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Human breast milk-derived extracellular vesicles alleviate Alzheimer’s disease phenotypes through delivering CD36

GSE327752 Mus musculus (mouse) RNA-seq (gene expression) 18 samples Submitted 2026/04/13 Platform GPL24247
At a glance

RNA-seq (gene expression) · mouse · 18 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
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.
Summary (written by the submitters)
Alzheimer's disease (AD) is an age-related progressive neurodegenerative disease that is the most common cause of dementia globally. To date, multiple types of extracellular vesicles (EVs), particularly those derived from stem cells, have been implicated to improve AD pathology, but the clinical applications of stem cell-derived EVs are restricted due to ethical/religious concerns, supply constraints, and other challenges.
Read the full summary ↓
Here we found that human breast milk-derived extracellular vesicles (HM-EVs) mitigated AD pathology and rescued cognitive decline of 5xFAD mice, a preclinical mouse model of AD. We identified CD36, a microglial anti-Aβ receptor as the key cargo to mediate the therapeutic effects of HM-EVs by enhancing microglial phagocytosis and Aβ clearance. Moreover, blockage of CD36 using a monoclonal antibody abolished HM-EVs-driven Aβ phagocytosis of microglia, therefore aggravating cognitive performance and pathological changes of 5xFAD mice. Together, our findings indicate HM-EVs as a novel intervention strategy of AD through delivering CD36.
This dataset
Open GSE327752 in finder → Find more mouse 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 GSE327752_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 PRJNA1452678 and SRA study SRP691275. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
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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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.