| Autor: |
Yun Deng, Thomas A Perry, Philippa Hulley, Rose A Maciewicz, Joanna Mitchelmore, Darryl Perry, Staffan Larsson, Sophie Brachat, André Struglics, C Thomas Appleton, Stefan Kluzek, Nigel K Arden, David Felson, Brian Marsden, Brian D M Tom, Laura Bondi, Mohit Kapoor, Vicky Batchelor, Jennifer Mackay-Alderson, Vinod Kumar, L Stefan Lohmander, Tim J Welting, David A Walsh, Ana M Valdes, STEpUP OA Consortium, Tonia L Vincent, Fiona E Watt, Luke Jostins-Dean |
| Jazyk: |
angličtina |
| Rok vydání: |
2024 |
| Předmět: |
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| Zdroj: |
PLoS ONE, Vol 19, Iss 11, p e0309677 (2024) |
| Druh dokumentu: |
article |
| ISSN: |
1932-6203 |
| DOI: |
10.1371/journal.pone.0309677 |
| Popis: |
ObjectivesTo develop a protocol for largescale analysis of synovial fluid proteins, for the identification of biological networks associated with subtypes of osteoarthritis.MethodsSynovial 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.ResultsA synovial fluid standardised protocol was optimised that had good reliability (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.ConclusionsWe 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. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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