GEO series

A long-read RNA sequencing and polysome profiling framework reveals transposable element–driven transcript diversity and translational rewiring in glioblastoma [LR]

GSE326349 Homo sapiens (human) RNA-seq (gene expression) 18 samples Submitted 2026/06/19 Platform GPL26167

At a glance

RNA-seq (gene expression) · human · 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: Homo sapiens (human)
Topic: GlioblastomaThis series is part of our curated briefing on it.
Samples: 18 samples: 6 conditions, mostly in triplicateGroups 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)

Transposable elements (TEs) account for over half of the human genome and are often derepressed in cancer. TEs can add cryptic splice sites, undergo exonization, and generate gene–TE fusion transcripts, but the combined effects of TEs on RNA processing and translation in glioblastoma stem cells (GSCs) remains incompletely elucidated. We combined long-read RNA sequencing with polysome profiling in four patient-derived GSCs and two neural stem cell (NSC) controls to resolve TE-associated transcript diversity and its relationship to ribosomal engagement.

Read the full summary ↓

Across GSCs, we identified 13,421 alternative splicing (AS) events, 3,077 of which contained TEs within 150 bp of splice junctions. AS sites proximal to TEs were associated with increased isoform switching compared to non–TE-associated AS sites (odds ratio 2.9 - 4.3). Moreover, AS isoforms generated from TE-proximal sites were more likely to exhibit altered ribosomal association (odds ratio 2.54). Directional shifts were observed, with shorter isoforms associating with monosome fractions and longer isoforms with polysome fractions. To enable systematic detection of gene - TE chimeric transcripts, we developed FuTER (Fusion TE Reporter), a long-read–based framework for identifying TE-associated fusions. Application to GSC datasets identified 78 GSC enriched fusion transcripts, several supported by breakpoint-spanning reads in polysome fractions, consistent with ribosome association. Our data suggest that TEs correlate with abnormal splicing activity and altered ribosome engagement in glioblastoma stem cells. By integrating long-read sequencing with polysome profiling and fusion detection, we establish a framework for analysis of TE-induced transcript diversity and its effects on cancer evolution and plasticity.

This dataset

Open GSE326349 in finder → Find more human RNA-seq datasets → Glioblastoma landscape →

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 GSE326349_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 PRJNA1445145 and SRA study SRP687783. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗

Study design

6 conditions, mostly in triplicate

GB2, GSC, ×3 GB11, GSC, ×3 GB13, GSC, ×3 GB24, GSC, ×3 H9, NSC, ×3 H14, NSC, ×3

Supports a between-group comparison across 18 samples.

How we read this: 6 replicated groups read from 18 sample titles; they account for 18 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

GSM9629732 GB2, GSC, replicate 1 GB2, GSC,
GSM9629733 GB2, GSC, replicate 2 GB2, GSC,
GSM9629734 GB2, GSC, replicate 3 GB2, GSC,
GSM9629735 GB11, GSC, replicate 1 GB11, GSC,
GSM9629736 GB11, GSC, replicate 2 GB11, GSC,
GSM9629737 GB11, GSC, replicate 3 GB11, GSC,
GSM9629738 GB13, GSC, replicate 1 GB13, GSC,
GSM9629739 GB13, GSC, replicate 2 GB13, GSC,
GSM9629740 GB13, GSC, replicate 3 GB13, GSC,
GSM9629741 GB24, GSC, replicate 1 GB24, GSC,
GSM9629742 GB24, GSC, replicate 2 GB24, GSC,
GSM9629743 GB24, GSC, replicate 3 GB24, GSC,
GSM9629744 H9, NSC, replicate 1 H9, NSC,
GSM9629745 H9, NSC, replicate 2 H9, NSC,
GSM9629746 H9, NSC, replicate 3 H9, NSC,
GSM9629747 H14, NSC, replicate 1 H14, NSC,
GSM9629748 H14, NSC, replicate 2 H14, NSC,
GSM9629749 H14, NSC, replicate 3 H14, NSC,

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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.