Biological Analysis Core

生物分析核心（BAC）的目标是应用多模态单细胞和成像技术 为了开发小鼠组织衰老图谱，在雄性和雌性小鼠的整个寿命期间， 多种菌株我们的老龄化和生物信息学专家团队拥有广泛的基础设施和独特的专业知识 这是理想的适合建立一个全面的地图鼠细胞衰老，包括其调节，细胞 表现，以及对组织环境内细胞通讯的影响。为了实现这一目标，我们将开发 使用基因组、成像和计算方法进行组织分析的集成管道， 衰老与单细胞和/或空间背景。我们将利用NIA小鼠纵向衰老研究 （SLAM）多品系小鼠衰老项目、转基因报告基因和衰老耗竭小鼠，以及广泛的 单细胞数据集结合新的迁移学习，以产生跨组织的SnCs识别， 血浆/血清和多种选择的组织（由于SnC的流行，它们与衰老的关联， 小鼠中的相关病理学，以及它们在人类SenNet计划中的纳入）。我们提议下列 特定目的，包括筛选和深入表征小鼠中SnC的多层次方法 在他们的生命周期中： 目的1：建立衰老小鼠和转基因动物模型的多模态分析，鉴定细胞衰老 以及它们对组织生理学的影响。 目的2：生成小鼠衰老的单细胞转录组和表观基因组测序图谱， 揭示组织特异性SnC身份。 目的3：使用成像确定组织中SnC对其邻近细胞生态系统的影响- 基于表型和空间衰老作图。

The goal of the Biological Analysis Core (BAC) is to apply multi-modal single-cell and imaging technologies toward developing an Atlas of Senescence in Murine Tissues in male and female mice across the lifespan in multiple strains. Our team of aging and bioinformatics experts have extensive infrastructure and unique expertise that is ideally suited to build a comprehensive map of murine cellular senescence, including its regulators, cellular manifestations, and impact on cell communication within the tissue environment. To achieve this, we will develop an integrated pipeline for tissue analysis using genomic, imaging, and computational methods to characterize senescence with single-cell and/or spatial context. We will leverage the NIA Study of Longitudinal Aging in Mice (SLAM) multi-strain mouse aging project, transgenic reporter and senescence-depleted mice, and extensive single-cell datasets combined with novel transfer learning to yield cross-tissue identification of SnCs in plasma/serum and multiple select tissues (chosen due to the prevalence of SnCs, their association with aging- related pathologies in mice, and their inclusion in the Human SenNet Program). We propose the following Specific Aims, which encompass a multi-tiered approach to screen and deeply characterize SnCs in murine tissues across their lifespan: Aim 1: Establish multi-modal profiling of aging mice and transgenic models to identify cell senescence in murine tissues across lifespan and their impact on tissue physiology. Aim 2: Generate single-cell transcriptomic and epigenomic sequencing atlases of mouse aging to reveal tissue-specific SnC identities. Aim 3: Determine the impact of SnCs in tissues on their neighboring cell ecosystems using imaging- based phenotypic and spatial senescent mapping.