GEO series

Lung NR3C1+ and CXCR6 High CD4 T cells distinguish immunopathogenesis of human emphysema

GSE302339 Homo sapiens (human) Single-cell RNA-seq 67 samples Submitted 2025/11/13 Platform GPL24676

At a glance

Single-cell RNA-seq · human · 67 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: Homo sapiens (human)
Samples: 67 samples: 67 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: Lung NR3C1(+) and CXCR6(high) T cells distinguish immunopathogenesis of human emphysemaCommunications biology · 2025

Summary (written by the submitters)

There is a significant knowledge gap in how T cells promote emphysema in smokers with chronic obstructive pulmonary disease (COPD). Single-cell RNA sequencing (scRNA seq) analysis of human samples and relevant clinical data could provide new mechanistic insights into disease pathogenesis. We generated an in-house human lung scRNA seq dataset with extensive disease characteristic annotation and analyzed a second independent scRNA seq dataset to examine the pathophysiological role of T cells in emphysema.

Read the full summary ↓

Comparisons of pulmonary immune landscapes in emphysematous (E)-COPD, non-emphysematous (NE)-COPD, and controls showed positive enrichment of T cells in E-COPD. Pathway analyses identified upregulated inflammatory states in CD4 T cells a distinguishing feature of E-COPD. Compared to controls, glucocorticoid receptor NR3C1 expressing CD4 T cells were enriched in NE-COPD but were reduced in E-COPD. Interactions between macrophages and NR3C1+ CD4 T cell subset via CXCL signaling were strongly predicted in E-COPD but were absent in NE-COPD and controls. The relative abundance of CD4 CXCR6high effector memory T cells positively correlated with preserved lung function in E-COPD but not in NE-COPD. Overall, these findings suggest that NR3C1+ and CXCR6high effector memory subsets of CD4 T cells distinguish the immune-pathophysiological features of emphysema in human lungs. Molecular targeting of relevant T cell subsets in emphysema could provide new therapeutic opportunities.

Published in

Lung NR3C1(+) and CXCR6(high) T cells distinguish immunopathogenesis of human emphysema

Zhang Y, Sauler M, Corry DB et al. · Communications biology 2025 · PMID 40993192 · doi:10.1038/s42003-025-08698-1

This dataset

Open GSE302339 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 GSE302339_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 PRJNA1282758 and SRA study SRP594944. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 40993192) ↗

Study design

67 samples, each with a distinct title — no replicated groups

Any grouping that defines a comparison lives in the sample characteristics, which GEO records separately from the titles.

How we read this: Read from 67 sample titles: 67 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.

Samples in this study

GSM9102249 s61-emphysematous-copd
GSM9102250 s74-emphysematous-copd
GSM9102251 s80-emphysematous-copd
GSM9102252 s1-emphysematous-copd
GSM9102253 s10-emphysematous-copd
GSM9102254 s11-emphysematous-copd
GSM9102255 s2-emphysematous-copd
GSM9102256 s23-emphysematous-copd
GSM9102257 s3-emphysematous-copd
GSM9102258 s76-emphysematous-copd
GSM9102259 s77-emphysematous-copd
GSM9102260 s78-emphysematous-copd
GSM9102261 s79-emphysematous-copd
GSM9102262 s81-emphysematous-copd
GSM9102263 s9-emphysematous-copd
GSM9102264 s93-emphysematous-copd
GSM9102265 s12-control
GSM9102266 s13-control
GSM9102267 s14-control
GSM9102268 s15-control
GSM9102269 s16-control
GSM9102270 s17-control
GSM9102271 s18-control
GSM9102272 s19-control
GSM9102273 s20-control
GSM9102274 s21-control
GSM9102275 s22-control
GSM9102276 s50-control
GSM9102277 s53-control
GSM9102278 s6-control
GSM9102279 s63-control
GSM9102280 s64-control
GSM9102281 s65-control
GSM9102282 s68-control
GSM9102283 s69-control
GSM9102284 s70-control
GSM9102285 s71-control
GSM9102286 s72-control
GSM9102287 s75-control
GSM9102288 s82-control

+ 27 more — browse all 67 samples with per-sample file links →

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Search all human RNA-seq datasets in GEO →

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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.