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

• 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
• 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

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-项目总结 衰老细胞（SC）是长寿命的炎性细胞，其由暴露于某些细胞因子引起。 压力源已经发现这些细胞随着衰老和许多与年龄相关的慢性疾病而增加， 一些研究已经将SC功能与多种疾病表型机械地联系起来。新疗法 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 这些SC在组织中的分布。在动物模型中，senolytics可以延迟，预防或改善虚弱， 心血管病理学、神经精神疾病和肝、肾、肌肉骨骼、肺、眼， 血液学、代谢和皮肤疾病以及其他临床表型。的当前方法 定量组织中的SC负荷依赖于一些典型标志物，如p16、p21、SA-βgal等，但它们的 特异性仍然是有争议的，并且这些标记物很少在人组织中共表达。目前，我们 缺乏对健康人体组织中估计SC负荷的完整描述， 衰老在这里，我们的目标是使用不同的技术无偏见地表征来自不同人体组织的SC。 平台和先进的分析，以开发在人体组织中的干细胞的阿特拉斯。由于SC的积累， 被认为先于疾病表型，鉴定SC的强大诊断方法也将使早期 检测那些有患慢性病风险的人。没有可靠的诊断来估计SC负担， 人类组织，（1）评估靶向干细胞的治疗将继续成为衰老清除药物的瓶颈 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 人口健康寿命的持续缩短。在这个建议中，我们将构建分子和 人类组织驻留SC的形态图，并创建多种机制来共享这些 结果与科学界。组织映射中心的数据分析核心将利用其 能够无偏地分析人体组织和血液，以预测人体衰老细胞负荷， 组织中SC生物标志物图谱。为了实现我们的目标，我们的数据分析核心将提供管道， 数据处理，数据分析算法，构建和共享组织中人类SC的图谱，以及一般的 通过联合体组织和数据协调中心（CODCC）协调数据，并与 细胞衰老网络（SenNet）。我们将建立，策划和注释一个跨人体组织的SC图谱 实现数据共享，并与SenNet协调协议和分析管道。该数据库将 允许用户查看和下载SC签名，并提供一个受控访问系统， 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 整个财团的其他类型的分析。