GEO series

Kinetical scRNAseq Analysis Reveals Immune Mechanisms Underlying Hindered Lung Recovery following Influenza Virus Infection in Aged Hosts

GSE271578 Mus musculus (mouse) Single-cell RNA-seq 12 samples Submitted 2025/12/28 Platform GPL28457

At a glance

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: 6 × Lung, Aged, Day vs 6 × Lung, Young, DayGroups 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.

Summary (written by the submitters)

Aging is a major risk factor for host morbidity and mortality upon acute respiratory virus infections. To better elucidate the immune determinants of viral pathogenesis during aging, we conducted a time-course single-cell RNA sequencing (scRNAseq) and high-dimensional flow cytometry analysis on the pulmonary responses against influenza infection in young or aged mice.

Read the full summary ↓

Notably, aged mice exhibited decreased populations of alveolar macrophages (AMs) and dendritic cells (DCs), alongside an increase in monocyte-derived macrophages (MoMs) and interstitial macrophages (IMs), which last weeks after acute viral infection. Additionally, there was enhanced accumulation of respiratory adaptive immune cell cells including tissue resident helper CD4 T cells (TRHs), CD8 tissue resident memory cells (TRMs) and a unique B cell population resembling age-associated B cells. Gene set enrichment analysis (GSEA) comparing lung transcriptomes of young and aged mice across time points highlighted persistent type I and type interferon signaling in aged hosts, especially in the macrophage population. Interestingly, inhibiting interferon signaling in aged mice after viral clearance led to ameliorated long-term sequelae, along with a decrease in IM and TRH populations. Our findings suggest that IFNα/γ signaling, particularly in MoM/IM, is pivotal in the development of long-term sequelae following acute respiratory infections in aged hosts.

This dataset

Open GSE271578 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 GSE271578_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: 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 PRJNA1132244 and SRA study SRP518189. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗

Study design

6 × Lung, Aged, Day vs 6 × Lung, Young, Day

Supports a between-group comparison across 12 samples.

How we read this: 2 replicated groups read from 12 sample titles; they account for 12 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

GSM8379993 Lung, Aged, Day0 Lung, Aged, Day
GSM8379994 Lung, Young, Day0 Lung, Young, Day
GSM8379995 Lung, Aged, Day2 Lung, Aged, Day
GSM8379996 Lung, Young, Day2 Lung, Young, Day
GSM8379997 Lung, Aged, Day9 Lung, Aged, Day
GSM8379998 Lung, Young, Day9 Lung, Young, Day
GSM8379999 Lung, Aged, Day14 Lung, Aged, Day
GSM8380000 Lung, Young, Day14 Lung, Young, Day
GSM8380001 Lung, Aged, Day30 Lung, Aged, Day
GSM8380002 Lung, Young, Day30 Lung, Young, Day
GSM8380003 Lung, Aged, Day61 Lung, Aged, Day
GSM8380004 Lung, Young, Day61 Lung, Young, Day

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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.