Digital spatial profiling of collapsing glomerulopathy

  1. Kelly D. Smith
  2. David K. Prince
  3. Kammi J. Henriksen
  4. Roberto F. Nicosia
  5. Charles E. Alpers
  6. Shreeram Akilesh

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Abstract

No abstract available

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  1. Version published to 10.1016/j.kint.2022.01.033
  2. ScreenIT

    SciScore for 10.1101/2021.09.08.459502: (What is this?)

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

    Table 1: Rigor

    EthicsIRB: Slide preparation for spatial profiling: This study was approved by the University of Washington and the University of Chicago’s institutional review boards.
    Sex as a biological variablenot detected.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.

    Table 2: Resources

    Antibodies
    SentencesResources
    The next day, following stringency washes and blocking, fluorescently labeled antibodies to detect CD45 (Novus, 2B11+PD7/26, #NBP2-34528AF647), smooth muscle actin (SMA; Abcam, 1A4, ab202368), pan-cytokeratin (nanoString, AE1/AE3, #121301310) and a DNA binding dye (ThermoFisher, Syto13, S7575) were applied to the slides for 1 hour at room temperature.
    CD45
    suggested: (Novus Cat# NB100-65096, RRID:AB_960384)
    #NBP2-34528AF647), smooth muscle actin
    suggested: None
    pan-cytokeratin
    suggested: None

    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:
    The main limitation of our study is the small number of samples; however, this is inherent to studying rare entities such as collapsing glomerulopathy. For comparison even Nephroseq, a large database with >2,100 patient datasets, only has expression data from 6 patients with collapsing glomerulopathy, the same number as in our study (www.nephroseq.org, accessed July 2021, University of Michigan, Ann Arbor, MI). Furthermore, the ability to correlate a single glomerulus’ histology to its expression profile is not technically possible with laser capture microdissection which is the most common method used in the Nephroseq dataset. Another shortcoming is the small number of genes available in the Cancer Transcriptome Atlas probeset that was available at the time we performed this study (1,825 cancer and immunology-focused genes). Since then, a Whole Transcriptome Atlas probeset encompassing ∼18,000 protein coding genes has been released which will enable whole transcriptome analyses of disease processes in kidney biopsies. Other limitations of our study include validation failures if glomeruli were not preserved on deeper levels used for RNA-ISH or immunohistochemistry and difficulty in establishing causality and timing of mechanisms, which is common in all human biopsy-based studies. However, even with these limitations, we uncovered remarkable heterogeneity in glomerular expression patterns by disease and histology using DSP. Importantly, we found that there was good correlatio...

    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.

  3. Version published to 10.1101/2021.09.08.459502v1 on bioRxiv