GEO series
Minimizing far-extending chromatin perturbation in genome editing preserves stem cell identity
GSE243065
Mus musculus (mouse)
ChIP-seq / ATAC-seq (genome binding and open chromatin)
783 samples
Submitted 2026/02/24
Platform GPL29480Platform GPL34281Platform GPL28330Platform GPL26624Platform GPL34328
At a glance
ChIP-seq / ATAC-seq (genome binding and open chromatin) · mouse · 783 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
- Mus musculus (mouse)
- Samples
- 783 samples: 135 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
- Minimizing far-extending chromatin perturbation in genome editing preserves stem cell identityCell stem cell · 2026
Summary (written by the submitters)
Advancing CRISPR/Cas9 genome editing demands a thorough understanding of its associated risks.Yet, most of risk assessments have focused on the mutagenic effects stemming from off-target cleavage in gene-coding regions, largely neglecting the implications of cleavage within the extensive non-coding genome.
Read the full summary ↓
We observed that CRISPR/Cas9-induced DNA cleavage, even at sites several kilobases away from the nearest regulatory elements, can trigger premature differentiation of somatic neural stem cell in vivo.Leveraging a refined multi-omics technology, we delved into the consequences of CRISPR/Cas9-induced DNA cleavage in these non-coding regions. Our findings reveal an impact, especially pronounced in cells with high stemness, where cleavage-induced disruptions can span long ranges, potentially culminating in the loss of stemness. These effects are tied to extensive chromatin perturbations. Building on this understanding, we used chromatin perturbation as a key marker to refine gene editing technologies that could minimize such disruptions.This key finding illuminates the path towards the development of safer and more broadly applicable genome editing technologies. This SuperSeries is composed of the SubSeries listed below.
Published in
Zhu M, Yuan J, Meng Q et al.
· Cell stem cell 2026
· PMID 41742419
· doi:10.1016/j.stem.2026.01.015
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 GSE243065_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: Look for peak files (.bed, .narrowPeak) and signal tracks (.bigWig) among the supplementary files; the raw reads are on ENA.
Also filed as BioProject PRJNA1016317 and SRA study SRP678684. Searching any of these in the dataset finder brings you back here.
Study design
135 conditions, mostly in triplicate
RNA-seq 20201013_nih3t3_cas9_wt ×6
RNA-seq 20210218_hek293t_be_EMX1 ×6
RNA-seq 20210218_hek293t_be_TYR ×6
Amplicon long-read sequencing NanogSE_edit ×3
Amplicon long-read sequencing Tcf19_edit ×3
RNA-seq 20200928_hek293t_cas9_EMX1_dead_sgrna ×3
RNA-seq 20200928_hek293t_cas9_EMX1 ×3
RNA-seq 20200928_hek293t_cas9_TYR_dead_sgrna ×3
+128 more
Supports a between-group comparison across 399 samples.
How we read this: 135 replicated groups read from the first 400 of 783 sample titles; they account for 399 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
- GSM9535931 Amplicon long-read sequencing NanogSE_edit_rep1 Amplicon long-read sequ…
- GSM9535932 Amplicon long-read sequencing NanogSE_edit_rep2 Amplicon long-read sequ…
- GSM9535933 Amplicon long-read sequencing NanogSE_edit_rep3 Amplicon long-read sequ…
- GSM9535934 Amplicon long-read sequencing NanogSE_wt_rep1 Amplicon long-read sequ…
- GSM9535935 Amplicon long-read sequencing NanogSE_wt_rep2 Amplicon long-read sequ…
- GSM9535936 Amplicon long-read sequencing Tcf19_edit_rep1 Amplicon long-read sequ…
- GSM9535937 Amplicon long-read sequencing Tcf19_edit_rep2 Amplicon long-read sequ…
- GSM9535938 Amplicon long-read sequencing Tcf19_edit_rep3 Amplicon long-read sequ…
- GSM9535939 Amplicon long-read sequencing Tcf19_wt_rep1 Amplicon long-read sequ…
- GSM9535940 Amplicon long-read sequencing Tcf19_wt_rep2 Amplicon long-read sequ…
- GSM9535941 RNA-seq 20200928_hek293t_cas9_EMX1_dead_sgrna_rep1 RNA-seq 20200928_hek293…
- GSM9535942 RNA-seq 20200928_hek293t_cas9_EMX1_dead_sgrna_rep2 RNA-seq 20200928_hek293…
- GSM9535943 RNA-seq 20200928_hek293t_cas9_EMX1_dead_sgrna_rep3 RNA-seq 20200928_hek293…
- GSM9535944 RNA-seq 20200928_hek293t_cas9_EMX1_rep1 RNA-seq 20200928_hek293…
- GSM9535945 RNA-seq 20200928_hek293t_cas9_EMX1_rep2 RNA-seq 20200928_hek293…
- GSM9535946 RNA-seq 20200928_hek293t_cas9_EMX1_rep3 RNA-seq 20200928_hek293…
- GSM9535947 RNA-seq 20200928_hek293t_cas9_TYR_dead_sgrna_rep1 RNA-seq 20200928_hek293…
- GSM9535948 RNA-seq 20200928_hek293t_cas9_TYR_dead_sgrna_rep2 RNA-seq 20200928_hek293…
- GSM9535949 RNA-seq 20200928_hek293t_cas9_TYR_dead_sgrna_rep3 RNA-seq 20200928_hek293…
- GSM9535950 RNA-seq 20200928_hek293t_cas9_TYR_rep1 RNA-seq 20200928_hek293…
- GSM9535951 RNA-seq 20200928_hek293t_cas9_TYR_rep2 RNA-seq 20200928_hek293…
- GSM9535952 RNA-seq 20200928_hek293t_cas9_TYR_rep3 RNA-seq 20200928_hek293…
- GSM9535953 RNA-seq 20200928_hek293t_cas9_ZSWIM3_dead_sgrna_rep1 RNA-seq 20200928_hek293…
- GSM9535954 RNA-seq 20200928_hek293t_cas9_ZSWIM3_dead_sgrna_rep2 RNA-seq 20200928_hek293…
- GSM9535955 RNA-seq 20200928_hek293t_cas9_ZSWIM3_dead_sgrna_rep3 RNA-seq 20200928_hek293…
- GSM9535956 RNA-seq 20200928_hek293t_cas9_ZSWIM3_rep1 RNA-seq 20200928_hek293…
- GSM9535957 RNA-seq 20200928_hek293t_cas9_ZSWIM3_rep2 RNA-seq 20200928_hek293…
- GSM9535958 RNA-seq 20200928_hek293t_cas9_ZSWIM3_rep3 RNA-seq 20200928_hek293…
- GSM9535959 RNA-seq 20200928_hek293t_cas9_cas9_only_vector_rep1 RNA-seq 20200928_hek293…
- GSM9535960 RNA-seq 20200928_hek293t_cas9_cas9_only_vector_rep2 RNA-seq 20200928_hek293…
- GSM9535961 RNA-seq 20200928_hek293t_cas9_cas9_only_vector_rep3 RNA-seq 20200928_hek293…
- GSM9535962 RNA-seq 20200928_hek293t_cas9_empty_vector_rep1 RNA-seq 20200928_hek293…
- GSM9535963 RNA-seq 20200928_hek293t_cas9_empty_vector_rep2 RNA-seq 20200928_hek293…
- GSM9535964 RNA-seq 20200928_hek293t_cas9_empty_vector_rep3 RNA-seq 20200928_hek293…
- GSM9535965 RNA-seq 20200928_hek293t_cas9_wt_rep1 RNA-seq 20200928_hek293…
- GSM9535966 RNA-seq 20200928_hek293t_cas9_wt_rep2 RNA-seq 20200928_hek293…
- GSM9535967 RNA-seq 20200928_hek293t_cas9_wt_rep3 RNA-seq 20200928_hek293…
- GSM9535968 RNA-seq 20201013_nih3t3_cas9_EMX1_dead_sgrna_rep1 RNA-seq 20201013_nih3t3…
- GSM9535969 RNA-seq 20201013_nih3t3_cas9_EMX1_dead_sgrna_rep2 RNA-seq 20201013_nih3t3…
- GSM9535970 RNA-seq 20201013_nih3t3_cas9_EMX1_dead_sgrna_rep3 RNA-seq 20201013_nih3t3…
+ 743 more — browse all 783 samples with per-sample file links →
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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.