GEO series

Multimodal single-cell analyses reveal distinct fusion-regulated transcriptional programs in Ewing sarcoma

GSE332630 Homo sapiens (human) Single-cell RNA-seq 22 samples Submitted 2026/05/20 Platform GPL24676Platform GPL30173

At a glance

Single-cell RNA-seq · human · 22 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)
Topic: Ewing sarcomaThis series is part of our curated briefing on it.
Samples: 22 samples: 22 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.

Summary (written by the submitters)

Ewing sarcoma (EwS) is a fusion-driven malignancy, peaking in adolescence. Although EwS tumors are driven uniquely by EWS::FLI1 and related fusions, patient outcomes vary greatly. If and how tumor plasticity of EWS::FLI1-regulated transcriptional signatures contribute to disease progression is not known. To address this, we utilized a single-cell co-assay of RNA and chromatin accessibility (ATAC) sequencing to identify gene regulatory networks in EwS.

Read the full summary ↓

By comprehensively characterizing regulatory elements across cell lines, we identified multiple unique modules of gene regulation. Differential usage and prevalence of these modules was evident across cell lines, associated with distinct epigenetic and transcriptomic signatures, and in specific cases, modifiable by exogenous TGF-β. When we examined primary EwS patient tumors, we observed these same regulatory modules were variably enriched both across and within tumors, highlighting the existence of intratumoral heterogeneity in gene regulatory networks. Our findings demonstrate that multiple, co-existing transcriptional programs shape the phenotypic diversity of EwS and suggest that the balance between these networks may have important implications for clinical outcomes and targeted therapy development.

This dataset

Open GSE332630 in finder → Find more human RNA-seq datasets → Ewing sarcoma 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 GSE332630_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 PRJNA1468702 and SRA study SRP702431. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗

Study design

22 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 22 sample titles: 22 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

GSM9751717 Multiome, A4573_A673_SKNMC ewing cell-lines, GEX
GSM9751718 Multiome, A4573_A673_SKNMC ewing cell-lines, ATAC
GSM9751719 Multiome, CHLA10_PDX305_TC32 ewing cell-lines, GEX
GSM9751720 Multiome, CHLA10_PDX305_TC32 ewing cell-lines, ATAC
GSM9751721 Multiome, CHLA9_RDES_TC71 ewing cell-lines, GEX
GSM9751722 Multiome, CHLA9_RDES_TC71 ewing cell-lines, ATAC
GSM9751723 Multiome, A673-shNS_CHLA10-shNS_PDX-132 cell-lines, GEX
GSM9751724 Multiome, A673-shNS_CHLA10-shNS_PDX-132 cell-lines, ATAC
GSM9751725 Multiome, A673-shFL1_CHLA10-shFL1_hMSC-240L-parent cell-lines , GEX
GSM9751726 Multiome, A673-shFL1_CHLA10-shFL1_hMSC-240L-parent cell-lines , ATAC
GSM9751727 Multiome, TC71-shNS_PDX305-shNS_U2OS-parent cell-lines , GEX
GSM9751728 Multiome, TC71-shNS_PDX305-shNS_U2OS-parent cell-lines , ATAC
GSM9751729 Multiome, TC71-shFL1_PDX305-shFL1_hMSC-parent cell-lines , GEX
GSM9751730 Multiome, TC71-shFL1_PDX305-shFL1_hMSC-parent cell-lines , ATAC
GSM9751731 Multiome, U2OS-empty_hMSC-empty_RD-parent cell-lines , GEX
GSM9751732 Multiome, U2OS-empty_hMSC-empty_RD-parent cell-lines , ATAC
GSM9751733 Multiome, U2OS-EFOE_hMSC-EFOE_H7-MSC-parent cell-lines , GEX
GSM9751734 Multiome, U2OS-EFOE_hMSC-EFOE_H7-MSC-parent cell-lines , ATAC
GSM9751735 Multiome, TC71_A673_CHLA10_vehicle ewing cell-lines, GEX
GSM9751736 Multiome, TC71_A673_CHLA10_vehicle ewing cell-lines, ATAC
GSM9751737 Multiome, TC71_A673_CHLA10_TGFB1 ewing cell-lines, GEX
GSM9751738 Multiome, TC71_A673_CHLA10_TGFB1 ewing cell-lines, ATAC

More Ewing sarcoma datasets

GSE250523 Single-cell RNA sequencing of bladder Ewing sarcoma 4 samples same disease briefing · shares single-cell, sarcoma, ewing
GSE293632 The hegemonic EWSR1::ETS oncogene overrules core regulatory circuitry mechanisms in Ewing sarcoma. 39 samples same disease briefing · shares sarcoma, ewing
GSE211743 KDM6A and B-mediated transcriptional activation of EWSR1-FLI1 targets coordinates demethylase dependent and independent mechanisms in Ewing sarcoma 99 samples same disease briefing · shares sarcoma, ewing
GSE299137 Autocrine TGFβ2 enforces a transcriptionally hybrid cell state in Ewing sarcoma 48 samples same disease briefing · shares sarcoma, ewing
GSE176339 The Ewing Sarcoma Cell Line Atlas (ESCLA) 306 samples same disease briefing · shares sarcoma, ewing
GSE299432 Transcriptional Rewiring of BET Inhibitor Treated Ewing Sarcoma Cells Augments their Dependency on Focal Adhesion Kinase 30 samples same disease briefing · shares sarcoma, ewing
GSE291136 Dynamic modelling of EWS::FLI1 fluctuations reveals molecular determinants of phenotypic tumor plasticity and prognosis in Ewing sarcoma 222 samples same disease briefing · shares sarcoma, ewing
GSE277083 Single cell RNA sequencing of Ewing sarcoma tumors demonstrate transcriptional heterogeneity and clonal evolution 15 samples same disease briefing · shares sarcoma, ewing

Search all human RNA-seq 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.