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Minimizing far-extending chromatin perturbation in genome editing preserves stem cell identity [Cut&Tag]

GSE320165 Homo sapiens (human) ChIP-seq / ATAC-seq (genome binding and open chromatin) 46 samples Submitted 2026/02/26 Platform GPL28330Platform GPL29480
At a glance

ChIP-seq / ATAC-seq (genome binding and open chromatin) · human · 46 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
46 samples: 23 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
Minimizing far-extending chromatin perturbation in genome editing preserves stem cell identityCell stem cell · 2026
Summary (written by the submitters)
While CRISPR/Cas9 holds therapeutic promise, broader application demands understanding complications in vast non-coding regions. We found that CRISPR/Cas9 can cause premature differentiation of neural stem cells in vivo and mouse embryonic stem cells in vitro, even when cleavage occurred at distant sites tens of kilobases away from the nearest regulatory elements.
Read the full summary ↓
To investigate this, we employed an integrated ATAC/RNA approach (AR-seq) and identified editing-induced chromatin accessibility change, with its scale varying by cell types. Cells with stemness are most affected, experiencing perturbations that extend over a hundred kilobases. Furthermore, even local DNA perturbations can disrupt CTCF- and condensate-associated chromatin architecture, causing distal transcriptional rewiring and ultimately loss of stemness identity. To minimize chromatin perturbations and preserve cell identity we refined gene editing strategies, including distance-aware sgRNA design, pharmacological attenuation of DNA resection, and alternative editing systems. This work paves the way for safer and broader application of genome editing technologies.
Published in
Minimizing far-extending chromatin perturbation in genome editing preserves stem cell identity
Zhu M, Yuan J, Meng Q et al. · Cell stem cell 2026 · PMID 41742419 · doi:10.1016/j.stem.2026.01.015
This dataset
Open GSE320165 in finder → Find more 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 GSE320165_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 PRJNA1426990 and SRA study SRP678688. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 41742419) ↗
Study design
23 conditions, mostly in duplicate
CUT&Tag hek293t_emx1_h3k27ac ×2 CUT&Tag hek293t_emx1_h3k27me3 ×2 CUT&Tag hek293t_emx1_h3k9me3 ×2 CUT&Tag hek293t_wt_h3k27ac ×2 CUT&Tag hek293t_wt_h3k27me3 ×2 CUT&Tag hek293t_wt_h3k9me3 ×2 CUT&Tag hek293t_zswim3_h3k27ac ×2 CUT&Tag hek293t_zswim3_h3k27me3 ×2 +15 more

Supports a between-group comparison across 46 samples.

How we read this: 23 replicated groups read from 46 sample titles; they account for 46 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

+ 6 more — browse all 46 samples with per-sample file links →

Datasets on a similar topic

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