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: 10458758
• 负责人: LINDA C GIUDICE
• 金额: $ 41.73万
• 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/10458758
• 关键词: Adaptive Immune System; Adhesions; Affect; Age; Anatomy; Biological Assay; Biological Process; Cell Communication; Cell Nucleus; 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; Disease; Disease stratification; Endometrial; Endometrium; Environment; Estrogens; Fibrosis; Flow Cytometry; Functional disorder; Genomics; Goals; Hormonal; Human; Image; Immune; Immune system; Immunologics; In Vitro; Infertility; Inflammatory; Lesion; Measurement; 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; Tumor-infiltrating immune cells; Universities; Woman; base; cell type; clinical care; community engagement; disease classification; disease heterogeneity; disorder subtype; effective therapy; endometriosis; eutopic endometrium; high dimensionality; improved; innovation; lens; macrophage; multidisciplinary; novel; peripheral blood; personalized diagnostics; personalized therapeutic; precision medicine; predictive modeling; prognostic value; reproductive; response; single cell technology; single-cell RNA sequencing; systemic inflammatory response; therapeutic target; therapeutically effective; transcriptome; transcriptome sequencing; 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、炎症性疾病，影响约10%的育龄妇女 女性，导致虚弱的骨盆疼痛，不孕症，生活质量下降。它的特点是 盆腔腹膜表面的尿道样组织的解剖学和表型多样性病变， 卵巢和盆腔器官的深层浸润，导致神经血管生成，纤维化，粘连，盆腔 疼痛和不孕症子宫内膜异位症的发病机制依赖于子宫内膜， 腹膜间皮瘤和结缔组织细胞以及局部免疫细胞应答的激活。有 先天性和适应性免疫系统的严重功能障碍，与无效的病灶清除相关 以及盆腔和全身炎症。由于子宫内膜异位症的临床分类不适应 缺乏疾病表达的异质性、诊断以及有效的治疗。因此，精确 了解子宫内膜异位症的细胞和分子病理学是改善子宫内膜异位症的关键先决条件。 疾病分类和告知诊断和治疗干预措施。项目1的目标是确定 免疫系统在单细胞水平上对子宫内膜异位症病理生物学的贡献， 数据驱动的策略，以推导和分子表征客观的疾病分类。在目标1中，我们 确定子宫内膜异位症病变的细胞组成和功能属性，其周围 腹膜/浆膜龛和在位子宫内膜通过单细胞转录组学标记的透镜 分辨率我们的假设是，病变及其生态位环境具有独特的和功能相关的 转录组特征在目标2中，我们将确定局部和外周免疫系统的贡献 子宫内膜异位症的病理生物学的研究。我们将检验一个假设， 和外周骨髓吞噬细胞系统在子宫内膜异位症妇女中功能障碍。局部和全身 将整合免疫学数据，以确定功能障碍的免疫学特征， 子宫内膜异位症疾病类型，沿着功能研究。最后，在目标3中，我们将利用无监督 机器学习技术整合子宫内膜异位症病变，周围组织， 子宫内膜，局部和外周免疫系统和临床数据到一个跨组织的预测模型， 疾病分类我们的综合方法将利用数百个现有的、临床上注释良好的 我们完善的人类子宫内膜组织和DNA库中的生物标本，并通过我们的 广泛的医生和外科医生合作者网络。这项研究的影响将是得出一个充满 子宫内膜异位症病变的转录组学和蛋白质组学分类，它们的生态位环境，在位子宫内膜， 以及单细胞分辨率的局部和外周免疫系统;基于 用于疾病分层的分子特征，并告知个性化的诊断和治疗目标;以及 提供了一个丰富的数据来源，为更大的研究社区集中在子宫内膜异位症和相关疾病。