GEO series
Minimizing far-extending chromatin perturbation in genome editing preserves stem cell identity [RNA-Seq]
GSE320162
Homo sapiens; Mus musculus
Expression profiling by high throughput sequencing
347 samples
Submitted 2026/02/26
Platform GPL29480Platform GPL34328Platform GPL34281Platform GPL28330
Summary
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. 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
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
Direct links to NCBI, no account and no request form: the whole study as GSE320162_RAW.tar, processed values as the series matrix, the supplementary file directory, and per-sample supplementary files for any of the 347 samples. Raw sequencing reads are also available from ENA.
Also filed as BioProject PRJNA1426989 and SRA study SRP678694. Searching any of these in the dataset finder brings you back here.
Study design
13 conditions, mostly in triplicate
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
RNA-seq 20200928_hek293t_cas9_TYR ×3
RNA-seq 20200928_hek293t_cas9_ZSWIM3_dead_sgr… ×3
RNA-seq 20200928_hek293t_cas9_ZSWIM3 ×3
RNA-seq 20200928_hek293t_cas9_cas9_only_vector ×3
RNA-seq 20200928_hek293t_cas9_empty_vector ×3
+6 more
Supports a between-group comparison across 39 samples.
13 replicated groups read from the first 40 of 347 sample titles; they account for 39 of them. Check it against the sample list below before relying on it.
Samples in this study
0 selected
- GSM9535941 RNA-seq 20200928_hek293t_cas9_EMX1_dead_sgrna_rep1
- GSM9535942 RNA-seq 20200928_hek293t_cas9_EMX1_dead_sgrna_rep2
- GSM9535943 RNA-seq 20200928_hek293t_cas9_EMX1_dead_sgrna_rep3
- GSM9535944 RNA-seq 20200928_hek293t_cas9_EMX1_rep1
- GSM9535945 RNA-seq 20200928_hek293t_cas9_EMX1_rep2
- GSM9535946 RNA-seq 20200928_hek293t_cas9_EMX1_rep3
- GSM9535947 RNA-seq 20200928_hek293t_cas9_TYR_dead_sgrna_rep1
- GSM9535948 RNA-seq 20200928_hek293t_cas9_TYR_dead_sgrna_rep2
- GSM9535949 RNA-seq 20200928_hek293t_cas9_TYR_dead_sgrna_rep3
- GSM9535950 RNA-seq 20200928_hek293t_cas9_TYR_rep1
- GSM9535951 RNA-seq 20200928_hek293t_cas9_TYR_rep2
- GSM9535952 RNA-seq 20200928_hek293t_cas9_TYR_rep3
- GSM9535953 RNA-seq 20200928_hek293t_cas9_ZSWIM3_dead_sgrna_rep1
- GSM9535954 RNA-seq 20200928_hek293t_cas9_ZSWIM3_dead_sgrna_rep2
- GSM9535955 RNA-seq 20200928_hek293t_cas9_ZSWIM3_dead_sgrna_rep3
- GSM9535956 RNA-seq 20200928_hek293t_cas9_ZSWIM3_rep1
- GSM9535957 RNA-seq 20200928_hek293t_cas9_ZSWIM3_rep2
- GSM9535958 RNA-seq 20200928_hek293t_cas9_ZSWIM3_rep3
- GSM9535959 RNA-seq 20200928_hek293t_cas9_cas9_only_vector_rep1
- GSM9535960 RNA-seq 20200928_hek293t_cas9_cas9_only_vector_rep2
- GSM9535961 RNA-seq 20200928_hek293t_cas9_cas9_only_vector_rep3
- GSM9535962 RNA-seq 20200928_hek293t_cas9_empty_vector_rep1
- GSM9535963 RNA-seq 20200928_hek293t_cas9_empty_vector_rep2
- GSM9535964 RNA-seq 20200928_hek293t_cas9_empty_vector_rep3
- GSM9535965 RNA-seq 20200928_hek293t_cas9_wt_rep1
- GSM9535966 RNA-seq 20200928_hek293t_cas9_wt_rep2
- GSM9535967 RNA-seq 20200928_hek293t_cas9_wt_rep3
- GSM9535968 RNA-seq 20201013_nih3t3_cas9_EMX1_dead_sgrna_rep1
- GSM9535969 RNA-seq 20201013_nih3t3_cas9_EMX1_dead_sgrna_rep2
- GSM9535970 RNA-seq 20201013_nih3t3_cas9_EMX1_dead_sgrna_rep3
- GSM9535971 RNA-seq 20201013_nih3t3_cas9_EMX1_rep1
- GSM9535972 RNA-seq 20201013_nih3t3_cas9_EMX1_rep2
- GSM9535973 RNA-seq 20201013_nih3t3_cas9_EMX1_rep3
- GSM9535974 RNA-seq 20201013_nih3t3_cas9_TYR_dead_sgrna_rep1
- GSM9535975 RNA-seq 20201013_nih3t3_cas9_TYR_dead_sgrna_rep2
- GSM9535976 RNA-seq 20201013_nih3t3_cas9_TYR_dead_sgrna_rep3
- GSM9535977 RNA-seq 20201013_nih3t3_cas9_TYR_rep1
- GSM9535978 RNA-seq 20201013_nih3t3_cas9_TYR_rep2
- GSM9535979 RNA-seq 20201013_nih3t3_cas9_TYR_rep3
- GSM9535980 RNA-seq 20201013_nih3t3_cas9_ZSWIM3_dead_sgrna_rep1
+ 307 more — browse all 347 samples with per-sample file links →
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