GEO series

scnasRNA-seq resolves in vivo single-cell RNA dynamics of immune cells during Salmonella infection

GSE279651 Mus musculus (mouse) Single-cell RNA-seq 12 samples Submitted 2024/10/17 Platform GPL21273

At a glance

Single-cell RNA-seq · mouse · 12 samples

What it is: Single-cell RNA-seqGene activity measured in thousands of individual cells, so cell types can be told apart.
Organism: Mus musculus (mouse)
Samples: 12 samples: 12 samples, each with a distinct title — no replicated groupsGroups 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: scIVNL-seq resolves in vivo single-cell RNA dynamics of immune cells during Salmonella infectionNature communications · 2025

Summary (written by the submitters)

The immune response against pathogens involves multiple cell state transitions and complex gene expression changes. Here we established an in vivo single-cell nascent RNA labeling sequencing method (scnasRNA-seq) and applied it to survey time-resolved RNA dynamics during immune response to acute enteric infection with Salmonella. We showed that detection of nascent RNA synthesis reflects more realistic information on cell activation and gene transcription than total RNA level.

Read the full summary ↓

Interplay of nascent RNA synthesis and RNA degradation together modulate dynamics of total RNA. We found that bone marrow macrophages are first primed at very early stage upon Salmonella infection. In contrast, the innate immune response of macrophages in intestine is limited. Notably, intestinal CD8+ T cells and plasma cells are rapidly and specifically activated at early stage post infection. Intestinal late enterocytes quickly express MHC-I molecules and present Salmonella antigen to CD8+ T cells for their activation, serving as antigen presenting cells for initiation of adaptive immunity. Our findings unveil novel RNA control strategies of immune cells and dynamic time course of immune response activation upon Salmonella infection, challenging the doctrine boundary between innate immunity and adaptive immunity against bacterial infection.

Published in

scIVNL-seq resolves in vivo single-cell RNA dynamics of immune cells during Salmonella infection

Xiong Z, Wu R, Wang Y et al. · Nature communications 2025 · PMID 40858611 · doi:10.1038/s41467-025-63155-1

This dataset

Open GSE279651 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 GSE279651_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: Single-cell studies usually ship their counts as 10x files (matrix.mtx, barcodes.tsv, features.tsv), an .h5 file, or an .rds / .h5ad object that Seurat or Scanpy can open. Look for those among the supplementary files.

Also filed as BioProject PRJNA1087550 and SRA study SRP495165. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 40858611) ↗

Study design

12 samples, each with a distinct title — no replicated groups

Any grouping that defines a comparison lives in the sample characteristics, which GEO records separately from the titles.

How we read this: Read from 12 sample titles: 12 distinct titles with little repetition. 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

GSM8577499 scnasRNA-seq of BM, 0h post salmonella infection
GSM8577500 scnasRNA-seq of BM, 2h post salmonella infection
GSM8577501 scnasRNA-seq of BM, 6h post salmonella infection
GSM8577502 scnasRNA-seq of BM, 12h post salmonella infection
GSM8577503 scnasRNA-seq of BM, 24h post salmonella infection
GSM8577504 scnasRNA-seq of BM, 48h post salmonella infection
GSM8577505 scnasRNA-seq of BM, 72h post salmonella infection
GSM8577506 Reference
GSM9009703 scnasRNA-seq of BM, 0h post salmonella infection, Batch2
GSM9009704 scnasRNA-seq of BM, 12h post salmonella infection, Batch2
GSM9009705 scnasRNA-seq of BM, 24h post salmonella infection, Batch2
GSM9009706 scnasRNA-seq of BM, 72h post salmonella infection, Batch2

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