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Linking trajectories of cerebrovascular remodeling and dysfunction to cross-species brain vessel transcriptome in Alzheimer’s disease

GSE291921 Mus musculus (mouse) RNA-seq (gene expression) 24 samples Submitted 2026/03/13 Platform GPL30172
At a glance

RNA-seq (gene expression) · mouse · 24 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
Mus musculus (mouse)
Samples
24 samples: 6 × APP-Male vs 6 × APP-Female vs 6 × WT-Male vs 6 × WT-FemaleGroups 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.
Summary (written by the submitters)
Growing evidence implicates dysfunctional brain vasculature occurring early in Alzheimer’s disease (AD) progression. Few studies have attempted to bridge transcriptomic signatures with early neuroimaging observations of vascular abnormalities. We leveraged longitudinal in vivo imaging of the cerebrovascular tree in APP/PS1 mice to identify when vascular abnormalities emerge, followed by bulk RNA-seq of isolated brain vessels at this time point.
Read the full summary ↓
At 9–11 months of age, AD mice exhibited microvascular remodeling and dysfunction, including increased tortuosity, decreased capillary blood flow and reduced vascular density. Enriched gene pathways highlighted increased neuroinflammation, dysfunctional angiogenesis, and deficits in actin-mediated muscle contractility. Mapping transcriptional changes to a mouse vascular atlas implicated endothelial cells and microglia in impaired angiogenesis, while mural cells showed actin-filament and ion channel deficits. Mapping to a human vascular atlas identified translational targets to alleviate actin-mediated contractility deficits. Our integrated framework advances cerebrovascular biomarker understanding and informs early-stage AD therapeutic targets.
This dataset
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.

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 GSE291921_RAW.tar (it can run to tens of gigabytes).

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.

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 PRJNA1235782 and SRA study SRP570311. Searching any of these in the dataset finder brings you back here.

Study design
6 × APP-Male vs 6 × APP-Female vs 6 × WT-Male vs 6 × WT-Female

Supports a between-group comparison across 24 samples.

How we read this: 4 replicated groups read from 24 sample titles; they account for 24 of them. 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
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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.