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Mitochondrial reverse electron transport in myeloid cells perpetuates neuroinflammation

GSE224366 Mus musculus (mouse) RNA-seq (gene expression) 13 samples Submitted 2025/01/31 Platform GPL24247
At a glance

RNA-seq (gene expression) · mouse · 13 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
Mus musculus (mouse)
Samples
13 samples: 5 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)
Sustained smouldering, or low grade, activation of myeloid cells is a common hallmark of several chronic neurological diseases, including multiple sclerosis. Distinct metabolic and mitochondrial features of myeloid cells guide diverse functional states. How these features act to perpetuate neuroinflammation is currently unknown. Using a multiomics approach, we identify a new molecular signature that perpetuates the activation of microglia by the reverse electron transport through complex I and the subsequent production of reactive oxygen species.
Read the full summary ↓
Blocking reverse electron transport in pro-inflammatory myeloid cells protects against neurotoxic damage and improves functional outcomes in animal disease models in vivo. Our data show that reverse electron transport in myeloid cells is a potential new therapeutic target to foster neuroprotection in smouldering inflammatory central nervous system diseases.
This dataset
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.

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 GSE224366_RAW.tar (it can run to tens of gigabytes).

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.

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 PRJNA930730 and SRA study SRP420657. Searching any of these in the dataset finder brings you back here.

Study design
5 conditions, mostly in triplicate
Control resident, biol ×3 Chronic EAE resident, biol ×3 Chronic EAE infiltrating, biol ×3 Peak EAE infiltrating, biol ×2 Peak EAE resident, biol ×2

Supports a between-group comparison across 13 samples.

How we read this: 5 replicated groups read from 13 sample titles; they account for 13 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
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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.