GEO series

A Single-cell Atlas of Schwannoma Across Genetic Backgrounds and Anatomic Locations

GSE292071 Homo sapiens (human) Single-cell RNA-seq 151 samples Submitted 2025/04/17 Platform GPL30173

At a glance

Single-cell RNA-seq · human · 151 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: 151 samples: 151 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 single-cell atlas of Schwannoma across genetic backgrounds and anatomic locationsGenome medicine · 2025

Summary (written by the submitters)

Background: Schwannomas are nerve sheath tumors arising at cranial and peripheral nerves, either sporadically or in patients with a schwannomatosis-predisposition syndrome. There is limited understanding of the transcriptional heterogeneity of schwannomas across genetic backgrounds and anatomic locations. Methods: Here, we prospectively profile by single-cell full-length transcriptomics tumors from 22 patients with NF2-related schwannomatosis, non-NF2-related schwannomatosis, and sporadic schwannomas, resected from cranial and peripheral nerves.

Read the full summary ↓

We profiled 11,373 cells, including neoplastic cells, fibroblasts, T cells, endothelial cells, myeloid cells and pericytes. Results: We characterize the intra-tumoral genetic and transcriptional heterogeneity of schwannoma, identifying six distinct transcriptional metaprograms, with gene signatures related to stress, myelin production, antigen presentation, interferon signaling, glycolysis and extracellular matrix. We demonstrate the robustness of our findings with analysis of an independent cohort. Conclusion: Overall, our atlas describes the spectrum of gene expression across schwannoma entities at the single-cell level and will serve as important resource for the community.

Published in

A single-cell atlas of Schwannoma across genetic backgrounds and anatomic locations

Gonzalez Castro LN, Gavish A, Bussema L et al. · Genome medicine 2025 · PMID 40217315 · doi:10.1186/s13073-025-01462-4

This dataset

Open GSE292071 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 GSE292071_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 PRJNA1236642 and SRA study SRP570675. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 40217315) ↗

Study design

151 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 151 sample titles: 151 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

GSM8848407 SCHW001-P2_
GSM8848408 SCHW001-P5_
GSM8848409 SCHW001-P6_
GSM8848410 SCHW001-P7_
GSM8848411 SCHW2_P1_
GSM8848412 SCHW2_P10_
GSM8848413 SCHW2_P11_
GSM8848414 SCHW2_P12_
GSM8848415 SCHW2_P3_
GSM8848416 SCHW2_P5_
GSM8848417 SCHW2_P6_
GSM8848418 SCHW2_P7_
GSM8848419 SCHW2_P8_
GSM8848420 SCHW2_P9_
GSM8848421 SCHW3_P1_
GSM8848422 SCHW3_P2_
GSM8848423 SCHW4_P1_
GSM8848424 SCHW4_P2_
GSM8848425 SCHW4_P3_
GSM8848426 SCHW4_P4_
GSM8848427 SCHW4_P5_
GSM8848428 SCHW4_P6_
GSM8848429 SCHW4_P7_
GSM8848430 SCHW4_P8_
GSM8848431 SCHW5_P1_
GSM8848432 SCHW5_P10_
GSM8848433 SCHW5_P2_
GSM8848434 SCHW5_P4_
GSM8848435 SCHW5_P5_
GSM8848436 SCHW5_P6_
GSM8848437 SCHW5_P7_
GSM8848438 SCHW5_P8_
GSM8848439 SCHW5_P9_
GSM8848440 SCHW6_P1_
GSM8848441 SCHW6_P10_
GSM8848442 SCHW6_P2_
GSM8848443 SCHW6_P3_
GSM8848444 SCHW6_P5_
GSM8848445 SCHW6_P6_
GSM8848446 SCHW6_P7_

+ 111 more — browse all 151 samples with per-sample file links →

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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.