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Dysregulated NK Cell Activation and Myeloid-Lymphoid Imbalance Underpin COPD Progression: Insights from High-Dimensional Immune Profiling and Smoking-Induced Immune Remodeling

GSE306950 Homo sapiens (human) RNA-seq (gene expression) 35 samples Submitted 2025/11/18 Platform GPL30209
At a glance

RNA-seq (gene expression) · human · 35 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
Homo sapiens (human)
Samples
35 samples: 12 × COPD_stage2 vs 10 × HD vs 7 × COPD_stage34 vs 6 × COPD_stage1Groups 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
Dysregulated NK cell activation and myeloid-lymphoid imbalance underpin COPD progression: insights from high-dimensional immune profiling and smoking-induced immune remodelingFrontiers in immunology · 2025
Summary (written by the submitters)
Background: Chronic obstructive pulmonary disease (COPD) is a significant global health concern, marked by persistent inflammation and immune dysregulation. Although it is widespread and has substantial clinical implications, the systemic immune mechanisms driving disease progression are not fully understood. Since blood contains a diverse array of immune cells and offers a non-invasive means of assessing immune homeostasis and overall physiological status, investigating immune dysregulation through blood sampling offers considerable value for both basic research and clinical application.
Read the full summary ↓
This approach can provide novel insights into the pathogenesis of COPD. Methods: This study employed high-dimensional flow cytometry and RNA sequencing to comprehensively characterize peripheral immune cells from a cohort of 69 COPD patients spanning clinical stages 1 to 4, alongside 41 healthy donors as controls. To capture granulocyte populations typically excluded from peripheral blood mononuclear cell analyses, fresh whole blood samples were analyzed directly. Results: Our study revealed a marked shift in the myeloid-lymphoid balance, characterized by elevated neutrophils, eosinophils, and classical monocytes that correlated with disease severity, alongside reduced CD8⁺ T cells and circulating T follicular helper cells. Transcriptional profiling identified oxidative stress pathways, T cell suppression, and aberrant natural killer (NK) cell activation as hallmarks of advanced COPD. Notably, activated Nkp44⁺ NK cells were significantly enriched in severe stages, implicating their role in perpetuating inflammation. Smoking exacerbated immune perturbations, including upregulated complement activation and B cell pathways, though cessation partially restored transcriptional homeostasis. Conclusions: This study underscores the value of peripheral immune profiling in capturing the heterogeneity of COPD. The results reveal systemic immune dysregulation-especially NK cell hyperactivity and point to potential therapeutic avenues aimed at modulating immune responses to slow disease progression.
Published in
Dysregulated NK cell activation and myeloid-lymphoid imbalance underpin COPD progression: insights from high-dimensional immune profiling and smoking-induced immune remodeling
Qiao Q, Yang Y, Huang K et al. · Frontiers in immunology 2025 · PMID 41050685 · doi:10.3389/fimmu.2025.1623319
This dataset
Open GSE306950 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 GSE306950_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 PRJNA1313939 and SRA study SRP616481. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 41050685) ↗
Study design
12 × COPD_stage2 vs 10 × HD vs 7 × COPD_stage34 vs 6 × COPD_stage1

Supports a between-group comparison across 35 samples.

How we read this: 4 replicated groups read from 35 sample titles; they account for 35 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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