An Integrative Bioinformatics Approach to Study Single Cancer Cell Heterogeneity

研究单个癌细胞异质性的综合生物信息学方法

• 批准号: 9095313
• 负责人: Lana X Garmire
• 金额: $ 18.04万
• 依托单位: UNIVERSITY OF HAWAII AT MANOA
• 依托单位国家: 美国
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-09-29 至 2019-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9095313
• 关键词: Acute Erythroblastic Leukemia; Address; Alternative Splicing; Award; Big Data; Bioinformatics; Biological; Biological Assay; Biopsy; Cancer Center; Cell Line; Cells; Clinical; Collaborations; Computer software; Computing Methodologies; Copy Number Polymorphism; Data; Data Analyses; Data Science; Data Set; Development; Development Plans; Diagnosis; Event; Evolution; Excision; Faculty; Failure; Galaxy; Generic Drugs; Genes; Genetic; Genetic Polymorphism; Genomics; Goals; Hawaii; Health; Heterogeneity; Human; Individual; Information Sciences; K-562; K562 Cells; Laboratories; Machine Learning; Malignant Neoplasms; Mentors; Messenger RNA; Methodology; Methods; Methylation; Microfluidic Microchips; Modeling; Molecular; Noise; Nucleotides; Pattern; Play; Population; Process; Quality Control; Reproducibility; Research Personnel; Research Project Grants; Resolution; Role; Sample Size; Sequence Analysis; Source; Statistical Methods; Technology; Testing; Therapeutic; Tissues; Universities; Variant; Work; base; cancer cell; cancer epidemiology; cancer heterogeneity; career; college; computer science; computerized data processing; data integration; data visualization; design; exome; exome sequencing; genome-wide; genomic data; genomic variation; improved; innovation; insertion/deletion mutation; insight; interest; learning strategy; leukemia; methylome; neoplastic cell; new technology; next generation sequencing; novel; open source; outcome forecast; research study; restriction enzyme; single cell analysis; single cell sequencing; success; tool; transcriptome; transcriptome sequencing; tumor; tumor heterogeneity; user-friendly

DESCRIPTION: My long term career goal is to become a leading expert in translational bioinformatics who creates, develops and applies computational and statistical methods to reveal landscapes of cancers and to identify strategies to cure cancers. Human cancers are highly heterogeneous. Such heterogeneity is the major source of the ultimate failure of most cancer agents. However, due to the limit of technologies, the intercellular heterogeneity has not been investigated genome wide, at single-cell level until recently. New technologies such as single-cell transcriptome sequencing (RNA-Seq) and exome have revealed new insights and more profound complexity than was previously thought. However, so far these technologies are limited to one assay per cell. It remains a grand challenge to perform multiple, integrative assays from the same single tumor cell, in particular, from those derived from small tumor biopsies. Given the stochasticity at the single cell resolution, reproducibility and sensitivity ar daunting tasks. To overcome this challenge, I have started the single cancer cell sequencing analysis project, in collaboration with Dr. Sherman Weissman at Yale University, who is also my co-mentor of this K01 proposal. My immediate career goal is to identify genome-wide heterogeneity among single cancer cells, using the erythroleukemia K562 cell line. Towards this, I am proposing a research project on an integrative bioinformatics approach to analyze multiple types of genomics data generated from the same single leukemia cells, a timely and critical topic. Specifically, I am interested in studying the following specific aims: (1) buildinga bioinformatics pipeline to study heterogeneity of single-cell RNA-Seq, (2) building a bioinformatics pipeline to study CpG methylome of single cells, (3) building a bioinformatics pipeline to study single-cell Exome-Seq, and (4) integrate the RNA-Seq, methylome and Exome-Seq data generated from the same single cells. These single cells genomic data are provided by Dr. Sherman Weissman's lab from 30 single K562 erythroleukemia cells. I will first construct and validate in parallel, the RNA-Seq, methylome, and Exome-Seq bioinformatics pipelines optimized for single-cell analysis, and then develop and validate an integrative platform to analyze these multiple types of high-throughput data. To accomplish the research project, and to successfully transit from a junior faculty to an expert of the field, I have developed a career plan with my mentoring committee composed of four world-class experts in different fields relevant to Big Data Science: Primary Mentor Dr. Jason Moore in Bioinformatics from Dartmouth College, Co-mentor Dr. Sherman Weissman in Single-cell Genomics and Genetics from Yale University, Co-mentor Dr. Herbert Yu in Cancer Epidemiology from University of Hawaii Cancer Center and Co-mentor Dr. Jason Leigh in Big Data Visualization from the Information and Computer Science Department of University of Hawaii Manoa. I will primarily work with my four co-mentors for planning the development of my career during this award.

描述：我的长期职业目标是成为转化生物信息学领域的领先专家，他们创建，开发和应用计算和统计方法来揭示癌症的景观并确定治愈癌症的策略。人类癌症是高度异质的。这种异质性是大多数癌症药物最终失败的主要来源。但是，由于技术的限制，直到最近，单细胞水平都没有在单细胞水平上研究细胞间的异质性。与以前认为的新技术（RNA-Seq）和外显子组等新技术相比，新见解和更深刻的复杂性。但是，到目前为止，这些技术仅限于每个细胞的一个测定法。从相同的单个肿瘤细胞中进行多种综合测定，特别是来自小型肿瘤活检的多个综合测定，这仍然是一个巨大的挑战。鉴于在单细胞分辨率下的随机性，可重复性和灵敏度AR艰巨的任务。为了克服这一挑战，我已经与耶鲁大学的谢尔曼·韦斯曼（Sherman Weissman）博士合作，启动了单个癌细胞测序分析项目，后者也是我的K01提案的同事。我的直接职业目标是使用红血病K562细胞系鉴定单个癌细胞中全基因组的异质性。为此，我提出了一项研究项目，讨论一种综合生物信息学方法，以分析从同一单一白血病细胞产生的多种类型的基因组学数据，这是一个及时且关键的主题。 Specifically, I am interested in studying the following specific aims: (1) buildinga bioinformatics pipeline to study heterogeneity of single-cell RNA-Seq, (2) building a bioinformatics pipeline to study CpG methylome of single cells, (3) building a bioinformatics pipeline to study single-cell Exome-Seq, and (4) integrate the RNA-Seq, methylome and Exome-Seq data由同一单元单元产生。这些单细胞基因组数据由Sherman Weissman博士的实验室提供，来自30个单个K562红血病细胞。我将首先并行构建和验证，RNA-seq，甲基组和外显性生物信息授权管道优化了用于单细胞分析的管道，然后开发和验证一个集成平台来分析这些多种类型的高通量数据。 To accomplish the research project, and to successfully transit from a junior faculty to an expert of the field, I have developed a career plan with my mentoring committee composed of four world-class experts in different fields relevant to Big Data Science: Primary Mentor Dr. Jason Moore in Bioinformatics from Dartmouth College, Co-mentor Dr. Sherman Weissman in Single-cell Genomics and Genetics from Yale University, Co-mentor Dr. Herbert Yu in夏威夷大学癌症中心的癌症流行病学和夏威夷大学信息和计算机科学系的大数据可视化的同事Jason Leigh博士。我将主要与我的四个法官合作，以计划在此奖项期间的职业发展。