GEO series

A longitudinal single-cell and spatial multiomic atlas of pediatric high-grade glioma

GSE292623 Homo sapiens (human) Single-cell RNA-seq 18 samples Submitted 2026/03/11 Platform GPL24676

At a glance

Single-cell RNA-seq · human · 18 samples

What it is: Single-cell RNA-seqGene activity measured in thousands of individual cells, so cell types can be told apart.
Organism: Homo sapiens (human)
Samples: 18 samples: 18 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: A longitudinal single-cell and spatial multiomic atlas of pediatric high-grade gliomaCell reports. Medicine · 2026

Summary (written by the submitters)

Pediatric high-grade glioma (pHGG) is an incurable central nervous system malignancy that is a leading cause of pediatric cancer death. While pHGG shares many similarities to adult glioma, it comprises distinct disease entities. In this study, we longitudinally profile a molecularly diverse cohort of 16 pHGG patients through single-nucleus RNA and ATAC sequencing, whole-genome sequencing, and CODEX spatial proteomics to capture the evolution of neoplastic and microenvironmental features during disease progression and treatment.

Read the full summary ↓

We define a set of core pHGG neoplastic cell states and observe differential tumor-myeloid interactions between malignant cell phenotypes. We find that essential neuromodulators and the interferon response are upregulated post-therapy and validated malignant cell-intrinsic targets. We observe an increase in oligodendrocytes upon progression and that they coordinate spatial motifs with proneural tumor cells. This multiomic atlas of longitudinal pHGG captures features of therapy response and provides a scalable reference for the study of pediatric brain tumors.

Published in

A longitudinal single-cell and spatial multiomic atlas of pediatric high-grade glioma

Sussman JH, Oldridge DA, Yu W et al. · Cell reports. Medicine 2026 · PMID 42030938 · doi:10.1016/j.xcrm.2026.102766

This dataset

Open GSE292623 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 GSE292623_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 PRJNA1240321 and SRA study SRP572256. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 42030938) ↗

Study design

18 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 18 sample titles: 18 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

GSM8862485 A-195NR
GSM8862486 B-195NR
GSM8862487 C-195NR
GSM8862488 D-195NR-4Gy
GSM8862489 E-195NR-4Gy
GSM8862490 F-195NR-4Gy
GSM8862491 G-1763NR
GSM8862492 H-1763NR
GSM8862493 I-1763NR
GSM8862494 J-1763NR-4Gy
GSM8862495 K-1763NR-4Gy
GSM8862496 L-1763NR-4Gy
GSM8862497 A-1746-0Gy
GSM8862498 B-1746-0Gy
GSM8862499 C-1746-0Gy
GSM8862500 D-1746-4Gy
GSM8862501 E-1746-4Gy
GSM8862502 F-1746-4Gy

Datasets on a similar topic

GSE196198 Longitudinal single-cell multiomics data for different severity of COVID-19 100 samples shares longitudinal, single-cell, multiomic
GSE282122 A longitudinal single-cell atlas of anti-tumour necrosis factor treatment in inflammatory bowel disease 216 samples shares longitudinal, single-cell
GSE324131 A single-cell and spatial atlas of prostate cancer reveals the combinatorial nature of gene modules underlying lineage plasticity and metastasis 38 samples shares single-cell, spatial
GSE176078 A single-cell and spatially resolved atlas of human breast cancers 26 samples shares single-cell, spatial
GSE278688 Integrated Single-Cell and Spatial Transcriptomics Uncover Distinct Cellular Subtypes Involved in Neural Invasion in PDAC [scRNA-Seq] 29 samples shares single-cell, spatial
GSE305146 Single-Cell Multiomic Atlas of Human Cortical Development in Down Syndrome - Fetal Cortex Multiome Dataset 92 samples shares single-cell, multiomic
GSE324206 Spatially-Resolved Multiomic Atlas of Leiomyosarcoma Identifies Two Clinically Relevant Epigenetically-Driven Cell States [scMultiomic] 32 samples shares multiomic, spatial
GSE290367 Single-cell multiomic integration identifies widespread, cell-type resolved fetal reactivation in the diseased human heart 90 samples shares single-cell, multiomic

Search all human RNA-seq datasets in GEO →

Share this dataset

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.