Omics information maximization in single-cell sequencing with hybrid molecular and computational approaches

使用混合分子和计算方法实现单细胞测序中的组学信息最大化

• 批准号: 10434956
• 负责人: Billy Tsz Cheong Lau
• 金额: $ 47.1万
• 依托单位: STANFORD UNIVERSITY
• 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 2025-06-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10434956
• 关键词: Algorithms; Bioinformatics; Biological Assay; Biological Models; Biology; Cells; Cellular biology; Characteristics; Complementary DNA; Complex; Consumption; DNA; Data; Data Set; Development; Dropout; Engineering; Genome; Genomics; Goals; Human; Human Biology; Hybrids; Libraries; Lymphocyte; Methods; Molecular; Molecular Computations; Peripheral; Phase; Polymerase; Preparation; Primer Extension; Problem Solving; Research; Sampling; Sepharose; Signal Transduction; Solid; Source; Techniques; Technology; Vision; assay development; base; cohort; computer framework; data integration; experimental study; human disease; improved; innovation; magnetic beads; preservation; programs; scale up; single cell sequencing; transcriptome sequencing

ABSTRACT The overall goal of the proposed research program is to improve our understanding of single cell biology through information maximization techniques, by applying molecular engineering and computational approaches in sequencing. Specifically, single cell sequencing is rapidly becoming the predominant method for studying human biology and disease because it removes the confounding factor of sequencing cell mixtures in bulk. However, it has major pitfalls: significant material consumption during library preparation, noisy data readouts and signal dropout, and unclear paths for data integration across datasets. The overall vision of the proposed research program is to develop a pan-omic analysis strategy that enables perpetual re-use of any single cell source material. It revolves around a hybrid molecular engineering and computational framework that is loosely inspired by principles found in computing. The experimental core of the proposed research program revolved around a new molecular technology referred to as APEX (‘Attachment- based Primer EXtension’). The major innovation of APEX is the covalent conjugation of genomic material (i.e. DNA or cDNA) to a solid phase support such as an agarose magnetic bead, followed by utilizing only polymerase- based assays for non-destructive molecular interrogation. In this project, we will focus APEX development on single cell transcriptome sequencing applications, with general applicability to genome biology. As a model system, we will utilize peripheral lymphocytes as they consist of complex subpopulations with distinct characteristics at multiple levels of omic features. The project will focus on assay development and optimization, development of bioinformatic algorithms for data integration, and scale up to large cohorts as a demonstration of the scalability of the technology.

摘要 拟议研究计划的总体目标是通过以下方式提高我们对单细胞生物学的理解 信息最大化技术，通过应用分子工程和计算方法， 测序具体而言，单细胞测序正迅速成为研究人类的主要方法。 因为它消除了批量测序细胞混合物的混杂因素。但 存在主要缺陷：在库准备期间消耗大量材料，数据读出和信号噪声 dropout和跨数据集的数据集成路径不明确。 拟议的研究计划的总体愿景是开发一个泛组学分析策略， 任何单细胞源材料的永久再利用。它围绕着一个混合分子工程， 一种松散的计算框架，其灵感来自于计算中发现的原理。的实验核心 拟议的研究计划围绕着一种新的分子技术，称为APEX（'附件- 基于Primer Extension '）。APEX的主要创新是基因组物质的共价缀合（即， DNA或cDNA）转移到固相支持物如琼脂糖磁珠上，然后仅利用聚合酶- 基于非破坏性分子询问的分析。 在这个项目中，我们将把APEX开发的重点放在单细胞转录组测序应用上， 对基因组生物学的普遍适用性。作为一个模型系统，我们将利用外周淋巴细胞，因为它们组成 在多个水平的组学特征上具有不同特征的复杂亚群。该项目将重点 分析开发和优化，开发用于数据集成的生物信息学算法， 作为该技术的可扩展性的证明。