Epigenomic profiling of complex tissues with single-cell CUT&RUN

通过单细胞 CUT 对复杂组织进行表观基因组分析

基本信息

批准号：
10610976
负责人：
Steven Henikoff
金额：
$ 53.01万
依托单位：
FRED HUTCHINSON CANCER CENTER
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：
2019-04-19 至 2024-01-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10610976
关键词：
ATAC-seq Achievement Algorithms Antibodies Bar Codes Base Pairing Basic Science Biological Brain CD34 gene Cell Line Cell Nucleus Cells ChIP-seq Chromatin Clinic Complex Computer software Coupled Custom DNA Data Development Developmental Biology Disease Drosophila genus Elements Enhancers Evaluation Gene Combinations Gene Expression Genetic Genetic Predisposition to Disease Genome Genomics Goals Hematopoietic Histones Human Human Genetics In Situ Individual Infrastructure K562 Cells Laboratories Length Ligation Lymphoid Cell Maps Methods Modification Molecular Nuclear Nucleic Acid Regulatory Sequences Nucleosomes Organ Pathway interactions Population Positioning Attribute Procedures Protocols documentation Publications RNA Polymerase II Reagent Regulator Genes Regulatory Element Research Resolution Series Site System Technology Testis Tissues United States National Institutes of Health Validation algorithm development base cell dimension cell type clinical application combinatorial computerized tools cost cost effective data quality epigenome epigenomics genome-wide histone modification imaginal disc indexing novel nuclease promoter single cell analysis single cell technology single-cell RNA sequencing software development symposium technology development tissue processing tool transcription factor transcriptome sequencing

项目摘要

Summary Human genetic regulatory elements remain poorly defined, in large part owing to technical limitations of methods that have been used to map specific components of the chromatin landscape. By far the most popular of these methods is ChIP-seq, which is used in thousands of laboratories and is a staple of large infrastructural projects such as NIH's ENCODE and Epigenomic Roadmap consortia. However, in 1½ years since its introduction, our novel Cleavage Under Targets & Release Using Nuclease (CUT&RUN) antibody-tethered nuclease method has surpassed standard ChIP-seq in efficiency and resolution by orders of magnitude. We have developed extensions to CUT&RUN that make it more generally applicable to biological problems, and have introduced a high- throughput automated format for research and clinical applications. To extend the utility of CUT&RUN to heterogeneous cells and tissues, we propose to develop two single-cell CUT&RUN strategies with distinct advantages. In both cases, we apply in situ ligation to CUT&RUN fragments in bulk, for which we present preliminary proof-of-concept data. One strategy uses direct barcoded amplification of CUT&RUN fragments in nanowell chip arrays and the other uses split-pool combinatorial barcoding. To help guide technology development and to further our understanding of important developmental pathways, we will apply single-cell CUT&RUN to human CD34+ primary hematopoietic cells and Drosophila germline tissues. We will also develop novel computational tools customized for CUT&RUN data that take advantage of the base-pair precision of cleavages by using fragment length and position for peak-calling and for identification of active genetic regulatory elements. We will use standard computational tools that have been developed for single-cell RNA-seq data to delineate cell-type, and we will develop software for simultaneous mapping of adjacent transcription factors, histone marks and RNA Polymerase II within single cells to deduce enhancer-promoter-gene combinations. Finally, we will exploit the ability of CUT&RUN to detect a new general nucleosome feature that we recently discovered in which regulatory elements are marked by asymmetrically unwrapped nucleosomes. Taken together, our proposal will introduce a low-cost high-throughput single-cell epigenome characterization strategy that applies to the wide variety of basic research and clinical applications that require information from the activity of genetic regulatory elements.

概括人类遗传调节元素的定义较差，在很大程度上是由于技术用于绘制染色质景观特定组成部分的方法的局限性。到目前为止，这些方法中最受欢迎的是Chip-Seq，它用于数千个实验室和是大型基础设施项目的主食，例如NIH的编码和表观基因组路线图财团。但是，自引入以来的1。5年后，我们的小说裂解目标和释放下使用核酸酶（切割和运行）抗体螺旋核酸酶方法已经浏览了标准芯片seq 通过数量级的效率和解决方案。我们已经开发了剪切和运行的扩展这使其更普遍地适用于生物学问题，并引入了高度用于研究和临床应用的吞吐量自动化格式。为了扩展切割和运行的效用到异质细胞和组织，我们建议开发两个具有独特优势的单细胞切割和运行策略。在这两种情况下，我们都将原位连接施加到散装剪切片段，为此我们提供初步的概念验证数据。一种策略使用纳米韦尔芯片阵列中切割和运行片段的直接条形码放大，另一个使用分裂池组合条形码。帮助指导技术发展并进一步了解重要的发育途径，我们将应用单细胞剪切和运行人CD34+原发性造血细胞和果蝇种系组织。我们还将发展用于剪切和运行数据的新型计算工具，以利用基础对通过使用碎片长度和位置进行峰值打击和识别裂解的精度主动遗传调节元素。我们将使用已有的标准计算工具为单细胞RNA-seq数据开发以描绘细胞类型，我们将开发用于简单地映射相邻转录因子，组蛋白标记和RNA聚合酶II 在单个细胞内推断增强子促销 - 基因组合。最后，我们将利用剪切和运行能够检测我们最近在中发现的新的一般核病特征哪些调节元件以不对称的未包装核小体为特征。综上所述，我们的建议将引入低成本的高通量单细胞表观基因组适用于各种基础研究和临床应用的表征策略这需要来自遗传调节元素活动的信息。