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Catalytic activity of nuclear METTL3 promotes functional m6A modifications that drive retinal development [mRNA-seq]

GSE287484 Mus musculus (mouse) RNA-seq (gene expression) 32 samples Submitted 2025/01/25 Platform GPL24247
At a glance

RNA-seq (gene expression) · mouse · 32 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
Mus musculus (mouse)
Samples
32 samples: 16 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 uncouples chromatin accessibility from transcription during retinal developmentStem cell reports · 2025
Summary (written by the submitters)
METTL3 catalyzes N6-methyladenosine (m6A) on mRNA, regulating RNA metabolism, but its role in early retinal development remains virtually unstudied. Using stem cell-derived 3D retinal organoids to model retinal progenitor cell (RPC) differentiation, we found that loss of METTL3 nuclear m6A enzymatic activity impairs formation of the Rx+ retinal anlage in vitro.
Read the full summary ↓
Through dCas13b-FTO m6A engineering, we demonstrate that m6A modifications at the Six3 3'UTR control its stability. While Mettl3 loss altered histone modifications, direct METTL3 chromatin targets were not transcriptionally functional. Integration of transcriptome-wide m6A and protein-RNA mapping with a degron-based strategy revealed immediate effects of METTL3 degradation in RPCs, uncovering a regulatory interaction between METTL3 and its RNA target Ythdf1. We demonstrate uncoupling of chromatin accessibility from changes in retinal transcription and m6A modifications. In vivo studies of Mettl3-deficient RPCs showed altered cell cycle and impaired retinal ganglion cell generation. Our findings establish METTL3-dependent m6A modifications as an essential post-transcriptional layer that drives retinal development.
Published in
METTL3 uncouples chromatin accessibility from transcription during retinal development
Xu J, Huang Y, Han Z et al. · Stem cell reports 2025 · PMID 41135527 · doi:10.1016/j.stemcr.2025.102690
This dataset
Open GSE287484 in finder → Find more mouse 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 GSE287484_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 PRJNA1213079 and SRA study SRP558519. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 41135527) ↗
Study design
16 conditions, mostly in duplicate
Rx-GFP d0 ×2 Rx-GFP d2 ×2 Rx-GFP d4 ×2 Rx-GFP d6 ×2 Mettl3 KO d0 ×2 Mettl3 KO d2 ×2 Mettl3 KO d4 ×2 Mettl3 KO d6 ×2 +8 more

Supports a between-group comparison across 32 samples.

How we read this: 16 replicated groups read from 32 sample titles; they account for 32 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
Datasets on a similar topic

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