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Deciphering Pre-existing and Induced 3D Genome Architecture Changes involved in Constricted Melanoma Migration (RNA-Seq)

GSE275395 Homo sapiens (human) RNA-seq (gene expression) 30 samples Submitted 2024/08/26 Platform GPL24676
At a glance

RNA-seq (gene expression) · human · 30 samples

What it is
RNA-seq (gene expression)Gene activity measured by sequencing. The summary does not mention single-cell work, so this is most likely bulk: one average per sample, the usual starting point for comparing groups.
Organism
Homo sapiens (human)
Samples
30 samples: 10 conditions, mostly in triplicateGroups 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
Deciphering pre-existing and induced 3D genome architecture changes involved in constricted melanoma migrationiScience · 2025
Summary (written by the submitters)
Metastatic cancer cells traverse tight junctions that exert forces on their nucleus and the genomic contents within. Cancerous tumors are highly heterogeneous and not all cells within them can achieve such a feat. Here, we investigated what initial genome architecture characteristics favor the constricted migratory ability of cancer cells and which arise only after passage through multiple constrictions.
Read the full summary ↓
We identified a cell surface protein (ITGB4) whose expression correlates with increased initial constricted migration ability in human melanoma A375 cells. Sorting out this subpopulation allowed us to identify cellular and nuclear features that pre-exist and favor migration, as well as alterations that only appear after cells have passed through constrictions. We identified specific genomic regions that experienced altered genome spatial compartment profiles only after constricted migration. Our study reveals 3D genome structure contributions to both selection and induction mechanisms of cell fate change during cancer metastasis.
Published in
Deciphering pre-existing and induced 3D genome architecture changes involved in constricted melanoma migration
Playter C, Golloshi R, Garretson JH et al. · iScience 2025 · PMID 40292313 · doi:10.1016/j.isci.2025.112346
This dataset
Open GSE275395 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 GSE275395_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: For RNA-seq the counts are usually a table among the supplementary files (look for 'counts', 'expression', 'FPKM' or 'TPM' in the file name). The series matrix of a sequencing study is sometimes nearly empty.

Also filed as BioProject PRJNA1150602 and SRA study SRP527917. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 40292313) ↗
Study design
10 conditions, mostly in triplicate
ITGB4(-) ×3 ITGB4(+) ×3 Top10-ITGB4neg ×3 Top10-ITGB4pos ×3 Bottom10-ITGB4neg ×3 Bottom10-ITGB4pos ×3 ITGB4(-)-BottomR4 ×3 ITGB4(+)-BottomR5 ×3 +2 more

Supports a between-group comparison across 30 samples.

How we read this: 10 replicated groups read from 30 sample titles; they account for 30 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
Datasets on a similar topic

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