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

GSE271284 Homo sapiens (human) RNA-seq (gene expression) 135 samples Submitted 2025/06/13 Platform GPL24676
At a glance

RNA-seq (gene expression) · human · 135 samples

What it is
RNA-seq (gene expression)Gene activity measured by sequencing. The summary does not mention single-cell work, so this is most likely bulk: one average per sample, the usual starting point for comparing groups.
Organism
Homo sapiens (human)
Samples
135 samples: 45 conditions, mostly in duplicateGroups are read from the sample names, not from the paper. Check the list below.
Data you can get
A processed table (series matrix) and supplementary files from NCBI; the raw sequencing reads from ENA. Which of these hold counts depends on the study, so check the file list.
Paper
Rewired type I IFN signaling is linked to age-dependent differences in COVID-19Cell reports. Medicine · 2025
Summary (written by the submitters)
Advanced age is the most important risk factor for severe or even lethal COVID-19, but a thorough understanding of the underlying differences in infection-induced inflammation and cellular pathogenesis is missing. In samples from SARS-CoV-2 infected patients aged 1 to 84 years, we observed a continuous rewiring of type I interferon signaling and signal transducer and activator of transcription (STAT) usage across monocytes, CD4+ T cells and B cells during aging.
Read the full summary ↓
In vitro investigations using cells from controls confirmed this as a general age-induced phenomenon. Conversion in interferon signaling from STAT1 to STAT3 was associated with increased inflammatory profiles, cytokine release and delayed contraction of infection-induced CD4+ T cells. A shift from solely interferon-responsive germinal center B (GCB) cells towards CD69high GCB and atypical B cells was linked to formation of IgA in children vs complement fixing IgG in adults. Our data provide molecular explanations for inflammation-prone responses to infections during aging.
Published in
Rewired type I IFN signaling is linked to age-dependent differences in COVID-19
Petrov L, Brumhard S, Wisniewski S et al. · Cell reports. Medicine 2025 · PMID 40834853 · doi:10.1016/j.xcrm.2025.102285
This dataset
Open GSE271284 in finder → Find more human RNA-seq datasets →
Download · which file do I need?

Direct links to NCBI and ENA, no account and no request form. Pick by what you want to do: take the small processed table to look, the supplementary files to re-analyse, or the original raw data to start over.

1 · Just have a look

Processed table

The series matrix: one table of values per sample as the submitters processed it. The smallest download, and it opens in Excel or R. Start here to see what the data look like.

Series matrix ↓
2 · Re-analyse it

Supplementary files

The count tables and other files the authors uploaded. Take only the samples you need, or the whole study as GSE271284_RAW.tar (it can run to tens of gigabytes).

All supplementary files ↗ Whole study · RAW.tar ↓
3 · Start from the raw reads

Raw sequencing reads

The original FASTQ files, straight from the sequencer, held at ENA. Large, and you align them yourself, so only choose this if you need to re-process the study from scratch.

Raw reads on ENA ↗

What the files usually look like: For RNA-seq the counts are usually a table among the supplementary files (look for 'counts', 'expression', 'FPKM' or 'TPM' in the file name). The series matrix of a sequencing study is sometimes nearly empty.

Also filed as BioProject PRJNA1130853. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 40834853) ↗
Study design
45 conditions, mostly in duplicate
C19-CB-0013 IFNa severe adult PBMC ×2 C19-CB-0049 mild adult PBMC ×2 C19-CB-0335 healthy child PBMC ×2 C19-CB-0389 healthy adult PBMC ×2 C19-CB-0477 symptomatic child PBMC ×2 C19-CB-50001 symptomatic child PBMC ×2 C19-CB-50051 asymptomatic child PBMC ×2 C19-CB-0016 IFNa severe adult PBMC ×2 +82 more

Supports a between-group comparison across 90 samples.

How we read this: 45 replicated groups read from 135 sample titles; they account for 90 of them. These groups are guessed from the sample names, not taken from the paper, so check them against the sample list below before relying on them.

Samples in this study

+ 95 more — browse all 135 samples with per-sample file links →

Datasets on a similar topic

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