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Adipose-derived Leptin and Complement Factor D mediate osteoarthritis severity and pain

GSE287251 Mus musculus (mouse) RNA-seq (gene expression) 17 samples Submitted 2025/01/16 Platform GPL24247
At a glance

RNA-seq (gene expression) · mouse · 17 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
Mus musculus (mouse)
Samples
17 samples: 8 conditions, mostly in duplicateGroups 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)
Obesity is a risk factor for osteoarthritis (OA), and leptin is among the adipokines implicated in obesity-induced OA. However, the specific role of leptin in OA severity and pain is not known. Using lipodystrophic (LD) mice, we show fat-secreted factors are required for knee OA development, implicating a fat-cartilage crosstalk. Fat pad implantation or systemic leptin restoration in LD mice reintroduced structural OA and pain, whereas implantation of leptin-deficient fat pad did not change OA susceptibility.
Read the full summary ↓
Isochronic parabiosis and spatial transcriptomics confirmed that fat-joint crosstalk likely occurred via soluble mediators. Global unsupervised multiomics of conditioned media from fat implants revealed that leptin exerts a regulatory effect on adipsin (or complement factor D), the activity of which modulates contrastive OA structural and pain phenotype. These findings suggest adipokines influence OA pathogenesis, providing conclusive evidence of a fat-joint crosstalk and implicating OA as a systemic disease of adipose tissue.
This dataset
Open GSE287251 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 GSE287251_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 PRJNA1211689 and SRA study SRP557918. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
8 conditions, mostly in duplicate
EVFN ×2 MKM ×2 EVFY ×2 ESM ×2 MKF ×2 EVM ×2 MWF ×2 MWM ×2 +1 more

Supports a between-group comparison across 16 samples.

How we read this: 8 replicated groups read from 17 sample titles; they account for 16 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
Datasets on a similar topic

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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.