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

基本信息

批准号：
10657366
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
项目类别：
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

项目摘要

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.

抽象的拟议的研究计划的总体目标是通过信息最大化技术，通过应用分子工程和计算方法测序。具体而言，单细胞测序迅速成为研究人类的主要方法生物学和疾病是因为它消除了大量测序细胞混合物的混杂因素。但是，它有重大的陷阱：图书馆准备期间的大量材料消耗，噪音数据读数和信号辍学和跨数据集的数据集成的不清楚路径。拟议的研究计划的总体愿景是制定泛滥分析策略，以实现永久重复任何单个单元源材料。它围绕混合分子工程和计算框架是受到计算中原理的启发的。实验核心拟议的研究计划围绕一种新的分子技术开发，称为Apex（'eTteconment-- 基于底漆的扩展”。顶点的主要创新是基因组材料的共价共轭（即 DNA或cDNA）到固相支撑，例如琼脂糖磁珠，然后仅使用聚合酶 - 基于非破坏性分子询问的测定法。在这个项目中，我们将把Apex开发集中在单细胞转录组测序应用程序上，并使用一般适用于基因组生物学。作为模型系统，我们将使用外周淋巴细胞在它们组成的情况下使用在多个级别的imic特征下具有不同特征的复杂亚群。该项目将集中关于测定开发和优化，用于数据集成的生物信息学算法的开发，并扩展最多可作为技术的可扩展性演示。

项目成果

期刊论文数量（5）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Profiling SARS-CoV-2 mutation fingerprints that range from the viral pangenome to individual infection quasispecies.

DOI：
10.1186/s13073-021-00882-2
发表时间：
2021-04-19
期刊：
Genome medicine
影响因子：
12.3
作者：
Lau BT;Pavlichin D;Hooker AC;Almeda A;Shin G;Chen J;Sahoo MK;Huang CH;Pinsky BA;Lee HJ;Ji HP
通讯作者：
Ji HP

Single-cell multi-gene identification of somatic mutations and gene rearrangements in cancer.