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Single-cell transcriptional patterns impairment in an adult neurogenesis model of Alzheimer´s disease

GSE307094 Homo sapiens (human) Single-cell RNA-seq 18 samples Submitted 2026/02/10 Platform GPL18573
At a glance

Single-cell RNA-seq · human · 18 samples

What it is
Single-cell RNA-seqGene activity measured in thousands of individual cells, so cell types can be told apart.
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.
Paper
Human neuronal differentiation under Aβ exposure: a single-cell transcriptomic and epigenomic datasetScientific data · 2026
Summary (written by the submitters)
Alzheimer’s disease (AD) disrupts neurogenesis and neuronal differentiation, processes critical for cognitive function. This study investigates transcriptional and regulatory changes during neuronal differentiation under the influence of amyloid-beta (Aβ) peptide using scRNA-seq and scATAC-seq approaches. Neuronal differentiation trajectories showed three distinct groups of differentially expressed genes (DEGs).
Read the full summary ↓
Notably, while some neurogenesis-related genes like VIM, MAPT, STMN2, and SLC17A6 were unaffected by Aβ, others, such as PAX6, NEFL, NEFM, and CALB1, displayed significant alterations, particularly in transitions to mature neuronal states. Functional enrichment analysis highlighted disruptions in ATP synthesis, oxidative phosphorylation, synaptic signaling, and cytoplasmic translation. Regulatory transcription factors (TFs) such as EGR1, GATA2, PAX6, and SOX2 were also implicated in these changes. Translational characterization revealed that the gene expression changes observed in the Aβ condition partially aligned with dysregulated gene patterns in hippocampal tissue from AD patients. Differential analysis across stages indicated that mature neuronal differentiation stages exhibited stronger concordance with AD-related gene expression. To understand the role of chromatin accessibility, scATAC-seq analysis identified dysregulation in gene regulatory networks (GRNs) driven by key TFs such as ASCL1, HOXB2, POU2F2, and ONECUT2. Notably, regulons promoting neurogenesis in control conditions were impaired or delayed under Aβ exposure, with regulons specific to mature neuronal transitions emerging only in the presence of Aβ. Functional analysis of these regulons revealed enrichment for pathways associated with differentiation, neuron migration, and synaptic regulation, suggesting Aβ-mediated disruption of neurogenesis-associated regulatory programs. In summary, this study highlights Aβ-induced transcriptional and regulatory disruptions during neuronal differentiation, linking these changes to AD pathology. It underscores the importance of transcriptional and epigenetic regulation in understanding AD and provides insights into how Aβ interferes with neurogenic processes critical for neuronal maturation and function.
Published in
Human neuronal differentiation under Aβ exposure: a single-cell transcriptomic and epigenomic dataset
Blanco-Luquin I, Martínez-de-Morentin X, Vilas-Zornoza A et al. · Scientific data 2026 · PMID 41807428 · doi:10.1038/s41597-026-06971-4
This dataset
Open GSE307094 in finder → Find more human ChIP / ATAC / CUT&Tag 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 GSE307094_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 PRJNA1314248 and SRA study SRP616657. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 41807428) ↗
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

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