GEO series

Microbiota-derived bile acid ameliorates lung inflammation through metabolic rewiring of alveolar macrophages

GSE307734 Mus musculus (mouse) RNA-seq (gene expression) 12 samples Submitted 2025/11/13 Platform GPL24247

At a glance

RNA-seq (gene expression) · mouse · 12 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: 12 samples: 3 × Lung, Non treated, vs 3 × Lung, isoLCA, vs 3 × Lung, LPS + isoLCA, vs 3 × Lung, LPS,Groups 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)

Using mouse models of airway inflammation, we identified a causal link between the microbiota-dependent bile acid isolithocholic acid and protection against lung immunopathology. We further found that this protection involves Sphingosine-1-phosphate receptor 2–dependent metabolic rewiring of alveolar macrophages as a key mechanism. Collectively, our findings demonstrate that isolithocholic acid, a microbiota-derived bile acid, can ameliorate lung inflammation through previously unrecognized pathways, highlighting its therapeutic potential—and that of its derivatives—in lung disease.

This dataset

Open GSE307734 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 GSE307734_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 PRJNA1327230 and SRA study SRP619057. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗

Study design

3 × Lung, Non treated, vs 3 × Lung, isoLCA, vs 3 × Lung, LPS + isoLCA, vs 3 × Lung, LPS,

Supports a between-group comparison across 12 samples.

How we read this: 4 replicated groups read from 12 sample titles; they account for 12 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

GSM9230031 Lung, Non treated, rep1 Lung, Non treated,
GSM9230032 Lung, Non treated, rep2 Lung, Non treated,
GSM9230033 Lung, Non treated, rep3 Lung, Non treated,
GSM9230034 Lung, isoLCA, rep1 Lung, isoLCA,
GSM9230035 Lung, isoLCA, rep2 Lung, isoLCA,
GSM9230036 Lung, isoLCA, rep3 Lung, isoLCA,
GSM9230037 Lung, LPS + isoLCA, rep1 Lung, LPS + isoLCA,
GSM9230038 Lung, LPS, rep1 Lung, LPS,
GSM9230039 Lung, LPS + isoLCA, rep2 Lung, LPS + isoLCA,
GSM9230040 Lung, LPS + isoLCA, rep3 Lung, LPS + isoLCA,
GSM9230041 Lung, LPS, rep2 Lung, LPS,
GSM9230042 Lung, LPS, rep3 Lung, LPS,

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