Biological Analysis Core

项目摘要 衰老细胞（SnC）随着年龄的增长而积累，并有助于驱动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。目前尚不清楚何时何地 随着年龄的增长，SnC在组织中出现，体内SnC的异质性如何，以及如何最好地识别它们，特别是 在人类身上。明尼苏达组织绘图中心（MN TMC）生物分析核心（BAC）的总体目标 是验证，优化，并应用最先进的方法，为散装和单细胞表征和空间， 在一定年龄范围内健康人体组织中SnC的时间分析。MN BAC将专注于脂肪， 来自健康人的骨骼肌、肝脏和卵巢组织，将由生物样本中心提供 （BSP）。BAC生成的分析数据将通过网络交付给数据分析核心（DAC） 用于与健康数据集成的门户网站，以开发4D SnC组织图谱，并最终建立模型/预测 SnC健康影响。BAC将由Paul Robbins共同指导，他是描述SnCs的专家， 和安德鲁纳尔逊，一个委员会认证的解剖和分子病理学家， 在人体组织的大量和单细胞分析方面具有丰富的经验。BAC分析工作流程将基于 完全在UMN现有的核心和研究所内，以确保稳定的基础设施和高质量的控制 标准：大学成像中心（由马克桑德斯指导），UMN基因组学中心（由 Kenny Beckman），质谱和蛋白质组学中心（CMSP，由Tim Griffin指导）和 明尼苏达州马约诊所的多个实验室（由Nathan LeBrasseur协调）。最先进的技术， 应用于定位SnCs的方法包括数字液滴PCR，单细胞和单核RNAseq，组织清除， RNAScope、CyTOF、IonPath多路复用离子束质谱成像、Visium空间基因表达，以及 NanoString GeoMx数字空间轮廓。此外，CMSP将使用蛋白质基因组学方法来识别 新的SnC特异性蛋白质序列作为生物标志物。BAC还将模拟早期和深度衰老， 体外使用诱导多能干细胞（iPSC）分化成肝细胞、胆管细胞、颗粒细胞、 细胞以及肌原细胞和脂肪细胞祖细胞。这些细胞将被各种各样的 验证SnC探针，鉴定新的SnC生物标志物，并表征衰老的演变 随着时间广泛地说，BAC建议：1）建立一个可重复的，经验证的和定量的管道， 检测和表征大量组织和单细胞制备物中的SnC的测定; 2）使用iPSC衍生的 多谱系的分化细胞作为验证分析工具和扩展 3）扩大数据生成管道并纳入新兴技术; 以及4）对四种组织中的SnC进行时空分析，以便DAC生成4D图谱 的SnCs。

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.