Human airway lineages derived from pluripotent stem cells reveal the epithelial responses to SARS-CoV-2 infection

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1. Version published to 10.1152/ajplung.00397.2021

   Mar 1, 2022
2. 

   ScreenIT

   Jul 9, 2021

   SciScore for 10.1101/2021.07.06.451340: (What is this?)

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

   Table 1: Rigor

   Ethics	not detected.
   Sex as a biological variable	not detected.
   Randomization	not detected.
   Blinding	Manual quantification of N+ cells were performed by distributing 10-15 images of iPSC-airway infected with SARS-CoV-2 stained with DAPI and N (>200 cells/image) to 5 blinded scorers.
   Power Analysis	not detected.
   Cell Line Authentication	not detected.

   Table 2: Resources

   Antibodies
   Sentences	Resources
   The antibodies used were; anti-SARS-CoV nucleoprotein (N) antibody (rabbit polyclonal, 1:2500, Rockland Immunochemicals, Cat #200-401-A50), anti-α-TUBULIN antibody (mouse monoclonal, 1:500 sigma cat# T6199), and anti-MUC5B antibody (mouse monoclonal, Santa Cruz Biotechnology, 1:500	anti-SARS-CoV nucleoprotein ( N ) sugges…

   SciScore for 10.1101/2021.07.06.451340: (What is this?)

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

   Table 1: Rigor

   Ethics	not detected.
   Sex as a biological variable	not detected.
   Randomization	not detected.
   Blinding	Manual quantification of N+ cells were performed by distributing 10-15 images of iPSC-airway infected with SARS-CoV-2 stained with DAPI and N (>200 cells/image) to 5 blinded scorers.
   Power Analysis	not detected.
   Cell Line Authentication	not detected.

   Table 2: Resources

   Antibodies
   Sentences	Resources
   The antibodies used were; anti-SARS-CoV nucleoprotein (N) antibody (rabbit polyclonal, 1:2500, Rockland Immunochemicals, Cat #200-401-A50), anti-α-TUBULIN antibody (mouse monoclonal, 1:500 sigma cat# T6199), and anti-MUC5B antibody (mouse monoclonal, Santa Cruz Biotechnology, 1:500	anti-SARS-CoV nucleoprotein ( N ) suggested: None anti-α-TUBULIN suggested: (Sigma-Aldrich Cat# T6199, RRID:AB_477583) anti-MUC5B suggested: None
   Next, cells were washed with PBS three times (5 minutes, room temperature), and incubated with secondary antibody (AlexaFluor 546 AffiniPure Donkey Anti-mouse IgG (H+L), 1:500, and AlexFluor 647 donkey anti-mouse IgG (H+L) 1:500, Jackson ImmunoResearch) for 2 hours at room temperature.	Anti-mouse IgG suggested: None
   To determine cellular tropism, Z-stack images of the infected transwells stained with either anti-SARS-CoV-2 N and α antibodies or anti-SARS-CoV-2 N and anti-mucin 5B antibodies were taken on Nikon Eclipse NiE.	anti-SARS-CoV-2 N and α suggested: None anti-SARS-CoV-2 N suggested: None anti-mucin 5B suggested: None
   Anti-MUC5AC (Abcam cat# ab198254) was applied and visualized with anti-rabbit NP (Roche) and anti-NP-AP (Roche) and detected with Discovery Yellow (Roche); counter stained with hematoxylin, rinsed with detergent, dehydrated, and cover-slipped with permanent mounting media.	Anti-MUC5AC suggested: (LSBio (LifeSpan Cat# LS-B1927-50, RRID:AB_1128083)
   Experimental Models: Cell Lines
   Sentences	Resources
   SARS-CoV-2 propagation and titration: SARS-CoV-2 stocks (isolate USA_WA1/2020, kindly provided by CDC’s Principal Investigator Natalie Thornburg and the World Reference Center for Emerging Viruses and Arboviruses (WRCEVA)) were grown in Vero E6 cells (ATCC CRL-1586) cultured in Dulbecco’s modified Eagle’s medium (DMEM) supplemented with 2% fetal calf serum (FCS), penicillin (50 U/mL), and streptomycin (50 mg/mL) and titrated as described previously38.	Vero E6 suggested: None
   Experimental Models: Organisms/Strains
   Sentences	Resources
   Custom configured targets include: CCL2/JE/MCP-1, CXCL-9/MIG, CXCL10/IP-10/CRG-2, GM-CSF, IFNβ, IL-6, TNFα, TRAIL/TNFSF10	CCL2/JE/MCP-1 suggested: None
   Software and Algorithms
   Sentences	Resources
   Each image analyzed using ImageJ and DAPI+ nuclei and N+ cells were quantified with the multi-point tool.	ImageJ suggested: (ImageJ, RRID:SCR_003070)
   Mean fluorescence intensity was measured to calculate final concentration in pg/mL using Bioplex200 and Bioplex Manager 5 software (Biorad).	Bioplex200 suggested: None Bioplex suggested: (BioPlex, RRID:SCR_016144)
   The quality of the raw data was assessed using FastQC v.0.11.774.	FastQC suggested: (FastQC, RRID:SCR_014583)
   The sequence reads were aligned to a combination of the human genome reference (GRCh38) and the SARS-CoV-2 reference (NC_045512) using STAR v.	STAR suggested: (STAR, RRID:SCR_004463)
   Counts per gene were summarized using the featureCounts function from the subread package v.1.6.276.	featureCounts suggested: (featureCounts, RRID:SCR_012919) subread suggested: (Subread, RRID:SCR_009803)
   The edgeR package v.	edgeR suggested: (edgeR, RRID:SCR_012802)
   The limma package v.	limma suggested: (LIMMA, RRID:SCR_010943)
   Differentially expressed genes for each comparison were visualized using Glimma v.	Glimma suggested: (Glimma, RRID:SCR_017389)

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   Results from OddPub: Thank you for sharing your data.

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   Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:

   This study is not without limitations. The iPSC-airway epithelium is transcriptionally similar to primary airway epithelium and can be used to functionally model diseases such as cystic fibrosis (CF) in vitro40. However, iPSC-derived cells differentiated into many cell types of different organs tend to be more fetal or immature than their endogenous counterparts in adults65–67, and there may be differences including receptor expression and cell type distributions. Whether our model reflects a more fetal or pediatric response to SARS-CoV-2 will require further investigation. Another limitation of this model is that the epithelial response is studied in isolation, and the complex interplay between epithelial, immune, interstitial, and endothelial cells that leads to COVID-19 pneumonia is not captured in our model. However, the iPSC system offers a reductionist, physiologically relevant model system to study the intrinsic epithelial response and provides key insights into the initial stages of COVID-19. Furthermore, given the clinical spectrum of disease severity caused by SARS-CoV-2 infection there is pressing need to further understand the mechanisms that lead to serve disease. Numerous genes, variants and pathways are implicated in modulating the response to infection and require further investigation15, 16. This iPSC-based platform, coupled with gene-editing technology, opens up future directions to evaluate the mechanisms of the airway response to SARS-CoV-2 response.

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   Results from TrialIdentifier: No clinical trial numbers were referenced.

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   Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

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   Results from JetFighter: Please consider improving the rainbow (“jet”) colormap(s) used on pages 34, 35 and 39. At least one figure is not accessible to readers with colorblindness and/or is not true to the data, i.e. not perceptually uniform.

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   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.

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   Results from scite Reference Check: We found no unreliable references.

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3. 

   Version published to 10.1101/2021.07.06.451340v1 on bioRxiv

   Jul 7, 2021