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Tracking and mitigating imprint erasure during induction of naïve human pluripotency at single-cell resolution [RNA-Seq]

GSE268535 Homo sapiens (human) Single-cell RNA-seq 32 samples Submitted 2025/02/12 Platform GPL24676Platform GPL34284
At a glance

Single-cell RNA-seq · human · 32 samples

What it is
Single-cell RNA-seqGene activity measured in thousands of individual cells, so cell types can be told apart.
Organism
Homo sapiens (human)
Samples
32 samples: 10 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
Tracking and mitigating imprint erasure during induction of naive human pluripotency at single-cell resolutionStem cell reports · 2025
Summary (written by the submitters)
Naïve human pluripotent stem cells (hPSCs) model the pre-implantation epiblast. However, parent-specific epigenetic marks (imprints) are eroded in naïve hPSCs, which represents an important deviation from the epiblast in vivo. To track the dynamics of imprint erasure during naïve resetting in real time, we established a dual-colored fluorescent reporter at both alleles of the imprinted SNRPN locus.
Read the full summary ↓
During primed-to-naïve resetting, SNRPN expression becomes biallelic in most naïve cells and biallelic SNRPN expression is irreversible upon re-priming. We utilized this live-cell reporter to evaluate chemical and genetic strategies to minimize imprint erasure. Decreasing the level of MEK/ERK inhibition or overexpressing the KRAB zinc finger protein ZFP57 protected a subset of imprints during naïve resetting. Combining these two strategies protected imprint levels to a further extent than either strategy alone. This study offers an experimental tool to track and enhance imprint stability during transitions between human pluripotent states in vitro.
Published in
Tracking and mitigating imprint erasure during induction of naive human pluripotency at single-cell resolution
Fischer LA, Meyer B, Reyes M et al. · Stem cell reports 2025 · PMID 39952244 · doi:10.1016/j.stemcr.2025.102419
This dataset
Open GSE268535 in finder → Find more human RNA-seq 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 GSE268535_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: Single-cell studies usually ship their counts as 10x files (matrix.mtx, barcodes.tsv, features.tsv), an .h5 file, or an .rds / .h5ad object that Seurat or Scanpy can open. Look for those among the supplementary files.

Also filed as BioProject PRJNA1117755 and SRA study SRP510424. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 39952244) ↗
Study design
10 conditions, mostly in duplicate
RNA-seq_H9-SRG_Primed ×2 RNA-seq_H9-SRG_0.5MEKi_1.0ERKi ×2 RNA-seq_H9-SRG_1.0MEKi_2.5ERKi ×2 RNA-seq_H9-SRG_0.5MEKi_0.5ERKi ×2 RNA-seq_H9-SRG_1.0MEKi_0.1ERKi ×2 RNA-seq_H9_Primed ×2 RNA-seq_H9_Naïve_rtTA ×2 RNA-seq_H9_Naïve_ZFP57_OE ×2 +14 more

Supports a between-group comparison across 20 samples.

How we read this: 10 replicated groups read from 32 sample titles; they account for 20 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
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