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力学

• 批准号: 10388258
• 负责人: Ilya Korsunskiy
• 金额: $ 9.81万
• 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/10388258
• 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; Institutes; Integrins; Joints; Lead; 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 Transduction; Statistical Data Interpretation; Synovial Membrane; Testing; Therapeutic; Time; Tissues; Training; base; 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 类风湿疾病周围的滑膜组织中成纤维细胞的病理扩张 关节炎（RA）和骨关节炎（OA）。最近的研究已经鉴定出分子和功能不同 使用单细胞RNA测序（SCRNASEQ）的滑膜成纤维细胞的表型。表型之一， 仅在滑膜的衬里室中发现并在RA和OA中扩展 实施体内组织破坏。先前的研究表明，滑膜成纤维细胞表型是 塑料，使其具有生物疗法的可能诱导，但由于失去了体外研究 他们的表型离体。我提出了两种新型策略，以建模 滑膜衬里表型。初步分析优先考虑TGF𝛽，这是一种已知驱动成纤维细胞的细胞因子 分化，在这两种策略中。第一个策略是基于成纤维细胞表型所在的观念 动态平衡，存在于人体组织的多个诱导阶段。 AIM 2将在 具有新型RNA速度算法的108个滑膜供体活检中的100,000多个成纤维细胞可推断衬里 成纤维细胞分化过程并提名驱动基因。 AIM 2要么单独执行108个 通过荟萃分析合并的分析或与Crescendo进行一项联合分析，将在AIM 1中开发 作为第一个多偏RNA速度分析。第二种策略是基于初步数据，表明 在体内表型丧失期间，在表型诱导中激活的基因被灭活。 AIM 3将直接 实验性地断言表型损失的动力学，多个时间分析150,000个成纤维细胞 用scrnaseq的点。 AIM 3A将测试外源TGF𝛽刺激以保持衬里的有效性 表型外体。 AIM 3B将从对生成的生成的复杂分析提名和测试更多途径 时间课数据。这些目的共同确定了衬里表型和燃料小说的分子驱动因素 靶向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具有专业知识。我开发了 用于单细胞整合的流行和谐算法，以自然方法出版，并首先撰写了 详细介绍体内artritice疾病的新型成纤维细胞亚型诱导的论文，按下 自然。完成拟议的研究将有助于我通过时间表数据分析来建立分析技能 并发展实验性成纤维细胞生物学的宝贵技能。我将培训Soumya Raychaudhuri博士 统计分析，合伙人迈克尔·布伦纳（Michael Brenner）博士，滑膜成纤维细胞生物学专家，顾问彼得 RNA Velocity的开发商Kharchenko，肯尼迪研究所主任Fiona Powrie博士 流变学和顾问克里斯托弗·巴克利（Christopher Buckley）博士，滑膜成纤维细胞生物学专家。有了这个多重的 纪律培训，我将成为采用计算和实验方法的首席研究员 翻译流变学研究。