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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.

项目总结