Biological Analysis Core

生物分析核心

• 批准号: 10675012
• 负责人: Nathan K LeBrasseur
• 金额: $ 147.82万
• 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/10675012
• 关键词: 3 year old; Ablation; Adipocytes; Adipose tissue; Age; Aging; Amino Acid Sequence; Animals; Architecture; Astrocytes; Atlases; Attenuated; Automobile Driving; Biological; Biological Assay; Biological Markers; Biological Models; Biology; Birth; Brain; CDKN2A gene; Cell Aging; Cells; Chromatin; Chronic Disease; Clinic; Collaborations; Cre driver; Data; Data Analyses; Data Set; Detection; Development; Disease; Electronic Health Record; Emerging Technologies; Ensure; Epigenetic Process; Evolution; Funding; Gene Expression; Generations; Genes; Genetic; Genome; Genomics; Goals; Hepatocyte; Heterogeneity; Human; Hybrids; Image; Inbreeding; Infrastructure; Ions; Laboratories; Liver; Longevity; Lung; Management Information Systems; Manuscripts; Maps; Mass Spectrum Analysis; Methodology; Methods; Microglia; Midwestern United States; Minnesota; Modeling; Morbidity - disease rate; Mouse Strains; Mus; Muscle; Muscle Cells; Neurons; Organ; Pathology; Pattern; Pharmaceutical Preparations; Phenotype; Physiology; Population; Proteomics; Publishing; Quality Control; Reporting; Reproducibility; Resources; Role; Sampling Errors; Scientist; Sensitivity and Specificity; Site; Skeletal Muscle; Structure of parenchyma of lung; Supercomputing; Technology; Time; Tissue Banks; Tissue Sample; Tissue atlas; Tissues; Transgenic Mice; Universities; Wild Type Mouse; aged; analytical tool; biomarker discovery; brain tissue; cell killing; cell preparation; cell type; cytokine; detection assay; digital; experience; frailty; healthspan; imaging facilities; improved; in vivo; innovation; instrumentation; member; model organism; mortality; nano-string; new technology; novel; preservation; progenitor; promote resilience; proteogenomics; scale up; senescence; single-cell RNA sequencing; skeletal tissue; spatiotemporal; therapeutic target; tissue mapping; tool; transcriptomics; wound healing

PROJECT SUMMARY Senescent cells (SnCs) accumulate with age and contribute to morbidity and mortality in model systems. SnCs also play a role in normal physiology, e.g., wound healing. Currently it is unclear when and where SnCs arise in tissues with age, how heterogenous SnCs are in vivo, and how to best identify them and their role in physiology vs. pathology, especially in humans. The goal of the Midwest Murine-Tissue Mapping Center (MM-TMC) Biological Analysis Core (BAC) is to leverage the utility of the mouse as a model organism to map SnCs, which will help inform the human SnC atlases under development by SenNet. We propose to validate, optimize, and apply state-of-the-art methodologies for bulk and single cell characterization and spatiotemporal analysis of SnCs in healthy mouse tissues over a range of ages in two genetic backgrounds. The MM-TMC BAC will focus on adipose, skeletal muscle, liver, brain, and lung tissues from inbred C57BL/6J and f1 hybrid (C57BL/6J:FVB/n) mice. The data generated by the BAC will be delivered to the Data Analysis Core (DAC) for integration to develop SnC atlases for the five tissues. The BAC will be led by Nathan LeBrasseur, an expert in the identification and characterization of SnCs in skeletal muscle and lung in mice and humans, and in biomarker discovery; Paul Robbins, an expert in senolytic development; and Laura Niedernhofer, an expert in the study of SnCs in transgenic mice. The three MPIs are part of a P01 led by Overall PI Sundeep Khosla, which develops, characterizes, and utilizes innovative transgenic mice that permit the induction of SnCs in a particular organ or cell type, report expression of the SnC-driving genes p16Ink4a or p21Cip1, or specifically kill cells expressing those genes. These mice will be important tools in SenNet for mapping efforts and validating probes to detect SnCs. The BAC analytical workflow will be based within existing cores at Mayo Clinic and University of Minnesota (UMN) to guarantee a stable infrastructure and high quality control standards: the UMN Imaging Centers, the UMN Genomics Center, Mayo CyTOF Core, the UMN Center for Mass Spectrometry and Proteomics (CMSP), the UMN Cytokine Reference Laboratory, and Minnesota Supercomputing Institute. These cores contain state- of-the-art instrumentation available for mapping SnCs: Ionpath Multiplexed Ion Beam Mass Imaging, Visium Spatial Gene Expression, and NanoString GeoMx Digital Spatial Profiling. In addition, the CMSP will use a proteogenomic approach to identify novel SnC-specific protein sequences as biomarkers. These unique resources, together with the MPIs’ expertise, will be valuable for building the 4D tissue atlases. Broadly, the BAC proposes to: 1) Establish a pipeline of reproducible, validated, and quantitative assays to detect and characterize SnCs in whole tissues and single cell preparations; 2) Use primary mouse cells as a controlled model for validating analytical tools, studying the evolution of SnCs over time, and identifying novel SnC biomarkers; 3) Scale-up the data generation pipeline and incorporate emerging technologies; and 4) Perform spatiotemporal analysis of SnCs in the five tissues to enable the DAC to generate 4D SnC atlases.

项目摘要 衰老细胞（SnC）随着年龄的增长而积累，并导致模型系统中的发病率和死亡率。SnCs 也在正常生理学中发挥作用，例如，伤口愈合目前尚不清楚SnCs何时何地出现 随着年龄的增长，SnC在体内的异质性如何，以及如何最好地识别它们及其在生理学中的作用 vs.病理学，尤其是人类。中西部小鼠组织绘图中心（MM-TMC）的目标 生物分析核心（BAC）是利用小鼠作为模式生物的效用来绘制SnCs， 将有助于为SenNet正在开发的人类SnC图谱提供信息。我们建议验证、优化和 应用最先进的方法进行批量和单细胞表征和时空分析， 两种遗传背景下不同年龄段健康小鼠组织中的SnC。MM-TMC BAC将专注于 对来自近交系C57 BL/6 J和f1杂种（C57 BL/6 J：FVB/n）的脂肪、骨骼肌、肝脏、脑和肺组织的影响 小鼠BAC生成的数据将交付给数据分析核心（DAC）进行集成，以开发 五种组织的SnC图谱。BAC将由Nathan LeBrasseur领导，他是鉴定专家， 小鼠和人类骨骼肌和肺中SnCs的表征，以及生物标志物的发现; Paul Robbins，senolytic发展专家; Laura Niedernhofer，SnCs研究专家， 转基因小鼠这三个MPI是由总体PI Sundeep Khosla领导的P01的一部分，该公司开发， 表征并利用允许在特定器官中诱导SnC的创新转基因小鼠，或 细胞类型，报告SnC驱动基因p16 Ink 4a或p21 Cip 1的表达，或特异性杀死表达这些基因的细胞 基因.这些小鼠将成为SenNet中的重要工具，用于映射工作和验证探针以检测SnC。 BAC分析工作流程将基于马约诊所和明尼苏达大学的现有核心 (UMN)为了保证稳定的基础设施和高质量的控制标准：UMN成像中心， UMN基因组学中心，马约CyTOF核心，UMN质谱和蛋白质组学中心（CMSP）， UMN细胞因子参考实验室和明尼苏达超级计算研究所。这些核心包含国家- 最先进的仪器可用于绘制SnCs：离子路径多路复用离子束质量成像，Visium 空间基因表达和NanoString GeoMx数字空间分析。此外，CMSP将使用 蛋白基因组学方法鉴定新的SnC特异性蛋白序列作为生物标志物。这些独特 资源，加上MPI的专业知识，将是宝贵的建立4D组织图谱。总的来说，BAC 建议：1）建立一个可重复的、经验证的和定量的检测管道，以检测和表征 全组织和单细胞制备物中的SnC; 2）使用原代小鼠细胞作为对照模型， 验证分析工具，研究SnC随时间的演变，并鉴定新的SnC生物标志物; 3） 扩大数据生成管道并纳入新兴技术;以及4）执行时空 分析五种组织中的SnC以使DAC能够生成4D SnC图谱。