GEO series

Single-Cell Fixed RNA Profiling of Osteosarcoma Evolution Reveals a Coordinated Multi-cellular Network Driving Metastasis

GSE318840 Homo sapiens (human) Single-cell RNA-seq 8 samples Submitted 2026/06/30 Platform GPL35417

At a glance

Single-cell RNA-seq · human · 8 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: OsteosarcomaThis series is part of our curated briefing on it.
Samples: 8 samples: 2 × Osteosarcoma_Naive vs 2 × Osteosarcoma_Treated vs 2 × Osteosarcoma_Recurrent vs 2 × Osteosarcoma_MetastasisGroups 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 remains a profound clinical challenge, particularly for patients with metastatic or recurrent disease where survival rates have stagnated for decades. In this study, we present the first systematic application of Single-cell Fixed RNA Profiling (FLEX) to the osteosarcoma clinical spectrum. We profiled over 40,000 cells across treatment-naïve, post-chemotherapy, recurrent, and lung metastatic stages.

Read the full summary ↓

By identifying the ADAMTS12/RNASE1/S100A4/PLVAP multi-cellular network, we move beyond simple cell-type identification toward an integrated understanding of how malignant cells and their microenvironment co-evolve. These findings offer a translational framework for improved risk stratification and the development of next-generation therapies designed to disrupt the interactive networks supporting metastatic osteosarcoma.

This dataset

Open GSE318840 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 GSE318840_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 PRJNA1420714 and SRA study SRP675669. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗

Study design

2 × Osteosarcoma_Naive vs 2 × Osteosarcoma_Treated vs 2 × Osteosarcoma_Recurrent vs 2 × Osteosarcoma_Metastasis

Supports a case/control comparison: 4 samples read as cases, 2 as controls.

How we read this: 4 replicated groups read from 8 sample titles; they account for 8 of them. 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

GSM9504297 Osteosarcoma_Naive_1 Osteosarcoma_Naive
GSM9504298 Osteosarcoma_Naive_2 Osteosarcoma_Naive
GSM9504299 Osteosarcoma_Treated_1 Osteosarcoma_Treated
GSM9504300 Osteosarcoma_Treated_2 Osteosarcoma_Treated
GSM9504301 Osteosarcoma_Recurrent_1 Osteosarcoma_Recurrent
GSM9504302 Osteosarcoma_Recurrent_2 Osteosarcoma_Recurrent
GSM9504303 Osteosarcoma_Metastasis_1 Osteosarcoma_Metastasis
GSM9504304 Osteosarcoma_Metastasis_2 Osteosarcoma_Metastasis

More Osteosarcoma datasets

GSE250015 Exploring the impact of PDGFD in osteosarcoma metastasis through single-cell sequencing analysis 2 samples same disease briefing · shares osteosarcoma, single-cell, metastasis
GSE162454 Single-cell RNA sequencing of human osteosarcoma 6 samples same disease briefing · shares osteosarcoma, single-cell
GSE293148 Metastasis-initiating osteosarcoma subpopulations establish paracrine interactions with both lung and tumor cells to create a metastatic niche 4 samples same disease briefing · shares osteosarcoma, metastasis
GSE216280 Single-cell RNA sequencing of peripheral blood mononuclear cell of human osteosarcoma 3 samples same disease briefing · shares osteosarcoma, single-cell
GSE188304 AMTB, a TRPM8 Antagonist, Suppresses Growth and Metastasis of Osteosarcoma through Repressing the TGFβ Signaling Pathway 2 samples same disease briefing · shares osteosarcoma, metastasis
GSE237070 Single-cell RNA sequencing of human various osteosarcoma tissues 9 samples same disease briefing · shares osteosarcoma, single-cell
GSE259286 In vitro modeling of nutritional and mitochondrial-targeted therapies for osteosarcoma 98 samples same disease briefing · shares osteosarcoma
GSE253548 The whole transcriptome analysis using FFPE and fresh tissue samples identifies the molecular fingerprint of osteosarcoma 90 samples same disease briefing · shares osteosarcoma

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.