Biological Analysis Core

项目摘要 衰老细胞(SNC)随着年龄的增长而积累，并在模型系统中导致发病率和死亡率的增加。 SNCs也在正常生理中发挥作用--例如，伤口愈合。目前尚不清楚时间和地点。 随着我们年龄的增长，SNCs在组织中的出现，SNCs在体内的异质性如何，以及如何最好地识别它们，尤其是 在人类身上。明尼苏达州组织测绘中心(MN TMC)生物分析核心(BAC)的总体目标 是验证、优化和应用最先进的方法来表征整体和单个细胞以及空间- 不同年龄范围的健康人体组织中SNCs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Biospecimen Core提供 (BSP)。由BAC生成的分析数据将通过网络传送到数据分析核心(DAC) 与健康数据集成的门户，以开发4D SNC组织图谱，并最终建立模型/预测 对SNC健康的影响。BAC将由保罗·罗宾斯共同执导，保罗·罗宾斯是表征SNC的专家，并在 和安德鲁·纳尔逊，一位董事会认证的解剖和分子病理学家， 在人体组织整体和单细胞分析方面有丰富的经验。BAC分析工作流程将基于 完全在UMN的现有核心和机构内，以确保稳定的基础设施和高质量控制 标准：大学成像中心(由马克·桑德斯指导)、UMN基因组中心(由 Kenny Beckman)、质谱学和蛋白质组学中心(CMSP，由Tim Griffin指导)和 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SNC的定位包括数字液滴聚合酶链式反应、单细胞和单核RNAseq、组织清除、 RNAScope、CyTOF、IonPath多路复用离子束质量成像、紫色空间基因表达和 纳米串GeoMx数字空间剖面图。此外，CMSP将使用蛋白质基因组学方法来鉴定 作为生物标志物的新的SNC特异性蛋白质序列。BAC还将在#年模拟早期和深度衰老 体外诱导多能干细胞分化为肝细胞、胆管细胞、颗粒细胞 细胞、成肌祖细胞和脂肪祖细胞。这些细胞会被一种不同的 以验证SNC探针，识别新的SNC生物标记物，并表征衰老的演变 随着时间的推移。大体上，BAC建议：1)建立可重复、有效和定量的 检测和表征散装组织和单细胞制剂中的SNCs的方法；2)使用IPSC来源的 多谱系分化细胞作为验证分析工具和扩展 SNC生物标志物曲目；3)扩大数据生成管道并采用新兴技术； 以及4)对四个组织中的SNCs进行时空分析，以便DAC生成4D图谱 SNC的。

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.