An integrated multiplexed genomic assay for low input clinical samples1

适用于低输入临床样品的综合多重基因组检测1

• 批准号: 9305830
• 负责人: AVIV REGEV
• 金额: $ 41.2万
• 依托单位: BROAD INSTITUTE, INC.
• 依托单位国家: 美国
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-06-24 至 2020-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9305830
• 关键词: Antiviral Agents; Autoimmunity; Automobile Driving; Biological Assay; Biological Preservation; Biopsy; Blood; Cell Count; Cells; Cellular Structures; Clinical; Communities; Complex; Complex Mixtures; Computational Biology; Computer software; Core Biopsy; DNA; Data; Data Quality; Data Set; Dendritic Cells; Development; Disease; Dissociation; Educational process of instructing; Engineering; Ensure; Enzymes; Erythrocytes; Excision; Fluorescence-Activated Cell Sorting; Future; Genes; Genomics; Glean; HIV; HIV/TB; Heterogeneity; Human; Imagery; Immune; Immune System Diseases; Immune system; Immunofluorescence Immunologic; Immunologics; Immunologist; Immunology; Individual; Knowledge; Letters; Link; Liquid substance; Masks; Measurement; Measures; Mechanics; Methods; Microfluidics; Molecular; Molecular Biology; Mucous Membrane; Mus; Noise; Optics; Pathogenicity; Pathway interactions; Population; Preparation; Procedures; Proteins; Protocols documentation; Quality Control; RNA; Reagent; Research Personnel; Resolution; Rheumatology; Sampling; Scheme; Scientist; Sorting - Cell Movement; Specimen; Standardization; Surface; Synovial Fluid; System; Systematic Bias; Systemic Lupus Erythematosus; Temperature; Testing; Training; Transcript; Variant; Walkers; Work; cell type; clinically relevant; cost effective; cytokine; experimental study; genome-wide; genomic data; genomic profiles; multidisciplinary; novel; pathogen; public health relevance; receptor; response; tool; transcription factor; transcriptome; transcriptome sequencing; translational approach; tumor

 DESCRIPTION (provided by applicant): The heterogeneity of the human immune system is essential for protecting us against myriad pathogens, but also poses measurement challenges, since we typically must either deeply profile samples as heterogeneous mixtures or measure only a few pre-selected variables in single cells. To understand the composition of limiting clinical immune isolates and their relation to disease, we must combine the breadth of genomic profiling with the resolution of single cell assays. Emerging single-cell profiling methods, such a single-cell RNA-seq (scRNA-Seq), provide an extraordinary opportunity to overcome these challenges. In recent proof-of-concept studies, we applied scRNA-Seq to murine immune and human tumor samples: we developed experimental and computational approaches for generating and analyzing scRNA-Seq data, and used them to examine >50,000 cells from diverse systems and samples. However, to realize the promise of these strategies for translational immunology, we must create, optimize and implement a scRNA-Seq platform that can be deployed for diverse clinical samples from core biopsies to FNAs to fluids. Here, we will optimize current protocols to create standard operating procedures (SOPs) and merge individual components into a standardized pipeline that can be provided to the immunology community. We will wrap and release SOPs for scRNA-Seq and realize protocols for linking single cell genomic data to more conventional immunology measurement, such as FACS or CyTOF (Aim 1). From our driving clinical collaborators (Kwon, Tsokos, Walker, Xavier), we will receive diverse clinical specimens (including biopsies, PBMCs, synovial) in IBD, HIV, TB, and SLE. We will develop and deploy experimental protocols to best extract disaggregated individual cells from each sample type for successful scRNA-Seq (Aim 2). We will optimize our existing computational pipelines for QC, analysis and visualization, wrap them into a streamlined package, and release it as a publicly available tool (Aim 3). Our project will create a robust workflow for single-cell transcriptome analysis applicable across diverse clinical sample types, and accessible to the entire immunology community.

 描述（由申请人提供）：人类免疫系统的异质性对于保护我们免受无数病原体的侵害至关重要，但也带来了测量挑战，因为我们通常必须将样品作为异质混合物进行深入分析，或者仅测量单细胞中的少数预选变量。为了了解限制性临床免疫分离株的组成及其与疾病的关系，我们必须将基因组分析的广度与单细胞测定的分辨率相结合。新兴的单细胞分析方法，如单细胞RNA-Seq（scRNA-Seq），为克服这些挑战提供了非凡的机会。在最近的概念验证研究中，我们将scRNA-Seq应用于小鼠免疫和人类肿瘤样本：我们开发了用于生成和分析scRNA-Seq数据的实验和计算方法，并使用它们来检查来自不同系统和样本的> 50，000个细胞。然而，为了实现这些策略对转化免疫学的承诺，我们必须创建、优化和实施scRNA-Seq平台，该平台可用于从核心活检到FNA再到液体的各种临床样本。在这里，我们将优化当前的协议，以创建标准操作程序（SOP），并将各个组件合并到可以提供给免疫学社区的标准化管道中。我们将包装和发布scRNA-Seq的SOP，并实现将单细胞基因组数据与更传统的免疫学测量（如FACS或CyTOF）联系起来的协议。我们将从我们的临床合作者（Kwon、Tsokos、步行者、Xavier）那里收到IBD、HIV、TB和SLE的各种临床标本（包括活检、PBMC、滑膜）。我们将开发和部署实验方案，以最好地从每种样品类型中提取解聚的单个细胞，以成功进行scRNA-Seq（目标2）。我们将优化我们现有的计算管道，用于QC，分析和可视化，将它们包装成一个精简的包，并将其作为一个公开可用的工具发布（目标3）。我们的项目将为单细胞转录组分析创建一个强大的工作流程，适用于各种临床样本类型，并可供整个免疫学界使用。