Mapping and validating senescent cells in human muscle, ovary and breast

生物分析核心--项目总结 巴克研究所组织映射中心(TMC)建议在三个人体和体细胞中映射衰老细胞 生殖组织；卵巢、乳房组织和骨骼肌。该领域的一个主要空白是定义 针对不同细胞和组织类型的特定细胞衰老标志物。我们建议通过定义以下内容来填补这一空白 人体组织衰老背景下的细胞衰老标记物。此核心的功能包括 深入了解衰老的多个方面，包括SASP、新的衰老解药和初步的 定义人体肌肉组织中衰老细胞的数据。在Biospecimen核心接收的组织将是 传送到生物分析核心，并接受旨在识别衰老的多个程序 细胞核或生物体液中的细胞标志(蛋白质或信使核糖核酸)，并在组织切片中得到证实。结果来自于 生物分析核心将被传达到数据分析核心，并通过 管理核心。生物分析核心将在空间上绘制并确定细胞的特征 健康人的卵巢、乳房和肌肉在整个衰老过程中的衰老 四个具体目标。1)确定大型衰老细胞的独特转录信号。我们将决定 大型衰老细胞的转录签名，在传统的单细胞工作流程中丢失 并使用这些数据来确定这种签名在乳房、卵巢和肌肉中的流行率。2)确定 乳房、卵巢和骨骼肌衰老的蛋白质特征。我们将全面分析 生物液分泌衰老相关分泌表型(SASP)蛋白及细胞培养 来自肌肉、乳房和卵巢的系统使用不同的衰老诱导剂和衰老剂。3)确定 乳房、卵巢和骨骼肌衰老的转录特征。我们将使用自举 在这一目标中，对来自组织的关键细胞类型的策略，以确定独特的单细胞衰老特征 从来自每个组织的原型细胞培养上的一系列衰老诱导剂衍生而来。我们将使用这些数据 为了使用SnRNA-seq将类似的签名映射回从完整组织导出的复杂的完全简档数据集， 单元格分配和表达式分析。4)确定衰老细胞的空间关系和频率 在组织切片中。我们将利用纳米串(数字空间剖面图)的新兴技术， 和10X技术(维特姆)来构建我们在前三个部分中发现的知识，目的是更好地理解 组织切片中衰老细胞的频率和亚型。与其他人一起 我们希望创建一个全面的空间地图和生殖组织衰老的特征 (乳房和卵巢)以及肌肉(一种体细胞组织)随年龄的性别差异和纵向差异。

BIOLOGICAL ANALYSIS CORE - PROJECT SUMMARY This Buck Institute Tissue Mapping Center (TMC) proposes to map senescent cells in three human somatic and reproductive tissues; ovaries, breast tissue and skeletal muscle. A major gap in the field has been to define specific cellular senescence markers for distinct cells and tissue types. We propose to fill this gap by defining markers of cellular senescence in the context of aging in human tissues. The capabilities of this core include a deep knowledge of multiple aspects of senescence encompassing the SASP, novel senolytics, and preliminary data defining senescent cells in human muscle tissue. Tissues received in the Biospecimen Core will be conveyed to the Biological Analysis Core and subjected to multiple procedures designed to identify senescent cell signatures (either protein or mRNA) in nuclei or biofluids, and confirmed in tissue sections. The results from the Biological Analysis Core will be conveyed to the Data Analysis Core, and coordinated through the Administrative Core. The Biological Analysis Core will spatially map and determine the signatures of cellular senescence in healthy human ovaries, breast, and muscle in both sexes across an aging continuum through four specific aims. 1) Determine the unique transcriptional signature of large senescent cells. We will determine the transcriptional signatures of large senescent cells, which are lost during conventional single cell workflows and use this data to determine the prevalence of such signatures in the breast, ovary, and muscle. 2) Determine senescent protein signatures of the breast, ovary, and skeletal muscle. We will comprehensively analyze secreted senescence-associated secretory phenotype (SASP) proteins from bio fluids, and cell culture systems from muscle, breast, and ovaries using different senescence inducers and senolytics. 3) Determine senescent transcriptional signatures of the breast, ovary, and skeletal muscle. We will use a bootstrapping strategy on key cell types from the tissues in this aim, to determine unique single cell senescent signatures derived from a range of senescent inducers on prototypical cell cultures from each tissue. We will use these data to map similar signatures back to complex fully profiled data sets derived from intact tissues using snRNA‐seq, cell assignment, and expression analysis. 4) Determine spatial relationship and frequencies of senescent cells in tissue sections. We will take advantage of emerging technologies from Nanostring (Digital Spatial Profiling), and 10X technologies (Visium) to build on our knowledge discovered in the first three aims to better understand frequency and subtypes of senescent cells within tissue sections from tissue sections. In conjunction with other cores we expect to create a comprehensive spatial map and signatures of senescence in reproductive tissues (breast and ovary) and also the sex-specific and longitudinal differences in muscle, a somatic tissue, with age.