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Exploring the Biological Mechanisms of Severe COVID-19 in the Elderly: Insights from an Aged Mouse Model

GSE274784 Mus musculus (mouse) RNA-seq (gene expression) 13 samples Submitted 2024/08/25 Platform GPL24247
At a glance

RNA-seq (gene expression) · mouse · 13 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
Mus musculus (mouse)
Samples
13 samples: 4 × C57BL/6j mice, three-week-old, infected, vs 3 × C57BL/6j mice, three-week-old, control, vs 3 × C57BL/6j mice, nineteen-month-old, control, vs 3 × C57BL/6j mice, nineteen-month-old, infected,Groups 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
Exploring the biological mechanisms of severe COVID-19 in the elderly: Insights from an aged mouse modelVirulence · 2025
Summary (written by the submitters)
Definitive parameters or mechanisms underlying the severity of COVID-19 in elderly people remain confused. Thus, this study seeks to elucidate the mechanism behind the increased vulnerability of elderly individuals to severe COVID-19. We employed an aged mouse model with a mouse-adapted SARS-CoV-2 strain to mimic the severe symptoms observed in elderly patients with COVID-19.
Read the full summary ↓
Comprehensive analyses of the whole lung were performed using transcriptome and proteome sequencing, comparing data from aged and young mice. For transcriptome analysis, bulk RNA sequencing was conducted using an Illumina sequencing platform.
Published in
Exploring the biological mechanisms of severe COVID-19 in the elderly: Insights from an aged mouse model
Ma L, Lin X, Xu M et al. · Virulence 2025 · PMID 40228062 · doi:10.1080/21505594.2025.2487671
This dataset
Open GSE274784 in finder → Find more mouse 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 GSE274784_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 PRJNA1148044 and SRA study SRP526302. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 40228062) ↗
Study design
4 × C57BL/6j mice, three-week-old, infected, vs 3 × C57BL/6j mice, three-week-old, control, vs 3 × C57BL/6j mice, nineteen-month-old, control, vs 3 × C57BL/6j mice, nineteen-month-old, infected,

Supports a case/control comparison: 7 samples read as cases, 6 as controls.

How we read this: 4 replicated groups read from 13 sample titles; they account for 13 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
Datasets on a similar topic

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