Using high dimensional molecular data to decipher gene dynamics underlying pathogenic synovial fibroblasts

利用高维分子数据破译致病性滑膜成纤维细胞的基因动态

• 批准号: 10601120
• 负责人: Ilya Korsunskiy
• 金额: $ 9.6万
• 依托单位: BRIGHAM AND WOMEN'S HOSPITAL
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-04-15 至 2026-03-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10601120
• 关键词: Adopted; Advisory Committees; Algorithms; Arthritis; Atlases; Autoimmune; Benchmarking; Biological Assay; Biological Response Modifier Therapy; Biology; Biopsy; Blocking Antibodies; Cells; Cellular biology; Cluster Analysis; Computational Biology; Data; Data Analyses; Data Set; Degenerative polyarthritis; Disease; Equilibrium; Fibroblasts; Future; Genes; Genetic; Genomics; Human; Immunology; In Vitro; Individual; Inflammation; Integrins; Joints; Ligands; Maintenance; Measures; Mediating; Mentors; Meta-Analysis; Methods; Modeling; Molecular; Molecular Profiling; NOTCH3 gene; Nature; Paper; Pathogenicity; Pathologic; Pathologic Processes; Pathway interactions; Phenotype; Population; Positioning Attribute; Principal Investigator; Process; Publishing; RNA; Recombinants; Reproducibility; Research; Rheumatoid Arthritis; Rheumatology; Role; Series; Signal Induction; Signal Transduction; Sorting; Statistical Data Interpretation; Synovial Membrane; Testing; Therapeutic; Time; Tissues; Training; cytokine; efficacy testing; genetic signature; high dimensionality; human tissue; in vivo; joint injury; morphogens; multidisciplinary; new therapeutic target; novel; novel strategies; receptor; single-cell RNA sequencing; skills; therapeutic target; transcriptome sequencing

Project Summary Pathological expansion of fibroblasts in the synovial tissue surrounding the joint drive disease in rheumatoid arthritis (RA) and osteoarthritis (OA). Recent studies have identified molecularly and functionally distinct phenotypes of synovial fibroblasts using single cell RNA sequencing (scRNAseq). One of the phenotypes, found exclusively in the lining compartment of the synovium and expanded in both RA and OA, has been implicated in tissue destruction in vivo. Previous studies have shown that synovial fibroblast phenotypes are plastic, making them potentially inducible with biological therapies but difficult to study in vitro, as they lose their phenotypes ex vivo. I propose two novel strategies to model the induction and maintenance of the synovial lining phenotype. Preliminary analyses prioritized TGF𝛽 , a cytokine known to drive fibroblast differentiation, in both strategies. The first strategy builds on the notion that fibroblast phenotypes are in dynamic equilibrium and exist at multiple stages of induction in human tissue. Aim 2 will model these states in over 100,000 fibroblasts from 108 synovial donor biopsies with the novel RNA velocity algorithm to infer lining fibroblast differentiations processes and nominate driver genes. Aim 2 will either perform 108 separate analyses combined through meta-analysis or do one joint analysis with Crescendo, to be developed in aim 1 as the first multi-donor RNA velocity analysis. The second strategy builds on preliminary data that show that genes activated in phenotype induction are inactivated during phenotype loss ex vivo. Aim 3 will directly experimentally assay the dynamics of phenotype loss ex vivo, profiling 150,000 fibroblast at multiple time points with scRNAseq. Aim 3a will test the efficacy of exogenous TGF𝛽 stimulation to maintain the lining phenotype ex vivo. Aim 3b will nominate and test more pathways from sophisticated analysis of the generated time-course data. Together, these aims will identify molecular drivers of the lining phenotype and fuel novel research on therapeutics to target lining fibroblasts. I have expertise in single cell computational biology and synovial fibroblast genomics. I developed the popular Harmony algorithm for single cell integration, published in Nature Methods and co-first authored a paper detailing the induction of a novel fibroblast subtype necessary for arthritic disease in vivo, in press at Nature. Completing the proposed research will help me build my analytical skills with time-course data analysis and develop invaluable skills in experimental fibroblast biology. I will train Dr. Soumya Raychaudhuri, in statistical analyses, co-mentor Dr. Michael Brenner, expert in synovial fibroblast biology, advisor Dr. Peter Kharchenko, developer of RNA velocity, advisor Dr. Fiona Powrie, director of the Kennedy Institute for Rheumatology, and advisor Dr. Christopher Buckley, expert in synovial fibroblast biology. With this multi- disciplinary training, I will be become a principal investigator applying computational and experimental methods to translational rheumatology research.

项目摘要 类风湿关节驱动病周围滑膜组织成纤维细胞的病理性扩张 关节炎（RA）和骨关节炎（OA）。最近的研究已经确定了分子和功能上的不同 使用单细胞RNA测序（scRNAseq）分析滑膜成纤维细胞的表型。其中一种表现型， 仅在滑膜的衬里隔室中发现，并在RA和OA中扩大， 与体内组织破坏有关。以前的研究表明，滑膜成纤维细胞表型是 塑料，使它们有可能用生物疗法诱导，但难以在体外研究，因为它们失去了 它们的表型。我提出了两个新的策略来模拟诱导和维护的 滑膜衬里表型。初步分析优先考虑TGFβ 1，一种已知驱动成纤维细胞的细胞因子 差异化，在两种策略中。第一种策略建立在成纤维细胞表型与细胞分化有关的概念上。 动态平衡，并存在于人体组织中的多个诱导阶段。Aim 2将对这些状态进行建模， 来自108个滑膜供体活检组织的超过100，000个成纤维细胞，使用新型RNA速度算法推断衬里 成纤维细胞分化过程和提名驱动基因。目标2将执行108个单独的 通过荟萃分析进行分析，或与Crescendo进行一项联合分析，将在目标1中开发 作为第一个多供体RNA速度分析。第二个战略建立在初步数据的基础上，这些数据表明， 在表型诱导中活化的基因在离体表型丧失期间失活。Aim 3将直接 实验测定离体表型丧失的动力学，在多个时间对150，000个成纤维细胞进行分析 用scRNAseq。目的3a将测试外源性TGFβ 1刺激维持衬里的功效 离体表型。Aim 3b将通过对生成的 时程数据总之，这些目标将确定内衬表型和燃料新的分子驱动程序 靶向衬里成纤维细胞的治疗研究。 我在单细胞计算生物学和滑膜成纤维细胞基因组学方面有专长。我开发了 一种流行的单细胞整合的和谐算法，发表在《自然方法》上， 详细介绍了体内关节炎疾病所必需的一种新的成纤维细胞亚型的诱导的论文， 自然完成建议的研究将帮助我建立我的分析能力与时间过程数据分析 并在实验性成纤维细胞生物学方面发展宝贵的技能。我将训练Soumya Raychaudhuri博士， 统计分析，共同导师Michael Brenner博士，滑膜成纤维细胞生物学专家，顾问Peter博士 Kharchenko，RNA速度的开发者，顾问Fiona Powrie博士，肯尼迪研究所所长， 流变学和顾问克里斯托弗巴克利博士，滑膜成纤维细胞生物学专家。有了这个多- 通过学科培训，我将成为一名应用计算和实验方法的首席研究员 转译流变学的研究。