GEO series

Common pathogenic mechanisms in the hippocampus across neurodegenerative dementias: Alzheimer's disease, Down syndrome, and Parkinson's disease

GSE318560 Homo sapiens (human) RNA-seq (gene expression) 20 samples Submitted 2026/02/05 Platform GPL24676

At a glance

RNA-seq (gene expression) · human · 20 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: 20 samples: 20 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: Common pathogenic mechanisms in the hippocampus across neurodegenerative dementias: Alzheimer's disease, Down syndrome, and Parkinson's diseaseNPJ dementia · 2026

Summary (written by the submitters)

Extensive evidence suggests overlapping pathological mechanisms in the brain of individuals with Parkinson´s disease dementia, Down syndrome dementia, and Alzheimer´s disease. For these neurodegenerative dementias, we observed that the chronological age did not align with their biological age, which was determined based on hippocampal transcript levels (i.e., transcriptional age).

Read the full summary ↓

Subsequently, we performed a transcriptomic analysis that corrected for the transcriptional age in the hippocampus of affected individuals, highlighting common underlying pathogenic mechanisms. There were 45 common differentially expressed genes (DEGs), whereas enriched functional terms were related to lysine N-methyltransferase activity and intermediate filament. Co-expression network analysis displayed a module that was significantly downregulated in the non-demented control group only. This module identified EHMT2 and LMNB2 as hub genes, which were also common DEGs. Overall, these findings uncover shared functional insights in the hippocampus, while specifically highlighting EHMT2 and LMNB2 as potential universal biomarkers or disease-altered targets across neurodegenerative dementias.

Published in

Common pathogenic mechanisms in the hippocampus across neurodegenerative dementias: Alzheimer's disease, Down syndrome, and Parkinson's disease

Crans RAJ, Fructuoso M, Bascón-Cardozo K et al. · NPJ dementia 2026 · PMID 42078116 · doi:10.1038/s44400-026-00075-x

This dataset

Open GSE318560 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 GSE318560_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 PRJNA1419230 and SRA study SRP674932. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 42078116) ↗

Study design

20 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 20 sample titles: 20 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

GSM9497559 HC_105, Control
GSM9497560 HC_108, Control
GSM9497561 HC_110, Control
GSM9497562 HC_109, Control
GSM9497563 HC_111, Control
GSM9497564 HC_025, PDD
GSM9497565 HC_027, PDD
GSM9497566 HC_178, PDD
GSM9497567 HC_176, PDD
GSM9497568 HC_131, AD
GSM9497569 HC_128, AD
GSM9497570 HC_148, AD
GSM9497571 HC_132, AD
GSM9497572 HC_130, AD
GSM9497573 HC_020, AD
GSM9497574 HC_061, DSD
GSM9497575 HC_069, DSD
GSM9497576 HC_064, DSD
GSM9497577 HC_066, DSD
GSM9497578 HC_063, DSD

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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.