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The effect of miR192-LNP on the regulation of innate immune response

GSE291607 Mus musculus (mouse) RNA-seq (gene expression) 18 samples Submitted 2025/12/31 Platform GPL34290
At a glance

RNA-seq (gene expression) · mouse · 18 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
18 samples: 6 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
Lipid nanoparticle-encapsulated microRNA-192: An anti-inflammatory adjuvant that enhances vaccine efficacy in aged miceMolecular therapy. Nucleic acids · 2026
Summary (written by the submitters)
Previously, we demonstrated that microRNA-192 (miR-192) is an aging-associated immune-regulatory microRNA suppressing the hyperinflammatory state observed in the elderly (Tsukamoto T et al., iScience, 2020). In this study, we utilized lipid nanoparticle (LNP) technology to deliver miR-192 into the macrophage cells and investigated its mechanism of inflammation regulation and potential therapeutic applications.
Read the full summary ↓
Murine macrophage RAW264.7 cells were pretreated with miR-192-LNP for 24 hours, followed by stimulation with lipopolysaccharide (LPS). To assess the impact of miR-192-LNP on the innate immune response against LPS, we performed bulk RNA sequencing (RNA-seq) to analyze gene expression alterations.
Published in
Lipid nanoparticle-encapsulated microRNA-192: An anti-inflammatory adjuvant that enhances vaccine efficacy in aged mice
Takagi Y, Nishimura T, Aktar S et al. · Molecular therapy. Nucleic acids 2026 · PMID 41458877 · doi:10.1016/j.omtn.2025.102784
This dataset
Open GSE291607 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 GSE291607_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 PRJNA1234487 and SRA study SRP569507. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 41458877) ↗
Study design
6 conditions, mostly in triplicate
RAW264.7, nonLNP, LPS 0hr, ×3 RAW264.7, NC-LNP, LPS 0hr, ×3 RAW264.7, miR192-LNP, LPS 0hr, ×3 RAW264.7, nonLNP, LPS 24hr, ×3 RAW264.7, NC-LNP, LPS 24hr, ×3 RAW264.7, miR192-LNP, LPS 24hr, ×3

Supports a between-group comparison across 18 samples.

How we read this: 6 replicated groups read from 18 sample titles; they account for 18 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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