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.

项目概要衰老细胞 (SnC) 随着年龄的增长而积累，并导致模型系统的发病率和死亡率。 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，组织、体内 SnC 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与病理学，尤其是人类中西部鼠组织图谱中心 (MM-TMC) 的目标。生物分析核心（BAC）是利用小鼠作为模型生物来绘制 SnCs 图谱，将有助于 SenNet 正在开发的人类 SnC 图集提供信息，我们建议对其进行验证、优化和改进。应用最先进的方法进行批量和单细胞表征以及时空分析 MM-TMC BAC 将重点关注两种遗传背景下不同年龄的健康小鼠组织中的 SnC。对近交 C57BL/6J 和 f1 杂种 (C57BL/6J:FVB/n) 的脂肪、骨骼肌、肝脏、脑和肺组织的影响 BAC生成的数据将被传送到数据分析核心（DAC）进行集成开发。这五种组织的 SnC 图集将由鉴定和鉴定专家 Nathan LeBrasseur 领导。小鼠和人类骨骼肌和肺中 SnC 的表征以及生物标志物的发现； Robbins，senolytic 开发专家；Laura Niedernhofer，SnCs 研究专家；这三种 MPI 是由总体 PI Sundeep Khosla 领导的 P01 的一部分，该项目开发了：描述并利用创新的转基因小鼠，允许在特定器官或细胞类型，报告 SnC 驱动基因 p16Ink4a 或 p21Cip1 的表达，或特异性杀死表达这些基因的细胞这些小鼠将成为 SenNet 中用于绘制地图和验证 SnC 探针的重要工具。 BAC 分析工作流程将基于梅奥诊所和明尼苏达大学的现有核心 (UMN) 确保稳定的基础设施和高质量控制标准：UMN 影像中心、 UMN 基因组学中心、Mayo 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- 可用于绘制 SnC 的最先进仪器：Ionpath 多重离子束质量成像、Visium 空间基因表达和 NanoString GeoMx 数字空间分析此外，CMSP 将使用蛋白质组学方法鉴定新的 SnC 特异性蛋白质序列作为这些独特的生物标志物。资源以及 MPI 的专业知识对于构建 BAC 的 4D 组织图谱非常有价值。建议： 1) 建立可重复、经过验证和定量的检测方法来检测和表征全组织和单细胞制剂中的 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 随着时间的推移的演变，并识别新型 SnC 生物标志物 3) 扩大数据生成管道并纳入新兴技术；4) 执行时空分析对五种组织中的 SnC 进行分析，使 DAC 能够生成 4D SnC 图集。