Center for Integrated Cellular Analysis

综合细胞分析中心

基本信息

批准号：
10176553
负责人：
Dan Landau
金额：
$ 293.59万
依托单位：
NEW YORK GENOME CENTER
依托单位国家：
美国
项目类别：
财政年份：
2020
资助国家：
美国
起止时间：
2020-06-01 至 2025-03-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10176553
关键词：
Academic Training Address Anatomy Atlases Awareness Basic Science Biological Biological Assay Cell Communication Cell Lineage Cells Cellular Indexing of Transcriptomes and Epitopes by Sequencing Chromatin Communities Complex Computer software Computing Methodologies Copy Number Polymorphism DNA Methylation Data Data Set Development Disease Education and Outreach Ensure Environmental Risk Factor Epitopes Genes Genetic Polymorphism Genome Genomics Goals Health Heritability Human body Immunofluorescence Immunologic In Situ Individual Inherited Joints Learning Measurement Measures Membrane Proteins Messenger RNA Methods Mission Modality Molecular New York City Output Phenotype Play Population Positioning Attribute Process Property Proteins Proteome Protocols documentation Recording of previous events Resolution Resource Sharing Role Series Signal Transduction Source Students Systems Biology Taxonomy Technology Time Variant analytical method base biological systems cell behavior clinical application combinatorial community engagement cost data integration deep learning epigenome experimental study extracellular indexing innovation insight molecular phenotype multimodality multiple omics novel strategies outreach reconstruction single cell sequencing single-cell RNA sequencing success tool transcriptome transcriptome sequencing transcriptomics whole genome

项目摘要

Abstract While rapid advances in single-cell RNA-sequencing are yielding comprehensive taxonomies of cell states in the human body, understanding the complex molecular and environmental factors that regulate cell behavior remains a central challenge. New methods for simultaneous measurement of multiple molecular modalities, spatial context, and lineage relationships are needed to address this goal, but are currently outside the scope of present technologies which largely focus on a single data type. We propose to create a Center for Integrated Cellular Analysis, with a mission to develop a comprehensive suite of technologies and analytical methods to measure and integrate the molecular and environmental determinants of cellular identity. To achieve these goals, we propose the following series of synergistic Aims that will be developed in parallel: 1) Develop massively- parallel assays to simultaneously profile multiple molecular components across millions of cells; 2) Identify the spatial and environmental determinants of cellular state in complex interacting populations; 3) Develop scalable platforms to profile inherited molecular components, and determine the role of cell lineage in establishing molecular and phenotypic differences across cells; and 4) Develop methods to harmonize single- cell profiles across distinct modalities, enabling the inference of cellular identity. Our Center will address critical challenges in data integration, and produce software and protocols that will be applicable to diverse biological systems. We will share these resources broadly with the community, alongside a broader educational focus to encourage New York City students from under-represented backgrounds to pursue academic training in Genomics and Systems Biology.

抽象的虽然单细胞 RNA 测序的快速进展正在产生全面的细胞状态分类学人体，了解调节细胞行为的复杂分子和环境因素仍然是一个主要挑战。同时测量多种分子模式的新方法，实现这一目标需要空间背景和血统关系，但目前超出了范围目前的技术主要集中于单一数据类型。我们建议建立一个综合中心细胞分析，其使命是开发一套全面的技术和分析方法，以测量并整合细胞身份的分子和环境决定因素。为了实现这些目标，我们提出以下一系列将并行制定的协同目标： 1) 大规模发展—— 并行测定可同时分析数百万个细胞中的多个分子成分； 2）识别复杂相互作用群体中细胞状态的空间和环境决定因素； 3）开发可扩展的平台来分析遗传分子成分，并确定细胞谱系在建立细胞间的分子和表型差异； 4）制定协调单一的方法跨不同模式的细胞概况，从而能够推断细胞身份。我们的中心将解决关键问题数据集成的挑战，并开发适用于不同生物的软件和协议系统。我们将与社区广泛分享这些资源，同时更广泛地关注教育鼓励来自弱势群体的纽约市学生在以下领域接受学术培训基因组学和系统生物学。