← BioTransfer GEO Dataset Finder
GEO series

Transcriptonal and epigenetic profiling of neuroblastoma cells based on CD49b expression

GSE207516 Mus musculus (mouse) ChIP-seq / ATAC-seq (genome binding and open chromatin) 26 samples Submitted 2023/12/05 Platform GPL24247Platform GPL24676
At a glance

ChIP-seq / ATAC-seq (genome binding and open chromatin) · mouse · 26 samples

What it is
ChIP-seq / ATAC-seq (genome binding and open chromatin)Where proteins bind the genome, or which regions are open and active.
Organism
Mus musculus (mouse)
Topic
NeuroblastomaThis series is part of our curated briefing on it.
Samples
26 samples: 25 distinct titles, 1 of them repeatedGroups 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)
This study was undertaken to assess heterogeneity within neuroblastoma cell populations, and to assess whether the integrin CD49b can distinguish different neuroblastoma phenotypes. Methods: We performed bulk RNA sequencing on Neuro-2a and SH-SY5Y neuroblastoma cells sorted based on CD49b expression, and CUT&RUN for H3K4me1 and H3K27ac on Neuro-2a cells sorted based on CD49b expression.
Read the full summary ↓
Results: We find that CD49b expression identifies transcriptionally distinct neuroblastoma cell populations. High CD49b expression marks cells expressing mesenchymal genes, while lack of CD49 denotes cells expression neuronal genes. CUT&RUN analysis shows that CD49b distinguishes cells with distinct enhancer and super enhancer profiles. Conclusion: RNA sequencing and CUT&RUN demonstrate that neuroblastoma cell lines include heterogeneous populations.
This dataset
Open GSE207516 in finder → Find more RNA-seq datasets → Neuroblastoma 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 GSE207516_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: Look for peak files (.bed, .narrowPeak) and signal tracks (.bigWig) among the supplementary files; the raw reads are on ENA.

Also filed as BioProject PRJNA855951 and SRA study SRP384995. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 38095019) ↗
Study design
25 distinct titles, 1 of them repeated
S1_S ×2

How we read this: Read from 26 sample titles: 25 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 Neuroblastoma datasets

Search all 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.