OPTICAL, FLUIDIC, AND MOLECULAR TECHNOLOGIES FOR SINGLE-CELL OMICS

单细胞组学的光学、流体和分子技术

• 批准号: 9753281
• 负责人: Aaron Streets
• 金额: $ 38.11万
• 依托单位: UNIVERSITY OF CALIFORNIA BERKELEY
• 依托单位国家: 美国
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-08-01 至 2022-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9753281
• 关键词: Cells; Chemicals; Complement; Complex; DNA-Protein Interaction; Data Set; Epigenetic Process; Gene Expression Profiling; Genes; Genetic Transcription; Genome; Goals; Image; In Situ; Lead; Lipids; Liquid substance; Measurement; Measures; Molecular; Phenotype; Population; Process; Proteins; RNA; Research; Stem cells; Techniques; Technology; Tissues; Transcript; Whole Organism; base; developmental disease; microfluidic technology; multimodality; multiple omics; novel strategies; programs; single cell technology; single-cell RNA sequencing; stem cell differentiation; technology development; tool; transcriptome; transcriptome sequencing

SUMMARY Stem cell differentiation takes place on a complex landscape defined by a continuum of intermediate cell states, branching trajectories, and highly coordinated regulatory dynamics. This complexity is often hidden to ensemble measurements, which are typically limited to measuring average values of markers in populations of cells. The recent and rapid emergence of high-throughput single-cell RNA sequencing has provided a powerful solution for dissecting transitions between cell states by measuring the expression of thousands of genes in large populations of cells. Complementary statistical tools can process these large data sets and identify distinct groups of cells base on their transcriptional profile. However, RNA transcript abundance does not always correlate with protein composition and there are many important intracellular molecules, like lipids and metabolites, which are not specifically encoded in the genome. Orthogonal molecular measurements of cell state could provide a powerful complement to transcriptome analysis for investigating regulatory dynamics during stem cell differentiation. This research program focuses on the development of technology to facilitate “multi-omic” measurements in single cells. We take advantage of three core technologies to measure cell state phenotypes. We use DamID to probe genome organization and protein-DNA interactions, single-cell RNAseq to provide whole-transcriptome gene expression profiling, and coherent Raman imaging to characterize the chemical composition of live cells. Microfluidic technology facilitates the integration of these techniques and enables multimodal measurement of single cells. This platform will provide a new approach for dissecting coordinated regulatory networks in differentiating stem cells. Our ultimate goal is to develop a tool to make all of these measurements in situ, in order to retain single-cell spatial information and cellular context in a developing tissue or whole organism.

概括 干细胞分化发生在由中间细胞状态的连续性定义的复杂景观上， 分支轨迹和高度协调的调节动力学。这种复杂性通常被隐藏为 合奏测量值通常仅限于测量标记的平均值 细胞。高通量单细胞RNA测序的最近和快速出现提供了强大的 通过测量成千上万基因在细胞态之间剖析细胞态之间过渡的溶液 大量细胞种群。互补的统计工具可以处理这些大数据集并确定 不同的细胞基于其转录谱。但是，RNA的成绩单丰度不 始终与蛋白质组成相关，并且存在许多重要的细胞内分子，例如脂质和 代谢物，未在基因组中特别编码。细胞的正交分子测量 状态可以为转录组分析提供有力的完成，以调查监管动态 在干细胞分化过程中。该研究计划着重于技术的发展 单细胞中的“多摩尼克”测量。我们利用三种核心技术来测量细胞状态 表型。我们使用DAMID探测基因组组织和蛋白-DNA相互作用，单细胞RNASEQ 提供全转录组基因表达分析和连贯的拉曼成像以表征 活细胞的化学组成。微流体技术促进了这些技术和 启用了单个细胞的多模式测量。该平台将提供一种新的剖析方法 分化干细胞中协调的调节网络。我们的最终目标是开发一种工具来使所有人 在原位测量中，以保留单细胞空间信息和蜂窝上下文 发展组织或整个生物体。