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Single cell analysis uncovers striking cellular heterogeneity of lung infiltrating Tregs during steroid-responsive vs. steroid-resistant allergic airway inflammation

GSE243653 Mus musculus (mouse) Single-cell RNA-seq 2 samples Submitted 2024/06/12 Platform GPL24247
At a glance

Single-cell RNA-seq · mouse · 2 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
2 samples
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
Single-Cell Analysis Uncovers Striking Cellular Heterogeneity of Lung-Infiltrating Regulatory T Cells during Eosinophilic versus Neutrophilic Allergic Airway InflammationJournal of immunology (Baltimore, Md. : 1950) · 2024
Summary (written by the submitters)
Allergic airway inflammation is a chronic inflammatory condition resulting from uncontrolled immune responses to environmental antigens. While it is well-established that allergic immune responses exhibit a high degree of diversity, driven by primary effector cell types like eosinophils, neutrophils, or CD4 T cells with distinct effector signatures, the exact mechanisms responsible for such pathogenesis remain largely elusive.
Read the full summary ↓
Foxp3+ regulatory T cells (Treg) is an indispensable immune regulator during chronic inflammation including allergic airway inflammation. Emerging evidence suggests that Tregs infiltrating inflamed tissues exhibit distinct phenotypes dependent on the specific tissue sites and can display significant heterogeneity and tissue residency. Whether diverse allergic inflammatory responses in the lung influences infiltrating Treg heterogeneity or Treg lung residency has not previously been explored. We employed an unbiased single-cell RNAseq approach to study lung-infiltrating Tregs in models of eosinophilic and neutrophilic airway inflammation models, in which Tregs are critical immune regulators of inflammation. We found that lung-infiltrating Tregs are highly heterogeneous and that Tregs displaying lung residency phenotypes are significantly different depending on the types of inflammation. Tregs expression of ST2, a receptor for alarmin cytokine IL-33, was predominantly induced by eosinophilic inflammation and by tissue residency. However, Treg-specific ST2 deficiency did not affect the development of eosinophilic allergic inflammation nor the generation of lung resident Tregs. These results uncover a striking heterogeneity among Tregs infiltrating the lungs during allergic airway inflammation. The results also indicate that varying types of inflammation may give rise to phenotypically distinct lung resident Tregs. Thus, the present study underscores a novel mechanism by which inflammatory environments may shape the composition of infiltrating Tregs, allowing them to regulate inflammatory responses through tissue-adapted mechanisms.
Published in
Single-Cell Analysis Uncovers Striking Cellular Heterogeneity of Lung-Infiltrating Regulatory T Cells during Eosinophilic versus Neutrophilic Allergic Airway Inflammation
Iamsawat S, Yu R, Kim S et al. · Journal of immunology (Baltimore, Md. : 1950) 2024 · PMID 38647384 · doi:10.4049/jimmunol.2300646
This dataset
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.

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 GSE243653_RAW.tar (it can run to tens of gigabytes).

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.

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 PRJNA1019336 and SRA study SRP462052. Searching any of these in the dataset finder brings you back here.

Samples in this study

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