Single Cell Genomics Core

单细胞基因组核心

• 批准号: 10663283
• 负责人: MICHAEL L WHITFIELD
• 金额: $ 31.87万
• 依托单位: DARTMOUTH COLLEGE
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-08-01 至 2024-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10663283
• 关键词: Antibiotic Resistance; Autoimmune Diseases; Autoimmunity; Bar Codes; Bioinformatics; Biological; Biological Process; Biological Sciences; Biology; Cancer Biology; Cell Separation; Cells; Centers of Research Excellence; Chemistry; Chromium; Chronobiology; Clinical; Collaborations; Communication; Communities; Computer software; Consultations; Cutaneous Melanoma; DNA sequencing; Data; Data Analyses; Databases; Development; Discipline; Disease; Dissociation; Educational process of instructing; Educational workshop; Ensure; Equipment; Event; Experimental Designs; Foundations; Funding; Gene Expression; Generations; Genomics; Goals; Grant; Health; Heterogeneity; Human Resources; Immune; Immune response; Immune system; Immunology; Immunotherapy; Individual; International; Internet; Laboratories; Leadership; Libraries; Link; Malignant Neoplasms; Management Information Systems; Mentors; Methodology; Methods; Molecular; Nerve Degeneration; Nucleic Acids; Organism; Patients; Phenotype; Population; Preparation; Privatization; Procedures; Process; Proteomics; Protocols documentation; Publishing; Quality Control; RNA; Reproducibility; Research; Research Personnel; Resistance; Resources; Sampling; Services; Skin; Standardization; System; Systemic Scleroderma; Technology; Time; Tissues; Translating; United States National Institutes of Health; Videoconferencing; Visualization; Whole Organism; Work; base; body system; cell type; clinically relevant; complex biological systems; computer infrastructure; data analysis pipeline; data pipeline; data quality; design; digital; epigenome; experience; experimental study; improved; innovation; insight; machine learning model; mathematical model; meetings; metabolomics; outcome prediction; protocol development; single-cell RNA sequencing; success; therapy resistant; tool; transcriptome sequencing; tumor; tumor progression

Core C: Single Cell Genomics (SCG) Core ABSTRACT High-throughput genomic, proteomic, metabolomic, and immune profiling technologies now provide a wealth of data that can be mined to interrogate basic biological process, changes in cellular or organismal populations, and the molecular bases of disease, at levels ranging from the whole organism to single cells. The generation and analysis of such data is now a pillar of research approaches and problems in the life sciences. The Center for Quantitative Biology (CQB) will first nucleate and then grow this fusion of wet bench “omics research” with bioinformatics at Dartmouth. Single Cell Genomics (SCG) has become an essential tool to understand diseases involving complex biological systems including cancer, neurodegenerative and immune/autoimmune diseases. The overall goal of the SCG core is to develop a centralized resource within the CQB to support individual project leaders and the Dartmouth community. The goal of this core is to implement, maintain and improve upon the technologies required to conduct SCG experiments in a rigorous and quality controlled manner. The SCG Core will work directly with COBRE leadership, project leaders, mentors and investigators to design robust experiments and will implement stringent quality control and sample tracking methodologies (Aim 1) to ensure generation of high quality, reproducible data. The SCG core will also maintain equipment and expertise to enable isolation, capture, library preparation and sequencing of DNA and RNA from single cells for COBRE investigators and the Dartmouth research community at large (Aim 2). Finally, in collaboration with the Analytics Core (Core B), the SCG Core will provide the computational infrastructure and expertise to analyze and visualize these single cell data, expediting the process from experimental design to biological insight (Aim 3). The SCG core will stay abreast of advancements in the field, serving as a hub of expertise and technology to investigators campus-wide.

核心C：单细胞基因组学（SCG）核心 摘要 高通量基因组学、蛋白质组学、代谢组学和免疫分析技术现在 提供了丰富的数据，可以挖掘询问基本的生物过程， 细胞或有机体群体，以及疾病的分子基础，水平从 整个生物体到单个细胞。这些数据的生成和分析现在是 生命科学的研究方法和问题。定量生物学中心 (CQB)将首先形成核，然后发展这种融合湿台“组学研究”与 达特茅斯的生物信息学单细胞基因组学（SCG）已成为一种重要的工具， 了解涉及复杂生物系统的疾病，包括癌症，神经退行性疾病， 和免疫/自身免疫性疾病。SCG核心的总体目标是开发一个集中的 CQB内部的资源，以支持个人项目负责人和达特茅斯社区。的 该核心的目标是实施、维护和改进执行所需的技术 SCG实验以严格和质量控制的方式进行。SCG核心将直接 与COBRE领导层、项目负责人、导师和研究人员一起设计强大的实验 并将实施严格的质量控制和样本跟踪方法（目标1），以确保 生成高质量、可再现的数据。SCG核心还将维护设备， 专业知识，使分离，捕获，文库制备和测序的DNA和RNA从 为COBRE研究人员和达特茅斯研究团体提供单细胞（目标2）。 最后，与分析核心（核心B）合作，SCG核心将提供 计算基础设施和专业知识来分析和可视化这些单细胞数据， 加快从实验设计到生物学洞察的过程（目标3）。SCG核心将 与该领域的进步保持同步，作为专业知识和技术的中心， 全校范围内的调查员