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CD38+CD93+ microglia and macrophages drive neuroinflammation through glycolysis-histone lactylation axis in multiple sclerosis model mice

GSE296797 Mus musculus (mouse) ChIP-seq / ATAC-seq (genome binding and open chromatin) 17 samples Submitted 2026/01/01 Platform GPL24247Platform GPL19057
At a glance

ChIP-seq / ATAC-seq (genome binding and open chromatin) · mouse · 17 samples

What it is
ChIP-seq / ATAC-seq (genome binding and open chromatin)Where proteins bind the genome, or which regions are open and active.
Organism
Mus musculus (mouse)
Samples
17 samples: 17 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)
Neuroinflammation is a pathological hallmark of several chronic neurological diseases, including multiple sclerosis (MS), and this process is driven by the sustained activation of myeloid cells. However, the pathogenic myeloid cells subset(s) and their regulation in MS remain unclear. Here, we identify a multiple sclerosis-associated microglia and macrophage (MSMM) subset defined as CD38+CD93+ that drives MS pathogenesis.
Read the full summary ↓
MSMM exhibits pro-inflammatory and neurotoxic characteristics accompanied by aberrant activation of glycolysis. Mechanistically, we found that aberrant activation of glycolysis results in excessive lactate production and elevation of histone lactylation in MSMM. Histone lactylation (H3K18la and H4K12la) in turn activates the transcriptional modules of genes governing neuroinflammation and glycolysis, thereby promotes the formation of MSMM. Blockade of MSMM with anti-CD38 plus anti-CD93 neutralizing antibodies ameliorates neuroinflammation and MS-like pathologies in an experimental autoimmune encephalomyelitis (EAE) mouse model of MS. Inhibition of glycolysis through Ldha knock out or LDHA inhibitor decreases the number of MSMM and attenuates EAE severity. Together, these findings reveal a MS-associated pathogenic cell subset and provide potential therapeutic approaches for MS.
This dataset
Open GSE296797 in finder → Find more mouse 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 GSE296797_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: Look for peak files (.bed, .narrowPeak) and signal tracks (.bigWig) among the supplementary files; the raw reads are on ENA.

Also filed as BioProject PRJNA1261065 and SRA study SRP584277. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
17 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 17 sample titles: 17 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
Datasets on a similar topic

Search all mouse RNA-seq datasets in GEO →

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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.