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Mislocalization of mitochondrial DNA and subcellular RNA is a convergent mechanism in neurodegenerative dementias and is rescued by VCP ATPase inhibition

GSE277599 Homo sapiens (human) RNA-seq (gene expression) 72 samples Submitted 2025/06/27 Platform GPL24676
At a glance

RNA-seq (gene expression) · human · 72 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
72 samples: 72 samples, each with a distinct title — no replicated groupsGroups 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
Mislocalization of nucleic acids is a convergent and targetable mechanism in Alzheimer's disease and frontotemporal dementiaCell reports · 2025
Summary (written by the submitters)
Nucleocytoplasmic transport defects have been observed in Alzheimer’s disease (AD) and frontotemporal dementia (FTD). However, there have been few systematic efforts to evaluate transcriptome-wide subcellular mRNA distribution in these conditions, which if deregulated may significantly compromise cellular homeostasis. Here, we assessed mRNA nucleocytoplasmic localization by performing transcriptome-wide profiling on nuclear and cytoplasmic fractions of human iPSC-derived cortical neurons from healthy individuals compared to those from both familial AD and FTD patients.
Read the full summary ↓
Our findings revealed that AD and FTD mutations induce significant changes in mRNA nucleocytoplasmic distribution. We additionally reveal the redistribution of mitochondrial-related transcripts across different mutations. The significant ‘leakage’ of mitochondrial RNA (mtRNA) into the cytosol in AD and FTD mutant neurons motivated us to examine the leakage of mtDNA. Importantly, we found abnormal cytoplasmic accumulation of mtDNA in AD and FTD cortical neurons, which is reversible with treatment using a pharmacological inhibitor of the VCP D2 ATPase domain.
Published in
Mislocalization of nucleic acids is a convergent and targetable mechanism in Alzheimer's disease and frontotemporal dementia
Hung C, Llorian M, Pal K et al. · Cell reports 2025 · PMID 40570368 · doi:10.1016/j.celrep.2025.115867
This dataset
Open GSE277599 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 GSE277599_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 PRJNA1162889 and SRA study SRP533692. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 40570368) ↗
Study design
72 samples, each with a distinct title — no replicated groups

Any grouping that defines a comparison lives in the sample characteristics, which GEO records separately from the titles.

How we read this: Read from 72 sample titles: 72 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

+ 32 more — browse all 72 samples with per-sample file links →

Datasets on a similar topic

Search all human 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.