An Integrative Bioinformatics Platform with Application in Single Cancer Cells

应用于单个癌细胞的综合生物信息学平台

• 批准号: 9321082
• 负责人: Lana X Garmire
• 金额: $ 34.45万
• 依托单位: UNIVERSITY OF HAWAII AT MANOA
• 依托单位国家: 美国
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-09-01 至 2020-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9321082
• 关键词: Acute Erythroblastic Leukemia; Acute Myelocytic Leukemia; Address; Big Data; Bioinformatics; Biological; Biological Assay; Cancer cell line; Cell Line; Cells; Collaborations; Communities; Computer software; Computing Methodologies; Copy Number Polymorphism; Data; Data Set; Development; Evolution; Generic Drugs; Genes; Genetic; Genetic Polymorphism; Genomics; Goals; Hematologist; Heterogeneity; Human; Individual; K-562; K562 Cells; Laboratories; Malignant Neoplasms; Messenger RNA; Methodology; Methods; Methylation; Modeling; Myeloid Leukemia; Noise; Nucleotides; Pathway interactions; Patients; Pattern; Play; Population; Quality Control; Research Personnel; Resolution; Role; Sample Size; Sampling; Services; Source; Stem cells; Technology; Testing; Tissues; Treatment Efficacy; Universities; Validation; Variant; base; cancer cell; carbene; data integration; design; exome; exome sequencing; experimental study; genome-wide; genomic variation; improved; insertion/deletion mutation; insight; leukemia; methylome; neoplastic cell; new technology; novel; restriction enzyme; single cell analysis; single cell sequencing; success; therapeutic target; tool; transcriptome; transcriptome sequencing; tumor; tumor heterogeneity; user-friendly

Project Summary Human cancers are highly heterogeneous. However, due to the limit of technologies, the intercellular heterogeneity was not detectable genome-wide at single-cell level until recently. New technologies such as single-cell RNA-Seq and exome-Seq have revealed new insights and more profound complexity than what was previously thought. In collaboration with Dr. Weissman's group in the Genetics Department at Yale University, it is now feasible to perform multiple integrative assays from the same single tumor cell. However, rigorous computational methods are still lagging behind, in order to solve the computational challenge and determine true heterogeneity rather than noise. Here we propose a user-friendly bioinformatics platform that is optimized to integrative multiple types of single-cell NGS data, in particular, transcriptome, exome and CpG methylome data that are all obtained from the same cell. Specifically, in this study we will first construct and validate in parallel three new NGS bioinformatics pipelines to enable single-cell based RNA-Seq, exome- Seq, and CpG methylome data. We will then develop and validate an integration pipeline to analyze multiple types of high- throughput data, exemplified by the RNA-Seq, exome-Seq and CpG methylome single-cell data. To test the software suite, we will first obtain data sets from erythroleukemia cell line K562. We will then utilize this bioinformatics suite to investigate heterogeneity in Myeloid Leukemia patient samples provided by hematologist Dr. Stephanie Halene at Yale Stem Cell Center. Beyond deciphering tumor heterogeneity, this user-friendly bioinformatics platform is expected to be used widely by the single-cell sequencing community.

项目摘要 人类癌症是高度异质性的。然而，由于技术的限制， 直到最近才在单细胞水平上检测到全基因组异质性。等新技术 单细胞RNA-Seq和exome-Seq揭示了新的见解和更深刻的复杂性， 以前认为。在与耶鲁大学遗传学系Weissman博士的团队合作下， 现在可以从同一单个肿瘤细胞进行多个综合测定。然而，严格 计算方法仍然落后，为了解决计算挑战并确定 真正的异质性，而不是噪音。在这里，我们提出了一个用户友好的生物信息学平台， 整合多种类型的单细胞NGS数据，特别是转录组、外显子组和CpG甲基化组 这些数据都是从同一个细胞中获得的。具体来说，在这项研究中，我们将首先构建和验证 并行三个新的NGS生物信息学管道，以实现基于单细胞的RNA-Seq，外显子组- Seq和CpG 甲基化数据。然后，我们将开发和验证集成管道，以分析多种类型的高- 通过RNA-Seq、外显子组-Seq和CpG甲基化组单细胞数据例示的通量数据。测试 软件套件，我们将首先从红白血病细胞系K562获得数据集。我们将利用这个 研究髓性白血病患者样本异质性的生物信息学套件，由 耶鲁干细胞中心的血液学家斯蒂芬妮·哈琳博士说。除了破译肿瘤异质性， 用户友好的生物信息学平台有望被单细胞测序界广泛使用。