← BioTransfer GEO Dataset Finder
GEO series

GPR34 regulation of disease-associated microglial states in iMGL

GSE327711 Homo sapiens (human) RNA-seq (gene expression) 60 samples Submitted 2026/04/10 Platform GPL34281
At a glance

RNA-seq (gene expression) · human · 60 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
Homo sapiens (human)
Samples
60 samples: 60 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)
GPR34 is a microglia-enriched GPCR whose expression is downregulated under several disease conditions, including Alzheimer’s disease (AD) and multiple sclerosis (MS). Despite this, its function is poorly understood in normal or disease conditions, as is its contribution to disease-related microglia states. Using RNA-sequencing, we find that microglia from global Gpr34 knockout (KO) mouse brains exhibited transcriptional shifts toward disease-associated microglia (DAM) and inflammatory profiles, partially mirroring the microglial phenotype seen in 5xFAD AD model mice.
Read the full summary ↓
Notably, when Gpr34 KO mice were crossed with 5xFAD mice, DAM transcriptional profiles and glial pathology were further exacerbated despite the already robust DAM signature driven by amyloidosis. This occurred without affecting amyloid plaque burden. In human stem cell-derived microglia (iMGLs), GPR34 KO strongly reduced calcium (Ca²⁺) and phosphorylated ERK (pERK) signaling in response to known GPR34 agonists, including lyso-phosphatidylserine (lysoPS) and myelin, and caused transcriptional alterations linked to immune regulation and cell proliferation. Interestingly, GPR34 loss selectively impaired phagocytosis of myelin but not amyloid-β or E. coli. Furthermore, GRP34 KO diminished, but did not abolish, the transcriptional response elicited by myelin. Together, these findings suggest that GPR34 is important for maintaining microglia homeostasis, mediating phagocytosis of and transcriptional response to myelin, and restraining microglial response to neurodegenerative disease conditions.
This dataset
Open GSE327711 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 GSE327711_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 PRJNA1451925 and SRA study SRP690963. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
60 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 60 sample titles: 60 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

+ 20 more — browse all 60 samples with per-sample file links →

Datasets on a similar topic

Search all human 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.