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A protective role for APP in nuclear waste clearance via lysosomal exocytosis

GSE272590 Homo sapiens (human) RNA-seq (gene expression) 18 samples Submitted 2026/06/11 Platform GPL24676
At a glance

RNA-seq (gene expression) · human · 18 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
18 samples: 18 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
A protective role for APP in nuclear waste clearance via lysosomal exocytosisProceedings of the National Academy of Sciences of the United States of America · 2026
Summary (written by the submitters)
Amyloid precursor protein (APP) is widely known for its role in Alzheimer’s disease (AD) pathogenesis through its proteolytic processing into amyloid-β peptides. However, its physiological function remains incompletely understood. Here, we uncover a protective role for full-length APP in facilitating the disposal of nuclear-derived debris under genotoxic stress.
Read the full summary ↓
In both cultured cells and in vivo mouse models, loss of APP leads to nuclear waste accumulation, increased inflammation, and cell death, whereas APP overexpression mitigates these effects. Mechanistically, we show that APP supports the extracellular release of nuclear material through lysosomal exocytosis. APP mutants associated with familial AD fail to mediate this process. Consistently, human AD brain tissue exhibits abnormal nuclear morphology, accumulation of nuclear waste in the cytoplasm, and reduced APP levels per neuron. These findings highlight a conserved cellular mechanism by which APP contributes to nuclear and cellular homeostasis, and suggest that impaired nuclear waste clearance may represent an underappreciated contributor to neurodegeneration.
Published in
A protective role for APP in nuclear waste clearance via lysosomal exocytosis
Dougnon G, Otsuka T, Nakamura Y et al. · Proceedings of the National Academy of Sciences of the United States of America 2026 · PMID 42275473 · doi:10.1073/pnas.2524190123
This dataset
Open GSE272590 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 GSE272590_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 PRJNA1137394 and SRA study SRP520851. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 42275473) ↗
Study design
18 samples, each with a distinct title — no replicated groups

Supports a case/control comparison: 6 samples read as cases, 3 as controls.

How we read this: Read from 18 sample titles: 18 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
Datasets on a similar topic

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