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GATA3 Differentially Regulates the Transcriptome via Its Zinc Finger 2-Modulated Phase Separation

GSE289230 Homo sapiens (human) RNA-seq (gene expression) 12 samples Submitted 2026/06/01 Platform GPL24676
At a glance

RNA-seq (gene expression) · human · 12 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
12 samples: 3 × MCF7_gata3_ko,WT, vs 3 × MCF7_gata3_ko,2R_A, vs 3 × MCF7_gata3_ko,308, vs 3 × MCF7_gata3_ko,330,Groups 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)
Phase separation (PS) underlies gene control by transcription factors. However, little is known about whether and how the DNA-binding domains (DBDs) regulate the PS for transcription factors to differentially regulate the transcriptome. The transcription factor GATA3, a master immune regulator, is frequently mutated in breast cancer. Here, we report that GATA3 undergoes its DBD-modulated PS to mediate the formation of chromatin condensates.
Read the full summary ↓
We show that the DBD regulates the GATA3 PS through its zinc finger 2 (ZnF2), which provides positive charges for multivalent electrostatic interactions mainly via two arginine amino acids, R329 and R330. Breast cancer GATA3 with ZnF2-defective mutations, when compared to those without, cause aberrant ZnF2-modulated PS and condensates to remodel the differentially regulated transcriptome, resulting in a favorable prognosis for patients and reduced tumor growth in mice. Thus, GATA3 represents a principle of how a transcription factor differentially regulates the transcriptome via its DBD-modulated PS.
This dataset
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.

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 GSE289230_RAW.tar (it can run to tens of gigabytes).

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.

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 PRJNA1221937 and SRA study SRP563021. Searching any of these in the dataset finder brings you back here.

Study design
3 × MCF7_gata3_ko,WT, vs 3 × MCF7_gata3_ko,2R_A, vs 3 × MCF7_gata3_ko,308, vs 3 × MCF7_gata3_ko,330,

Supports a between-group comparison across 12 samples.

How we read this: 4 replicated groups read from 12 sample titles; they account for 12 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
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