1. SciScore for 10.1101/2021.10.09.463766: (What is this?)

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

    Table 1: Rigor

    Ethicsnot detected.
    Sex as a biological variablenot detected.
    RandomizationCell counting was performed manually on random sections from different organoids, with a similarly sized area.
    Blindingnot detected.
    Power Analysisnot detected.
    Cell Line Authenticationnot detected.

    Table 2: Resources

    Antibodies
    SentencesResources
    The sections were blocked and permeabilized with 10% normal donkey serum (Abcam) and 0.3% Triton X in PBS for 1 hour at room temperature (RT), and then incubated with the primary antibodies diluted in blocking medium overnight at 4°C.
    normal donkey serum ( Abcam )
    suggested: None
    ACE2 blocking: Retinal organoids on day 90 of differentiation were incubated at RT for 1 hour in PBS with either a goat anti-ACE2 antibody (AF933, R&D Systems) or a normal goat IgG control (AB-108-C, R&D Systems), at a concentration of 100 μg/mL before being infected with SARS-CoV-2.
    anti-ACE2
    suggested: None
    IgG
    suggested: (R and D Systems Cat# AB-108-C, RRID:AB_354267)
    Experimental Models: Cell Lines
    SentencesResources
    The virus was propagated on VeroE6-TMPRSS2 cells using DMEM (Sigma) supplemented with 2% (v/v)
    VeroE6-TMPRSS2
    suggested: JCRB Cat# JCRB1819, RRID:CVCL_YQ49)
    Plaque assay: VeroE6 cells were grown to a confluent monolayer in 6-well plates.
    VeroE6
    suggested: JCRB Cat# JCRB1819, RRID:CVCL_YQ49)
    Software and Algorithms
    SentencesResources
    DNA library quality and concentration were analyzed on an Agilent Bioanalyzer DNA Chip.
    Agilent Bioanalyzer
    suggested: None
    Bioinformatic analysis: The sequencing data was first demultiplexed using the Illumina software bcl2fastq v2.20.0.
    bcl2fastq
    suggested: (bcl2fastq , RRID:SCR_015058)
    The quality of the resulting FASTQ files was then evaluated with the program FastQC v0.11.8 (https://www.bioinformatics.babraham.ac.uk/projects/fastqc/).
    FastQC
    suggested: (FastQC, RRID:SCR_014583)
    Finally, the alignment was performed using STAR v2.7.7a (Dobin et al., 2013) with default parameters and the extra instruction “–-quantMode GeneCounts” to create additional gene count tables.
    STAR
    suggested: (STAR, RRID:SCR_004463)
    The following secondary analysis was performed in the statistical environment R v4.0.3 (https://www.r-project.org/).
    https://www.r-project.org/
    suggested: (R Project for Statistical Computing, RRID:SCR_001905)
    Differential gene expression was assessed based on a Negative Binomial distribution through the package DEseq2 v1.30.1 (Love et al., 2014).
    DEseq2
    suggested: (DESeq2, RRID:SCR_015687)
    Heatmap representation of the expression of selected genes was created with the package pheatmap v1.0.12 using the values obtained after applying the regularized log transformation over the raw counts.
    pheatmap
    suggested: (pheatmap, RRID:SCR_016418)
    GO and KEGG pathway analyses were performed by separately collecting all upregulated genes with a fold change of ≥2 and all downregulated genes with a fold change of ≤ −2 and uploading the gene lists to the Enrichr software (Chen et al., 2013; Kuleshov et al., 2016).
    KEGG
    suggested: (KEGG, RRID:SCR_012773)
    Enrichr
    suggested: (Enrichr, RRID:SCR_001575)
    Statistical analysis: Statistical analysis was done using GraphPad Prism 9.
    GraphPad Prism
    suggested: (GraphPad Prism, RRID:SCR_002798)

    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:
    While our study indicates that SARS-CoCV-2 can infect and replicate in retinal cells in organoids, our experimental system has some limitations. Retinal organoids contain some RPE tissue together with neuro-retinal tissue but are devoid of other important tissues such as the cornea and the retinal vasculature present in the physiological environment (Achberger et al., 2019). This limits the ability to use the retinal organoid model to study the entry and route of SARS-CoV-2 infection in the retina and the interaction between the retina and other tissues during infection. Moreover, retinal organoids lack specialized immune cells such as microglia, which have an important role in retinal inflammation (Rashid et al., 2019), and thus cannot be used to fully characterize the retinal inflammatory response to SARS-CoV-2. Retinal organoids also represent the embryonic retina rather than a mature retina, and thus some age-related differences might affect retinal SARS-CoV-2 infection and pathology. Yet, the fact that SARS-CoV-2 also does infect relatively mature neurons and cause the upregulation of inflammatory genes in our organoids, combined with the growing evidence of retinal involvement in patients with COVID-19, indicates the relevance of the data produced from our retinal organoids and the need for further investigations into COVID-19–related retinal pathologies.

    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: Please consider improving the rainbow (“jet”) colormap(s) used on pages 27, 29, 30 and 31. At least one figure is not accessible to readers with colorblindness and/or is not true to the data, i.e. not perceptually uniform.


    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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