変形性関節症治療の新しい知見

タイトル: Methodological development of molecular endotype discovery from synovial fluid of individuals with knee osteoarthritis: the STEpUP OA Consortium

概要: ObjectivesTo develop and validate a pipeline for quality controlled (QC) protein data for largescale analysis of synovial fluid (SF), using SomaLogic technology. DesignKnee SF and associated clinical data were from partner cohorts. SF samples were centrifuged, supernatants stored at -80 {degrees}C, then analysed by SomaScan Discovery Plex V4.1 (>7000 SOMAmers/proteins). SettingAn international consortium of 9 academic and 8 commercial partners (STEpUP OA). Participants1746 SF samples from 1650 individuals comprising OA, joint injury, healthy controls and inflammatory arthritis controls, divided into discovery (n=1045) and replication (n=701) datasets. Primary and secondary outcome measuresAn optimised approach to standardisation was developed iteratively, monitoring reliability and precision (comparing coefficient of variation [%CV] of pooled SF samples between plates and correlation with prior immunoassay for 9 analytes). Pre-defined technical confounders were adjusted for (by Limma) and batch correction was by ComBat. Poorly performing SOMAmers and samples were filtered. Variance in the data was determined by principal component (PC) analysis. Data were visualised by Uniform Manifold Approximation and Projection (UMAP). ResultsOptimal SF standardisation aligned with that used for plasma, but without median normalisation. There was good reliability (80% of SOMAmers in pooled samples) and overall good correlation with immunoassay. PC1 accounted for 48% of variance and strongly correlated with individual SOMAmer signal intensities (median correlation coefficient 0.70). These could be adjusted using an intracellular protein score. PC2 (7% variance) was attributable to processing batch and was batch-corrected by ComBat. Lesser effects were attributed to other technical confounders. Data visualisation by UMAP revealed clustering of injury and OA cases in overlapping but distinguishable areas of high-dimensional proteomic space. ConclusionsWe define a standardised approach for SF analysis using the SOMAscan platform and identify likely intracellular protein as being a major driver of variance in the data. Strengths and limitationsO_LIThis is the largest number of individual synovial fluid samples analysed by a high content proteomic platform (SomaLogic technology) C_LIO_LISomaScan offers reliable, precise relative SF data following standardisation for over 6000 proteins C_LIO_LISignificant variance in the data was driven by a protein signal which is likely intracellular in origin: it is not yet clear whether this is due to technical considerations, normal cell turnover or relevant pathological processes C_LIO_LIAdjusting for confounding factors might conceal the true structure of the data and reduce the ability to detect molecular endotypes within disease groups C_LI