Versatile, exponentially scalable methods for single cell molecular profiling

用于单细胞分子分析的多功能、指数扩展方法

基本信息

批准号：
10018642
负责人：
Jay Ashok Shendure
金额：
$ 98.96万
依托单位：
UNIVERSITY OF WASHINGTON
依托单位国家：
美国
项目类别：
财政年份：
2019
资助国家：
美国
起止时间：
2019-09-15 至 2023-06-30
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10018642
关键词：
A549 ATAC-seq Adoption Architecture Atlases Binding Biological Assay Biological Models Cell Nucleus Cells Cellular Assay Chemicals Chromatin Chromosomes Communities Computer software DNA Data Analyses Disease Emulsions Functional disorder Gene Expression Regulation Genes Genetic Genetic Screening Genetic Transcription Genome Genomics Goals Human Biology Human body Individual Kinetics Label Measures Messenger RNA Methods Microfluidics Modeling Modernization Molecular Molecular Biology Molecular Conformation Molecular Profiling Normal Cell Nucleic Acids Organism Pathology Phenotype Physiologic pulse Proteins Protocols documentation RNA Regulatory Element Reporter Research Resolution Standardization Technology Transcription Initiation Work base chemical genetics combinatorial cost effective human disease human tissue indexing novel strategies transcription factor transcriptome transcriptome sequencing

项目摘要

The field of single cell genomics is exploding. However, the vast majority of studies restrict themselves to quantifying mRNA transcription, typically in a few thousand cells. We have recently pioneered a new class of methods based on the concept of single cell combinatorial indexing (“sci”), wherein several rounds of splitting, molecular indexing, and pooling are used to uniquely label nucleic acids of cells or nuclei, without requiring the isolation or compartmentalization of each cell. The number of cells that can be uniquely labeled scales exponentially with the number of rounds of indexing, e.g. millions of cells can be profiled with as few as three rounds of indexing. Since 2015, we have developed sci- methods for quantifying chromatin accessibility (sci-ATAC-seq), transcription (sci-RNA-seq), chromatin architecture (sci-Hi-C), and genome sequence (sci-LIANTI), as well as a co-assay of chromatin accessibility and transcription (sci-CAR). Here, we propose to develop a much broader range of single cell methods, all based on the unifying concept of single cell combinatorial indexing. In our first aim, we will develop additional “single channel” sci- assays of various aspects of molecular state. In our second aim, we will develop additional “two channel” sci- assays, e.g. co-assays of RNA and DNA. In our third aim, we will adapt sci- assays to enable large-scale chemical and genetic screens in single cells. In our final aim, we will work to make the methods and associated software widely available to the research community. As a versatile, exponentially scalable platform, we anticipate that single cell combinatorial indexing will deepen and broaden the impact of single cell genomics for diverse goals, including for descriptive molecular atlases of organisms, for functional studies of genes and regulatory elements, and for modeling gene regulation.

单细胞基因组学领域正在爆炸。但是，绝大多数研究局限于量化mRNA转录，通常在几千个细胞中。我们最近开创了新的一类基于单细胞组合索引（“ SCI”）的概念的方法，其中几轮分裂，分子索引和合并用于独特地标记细胞或核的核酸，而无需每个细胞的分离或分区化。可以唯一标记量表的单元格数指数为索引的回合数量，例如数以百万计的细胞可以用最少的三个细胞进行分析索引的回合。自2015年以来，我们开发了用于量化染色质可及性的Sci-方法（SCI-ATAC-SEQ），转录（SCI-RNA-SEQ），染色质结构（SCI-HI-C）和基因组序列（SCI-Lianti），以及染色质可及性和转录的共同测定（SCI-CAR）。在这里，我们建议开发更广泛的单细胞方法，这都是基于单个单元的统一概念组合索引。在我们的第一个目标中，我们将开发各种的其他“单渠道” Sci-says 分子状态的各个方面。在我们的第二个目标中，我们将开发其他“两个渠道” Sci-says，例如 RNA和DNA的共同测量。在我们的第三个目标中，我们将调整科学分析以实现大型化学化学和单细胞中的遗传筛选。在我们的最终目标中，我们将努力制造方法和相关的软件研究界广泛使用。作为一个多功能，指数性可扩展的平台，我们预计单细胞组合索引将加深并扩大单细胞基因组学对潜水员目标的影响，包括生物体的描述性分子图谱，用于基因和调节的功能研究元素，用于建模基因调节。