Biological Analysis Core

生物分析核心

• 批准号: 10385165
• 负责人: Paul D. Robbins
• 金额: $ 82.8万
• 依托单位: UNIVERSITY OF MINNESOTA
• 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/10385165
• 关键词: Biological; Analysis; Core

Project Summary Senescent cells (SnCs) accumulate with age and contribute to driving morbidity and mortality in model systems. SnCs also play a role in normal physiology – for example, wound healing. It is currently unclear when and where SnCs arise in tissues as we age, how heterogenous SnCs are in vivo, and how to best identify them, especially in humans. The overall goal of the Minnesota Tissue Mapping Center (MN TMC) Biological Analysis Core (BAC) is to validate, optimize, and apply state-of-the-art methods for bulk and single cell characterization and spatio- temporal analysis of SnCs in healthy human tissues over a range of ages. The MN BAC will focus on adipose, skeletal muscle, liver, and ovarian tissues from healthy humans, which will be provided by the Biospecimen Core (BSP). The analytical data generated by the BAC will be delivered to the Data Analysis Core (DAC) via a web portal for integration with health data to develop 4D SnC tissue atlases and, eventually, models/predictions of SnC health impact. The BAC will be co-directed by Paul Robbins, an expert in characterizing SnCs and in the development of senolytics, and Andrew Nelson, a board-certified anatomic and molecular pathologist with extensive experience in bulk and single cell analysis of human tissue. The BAC analytical workflow will be based entirely within existing cores and institutes at UMN to guarantee stable infrastructure and high quality control standards: the University Imaging Centers (directed by Mark Sanders), the UMN Genomics Center (directed by Kenny Beckman), the Center for Mass Spectrometry and Proteomics (CMSP, directed by Tim Griffin) and multiple labs at Mayo Clinic in Minnesota (coordinated by Nathan LeBrasseur). State-of-the-art technologies to be applied to mapping SnCs include digital droplet PCR, single cell and single nucleus RNAseq, tissue clearing, RNAScope, CyTOF, 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. The BAC will also model early and deep senescence in vitro using induced pluripotent stem cells (iPSCs) differentiated into hepatocytes, cholangiocytes, granulosa cells, and myogenic and adipocyte progenitors. These cells will be induced to undergo senescence by a variety of stressors to validate SnC probes, identify new SnC biomarkers, and characterize the evolution of senescence over time. Broadly, the BAC proposes to: 1) Establish a pipeline of reproducible, validated, and quantitative assays to detect and characterize SnCs in bulk tissues and single cell preparations; 2) Use iPSC-derived differentiated cells of multi-lineages as a controlled model for validating analytical tools and expanding the repertoire of SnC biomarkers; 3) Scale-up the data generation pipeline and incorporate emerging technologies; and 4) Perform spatiotemporal analysis of SnCs in the four tissues in order for the DAC to generate a 4D atlas of SnCs.

项目摘要 衰老细胞（SNC）随着年龄的增长而积累，并有助于模型系统中的发病率和死亡率。 SNC在正常生理学中也起作用 - 例如，伤口愈合。目前尚不清楚何时何地 随着年龄的增长，SNC在组织中出现，异源SNC在体内以及如何最好地识别它们，尤其是 在人类中。明尼苏达州组织映射中心（MN TMC）生物分析核心（BAC）的总体目标 是为了验证，优化和应用最先进的方法，以进行批量和单细胞表征和空间 在一系列年龄段的健康人体组织中SNC的临时分析。 MN BAC将专注于脂肪， 健康人的骨骼肌，肝脏和卵巢组织 （BSP）。 BAC生成的分析数据将通过Web传递到数据分析核心（DAC） 与健康数据集成以开发4D SNC组织地图集的门户，并最终是模型/预测 SNC健康影响。 BAC将由Paul Robbins共同指导，Paul Robbins是SNC的特征和 Senolotics的发展和Andrew Nelson，是董事会认证的解剖学和分子病理学家 在人体组织的批量和单细胞分析方面的丰富经验。 BAC分析工作流将基于 完全在UMN的现有核心和机构中，以确保稳定的基础设施和高质量控制 标准：大学成像中心（由Mark Sanders执导），UMN基因组中心（由 肯尼·贝克曼 明尼苏达州梅奥诊所的多个实验室（由Nathan Lebrasseur协调）。最先进的技术 应用于映射SNC包括数字液滴PCR，单细胞和单核RNASEQ，组织清除， rnascope，cytof，离子Pather多路复用离子梁质量成像，visium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来识别 新型SNC特异性蛋白序列作为生物标志物。 BAC还将在 体外使用诱导多能干细胞（IPSC）分化为肝细胞，胆管细胞，颗粒 细胞以及肌原性和脂肪细胞祖细胞。这些细胞将被诱导通过一种多种 验证SNC问题的压力源，识别新的SNC生物标志物并表征衰老的演变 随着时间的推移。从广义上讲，BAC提出的提案：1）建立可再现，验证和定量的管道 检测和表征散装组织和单细胞制剂中SNC的测定； 2）使用IPSC衍生 多边形的分化细胞作为验证分析工具并扩展的受控模型 SNC生物标志物的曲目； 3）扩大数据生成管道并结合新兴技术； 4）对四个组织中的SNC进行空间临时分析，以使DAC产生4D地图集 SNC。