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

项目摘要 下一代测序技术的出现极大地增强了检测亚细胞的能力， 细胞群和扩大我们对生物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，模糊了DNA或RNA序列。 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 能够系统地研究广泛的组织和细胞群体中的细胞异质性。 然而，仍然存在重大挑战。其中最主要的是高成本，低吞吐量，依赖定制 或商业上不可用的设备，以及准确检测低频单核苷酸的能力有限 变体。因此，有必要通过减少或消除这些问题来使SCS“民主化”。为此，我们 该提案利用了一种新的基于连接的方法来组合细胞索引， 增加了可以被分析的单个细胞的数量，同时消除了定制的 设备.我们最初的方法，我们最初称之为基于分裂池连接的转录组学 测序（SPLiT-Seq）能够对> 150，000个个体细胞的转录谱进行去卷积， >99.9%的准确性。这种方法利用了组合细胞索引的概念， 每个细胞中的所有核酸的短条形码序列的独特组合，使得所有读段共享 该组合可以被确定地确定为源自相同的细胞。重要的是，这种方法 不局限于RNA。因此，本提案旨在充分开发我们的基于连接的分裂池细胞 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体 检测（SNV）。具体目标1将侧重于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。由于以下因素的组合，SCS中的低频SNV检测是困难的 现代测序平台的相对高的错误率和在样品制备期间引入的错误。 因此，在特定目标2中，我们建议将我们的超精确双链测序技术与我们的 组合细胞索引方法。

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.