SARS-CoV-2 infection of human iPSC–derived cardiac cells reflects cytopathic features in hearts of patients with COVID-19

  1. Juan A. Perez-Bermejo
  2. Serah Kang
  3. Sarah J. Rockwood
  4. Camille R. Simoneau
  5. David A. Joy
  6. Ana C. Silva
  7. Gokul N. Ramadoss
  8. Will R. Flanigan
  9. Parinaz Fozouni
  10. Huihui Li
  11. Pei-Yi Chen
  12. Ken Nakamura
  13. Jeffrey D. Whitman
  14. Paul J. Hanson
  15. Bruce M. McManus
  16. Melanie Ott
  17. Bruce R. Conklin
  18. Todd C. McDevitt

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Abstract

Infection of human iPSC–derived cardiomyocytes by SARS-CoV-2 leads to specific cytopathic features reflected in patient autopsy samples.

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  1. Version published to 10.1126/scitranslmed.abf7872
  2. ScreenIT

    SciScore for 10.1101/2020.08.25.265561: (What is this?)

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

    Table 1: Rigor

    Institutional Review Board Statementnot detected.
    RandomizationEach datapoint represents the normalized sum of counts for nine randomly acquired fields of view in a separate well using high magnification (40x).
    BlindingAnalysis of immunofluorescence images: Immunostained images of cells were coded and manually counted by four blinded individuals, with a 20% overlap for concordance.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.
    Cell Line Authenticationnot detected.

    Table 2: Resources

    Antibodies
    SentencesResources
    On day 8, all cells were cryo-preserved and a fraction of ECs were assayed for >95% purity by flow cytometry using antibodies against mature EC markers CD31 and CDH5.
    antibodies against mature EC markers CD31
    suggested: None
    CDH5
    suggested: None
    Experimental Models: Cell Lines
    SentencesResources
    Briefly, virus was diluted 1:102-1:106 and incubated for 1 hour on Vero cells before an overlay of Avicel and complete DMEM (Sigma Aldrich, SLM-241) was added.
    Vero
    suggested: None
    Software and Algorithms
    SentencesResources
    Samples were demultiplexed and aligned to GRCh38 with CellRanger v3.0.2.
    CellRanger
    suggested: None
    Individual cell UMIs were filtered using Seurat v3.2.05, keeping only cells with at least 1,000 reads, 300 detected genes, and less than 10% mitochondrial reads.
    Seurat
    suggested: (SEURAT, RRID:SCR_007322)
    Molecular Devices) and processed using ZenBlue and ImageJ.
    ImageJ
    suggested: (ImageJ, RRID:SCR_003070)
    Bioinformatic analyses of transcriptomic data: Samples were demultiplexed using bcl2fastq v2.20.0 and aligned to both GRCh38 and the SARS-CoV-2 reference sequence (NC_045512) using hisat2 v2.1.08
    bcl2fastq
    suggested: (bcl2fastq , RRID:SCR_015058)
    hisat2
    suggested: (HISAT2, RRID:SCR_015530)
    Aligned reads were converted to counts using featureCounts v1.6.29.
    featureCounts
    suggested: (featureCounts, RRID:SCR_012919)
    Cell-type clustering, gene loadings, and technical replication were assessed using the PCA and MDS projections implemented in scikit-learn v0.2310.
    scikit-learn
    suggested: (scikit-learn, RRID:SCR_002577)
    Differential expression analysis was performed using edgeR v3.30.211 with limma/voom v3.44.3 normalization12 and GO term enrichment analysis was performed using clusterProfiler v3.16.013.
    edgeR
    suggested: (edgeR, RRID:SCR_012802)
    limma/voom
    suggested: None
    clusterProfiler
    suggested: (clusterProfiler, RRID:SCR_016884)
    Pathways for sarcomere organization and the LINC complex were adapted from WikiPathways (WP383 and WP4535 respectively) using Cytoscape v2.8.014.
    WikiPathways
    suggested: (WikiPathways, RRID:SCR_002134)
    Cytoscape
    suggested: (Cytoscape, RRID:SCR_003032)
    The sections were imaged using a Tecnai 12 120kV TEM (FEI, Hillsboro, OR, USA), data recorded using an UltraScan 1000 with Digital Micrograph 3 software (Gatan Inc., Pleasanton, CA, USA), and montaged datasets were collected with SerialEM (bio3d.colorado.edu/SerialEM) and reconstructed using IMOD eTOMO (bio3d.colorado.edu/imod).
    SerialEM
    suggested: (SerialEM, RRID:SCR_017293)
    Nuclei counts were performed automatically using the EBImage package15 on R16.
    EBImage
    suggested: None

    Results from OddPub: Thank you for sharing your 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.

    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 10.1101/2020.08.25.265561v2 on bioRxiv
  4. Version published to 10.1101/2020.08.25.265561v1 on bioRxiv