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Multi-omics analysis identifies a microbiota–bile acid–TLR signaling axis driving bladder injury in interstitial cystitis

GSE310557 Homo sapiens (human) Single-cell RNA-seq 17 samples Submitted 2025/11/21 Platform GPL34284
At a glance

Single-cell RNA-seq · human · 17 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
Homo sapiens (human)
Samples
17 samples: 10 × Human HIC bladder, biol vs 7 × Human control bladder, biolGroups 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
Multi-omics analysis identifies a microbiota-bile acid-TLR signaling axis driving bladder injury in interstitial cystitisNature communications · 2025
Summary (written by the submitters)
Hunner-type interstitial cystitis/bladder pain syndrome (HIC) is a complex and clinically challenging chronic disorder characterized by bladder-centered symptoms, including persistent pelvic pain and urinary urgency frequency, with elusive pathogenesis involving poorly defined multi-system interactions and systemic implications. To investigate upstream drivers of urothelial injury, we employed an integrative multi-omics approach, incorporating metagenomic sequencing, targeted metabolomics of urine and serum, and single-cell RNA sequencing.
Read the full summary ↓
Our analysis identified Enterococcus avium, Megasphaera micronuciformis, and Fusobacterium nucleatum as the significantly enriched species in HIC patients, with bile acid metabolism appearing as a prominently altered pathway. Metabolomic profiling revealed the elevated levels of taurochenodeoxycholic acid (TCDCA) and tauroursodeoxycholic acid, while single-cell transcriptomic data pointed to TLR3 signaling as a key mediator of urothelial injury. Cross-omics correlation proposed a mechanistic axis involving E. avium, TCDCA, and TLR3 activation. Functional validation using fecal microbiota transplantation, E. avium transplantation, and TCDCA administration demonstrated that this axis contributes to the urothelial barrier disruption and inflammatory responses via TLR3. Intervention with a TLR3 inhibitor demonstrated therapeutic potential. These findings shed light on the pathogenesis of HIC, highlighting the potential role of the gut-bladder axis in disease development. This may contribute not only to the discovery of novel biomarkers but also to the development of personalized treatment strategies targeting specific molecular and microbial pathways.
Published in
Multi-omics analysis identifies a microbiota-bile acid-TLR signaling axis driving bladder injury in interstitial cystitis
Peng L, Chen JW, Chen YZ et al. · Nature communications 2025 · PMID 41457077 · doi:10.1038/s41467-025-68060-1
This dataset
Open GSE310557 in finder → Find more human 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 GSE310557_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 PRJNA1366500 and SRA study SRP646949. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 41457077) ↗
Study design
10 × Human HIC bladder, biol vs 7 × Human control bladder, biol

Supports a between-group comparison across 17 samples.

How we read this: 2 replicated groups read from 17 sample titles; they account for 17 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
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