Objectives: To develop a protocol for largescale analysis of synovial fluid proteins, for the
identification of biological networks associated with subtypes of osteoarthritis.
Methods: Synovial Fluid To detect molecular Endotypes by Unbiased Proteomics in Osteoarthritis
(STEpUP OA) is an international consortium utilising clinical data (capturing pain, radiographic severity
and demographic features) and knee synovial fluid from 17 participating cohorts. 1746 samples from
1650 individuals comprising OA, joint injury, healthy and inflammatory arthritis controls, divided into
discovery (n=1045) and replication (n=701) datasets, were analysed by SomaScan Discovery Plex V4.1
(>7000 SOMAmers/proteins). An optimised approach to standardisation was developed. Technical
confounders and batch-effects were identified and adjusted for. Poorly performing SOMAmers and
samples were excluded. Variance in the data was determined by principal component (PC) analysis.
Results: A synovial fluid standardised protocol was optimised that had good reliability (<20 % co efficient of variation for >80% of SOMAmers in pooled samples) and overall good correlation with
immunoassay. 1720 samples and >6290 SOMAmers met inclusion criteria. 48% of data variance (PC1)
was strongly correlated with individual SOMAmer signal intensities, particularly with low abundance
proteins (median correlation coefficient 0.70), and was enriched for nuclear and non-secreted
proteins. We concluded that this component was predominantly intracellular proteins, and could be
adjusted for using an ‘intracellular protein score’ (IPS). PC2 (7% variance) was attributable to
processing batch and was batch-corrected by ComBat. Lesser effects were attributed to other
technical confounders. Data visualisation revealed clustering of injury and OA cases in overlapping but
distinguishable areas of high-dimensional proteomic space.
Conclusions: We have developed a robust method for analysing synovial fluid protein, creating a
molecular and clinical dataset of unprecedented scale to explore potential patient subtypes and the molecular pathogenesis of OA. Such methodology underpins the development of new approaches to
tackle this disease which remains a huge societal challenge.