Development of new tools for Single Cell Analysis

描述（由申请人提供）：单细胞分析变得越来越重要，因为很明显，集合测量掩盖了细胞群体中生物学的多样性。单细胞基因分型和表型分型对于定义不同细胞类型（包括病变细胞和正常细胞）的功能异质性是必要的。该项目的总体目标是开发用于进行高分辨率单细胞分析的新型生物分析工具。该项目涉及化学、分子生物学、生物化学、遗传学和生物工程等学科，由1名高级首席研究员、1名博士后和2名研究生组成。具体目标1：开发一种高保真的单DNA分子基因分型方法，用于同时分析数千个分子，并具有询问多个snp的能力。我们计划采用光纤微孔阵列来开发一种强大、高效、快速、经济的方法，同时从数千个单个细胞中对单个DNA分子进行基因分型。具体目标2：开发一种分离单细胞、捕获基因组DNA并对捕获的DNA进行基因分型的方法。在这个目标中，我们将把在Specific Aim 1中开发的方法应用于整个细胞。成千上万的单个大肠杆菌细胞将被捕获在单个孔中，裂解，基因组DNA片段化，DNA捕获在单个孔的表面，然后进行基因分型。具体目标3：建立一种高分辨率的单分子分析方法，用于分析单个细胞的内容物。我们将开发对单个细胞内容物进行高分辨率单分子计数的方法。单细胞将被分离、裂解，其内容物将被捕获在微孔阵列中，以便分离单个mRNA和蛋白质分子。然后，我们将使用各种方法来计算单个分子。这些目标的成功将提供一种强大的新技术，广泛适用于生物学研究的多个领域。对于生物学家来说，它提供了描述细胞群体复杂遗传学的能力，为临床医生提供了更好的诊断机会，并将群体遗传学与治疗反应和临床结果联系起来。对于实验工作者来说，它为研究肿瘤进展和对实验疗法的反应等现象提供了新的途径。对于研究衰老的研究人员来说，它提供了监测体细胞遗传学随年龄变化以及影响这些变化的因素的能力。对于遗传学家来说，它提供了低成本的能力来构建遗传图谱，识别控制重组的反式作用元件，并以高分辨率监测重组产物的分子结构。我们的长期目标是将已开发的技术应用于各种细胞类型，以研究基础细胞生物学，并了解细胞间和细胞群体差异如何导致不同的细胞命运并可能定义疾病。

DESCRIPTION (provided by applicant): Single cell analysis is becoming increasingly important as it is clear that ensemble measurements mask the diversity of the biology in cell populations. Single cell genotyping and phenotyping are necessary to define functional heterogeneity in varying cell types including diseased vs. normal cells. The overall goal of this project is to develop new bioanalytical tools for performing high resolution single cell analysis. This interdisciplinary project involves Chemistry, Molecular Biology, Biochemistry, Genetics, and Bioengineering and will be conducted by a team including a senior Principal Investigator, one postdoctoral associate, and two graduate students. Specific Aim 1-Develop a high fidelity single DNA molecule genotyping approach for analyzing thousands of molecules simultaneously and with the ability to interrogate multiple SNPs. We plan to employ fiber optic microwell arrays to develop a robust, efficient, fast, and affordable method of simultaneously genotyping single DNA molecules from thousands of individual cells. Specific Aim 2-Develop an approach to isolating single cells, capturing genomic DNA, and genotyping the captured DNA. In this Aim, we will apply the methods developed in Specific Aim 1 to whole cells. Thousands of single E. coli cells will be captured in individual wells, lysed, genomic DNA fragmented, the DNA captured on the surface of the individual wells, and then genotyped. Specific Aim 3-Develop a high resolution single molecule analysis method for analyzing the contents of single cells. We will develop methods for performing high resolution single molecule counting of the contents of individual cells. Single cells will be isolated, lysed, and their contents will be captured in microwell arrays such that individual mRNA and protein molecules are isolated. We will then use a variety of methods that enable us to count the individual molecules. Success of these aims will provide a powerful new technology broadly applicable to multiple areas of biological research. For biologists, it provides the ability to describe the complex genetics of cell populations, providing clinicians with improved opportunities for diagnostics, and relating population genetics with responses to therapy and clinical outcomes. For experimentalists, it provides new ways to study phenomena such as tumor progression and responses to experimental therapies. For researchers on aging, it provides the ability to monitor changes in somatic genetics with age and the factors influencing them. For geneticists, it provides a low cost ability to construct genetic maps, to identify trans-acting elements controlling recombination, and to monitor the molecular structure of recombination products with high resolution. Our long term goal is to apply the developed technology to a variety of cell types to study fundamental cell biology and to understand how cell-to-cell and cell population differences may lead to different cell fates and may define disease. PUBLIC HEALTH RELEVANCE: Individual cells in a population, such as are present in bacterial infections, as well as diseased tissues such as cancer, exhibit random behaviors such that different cells may behave quite differently relative to the average behavior of the population and can lead to such conditions as bacterial antibiotic resistance and cancer metastasis. The goal of this project is to develop new tools for analyzing single cells to characterize and understand the differences between cells in a population.