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组织映射中心（TMC）生物分析核心将负责产生高 分辨率和高含量数据集，以定义不陈年的感官细胞及其微环境 人体组织，并测量此类细胞如何比较一系列年龄。我们将利用最先进的 分解组织和完整组织切片上的单细胞技术研究该生物学。我们将 与我们的Kapp-Sen Biospecimen Core协调以获得高质量的人类正常肾脏，胰腺， 胎盘和脂肪组织。通过采用公正的基于测序的单细胞分辨率方法，我们将 生成高含量的空间分辨数据，以识别感觉细胞。我们将与 我们的KAPP-SEN数据分析核心核心，以发现人类感觉细胞的全面mRNA生物标志物。一个 将在高较高的组织切片中检测到从这些生物标志物中得出的目标表位的选择 使用高度多重抗体成像方法的分辨率（1 µM）。在 人体组织和离体和诱导的多能干细胞（IPSC）模型将进一步告知和验证 在完整的人体组织中，感应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的实现其目标： 目标1。建立最佳组织解离和制备技术以实施分离和 用于鉴定人类组织中感觉细胞的空间分辨单细胞转录组方法。 目标2。缩放和标准化管道以产生高质量，高分辨率和高通量 数据集和构造四个目标时序中细胞感应的地图。 目标3。鉴定人类感觉细胞的mRNA生物标志物并构建和应用多重抗体 来自这些的面板。 目标4。利用离体模型进一步表征了感觉细胞的功能特征。 总之，这种分析方法将在1 µm分辨率下定义感应的全面组织特征 并开始发现感觉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有关感应相关的分泌蛋白的见解，这些蛋白可能会告知 感染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 在各种各样的人体组织中。