Biological Analysis Core

生物分析核心

基本信息

批准号：
10552988
负责人：
Nathan K LeBrasseur
金额：
$ 158.7万
依托单位：
UNIVERSITY OF MINNESOTA
依托单位国家：
美国
项目类别：
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/10552988
关键词：
3 year old Adipocytes Adipose tissue Age Aging Amino Acid Sequence Animal Model 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 FVB/N Mouse Funding Gene Expression Generations Genes Genetic Genome Genomics Goals Hepatocyte Heterogeneity Human Hybrids Image Inbreeding Infrastructure Institutes Ions Laboratories Liver 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 Play 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 atlas Tissues Transgenic Mice Universities Wild Type Mouse aged analytical tool animal tissue base biomarker discovery brain tissue cell killing cell preparation cell type cohesion cytokine digital experience frailty healthspan imaging facilities improved in vivo innovation instrumentation member mortality nano-string new technology novel preservation progenitor proteogenomics resilience scale up senescence single-cell RNA sequencing spatiotemporal therapeutic target tissue mapping tool transcriptomics wound healing

项目摘要

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。 SNC 还在正常生理学中发挥作用，例如伤口愈合。目前尚不清楚SNC在何时何地出现随着年龄的增长，异源SNC在体内的方式以及如何最好地识别它们及其在生理中的作用 vs.病理学，尤其是在人类中。中西部鼠组织映射中心（MM-TMC）的目标生物分析核心（BAC）是要利用小鼠作为模型生物的效用来映射SNC，这将有助于告知Sennet正在开发的人类SNC地图集。我们建议验证，优化和应用最先进的方法，用于批量和单细胞表征和空间时间分析在两个遗传背景中，健康小鼠组织中的SNC在各种年龄范围内。 MM-TMC BAC将聚焦关于脂肪，骨骼肌，肝脏，大脑和肺组织，来自近交C57BL/6J和F1杂种（C57BL/6J：FVB/N）老鼠。 BAC生成的数据将被传递到数据分析核心（DAC）以进行集成以开发五个组织的SNC地图集。 BAC将由Nathan Lebrasseur领导，该识别专家在小鼠和人类中的骨骼肌和肺中SNC的表征，以及生物标志物的发现；保罗罗宾斯（Robbins），鼻溶性开发专家；和SNC研究专家Laura Niedernhofer 转基因小鼠。这三个MPI是由Pi Sundeep Khosla总体领导的P01的一部分，该Pi Sundeep Khosla是发展的。表征并利用允许在特定器官或细胞类型，报告SNC驱动基因P16INK4A或P21CIP1的报告表达，或专门杀死表达这些的细胞基因。这些小鼠将是Sennet中的重要工具，用于映射工作和验证问题以检测SNC。 BAC分析工作流将基于Mayo诊所和明尼苏达大学的现有核心内（UMN）保证稳定的基础设施和高质量控制标准：UMN成像中心， UMN基因组学中心，Mayo Cytof Core，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州超级计算研究所。这些核心包含状态 - 可用于映射SNC的艺术仪器：离子Pather多路复用离子束质量成像，森林空间基因表达和纳米弦Geomx数字空间分析。此外，CMSP将使用蛋白质生成方法将新型SNC特异性蛋白序列鉴定为生物标志物。这些独特资源以及MPI的专业知识，对于建造4D组织地图集将是有价值的。从广义上讲，bac 提案：1）建立可再现，经过验证和定量测定的管道，以检测和表征整个组织和单细胞制剂中的SNC； 2）使用原代小鼠细胞作为受控模型验证分析工具，研究SNC随着时间的流逝的演变以及识别新型SNC生物标志物； 3）扩展数据生成管道并结合新兴技术； 4）执行时空分析五个组织中的SNC，使DAC能够生成4D SNC地图集。