Table S1).
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Cell Line Authenticationnot detected.

    Table 2: Resources

    Secondary antibodies were diluted in 5% skim milk in PBST as follows: Goat anti-Human IgG (H+L) Secondary Antibody, HRP (1:30,000;
    anti-Human IgG
    suggested: None
    Sigma): anti-human IgA HRP antibody (1:5,000; Sigma) and incubated for 1 hour at room temperature.
    anti-human IgA
    suggested: None
    Pearson correlation analysis was performed using the Hmisc v4.4-2 package in R to determine correlations between anti-Spike and anti-RBD antibody titres, flow cytometry data and BTM activity scores.
    suggested: None
    suggested: None
    Experimental Models: Cell Lines
    SARS-CoV-2 protein purification and ELISA: Prefusion SARS-CoV-2 ectodomain (isolate WHU1, residues1-1208) with HexaPro mutations (76) (kindly provided by Adam Wheatley) and SARS-Cov-2 receptor-binding domain (RBD) with C-terminal His-tag (77) (residues 319-541; kindly provided by Florian Krammer) were overexpressed in Expi293 cells and purified by Ni-NTA affinity and size- exclusion chromatography.
    suggested: RRID:CVCL_D615)
    Software and Algorithms
    AUC calculation was performed using Prism GraphPad, where the X-axis is half log10 of sera dilution against OD450 on Y-axis.
    suggested: (GraphPad Prism, RRID:SCR_002798)
    Compensation was set with beads matched to each panel antibody combination using spectral compensation using FlowJo Software V10.
    suggested: (FlowJo, RRID:SCR_008520)
    RNA-Seq analysis: Sequence read quality was assessed using FastQC version 0.11.4 (78) and summarised with MultiQC version 1.8 (79) prior to quality control with Trimmomatic version 0.38 (80) with a window size of 4 nucleotides and an average quality score of 25.
    suggested: (FastQC, RRID:SCR_014583)
    suggested: (MultiQC, RRID:SCR_014982)
    suggested: (Trimmomatic, RRID:SCR_011848)
    Reads that passed all quality control steps were then aligned to the mouse genome (GRCh38 assembly) using HISAT2 version 2.1.0 (81).
    suggested: (HISAT2, RRID:SCR_015530)
    The gene count matrix was generated with FeatureCounts version 1.5.0-p2 (82) using the union model with Ensembl version 101 annotation.
    suggested: (featureCounts, RRID:SCR_012919)
    suggested: (Ensembl, RRID:SCR_002344)
    The count matrix was then imported into R version 4.0.3 for further analysis and visualisation in ggplot2 v2.3.3.
    suggested: (ggplot2, RRID:SCR_014601)
    To assess if differential gene expression was primarily driven by differences in the proportion of any major immune cell population (i.e. LD granulocytes, LD neutrophils, CXCR3+ neutrophils, monocytes, lymphocytes, CD56++ NK cells, CD19+ B cells, CD3+ T cells, NKT cells, CD4+ T cells or CD8+ T cells), we additionally fit the frequency of each population in each individual into the EdgeR model and reperformed the differential gene expression and pathway overrepresentation analysis.
    suggested: (edgeR, RRID:SCR_012802)
    Gene Set Variation Analysis (GSVA) (44) was used to calculate a per sample activity score for each of the modules (excluding unannotated modules labelled as ‘TBA’). limma v3.46.0 was used to identify modules that were differentially active in at least one timepoint.
    suggested: (LIMMA, RRID:SCR_010943)
    Correlation networks were exported to Cytoscape v3.8.1 for visualisation.
    suggested: (Cytoscape, RRID:SCR_003032)

    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.

    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.

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