GEO series

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

GSE286415 Homo sapiens (human) ChIP-seq / ATAC-seq (genome binding and open chromatin) 12 samples Submitted 2025/07/14 Platform GPL24676

At a glance

ChIP-seq / ATAC-seq (genome binding and open chromatin) · human · 12 samples

What it is: ChIP-seq / ATAC-seq (genome binding and open chromatin)Where proteins bind the genome, or which regions are open and active.
Organism: Homo sapiens (human)
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 GSE286415 in finder → Find more human ChIP / ATAC / CUT&Tag 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 GSE286415_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: Look for peak files (.bed, .narrowPeak) and signal tracks (.bigWig) among the supplementary files; the raw reads are on ENA.

Also filed as BioProject PRJNA1209099 and SRA study SRP556582. 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

GSM8727350 HGG1C(GBM1C)
GSM8727351 HGG1M(GBM1M)
GSM8727352 HGG2C(GBM2C)
GSM8727353 HGG2M(GBM2M)
GSM8727354 HGG3A(GBM3A)
GSM8727355 HGG3B(GBM3B)
GSM8727356 HGG4C(GBM4C)
GSM8727357 HGG4M(GBM4M)
GSM8727358 HGG5A(GBM5A)
GSM8727359 HGG5B(GBM5B)
GSM8727360 HGG6C(GBM6C)
GSM8727361 HGG6M(GBM6M)

Datasets on a similar topic

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GSE271689 Enhancing Immunotherapy Outcomes: Spatial Multi-Omics Predictive Models for Non-Small Cell Lung Cancer 586 samples shares multi-omics, spatial
GSE274284 Multi-omics dissection of human RAG deficiency reveals distinctive patterns of immune dysregulation but a common inflammatory signature 63 samples shares multi-omics, signature
GSE273848 Definition of a multi-omics signature for Esophageal Adenocarcinoma prognosis prediction [totalRNA-seq] 40 samples shares multi-omics, signature
GSE291865 Multi-omics analyses construct an inflammatory response based prognostic gene signature for cervical cancer and suggest tumor infiltrating monocytes subgroups as key players 23 samples shares multi-omics, signature
GSE277387 Single-Cell and Spatial Multi-omics Reveal Interferon Signaling in the Pathogenesis of Perianal Fistulizing Crohn’s Disease 21 samples shares multi-omics, spatial
GSE318564 Mapping the spatial architecture of glioblastoma from core to edge delineates niche-specific tumor cell states and intercellular interactions [scRNA-seq] 13 samples shares glioblastoma, spatial

Search all human ChIP / ATAC / CUT&Tag 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.