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PD-1high memory-phenotype CD4 T cells mediate type 2 bystander immunity and promote pulmonary fibrosis via BATF/amphiregulin axis

GSE295241 Mus musculus (mouse) Single-cell RNA-seq 19 samples Submitted 2025/05/31 Platform GPL34290Platform GPL24247
At a glance

Single-cell RNA-seq · mouse · 19 samples

What it is
Single-cell RNA-seqGene activity measured in thousands of individual cells, so cell types can be told apart. Read from the summary: GEO's own type field only says 'expression profiling by sequencing', so confirm it in the paper.
Organism
Mus musculus (mouse)
Samples
19 samples
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)
Single-cell RNA sequencing(scRNA-seq) was performed to investigate the differentiation process of steady-state CD4 T cells following treatmernt with IL-25 and IL-33, after initial isolation via FACS. This analysis revealed that steady-state PD-1high CD4 T cell differentiate into patogenic Th2 cells, characterized by the expression of IL-13, IL-5 and amphireguling.
Read the full summary ↓
These findings suggest that steady-state PD-1high CD4 T cells may contribute to pulmonary fibrosis through its differntiation into pathogenic Th2 cells. This dataset includes previously published scRNA-seq data from [Min-Ji Cho et al., 2023] To investigate the tissue-specific roles of Pdcd1 in CD4⁺ T cells, bulk RNA sequencing was performed on Pdcd1^high and Pdcd1^low CD4⁺ T cells isolated from the spleen and lungs of mice. Differential gene expression analysis revealed that Pdcd1^high CD4⁺ T cells in both tissues were enriched for Th2- and fibrosis-related gene signatures. Notably, chemokine receptor expression profiles differed between spleen and lung, suggesting tissue-specific transcriptional programs associated with Pdcd1 expression.
This dataset
Open GSE295241 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 GSE295241_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 PRJNA1253748 and SRA study SRP580307. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Samples in this study

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+ 19 more — browse all 19 samples with per-sample file links →

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