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METTL3 safeguards cell identity and epigenome of human trophoblast stem cells

GSE280039 Homo sapiens (human) ChIP-seq / ATAC-seq (genome binding and open chromatin) 78 samples Submitted 2025/11/11 Platform GPL24676
At a glance

ChIP-seq / ATAC-seq (genome binding and open chromatin) · human · 78 samples

What it is
ChIP-seq / ATAC-seq (genome binding and open chromatin)Where proteins bind the genome, or which regions are open and active.
Organism
Homo sapiens (human)
Samples
78 samples: 32 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
METTL3 safeguards cell identity and epigenome of human trophoblast stem cellsNature communications · 2025
Summary (written by the submitters)
N6-methyladenosine (m6A) is the most prevalent mRNA modification with diverse regulatory functions in mammalian cells. However, its significance in the human trophoblast lineage and human placenta development remains largely unexplored. Here, we found that in human pregnancy, METTL3 and METTL14, the primary enzymes responsible for m6A deposition, specifically mark cytotrophoblast cells in the human placenta.
Read the full summary ↓
By utilizing a METTL3 inducible knockout (iKO) system, we demonstrated the essential role of METTL3 in generating human trophoblast stem cells (hTSCs) from human expanded potential stem cells (hESPCs). Furthermore, METTL3 is critical for the self-renewal ability and cellular identity in the established hTSCs. Mechanistically, METTL3-dependent m6A modification regulates histone modifiers such as EP300 and EZH2 to maintain the epigenetic homeostasis of hTSCs. The depletion of METTL3 in hTSCs results in epigenetic dysregulation, leading to activation of inflammation and premature senescence, which are associated with the preeclampsia phenotype. Notably, our findings from in vitro model show that inhibiting EP300 could efficiently rescue the preeclampsia signature at the transcriptomic level. These results underscore the critical roles of METTL3-dependent m6A RNA methylation in human placentation and the development of placental diseases.
Published in
METTL3 safeguards cell identity and epigenome of human trophoblast stem cells
Fu H, Chen C, Huang Y et al. · Nature communications 2025 · PMID 41402305 · doi:10.1038/s41467-025-67484-z
This dataset
Open GSE280039 in finder → Find more human ChIP / ATAC / CUT&Tag 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 GSE280039_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: Look for peak files (.bed, .narrowPeak) and signal tracks (.bigWig) among the supplementary files; the raw reads are on ENA.

Also filed as BioProject PRJNA1176010 and SRA study SRP540159. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 41402305) ↗
Study design
32 conditions, mostly in duplicate
T3PLUSDOX ×2 T3MINUSDOXP1 ×2 T3MINUSDOXP2 ×2 T4PLUSDOX ×2 T4MINUSDOXP1 ×2 T4MINUSDOXP2 ×2 A485 ×2 control ×2 +38 more

Supports a between-group comparison across 64 samples.

How we read this: 32 replicated groups read from 78 sample titles; they account for 64 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

+ 38 more — browse all 78 samples with per-sample file links →

Datasets on a similar topic

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