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Gut Microbiota Dysbiosis Remodels the Multi-Tissue Transcriptional Landscape of the Host

GSE322803 Mus musculus (mouse) Single-cell RNA-seq 54 samples Submitted 2026/03/27 Platform GPL24247
At a glance

Single-cell RNA-seq · mouse · 54 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
54 samples: 18 conditions, mostly in triplicateGroups 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)
The gut microbiota exerts a profound influence on host physiology, but its systemic impact on gene expression across diverse tissues remains poorly characterized. This study investigated the transcriptional effects of gut microbiota depletion and restoration in mice across six tissues (colon, jejunum, liver, heart, lung, and kidney) using whole-transcriptome sequencing.
Read the full summary ↓
We found that the presence of gut microbiota significantly altered the transcriptome, with the most pronounced effects in the colon. Using a linear mixed-effects model, we identified 7,365 effector genes. Tissue-specific analysis revealed that these genes were associated with distinct functional pathways, such as immunity in the gut and lung, and metabolism in the liver. Further refinement with LASSO regression pinpointed gut microbiota-mediated key effector genes, whose expression levels were significantly associated with patient survival in corresponding human cancers (e.g., LIHC, LUAD, KIRC). Furthermore, we observed a widespread remodeling of competing endogenous RNA (ceRNA) networks by the gut microbiota. Single-cell data analysis highlighted a potential gut-liver axis interaction, primarily mediated by colonic enterocytes and hepatic cholangiocytes, meanwhile gut microbiota repressed the transcription initiation of Noct in colonic enterocytes, whose expression level was significantly negatively correlated to gut-liver axis interaction. Our findings provide a comprehensive map of the multi-tissue transcriptional landscape shaped by the gut microbiota, revealing tissue-specific regulatory mechanisms and identifying key genes with potential clinical relevance in cancer.
This dataset
Open GSE322803 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 GSE322803_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 PRJNA1431786 and SRA study SRP680833. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
18 conditions, mostly in triplicate
colonGF ×3 colonFMT ×3 colonSPF ×3 heartGF ×3 heartFMT ×3 heartSPF ×3 jejunumGF ×3 jejunumFMT ×3 +10 more

Supports a between-group comparison across 54 samples.

How we read this: 18 replicated groups read from 54 sample titles; they account for 54 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

+ 14 more — browse all 54 samples with per-sample file links →

Datasets on a similar topic

Search all mouse RNA-seq datasets in GEO →

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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.