Reverse Engineering the Extracellular Neighborhood to Support the Functional Tissue Unit: A Use Case to Restore Ovarian Function

项目概要/摘要 静态的人类生物分子图谱缺少对建立健康状况至关重要的维度。我们 我们拥有独特的资源和专业知识，可以填补这些方面的空白， 人类卵巢HuBMAP数据。在这里，我们将通过界定发展方面的变化， 间质细胞跨越青春期过渡;我们将通过探测生物化学创造空间维度 细胞外基质（ECM）在解剖区室和功能细胞单位的物理线索 我们将通过研究功能细胞单位的变化来创建时间维度 在卵质成熟和激素产生期间。如果没有这些增加的维度，HuBMAP数据集 在识别和理解疾病模式方面效果较差。我们的长期目标是提高 利用现有和新的数据集，为再生医学技术提供信息， 卵巢早衰患者生育和激素恢复选择的安全性、有效性和寿命 子宫内膜异位症（POI）是一种在美国约1%的女性中发生的疾病。有POI的人 生活质量降低，预期寿命缩短约2年，包括认知和 心血管疾病我们将纳入宾夕法尼亚大学（UPENN）HuBMAP组织的数据 制图中心（TMC）和密歇根大学（UM）Chan Zuckerberg Initiative Human Cell Atlas（HCA） 健康成年人卵巢的多组图谱。范德比尔特大学生物分子多模态成像中心 （BIOMIC）开发了成像和生物计算分析管道，可实现3D多模型重建 和分子分析此外，我们已经在模型和人上生成了ECM和相关蛋白质图谱， 卵巢，与西北大学（NU）HuBMAP快速技术实施（RTI）合作 中心最后，我们开发了工程工具，如支持卵泡的支架， 从人类诱导多能干细胞（iPSC）中制备卵巢上皮细胞的方案。这些 资源和合作将提供有价值的组织和功能单位的见解，并结合我们的 组织工程的专业知识，将使我们能够反向工程细胞外邻居，支持 卵巢卵泡生长，以提高生育能力和激素恢复的选择。我们将（1）增加发展和 人类卵巢图谱的空间维度以识别影响卵泡发生的邻近区域的变化， (2)展示HuBMAP数据和工具的实用性，以解决重要的生物学问题， 卵巢和反向工程支持卵泡生长的邻域，以及（3）反向工程个性化的 通过定义和验证细胞对人类iPSC的反应， ECM环境。通过实现这些里程碑，我们将增加HuBMAP卵巢图谱的功能， 展示这些数据集的实用性，并为未来的再生能源开发管道和技术。 可以从其他HuBMAP组织图谱开发的技术。

PROJECT SUMMARY/ABSTRACT Static human biomolecular atlases miss dimensionality that is critical for establishing the healthy condition. We are uniquely positioned with rare resources and expertise to fill in these dimensions and enhance the healthy human ovary HuBMAP data. Here, we will contribute the developmental dimension by defining the change in interstitial cells across the pubertal transition; we will create the spatial dimension by probing the biochemical and physical cues of the extracellular matrix (ECM) across anatomical compartments and functional cell unit neighborhoods, and we will create the temporal dimension by investigating the changes in functional cell units during ooplasm maturation and hormone production. Without these added dimensions, the HuBMAP datasets are less effective in identifying and understanding disease modalities. Our long-term goal is to increase the utility of the ovary atlas using existing and new datasets to inform regenerative medicine technologies that improve the safety, efficacy and longevity of fertility and hormone restoration options for patients with premature ovarian insufficiency (POI), a disease that occurs in approximately 1% of women in the USA. Individuals with POI have a reduced quality of life and life expectancy of ~ 2 years shorter from comorbidities that include cognitive and cardiovascular diseases. We will incorporate data from the University of Pennsylvania (UPENN) HuBMAP Tissue Mapping Center (TMC) and the University of Michigan (UM) Chan Zuckerberg Initiative Human Cell Atlas (HCA) multiome maps of healthy adult ovaries. The Vanderbilt University Biomolecular Multimodal Imaging Center (BIOMIC) developed imaging and biocomputational analysis pipelines that enable 3D multimodel reconstruction and molecular profiling. Additionally, we have generated ECM and associated protein maps on model and human ovaries, in collaboration with the Northwestern University (NU) HuBMAP Rapid Technology Implementation (RTI) center. Finally, we have developed engineering tools, such as scaffolds that support ovarian follicles and protocols to make ovarian hormone-producing cells from human induced pluripotent stem cells (iPSCs). These resources and collaborations will provide valuable tissue and functional unit insights and, combined with our expertise in tissue engineering, will enable us to reverse engineer the extracellular neighborhood that supports ovarian follicle growth to improve fertility and hormone restoration options. We will (1) add developmental and spatial dimensions to the human ovary atlas to identify changes in the neighborhood that impact folliculogenesis, (2) demonstrate the utility of the HuBMAP data and tools to address important biological questions within the ovary and reverse engineer a neighborhood that supports follicle growth, and (3) reverse engineer a personalized ovarian hormone-replacement therapy from human iPSCs by defining and validating the response of cells to ECM environments. By achieving these milestones, we will add to the functionality of the HuBMAP ovary atlas, demonstrate the utility of these datasets, and develop pipelines and technologies for future regenerative technologies that may be developed from other HuBMAP tissue atlases.