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.

项目总结