Longitudinal Multi-omics Analyses Identify Responses of Megakaryocytes, Erythroid Cells, and Plasmablasts as Hallmarks of Severe COVID-19
This article has been Reviewed by the following groups
Listed in
- Evaluated articles (ScreenIT)
Abstract
No abstract available
Article activity feed
-
SciScore for 10.1101/2020.09.11.20187369: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
Antibodies Sentences Resources Anti-SARS-CoV-2 specific antibodies: Anti-SARS-CoV-2-specific IgA and IgG was quantified by CE-certified ELISA (EUROIMMUN, Lübeck, Germany). Anti-SARS-CoV-2suggested: (PhosphoSolutions Cat# CoV2-2G1, RRID:AB_2868397)Anti-SARS-CoV-2-specific IgAsuggested: NoneB cell subsets were stained using antibodies against CD19 (clone REA675, Miltenyi), CD19suggested: NoneSoftware and Algorithms Sentences Resources Standard curves and cytokine concentrations were calculated using linear regression in Microsoft Excel GraphPad Prism (Graphpad Software Inc, San Diego, US) Microsoft Excel GraphPad Prismsuggested: NoneAdapt… SciScore for 10.1101/2020.09.11.20187369: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
Antibodies Sentences Resources Anti-SARS-CoV-2 specific antibodies: Anti-SARS-CoV-2-specific IgA and IgG was quantified by CE-certified ELISA (EUROIMMUN, Lübeck, Germany). Anti-SARS-CoV-2suggested: (PhosphoSolutions Cat# CoV2-2G1, RRID:AB_2868397)Anti-SARS-CoV-2-specific IgAsuggested: NoneB cell subsets were stained using antibodies against CD19 (clone REA675, Miltenyi), CD19suggested: NoneSoftware and Algorithms Sentences Resources Standard curves and cytokine concentrations were calculated using linear regression in Microsoft Excel GraphPad Prism (Graphpad Software Inc, San Diego, US) Microsoft Excel GraphPad Prismsuggested: NoneAdapters and low-quality bases from the RNA-seq reads were removed using Trim Galore (version 0.4.4), which is a wrapper tool for Cutadapt and FastQC. Trim Galoresuggested: (Trim Galore, RRID:SCR_011847)FastQCsuggested: (FastQC, RRID:SCR_014583)The expression counts were normalized across samples using the DESeq normalization method. DESeqsuggested: (DESeq, RRID:SCR_000154)Differential expression analysis: Differentially expressed genes between healthy controls and each of the COVID pseudotime samples were identified using the Bioconductor package DESeq2 (version 1.20.0). DESeq2suggested: (DESeq, RRID:SCR_000154)Virus mRNA detection: To quantify the amount of virus present in the blood of COVID-19 patients, reads from whole blood RNA-seq data were first aligned to the human reference genome (GRCh38) using STAR with default parameters. STARsuggested: (STAR, RRID:SCR_015899)DNA methylation data analysis: DNA methylation data was analysed using the Bioconductor package RnBeads (version 1.12.1). Bioconductorsuggested: (Bioconductor, RRID:SCR_006442)Differentially methylated positions (DMPs) between healthy controls and each of the COVID-19 pseudotime samples as well as between sequential COVID-19 pseudotime samples were identified using the automatically selected rank cutoff of RnBeads. RnBeadssuggested: (RnBeads, RRID:SCR_010958)Gene set enrichment analysis (GSEA) was conducted for the co-expression modules using GSEA desktop application. Gene set enrichment analysissuggested: (Gene Set Enrichment Analysis, RRID:SCR_003199)KEGG and GO (Biological Processes) genes sets were conducted for each of the modules by ranking all genes by the module membership score. KEGGsuggested: (KEGG, RRID:SCR_012773)Predicted transcription factor binding sites (TFBS) enriched in DMPs were identified by conducting enrichment analysis using the Bioconductor package LOLA (version 1.14.0). LOLAsuggested: NoneAnalyses were performed using FlowJo v10 FlowJosuggested: (FlowJo, RRID:SCR_008520)(FlowJo LLC, Beckton Dickinson, Ashland, Oregon, US) and Graphpad Prism 8 (GraphPad Software, San Diego, California USA). GraphPadsuggested: (GraphPad Prism, RRID:SCR_002798)Each sample was mapped to GRCh38 Homo sapiens reference genome, in order to produce their respective count matrices. scRNA-seq data quality control and data analysis: Raw feature-barcode matrixes were filtered using Seurat package (version 3.1.5) in R environment 113,114; low quality cells that were potentially disrupted or doublets cells were removed from the analysis using number of features (number of reads mapping to gene between [200;5000]) or percentage of mitochondria (lower than 25%). Seuratsuggested: (SEURAT, RRID:SCR_007322)Differentially expressed genes between healthy controls and each of the COVID pseudotimes was identified using MAST 115 and GO enrichment analysis was performed with TopGO package for R (version 2.38.1) 116 and GO terms of interest selected based on fisher classic test statistic (p-value < 0.05). MASTsuggested: (MAST, RRID:SCR_016340)TopGOsuggested: (topGO, RRID:SCR_014798)All values were transformed to mM to correspond with mean values of the HMDB database (cut-off of values > 10-6). HMDBsuggested: (HMDB, RRID:SCR_007712)Normalized counts were converted into TPM-values, and ENSEMBL gene names were mapped to Recon 2.2 120,126. ENSEMBLsuggested: (Ensembl, RRID:SCR_002344)Based on an updated version of Recon 2.2 simulated in the serum metabolic environment, a consistent model was generated using FASTCORE’s FASTcc algorithm in MATLAB. MATLABsuggested: (MATLAB, RRID:SCR_001622)Identification of upstream regulatory signalling pathways from downstream gene expression was performed on t-statistic values from viper against the Omnipath interaction database (R package OmnipathR version 1.2.1) applying CARNIVAL (version 1.0.1 with IBM Cplex solver as network optimizer) 47. OmnipathRsuggested: NoneThe significance in the differential regulation of transcription factors over time in the non-survivor vs. the survivor groups of cohort 3 was quantified via a moderated t-test using limma 91, while accounting for patient correlation using a block design and limma’s duplicateCorrelation function. limmasuggested: (LIMMA, RRID:SCR_010943)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:Limitations of our study are given by the relatively low sample size of the initial two-centre cohort (cohort 1), which we aimed to compensate by two independent validation cohorts. The initial findings, which point to novel processes and potential biomarkers for a severe disease trajectory, mandate prospective validation. Collectively, our study shows that circulating non-immune cells, most prominently megakaryocytes and cells of the erythroid lineage, as well as specific shifts of metabolic properties across cell types, are to be considered as an integral part of COVID-19 induced pathology. Our analysis thus provides insights into the broad effects of SARS-CoV-2 infection beyond classical immune cells and may serve as an important entry point to develop biomarkers and targeted treatments of patients with COVID-19. Our findings of interferon-activated circulating megakaryocytes may help better explaining and treating the intricate, but clinically highly relevant association of COVID-19 with thrombotic events.
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.
-
-
