Journal Article

Development of methodology to support molecular endotype discovery from synovial fluid of individuals with knee osteoarthritis: The STEpUP OA consortium

Lohmander, L. Stefan, Jostins-Dean, Luke, Tom, Brian D. M., Walsh, David A., Vincent, Tonia L., Mackay-Alderson, Jennifer, Hulley, Philippa, Felson, David, Batchelor, Vicky, Marsden, Brian, Deng, Yun, Perry, Thomas A., Perry, Darryl, Valdes, Ana M., Brachat, Sophie, Larsson, Staffan, Appleton, C. Thomas, Maciewicz, Rose A., Welting, Tim J., Kapoor, Mohit, Kumar, Vinod, Struglics, André, Bondi, Laura, Kluzek, Stefan, Arden, Nigel K., Mitchelmore, Joanna, Watt, Fiona E.

2024

Overview

To develop a protocol for largescale analysis of synovial fluid proteins, for the identification of biological networks associated with subtypes of osteoarthritis. 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. 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. 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.

• Publisher: Public Library of Science,Public Library of Science (PLoS)
• Subject: Adult / Aged / Arthritis / Batch processing / Biology and Life Sciences / Biomarkers / Cartilage / Clinical Medicine / Clustering / Computer and Information Sciences / Consortia / Correlation coefficient / Correlation coefficients / Data analysis / Data visualization / Datasets / Dimensional analysis / Disease / Ethics / FDA approval / Female / Health aspects / Health risk assessment / Humans / Hypotheses / Immunoassay / Inflammation / Injury analysis / Intracellular / Investigations / Klinisk medicin / Knee / Male / Medical and Health Sciences / Medical research / Medicin och hälsovetenskap / Medicine and Health Sciences / Medicine, Experimental / Methodology / Middle Aged / Orthopaedics / Ortopedi / Osteoarthritis / Osteoarthritis, Knee - metabolism / Pathogenesis / Pathology / Physiological aspects / Proteins / Proteomics / Proteomics - methods / Quality control / Research and Analysis Methods / Scientific visualization / Social Sciences / Standardization / Synovial fluid / Synovial Fluid - chemistry / Synovial Fluid - metabolism