Leveraging Single-Cell Technologies to Elucidate Niche Environments and Immune Mechanisms Involved in Endometriosis Pathogenesis, Pathophysiology, and Disease Stratification

利用单细胞技术阐明参与子宫内膜异位症发病机制、病理生理学和疾病分层的利基环境和免疫机制

• 批准号: 10699965
• 负责人: LINDA C GIUDICE
• 金额: $ 41.5万
• 依托单位: UNIVERSITY OF CALIFORNIA, SAN FRANCISCO
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-08-01 至 2026-07-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10699965
• 关键词: Adaptive Immune System; Adhesions; Affect; Age; Anatomy; Biological Assay; Biological Process; Cell Communication; Cell physiology; Cells; Characteristics; Chronic; Classification; Clinical; Clinical Data; Communities; Complex; Computational Science; Connective Tissue Cells; Cytometry; DNA Library; Data; Data Sources; Detection; Diagnosis; Diagnostic; Dimensions; Disease; Disease stratification; Endometrial; Endometrium; Environment; Estrogens; Fibrosis; Flow Cytometry; Functional disorder; Genomics; Goals; Hormonal; Human; Image; Immune; Immune system; Immunologics; In Vitro; Infertility; Infiltration; Inflammatory; Innate Immune System; Lesion; Macrophage; Measurement; Mesothelium; Metadata; Microscope; Molecular; Molecular Profiling; Myelogenous; Organ; Ovary; Pain; Pathogenesis; Pathology; Pathway interactions; Patients; Pelvic Pain; Pelvis; Peripheral; Peritoneal; Peritoneum; Phagocytes; Phase; Phenotype; Physicians; Population; Proteins; Proteomics; Protocols documentation; Quality of life; Research; Resolution; Surgeon; System; Taxonomy; Techniques; Technology; Testing; Therapeutic Intervention; Tissue Banks; Tissues; Training; Universities; Woman; cell type; clinical care; community engagement; disease classification; disease heterogeneity; disorder subtype; effective therapy; endometriosis; eutopic endometrium; high dimensionality; immune cell infiltrate; improved; innovation; lens; multidisciplinary; novel; peripheral blood; personalized diagnostics; personalized therapeutic; precision medicine; predictive modeling; prognostic value; reproductive; response; single cell technology; single nucleus RNA-sequencing; single-cell RNA sequencing; systemic inflammatory response; therapeutic target; therapeutically effective; transcriptome; transcriptomics; unsupervised learning

ABSTRACT – PROJECT 1 Endometriosis is a chronic, estrogen-dependent, inflammatory disease that affects ~10% of reproductive age women, resulting in debilitating pelvic pain, infertility, and compromised quality of life. It is characterized by anatomically and phenotypically diverse lesions of endometrial-like tissue superficially on the pelvic peritoneum, the ovaries and deeply infiltrating into pelvic organs, with resulting neuroangiogenesis, fibrosis, adhesions, pelvic pain and infertility. The pathogenesis of endometriosis relies on complex interactions between endometrial, peritoneal mesothelial and connective tissue cells and activation of local immune cell responses. There is profound dysfunction of the innate and adaptive immune systems, associated with inefficient lesion clearance and pelvic and systemic inflammation. As clinical classifications of endometriosis are maladapted to the heterogeneity of disease expression, diagnostics as well as effective treatments are lacking. Thus, precise understanding of the cellular and molecular pathobiology of endometriosis is a critical prerequisite to improve disease classification and inform diagnostic and therapeutic interventions. The goal of Project 1 is to determine the contribution of the immune system to the pathobiology of endometriosis on a single cell level, and using a data-driven strategy to derive and molecularly characterize objective disease classification. In Aim 1, we will determine the cellular composition and functional attributes of endometriosis lesions, their surrounding peritoneal/serosal niches, and eutopic endometrium through the lens of transcriptomic signatures at single cell resolution. Our hypothesis is that lesions and their niche environments have unique and functionally relevant transcriptomic signatures. In Aim 2, we will determine the contribution of the local and peripheral immune system to the pathobiology of endometriosis leveraging CYTOF technology. We will test the hypothesis that the local and peripheral myeloid phagocyte systems are dysfunctional in women with endometriosis. Local and systemic immunological data will be integrated to identify immunological signatures of dysfunctionality and to differentiate endometriosis disease types, along with functional studies. Finally, in Aim 3, we will leverage unsupervised machine learning techniques to integrate single-cell assessment of endometriosis lesions, surrounding tissue, endometrium, the local and peripheral immune systems and clinical data into a cross-tissue predictive model of disease classification. Our integrated approach will leverage hundreds of existing, clinically well-annotated biospecimens in our well established Human Endometrial Tissue & DNA Bank and ongoing accrual through our extensive network of physician and surgeon collaborators. The impact of this study will be to derive a replete transcriptomic and proteomic taxonomy of endometriosis lesions, their niche environments, eutopic endometrium, and the local and peripheral immune systems at single cell resolution; identify disease subtypes based on molecular signatures for disease stratification and inform personalized diagnostic and therapeutic targets; and provide a rich data source for the greater research community focused on endometriosis and related disorders.

摘要 - 项目1 子宫内膜异位症是一种慢性，雌激素依赖性的炎症性疾病，影响了约10％的生殖年龄 妇女，导致骨盆疼痛，不育和生活质量造成的衰弱。它的特征是 在解剖学和表型上多种多样的子宫内膜样组织在骨盆腹膜上超级损伤， 卵巢并深深渗入骨盆器官，导致神经血管生成，纤维化，粘合剂，骨盆 疼痛和不育。子宫内膜异位症的发病机理取决于子宫内膜的复杂相互作用， 腹膜间皮和连接的组织细胞以及局部免疫细胞反应的激活。有 先天性和适应性免疫系统的严重功能障碍，与效率低下的病变有关 和骨盆和全身炎症。由于子宫内膜异位症的临床分类不适为 缺乏疾病表达，诊断和有效治疗的异质性。那，确切地说 了解子宫内膜异位症的细胞和分子病理生物学是改善的关键先决条件 疾病分类并告知诊断和治疗干预措施。项目1的目标是确定 免疫系统对单个细胞水平上子宫内膜异位症病理生物学的贡献，并使用 数据驱动的策略以得出和分子表征客观疾病分类。在AIM 1中，我们将 确定子宫内膜异位病变的细胞组成和功能属性，其周围环境 通过单细胞处的转录组特征的镜头 解决。我们的假设是，病变及其利基环境具有独特且功能相关 转录组签名。在AIM 2中，我们将确定局部和周围免疫系统的贡献 掌握细胞胞菌技术的子宫内膜异位症病理生物学。我们将测试当地的假设 子宫内膜异位症女性的外周髓性吞噬系统和外周髓样细胞系统功能失调。本地和系统 免疫数据将集成以识别功能障碍的免疫学特征，并区分 子宫内膜异位疾病类型以及功能研究。最后，在AIM 3中，我们将利用无监督的 机器学习技术以整合子宫内膜异位病变的单细胞评估，周围组织， 子宫内膜，局部和周围免疫系统以及临床数据，成为跨组织预测模型 疾病分类。我们集成的方法将利用数百种现有的，临床上良好的现有 在我们建立的人类子宫内膜组织和DNA库中的生物测量以及通过我们的持续应计 广泛的身体和外科医生合作者网络。这项研究的影响将是得出重复 子宫内膜异位病变的转录组和蛋白质组学分类学，其利基环境，Eutopic子宫内膜， 以及单细胞分辨率下的局部和周围免疫系统；根据 疾病分层的分子特征并为个性化的诊断和治疗靶标提供信息；和 为更大的研究社区提供了丰富的数据源，该研究社区的重点是子宫内膜异位症和相关疾病。