The protein expression profile of ACE2 in human tissues

  1. Feria Hikmet
  2. Loren Méar
  3. Åsa Edvinsson
  4. Patrick Micke
  5. Mathias Uhlén
  6. Cecilia Lindskog

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Abstract

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  1. Version published to 10.15252/msb.20209610
  2. ScreenIT

    SciScore for 10.1101/2020.03.31.016048: (What is this?)

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

    Table 1: Rigor

    Institutional Review Board StatementIRB: All samples were anonymized for personal identity by following the approval and advisory report from the Uppsala Ethical Review Board (Ref # 2002-577, 2005-388, 2007-159, 2011-473).
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variableblot: Protein extracts were generated from fresh frozen lung (male/female), kidney, colon, tonsil using a ProteoExtract Complete Mammalian Proteome Extraction Kit (cat#539779

    Table 2: Resources

    Antibodies
    SentencesResources
    Primary antibodies towards human ACE2 were the polyclonal rabbit IgG antibody HPA000288, RRID: AB_1078160, (Atlas Antibodies AB, Bromma, Sweden) and monoclonal mouse IgG antibody MAB933, RRID: AB_2223153, (R&D Systems, Minneapolis, MN).
    ACE2 the polyclonal
    detected: (Atlas Antibodies Cat# HPA000288, RRID:AB_1078160)
    mouse IgG
    detected: (R and D Systems Cat# MAB933, RRID:AB_2223153)
    The membranes were incubated with two different primary antibodies towards ACE2: HPA000288 (Atlas Antibodies AB) and MAB933 (R&D Systems).
    ACE2
    suggested: (Sigma-Aldrich Cat# HPA000288, RRID:AB_1078160)
    HRP-conjugated antibodies were used as secondary antibodies (Swine anti-rabbit 1:3000
    anti-rabbit
    suggested: None
    Data availability: High-resolution images corresponding to immunohistochemically stained TMA cores from three individuals in 44 different tissue types are readily available in version 19.3 of the Human Protein Atlas (https://www.proteinatlas.org), using both the HPA000288 antibody and the MAB933 antibody.
    MAB933
    suggested: None
    Software and Algorithms
    SentencesResources
    Data sources: RNA expression data from HPA(Uhlen et al., 2015), GTEx(Keen & Moore, 2015), FANTOM5(Yu et al., 2015) as well as the normalized RNA expression dataset were retrieved from the HPA database (http://www.proteinatlas.org).
    http://www.proteinatlas.org
    suggested: (HPA, RRID:SCR_006710)
    The lung scRNA-seq data (gene raw counts) were downloaded from the Gene Expression Omnibus (GEO) (https://www.ncbi.nlm.nih.gov/geo/) database under the series numbers: GSE130148 (Viera Braga, 2019), GSE134355 (Han et al., 2020) (samples: GSM4008628, GSM4008629, GSM4008630, GSM4008631, GSM4008632 and GSM4008633) and GSE122960 (Reyfman et al., 2019) (samples: GSM3489182, GSM3489185, GSM3489187, GSM3489189, GSM3489191, GSM3489193, GSM3489195 and GSM3489197).
    Gene Expression Omnibus
    suggested: (Gene Expression Omnibus (GEO, RRID:SCR_005012)
    ACE2 mass spectrometry-based expression among different tissues was assessed by retrieving data in two proteomics databases: Protein abundance database (PaxDB) (Wang et al, 2015) and ProteomicsDB (Schmidt et al, 2018).
    ProteomicsDB
    suggested: (ProteomicsDB, RRID:SCR_015562)
    The gene expression data were normalized using Seurat default settings, and cell clustering was based on the 5,000 most highly variable genes.
    Seurat
    suggested: (SEURAT, RRID:SCR_007322)

    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:
    Technical limitations related to the different techniques used for tissue dissociation result in a lower amount of starting material for sequencing. The proportions of different cell types analyzed within a tissue may be biased and not reflect the true biological proportions, due to some cell types being less tolerant for dissociation. It may also lead to the “drop out” phenomenon, where a gene showing high expression levels in a certain cell lacks expression in other cells corresponding to the same cell type (Haque et al, 2017). Other limitations include challenges related to interpretation and analysis of the complex datasets, involving quality control for removing low-quality scRNA-seq data, normalization and manual annotation of cell clusters. It is also important to note that scRNA-seq data generated by different methods or platforms may lead to batch effects. To be able to compare expression data based on scRNA-seq datasets from different sources across diverse cell and tissue types in a consistent way, the global Human Cell Atlas effort promotes standardization of methods and strategies used for both scRNA-seq protocols and data analysis (Ding et al, 2020; Mereu, 2020). Further advances in this emerging field are likely to lead to more refined maps on the single cell transcriptomes of different human tissues and organs in health and disease. As various methods for mRNA and protein detection have different advantages and disadvantages, an integrated omics approach combi...

    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.

  3. Version published to 10.1101/2020.03.31.016048v2 on bioRxiv
  4. Version published to 10.1101/2020.03.31.016048v1 on bioRxiv