Mapping gene-by-environment interactions using multiplexed single cell RNA-sequencing

使用多重单细胞 RNA 测序绘制基因与环境相互作用图谱

• 批准号: 10409737
• 负责人: Chun Jimmie Ye
• 金额: $ 101.73万
• 依托单位: UNIVERSITY OF CALIFORNIA, SAN FRANCISCO
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-08-07 至 2024-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10409737
• 关键词: Affect; Animal Model; Bar Codes; Benchmarking; Biological; Biological Assay; Cells; Chromosome Mapping; Collaborations; Computational Biology; Computing Methodologies; Data; Development; Dimensions; Disease; Dropout; Environment; Environmental Exposure; Gene Expression; Genes; Genetic; Genetic study; Genome; Genome engineering; Genomics; Genotype; Glean; Heritability; Human; Human Genetics; Immune; Immune response; In Vitro; Individual; Knowledge; Label; Light; Lipids; Maps; Methods; Microfluidics; Modeling; Nature; Oligonucleotides; Peripheral; Phenotype; Population; Process; Quantitative Trait Loci; Regulatory Element; Research; Sampling; Scheme; Science; Source; Statistical Models; Stimulus; Technology; Testing; Time; Transcript; Variant; cohort; combinatorial; computational pipelines; computer framework; cost; cost effective; exome sequencing; experience; experimental study; gene environment interaction; gene expression variation; genetic architecture; genetic variant; genome sequencing; genome-wide; immune activation; indexing; insight; multidisciplinary; risk prediction model; single-cell RNA sequencing; targeted sequencing; theories; trait; transcriptome sequencing

ABSTRACT While common variants are known to additively contribute to gene expression variation, there has been limited statistical evidence of gene-by-environment interactions (GxE) in humans. This is because even the largest expression quantitative trait loci (eQTL) studies have little statistical power to detect GxE interactions given the large number of segregating loci and extensive variability in environmental exposure. We hypothesize that the integration of multiplexed perturbations and single-cell RNA-sequencing is an efficient strategy for mapping GxE interactions in large population cohorts. However, current approaches are not scalable to sequencing 107 cells across 104 samples (i.e. 103 donors by 10 conditions) needed for sufficiently powered perturbation screens in human cohorts. In this proposal, we will first develop a cost-effective single-cell RNA-sequencing approach called DIT-seq that reduces the cost of sequencing to $0.06/cell (Aim 1). We will then develop strategies for encoding environmental perturbations using sample multiplexing to map and characterize GxE interactions in the human immune response (Aim 2). Finally, we will develop a new statistical model and a computational pipeline for efficient hypothesis testing using tens of millions of cells (Aim 3). The experimental and computational technologies proposed have the potential to create fundamental new ways to study genotype-phenotype relationships and the biological insights gained could shed light on the genetic architecture of gene expression and facilitate the interpretation of disease-associations from large-scale genome and exome sequencing studies.

抽象的 虽然已知常见变体有助于基因表达变异，但有限 人类基因相互作用（GXE）的统计证据。这是因为甚至最大 表达定量性状基因座（EQTL）研究几乎没有统计能力来检测GXE相互作用 大量分离基因座和环境暴露的广泛可变性。我们假设 多路复用扰动和单细胞RNA序列的整合是绘制GXE的有效策略 大量人群中的相互作用。但是，当前方法无法扩展对107个细胞进行测序 在104个样本中（即103个捐赠者，按10条条件）需要足够动力的扰动屏幕 人类人群。在此提案中，我们将首先开发一种称为成本效益的单细胞RNA测序方法 dit-seq将测序成本降低到$ 0.06/cell（AIM 1）。然后，我们将制定编码策略 使用样品多路复用来映射和表征人类中的GXE相互作用的环境扰动 免疫反应（AIM 2）。最后，我们将开发一个新的统计模型和一个计算管道 使用数千万细胞进行有效的假设检验（AIM 3）。实验和计算 提出的技术有可能创建基本的新方法来研究基因型 - 表型 关系和获得的生物学见解可以阐明基因表达的遗传结构 并促进了大规模基因组和外显子组测序研究中疾病缔合的解释。