Utilizing single-cell Massively Parallel Reporter Assays to dissect gene by environment interactions

项目摘要/摘要 我建议开发一种新的分析方法，单细胞大规模平行报告分析(ScMPRA)，通过 环境相互作用(GxE)，特别是更好地理解核苷酸变异是如何 顺式调控元件与单细胞的可变环境相互作用，调节基因的表达。 我假设细胞环境通过调节基因的活性来改变基因的表达。 调控元件，由于核苷酸变异，这些元件的行为可能会有所不同。目前，没有 可用于在不同蜂窝环境中对变体进行功能性验证的高吞吐量方法。至 为了解决这个问题，我将采用我们开发的基于慢病毒的大规模并行报告程序分析(LentiMPRA) 实验室并将其与10倍基因组学scRNA-seq技术结合使用，允许同时 通过转录组对细胞环境进行量化，并用LentiMPRA对增强子活性进行量化 建造。这项测试将提供一个新的平台来测试驱动基因变化的变异之间的因果关系 以高通量的方式在特定环境中表达。我会用这项技术来研究和 验证GxE在不同细胞类型(Aim1)和不同免疫刺激条件下(Aim 2)的有效性 免疫激活与潜在遗传变异的关系。我们将使用线性混合模型 (LMM)，以量化SNPs与环境之间的相互作用，包括细胞类型、细胞状态和供体。 这项工作将绘制对细胞敏感的增强子及其疾病相关变体的图谱 环境，提供了一个平台来为与特定环境交互的变量分配因果关系。

PROJECT SUMMARY/ABSTRACT I propose to develop a novel assay, single-cell Massively Parallel Reporter Assay (scMPRA), to study gene by environment interactions (GxE) and in particular gain a greater understanding of how nucleotide variation in cis-regulatory elements interact with the variable environments of single cells to modulate gene expression. I hypothesize that the cellular environment modifies gene expression by modulating the activity of regulatory elements and these can behave differently due to nucleotide variants. Currently, there are no high-throughput methods available to functionally validate variants across different cellular environments. To address this, I will adapt a lentivirus based Massively Parallel Reporter Assay (lentiMPRA) developed in our lab and use it in combination with 10x Genomics scRNA-seq technology to allow the simultaneous quantification of the cellular environment via the transcriptome and of enhancer activity with the lentiMPRA construct. This assay will provide a novel platform to test the causality of variants driving changes in gene expression in specific environments in a high throughput manner. I will use this technology to study and validate GxE in distinct cell types (Aim1) and in different immune stimulation conditions (Aim 2) to dissect the relationship between immune activation and underlying genetic variation. We will use linear mixed models (LMMs) to quantify the interaction between SNPs and environments including, cell type, cell state, and donor. This work will map enhancers and their disease associated variants that are sensitive to the cellular environment, providing a platform to assign causality to variants that interact with specific environments.