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Epigenetic heterogeneity and state-specific transcriptional programs determine differential drug response in osteosarcoma [RNA-seq]

GSE270505 Homo sapiens (human) Single-cell RNA-seq 19 samples Submitted 2025/09/29 Platform GPL24676Platform GPL20301
At a glance

Single-cell RNA-seq · human · 19 samples

What it is
Single-cell RNA-seqGene activity measured in thousands of individual cells, so cell types can be told apart. 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
OsteosarcomaThis series is part of our curated briefing on it.
Samples
19 samples: 19 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)
Osteosarcoma is a genomically complex tumor with structural rearrangements, aneuploidy, and chromosomal alterations, resulting in significant intertumoral heterogeneity. This complexity hampers the identification of key oncogenic pathways and subtypes. We hypothesized that chromatin accessibility could uncover molecular features to clarify their transcriptional programs, suggesting new therapies.
Read the full summary ↓
Using ATAC-seq, H3K27ac profiling, and single-cell multiome analysis, we identified two distinct cellular states in osteosarcoma driven by unique transcription factor networks linked to normal bone development. These subtypes can be detected in patient samples via a specific gene expression signature with prognostic value.
This dataset
Open GSE270505 in finder → Find more human RNA-seq datasets → Osteosarcoma 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 GSE270505_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 PRJNA1126765 and SRA study SRP515379. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
19 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 19 sample titles: 19 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
More Osteosarcoma datasets

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.