GEO series

Single cell transcriptomics in blood of patients with chronic obstructive pulmonary disease

GSE249584 Homo sapiens (human) Single-cell RNA-seq 15 samples Submitted 2024/10/01 Platform GPL24676

At a glance

Single-cell RNA-seq · human · 15 samples

What it is: Single-cell RNA-seqGene activity measured in thousands of individual cells, so cell types can be told apart.
Organism: Homo sapiens (human)
Samples: 15 samples: 15 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: Single cell transcriptomics in blood of patients with chronic obstructive pulmonary diseaseBMC pulmonary medicine · 2025

Summary (written by the submitters)

Background Chronic obstructive pulmonary disease (COPD) is a leading cause of morbidity and mortality worldwide. Single-cell RNA sequencing (scRNA-seq) provides gene expression profiles at the single-cell level. Hence, we evaluated gene expression in the peripheral blood of patients with COPD. Methods Peripheral blood samples from seven healthy controls and eight patients with COPD were obtained in this study.

Read the full summary ↓

The 10X Genomics Chromium Instrument and cDNA synthesis kit was utilized to generate a barcoded cDNA library for single cell RNA-sequencing. We compared the scRNA-seq data between the COPD and control groups using computational analysis. Functional analyses were performed using Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway analyses. Results scRNA-seq was used to analyze the transcriptome of peripheral blood mononuclear cells from seven normal controls and eight patients with COPD. We found increased numbers of monocyte macrophages in the COPD group compared to those in the normal control group. Among the differentially expressed genes (DEGs) in monocyte-macrophages, we identified five upregulated genes (HLA-DRB5, ITGB2, EGR1, CXCL8, and CCL4) and seven downregulated genes (FOLR3, RPS4Yq, CD52, LY6E, HLA-DQB1, G0S2, and CCL3L1) in the COPD group compared to the normal control group. Conclusions Using scRNA-seq, we found differences in cell type distribution, especially in monocyte macrophages. Several upregulated and downregulated genes were found in the monocyte-macrophages of the COPD groups.

Published in

Single cell transcriptomics in blood of patients with chronic obstructive pulmonary disease

Heo Y, Kim J, Hong SH et al. · BMC pulmonary medicine 2025 · PMID 39810158 · doi:10.1186/s12890-024-03475-y

This dataset

Open GSE249584 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 GSE249584_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 PRJNA1049768 and SRA study SRP476406. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 39810158) ↗

Study design

15 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 15 sample titles: 15 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

GSM7950677 20_00269_LI_SING ,COPD, scRNAseq
GSM7950678 20_00271_LI_SING,COPD, scRNAseq
GSM7950679 20_00270_LI_SING,COPD, scRNAseq
GSM7950680 20_00267_LI_SING,Control, scRNAseq
GSM7950681 20_00262_LI_SING,Control, scRNAseq
GSM7950682 20_00263_LI_SING,Control, scRNAseq
GSM7950683 20_00266_LI_SING,Control, scRNAseq
GSM7950684 20_00264_LI_SING,Control, scRNAseq
GSM7950685 20_00265_LI_SING,COPD, scRNAseq
GSM7950686 20_00196_LI_SING,COPD, scRNAseq
GSM7950687 20_00193_LI_SING,Control, scRNAseq
GSM7950688 20_00194_LI_SING,COPD, scRNAseq
GSM7950689 20_00195_LI_SING,COPD, scRNAseq
GSM7950690 20_00197_LI_SING,COPD, scRNAseq
GSM7950691 20_00198_LI_SING,Control, scRNAseq

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