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HP1α depletion and TGFβ activation exert antagonistic effects on 3D genome organization

GSE316327 Homo sapiens (human) RNA-seq (gene expression) 18 samples Submitted 2026/01/20 Platform GPL24676Platform GPL18573
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: 7 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.
Summary (written by the submitters)
The three-dimensional (3D) organization of the human genome plays a critical role in regulating gene expression and is frequently disrupted in cancer. However, how key factors like Heterochromatin Protein 1 alpha (HP1α) and Transforming Growth Factor beta (TGFβ) remodel this architecture to drive tumorigenesis remains poorly understood.
Read the full summary ↓
We investigated the effects of HP1α knockdown and TGFβ treatment on higher-order chromatin structure and gene expression in human mammary epithelial cells. Our findings reveal that HP1α depletion and TGFβ stimulation exert distinct and opposing effects on genome compartmentalization and subcompartmentalization. HP1α knockdown drives a genome-wide shift of chromatin from transcriptionally inactive B compartments to active A compartments. This is accompanied by a stepwise redistribution of A subcompartments toward the most transcriptionally active state (A3), and the upregulation of oncogenic genes involved in EMT and proliferation. In contrast, TGFβ treatment promotes chromatin compaction, increases the proportion of B compartments, and drives a stepwise reduction in active A subcompartments. Our results highlight the differential roles of HP1α and TGFβ in shaping the 3D genome and underscore how precise, stepwise architectural changes contribute to malignant transformation. This study provides critical insight into how chromatin architecture acts as a regulatory layer in breast cancer development.
This dataset
Open GSE316327 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 GSE316327_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 PRJNA1402322 and SRA study SRP661867. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
7 conditions, mostly in triplicate
MCF10A ×3 MCF10A shHPA, ×3 MCF10A TGFB, ×3 MCF10A shScr, ×3 MCF10A, ×2 MCF10A shHPA ×2 MCF10A TGFB ×2

Supports a between-group comparison across 18 samples.

How we read this: 7 replicated groups read from 18 sample titles; they account for 18 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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