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

• 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
• 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

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。 细胞群并扩展我们对有机体生物学的基本理解。然而，典型的 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 对单个细胞进行测序分析。单细胞测序（SCS）技术的发展 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、依赖定制 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 变种。因此，需要通过减少或消除这些问题来实现 SCS 的“民主化”。为此，我们的 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 设备。我们最初的方法，最初称为基于分割池连接的转录组学 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。该方法利用了组合细胞索引的概念，该概念将 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 可以明确地确定该组合源自同一细胞。重要的是，这种方法是 本质上不限于RNA。因此，该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 检测（SNV）。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 和基因组 DNA 的细胞索引。由于以下因素的结合，低频 SNV 检测在 SCS 中很困难 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。