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Chromatin accessibility profiling of HSPC-derived erythroblast differentiation [ATAC-seq]

GSE291386 Homo sapiens (human) ChIP-seq / ATAC-seq (genome binding and open chromatin) 165 samples Submitted 2025/12/15 Platform GPL34284
At a glance

ChIP-seq / ATAC-seq (genome binding and open chromatin) · human · 165 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
165 samples: 68 conditions, mostly in duplicateGroups 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
Directing stem cell differentiation by chromatin state approximationNucleic acids research · 2026
Summary (written by the submitters)
A prime goal of regenerative medicine is to replace dysfunctional cells in the body. To design protocols for producing target cells in the laboratory, one may need to consider exponentially large combinations of culture components. Here, we tested the potential of iteratively approximating the target phenotype by quantifying the distance between chromatin profiles (ATAC-seq) of differentiating cells in vitro and their in-vivo counterparts.
Read the full summary ↓
We tested this approach on the well-studied generation of erythroblasts from haematopoietic stem cells, evaluating a fixed number of components over two sequential differentiation rounds (8x8 protocols). We found that the most erythroblast-like cells upon the first round yielded the most erythroblast-like cells at the second round, suggesting that greedy selection by chromatin approximation can be a viable optimisation strategy. Furthermore, by targeting transcriptional regulators linked to chromatin regions that were incompletely reprogrammed even after two rounds of differentiation, we could make a data-driven selection of additions to the protocol that further improved erythropoiesis. In future, our methodology can help craft notoriously difficult cells in vitro, such as B cells.
Published in
Directing stem cell differentiation by chromatin state approximation
Montano-Gutierrez LF, Müller S, Kutschat AP et al. · Nucleic acids research 2026 · PMID 41734818 · doi:10.1093/nar/gkag124
This dataset
Open GSE291386 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 GSE291386_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 PRJNA1233285 and SRA study SRP568712. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 41734818) ↗
Study design
68 conditions, mostly in duplicate
RBC-Ve2-IL4-1 ×3 RBC-Ve2-IL4-2 ×3 RBC-Ve2-TGF-1 ×3 RBC-Ve2-5E ×3 RBC-step1-1 ×2 RBC-step1-2 ×2 RBC-step1-3 ×2 RBC-step1-4 ×2 +85 more

Supports a between-group comparison across 140 samples.

How we read this: 68 replicated groups read from 165 sample titles; they account for 140 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

+ 125 more — browse all 165 samples with per-sample file links →

Datasets on a similar topic

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