GEO series

Lung microenvironment effects on T cells following Francisella tularensis vaccination

GSE297708 Mus musculus (mouse) Single-cell RNA-seq 11 samples Submitted 2025/11/21 Platform GPL21626

At a glance

Single-cell RNA-seq · mouse · 11 samples

What it is: Single-cell RNA-seqGene activity measured in thousands of individual cells, so cell types can be told apart. Read from the summary: GEO's own type field only says 'expression profiling by sequencing', so confirm it in the paper.
Organism: Mus musculus (mouse)
Samples: 11 samples: 9 distinct titles, 2 of them repeatedGroups 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: Non-hematopoietic tryptophan metabolism is a driver of ineffective T cell responses during secondary pulmonary bacterial infectionJournal of immunology (Baltimore, Md. : 1950) · 2025

Summary (written by the submitters)

Pulmonary infections often fail to produce long-lived immune memory and the underlying mechanism(s) remain unclear. We undertook a multiomics approach to compare adaptive immunity in two bacterial infections: Bordetella pertussis and Francisella tularensis. Both infections require CD4+ T cells, but B. pertussis results in long-lived memory responses whereas F.

Read the full summary ↓

tularensis does not. Single cell RNA sequencing and flow cytometry revealed CD4+ T cells favored proliferation immediately after secondary B. pertussis infection but stagnated during F. tularensis challenge. Instead, rapid IFN-γ production by F. tularensis-elicited T cells induced IDO1 expression in endothelial cells, depleting pulmonary tryptophan and increasing kynurenine. This metabolic shift correlated with poor T cell proliferation and retention. Additionally, IDO1 knock-out resulted in larger T cell pools and improved survival during F. tularensis infection. Imaging confirmed the spatial association of immune cell centers and IDO1-expressing macrovascular endothelial cells underscoring the microenvironment’s impact on T cell function. Our study highlights the utility of multiomics in understanding cellular interactions within complex tissues.

Published in

Non-hematopoietic tryptophan metabolism is a driver of ineffective T cell responses during secondary pulmonary bacterial infection

Roberts LM, Arakkal L, Wehrly T et al. · Journal of immunology (Baltimore, Md. : 1950) 2025 · PMID 40972009 · doi:10.1093/jimmun/vkaf197

This dataset

Open GSE297708 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 GSE297708_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 PRJNA1265748 and SRA study SRP586730. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 40972009) ↗

Study design

9 distinct titles, 2 of them repeated

Naïve, Bp challenge d ×2 Bp Vx, Bp challenge d ×2

How we read this: Read from 11 sample titles: 9 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

GSM8997175 Naïve d2
GSM8997176 Bp Vx, no challenge d2
GSM8997177 Naïve, Bp challenge d2
GSM8997178 Bp Vx, Bp challenge d2
GSM8997179 RML Vx, no challenge d2
GSM8997180 Naïve, S4 challenge d2
GSM8997181 RML Vx, S4 challenge d2
GSM8997182 Naïve, Bp challenge d8
GSM8997183 Bp Vx, Bp challenge d8
GSM8997184 RML Vx, S4 challenge d8 protected
GSM8997185 RML Vx, S4 challenge d8 not protected

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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.