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Transcriptomic analysis of RNA Granules and Exosomes: Unmasking the Intra- and Intercellular organelle overlap in Huntington's Disease

GSE261143 Homo sapiens (human) RNA-seq (gene expression) 20 samples Submitted 2025/03/19 Platform GPL24676
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)
Samples
20 samples: 5 × control_exosome - biol vs 5 × HD_exosome - biol vs 5 × HD_SG - biol vs 5 × control_SG - biolGroups 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
Transcriptomic analysis of intracellular RNA granules and small extracellular vesicles: Unmasking their overlap in a cell model of Huntington's diseaseMolecular and cellular probes · 2025
Summary (written by the submitters)
Introduction:Repetitive RNA sequences like CAG repeats that trigger aberrant RNA-protein interactions cause several neurodegenerative diseases including Huntington's disease (HD). Importantly, these aberrant RNA-protein complexes also seed the formation of cytoplasmic liquid-like granules, such as the stress granules. Emerging evidence demonstrates that granules formed via liquid-liquid phase separation can mature into gel-like inclusions that persist within the cell and may act as precursor to aggregates that occur in patients’ tissue.
Read the full summary ↓
Thus, deregulation of RNA granule biology is an important component of neurodegeneration. Interestingly, the formation of intracellular membrane-less organelles like stress granules increases along with the secretion of exosomes. Exosomes are small membrane-bound vesicles that are secreted generally by all cell types. They may participate in the spreading of misfolded proteins and aberrant RNA-protein complexes across the central nervous system in neurodegenerative diseases like HD. Moreover, due to their capability to cross the blood-brain barrier, exosomes hold great potential as sources of biomarkers available from the periphery, e.g., from blood. In this study, we performed a comparative transcriptomic analysis of exosomes and RNA granules in an HD model.Methods: Exosomes and RNA granules were isolated from an HD cell model, stably expressing HTT exon 1 with 83 CAG repeats under an inducible promoter. Both exosomes and RNA granules were isolated from induced (HD) and non-induced (control) cells and analysed by RNA sequencing.Results: Comparative analysis between the transcriptomics data of HD RNA granules and exosomes showed that: (I) intracellular RNA granules and extracellular RNA vesicles share content, (II) several non-coding RNAs translocate to RNA granules, (III) the composition of RNA granules and exosomes is affected by expression of mutant HTT.Discussion: Our data show that intracellular RNA granules and exosomes share content, suggesting that formation of RNA granules and exosome loading may be related. Moreover, our data suggest that both intracellular RNA granules and extracellular RNA vesicles may serve as a source for diagnostic strategies. For example, disease-specific RNA-signatures of exosomes can serve as biomarker of central nervous system diseases. However, the cell-type specific signature of EV-secreted RNAs may make it difficult to detect these biomarkers in blood.
Published in
Transcriptomic analysis of intracellular RNA granules and small extracellular vesicles: Unmasking their overlap in a cell model of Huntington's disease
Nabariya DK, Knüpfer LM, Hartwich P et al. · Molecular and cellular probes 2025 · PMID 40090627 · doi:10.1016/j.mcp.2025.102026
This dataset
Open GSE261143 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 GSE261143_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 PRJNA1085382 and SRA study SRP494029. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 40090627) ↗
Study design
5 × control_exosome - biol vs 5 × HD_exosome - biol vs 5 × HD_SG - biol vs 5 × control_SG - biol

Supports a between-group comparison across 20 samples.

How we read this: 4 replicated groups read from 20 sample titles; they account for 20 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
Datasets on a similar topic

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