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APOE determines cell type-specific pathological landscape of Alzheimer's disease

GSE237718 Homo sapiens (human) Single-nucleus RNA-seq 56 samples Submitted 2025/03/17 Platform GPL20301
At a glance

Single-nucleus RNA-seq · human · 56 samples

What it is
Single-nucleus RNA-seqGene activity measured in individual nuclei (used when whole cells are hard to isolate, for example from frozen tissue). Read from the summary: GEO's own type field only says 'expression profiling by sequencing', so confirm it in the paper.
Organism
Homo sapiens (human)
Samples
56 samples: 56 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
APOE genotype determines cell-type-specific pathological landscape of Alzheimer's diseaseNeuron · 2025
Summary (written by the submitters)
The apolipoprotein E (APOE) gene is the strongest genetic risk modifier for Alzheimer's disease (AD), with the APOE4 allele increasing risk and APOE2 decreasing it compared to the common APOE3 allele. Using single-nuclei RNA sequencing of the temporal cortex from APOE2 carriers, APOE3 homozygotes, and APOE4 carriers, we found that AD-associated transcriptomic changes were highly APOE genotype-dependent.
Read the full summary ↓
Comparing AD with controls, APOE2 carriers showed upregulated synaptic and myelination-related pathways, preserving synapses and myelination at the protein level. Conversely, these pathways were downregulated in APOE3 homozygotes, resulting in reduced synaptic and myelination proteins. In APOE4 carriers, excitatory neurons displayed reduced synaptic pathways similar to APOE3, but oligodendrocytes showed upregulated myelination pathways like APOE2. However, their synaptic and myelination protein levels remained unchanged or increased. APOE4 carriers also showed increased pro-inflammatory signatures in microglia but reduced responses to amyloid-β pathology. These findings reveal APOE genotype-specific molecular alterations in AD across cell types.
Published in
APOE genotype determines cell-type-specific pathological landscape of Alzheimer's disease
Li Z, Martens YA, Ren Y et al. · Neuron 2025 · PMID 40112813 · doi:10.1016/j.neuron.2025.02.017
This dataset
Open GSE237718 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 GSE237718_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: Single-cell studies usually ship their counts as 10x files (matrix.mtx, barcodes.tsv, features.tsv), an .h5 file, or an .rds / .h5ad object that Seurat or Scanpy can open. Look for those among the supplementary files.

Also filed as BioProject PRJNA996223 and SRA study SRP450199. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 40112813) ↗
Study design
56 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 56 sample titles: 56 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

+ 16 more — browse all 56 samples with per-sample file links →

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.