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Nitric oxide drives proteomic diversity through alternative splicing [Fibroblast RNA-Seq]

GSE286542 Mus musculus (mouse) RNA-seq (gene expression) 16 samples Submitted 2026/05/21 Platform GPL34328
At a glance

RNA-seq (gene expression) · mouse · 16 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
16 samples: 4 × WT PTB (flox) + cytokines, biol vs 4 × C250S PTB + cytokines, biol vs 4 × WT PTB (flox), untreated, biol vs 4 × C250S PTB, untreated, 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
Nitric oxide drives proteomic diversity through alternative splicingMolecular cell · 2026
Summary (written by the submitters)
Redox signaling by nitric oxide (NO) is estimated to control the large part of the global proteome via S-nitrosylation (SNO-modification). Here we report that RNA-binding proteins (RBPs) represent the most significantly enriched class of S-nitrosylation targets, with broad coverage of spliceosomal factors. We demonstrate that NO regulates alternative splicing (AS) and that S-nitrosylation of PTBP1, a central regulator of AS, can massively shift and contextually alter gene expression, while further enriching the transcriptome for SNO sites.
Read the full summary ↓
PTBP1 S-nitrosylation changes RNA-binding domain conformation, RNA motif recognition, protein–RNA and protein–protein interactions, and intracellular trafficking to impact pathways for viral infection and neurodegeneration. Levels of SNO-PTBP1 are reduced in mouse and human Alzheimer’s brains and correlate with adverse clinical outcomes. Overall, SNO-RBPs are characterized by conservation across diverse lineages and SNO sites and provide a blueprint for redox regulation of both transcriptome and proteome in physiology and disease.
Published in
Nitric oxide drives proteomic diversity through alternative splicing
Schindler JC, Seth P, Hausladen A et al. · Molecular cell 2026 · PMID 42167237 · doi:10.1016/j.molcel.2026.04.024
This dataset
Open GSE286542 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 GSE286542_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 PRJNA1209879 and SRA study SRP556966. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 42167237) ↗
Study design
4 × WT PTB (flox) + cytokines, biol vs 4 × C250S PTB + cytokines, biol vs 4 × WT PTB (flox), untreated, biol vs 4 × C250S PTB, untreated, biol

Supports a between-group comparison across 16 samples.

How we read this: 4 replicated groups read from 16 sample titles; they account for 16 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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