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Role of MAIT cells in Glioblastoma

GSE289864 Homo sapiens (human) Single-cell RNA-seq 46 samples Submitted 2025/11/14 Platform GPL24676
At a glance

Single-cell RNA-seq · human · 46 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
Homo sapiens (human)
Topic
GlioblastomaThis series is part of our curated briefing on it.
Samples
46 samples: 46 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
Characterization of MAIT cells in glioblastoma reveals a potential immunosuppressive role through a MAIT-neutrophil axisNeuro-oncology advances · 2025
Summary (written by the submitters)
Glioblastoma (GBM) is the most aggressive primary brain cancer in adults and remains incurable. We used multi-omics and flow cytometry methods to investigate the role of mucosal-associated invariant T (MAIT) cells in GBM. In bulk RNA sequencing data analysis of GBM tissues, MAIT cell gene signature significantly correlated with poor patient survival.
Read the full summary ↓
A scRNA-seq of CD45+ cells from 23 GBM tissue samples showed a diverse immune population in the GBM tumor microenvironment. Of the 23 GBM tissue samples analyzed, 15 (65.2%) were positive for MAIT cells. MAIT cells clustered with cell populations expressing a high level of transcription factor gene RORC, suggesting the enrichment of MAIT17 subset. The MAIT cell signature significantly correlated with the activity of tumor-associated neutrophils (TANs) and myeloid-derived suppressor cells (MDSCs). Multiple immune suppressive genes known to be used by TANs/MDSCs were upregulated in MAIT-positive tumors. Spatial imaging analysis of GBM tissues showed that all specimens were positive for both MAIT cells and TANs and localized enrichment of TANs. These findings highlight the MAIT-TAN/MDSC axis as a novel therapeutic target to modulate GBM’s immunosuppressive tumor microenvironment.
Published in
Characterization of MAIT cells in glioblastoma reveals a potential immunosuppressive role through a MAIT-neutrophil axis
Keretsu S, Hana T, Lee A et al. · Neuro-oncology advances 2025 · PMID 41030524 · doi:10.1093/noajnl/vdaf173
This dataset
Open GSE289864 in finder → Find more human RNA-seq datasets → Glioblastoma landscape →
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 GSE289864_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 PRJNA1225213 and SRA study SRP564670. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 41030524) ↗
Study design
46 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 46 sample titles: 46 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

+ 6 more — browse all 46 samples with per-sample file links →

More Glioblastoma datasets

Search all human 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.