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

Deng, Yun ; Perry, Thomas A. ; Hulley, Philippa ; Maciewicz, Rose A. ; Mitchelmore, Joanna ; Perry, Darryl ; Larsson, Staffan LU orcid ; Brachat, Sophie ; Struglics, Andre LU and Appleton, C. Thomas , et al. (2024) In PLoS ONE 19(11 November).
Abstract

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... (More)

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 batcheffects 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.

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organization
publishing date
type
Contribution to journal
publication status
published
subject
in
PLoS ONE
volume
19
issue
11 November
article number
e0309677
publisher
Public Library of Science (PLoS)
external identifiers
  • pmid:39556578
  • scopus:85209690234
ISSN
1932-6203
DOI
10.1371/journal.pone.0309677
language
English
LU publication?
yes
additional info
Publisher Copyright: © 2024 Deng et al.This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
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6a4048cb-d830-4695-b413-aa568f3a7a20
date added to LUP
2025-01-13 16:38:01
date last changed
2025-07-01 06:37:56
@article{6a4048cb-d830-4695-b413-aa568f3a7a20,
  abstract     = {{<p>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 (&gt;7000 SOMAmers/proteins). An optimised approach to standardisation was developed. Technical confounders and batcheffects 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 (&lt;20% co-efficient of variation for &gt;80% of SOMAmers in pooled samples) and overall good correlation with immunoassay. 1720 samples and &gt;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.</p>}},
  author       = {{Deng, Yun and Perry, Thomas A. and Hulley, Philippa and Maciewicz, Rose A. and Mitchelmore, Joanna and Perry, Darryl and Larsson, Staffan and Brachat, Sophie and Struglics, Andre and Appleton, C. Thomas and Kluzek, Stefan and Arden, N. K. and Felson, David and Marsden, Brian and Tom, Brian D.M. and Bondi, Laura and Kapoor, Mohit and Batchelor, Vicky and Mackay-Alderson, Jennifer and Kumar, Vinod and Lohmander, L. Stefan and Welting, Tim J. and Walsh, David A. and Valdes, Ana M. and Vincent, Tonia L. and Watt, Fiona E. and Jostins-Dean, Luke}},
  issn         = {{1932-6203}},
  language     = {{eng}},
  number       = {{11 November}},
  publisher    = {{Public Library of Science (PLoS)}},
  series       = {{PLoS ONE}},
  title        = {{Development of methodology to support molecular endotype discovery from synovial fluid of individuals with knee osteoarthritis : The STEpUP OA consortium}},
  url          = {{http://dx.doi.org/10.1371/journal.pone.0309677}},
  doi          = {{10.1371/journal.pone.0309677}},
  volume       = {{19}},
  year         = {{2024}},
}