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Aberrant splicing in Huntington’s disease accompanies disrupted TDP-43 activity and altered m6A RNA modifications

GSE278354 Mus musculus (mouse) RNA-seq (gene expression) 60 samples Submitted 2025/01/02 Platform GPL24247Platform GPL24676
At a glance

RNA-seq (gene expression) · mouse · 60 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
60 samples: 52 distinct titles, 4 of them repeatedGroups 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
Aberrant splicing in Huntington's disease accompanies disrupted TDP-43 activity and altered m6A RNA modificationNature neuroscience · 2025
Summary (written by the submitters)
Huntington’s disease (HD) is a neurodegenerative disorder caused by a CAG repeat expansion in the first exon of the HTT gene, leading to altered gene expression. However, the mechanisms leading to disrupted RNA processing in HD remain unclear. Here, we identify the RNA-binding TDP-43 and the N6-methyladenosine (m6A) writer protein METTL3 to be upstream regulators of exon skipping in multiple HD systems.
Read the full summary ↓
Dysregulated nuclear localization of TDP-43 and cytoplasmic accumulation of phosphorylated TDP-43 is shown to be present in HD mice and human brains with TDP-43 co-localizing with HTT nuclear aggregate-like bodies distinct from mutant HTT inclusions and from previously observed TDP-43 pathologies. The binding of TDP-43 onto RNAs encoding HD-associated differentially expressed and aberrantly spliced genes is decreased. Finally, m6A RNA modification is reduced on RNAs abnormally expressed in the striatum from HD R6/2 mouse brain, including at clustered sites adjacent to TDP-43 binding sites. Our evidence supports TDP-43 loss of function coupled with altered m6A modification as a mechanism underlying alternative splicing in HD and highlights the critical nature of TDP-43 loss of function across multiple neurodegenerative diseases.
Published in
Aberrant splicing in Huntington's disease accompanies disrupted TDP-43 activity and altered m6A RNA modification
Nguyen TB, Miramontes R, Chillon-Marinas C et al. · Nature neuroscience 2025 · PMID 39762660 · doi:10.1038/s41593-024-01850-w
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 GSE278354_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 PRJNA1166871 and SRA study SRP535566. Searching any of these in the dataset finder brings you back here.

Study design
52 distinct titles, 4 of them repeated
D37 MSNs from 18Q iPSCs with siSCR ×3 D37 MSNs from 18Q iPSCs with siTDP-43 ×3 D37 MSNs from 50Q iPSCs with siSCR ×3 D37 MSNs from 50Q iPSCs with siTDP-43 ×3

How we read this: Read from 60 sample titles: 52 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

+ 20 more — browse all 60 samples with per-sample file links →

Datasets on a similar topic

Search all RNA-seq datasets in GEO →

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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.