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Multi-tier signaling and epigenomic reprogramming drive microglial inflammatory states and functions associated with demyelination [RNA-seq]

GSE302857 Mus musculus (mouse) RNA-seq (gene expression) 81 samples Submitted 2026/04/29 Platform GPL34290
At a glance

RNA-seq (gene expression) · mouse · 81 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
81 samples: 81 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)
The extensive heterogeneity of microglia inflammatory states accompanying neurodegenerative diseases underscores the complex molecular mechanisms that regulate these cells. Here, we report on transcriptional effectors that control microglial inflammatory state polarization associated with brain demyelination in mice. Using flow cytometry, microscopy and RNA-seq, we identified two dominant, functionally distinct states of Clec7a+CD229+ inflammatory microglia, discriminated from one another by CD11c expression.
Read the full summary ↓
Epigenomic analyses implicated extensive genome-wide nucleosome remodeling to the polarization process, driven by state-associated stimulation of transcription factors that included Pu.1, AP-1, Bhlhe40 and Egr2, as well as re-calibration of homeostatic input provided by Mef2. Notably, a H3K27me3-based epigenetic gatekeeping mechanisms controls transcription of Egr2 and Bhlhe40 genes. Loss-of-function experiments validated the physiological relevance of Trem2, Mef2a and Egr2 to the microglial inflammatory state polarization process in the demyelinating brain. Therefore, distinct configuration of transcriptional input cooperate with epigenetic mechanisms to specify microglial inflammatory states and functions.
This dataset
Open GSE302857 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 GSE302857_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 PRJNA1292476 and SRA study SRP601460. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
81 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 81 sample titles: 81 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

+ 41 more — browse all 81 samples with per-sample file links →

Datasets on a similar topic

Search all mouse 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.