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Exploratory investigation of changes in blood transcriptome following mepolizumab treatment for asthma

GSE334345 Homo sapiens (human) RNA-seq (gene expression) 54 samples Submitted 2026/06/08 Platform GPL24676
At a glance

RNA-seq (gene expression) · human · 54 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
54 samples: 14 conditions, mostly in triplicateGroups 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)
Objective: To evaluate the effects of mepolizumab therapy on the blood transcriptome in patients with severe eosinophilic asthma (SEA) and to determine whether changes in gene expression predict responsiveness to mepolizumab treatment. Methods: Fourteen consecutive patients diagnosed with SEA were prospectively enrolled. Each patient had sequential blood samples obtained pre-initiation and at 4, 8, and 12 weeks post-initiation of mepolizumab.
Read the full summary ↓
Peripheral blood mononuclear cells (PBMCs) were isolated from blood samples for bulk RNA sequencing. Paired differential expression (DE) gene analysis was performed comparing baseline gene expression versus all timepoints. Changes in gene expression were also compared between responders versus nonresponders to mepolizumab treatment. Finally, gene set enrichment analysis was evaluated across timepoints and its interaction with the response to mepolizumab treatment. Results: Principal component analysis of all genes demonstrated a homogeneous distribution, which did not separate into distinct groups based on either timepoint or therapeutic response. At week 12, 149 genes had significant DE (FDR < 0.05) compared to baseline. When performing all-way timepoint comparisons, 74 genes (63 protein-coding) were found to be downregulated at weeks 4, 8 and 12 compared to baseline, but no differences were observed between post baseline timepoints. Twenty-eight genes (16 protein-coding) did change with progression of time and were associated with response to therapy, and 56 protein-coding genes distinguished responders and nonresponders. At week 12, nonresponders had a high enrichment of genes involved with signaling pathways, including cytokines, growth factors, cell differentiation markers, and protein kinases. Conclusion: In this exploratory study, mepolizumab use was associated with limited global transcriptomic separation with some preliminary differential-expression and enrichment findings. No definitive mechanistic or predictive response signatures were identified due to the study limitations. Future studies, including larger cohorts followed for longer durations with interrogation of specific immune subsets, may offer additional insight into the pathophysiology of severe eosinophilic asthma and into predicting response to mepolizumab treatment.
This dataset
Open GSE334345 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 GSE334345_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 PRJNA1474833 and SRA study SRP706761. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
14 conditions, mostly in triplicate
010_Week ×3 007_Week ×3 008_Week ×3 009_Week ×3 011_Week ×3 012_Week ×3 013_Week ×3 014_Week ×3 +21 more

Supports a between-group comparison across 39 samples.

How we read this: 14 replicated groups read from 54 sample titles; they account for 39 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

+ 14 more — browse all 54 samples with per-sample file links →

Datasets on a similar topic

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