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Paradoxical Phenotype of Fibromyalgia Neutrophils: Elevated Baseline Inflammation but Blunted Response to Stimulation

GSE334369 Homo sapiens (human) Single-cell RNA-seq 206 samples Submitted 2026/06/08 Platform GPL24676
At a glance

Single-cell RNA-seq · human · 206 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
206 samples: 206 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.
Summary (written by the submitters)
Fibromyalgia (FM) is a long-term pain condition of unknown etiology. We investigated transcriptomic responses of FM and healthy control neutrophils to inflammatory stimuli. We observed a state of inflammation in neutrophils from FM patients. However, FM neutrophils were unable to efficiently respond to lipopolysaccharide (LPS). This impairment was especially characteristic of FM patients with no improvement after 5 years post-diagnosis in comparison with those who did improve.
Read the full summary ↓
Further analysis identified NF-κB suppression as a key biological process associated with low grade inflammation and LPS non-responsiveness in FM neutrophils. The clinically used NF-κB activator, bryostatin, alleviated hypersensitivity in mice treated with FM plasma, pointing to controlled inflammation induction through reactivation of the NF-κB pathway as a possible therapeutic target for FM treatment. Our whole blood single-cell RNA sequencing replicated this NF-κB-driven inflammation and revealed that this inflammatory signature is strongly pronounced not only in neutrophils, but across a broad range of immune cells.
This dataset
Open GSE334369 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 GSE334369_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 PRJNA1472995 and SRA study SRP705608. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
206 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 206 sample titles: 206 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

+ 166 more — browse all 206 samples with per-sample file links →

Datasets on a similar topic

Search all human RNA-seq datasets in GEO →

Share this dataset

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.