Massively Parallel Single Cell Detection of Rare Variants with Split-Pool Combinatorial Indexing

使用分池组合索引大规模并行单细胞检测稀有变异

基本信息

批准号：
10025975
负责人：
Scott Robert Kennedy
金额：
$ 62.2万
依托单位：
UNIVERSITY OF WASHINGTON
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：
2020-09-01 至 2023-08-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10025975
关键词：
Acute Address Aneuploidy Bar Codes Base Sequence Biological Biological Assay Biological Process Biology CRISPR/Cas technology Cell Line Cells Custom DNA DNA amplification DNA sequencing Data Detection Development Equipment Frequencies Gene Expression Genetic Heterogeneity Genetic Transcription Genetic Variation Genome Genomic DNA Genomics Goals Health Heterogeneity Human Human Cell Line In Situ Individual Investigation Ligation Methods Microfluidics Modernization Molecular Morphologic artifacts Mus Mutation Nucleic Acids Pathologic Processes Performance Phase Population Preparation Protocols documentation RNA Reporting Resolution Reverse Transcription Sampling Single Nucleotide Polymorphism Somatic Mutation Source Structure Technology Testing Tissues Transcript Variant Work base combinatorial cost design epigenomics experimental study high throughput screening indexing insight instrument interest nano next generation sequencing novel novel strategies rare variant restriction enzyme sequencing platform single cell analysis single cell sequencing single cell technology transcriptomics variant detection

项目摘要

PROJECT SUMMARY The advent of next generation sequencing technologies has dramatically enhanced the ability to detect sub- populations of cells and expanding our fundamental understanding of organismal biology. However, typical sequencing protocols use bulk DNA or RNA mixed from thousands to millions of cell as input, obscuring the specific sequencing information from any given cell. The only way to directly study cellular heterogeneity is to perform sequencing analysis of individual cells. Development of single-cell sequencing (SCS) technologies has enabled systematic investigation of cellular heterogeneity in a wide range of tissues and cell populations. However, significant challenges remain. Chief among them are high cost, low throughput, reliance on customized or commercially unavailable equipment, and limited ability to accurately detect low frequency single nucleotide variants. As such, there is a need to ‘democratize’ SCS by reducing or eliminating these issues. To that end, our proposal makes use of a new ligation-based approach to combinatorial cellular indexing that dramatically increases the number of individual cells that can be assayed while eliminating the need for customized equipment. Our original approach, which we originally termed Split-Pool Ligation-based Transcriptomic Sequencing (SPLiT-Seq), is able to deconvolve the transcriptional profiles of >150,000 individual cells with >99.9% accuracy. This approach makes use of the concept of combinatorial cellular indexing which ligates a unique combination of short barcode sequences to all the nucleic acids in each cell, such that all reads sharing this combination can be definitively determined to be derived from the same cell. Importantly, this approach is not inherently limited to RNA. Therefore, this proposal aims to fully develop our ligation-based split-pool cellular indexing approach for use in DNA-based applications with a special emphasis on rare single nucleotide variant detection (SNV). Specific Aim 1 will focus on strategies for in situ genome fragmentation and optimizing ligation and cellular indexing of genomic DNA. Low frequency SNV detection is difficult in SCS due to a combination of relatively high error-rates of modern sequencing platforms and errors introduced during sample preparation. Therefore, in Specific Aim 2, we propose to integrate our ultra-accurate Duplex Sequencing technology with our combinatorial cellular indexing approach.

项目概要下一代测序技术的出现极大地增强了检测亚组的能力。然而，典型的细胞群体并扩大了我们对生物生物学的基本理解。测序方案使用从数千到数百万个细胞混合的大量 DNA 或 RNA 作为输入，从而模糊了来自任何给定细胞的特定测序信息直接研究细胞异质性的唯一方法是单细胞测序（SCS）技术的发展已经实现了对单个细胞的测序分析。使得能够对广泛的组织和细胞群中的细胞异质性进行系统研究。然而，重大挑战仍然存在，其中最主要的是成本高、吞吐量低、对定制的依赖。或商业上无法获得的设备，以及准确检测低频单核苷酸的能力有限因此，有必要通过减少或消除这些问题来实现 SCS 的“民主化”。该提案利用了一种新的基于连接的方法来进行组合细胞索引，显着增加了可检测的单个细胞的数量，同时消除了定制的需要我们最初的方法，我们最初将其称为基于分池连接的转录组学。测序 (SPLiT-Seq) 能够对超过 150,000 个单个细胞的转录谱进行解卷积 >99.9% 的准确度该方法利用了连接 a 的组合细胞索引的概念。每个细胞中所有核酸的短条形码序列的独特组合，使得所有读数共享重要的是，这种方法可以明确地确定来自同一细胞。因此，本提案旨在充分开发我们基于连接的分割池细胞。用于基于 DNA 的应用的索引方法，特别强调罕见的单核苷酸变异具体目标 1 将重点关注原位基因组片段化和优化连接的策略。由于以下因素的结合，SCS 中的低频 SNV 检测很困难。现代测序平台的错误率相对较高，并且在样品制备过程中引入了错误。因此，在具体目标 2 中，我们建议将我们的超精确双工测序技术与我们的组合细胞索引方法。