GEO series

Single-cell multi-omic sequencing reveals cell-specific transcriptomic and chromatin accessibility profiles in gut microbiome metabolite butyrate-produced pain modulation

GSE285766 Mus musculus (mouse) Single-cell RNA-seq 12 samples Submitted 2026/04/22 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: Single-cell multi-omics sequencing reveals cell-specific transcriptomic and chromatin accessibility profiles in gut microbiome metabolite butyrate-produced pain modulationInternational journal of oral science · 2026

Summary (written by the submitters)

Pain is a predominant symptom of temporomandibular joint (TMJ) disorders, presenting significant clinical challenges due to their complexity and limited treatment options. This study investigated the therapeutic potential of butyrate, a gut microbiome metabolite, in a complete Freund’s adjuvant (CFA)-induced mouse model of TMJ inflammatory pain.

Read the full summary ↓

Butyrate administration significantly alleviated TMJ pain and restored butyrate levels in mouse feces, plasma, and the spinal trigeminal nucleus caudalis (Sp5C). Additionally, it reversed TMJ pain-induced reductions in acetylation within Sp5C neurons, a critical epigenetic mechanism linked to pain states. Utilizing single-nucleus RNA sequencing (snRNA-seq) and single-nucleus ATAC sequencing (snATAC-seq), we profiled transcriptional and chromatin accessibility changes at the single-cell level.

Published in

Single-cell multi-omics sequencing reveals cell-specific transcriptomic and chromatin accessibility profiles in gut microbiome metabolite butyrate-produced pain modulation

Tao R, Liu S, Crawford J et al. · International journal of oral science 2026 · PMID 41997906 · doi:10.1038/s41368-026-00432-9

This dataset

Open GSE285766 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 GSE285766_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 PRJNA1205792 and SRA study SRP555025. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 41997906) ↗

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

GSM8708534 Sp5C, Saline injection+Glycerol via oral gavage,rep1, GEX
GSM8708535 Sp5C, Saline injection+Glycerol via oral gavage,rep1, ATAC
GSM8708536 Sp5C, Saline injection+Glycerol via oral gavage,rep2, GEX
GSM8708537 Sp5C, Saline injection+Glycerol via oral gavage,rep2, ATAC
GSM8708538 Sp5C, CFA injection+Glycerol via oral gavage,rep1, GEX
GSM8708539 Sp5C, CFA injection+Glycerol via oral gavage,rep1, ATAC
GSM8708540 Sp5C, CFA injection+Glycerol via oral gavage,rep2, GEX
GSM8708541 Sp5C, CFA injection+Glycerol via oral gavage,rep2, ATAC
GSM8708542 Sp5C, CFA injection+Tributyrin via oral gavage,rep1, GEX
GSM8708543 Sp5C, CFA injection+Tributyrin via oral gavage,rep1, ATAC
GSM8708544 Sp5C, CFA injection+Tributyrin via oral gavage,rep2, GEX
GSM8708545 Sp5C, CFA injection+Tributyrin via oral gavage,rep2, ATAC

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