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The Gain-of-Function TREM2-T96K Mutation Increases Risk for Alzheimer’s Disease by Impairing Microglial Function

GSE307564 Mus musculus (mouse) RNA-seq (gene expression) 44 samples Submitted 2025/09/17 Platform GPL30172
At a glance

RNA-seq (gene expression) · mouse · 44 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
44 samples
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
The gain-of-function TREM2-T96K mutation increases risk for Alzheimer's disease by impairing microglial functionNeuron · 2026
Summary (written by the submitters)
The aim of this study is to characterize the underlying molecular mechanisms of the gain-of-function Trem2T96K mutation in Alzheimer’s disease (AD) pathogenesis by using the constitutive Trem2T96K knock-in mouse crossed to the 5xFAD mouse model of AD. Gene expression analyses were performed on microglial cells isolated from male and female Trem2+/+ (WT), 5xFAD;Trem2+/+, 5xFAD;Trem2T96K/+, and 5xFAD;Trem2T96K/T96K mice at 8 months of age to analyze the impact of the Trem2T96K mutation on the transcriptome of microglia in the setting of neuroinflammation (i.e.
Read the full summary ↓
in the 5xFAD brain). The results revealed that Trem2T96K reprogrammed microglial gene expression and downregulated signaling pathways related to microglial function, specifically, in female 5xFAD mice.
Published in
The gain-of-function TREM2-T96K mutation increases risk for Alzheimer's disease by impairing microglial function
Pilat DJ, Le H, Prokopenko D et al. · Neuron 2026 · PMID 41109213 · doi:10.1016/j.neuron.2025.09.032
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 GSE307564_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 PRJNA1322294 and SRA study SRP618657. Searching any of these in the dataset finder brings you back here.

Samples in this study

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+ 44 more — browse all 44 samples with per-sample file links →

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