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中文摘要
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摘要 虽然单细胞RNA测序的快速进展正在产生细胞状态的全面分类,但在细胞中, 人体,了解调节细胞行为的复杂分子和环境因素 仍然是一个核心挑战。用于同时测量多种分子模式的新方法, 空间背景和血统关系是实现这一目标所必需的,但目前不在 目前的技术主要集中在一个单一的数据类型。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, 测量和整合细胞身份的分子和环境决定因素。为了实现这些目标, 我们提出了以下一系列协同目标,这些目标将并行发展:1)大规模发展- 平行测定,以同时分析数百万个细胞中的多种分子组分; 2)鉴定 复杂相互作用种群中细胞状态的空间和环境决定因素; 3)发展 可扩展的平台来分析遗传的分子成分,并确定细胞谱系在 建立跨细胞的分子和表型差异;以及4)开发方法以协调单个细胞, 不同模态的细胞谱,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,并产生软件和协议,将适用于不同的生物 系统.我们将与社会各界广泛分享这些资源,同时将教育重点放在更广泛的方面, 鼓励来自代表性不足背景的纽约市学生在以下方面接受学术培训: 基因组学和系统生物学。
英文摘要
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.
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Single-Cell Multi-omics to Link Clonal Mosaicism (CM) Genotypes with Chromatin, Epigenomic, Transcriptomic and Protein Phenotypes
Genome-wide mutational integration for ultra-sensitive plasma tumor burden monitoring in immunotherapy
Expanding the GoT toolkit to link single-cell clonal genotypes with protein, transcriptomic, epigenomic and spatial phenotypes
Genome-wide mutational integration for ultra-sensitive plasma tumor burden monitoring in immunotherapy
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