GEO series
Minimizing far-extending chromatin perturbation in genome editing preserves stem cell identity [RNA-Seq]
GSE320162
Homo sapiens (human)
RNA-seq (gene expression)
347 samples
Submitted 2026/02/26
Platform GPL29480Platform GPL34328Platform GPL34281Platform GPL28330
At a glance
RNA-seq (gene expression) · human · 347 samples
- What it is
- RNA-seq (gene expression)Gene activity measured by sequencing. The summary does not mention single-cell work, so this is most likely bulk: one average per sample, the usual starting point for comparing groups.
- Organism
- Homo sapiens (human)
- Samples
- 347 samples: 117 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)
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
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 GSE320162_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: For RNA-seq the counts are usually a table among the supplementary files (look for 'counts', 'expression', 'FPKM' or 'TPM' in the file name). The series matrix of a sequencing study is sometimes nearly empty.
Also filed as BioProject PRJNA1426989 and SRA study SRP678694. Searching any of these in the dataset finder brings you back here.
Study design
117 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
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
+109 more
Supports a between-group comparison across 347 samples.
How we read this: 117 replicated groups read from 347 sample titles; they account for 347 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
- 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…
- GSM9535971 RNA-seq 20201013_nih3t3_cas9_EMX1_rep1 RNA-seq 20201013_nih3t3…
- GSM9535972 RNA-seq 20201013_nih3t3_cas9_EMX1_rep2 RNA-seq 20201013_nih3t3…
- GSM9535973 RNA-seq 20201013_nih3t3_cas9_EMX1_rep3 RNA-seq 20201013_nih3t3…
- GSM9535974 RNA-seq 20201013_nih3t3_cas9_TYR_dead_sgrna_rep1 RNA-seq 20201013_nih3t3…
- GSM9535975 RNA-seq 20201013_nih3t3_cas9_TYR_dead_sgrna_rep2 RNA-seq 20201013_nih3t3…
- GSM9535976 RNA-seq 20201013_nih3t3_cas9_TYR_dead_sgrna_rep3 RNA-seq 20201013_nih3t3…
- GSM9535977 RNA-seq 20201013_nih3t3_cas9_TYR_rep1 RNA-seq 20201013_nih3t3…
- GSM9535978 RNA-seq 20201013_nih3t3_cas9_TYR_rep2 RNA-seq 20201013_nih3t3…
- GSM9535979 RNA-seq 20201013_nih3t3_cas9_TYR_rep3 RNA-seq 20201013_nih3t3…
- GSM9535980 RNA-seq 20201013_nih3t3_cas9_ZSWIM3_dead_sgrna_rep1 RNA-seq 20201013_nih3t3…
+ 307 more — browse all 347 samples with per-sample file links →
Datasets on a similar topic
- GSE320163 Minimizing far-extending chromatin perturbation in genome editing preserves stem cell identity [ATAC-Seq] 377 samples shares far-extending, perturbation, minimizing, chromatin, preserves, identity, editing
- GSE320165 Minimizing far-extending chromatin perturbation in genome editing preserves stem cell identity [Cut&Tag] 46 samples shares far-extending, perturbation, minimizing, chromatin, preserves, identity, editing
- GSE297591 Cytotoxicity caused by 5-methylcytosine depletion on chromatin-associated RNA by 5-azacytosine in leukemia [RNA-seq] 96 samples shares chromatin, rna-seq
- GSE300418 RNA promotes nucleation and oncogenic activity of chromatin-associated condensates [RNA-Seq] 28 samples shares chromatin, rna-seq
- GSE205246 H3K27me3 spreading organizes canonical PRC1 chromatin architecture to regulate developmental programs [RNA-seq] 25 samples shares chromatin, rna-seq
- GSE338103 Linker histone H1.0 and JUNB Interaction Maintains Lineage-Specific and Age-Dependent Chromatin Architecture to Safeguard Cell Identity [ATAC-seq] 14 samples shares chromatin, identity
- GSE295888 SIRT7 regulates NUCKS1 chromatin binding to elicit metabolic and inflammatory gene expression in senescence and liver aging. 30 samples shares chromatin
- GSE65360 Single-cell chromatin accessibility data using scATAC-seq 1632 samples shares chromatin
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.