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Comparison of expression profiles of Control and CDDO-Im treatment microglia under IFN-gamma or Amyloid beta stimulation

GSE301269 Homo sapiens (human) Single-cell RNA-seq 24 samples Submitted 2025/11/27 Platform GPL28038
At a glance

Single-cell RNA-seq · human · 24 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
24 samples: 24 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.
Paper
NRF2 activation by CDDO-Im regulates inflammatory and autophagy pathways in human microglial cellsFree radical biology & medicine · 2026
Summary (written by the submitters)
Alzheimer’s disease (AD) is characterized by amyloid-beta (Amyloid beta) plaques and neurofibrillary tangles, accompanied by elevated oxidative stress and inflammation. Microglia, the resident macrophages in the brain, play a key protective role by clearing plaques and damaged neurons. NRF2 (Nuclear factor erythroid 2-related factor 2) is a master regulator of cytoprotection against oxidative stress, whose activation alleviates oxidative damage, neuroinflammation, and cognitive deficits in AD models.
Read the full summary ↓
However, direct targets of NRF2 in microglia remain unclear. In this study, we demonstrate that NRF2 activation by CDDO-Im significantly suppresses inflammation in human microglial cells (HMC3) stimulated by IFN-gamma or Amyloid beta. Through integrative RNA-sequencing and ChIP-sequencing analysis of NRF2, we identified five representative NRF2 directly target genes involved in inflammation (e.g., IL6, CDK6) and another five in autophagy (e.g., TFE3, SQSTM1). Public single-cell transcriptomic data further underscored the critical role of microglia in NRF2-mediated autophagy regulation within AD brains. Our findings revealed new direct NRF2 target genes, highlighting dual role of NRF2 in suppressing inflammation and enhancing autophagy, thus providing novel insights for therapeutic interventions in AD.
Published in
NRF2 activation by CDDO-Im regulates inflammatory and autophagy pathways in human microglial cells
Chu CT, Uruno A, Suzuki T et al. · Free radical biology & medicine 2026 · PMID 41274321 · doi:10.1016/j.freeradbiomed.2025.11.046
This dataset
Open GSE301269 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 GSE301269_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 PRJNA1280717 and SRA study SRP593766. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 41274321) ↗
Study design
24 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 24 sample titles: 24 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

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