Bio-Analysis Core

生物分析核心

• 批准号: 10701913
• 负责人: GEORGE A KUCHEL
• 金额: $ 138.79万
• 依托单位: UNIVERSITY OF CONNECTICUT SCH OF MED/DNT
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-30 至 2026-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10701913
• 关键词: Adipocytes; Adipose tissue; Age; Aging; Antibodies; Biological; Biological Assay; Biological Markers; Biology; Blood; Cell Aging; Cells; Cellular biology; Collaborations; Cytometry; Data; Data Analyses; Data Set; Development; Dissociation; Drug Targeting; Drug usage; Ensure; Epitopes; Faculty; Formalin; Foundations; Gene Expression; Human; Human BioMolecular Atlas Program; Huntington gene; Image; Islet Cell; Kidney; Knowledge; Laboratories; Maps; Measures; Messenger RNA; Methods; Modeling; Molecular; Mus; Pancreas; Paraffin Embedding; Pharmaceutical Preparations; Placenta; Population; Preparation; Protein Secretion; RLK5-associated protein phosphatase; Research; Research Personnel; Resolution; Running; Standardization; System; Techniques; Tissue Embedding; Tissue Model; Tissues; Transcriptional Regulation; Work; aged; design; epigenomics; experience; experimental study; functional gain; high resolution imaging; human model; human tissue; imaging approach; induced pluripotent stem cell; insight; member; meter; mouse model; novel; response; senescence; single cell technology; single nucleus RNA-sequencing; single-cell RNA sequencing; stem cell model; success; tissue mapping; tool; transcriptome; transcriptomics; treatment response

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.

KAPP-Sen组织绘图中心（TMC）生物分析核心将负责生成高- 高分辨率和高内容的数据集来定义老年非疾病患者中的衰老细胞及其微环境 人体组织，并测量这些细胞在不同年龄段的比较情况。我们将利用最先进的 采用单细胞技术对解离组织和完整组织切片进行研究，以研究这种生物学。我们将 与我们的KAPP-Sen生物样本中心协调，获得高质量的人类正常肾脏，胰腺， 胎盘和脂肪组织。通过采用无偏的、基于测序的单细胞分辨率方法，我们将 产生高含量的空间分辨数据，从而能够识别衰老细胞。我们将与 我们的KAPP-Sen数据分析核心发现人类衰老细胞的全面mRNA生物标志物。一 将在组织切片中以高浓度检测来源于这些生物标志物的靶表位的选择 分辨率（1 µm），利用高度多重抗体成像方法。附加切线实验 人类组织和离体和诱导多能干细胞（iPSC）模型将进一步提供信息和验证 衰老的签名，并确定相关的表观基因组特征，在完整的人体组织。 生物分析核心将通过以下目标实现其目标： 目标1.建立最佳的组织分离和制备技术， 空间分辨的单细胞转录组方法用于鉴定人组织中的衰老细胞。 目标2.扩展和标准化管道以生成高质量、高分辨率和高吞吐量 数据集并构建四种靶组织中细胞衰老的地图。 目标3.鉴定人衰老细胞的mRNA生物标志物，构建并应用复合抗体 面板源自这些。 目标4。利用离体人体模型进一步表征衰老细胞的功能特征。 总之，这种分析方法将以1 μm的分辨率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，本发明还提供了一种方法， 产生的数据集将提供对衰老相关分泌蛋白的深入了解， 衰老的血液生物标志物的设计。总之，我们的方法及其相关工具将适用于 广泛的人体组织类型。