Does differential senescence between fibroblast subsets explain the increased progression towards Rheumatoid Arthritis and Osteoarthritis with age?

这个博士研究生有三个目标：Aim 1：在不同年龄段的性别匹配患者中，滑膜成纤维细胞衬里和衬里下层成纤维细胞亚群中一组衰老标志物的分子表征（20岁-80岁）伴OA和RA目标2：衬里衰老标志与亚细胞衰老关系的生物信息学分析人成纤维细胞亚群中的衬里成纤维细胞与从已建立的炎性关节炎（CIA）和退行性关节炎的小鼠模型分析的亚群相比内侧半月板的不稳定（DMM）目的3：使用多重免疫荧光（CODEX）和RNA范围分析，建立OA和RA成纤维细胞滑膜中衰老成纤维细胞亚群的解剖定位，并与CIA和DMM滑膜进行比较。我们将如[3]所述在一定年龄范围内（50-80岁）消化人RA和OA滑膜，并在使用CD 45/THY 1/PDPN进行FACS分选后鉴定衬里层（LL），我们将使用一组已知的衰老标志来测试这三个亚组中的差异表达。这些包括通过FACS测定的线粒体健康和ROS [4]、溶酶体健康（通过SenTraGor或IHC测定的脂褐质、通过14 C缬氨酸蛋白降解测定（Wade-Martins，Oxford）测定的溶酶体功能、DNA损伤、通过质谱测定的蛋白质损伤（Kessler，Oxford）、SASP（衰老相关分泌表型，通过细胞内FACS & ELISA测定）、复制性衰老和自噬通量（如所述[5]）。我们从NIH AMP研究和之前基于组织的研究[6]的数据中了解到，至少20例OA和RA患者样本将提供足够的把握度来证明30个独立年代（50- 60岁、60- 70岁、70- 80岁）患者组之间的差异。将使用transSMART将临床数据与实验室结果相关联：transSMART是一个开源和社区驱动的临床和转化研究信息学和数据共享平台，我们的临床数据库位于其中。目标二：将通过与NIH加速医学合作伙伴关系（AMP）的当前人类单细胞数据集进行比较来评估目标1的功能相关性，我们已经为该数据集做出了贡献[6]，该数据集来自牛津大学提供的RA（CIA）和OA（DMM）小鼠模型。此外，将对小鼠和人成纤维细胞进行明确的跨物种比对，以无偏倚地评估与衰老标志相关的保守成纤维细胞亚型的存在。目标三：我们在开发和应用多参数免疫组织化学（使用CODEX（多达40个参数）成像系统和共聚焦成像Zeiss 880 Airyscan（多达8个参数，高空间分辨率）和RNAscope）以开发滑膜中基因表达的细胞位置空间图方面具有经验。利用这种方法，我们将验证目标1中确定的关键衰老标志在人OA和RA滑膜组织以及小鼠CIA和DMM组织切片中的位置。我们使用FFPE和冷冻切片的组合来验证鉴定的基因表达盒。将使用商业（Imaris、CODEX Analysis Pipeline、Zen）和开源图像分析包（ImageJ、CellProfiler）的组合进行图像分析。

This PhD studentship has three aims:Aim1: Molecular characterization of a panel of senescence markers in synovial fibroblast lining and sub-lining layer fibroblast subsets in sex matched patients across the spectrum of ages (20ys-80ys) with OA and RA Aim 2: Bioinformatic analysis of the relationship between the senescence hallmarks in lining and sub-lining fibroblasts in human fibroblast subsets compared to subsets analysed from established mouse models of inflammatory arthritis (CIA) and degenerative arthritis the destabilization of medial meniscus (DMM)Aim 3: Establish the anatomical localization of senescent fibroblast subsets in OA and RA fibroblast synovium compared to CIA and DMM synovium using multiplex immunofluorescence (CODEX) and RNA scope analysisObjective 1: We will digest human RA and OA synovium as described [3] over a range of ages (50-80) and following FACS sorting using CD45/THY1/PDPN to identify Lining Layer (LL), sub-lining (SL) and pericytes (PC) we will use test a panel of known senescence hallmarks for differential expression in these three subsets. These include mitochondrial health and ROS by FACS [4], lysosomal health (lipofuscin by SenTraGor or IHC, lysosomal function by 14C valine protein degradation assay (Wade-Martins, Oxford), DNA damage, protein damage by mass spec (Kessler, Oxford), SASP (senescence associated secretory phenotype, by intracellular FACS & ELISA) replicative senescence and autophagic flux (as described [5]). We know from data in the NIH AMP study, and previous tissue-based studies [6] that a minimum of 20 patient samples from OA and RA will provide sufficient power to demonstrate differences between groups of patients over three independent decades; 50-60yrs, 60-70yrs, 70-80yrs. Clinical data will be correlated with laboratory findings using tranSMART: An Open Source and Community-Driven Informatics and Data Sharing Platform for Clinical and Translational Research in which our clinical databases are housed. Objective 2: The functional relevance of findings Objective 1 will be assessed by comparison with current human single-cell datasets from the NIH Accelerating Medicine Partnership (AMP) to which we have contributed [6] with datasets generated from mouse models of RA (CIA) and OA (DMM) that are available in Oxford. In addition, explicit cross-species alignment of mouse and human fibroblasts will be performed in order for unbiased assessment of the existence of conserved fibroblast subtypes relating to senescence hallmarks. Objective 3: We have experience in the development and application of multi-parameter immunohistochemistry (using CODEX (up to 40 parameters) imaging system and confocal imaging Zeiss 880 Airyscan (up to 8 parameter, high spatial resolution) and RNAscope to develop spatial maps of cellular location with gene expression in the synovium. Utilizing this approach, we will verify the location of key senescence hallmarks identified in Objective 1 in tissue sections from human OA and RA synovial tissue as well as mouse CIA and DMM. We use a combination of FFPE and frozen sections to validate gene expression cassettes identified. Image analysis will be done using a combination of commercial (Imaris, CODEX Analysis Pipeline, Zen) and open source image analysis packages (ImageJ, CellProfiler).