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p53 safeguards chemical reprogramming of human somatic cells towards pluripotency [bulk RNA-seq]

GSE287612 Homo sapiens (human) RNA-seq (gene expression) 53 samples Submitted 2026/03/20 Platform GPL24676Platform GPL20795
At a glance

RNA-seq (gene expression) · human · 53 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
53 samples: 19 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
p53 safeguards chemical reprogramming of human somatic cells toward pluripotencyCell · 2026
Summary (written by the submitters)
Cell fate manipulation is powerful for generating desired cell types through reprogramming, such as induced pluripotent stem cells (iPSCs), with broad biological applications. However, reprogramming can be risky due to dramatic changes in cell identity, necessitating strict regulation to ensure safety and efficacy. p53 is essential for maintaining genome stability to prevent abnormalities induced by changes of cell states, which however, functionally counters Yamanaka factors for efficient reprogramming.
Read the full summary ↓
Thus, delicately balancing p53 activity for efficient reprogramming with highly quality has proven challenging. Here, we demonstrate that efficient human chemical reprogramming is safeguarded by p53, in distinction from Yamanaka factors-mediated reprogramming. Suppressing p53 activity unexpectedly and substantially hinders the generation of chemically induced pluripotent stem cells (CiPSCs). Notably, p53 precisely regulates the mesenchymalization occurred at the early reprogramming stage where cell plasticity is re-established and curbs excessive epithelial-mesenchymal transition (EMT). Furthermore, the retinoid acid receptor agonist, TTNPB, leverages p53’s anti-metastatic functions to facilitate CiPSC generation by activating BTG2. Our results demonstrate that p53 activity distinguishes chemical reprogramming from Yamanaka factors-mediated reprogramming, and offer a promising strategy that delicately balances p53 with reprogramming for both safe and efficient cell fate manipulation.
Published in
p53 safeguards chemical reprogramming of human somatic cells toward pluripotency
Cheng L, Wang Y, Yang Z et al. · Cell 2026 · PMID 41999746 · doi:10.1016/j.cell.2026.03.038
This dataset
Open GSE287612 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 GSE287612_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: 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 PRJNA1213820 and SRA study SRP558800. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 41999746) ↗
Study design
19 conditions, mostly in duplicate
DMSO ×2 DMSO+C6A ×2 DMSO+C6NA ×2 DMSO+TTNPB ×2 NC-N ×2 NC ×2 S1DMSO ×2 S1DMSO+C ×2 +26 more

Supports a between-group comparison across 38 samples.

How we read this: 19 replicated groups read from 53 sample titles; they account for 38 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

+ 13 more — browse all 53 samples with per-sample file links →

Datasets on a similar topic

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