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Long read RNA sequencing reveals a new long non-coding RNA that regulates somatic cell reprogramming and pluripotency

GSE282319 Mus musculus (mouse) RNA-seq (gene expression) 45 samples Submitted 2026/08/01 Platform GPL26624Platform GPL24973
At a glance

RNA-seq (gene expression) · mouse · 45 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
45 samples: 45 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)
The recent development of long-read RNA sequencing (lrRNAseq) technologies has paved the way to the exploration of RNA isoform diversity and its relevance to better understand the gene regulatory network that sustains cell fate and plasticity. However, drawing conclusions regarding the functionality of these newly identified isoforms is still very scarce.
Read the full summary ↓
Here, we devised a very efficient system to reprogram cells towards an induced pluripotent cell state and leveraged the strengths of lrRNAseq in order to study the isoform diversity of reprogramming. This strategy enabled us to identify differential isoforms throughout the reprogramming process, with a pressure on novel non-coding isoforms to be regulated. More specifically, we highlight a new long non-coding RNA (lncRNA) whose isoform switch during reprogramming enhances the conversion of cells’ identity towards pluripotency. Conversely, we show that knocking-down this lncRNA in mouse embryonic stem cells hinders the maintenance of a pluripotent state and primes the cells for differentiation. Together, our study provides a comprehensive resource to study full-length isoform regulation and mechanisms at stake during reprogramming, but more importantly it demonstrates the power of lrRNAseq to identify functionally relevant gene isoforms in the context of pluripotency.
This dataset
Open GSE282319 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 GSE282319_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 PRJNA1188123 and SRA study SRP546220. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
45 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 45 sample titles: 45 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

+ 5 more — browse all 45 samples with per-sample file links →

Datasets on a similar topic

Search all mouse RNA-seq datasets in GEO →

Share this dataset

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.