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Distinct Myeloid Derived Suppressor Cell Populations in Human Glioblastoma

GSE278456 Homo sapiens (human) Single-cell RNA-seq 39 samples Submitted 2025/01/23 Platform GPL24676
At a glance

Single-cell RNA-seq · human · 39 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
39 samples
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
Distinct myeloid-derived suppressor cell populations in human glioblastomaScience (New York, N.Y.) · 2025
Summary (written by the submitters)
The diversity of transcriptional programs and cellular plasticity of glioma-associated myeloid cells, and thus their contribution to tumor growth and immune evasion, is poorly understood. We performed single cell RNA-sequencing of immune and tumor cells from 33 glioma patients of varying tumor grades.
Read the full summary ↓
We identified two populations characteristic of myeloid derived suppressor cells (MDSC) that were unique to IDH wild type glioblastomas (GBM) and absent in IDH mutant low-grade gliomas and IDH mutant grade IV astrocytomas: i) an early progenitor population (E-MDSC) characterized by strong upregulation of multiple catabolic, anabolic, oxidative stress, and hypoxia pathways typically observed within tumor cells themselves, and ii) a monocytic MDSC (M-MDSC) population. Spatial transcriptomics demonstrated that the E-MDSCs geographically co-localize with a subset of highly metabolic glioma stem-like tumor cells with a mesenchymal program in the pseudopalisading region, a pathognomonic feature of GBMs associated with poor prognosis. Ligand-receptor interaction analysis revealed symbiotic cross-talk between the stem-like tumor cells and E-MDSCs in GBM, whereby glioma stem cells produce specific chemokines attracting E-MDSCs, which in turn produce growth and survival factors for the tumor cells; in particular the FGFR1 ligand FGF11. This axis is not present in IDH mutant gliomas, associated with hypermethylation and repressed gene expression of the relevant chemokine genes. Our large-scale single-cell analysis elucidated unique MDSC populations that may facilitate GBM progression and mediate tumor immunosuppression.
Published in
Distinct myeloid-derived suppressor cell populations in human glioblastoma
Jackson C, Cherry C, Bom S et al. · Science (New York, N.Y.) 2025 · PMID 39818911 · doi:10.1126/science.abm5214
This dataset
Open GSE278456 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 GSE278456_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 PRJNA1167305. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 39818911) ↗
Study design
A cohort of 39 individual samples

The grouping that defines any comparison lives in the sample characteristics, which GEO records separately from the titles.

How we read this: Sample titles run GBM006 … GBM090 and carry no group information.

Samples in this study
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.