Methodological development of molecular endotype discovery from synovial fluid of individuals with knee osteoarthritis: the STEpUP OA Consortium
Methodological development of molecular endotype discovery from synovial fluid of individuals with knee osteoarthritis: the STEpUP OA Consortium
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从膝骨关节炎患者滑液中发现分子内型的方法学进展：STEpUP OA 联盟
DOI：
10.1101/2023.08.14.23294059
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发表时间：
2023
期刊：
影响因子：
--
通讯作者：
Deng Y
中科院分区：
文献类型：
--
作者：
Deng Y
目的开发和验证用于大规模滑液（SF）分析的质量控制（QC）蛋白质数据管道，使用SomaLogic技术。DesignKnee SF和相关临床数据来自合作伙伴队列。将SF样品离心，将上清液储存在-80 °C下，然后通过SomaScan Discovery ™ V4.1进行分析（>7000 SOMAmer/蛋白质）。设置由9个学术和8个商业合作伙伴组成的国际联盟来自1650个个体的1746个SF样品，包括OA、关节损伤、健康对照和炎性关节炎对照，分为发现（n=1045）和复制（n=701）数据集。主要和次要结果测量迭代地开发了一种优化的标准化方法，监测可靠性和精确度（比较板之间“合并”SF样品的变异系数[%CV]以及与9种分析物的先前免疫测定的相关性）。调整预定义的技术混杂因素（通过Limma），并通过ComBat进行批次校正。表现不佳的SOMAmer和样品被过滤。通过主成分（PC）分析确定数据的方差。数据可视化均匀流形近似和投影（UMAP）。ResultsOptimal SF standardisation对齐用于血浆，但没有中位数归一化。具有良好的可靠性（<20 %CV for >合并样品中SOMAmer的80%），并且与免疫测定具有总体良好的相关性。PC 1占方差的48%，与个体SOMAmer信号强度强相关（中位相关系数0.70）。这些可以使用“细胞内蛋白质评分”进行调整。PC 2（7%方差）归因于处理批次，并通过ComBat进行批次校正。较小的影响归因于其他技术混杂因素。通过UMAP的数据可视化显示了损伤和OA病例在重叠但可区分的高维蛋白质组学空间区域中的聚集。结论我们使用SOMAscan平台定义了SF分析的标准化方法，并将可能的“细胞内”蛋白确定为数据方差的主要驱动因素。优势和局限性这是通过高含量蛋白质组学平台分析的最大数量的个体滑液样本（SomaLogic technology）SomaScan提供了可靠、精确的相对SF数据，经过6000多种蛋白质的标准化处理。数据中的显著差异是由可能来源于细胞内的蛋白质信号驱动的：目前尚不清楚这是否出于技术考虑，正常的细胞更新或相关的病理过程。调整混杂因素可能会掩盖数据的真实结构，并降低识别的能力。检测疾病组内的“分子内型”
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 °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 (<20 %CV for >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 limitationsThis is the largest number of individual synovial fluid samples analysed by a high content proteomic platform (SomaLogic technology)SomaScan offers reliable, precise relative SF data following standardisation for over 6000 proteinsSignificant 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 processesAdjusting for confounding factors might conceal the true structure of the data and reduce the ability to detect ‘molecular endotypes’ within disease groups
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影响因子：
4.6
作者：
Jayadev C;Hulley P;Swales C;Snelling S;Collins G;Taylor P;Price A
通讯作者：
Price A
影响因子：
13.3
作者：
Watt, Fiona E.;Paterson, Erin;Freidin, Andrew;Kenny, Mark;Judge, Andrew;Saklatvala, Jeremy;Williams, Andy;Vincent, Tonia L.
通讯作者：
Vincent, Tonia L.
影响因子：
3.7
作者：
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通讯作者：
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影响因子：
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作者：
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影响因子：
3.4
作者：
Raffield, Laura M.;Dang, Hong;Bowler, Russell P.
通讯作者：
Bowler, Russell P.