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A Fast Chemical Reprogramming System to Generate Human Pluripotent Stem Cells [RNA-seq]

GSE264658 Homo sapiens (human) RNA-seq (gene expression) 31 samples Submitted 2024/10/27 Platform GPL24676Platform GPL20795
At a glance

RNA-seq (gene expression) · human · 31 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
31 samples: 31 samples, each with a distinct title — no replicated groupsGroups 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
A rapid chemical reprogramming system to generate human pluripotent stem cellsNature chemical biology · 2025
Summary (written by the submitters)
Chemical reprogramming enables the generation of human pluripotent stem cells (hCiPSCs) from somatic cells using small molecules, providing a novel and promising strategy for regenerative medicine. However, the current method for generating hCiPSCs is time consuming, and some cell lines from different donors are resistant to chemical induction, significantly limiting the utility of these cells for personalized medicine.
Read the full summary ↓
Here, we developed a fast-reprogramming system capable of generating hCiPSCs in as few as 10 days. This accelerated method enables efficient generation of hCiPSCs with a consistent 100% success rate across 15 different donors. In particular, the efficiency of reprogramming within 16 days for cells refractory to previously reported protocols increased more than 30-fold. Mechanistically, we identified KAT3A/B and KAT6A as key epigenetic obstacles, as suppression of these factors facilitated the transition of somatic cells to a poised state by triggering switches in the epigenome, a process essential for this fast-reprogramming system. These results highlight the superiority of this novel system for generating hCiPSCs, which represents a next-generation approach for manufacturing cells for clinical applications. This work also advances our understanding of human cell reprogramming and paves the way for broader applications of chemical reprogramming in regenerative medicine.
Published in
A rapid chemical reprogramming system to generate human pluripotent stem cells
Wang Y, Peng F, Yang Z et al. · Nature chemical biology 2025 · PMID 39753706 · doi:10.1038/s41589-024-01799-8
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 GSE264658_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 PRJNA1103754 and SRA study SRP503580. Searching any of these in the dataset finder brings you back here.

Study design
31 samples, each with a distinct title — no replicated groups

Any grouping that defines a comparison lives in the sample characteristics, which GEO records separately from the titles.

How we read this: Read from 31 sample titles: 31 distinct titles with little repetition. 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
Datasets on a similar topic

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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.