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Ubiquitin-Proteasome System Dysregulation in Alzheimer's Disease Impacts Protein Abundance

GSE327021 Homo sapiens (human) RNA-seq (gene expression) 24 samples Submitted 2026/05/04 Platform GPL34284
At a glance

RNA-seq (gene expression) · human · 24 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
24 samples: 24 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.
Summary (written by the submitters)
Alzheimer's disease (AD) is a relentlessly progressive, fatal neurodegenerative disorder that results in widespread protein dysfunctions. However, the full extent of aberrant proteomic changes in AD and their impact on cellular physiology remains unknown. This is, in part, because of the challenges of comprehensively measuring the proteome.
Read the full summary ↓
Here, we used plexDIA, an approach that provides deep proteomic coverage and high throughput by parallelizing the acquisition of peptides and samples, to characterize proteomic changes in AD. Using human dorsolateral prefrontal cortex tissue, we identified 281 differentially abundant proteins in AD. By systematically analyzing compartment and protein complex-specific shifts in protein abundance, we identified an AD-specific decrease in levels of the 20S proteasome, the catalytic core of the cell's primary protein degradation pathway. This alteration was accompanied by widespread decreases in proteasome subunit stoichiometries. Many proteasome substrate proteins were negatively correlated with 20S levels and increased in AD, suggesting that reduced 20S levels leads to abnormal protein accumulation. By analyzing proteins increased in AD, we identify key properties of such proteins: They have fast degradation rates, they contain signal sequences that allow them to be targeted for proteasomal degradation, and they are targeted by quality control pathways that recognize mislocalized proteins. Changes in these gene products at the protein and mRNA levels were highly discordant, providing additional evidence for increased protein abundance driven by impaired clearance. We also identified coherent sets of ubiquitin system enzymes, proteins that target substrates for proteasomal degradation, whose levels robustly discriminate AD from non-AD samples. One subset exhibited consistent increases in AD, while another, which contained the tau E3 ligase Cul5, exhibited consistent decreases, revealing complex changes in the ubiquitin system in AD. Taken together, our results suggest that decreased ubiquitin-proteasome system capacity and impaired clearance of short-lived and mislocalized proteins contribute substantially to proteopathic burden in AD.
This dataset
Open GSE327021 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 GSE327021_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 PRJNA1449257 and SRA study SRP689456. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
24 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 24 sample titles: 24 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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