Rewired type I IFN signaling is linked to age-dependent differences in COVID-19

Tools

Item Type:	Dataset
Title:	Rewired type I IFN signaling is linked to age-dependent differences in COVID-19
Creators Name:	Petrov, Lev, Brumhard, Sophia, Wisniewski, Sebastian, Georg, Philipp, Hiller, Anna, Astaburuaga, Rosario, Blüthgen, Nils, Wyler, Emauel, Vogt, Katrin, Dey, Hannah, von Stillfried, Saskia, Iwert, Christina, Bülow, Roman, Maas, Lukas, Langner, Christine, Meyer, Tim, Loske, Jennifer, Eils, Roland, Lehmann, Irina, Ondruschka, Benjamin, Ralser, Markus, Trimpert, Jakob, Boor, Peter, Bedoui, Sammy, Meisel, Christian, Mall, Marcus, Corman, Victor, Sander, Leif Erik, Röhmel, Jobst and Sawitzki, Birgit
Abstract:	This repository contains code and additional resources that can be used to reproduce the analysis from Petrov. et. al. "Rewired type I IFN signaling is linked to age-dependent differences in COVID-19" as well as some supplementary figures and tables. Corresponding whole blood CyTOF and pre-processed PBMC scRNAseq data can be found on Figshare https://figshare.com/s/dc50797136d4d90c6f48. PBMC scRNAseq data is also deposited as pre-filtered count tables accompanied by metadata on GEO under the GSE271284 accession number. Please see Resources Table from the publication as well as comments inside of scripts for instructions on how to use the code provided. -Gating_CyTOF.pdf shows pre-gating strategy for the CyTOF data. -Gating_PBMC_activation.pdf shows pre-gating strategy from the in vitro activation experiment flow cytometry. -Gating_pSTAT_PBMC_activation.pdf shows pre-gating strategy from the in vitro STAT phosphorylation experiment flow cytometry. -Mendeley Figure 1.pdf shows the relationship between main CyTOF findings and days post symptom onset. -Mendeley Figure 2.pdf shows boxplots with an alternative stratification of patients according to the disease severity for the clusters highlighed in the publication. -Mendeley Supplementary Table 1.xlsx contains information on the number of cells included in the CyTOF and scRNAseq analyses (by cluster, patient group). The scripts have been tested using Ubuntu 20.04.5 LTS under WSL2, Windows 11 and R 4.2.2 (patched) as well as RStudio (versions 2022.03 till 2023.09). List of package versions is presented in the Resources Table and stated in the Methods section of the publication. No specific installation is required to run the scripts besides R language (4.2.2 tested) and the packages listed in the beginning of the master scripts. There, user will also find a number of useful links if they e.g. choose to run R in WSL2. In order to run the scripts on data, user needs to download the data, including the metafiles, from either the Figshare repository (https://figshare.com/s/dc50797136d4d90c6f48) or the GEO repository (GSE271284), replicate the folder structure as stated in the scripts or change the paths to correspond to the chosen folder structure and run the scripts starting with the respective master script. Expected output includes .csv tables, .png and .pdf plots as well as normalized .fcs files (for CyTOF normalization script). It is recommended to use a single module (e.g. B cells) to demo the code. Expected runtime varies based on the system, but should not be more than a couple of hours for both the CyTOF and the scRNAseq scripts. Script output should be reproducible if using the same seed input value as seen in the script files.
Keywords:	Immunology, Bioinformatics, Single-Cell RNA Sequencing, Mass Cytometry
Source:	Mendeley Data
Publisher:	Elsevier
Date:	17 June 2025
Official Publication:	https://doi.org/10.17632/kz7cpw3bnt.1
Related to:	URL URL Type https://edoc.mdc-berlin.de/id/eprint/24885/ Publication

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