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Single-cell transcriptome analysis of wildtype, TREM2 knockout, GPR34 knock-out or TREM2/GPR34 double knock-out primary microglia from wildtype and APP knock-in mice (8 months)

GSE293041 Mus musculus (mouse) Single-cell RNA-seq 10 samples Submitted 2026/07/21 Platform GPL24247
At a glance

Single-cell RNA-seq · mouse · 10 samples

What it is
Single-cell RNA-seqGene activity measured in thousands of individual cells, so cell types can be told apart.
Organism
Mus musculus (mouse)
Samples
10 samples: 10 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)
Microglia are broadly implicated in modifying disease risk in the central nervous system (CNS). Identifying regulators of microglia state is critical for elucidating their role in disease and identifying novel drug targets. GPR34 is a G-protein coupled receptor expressed in homeostatic microglia where its function is not well understood.
Read the full summary ↓
Like TREM2, GPR34 detects lipid ligands. In both healthy and amyloid mouse models, Gpr34 KO accelerated microglial state transcriptionally and histologically consistent conversion of homeostatic microglia to a disease-associated microglia (DAM) state. Shifts in microglial state were accompanied by increased expression of multiple metabolic gene sets.
This dataset
Open GSE293041 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 GSE293041_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 PRJNA1242426 and SRA study SRP573403. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
10 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 10 sample titles: 10 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.