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Hijacking innate immunity to enhance mRNA therapeutics

GSE291337 Homo sapiens (human) RNA-seq (gene expression) 27 samples Submitted 2026/03/25 Platform GPL24676
At a glance

RNA-seq (gene expression) · human · 27 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)
Samples
27 samples: 9 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.
Paper
Hijacking innate immunity to enhance mRNA therapeutics by blocking IFN-P-body-XRN1 axis-mediated degradationNature communications · 2026
Summary (written by the submitters)
The therapeutic success of in vitro-transcribed (IVT) mRNA depends on its stability and efficient translation. However, IVT mRNA is highly susceptible to immune-mediated degradation, limiting its efficacy. Here, we co-transfected IVT mCherry mRNA with immune suppressor M or SOCS1 mRNA in human umbilical vein endothelial cells (HUVECs) for 6h and 24h to explore whether hijacking innate immunity could enhance expression and half-life of IVT mRNA.
Read the full summary ↓
Subsequently, RNA-seq was performed to investigate the transcriptomic changes in HUVECs after transfecting IVT mRNA. We sought to clarify the potential mechanism underlying degradation of IVT mRNA.
Published in
Hijacking innate immunity to enhance mRNA therapeutics by blocking IFN-P-body-XRN1 axis-mediated degradation
Zhang T, Peng X, Qin J et al. · Nature communications 2026 · PMID 41997928 · doi:10.1038/s41467-026-72025-3
This dataset
Open GSE291337 in finder → Find more human 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 GSE291337_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 PRJNA1232936 and SRA study SRP568583. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 41997928) ↗
Study design
9 conditions, mostly in triplicate
CT-6h ×3 mCherry-6h ×3 mCherry-24h ×3 mCherry+Luc-6h ×3 mCherry+Luc-24h ×3 mCherry+Mpro-6h ×3 mCherry+Mpro-24h ×3 mCherry+SOCS1-6h ×3 +1 more

Supports a between-group comparison across 27 samples.

How we read this: 9 replicated groups read from 27 sample titles; they account for 27 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
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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.