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

摘要-项目1 子宫内膜异位症是一种慢性、雌激素依赖的炎症性疾病，影响约10%的生育年龄。 导致虚弱的盆腔疼痛、不孕不育和生活质量下降。它的特点是 盆腔腹膜表面有解剖和表型不同的子宫内膜样组织病变， 卵巢和盆腔器官的深度渗透，导致神经血管生成、纤维化、粘连、盆腔 疼痛和不孕不育。子宫内膜异位症的发病机制依赖于子宫内膜、子宫内膜和子宫内膜之间复杂的相互作用。 腹膜间皮和结缔组织细胞与局部免疫细胞反应的激活。的确有 先天性和获得性免疫系统严重功能障碍，与病变清除效率低下有关 以及盆腔和全身炎症。由于子宫内膜异位症的临床分类与 缺乏疾病表达的异质性、诊断以及有效的治疗。因此，精确度 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前提 疾病分类，并为诊断和治疗干预措施提供信息。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，并使用 数据驱动的策略，以派生和分子表征客观的疾病分类。在目标1中，我们将 确定子宫内膜异位症病变及其周围的细胞成分和功能属性 通过单细胞转录信号的透镜观察腹膜/浆膜壁龛和在位内膜 决议。我们的假设是，病变及其生态位环境具有独特的和功能上的相关性 转录签名。在目标2中，我们将确定局部和外周免疫系统的贡献 利用细胞学技术研究子宫内膜异位症的病理生物学。我们将检验这一假设，即本地 子宫内膜异位症患者外周血髓系巨噬细胞系统功能紊乱。局部性和系统性 将整合免疫学数据，以识别功能障碍的免疫学特征并区分 子宫内膜异位症疾病类型，以及功能研究。最后，在目标3中，我们将利用无人监督 机器学习技术集成了对子宫内膜异位症病变、周围组织、 将子宫内膜、局部和外周免疫系统和临床数据纳入跨组织预测模型 疾病分类。我们的集成方法将利用数百个现有的、临床上注释良好的 我们已建立的人类子宫内膜组织中的生物标本&DNA库和通过我们的 广泛的内科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完整的 子宫内膜异位症病变、其生态位环境、在位内膜 以及局部和外周免疫系统在单细胞分辨率下；识别疾病亚型 用于疾病分层的分子签名，并为个性化诊断和治疗目标提供信息；以及 为关注子宫内膜异位症和相关疾病的更大研究社区提供丰富的数据来源。

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.