Bio-Analysis Core

Kapp-Sen组织测绘中心(TMC)生物分析核心将负责生成高 用于确定老年非疾病患者衰老细胞及其微环境的分辨率和高含量数据集 人体组织，并测量这些细胞在不同年龄段之间的比较。我们将利用最先进的技术 在分离的组织和完整的组织切片上应用单细胞技术来研究这种生物学。我们会 与我们的Kapp-Sen Biosecimen Core合作，获得高质量的人类正常肾、胰腺、 胎盘和脂肪组织。通过使用无偏见的、基于测序的单细胞解析方法，我们将 生成高含量的空间分辨率数据，以便能够识别衰老细胞。我们将与 我们的Kapp-Sen数据分析核心为人类衰老细胞发现全面的信使核糖核酸生物标记物。一个 从这些生物标志物衍生的目标表位的选择将在组织切片中被检测到 分辨率(1微米)采用高度多元化的抗体成像方法。中的附加切线实验 人体组织和体外和诱导多能干细胞(IPSC)模型将进一步告知和验证 在完整的人体组织中，识别衰老特征，并识别相关的表观基因组特征。 生物分析核心将通过以下目标实现其目标： 目的1.建立最佳的组织解离和制备技术，以实现解离和 用于鉴定人体组织中衰老细胞的空间分辨单细胞转录组方法。 目标2.扩展和标准化流水线，以产生高质量、高分辨率和高吞吐量 并构建四个目标组织中细胞衰老的数据集和构建图。 目的3.鉴定人衰老细胞的mRNA生物标志物，构建并应用多重抗体 从这些派生的面板。 目的4.利用体外人体模型进一步表征衰老细胞的功能特征。 总而言之，这种分析方法将在1微米分辨率下定义衰老的全面组织特征 并开始揭示衰老细胞的分子基础及其对治疗的反应。此外, 生成的数据集将提供对衰老相关分泌蛋白的洞察，这些蛋白可能会告诉 衰老血液生物标志物的设计。总之，我们的方法及其相关工具将是适用的 在各种各样的人体组织类型上。

The KAPP-Sen Tissue Mapping Center (TMC) Biological Analysis Core will be responsible for generating high- resolution and high-content datasets to define senescent cells and their microenvironment in aged non-diseased human tissues, and measure how such cells compare across a range of ages. We will utilize state-of-the-art single cell technologies on dissociated tissues and on intact tissue sections to study this biology. We will coordinate with our KAPP-Sen Biospecimen Core to obtain high-quality human normal kidney, pancreas, placenta, and adipose tissue. By employing unbiased, sequencing-based, single-cell resolution methods, we will generate high-content spatially resolved data to enable the identification of senescent cells. We will work with our KAPP-Sen Data Analysis Core to discover comprehensive mRNA biomarkers for human senescent cells. A selection of target epitopes derived from these biomarkers will be detected within tissue sections at high resolution (1 µm) utilizing a highly multiplex antibody imaging approach. Additional tangential experiments in human tissues and ex vivo and induced pluripotent stem cell (iPSC) models will further inform and validate senescence signatures, and identify associated epigenomic features, within intact human tissues. The Biological Analysis Core will achieve its objectives through the following Aims: Aim 1. To establish optimal tissue dissociation and preparation techniques to implement both dissociative and spatially-resolved single-cell transcriptome methods for the identification of senescent cells in human tissues. Aim 2. To scale and standardize the pipeline to generate high-quality, high-resolution, and high-throughput datasets and construct maps of cellular senescence in the four target tissues. Aim 3. To identify mRNA biomarkers of human senescent cells and construct and apply a multiplex antibody panel derived from these. Aim 4. Leverage ex vivo human models to further characterize the functional features of senescent cells. Together, this analytic approach will define the comprehensive tissue signature of senescence at 1 µm resolution and begin to uncover the molecular foundations of the senescent cell and its response to therapy. In addition, the data set generated will provide insight into senescence-associated secreted proteins that may inform the design of blood biomarker of senescence. Altogether, our approach and its associated tools will be applicable across a wide array of human tissues types.