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Co-translational profiling in the cardiac endothelium in response to LPS-induced inflammation in female mice in vivo: a proof-of-concept approach

GSE273368 Mus musculus (mouse) RNA-seq (gene expression) 16 samples Submitted 2026/06/19 Platform GPL24247
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 × heart, Pre-IP, control, biol vs 4 × heart, Pre-IP, experimental, biol vs 4 × heart, IP, control, biol vs 4 × heart, IP, experimental, 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
Co-translational profiling in the cardiac endothelium in response to LPS-induced inflammation in female mice in vivo: a proof-of-concept approachMolecular and cellular biochemistry · 2026
Summary (written by the submitters)
Producing functional proteins involves multiple steps during mRNA translation on the ribosomes. However, co-translational regulatory mechanisms remain poorly characterized in intact mammalian systems. As a proof-of-concept, we developed a multi-omics approach to investigate endothelial-specific, co-translational regulation by modifying the translating ribosome affinity purification (TRAP) in vivo.
Read the full summary ↓
We simultaneously co-immunoprecipitated (IP) polysome-associated mRNAs and proteins from the hearts of hemagglutinin-tagged ribosomal protein L22 mice (RiboTag) crossed with inducible endothelial-specific Cdh5CreERT2 mice (RiboTagEC). To perturb endothelial function, female mice were injected with E. coli lipopolysaccharide (LPS) (6 mg/Kg, i.p., 12 h). Hearts were homogenized, with ~ 10% used for input RNA-Seq and proteomics controls, and the remainder for IP of ribosome-bound polyadenylated mRNA and proteins. Endothelial cell transcripts (pecam1, cdh5) were enriched > 5-fold, while markers characteristic of other cell types were significantly depleted (< 0.05 q-value). We aligned transcriptomic and proteomic datasets (> 1250 overlapping terms) to identify pathways associated with concordant and discordant co-translational regulation. LPS was identified as the upstream regulator of the co-translational dataset that was concordantly regulated. Upregulated mRNAs but not proteins related to glycolysis were discordantly regulated. These findings validate our proof-of-concept multi-omics approach as a predictive platform for identifying disease-relevant pathways regulated at the co-translational level in vivo.
Published in
Co-translational profiling in the cardiac endothelium in response to LPS-induced inflammation in female mice in vivo: a proof-of-concept approach
Warren CM, Swaminathan B, Langa P et al. · Molecular and cellular biochemistry 2026 · PMID 42048039 · doi:10.1007/s11010-026-05551-9
This dataset
Open GSE273368 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 GSE273368_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 PRJNA1141492 and SRA study SRP523014. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 42048039) ↗
Study design
4 × heart, Pre-IP, control, biol vs 4 × heart, Pre-IP, experimental, biol vs 4 × heart, IP, control, biol vs 4 × heart, IP, experimental, 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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