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

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 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.

Fig 1

(A) Variation explained (%) by the top 10 PCs derived from the standardised log abundance proteomic data. (B) Correlation between PC1 and protein abundance, with two high-abundance proteins (albumin, a soluble serum protein, and LDH, an intracellular protein) marked. Protein abundance is calculated as the standardised RFU for each protein adjusted by the protein’s dilution factor used in the SomaScan assay (the "dilution bin"). (C) Comparison of variation explained (%) by PC1 between 18 pairs of SF samples that were centrifuged (spun) or not (unspun) after aspiration and prior to freezing, with paired samples from the same participant joined by separate lines. Red lines show samples that had an increased PC1 prior to spinning, and the green line where it was decreased. Correlation between PC1 and intracellular protein score (D) before and (E) after IPS adjustment. (F) Variation explained by the top 10 PCs derived from the batch corrected and IPS adjusted log abundance proteomic data. In all cases, correlation is measured using the Pearson correlation coefficient. IPS, Intracellular Protein Score; PC, principal component; LDH, Lactate dehydrogenase.

Fig 2

(A) Distribution of the second principal component (PC2) derived from the standardised log abundance data, showing a bimodal distribution. (B) UMAP visualisation of two reduced dimensions (D1 and D2) of the top PCs of the standardised log abundance data. (C) Example of a strongly bimodal protein measurement, TSG101, RFU (y-axis) against Oxford laboratory processing order (x-axis) and coloured by laboratory processing batch (with only points within the same processing batch connected by lines). Note that the ‘flipping’ between high and low signal status occurred primarily when processing batch changed, and only rarely within processing batch. This effect was particularly strong among sample batches that were processed later in processing order. (D) The same example protein measurement for three independent SF samples before (original) and after they were re-processed and re-assayed, showing that bimodal status changed after laboratory re-processing. (E) Distribution of PC2 derived from standardised log abundance data, showing the two probability density functions of the Gaussian Mixture Model used to classify samples into the two bimodal signal status groups. (F) UMAP visualisation of two reduced dimensions (D1 and D2) of the top PCs of the standardised log abundance data, colored by the inferred bimodal signal status. (G) Histogram of PC2 of the batch corrected log abundance data, with the now near-identical distributions of the two bimodal signal status groups shown as colored lines, (H) UMAP visualisation on two reduced dimensions (D1 and D2) of the top PCs of the batch corrected log abundance data, colored by the inferred bimodal signal status. RFUs, relative fluorescence units; PC, Principal Component; TSG101, Tumor susceptibility gene 101 protein; UMAP, Uniform Manifold Approximation and Projection.

Fig 3. Overview of the final data processing and quality control pipeline for synovial fluid SOMAscan data used by the STEpUP OA consortium, broken down into three stages: Standardisation (yellow box), technical confounder correction (blue box) and filtering (green box).

More details on filtering thresholds, and the number removed by each filter, can be found in S4 Table.

Fig 4. Visualisation of selected predefined confounders against select principal components of the batch corrected, filtered, IPS adjusted data.

(A) The average value of PC9 (most strongly associated with plate position) by sample well position, (B-F) visualisation of the two PCs most strongly associated with each confounder, coloured by confounder value. Pre-defined confounders shown are (B) blood staining grade of sample after aspiration assessed by visual inspection, (C) volume of sample taken during aspiration, (D) age of the sample in years, measured from aspiration to sample processing at Oxford, (E) the number of times the sample was thawed and re-frozen before sample processing at Oxford, (F) the disease group of the sample (osteoarthritis [OA], acute knee injury [Injury], healthy control, inflammatory arthritis control).

Fig 5

UMAP visualisation of two reduced dimensions (D1 and D2) of the top PCs of the log abundance data with (A) and without (B) IPS adjustment followed by filtering, coloured by disease group. These groups were osteoarthritis (OA, acute knee injury (injury), healthy controls, inflammatory arthritis controls. UMAP, Uniform Manifold Approximation and Projection.