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Chronic IFN-γ Exposure Drives Divergent Cellular and Molecular Responses in Glioblastoma Subtypes

GSE304761 Homo sapiens (human) Single-cell RNA-seq 30 samples Submitted 2025/08/12 Platform GPL24676
At a glance

Single-cell RNA-seq · human · 30 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
30 samples: 10 conditions, mostly in triplicateGroups 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)
Background: Glioblastoma (GBM) is an aggressive primary brain tumor with poor prognosis and limited treatments. Interferon-gamma (IFN-γ) plays a complex role in modulating immune and tumor responses, and its long-term impact on GBM remains unclear. Methods: Human GBM cell lines U87 and U251 were subjected to up to 28 days of chronic IFN-γ exposure and assessed for morphology, gene expression, protein dynamics, and cytokine secretion.
Read the full summary ↓
RNA sequencing, automated Western blotting, and single-cell proteomics were employed to uncover distinct cellular and molecular adaptations to prolonged IFN-γ signaling. Results: Chronic IFN-γ exposure induced significantly divergent phenotypic changes in U87 and U251 cells. U87 cells formed neurosphere-like structures and upregulated STAT1/STAT3 signaling, suggesting an immunogenic response. In contrast, U251 cells displayed elongated morphologies and activated PI3K-Akt and Erk pathways, promoting survival and immune evasion. Differential gene expression analysis highlighted unique temporal profiles, with U251 cells exhibiting higher expression of immune resistance genes such as PD-L1 and VEGF. Secretome profiling further demonstrated contrasting cytokine landscapes: U87 cells secreted pro-inflammatory cytokines, while U251 cells produced immunosuppressive factors. Conclusions: Chronic IFN-γ exposure drives distinct molecular and phenotypic changes in GBM subtypes, reflecting their intrinsic heterogeneity. While IFN-γ enhances immune activation, prolonged exposure may also promote immune escape. These findings emphasize the need for therapeutic strategies combining IFN-γ with immune checkpoint inhibitors and targeted pathway modulation to optimize GBM treatment outcomes.
This dataset
Open GSE304761 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 GSE304761_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 PRJNA1302463 and SRA study SRP607082. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
10 conditions, mostly in triplicate
U87-Control, biol ×3 U87-D3, biol ×3 U87-D14, biol ×3 U87-D28W, biol ×3 U87-D28WO, biol ×3 U251-Control, biol ×3 U251-D3, biol ×3 U251-D14, biol ×3 +2 more

Supports a between-group comparison across 30 samples.

How we read this: 10 replicated groups read from 30 sample titles; they account for 30 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.

Samples in this study
More Glioblastoma datasets

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