GEO series

Large-scale discovery of potent, compact and lineage specific enhancers for gene therapy vectors [DNaseI-seq]

GSE252160 Homo sapiens (human) ChIP-seq / ATAC-seq (genome binding and open chromatin) 3 samples Submitted 2024/05/01 Platform GPL24676Platform GPL18573

At a glance

ChIP-seq / ATAC-seq (genome binding and open chromatin) · human · 3 samples

What it is: ChIP-seq / ATAC-seq (genome binding and open chromatin)Where proteins bind the genome, or which regions are open and active.
Organism: Homo sapiens (human)
Samples: 3 samples
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: Large-scale discovery of potent, compact and erythroid specific enhancers for gene therapy vectorsNature communications · 2025

Summary (written by the submitters)

Regulation of gene expression during cell development and differentiation is chiefly orchestrated by distal noncoding regulatory elements that precisely modulate cell selective gene activity. Gene therapy vectors rely on the cellular and context specificity of regulatory DNA elements to express therapeutic transgenes in the correct location and time.

Read the full summary ↓

Here, we develop a straight-forward, one-shot approach to screen putative regulatory sequences identified in large-scale epigenomics profiling experiments for precise and programmable control of transgenes encoded within gene therapy viral vectors. We designed a library of 15,000 short sequences (~200bp) derived from a set of developmentally active DHS elements during human ex vivo erythropoiesis and cloned them into a GFP reporter lentiviral vector. In an erythroid progenitor cell line, these elements display a gradient of transcriptional enhancer activity, with some demonstrating equivalent activity to the canonical β-globin μLCR despite a 9-fold smaller size. We show that these elements are both highly cell type restricted and developmental stage specific both in vitro and in vivo. Finally, we replace the μLCR element with one of the novel short enhancers in a β-thalassemia lentiviral therapeutic vector and efficiently correct the thalassemic phenotype in patient-derived HSPCs. More broadly, our approach provides further insights into enhancer biology with wider implications into the development of highly cell type specific and efficacious viral vectors for human gene therapy.

Published in

Large-scale discovery of potent, compact and erythroid specific enhancers for gene therapy vectors

Psatha N, Sova P, Georgolopoulos G et al. · Nature communications 2025 · PMID 40346084 · doi:10.1038/s41467-025-59235-x

This dataset

Open GSE252160 in finder → Find more human ChIP / ATAC / CUT&Tag 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 GSE252160_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: Look for peak files (.bed, .narrowPeak) and signal tracks (.bigWig) among the supplementary files; the raw reads are on ENA.

Also filed as BioProject PRJNA1057940 and SRA study SRP480338. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 40346084) ↗

Samples in this study

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+ 3 more — browse all 3 samples with per-sample file links →

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