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Diversity and Heterogeneity of Extracellular Vesicles and Brain-Penetrating Amembranous Non-Vesicular Nanoparticles in Glioblastoma

GSE286513 Homo sapiens (human) RNA-seq (gene expression) 20 samples Submitted 2025/11/18 Platform GPL11154
At a glance

RNA-seq (gene expression) · human · 20 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
Homo sapiens (human)
Topic
GlioblastomaThis series is part of our curated briefing on it.
Samples
20 samples: 20 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
Heterogeneity of Extracellular Vesicles and Non-Vesicular Nanoparticles in GlioblastomaJournal of extracellular vesicles · 2025
Summary (written by the submitters)
Advances in understanding intercellular communication through extracellular vesicles (EVs) and non-vesicular extracellular nanoparticles (NVEPs) have been limited by their cellular heterogeneity and unclear origins. Using sequential ultracentrifugation, we identified high- and low-density EVs and performed multi-omics analyses on four EV/NVEP categories, including supermeres.
Read the full summary ↓
Our findings reveal that extracellular RNA, RNA-binding proteins, and other cellular proteins are differentially expressed among EVs and NVEPs. Notably, supermeres can penetrate the blood-brain barrier, in a manner dependent on their intact RNA and protein structure, and distribute throughout the CNS. Supermeres preferentially interact with microglia at higher levels than other CNS cell types. These interactions lead to decreased TGFβ expression, suggesting an immunomodulatory role. This study highlights supermeres as a promising platform for CNS therapeutic delivery.
Published in
Heterogeneity of Extracellular Vesicles and Non-Vesicular Nanoparticles in Glioblastoma
Xu T, Paulo JA, Zhang P et al. · Journal of extracellular vesicles 2025 · PMID 41039818 · doi:10.1002/jev2.70168
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 GSE286513_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 PRJNA1209840 and SRA study SRP557086. Searching any of these in the dataset finder brings you back here.

Study design
20 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 20 sample titles: 20 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
More Glioblastoma datasets

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.