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Innate Lymphoid Cells Integrate Sensing and Plasticity to Control Fungal Infections [whole lung tissue after ILC transfer_Afu infected]

GSE293054 Mus musculus (mouse) RNA-seq (gene expression) 10 samples Submitted 2026/01/01 Platform GPL24247
At a glance

RNA-seq (gene expression) · mouse · 10 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
10 samples: 4 × lung_tissue_TecILC_transfer vs 3 × lung_tissue_no transfer_ctrl vs 3 × lung_tissue_wtILC_transferGroups 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)
Innate lymphoid cells (ILCs) are crucial regulators of tissue immunity, yet their role in antifungal defense remains incompletely understood. Through our findings, we demonstrate that pulmonary ILCs can sense fungal pathogens by recognizing fungal cell wall components via pattern recognition receptors, leading to the activation and type-1 polarization in vitro.
Read the full summary ↓
Mechanistically, we identify Syk/Akt-dependent signaling as one of the key drivers of ILC activation upon fungal encounters. Aspergillus fumigatus infection reshapes this response in vivo, where elevated TGF-β and IL-1β levels facilitate a shift from type-1 ILC activity to IL-17A release. Ultimately, IL-17-producing ILCs are crucial in resolving the infection and restoring normal tissue homeostasis. Moreover, adoptive transfer models in lymphocyte-deficient Rag2-/-γc-/- mice reveal that ILCs restrain excessive inflammation while hindering fungal clearance, highlighting their dual role in regulating antifungal immunity. Remarkably, ILCs lacking the non-receptor tyrosine kinase Tec disrupts caspase-8 activation, leading to enhanced proliferation of type-1 ILCs. Consequently, improved fungal clearance enhances host survival by strengthening antifungal immunity. Our findings uncover hitherto unrecognized roles of ILCs as fungal sensors and early modulators of antifungal immunity. Therefore, targeting Tec kinase signaling in ILCs holds great promise as a therapeutic option to enhance antifungal immunity, especially in patients at risk for invasive A. fumigatus infections.
This dataset
Open GSE293054 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 GSE293054_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 PRJNA1242695 and SRA study SRP573507. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
4 × lung_tissue_TecILC_transfer vs 3 × lung_tissue_no transfer_ctrl vs 3 × lung_tissue_wtILC_transfer

Supports a between-group comparison across 10 samples.

How we read this: 3 replicated groups read from 10 sample titles; they account for 10 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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