Disease-associated microglia adopt stage-specific phenotypes that regulate T cell fate and immunity in glioma
Single-cell RNA-seq · mouse · 11 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
- 11 samples: 3 × D10 vs 3 × D20 vs 3 × D30 vs 2 × NGroups 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
- Disease-associated microglia adopt stage-specific phenotypes that regulate T cell fate and immunity in gliomaImmunity · 2026
Read the full summary ↓
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.
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.
Supplementary files
The count tables and other files the authors uploaded. Take only the samples you need, or the whole study as GSE171081_RAW.tar (it can run to tens of gigabytes).
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: 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 PRJNA718289 and SRA study SRP312606. Searching any of these in the dataset finder brings you back here.
Supports a between-group comparison across 11 samples.
How we read this: 4 replicated groups read from 11 sample titles; they account for 11 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.
- GSM5218446 D10-3 D10
- GSM5218447 D20-3 D20
- GSM5218448 D30-3 D30
- GSM6285595 D10-1 D10
- GSM6285596 D20-1 D20
- GSM6285597 D30-1 D30
- GSM6285598 D10-2 D10
- GSM6285599 D20-2 D20
- GSM6285600 D30-2 D30
- GSM6285601 N-1 N
- GSM6285602 N-2 N
- GSE327428 GPR34 regulation of disease-associated microglial states in mice 96 samples shares disease-associated, microglia
- GSE223424 Immune checkpoint TIM-3 regulates microglia and Alzheimer’s disease 57 samples shares microglia, regulate
- GSE252628 Microglia-astrocyte crosstalk regulates synapse remodeling via Wnt signaling 56 samples shares microglia, regulate
- GSE278199 Microglia regulate cortical remyelination via ΤNFR1-dependent phenotypic polarization [scRNA-Seq] 45 samples shares microglia, regulate
- GSE310017 Disease-associated mutations on the STAT5B SH2 domain regulate cholesterol and lipid metabolism in liver [RNA-Seq liver] 37 samples shares disease-associated, regulate
- GSE327604 Epigenetic control of microglial mitochondrial immunity by KAT7 drives Alzheimer’s disease pathogenesis [RNA-Seq] 12 samples shares microglia, immunity
- GSE220043 Arresting the Bad Seed: HDAC3 Regulates Proliferation of Different Microglia after Ischemic Stroke 22 samples shares microglia, regulate
- GSE264105 Plate-based scRNA-seq of plasma-positive microglia and plasma-negative microglia in young mice 1111 samples shares microglia
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.