GEO series

Spatial multi-omics defines a shared glioblastoma infiltrative signature at the resection margin [RNA-seq]

GSE286419 Homo sapiens (human) Single-nucleus RNA-seq 12 samples Submitted 2025/07/14 Platform GPL24676

At a glance

Single-nucleus RNA-seq · human · 12 samples

What it is: Single-nucleus RNA-seqGene activity measured in individual nuclei (used when whole cells are hard to isolate, for example from frozen tissue). 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: 12 samples: 12 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: Spatial Multiomics Defines a Shared Tumor Infiltrative Signature at the Resection Margin in High-Grade GliomasCancer research · 2025

Summary (written by the submitters)

Glioblastoma (GBM) remains an untreatable disease. Understanding of GBM’s infiltrative biology at the resection margin is limited, despite causing disease recurrence and progression. To address this, we generated a high-throughput single-nucleus (sn)RNA-seq and snATAC-seq multi-omic dataset from six tumors with distinct genomic drivers and combined it with spatial transcriptomics to characterize the unique molecular phenotype of GBM near the margin.

Read the full summary ↓

By contrasting GBM-specific biology in matching “Core” vs. “Margin” dissections, we define unique, shared “GBM infiltration” and chromatin accessibility signatures near the margin. We prioritize EGFR as a top differentially expressed and accessible “Margin” marker across GBM subtypes, show its dynamic expression along a core-to-margin infiltration trajectory, and validate its role in migration through CRISPR/Cas9 deletion in two patient-derived models. ChIP-seq studies furthermore corroborate preferential TEAD1 binding at EGFR’s accessible regulatory elements. This validated multi-omic dataset enables further studies into tumor and microenvironment biology in the context of residual GBM disease.

Published in

Spatial Multiomics Defines a Shared Tumor Infiltrative Signature at the Resection Margin in High-Grade Gliomas

Pai B, Ramos SI, Cheng WS et al. · Cancer research 2025 · PMID 40759029 · doi:10.1158/0008-5472.CAN-24-4708

This dataset

Open GSE286419 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 GSE286419_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 PRJNA1209108 and SRA study SRP556576. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 40759029) ↗

Study design

12 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 12 sample titles: 12 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

GSM8727406 HGG1C(GBM1C)
GSM8727407 HGG1M(GBM1M)
GSM8727408 HGG2C(GBM2C)
GSM8727409 HGG2M(GBM2M)
GSM8727410 HGG3A(GBM3A)
GSM8727411 HGG3B(GBM3B)
GSM8727412 HGG4C(GBM4C)
GSM8727413 HGG4M(GBM4M)
GSM8727414 HGG5A(GBM5A)
GSM8727415 HGG5B(GBM5B)
GSM8727416 HGG6C(GBM6C)
GSM8727417 HGG6M(GBM6M)

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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.