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Genetic and Epigenetic Regulation of Pvt1 Monoallelic Expression

GSE287458 Mus musculus (mouse) RNA-seq (gene expression) 101 samples Submitted 2025/09/26 Platform GPL24247
At a glance

RNA-seq (gene expression) · mouse · 101 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
101 samples: 101 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
Genetic and chromatin regulation of Pvt1 monoallelic expressionCell reports · 2025
Summary (written by the submitters)
Each gene exists as two copies in the same nucleus of mammalian cells. Although most genes are equivalently controlled on both alleles, Random Monoallelic Expressing (RME) genes stably maintain the expression from only one of two alleles, but the mechanisms and functional consequences of RME remains unclear. Here we performed allele-specific RNA-seq on over 100 neural progenitor cell (NPC) clonal lines to reveal the extent of RME for genes across clonal lines.
Read the full summary ↓
Interestingly out of 297 autosomal RME genes, Pvt1, a well-studied oncogenic long non-coding RNA, is monoallelically expressed with a skewed genetic bias towards the Castaneous allele in F1-hybrid NPCs. In the absence of genetic differences, allelic tracking by gene editing reveals Pvt1 undergoes balanced RME that is maintained across somatic cell generations. Monoallelic expression is maintained by a poised Pvt1 promoter with bivalent active histone marks, H3K4me3 and H3K27ac, and silencing histone marks, H3K4me3 and H3K27me3, demonstrating its ability to switch transcriptional states depending on the cellular context. Leveraging our large RNA-seq data set and the genetic skew, we use differential gene expression analysis on clonal lines based on their Pvt1 allelic status to reveal a transcription factor, Tfap2a, with differential binding at the Pvt1 promoter, providing a mechanism for the initiation and allelic choice for RME genes. Additionally, we demonstrate monoallelic Pvt1 expression results in an increase in Pvt1 expression leading to a growth advantage relative to Pvt1 biallelic expressing clonal lines These findings provide an example of how genetic differences can skew a stochastic process which results in an epigenetic phenomenon with a phenotypic consequence in early development.
Published in
Genetic and chromatin regulation of Pvt1 monoallelic expression
Luong C, Chen M, Belk JA et al. · Cell reports 2025 · PMID 41223055 · doi:10.1016/j.celrep.2025.116554
This dataset
Open GSE287458 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 GSE287458_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 PRJNA1213040 and SRA study SRP558491. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 41223055) ↗
Study design
101 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 101 sample titles: 101 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

+ 61 more — browse all 101 samples with per-sample file links →

Datasets on a similar topic

Search all mouse RNA-seq datasets in GEO →

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