Developing a whole-genome sequencing method for single human cells

开发单个人类细胞的全基因组测序方法

• 批准号: 8550031
• 负责人: Nicholas Navin
• 金额: $ 19.16万
• 依托单位: UNIVERSITY OF TX MD ANDERSON CAN CTR
• 依托单位国家: 美国
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-09-24 至 2014-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8550031
• 关键词: Address; Base Pairing; Biological Phenomena; Breast Cancer Cell; Cancer Patient; Cancer cell line; Cell Culture System; Cell Culture Techniques; Cell Line; Cell Lineage; Cells; Cleaved cell; Clinical; Cluster Analysis; Communicable Diseases; Complex; DNA; DNA Library; DNA Sequence; Data; Defect; Development; Developmental Biology; Diagnosis; Early Diagnosis; Evolution; Fertilization in Vitro; Fibroblasts; Genetic Heterogeneity; Genome; Genomics; Genotype; Goals; Grant; Heterogeneity; Human; Immune System Diseases; Immunology; Investigation; Knowledge; Malignant Neoplasms; Mammalian Cell; Mammary Neoplasms; Methodology; Methods; Microbiology; Mission; Modality; Modeling; Morbidity - disease rate; Mutation; Nature; Neoplasm Metastasis; Neurobiology; Patients; Point Mutation; Polymerase; Population; Primary Neoplasm; Public Health; Reading; Reagent; Reporting; Research; Resistance; Resolution; Sampling; Seeds; Signal Transduction; Solid Neoplasm; Somatic Mutation; Technology; Testing; Tissues; Tn5 transposase; United States National Institutes of Health; Validation; Variant; Work; base; cancer cell; cancer genome; chemotherapy; cost effective; developmental disease; genome sequencing; genome-wide; human disease; improved; innovation; interest; malignant breast neoplasm; mortality; neoplastic cell; nervous system disorder; new technology; next generation sequencing; novel; programs; research study; response; single cell analysis; tool; tumor; tumor progression

DESCRIPTION (provided by applicant): Current genomic methods are limited to reporting an average signal from a complex population of cells because they require a large amount of input material. In complex tissues such as tumors, where genetic heterogeneity is common, important information may be lost. To address this problem, we propose to develop a whole-genome sequencing method that can obtain high-coverage (>80%) sequencing data from the genome of a single human cell. From this data we will identify the full spectrum of somatic mutations, including point mutations, indels, structural variants and copy number aberration that are present in the genome of a single cell. By comparing the genomes of multiple cells we can delineate clonal diversity and infer how tumor genomes evolve complex somatic mutations. To do this, we propose to develop an innovative method called Cell-Seq which combines minimal isothermal amplification with a Phi29 polymerase and a Tn5 transposase that can simultaneously cleave DNA fragments and add adapters for next-generation sequencing, starting with only a single cell. In aim 1 we will develop and optimize the Cell-Seq method. In aim 2 we will validate the method in two clonal cell cultures by comparing the genomes of single cells to million cell samples, to determine error rates and identify potential biases associated with the method. In aim 3 we will apply Cell-Seq to a human breast tumor sample and sequence the genomes of 10 single cells to investigate clonal diversity and genome evolution. We hypothesize that breast tumors are organized into one or more major clonal subpopulations that stably expand to form the tumor mass, not millions of diverse clones, as the prevailing model for tumor progression assumes. We expect that clustering analysis will show evidence for a few major groups, and that within each group genomic mutations will be highly similar. The proposed single-cell sequencing approach is innovative because it can fully resolve heterogeneity in complex populations of cells, whereas standard bulk genomic methods are limited to reporting an average signal. This research is significant because achieving these aims will improve our fundamental understanding of clonal diversity in human breast cancers, and our knowledge of how tumor genomes evolve complex somatic mutations. Our long-term goal is to use single-cell sequencing to study how single cells from human tumors seed metastases and evolve resistance to chemotherapy. Our work is directly aligned with the mission of the NIH to reduce mortality rates in breast cancer through the development of new modalities for early detection and diagnosing heterogeneity in tumors. Our work is also aligned with the goal of the SCAP to cure human diseases through the development of new single-cell genomic technologies. In addition to benefiting the study of cancer, we expect that our tools will have a broad positive impact on many other human diseases, including neurological disorders, immunological diseases, developmental defects and infectious disease.

描述（由申请人提供）：当前的基因组方法仅限于报告来自复杂细胞群体的平均信号，因为它们需要大量的输入材料。在复杂的组织如肿瘤中，遗传异质性是常见的，重要的信息可能会丢失。为了解决这个问题，我们建议开发一种全基因组测序方法，该方法可以从单个人类细胞的基因组中获得高覆盖率（>80%）的测序数据。从这些数据中，我们将确定体细胞突变的全谱，包括单细胞基因组中存在的点突变，插入缺失，结构变异和拷贝数畸变。通过比较多个细胞的基因组，我们可以描绘克隆多样性，并推断肿瘤基因组如何演变复杂的体细胞突变。为此，我们建议开发一种名为Cell-Seq的创新方法，该方法将最小等温扩增与Phi 29聚合酶和Tn 5转座酶相结合，可以同时切割DNA片段并为下一代测序添加衔接子，仅从单个细胞开始。在目标1中，我们将开发和优化Cell-Seq方法。在目标2中，我们将通过比较单细胞与百万细胞样本的基因组，在两种克隆细胞培养物中验证该方法，以确定错误率并识别与该方法相关的潜在偏倚。在目标3中，我们将Cell-Seq应用于人类乳腺肿瘤样本，并对10个单细胞的基因组进行测序，以研究克隆多样性和基因组进化。我们假设乳腺肿瘤被组织成一个或多个主要的克隆亚群，这些亚群稳定地扩增形成肿瘤块，而不是像肿瘤进展的流行模型所假设的那样，有数百万个不同的克隆。我们期望聚类分析将显示几个主要群体的证据，并且在每个群体中基因组突变将高度相似。所提出的单细胞测序方法是创新的，因为它可以完全解决复杂细胞群体中的异质性，而标准的批量基因组方法仅限于报告平均信号。这项研究意义重大，因为实现这些目标将提高我们对人类乳腺癌克隆多样性的基本理解，以及我们对肿瘤基因组如何演变复杂体细胞突变的了解。我们的长期目标是使用单细胞测序来研究来自人类肿瘤的单细胞如何播种转移并进化出对化疗的抗性。我们的工作与NIH的使命直接一致，即通过开发早期检测和诊断肿瘤异质性的新模式来降低乳腺癌的死亡率。我们的工作也符合SCAP的目标，即通过开发新的单细胞基因组技术来治愈人类疾病。除了有利于癌症的研究外，我们预计我们的工具将对许多其他人类疾病产生广泛的积极影响，包括神经系统疾病，免疫性疾病，发育缺陷和传染病。