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Gene Expression Networks of Synchronized Cells Reveal Alzheimer's Disease in Autopsy-Validated Skin and Blood Samples

GSE303404 Homo sapiens (human) RNA-seq (gene expression) 18 samples Submitted 2025/07/30 Platform GPL18573
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.
Summary (written by the submitters)
Late-onset Alzheimer’s disease (LOAD), which accounts for more than 95% of cases, lacks a well-defined gene expression signature, unlike early-onset forms caused by autosomal dominant mutations in PSEN1, PSEN2, and APP. Although early and late onset Alzheimer’s share core neuropathological features, their genetic convergence had not previously been demonstrated.
Read the full summary ↓
In this study, synchronized skin fibroblasts from autopsy-confirmed LOAD patients revealed 20 significantly dysregulated genes (Adjusted P ≤ 0.05) that clearly distinguished LOAD from non-Alzheimer’s dementias. Among these, NHLH1, URB2, and WASF2 were also dysregulated in blood B lymphocytes, supporting a cross-tissue “modular resilience” framework. An additional 12 dysregulated gene families in skin were validated in blood, resulting in 15 LOAD-associated gene families shared across tissues. Furthermore, five genes—CARNS1, NORAD, SCG2, SNHG14, and WASF2—were differentially expressed in skin fibroblasts and dysregulated in the prefrontal cortex, indicating a multi-layered resilience module connecting peripheral and central tissues. These genes participate in metabolic buffering, genomic stability, secretory organization, epigenetic regulation, and cytoskeletal remodeling. Their coordinated dysfunction suggests a systems-level impairment in stress adaptation relevant to AD biology. Overall, the results reveal previously unrecognized gene-network convergence between familial early-onset and sporadic late-onset Alzheimer’s disease. The robust cross-tissue reproducibility of LOAD dysregulated gene networks highlights the potential of synchronized peripheral cells as accessible sources for disease-specific genomic profiling. This approach may enable the development of personalized biomarkers and targeted therapeutic strategies for Alzheimer’s disease.
This dataset
Open GSE303404 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 GSE303404_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 PRJNA1295357 and SRA study SRP602924. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
18 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 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

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.