GEO series

Transcriptomic Profiling of Murine Macrophages During Francisella tularensis Infection

GSE337952 Mus musculus (mouse) RNA-seq (gene expression) 20 samples Submitted 2026/07/15 Platform GPL24247

At a glance

RNA-seq (gene expression) · mouse · 20 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: 20 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)

Francisella tularensis is a highly virulent intracellular bacterium and the causative agent of tularemia, a severe disease posing a significant bioterrorism threat. During infection, these bacteria rapidly subvert host immune responses, particularly within macrophages, their primary replication niche. However, the temporal dynamics of the host macrophage response remain incompletely characterized.

Read the full summary ↓

Here, we announce comprehensive time-course RNA-seq datasets that systematically capture the global transcriptional profile of murine RAW 264.7 macrophages across both early and late stages of F. tularensis infection.

This dataset

Open GSE337952 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 GSE337952_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 PRJNA1491986 and SRA study SRP716272. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗

Study design

7 conditions, mostly in triplicate

t=0, ×3 t=0.5h, ×3 t=1.5h, ×3 t=4h, ×3 t=8h, ×3 t=24h, ×3 t=48h, ×2

Supports a between-group comparison across 20 samples.

How we read this: 7 replicated groups read from 20 sample titles; they account for 20 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

GSM9863375 t=0, rep1 t=0,
GSM9863376 t=0, rep2 t=0,
GSM9863377 t=0, rep3 t=0,
GSM9863378 t=0.5h, rep1 t=0.5h,
GSM9863379 t=0.5h, rep2 t=0.5h,
GSM9863380 t=0.5h, rep3 t=0.5h,
GSM9863381 t=1.5h, rep1 t=1.5h,
GSM9863382 t=1.5h, rep2 t=1.5h,
GSM9863383 t=1.5h, rep3 t=1.5h,
GSM9863384 t=4h, rep1 t=4h,
GSM9863385 t=4h, rep2 t=4h,
GSM9863386 t=4h, rep3 t=4h,
GSM9863387 t=8h, rep1 t=8h,
GSM9863388 t=8h, rep2 t=8h,
GSM9863389 t=8h, rep3 t=8h,
GSM9863390 t=24h, rep1 t=24h,
GSM9863391 t=24h, rep2 t=24h,
GSM9863392 t=24h, rep3 t=24h,
GSM9863393 t=48h, rep1 t=48h,
GSM9863394 t=48h, rep2 t=48h,

Datasets on a similar topic

GSE174676 Metabolic rewiring tunes dermal macrophages in staphylococcal skin infection 36 samples shares macrophages
GSE313869 Impact of Fc engineering on the transcriptional profile of monocytes and interstitial macrophages in the lungs of Hu-FcgR Tg mice after MA30 infection 36 samples shares macrophages
GSE264083 Mycobacterium abscessus infection in fetal liver derived alveolar-like macrophages activates distinct inflammatory responses compared to myeloid-derived macrophages 36 samples shares macrophages
GSE232854 Control of Mycobacterium abscessus pulmonary infection requires IFN- γ Induced formation of foam macrophages 22 samples shares macrophages
GSE263892 Single-cell analysis reveals Mycobacterium tuberculosis ESX-1-mediated accumulation of permissive macrophages in infected mouse lungs 6078 samples shares macrophages
GSE325061 Ubiquitylation by the GID/CTLH complex regulates the metabolic and innate immune response of macrophages to infection by Mycobacterium tuberculosis 12 samples shares macrophages
GSE304706 Persistent ER stress in macrophages fuels age-related inflammation and vulnerability to infection 8 samples shares macrophages
GSE325329 Transcriptomic profiling of macrophages that phagocytosed apoptotic Treg or conventional CD4⁺ T (Tconv) cells under different polarization conditions [RNA-Seq] 28 samples shares macrophages

Search all mouse RNA-seq datasets in GEO →

Share this dataset

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.