← BioTransfer GEO Dataset Finder
GEO series

Intracellular accumulation of amyloid-ß is a marker of selective neuronal vulnerability in Alzheimer’s disease

GSE297004 Homo sapiens (human) RNA-seq (gene expression) 53 samples Submitted 2025/05/19 Platform GPL20301
At a glance

RNA-seq (gene expression) · human · 53 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
53 samples: 53 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
Intracellular accumulation of amyloid-ß is a marker of selective neuronal vulnerability in Alzheimer's diseaseNature communications · 2025
Summary (written by the submitters)
Defining how amyloid-β and pTau together lead to neurodegeneration is fundamental to understanding Alzheimer’s disease (AD). We used imaging mass cytometry to identify neocortical neuronal subtypes lost with AD in post-mortem brain middle temporal gyri from non-diseased and AD donors. Here we showed that L5,6 RORB+FOXP2+ and L3,5,6 GAD1+FOXP2+ neurons, which accumulate amyloid-β intracellularly from early Braak stages, are selectively vulnerable to degeneration in AD, while L3 RORB+GPC5+ neurons, which accumulate pTau but not amyloid-β, are not lost even at late Braak stages.
Read the full summary ↓
We discovered spatial associations between activated microglia and these vulnerable neurons and found that vulnerable RORB+FOXP2+ neuronal transcriptomes are enriched selectively for pathways involved in inflammation and glycosylation and, with progression to AD, also protein degradation. Our results suggest that the accumulation of intraneuronal amyloid-β, which is associated with glial inflammatory pathology, may contribute to the initiation of degeneration of these vulnerable neurons.
Published in
Intracellular accumulation of amyloid-ß is a marker of selective neuronal vulnerability in Alzheimer's disease
Caramello A, Fancy N, Tournerie C et al. · Nature communications 2025 · PMID 40467545 · doi:10.1038/s41467-025-60328-w
This dataset
Open GSE297004 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 GSE297004_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 PRJNA1262360. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 40467545) ↗
Study design
53 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 53 sample titles: 53 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

+ 13 more — browse all 53 samples with per-sample file links →

Datasets on a similar topic

Search all human RNA-seq datasets in GEO →

Share this dataset

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.