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

利用多尺度深度表型分析和应用机器学习来预测人类衰老细胞负担

基本信息

批准号：
10684954
负责人：
David Furman
金额：
$ 30.24万
依托单位：
BUCK INSTITUTE FOR RESEARCH ON AGING
依托单位国家：
美国
项目类别：
财政年份：
2021
资助国家：
美国
起止时间：
2021-09-30 至 2026-08-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10684954
关键词：
Age Aging Algorithms Animal Model Apoptosis Apoptotic Atlases Biological Biological Markers Blood Breast Cardiovascular Pathology Cell Aging Cell Nucleus Cell physiology Cells Chronic Disease Collaborations Communities Data Data Analyses Data Coordinating Center Data Set Databases Development Diagnostic Diagnostic Procedure Disease Drug Targeting Early Diagnosis Ensure Exposure to Eye diseases Female Follicular Fluid Future Gene Expression Generations Genotype Goals Health Hematological Disease Human Human body Image Individual Inflammation Mediators Inflammatory Kidney Diseases Link Liquid substance Liver diseases Lung diseases Machine Learning Mammary Gland Parenchyma Maps Measures Metabolic Diseases Methods Molecular Morphology Muscle Musculoskeletal Diseases Ovary Participant Phenotype Plasma Population Primary Cell Cultures Procedures Process Production Proteome Proteomics Protocols documentation Public Health Quality Control Research Research Personnel Resources Risk Sampling Specificity Stains Statistical Methods Surrogate Markers System Techniques Tissue Banks Tissues Urine Validation Visualization Work advanced analytics age related artificial intelligence method clinical phenotype data analysis pipeline data infrastructure data sharing data visualization diagnostic tool disease phenotype drug development ethical, legal, and social implication experience frailty genetic signature healthspan human tissue improved in vivo method development neuropsychiatry novel novel therapeutics predictive tools preservation prevent proteomic signature reproductive reproductive longevity screening senescence single nucleus RNA-sequencing skin disorder stressor targeted treatment technology platform tissue mapping transcriptomics

项目摘要

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.

数据分析核心 - 项目摘要衰老细胞 (SC) 是长寿命的炎症细胞，是由于暴露于某些细胞而产生的。人们发现这些细胞会随着年龄的增长以及许多与年龄相关的慢性疾病而增加。一些研究将 SC 功能与多种疾病表型建立了机制联系。靶向 SC（senolytics）可以通过暂时禁用 SC 上的抗凋亡网络并引起细胞凋亡来发挥作用在动物模型中，senolytics 可以延缓、预防或改善虚弱，心血管病理学、神经精神疾病和肝脏、肾脏、肌肉骨骼、肺、眼睛、血液学、代谢和皮肤疾病等临床表型的当前方法。 SC 量化组织负荷依赖于一些经典标记，例如 p16、p21、SA-βgal 等，但它们的特异性仍然存在争议，并且这些标记物很少在人体组织中共表达，因此，目前，我们。缺乏对健康状态下和在治疗期间人类组织中估计的 SC 负担的完整了解在这里，我们的目标是使用不同的技术公正地表征来自不同人体组织的 SC。自 SC 积累以来，Atlas of SC 在人体组织中开发了平台和高级分析。被认为先于疾病表型，识别 SC 的强大诊断方法也将能够及早发现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 SC 负担。人体组织，(1) 针对 SC 的治疗评估将继续成为 senolytic 药物的瓶颈 (2) 慢性病将继续成为日益严重的公共卫生问题，这将导致在这个提案中，我们将构建分子和健康寿命的稳步缩短。人类组织驻留 SC 的形态图，并创建多种机制来共享这些图组织绘图中心的数据分析核心将利用其成果。能够公正地分析人体组织和血液，以预测人体衰老细胞负担并创造为了实现我们的目标，我们的数据分析核心将为组织中的 SC 生物标志物提供管道。数据处理、数据分析算法、构建和共享组织中人类 SC 的图谱以及一般知识通过联盟组织和数据协调中心 (CODCC) 以及与我们将构建、管理和注释跨人体组织的细胞衰老网络 (SenNet)。并与 SenNet 数据库实现数据共享并协调协议和分析管道。允许用户查看和下载SC签名，并为去识别化提供受控访问系统个体水平的单核表达、成像和蛋白质组数据该资源也将为许多人服务。整个联盟的其他类型的分析。