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Monocyte-derived IL-10 drives sex differences in pain duration

GSE316149 Homo sapiens (human) RNA-seq (gene expression) 227 samples Submitted 2026/01/14 Platform GPL20301
At a glance

RNA-seq (gene expression) · human · 227 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
Homo sapiens (human)
Samples
227 samples: 227 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
Monocyte-derived IL-10 drives sex differences in pain durationScience immunology · 2026
Summary (written by the submitters)
Women frequently experience longer-lasting pain than men, indicating delayed pain resolution. While we understand that pain can be orchestrated by the close interplay between the sensory nervous system and the immune system, it remains unclear whether this interaction contributes to the longer duration of pain in women. Here, we demonstrate that interleukin (IL)-10+ monocytes resolve inflammatory pain by signaling to IL-10R1+ sensory neurons in a mouse model of skin inflammation.
Read the full summary ↓
A higher number of IL-10+ monocytes in males enables faster resolution of inflammatory pain compared to females. In both sexes, pain resolution was impaired by deleting Il10 from monocytes or Il10ra from sensory neurons. Similarly, in humans, men’s pain resolved faster than women’s after traumatic injury, which was associated with higher levels of circulating monocytes and IL-10 in men. In mice, androgen signaling promotes IL-10 production by monocytes, resulting in sex differences in the number of IL-10+ monocytes. Enhancing IL-10+ monocytes in the inflamed skin through Resolvin D1 treatment accelerated pain resolution in both sexes of mice. Our findings uncover a novel role for peripheral IL-10+ monocytes in driving sex differences in pain resolution. These results reveal a previously underappreciated contribution of immune cells to resolving pain and preventing the transition to chronic pain.
Published in
Monocyte-derived IL-10 drives sex differences in pain duration
Sim J, O'Guin E, Sugimoto C et al. · Science immunology 2026 · PMID 41719383 · doi:10.1126/sciimmunol.adx0292
This dataset
Open GSE316149 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 GSE316149_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 PRJNA1401725. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 41719383) ↗
Study design
227 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 227 sample titles: 227 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

+ 187 more — browse all 227 samples with per-sample file links →

Datasets on a similar topic

Search all human 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.