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Single-nucleus multiomics reveals the regulatory programs in three brain regions of sporadic early-onset Alzheimer's Disease

GSE272082 Homo sapiens (human) Single-nucleus RNA-seq 42 samples Submitted 2025/11/19 Platform GPL24676Platform GPL34281
At a glance

Single-nucleus RNA-seq · human · 42 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).
Organism
Homo sapiens (human)
Samples
42 samples: 42 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
Single-nucleus multiomics reveals the disrupted regulatory programs in three brain regions of sporadic early-onset Alzheimer's diseaseScience advances · 2025
Summary (written by the submitters)
Sporadic early-onset Alzheimer’s Disease (sEOAD) represents a significant but less-studied subtype of Alzheimer’s Disease. In this study, we generated a single-nucleus multiome atlas from over 70,000 nuclei derived from the postmortem prefrontal cortex, entorhinal cortex, and hippocampus of four sEOAD patients and five control donors.
Read the full summary ↓
Comprehensive analyses were conducted to delineate cell type-specific transcriptomic changes and linked candidate cis-regulatory elements (cCREs) across these brain regions. We identified and prioritized seven conservative transcription factors in glial cells and neurons in multiple brain regions, including RFX4 in astrocytes and IKZF1 in microglia, which are implicated in the regulation of sEOAD-associated genes. Moreover, we investigated altered intercellular signaling between glial cells and neurons, highlighting their regulatory potential on gene expression in receiver cells. Finally, we reported an enrichment of late-onset Alzheimer’s Disease risk loci within microglial cCREs linked to sEOAD-associated genes. This atlas enhances our understanding of the transcriptional and chromatin dynamics in sEOAD and provides a foundational resource for further pathological investigations.
Published in
Single-nucleus multiomics reveals the disrupted regulatory programs in three brain regions of sporadic early-onset Alzheimer's disease
Liu A, Citu C, Enduru N et al. · Science advances 2025 · PMID 41406216 · doi:10.1126/sciadv.adw4917
This dataset
Open GSE272082 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 GSE272082_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 PRJNA1135080 and SRA study SRP519702. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 41406216) ↗
Study design
42 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 42 sample titles: 42 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

+ 2 more — browse all 42 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.