GEO series

Proneural-Mesenchymal hybrid glioblastoma cells are resistant to therapy and dependent on nuclear import-bulk RNAseq

GSE256064 Homo sapiens (human) Single-cell RNA-seq 56 samples Submitted 2025/07/09 Platform GPL24676

At a glance

Single-cell RNA-seq · human · 56 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)
Samples: 56 samples: 17 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.
Paper: Proneural-mesenchymal hybrid glioblastoma cells are resistant to therapy and dependent on nuclear importNeuro-oncology · 2025

Summary (written by the submitters)

Abstract Background Despite extensive research efforts, glioblastoma (GBM) remains a deadly disease with poor prognosis. Although previous studies have identified various cell states within GBM tumors, the molecular mechanism underlying adaptive GBM cell plasticity induced by conventional therapy remains unclear. Methods We used fluorescent reporters for proneural (PN) and mesenchymal (MES) subtypes to monitor GBM cell plasticity in real-time across multiple patient-derived cell lines.

Read the full summary ↓

This approach revealed cells that concurrently expressed both proneural and mesenchymal markers. To investigate this unique hybrid population, we implemented a comprehensive methodological approach encompassing bulk and single-cell RNA sequencing, single-cell ChIP sequencing, nuclear proteomics, high-resolution imaging, orthotopic mouse models, clinical dataset analysis, and pharmacological and genetic techniques. This multifaceted strategy allowed us to gain functional and molecular insights into this distinct cellular population. Results We showed that these hybrid cells are increased by conventional therapies, and are resistant to these therapies. At the molecular level, hybrid cells display significant alterations in chromatin structure and nuclear protein composition, elevated transcriptional activity, Myc activation, and improved transport between the nucleus and cytoplasm. Genetic and pharmaceutical inhibition of the nuclear import/export shuttling machinery, increased in hybrid cells, effectively suppressed adaptive GBM cell plasticity and hybrid identity, thereby enhancing the sensitivity of GBM cells to therapies. Conclusion Our results indicate that GBM hybrid cells play a crucial role in chemoradiation resistance. The nuclear transport machinery presents a potential therapeutic target for hybrid cells, offering a way to counteract the typical resistance to treatment observed in GBM.

Published in

Proneural-mesenchymal hybrid glioblastoma cells are resistant to therapy and dependent on nuclear import

Bourmeau G, Anezo O, Raymond J et al. · Neuro-oncology 2025 · PMID 40627673 · doi:10.1093/neuonc/noaf160

This dataset

Open GSE256064 in finder → Find more human 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 GSE256064_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 PRJNA1077980 and SRA study SRP490328. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 40627673) ↗

Study design

17 conditions, mostly in triplicate

MGG8_GB-Hybrid ×6 MGG4-drug-CTL ×4 MGG4-selinexo ×4 MGG4-vitro-CTL ×3 MGG4-vitro-I ×3 MGG8-vitro-CTL ×3 MGG8-vitro-I ×3 P3-vitro-CTL ×3 +9 more

Supports a between-group comparison across 56 samples.

How we read this: 17 replicated groups read from 56 sample titles; they account for 56 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

GSM8084922 MGG4-vitro-CTL-001 MGG4-vitro-CTL
GSM8084923 MGG4-vitro-CTL-002 MGG4-vitro-CTL
GSM8084924 MGG4-vitro-IR-002 MGG4-vitro-I
GSM8084925 MGG4-vitro-IR-001 MGG4-vitro-I
GSM8084926 MGG4-vitro-CTL-003 MGG4-vitro-CTL
GSM8084927 MGG4-vitro-IR-003 MGG4-vitro-I
GSM8084928 MGG8-vitro-CTL-001 MGG8-vitro-CTL
GSM8084929 MGG8-vitro-CTL-002 MGG8-vitro-CTL
GSM8084930 MGG8-vitro-CTL-003 MGG8-vitro-CTL
GSM8084931 MGG8-vitro-IR-001 MGG8-vitro-I
GSM8084932 MGG8-vitro-IR-002 MGG8-vitro-I
GSM8084933 MGG8-vitro-IR-003 MGG8-vitro-I
GSM8084934 P3-vitro-CTL-001 P3-vitro-CTL
GSM8084935 P3-vitro-CTL-002 P3-vitro-CTL
GSM8084936 P3-vitro-CTL-003 P3-vitro-CTL
GSM8084937 P3-vitro-IR-001 P3-vitro-I
GSM8084938 P3-vitro-IR-002 P3-vitro-I
GSM8084939 P3-vitro-IR-003 P3-vitro-I
GSM8084940 MGG8_GB-Hybrid_002 MGG8_GB-Hybrid
GSM8084941 MGG8_MES_001 MGG8_MES
GSM8084942 MGG8_PN_002 MGG8_PN
GSM8084943 MGG8_GBH-neg_003 MGG8_GBH-neg
GSM8084944 MGG8_PN_003 MGG8_PN
GSM8084945 MGG8_GBH-neg_001 MGG8_GBH-neg
GSM8084946 MGG8_PN_001 MGG8_PN
GSM8084947 MGG8_GBH-neg_002 MGG8_GBH-neg
GSM8084948 MGG8_MES_003 MGG8_MES
GSM8084949 MGG8_GB-Hybrid_001 MGG8_GB-Hybrid
GSM8084950 MGG8_GB-Hybrid_003 MGG8_GB-Hybrid
GSM8084951 MGG8_MES_002 MGG8_MES
GSM8084952 MGG4_GB-Hybrid_001 MGG4_GB-Hybrid
GSM8084953 MGG4_GBH-neg_001 MGG4_GBH-neg
GSM8084954 MGG4_GB-Hybrid_002 MGG4_GB-Hybrid
GSM8084955 MGG4_GBH-neg_002 MGG4_GBH-neg
GSM8084956 MGG4_GB-Hybrid_003 MGG4_GB-Hybrid
GSM8084957 MGG4_GBH-neg_003 MGG4_GBH-neg
GSM8084958 MGG8_GB-Hybrid_1 MGG8_GB-Hybrid
GSM8084959 MGG8_bulk_1 MGG8_bulk
GSM8084960 MGG8_GB-Hybrid_2 MGG8_GB-Hybrid
GSM8084961 MGG8_bulk_2 MGG8_bulk

+ 16 more — browse all 56 samples with per-sample file links →

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