Single-cell gene expression analysis using lung derived cells from SARS-CoV-2-infected mice in aged and adult mice
Single-cell RNA-seq · mouse · 12 samples
- What it is
- Single-cell RNA-seqGene activity measured in thousands of individual cells, so cell types can be told apart.
- Organism
- Mus musculus (mouse)
- Samples
- 12 samples: 12 samples, each with a distinct title — no replicated groupsGroups 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
- Dynamics of lung-infiltrating virus-specific T cells associated with age-dependent SARS-CoV-2 pneumonia severityPLoS pathogens · 2026
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.
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.
Supplementary files
The count tables and other files the authors uploaded. Take only the samples you need, or the whole study as GSE291215_RAW.tar (it can run to tens of gigabytes).
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.
What the files usually look like: Single-cell studies usually ship their counts as 10x files (matrix.mtx, barcodes.tsv, features.tsv), an .h5 file, or an .rds / .h5ad object that Seurat or Scanpy can open. Look for those among the supplementary files.
Also filed as BioProject PRJNA1232258 and SRA study SRP568188. Searching any of these in the dataset finder brings you back here.
Any grouping that defines a comparison lives in the sample characteristics, which GEO records separately from the titles.
How we read this: Read from 12 sample titles: 12 distinct titles with little repetition. 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.
- GSM8831041 Lung cell, aged, SARS-CoV2-infected, GEX
- GSM8831042 Lung cell, aged, uninfected, GEX
- GSM8831043 Lung cell, adult, SARS-CoV2-infected, GEX
- GSM8831044 Lung cell, adult, uninfected, GEX
- GSM8831045 Lung cell, aged, SARS-CoV2-infected, TCR
- GSM8831046 Lung cell, aged, uninfected, TCR
- GSM8831047 Lung cell, adult, SARS-CoV2-infected, TCR
- GSM8831048 Lung cell, adult, uninfected, TCR
- GSM8831049 Lung cell, aged, SARS-CoV2-infected, BCR
- GSM8831050 Lung cell, aged, uninfected, BCR
- GSM8831051 Lung cell, adult, SARS-CoV2-infected, BCR
- GSM8831052 Lung cell, adult, uninfected, BCR
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Metadata from NCBI GEO, cached and refreshed periodically — the NCBI page above is authoritative. Downloads link straight to NCBI/ENA; nothing is proxied through BioTransfer.