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METTL3-mediated m6A modification controls Tcra recombination center formation and thymic T cell development

GSE333480 Mus musculus (mouse) ChIP-seq / ATAC-seq (genome binding and open chromatin) 98 samples Submitted 2026/06/01 Platform GPL24247
At a glance

ChIP-seq / ATAC-seq (genome binding and open chromatin) · mouse · 98 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
Mus musculus (mouse)
Samples
98 samples: 32 conditions, mostly in triplicateGroups 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.
Summary (written by the submitters)
N6-methyladenosine (m6A) is the most prevalent internal modification of RNA, and METTL3 functions as the catalytic core of the methyltransferase complex. While m6A regulates T cell activation and differentiation, whether this RNA modification participates in antigen receptor V(D)J recombination remains unknown. Using conditional Mettl3 knockout mice and multi-omic profiling, we examined m6A function in thymocyte development and Tcra rearrangement.
Read the full summary ↓
Mettl3 deficiency reduced thymic cellularity by about 20-30%, substantially decreased double-positive (DP) thymocytes, diminished peripheral CD8+ T cells, and selectively impaired Tcra but not Tcrb rearrangement. Mechanistically, m6A specifically decorated TEA germline transcripts and maintained recombination center architecture. METTL3 loss abolished m6A enrichment at TEA, diminished Eα-TEA chromatin looping, and reduced germline transcription. Concurrently, rearranged Tcra translation efficiency fell to about 40% of wild-type levels, significantly reducing TCR-CD3 surface expression on DP, CD4 SP, and CD8 SP cells. Mettl3 knockout further upregulated DNA repair genes such as Lig4, Fignl1, and Rad51ap1, activated the interferon pathway, and increased γH2A.X DNA double-strand break markers and double-stranded RNA accumulation in DP thymocytes. Collectively, these results establish RNA m6A modification as a previously unrecognized regulatory layer of V(D)J recombination that controls recombination center formation and preserves genomic integrity during Tcra gene rearrangement.
This dataset
Open GSE333480 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 GSE333480_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 PRJNA1471173 and SRA study SRP704172. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
32 conditions, mostly in triplicate
DP-Mettl3WT-RNAseq ×4 thymus-Mettl3KO-TRA-RACE ×3 thymus-Mettl3WT-TRA-RACE ×3 thymus-Mettl3KO-TRB-RACE ×3 thymus-Mettl3WT-TRB-RACE ×3 thymus-Mettl3KO-TRD-RACE ×3 thymus-Mettl3WT-TRD-RACE ×3 thymus-Mettl3KO-TRAV14-HTGTS ×3 +25 more

Supports a between-group comparison across 97 samples.

How we read this: 32 replicated groups read from 98 sample titles; they account for 97 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

+ 58 more — browse all 98 samples with per-sample file links →

Datasets on a similar topic

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