GEO series

Life-long microbiome rejuvenation improves intestinal metabolism and inflammaging in mice [scRNA-seq]

GSE272842 Mus musculus (mouse) Single-cell RNA-seq 12 samples Submitted 2025/03/12 Platform GPL24247

At a glance

Single-cell RNA-seq · mouse · 12 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: 12 samples: 12 samples, each with a distinct title — no replicated groupsGroups 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: Life-long microbiome rejuvenation improves intestinal barrier function and inflammaging in miceMicrobiome · 2025

Summary (written by the submitters)

Alterations in composition and function of the intestinal microbiota have been observed in organismal aging across a broad spectrum of animal phyla. Recent findings, which have been derived mostly in simple animal models, have even established a causal relationship between age-related microbial shifts and lifespan, suggesting microbiota-directed interventions as a potential tool to decelerate aging processes.

Read the full summary ↓

To test whether a life-long microbiome rejuvenation strategy could delay or even prevent aging in mammals, we performed recurrent fecal microbial transfer (FMT) in mice throughout life. Transfer material was either derived from 8-week-old mice (young microbiome, yMB) or from animals of the same age as the recipients (isochronic microbiome, iMB) as control. Physiological responses were analyzed by rotarod and a grip strength test, intestinal barrier function by FITC gavage and LAL assay, transcriptional responses by single cell RNA sequencing and microbiome function by 16S profiling and metagenomics. Colonization with yMB improved coordination and intestinal permeability compared to iMB. yMB encoded fewer pro-inflammatory factors and altered metabolic pathways favoring oxidative phosphorylation. Ecological interactions among bacteria in yMB were more antagonistic than in iMB hinting at more stable microbiome communities. Single cell RNA sequencing analysis of intestinal mucosa revealed a salient shift of cellular phenotypes in the yMB group with markedly increased ATP synthesis and mitochondrial pathways in enterocytes and TA cells but reduced inflammatory signaling in macrophages. Taken together, we demonstrate that life-long and repeated transfer of microbiota material from young mice improved age-related processes including motor coordination, intestinal permeability and immune cell phenotypes.

Published in

Life-long microbiome rejuvenation improves intestinal barrier function and inflammaging in mice

Sommer F, Bernardes JP, Best L et al. · Microbiome 2025 · PMID 40176137 · doi:10.1186/s40168-025-02089-8

This dataset

Open GSE272842 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 GSE272842_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 PRJNA1139098 and SRA study SRP521558. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 40176137) ↗

Study design

12 samples, each with a distinct title — no replicated groups

Any grouping that defines a comparison lives in the sample characteristics, which GEO records separately from the titles.

How we read this: Read from 12 sample titles: 12 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

GSM8413540 aging-MB_Final_54_LP [scRNA-seq]
GSM8413541 aging-MB_Final_54_IEC [scRNA-seq]
GSM8413542 aging-MB_Final_36_IEC [scRNA-seq]
GSM8413543 aging-MB_Final_36_LP [scRNA-seq]
GSM8413544 8w_Final_16_LP [scRNA-seq]
GSM8413545 8w_Final_16_IEC [scRNA-seq]
GSM8413546 aging-MB_Final_10_LP [scRNA-seq]
GSM8413547 aging-MB_Final_10_IEC [scRNA-seq]
GSM8413548 8w_Final_3_LP [scRNA-seq]
GSM8413549 8w_Final_3_IEC [scRNA-seq]
GSM8413550 8w_Final_2_LP [scRNA-seq]
GSM8413551 8w_Final_2_IEC [scRNA-seq]

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