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Innate Imprinting of Transcriptional Trajectories Governs Respiratory TRM Fate and Persistence

GSE320474 Mus musculus (mouse) RNA-seq (gene expression) 30 samples Submitted 2026/03/05 Platform GPL34290Platform GPL24247
At a glance

RNA-seq (gene expression) · mouse · 30 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
30 samples: 7 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.
Summary (written by the submitters)
Respiratory T cell immunity wanes rapidly, and inducing durable mucosal memory remains a major challenge. We demonstrate that precision adjuvancy, using distinct PRR agonists to shape innate inflammation, imprints transcriptional trajectories that govern CD8 T cell fate. Using a nano-emulsion platform, TLR4 signaling induces neutrophilic inflammation and a C/EBP–NF-κB–HIF program that promotes durable, stem-like Type 3 CD127⁺ TRMs.
Read the full summary ↓
Conversely, STING drives DC–monocytic inflammation and activates an IRF–STAT–T-bet axis that programs attrition-prone Type 1 TRMs. We reconcile systemic and tissue divergence models by identifying distinct TRM precursors. During influenza, Ly6CloPD-1hiLAG-3hiCD69hi precursors differentiate via local reprogramming (tissue divergence). In contrast, vaccination generates Ly6CloLAG-3loCD103hi precursors in lymph nodes and lungs that bypass inhibitory stages (systemic divergence). Mechanistically, T-bet limits TRM precursor formation and differentiation into long-lived Type 3 TRMs. Thus, selecting precision adjuvants that limit T-bet activity is critical for durable mucosal T cell immunity.
This dataset
Open GSE320474 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 GSE320474_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 PRJNA1428270 and SRA study SRP679333. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
7 conditions, mostly in triplicate
AGLA_Pri_L ×3 ACDN_Pri_L ×3 ACPG_Pri_L ×3 AGLA_Sec_L ×3 ACDN_Sec_L ×3 ACPG_Sec_L ×3 Ctrl_L ×3 ACDN_PEAK ×1 +8 more

Supports a between-group comparison across 21 samples.

How we read this: 7 replicated groups read from 30 sample titles; they account for 21 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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