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Exploring the Factors regulating RNA Enrichment in EVs

GSE279376 Homo sapiens (human) RNA-seq (gene expression) 12 samples Submitted 2025/07/07 Platform GPL24676
At a glance

RNA-seq (gene expression) · human · 12 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)
Samples
12 samples: 12 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
Defining the Parameters for Sorting of RNA Cargo Into Extracellular VesiclesJournal of extracellular vesicles · 2025
Summary (written by the submitters)
Extracellular vesicles (EVs) are small particles that are released by cells and mediate cell-cell communication by transferring bioactive molecules such as RNA. RNA cargo of EVs, including coding and non-coding RNAs, can change the behavior of recipient cells, affecting processes including gene expression, proliferation, and apoptosis.
Read the full summary ↓
CircRNAs are stable and resistant to degradation and have been shown to be enriched in EVs. They play key roles in gene regulation and are also emerging as promising biomarkers for disease diagnosis due to their stability and disease-specific expression. While microRNAs (miRNAs) are the most well studied RNA cargo of EVs, very little is known about the mechanisms of enrichment of circular RNAs (circRNAs) as well as long linear RNAs. Here, we take a comprehensive genome-wide approach to investigate the role of structuredness and shape along with GC%, size, exon count, and coding potential, in the sorting and enrichment of circular and long linear RNAs into EVs. We developed a model using these parameters to predict the likelihood of EV packaging of RNA and it was validated by using single molecule RNA imaging of EV bound RNAs. Furthermore, we found that structuredness could explain the relative enrichment of circRNAs over their linear counterparts. These results were validated on existing public databases of circular and linear RNAs in EVs. By identifying and analyzing these factors, we aim to better understand the complex mechanisms behind EV-mediated RNA transfer and its impact on cell communication in both health and disease. This mechanistic understanding of RNA enrichment in EVs is crucial for engineering EVs with selective RNA cargo.
Published in
Defining the Parameters for Sorting of RNA Cargo Into Extracellular Vesicles
Abdelgawad A, Huang Y, Gololobova O et al. · Journal of extracellular vesicles 2025 · PMID 40620070 · doi:10.1002/jev2.70113
This dataset
Open GSE279376 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 GSE279376_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 PRJNA1172314 and SRA study SRP538257. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 40620070) ↗
Study design
12 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 12 sample titles: 12 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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