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PPAR agonist, DTMB reduces chronic neuroinflammation in 5xFAD mice model.

GSE184855 Mus musculus (mouse) RNA-seq (gene expression) 22 samples Submitted 2025/09/01 Platform GPL17021
At a glance

RNA-seq (gene expression) · mouse · 22 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
22 samples: 8 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)
Abnormal production of amyloid-beta (Aβ) plaque and chronic neuroinflammation are commonly observed in the brain of patients with Alzheimer’s disease, and both of which induce neuronal cell death, loss of memory, and cognitive dysfunction. However, many of the drugs targeting the production of Aβ peptides have been unsuccessful in treating Alzheimer’s disease.
Read the full summary ↓
In this study, we identified synthetic novel peroxisome proliferator activating receptor (PPAR) agonist, DTMB, which can ameliorate the chronic inflammation and Aβ pathological progression of Alzheimer’s disease. We discovered that DTMB attenuated the proinflammatory cytokine production of microglia by reducing the protein level of NF-κB. DTMB also improved the learning and memory defects and reduced the amount of Aβ plaque in the brain of 5xFAD mice. This reduction in Aβ pathology was attributed to the changes in gliosis and chronic inflammation level. Additionally, bulk RNA sequencing showed that genes related to inflammation and cognitive function were changed in the hippocampus and cortex of DTMB treated mice. Our findings demonstrate that DTMB has the potential to be a novel therapeutic agent for Alzheimer’s disease.
This dataset
Open GSE184855 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 GSE184855_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 PRJNA766662 and SRA study SRP338932. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
8 conditions, mostly in triplicate
Hippocampus_WT-V ×3 Hippocampus_WT-DTMB ×3 Hippocampus_TG-DTMB ×3 Cortex_WT-V ×3 Cortex_TG-V ×3 Cortex_TG-DTMB ×3 Hippocampus_TG-V ×2 Cortex_WT-DTMB ×2

Supports a between-group comparison across 22 samples.

How we read this: 8 replicated groups read from 22 sample titles; they account for 22 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
Datasets on a similar topic

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