Midwest Murine-Tissue Mapping Center (MM-TMC)

中西部鼠组织绘图中心 (MM-TMC)

• 批准号: 10552986
• 负责人: David A Bernlohr
• 金额: $ 270万
• 依托单位: UNIVERSITY OF MINNESOTA
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-08-02 至 2026-07-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10552986
• 关键词: Ablation; Adipose tissue; Aging; Alzheimer' s Disease; Architecture; Atlases; Attenuated; Award; Bioinformatics; Biological; Biological Markers; Biological Process; Biology; Birth; Brain; Cell Aging; Cells; Cellular biology; Chromatin; Clinic; Clinical Trials; Collaborations; Collection; Communication; Communities; Complex; Core Facility; DNA Methylation; Data; Data Analyses; Data Collection; Data Coordinating Center; Data Discovery; Data Science; Data Set; Detection; Disease; Embryonic Development; Equipment; Evolution; Excision; FAIR principles; Gene Expression; Generations; Genetic; Genome; Genomics; Goals; Human; Human Resources; Immune system; Immunology; Immunotherapy; Infrastructure; Institutes; Institution; International; Knowledge; Label; Lead; Link; Liver; Location; Longevity; Lung; Maps; Measurement; Measures; Metadata; Methods; Midwestern United States; Minnesota; Modeling; Molecular; Multiplexed Ion Beam Imaging; Mus; Muscle; Organ; Organism; Pathologic; Pathology; Pharmaceutical Preparations; Pharmacology; Phenotype; Physiological; Physiology; Play; Procedures; Production; Proteomics; Quality Control; Recording of previous events; Research; Research Personnel; Role; Skeletal Muscle; Structure of parenchyma of lung; Technology; Time; Tissue Sample; Tissues; Transgenic Animals; Transgenic Mice; Tumor Suppression; United States National Institutes of Health; Universities; Visualization; Visualization software; Wild Type Mouse; Work; age related; aged; aging population; base; brain tissue; cohesion; data management; data modeling; data sharing; digital; experience; extracellular; frailty; imaging informatics; improved; innovation; member; mouse development; mouse model; nano-string; novel; pathology imaging; phenotypic biomarker; quality assurance; reagent standard; resilience; response; senescence; single cell analysis; stressor; success; technology development; tissue mapping; wound healing

PROJECT SUMMARY Senescent cells (SnCs) play a causal role in aging and numerous age-related diseases. However, they also contribute to beneficial biology such as wound healing and tissue remodeling. Both physiological and pathological roles are linked to the secretome of SnCs and their complex interaction with the immune system, which is thought to play an important role in clearing SnCs. Much of what has been learned about SnCs is derived from studies in mice, where it has been clearly demonstrated that genetic or pharmacologic removal of SnCs in aged or diseased organisms reduces frailty; improves strength, endurance, and resilience; and attenuates a variety of age-related diseases, including Alzheimer’s. Discovering pharmacologic approaches to remove disease-causing SnCs in humans could have a tremendous impact on our aging population. However, much needs to be learned about SnCs to deploy such approaches safely and effectively in humans. This project aims to establish a Tissue Mapping Center (TMC) across multiple institutions with demonstrated expertise in SnCs and cell mapping to achieve a common goal; construction of a 4D atlas of SnCs in mouse tissues. The Midwest Murine-TMC (MM-TMC) proposes to focus on 5 key tissues: mouse liver, adipose, lung, muscle, and brain. This selection is based on MM-TMC’s expertise in the biology, cell biology, and immunology of these organs; lengthy experience studying SnCs in these organs; and applying single cell and spatial technologies to study these organs. The MM-TMC and its Administrative Core will be led by PIs with complementary expertise in organ biology and senescence. The Biological Analysis Core will be led by three MPIs who have a long history of collaboration around studying SnCs in aged mice and working with unique and innovative transgenic animals that enable the production or ablation of lineage-specific SnCs. The Data Analysis Core will be led by two bioinformaticians with expertise in single cell analysis, cell mapping, and multiplexing complex and disparate data sets to deeply phenotype cells. Other key personnel add expertise in pathology, imaging, spatial mapping of SnCs, tissue clearing, transgenic mice, and development of senolytics and immunotherapies to target SnCs. A unique feature of the proposed MM-TMC is that the data collection will be done within existing cores largely at the University of Minnesota by staff with expertise in state-of-the art spatial mapping platforms. This provides stability in the analytical pipeline and in-place quality control and assurance mechanisms. Through systematic and methodical study of SnCs in mouse tissues, the MM-TMC will make significant contributions informing and validating the SenNet human atlas and will work closely with NIH, other TMCs, Technology Development awardees, and the Consortium Organization and Data Coordinating Center to develop and adhere to standards created by SenNet to accelerate the production of rigorous SnC tissue maps in both species.

项目概要 衰老细胞 (SnC) 在衰老和许多与年龄相关的疾病中发挥着因果作用。 有助于有益的生物学，例如伤口愈合和组织重塑。 病理作用与 SnCs 的分泌组及其与免疫系统的复杂相互作用有关， 人们认为这在清除 SnC 方面发挥着重要作用，我们对 SnC 的了解很多都是源自于此。 小鼠研究已清楚地证明，通过基因或药物方法去除 SnC 衰老或患病会降低机体的虚弱程度；提高力量、耐力和恢复能力； 探索消除各种与年龄相关的疾病，包括阿尔茨海默病。 人类中致病的 SnC 可能会对人口老龄化产生巨大影响。 需要了解 SnC 才能在人体中安全有效地部署此类方法。 跨多个具有 SnC 领域专业知识的机构建立组织作图中心 (TMC) 和细胞图谱以实现共同目标；构建小鼠组织中 SnC 的 4D 图谱。 Murine-TMC (MM-TMC) 建议重点关注 5 个关键组织：小鼠肝脏、脂肪、肺、肌肉和大脑。 选择基于 MM-TMC 在这些器官的生物学、细胞生物学和免疫学方面的专业知识； 研究这些器官中的 SnC 的经验；并应用单细胞和空间技术来研究这些 MM-TMC 及其行政核心将由在器官领域具有互补专业知识的 PI 领导。 生物分析核心将由三位具有悠久历史的 MPI 领导。 围绕研究老年小鼠的 SnCs 以及与独特和创新的转基因动物合作进行合作 数据分析核心将由两个人领导。 生物信息学家，在单细胞分析、细胞图谱以及复杂和不同的多重分析方面具有专业知识 其他关键人员增加了病理学、成像、空间绘图方面的专业知识。 SnCs、组织透明化、转基因小鼠以及针对 SnCs 的 senolytics 和免疫疗法的开发。 所提出的 MM-TMC 的一个独特之处在于，数据收集将在现有核心内完成，主要在 明尼苏达大学的工作人员在最先进的空间测绘平台方面拥有专业知识。 通过系统化的分析流程和到位的质量控制和保证机制的稳定性。 以及对小鼠组织中 SnC 的系统研究，MM-TMC 将做出重大贡献 验证 SenNet 人类图谱并将与 NIH、其他 TMC、技术开发密切合作 获奖者以及联盟组织和数据协调中心制定并遵守标准 由 SenNet 创建，旨在加速这两个物种中严格的 SnC 组织图的生成。