← BioTransfer GEO Dataset Finder
GEO series

Allelic topological centering by transcription factors drives oncogenic multi-enhancer transcriptional regulation [RNA-seq]

GSE293331 Homo sapiens (human) RNA-seq (gene expression) 54 samples Submitted 2025/11/13 Platform GPL18573
At a glance

RNA-seq (gene expression) · human · 54 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
54 samples: 18 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
Lineage-determining transcription factors constrain cohesin to drive multi-enhancer oncogene regulationNature cell biology · 2026
Summary (written by the submitters)
Multiple enhancers, often located across vast genomic distances, regulate key genes in cancer. However, how organization of chromatin topology at individual allele enables cancer type-restricted multi-enhancer gene regulation remains unclear. Using acute protein degradation and time-course population-average chromatin conformation capture in lymphoma, we found that the B-cell lineage-determining transcription factor EBF1 preferentially positions multiple enhancers at loci containing sparsely distributed genes essential for B-cell identity and oncogenesis.
Read the full summary ↓
Our time-resolved sub-diffraction optical tracing of chromatin architecture of over 100,000 alleles further revealed diverse topological conformations that facilitate multi-enhancer interactions in individual lymphoma cells. Mechanistically, we found that positioning of enhancers at the topological center of the allele is required for their interactions with the target promoter, with EBF1 serving as a barrier to the loop extruding cohesin on the enhancers. These findings, which we demonstrate their generalizability to the T-cell lineage-determining transcription factor TCF1 in T-cell leukemia, suggest that lineage-determining transcription factors establish local radial positioning of enhancers and promoters to enable multi-enhancer regulation of key genes in cancer.
Published in
Lineage-determining transcription factors constrain cohesin to drive multi-enhancer oncogene regulation
Zhou Y, Jay A, Burget N et al. · Nature cell biology 2026 · PMID 41331087 · doi:10.1038/s41556-025-01827-2
This dataset
Open GSE293331 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 GSE293331_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 PRJNA1244138 and SRA study SRP575406. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 41331087) ↗
Study design
18 conditions, mostly in triplicate
Granta519EBF1KIcl29,0hr,RNAseq, ×3 Granta519EBF1KIcl29,6hr,RNAseq, ×3 Granta519EBF1KIcl29,24hr,RNAseq, ×3 Granta519EBF1KIcl97,0hr,RNAseq, ×3 Granta519EBF1KIcl97,6hr,RNAseq, ×3 Granta519EBF1KIcl97,24hr,RNAseq, ×3 Granta519EBF1KIcl97,6hWO,RNAseq, ×3 Granta519EBF1KIcl97,24hWO,RNAseq, ×3 +10 more

Supports a between-group comparison across 54 samples.

How we read this: 18 replicated groups read from 54 sample titles; they account for 54 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

+ 14 more — browse all 54 samples with per-sample file links →

Datasets on a similar topic

Search all human 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.