Leveraging Multi-Scale Deep Phenotyping and Applied Machine Learning to Predict Senescent Cell Burden in Humans

数据分析核心--项目总结 衰老细胞(SCs)是接触某些细胞后产生的长寿的炎性细胞。 压力源。这些细胞被发现随着年龄的增长而增加，在许多与年龄相关的慢性疾病中， 一些研究从机制上将SC功能与多种疾病表型联系起来。新疗法 靶向SCs(感觉神经降解剂)可以通过暂时禁用SCs上的抗凋亡网络并导致细胞凋亡来发挥作用 组织内的干细胞。在动物模型中，感觉剂可以延缓、预防或改善虚弱， 心血管病理，神经精神疾病和肝，肾，肌肉骨骼，肺，眼， 血液学、代谢和皮肤病，以及其他临床表型。当前的方法是 组织中SC负荷的量化依赖于几个典型的标志物，如p16、p21、SA-β、Gal等，但它们的 特异性仍有争议，这些标记物很少在人体组织中共表达。因此，目前我们 缺乏对人体各组织在健康和健康期间的SC负荷估计的完整图景 衰老。在这里，我们的目标是使用不同的技术来公正地鉴定来自不同人体组织的干细胞 将在人体组织干细胞图谱上开发平台和高级分析。由于SCs的积累 被认为是疾病表型的先兆，识别干细胞的可靠诊断方法也将使早期 检测那些有患慢性病风险的人。如果没有可靠的诊断来估计供应链负担， 人体组织，(1)针对干细胞的治疗评估将继续是敏感药物的瓶颈 发展和(2)慢性疾病将继续成为一个日益严重的公共卫生问题，这将导致 人口健康寿命稳步下降。在这个方案中，我们将构建分子和 人类组织驻留干细胞的形态图谱并创建多种机制来共享这些图谱 与科学界的合作成果。组织测绘中心的数据分析核心将利用其 能够无偏见地描述人类组织和血液，以预测人类衰老的细胞负担并创造 组织中SC生物标记物的图谱。为了实现我们的目标，我们的数据分析核心将为 数据处理，数据分析的算法，构建和共享组织中的人体干细胞地图，以及一般 通过联合体组织和数据协调中心(CODCC)和与 细胞衰老网络(Sennet)。我们将建立、整理和注释人体组织的SCS图谱 并与Sennet实现数据共享，协调协议和分析管道。该数据库将 允许用户查看和下载SCS签名，并为身份识别提供受控访问系统 个体水平的单核表达、成像和蛋白质组学数据。这一资源也将为许多人服务 整个财团的其他类型的分析。

DATA ANALYSIS CORE - PROJECT SUMMARY Senescent cells (SCs) are long-lived inflammatory cells that ensue from the exposure to certain cellular stressors. These cells have been found to increase with aging and in many age-related chronic diseases and some studies have mechanistically linked SC function with a variety of disease phenotypes. New therapies targetingSCs (senolytics) can act by transiently disabling anti-apoptotic networks on SCs and causing apoptosis of those SCs within a tissue. In animal models, senolytics can delay, prevent or improve frailty, cardiovascular pathology, neuropsychiatric conditions and liver, kidney, musculoskeletal, lung, eye, haematological, metabolic and skin disorders, among other clinical phenotypes. Current methods to quantify SC burden in tissues rely on a few canonical markers such as p16, p21, SA-βgal, etc. but their specificity is still debatable and these markers are seldom co-expressed in human tissues. Thus, at present, we lack a complete picture of the estimated SC burden across tissues in humans in health and during aging. Here, we aim to unbiasedly characterize SCs from different human tissues using different technological platforms and advanced analytics to develop at Atlas of SCs in human tissues. Since the accumulation of SCs is thought to precede disease phenotypes, robust diagnostic methods to identify SCs will also enable early detection of those at risk for developing chronic disease. Without robust diagnostics to estimate SC burden in human tissues, (1) assessment of therapies targeting SCs will continue to be a bottleneck in senolytic drug development and (2) chronic disease will continue to be a growing public health concern which will lead to a steady reduction in the populations' health span. In this proposal, we will construct molecular and morphological maps for tissue-resident SCs in humans and create multiple mechanisms to share these results with the scientific community. The Data Analysis Core of the Tissue Mapping Center will harness its ability to unbiasedly profile human tissues and blood to predict the senescent cell burden in humans and create an Atlas of SC biomarkers in tissues. To achieve our goals, our Data Analysis Core will provide pipelines for data processing, algorithms for data analysis, construct and share a map of human SCs in tissues, and general coordination of data through the Consortium Organization and Data Coordination Center (CODCC) and with Cellular Senescence Network (SenNet). We will build, curate, and annotate a SCs atlas across human tissues and implement data sharing and coordinate protocols and analytic pipelines with SenNet. The database will allow users to view and download SCs signatures, and provide a controlled access system for de-identified individual-level single nuclei expression, imaging and proteomic data. This resource will also serve for many additional kinds of analyses throughout the consortium.