WU-SN-TMC Bio-Analysis Core

生物学分析核心摘要/摘要 衰老与许多人类疾病和模式生物中衰老细胞的消除有关 减缓，在某些情况下逆转许多疾病。这些发现给我们带来了希望， 对衰老的理解将导致治疗人类疾病的新疗法。尽管重要的是 细胞衰老，我们缺乏可靠的方法来识别，分离和操纵衰老细胞。新 方法和技术使我们的团队能够全面识别衰老细胞。反过来，这将 能够深入分析衰老细胞的生物标志物、空间分布、行为和功能， 人组织和时间。生物分析核心（BAC）的主要目标 在华盛顿大学衰老绘图中心（WU-SN-TMC）内， 成像数据，以建立跨组织类型的细胞衰老的高分辨率、多维图谱， 生命周期，以促进基础和转化研究。尽管衰老细胞被几种已知的 随着标记物的增加，越来越清楚的是，并非所有的衰老细胞都表达相同的标记物组， 可能是有限的。静态通用组织不可知衰老标记物可能不 存在.相反，可能存在动态衰老特征（基因表达模式由频繁， 但是一些“已知”标记和其他尚未鉴定的标记的非强制性存在），这可能与 细胞类型年龄和环境刺激。我们建议在必要时使用最先进的技术 在绘制各种组织中的衰老细胞时识别这些特征的尺度、深度和分辨率 不同年龄的环境。我们将绘制四种组织中的衰老细胞及其相关表型 (bone骨髓、乳腺、肝脏和结肠），根据其临床重要性、多样性、与 衰老和可用性。我们将利用我们为几个国家开发的非常成功的基础设施， 以大型项目为跳板，结合衰老研究方面的内部专业知识， 具有转录组范围覆盖、单细胞分辨率和空间配准的细胞衰老图谱。 我们将利用我们在单细胞组学方面的经验，建立一个全面的表观遗传学和转录组学。 靶组织中的景观。我们将添加空间维度来构建2D和3D分子图谱， 整合了空间转录组学、CODEX和光片显微镜。我们还将使用蛋白质组学来研究 衰老细胞的生物标志物和分泌组。为了验证我们的结果，我们将使用现有的和新的 开发了小鼠模型来跟踪不同年龄的衰老细胞，有或没有扰动。这些研究 这将使我们能够改进衰老细胞的检测方法，并验证衰老细胞中鉴定的生物标志物特征。 人类研究BAC将与WU-SN-TMC内的其他核心密切合作， 以前所未有的细胞分辨率和基因覆盖率绘制分子和细胞衰老组织图谱， 尽可能高的效率。

Biological Analysis Core Summary/Abstract Senescence is implicated in many human diseases and the elimination of senescent cells in model organisms slows, and in some cases reverses many diseases. These findings raise hope that a detailed mechanistic understanding of senescence will lead to novel therapies to treat human disease. Despite the importance of cellular senescence, we lack robust methods to identify, isolate, and manipulate senescent cells. New approaches and technologies position our team to comprehensively identify senescent cells. This, in turn, will enable in-depth analyses of biomarkers, spatial distribution, behavior, and function of senescent cells across human tissues and time in a high throughput manner. The primary goal for the Biological Analysis Core (BAC) within Washington University Senescence Mapping Center (WU-SN-TMC) is to generate multi-omics and imaging data to build high resolution, multi-dimensional atlases of cellular senescence across tissue types and lifespan to facilitate basic and translational research. Although senescent cells are tracked by several known markers, it is increasingly clear that not all senescent cells express the same set of markers and the usefulness of individual senescent markers may be limited. Static universal tissue agnostic senescence markers may not exist. Instead, there are likely dynamic senescence signatures (gene expression patterns anchored by frequent, but unobligated presence of some of the “known” markers and others yet to be identified), that may differ with cell type, age, and environmental stimuli. We propose using cutting edge enabling technologies at necessary scale, depth, and resolution to identify such signatures while mapping senescent cells in various tissue environments at different ages. We will map senescent cells and their associated phenotypes in four tissues (bone marrow, breast, liver, and colon), chosen for their clinical importance, diversity, relevance to senescence, and availability. We will utilize the highly successful infrastructure we have developed for several large-scale projects as a springboard, combined with in-house expertise in senescence research, to generate cellular senescence atlases with transcriptome-wide coverage, single cell resolution, and spatial registration. We will use our experience in single cell omics to establish a comprehensive epigenetic and transcriptomic landscape in targeted tissues. We will add the spatial dimension to build 2D and 3D molecular atlases by incorporating spatial transcriptomics, CODEX, and light sheet microscopy. We will also use proteomics to study the biomarkers and secretome of senescent cells. To validate our results, we will use established and newly developed mouse models to track senescent cells at different ages with or without perturbations. These studies will allow us to improve detection methods for senescent cells and validate biomarker signatures identified in human studies. BAC will work closely with the other Cores within WU-SN-TMC to produce multi-dimensional molecular and cellular senescence tissue atlases at unprecedented cellular resolution and gene coverage with the highest efficiency possible.