← BioTransfer GEO Dataset Finder
GEO series

A transcriptomic microglia taxonomy across mouse and human pathologies

GSE304010 Mus musculus (mouse) Single-cell RNA-seq 74 samples Submitted 2026/02/09 Platform GPL21626
At a glance

Single-cell RNA-seq · mouse · 74 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
Mus musculus (mouse)
Samples
74 samples: 74 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
A transcriptomic microglia taxonomy across mouse and human pathologiesNature immunology · 2026
Summary (written by the submitters)
Microglia are the key immune cells involved in virtually all diseases of the central nervous system (CNS). Numerous previous single-cell sequencing approaches to the study of microglia have revealed extensive diversity in this cell type in the human and mouse brain in different conditions. Importantly, a unifying, comprehensive, disease- and species spanning transcriptomic immune cell-focused atlas of the perturbed CNS, including true single-cell spatial transcriptomic information on neighborhood relationships, is lacking.
Read the full summary ↓
Here, we generated a novel large-scale single-cell RNA-sequencing-derived taxonomy through the analysis of > 1 million CNS cells enriched for myeloid cells across > 30 different pathologies and conditions. Disease-associated human microglia, together with CNS-associated macrophages and monocytes, were differentiated iteratively into 27 superclusters and 192 clusters, showing substantial overlap with their counterparts in the corresponding murine pathologies. This top-down approach, in combination with targeted and genome-wide subcellular spatial transcriptomics, enabled us to examine and compare the spatial interactome of the identified superclusters and clusters within and across pathologies and species. Using cell-specific mutants, we further characterized the underlying transcriptional programs of activation-associated microglial clusters, identifying distinct dependencies on type I and II interferons and colony stimulating factor 1 receptor pathways. Our data provide new insights into the spatial dynamics of the endogenous CNS immune system during development, health and disease in human and mouse.
Published in
A transcriptomic microglia taxonomy across mouse and human pathologies
Chhatbar C, Sankowski R, Schulz M et al. · Nature immunology 2026 · PMID 41882205 · doi:10.1038/s41590-026-02472-z
This dataset
Open GSE304010 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 GSE304010_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 PRJNA1298755 and SRA study SRP604605. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 41882205) ↗
Study design
74 samples, each with a distinct title — no replicated groups

Supports a case/control comparison: 1 samples read as cases, 22 as controls.

How we read this: Read from 74 sample titles: 74 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

+ 34 more — browse all 74 samples with per-sample file links →

Datasets on a similar topic

Search all mouse 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.