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H3K27me3 chromatin heterogeneity reveals variable cell responses to estrogen and endocrine treatment [RNA-seq]

GSE280617 Homo sapiens (human) RNA-seq (gene expression) 18 samples Submitted 2026/02/11 Platform GPL24676
At a glance

RNA-seq (gene expression) · human · 18 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
18 samples: 18 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.
Paper
H3K27me3 chromatin heterogeneity reveals variable cell responses to estrogen and endocrine treatmentbioRxiv : the preprint server for biology · 2025
Summary (written by the submitters)
Gene expression heterogeneity creates subpopulations of tumor cells with increased resistance, posing a challenge to effective cancer treatment. This non-genetic variability stems from the stochastic nature of transcription, where gene activation occurs in bursts of RNA synthesis. The estrogen-responsive TFF1 gene is heterogeneously expressed due to periods of inactivity lasting several days.
Read the full summary ↓
Here we uncover the mechanism driving heterogeneous TFF1 activity by examining cells sorted based on TFF1 activity levels (High vs Low TFF1 expressing cells). In both populations, genome-wide assays and live cell imaging were performed to assess chromatin state, ERα occupancy, allele activation and transcriptional bursting behavior.
Published in
H3K27me3 chromatin heterogeneity reveals variable cell responses to estrogen and endocrine treatment
Chapus F, Day CR, Kammel LG et al. · bioRxiv : the preprint server for biology 2025 · PMID 41000645 · doi:10.1101/2025.09.18.677184
This dataset
Open GSE280617 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 GSE280617_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 PRJNA1179500 and SRA study SRP541867. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 41000645) ↗
Study design
18 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 18 sample titles: 18 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
Datasets on a similar topic

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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.