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.

产品说明：我的长期职业目标是成为翻译生物信息学的领先专家，创造，开发和应用计算和统计方法来揭示癌症的景观并确定治疗癌症的策略。人类癌症是高度异质性的。这种异质性是大多数抗癌药物最终失败的主要原因。然而，由于技术的限制，直到最近才在单细胞水平上研究全基因组范围内的细胞间异质性。单细胞转录组测序（RNA-Seq）和外显子组等新技术揭示了新的见解和比以前认为的更深刻的复杂性。然而，到目前为止，这些技术仅限于每个细胞一次测定。从相同的单个肿瘤细胞，特别是从源自小肿瘤活检的那些肿瘤细胞进行多个综合测定仍然是一个巨大的挑战。鉴于单细胞分辨率的随机性，重复性和灵敏度是一项艰巨的任务。为了克服这一挑战，我与耶鲁大学的谢尔曼·韦斯曼博士合作，启动了单个癌细胞测序分析项目，他也是我K 01方案的共同导师。我的近期职业目标是使用红白血病K562细胞系鉴定单个癌细胞中的全基因组异质性。为此，我提出了一个关于综合生物信息学方法的研究项目，以分析从相同的单个白血病细胞产生的多种类型的基因组学数据，这是一个及时和关键的主题。具体而言，我有兴趣研究以下具体目标：（1）建立一个生物信息学管道来研究单细胞RNA-Seq的异质性，（2）建立一个生物信息学管道来研究单细胞的CpG甲基化组，（3）建立一个生物信息学管道来研究单细胞Exome-Seq，（4）整合从相同单细胞产生的RNA-Seq，甲基化组和Exome-Seq数据。这些单细胞基因组数据由谢尔曼韦斯曼博士的实验室从30个单个K562红白血病细胞中提供。我将首先并行构建和验证针对单细胞分析优化的RNA-Seq、甲基化组和Exome-Seq生物信息学管道，然后开发和验证一个综合平台来分析这些多种类型的高通量数据。为了完成研究项目，并成功地从初级教师过渡到该领域的专家，我与我的指导委员会一起制定了一个职业计划，该指导委员会由四位世界级的专家组成，他们来自与大数据科学相关的不同领域：达特茅斯学院生物信息学的主要导师Jason摩尔博士，耶鲁大学单细胞基因组学和遗传学的共同导师谢尔曼韦斯曼博士，夏威夷大学癌症中心癌症流行病学的共同导师赫伯特余博士和夏威夷马诺阿大学信息和计算机科学系大数据可视化的共同导师杰森利博士。我将主要与我的四位共同导师一起规划我在此期间的职业发展。