Deciphering pro- and antiviral factors for CMV infection by heterogeneity sequencing

通过异质性测序破译巨细胞病毒感染的促病毒因子和抗病毒因子

• 批准号: 438122098
• 负责人: Professor Dr. Lars Dölken
• 金额: --
• 依托单位: Institut für Virologie und Immunbiologie
• 依托单位国家: 德国
• 项目类别: Research Grants
• 资助国家: 德国
• 项目状态: 未结题
• 来源: https://gepris.dfg.de/gepris/projekt/438122098?language=en
• 关键词: Deciphering; pro; antiviral; factors; CMV

During the last three years, we developed single cell SLAM-seq (scSLAM-seq) and the bioinformatic tool GRAND-SLAM to study real-time transcriptional responses at single cell level. Our approach enables dose-response analysis at single cell level. We employed scSLAM-seq to detail transcriptional regulation and the underlying molecular mechanism during lytic murine cytomegalovirus (CMV) of fibroblast at unprecedented resolution. Here, we aim to extend the computational methods, pioneer scSLAM-seq for high-throughput droplet-sequencing-based approaches and exploit its full potential for functional genomics analysis.1. In the past months, we adapted scSLAM-seq for the commercial 10x Genomics Chromium platform. This now enables temporal analyses of thousands of single cells, and makes these techniques broadly applicable. We therefore expect that scSLAM-seq will be a widely used technique in many areas of research. At the moment there is no computational tool available to analyze such data for thousands of cells. We will develop GRAND-SLAM into the standard tool for such experiments.2. We have prototypical implementations for quality control and explorative analysis of such data. We will extend those, streamline them into an R package, making them publicly available and usable. Here, the main goal is to enable reproducible analyses and to identify pitfalls that, in our experience, often occur in such experiments. This is essential for the objectives in this proposal as well as for the further use of our technique by other researchers.3. We will develop "heterogeneity sequencing". scSLAM-seq allows to relate the state of each cell before perturbation to the state after perturbation. This allows to exploit the heterogeneity of the cells "before" in order to recognize functional relationships to any parameter "after". We will develop a flexible statistical framework (and embed it in the R package) to perform such analyses taking into account the peculiarities of single cell data (sparsity, drop-outs, over-dispersion).4. We will use "heterogeneity sequencing" to identify pro- and antiviral genes relevant for lytic CMV infection. This includes factors that play a role in overall infection efficiency, as well as more fine-grained analyses to dissect influences on specific phases of infection or the cellular immune response. Candidates will be validated using gene knockdowns and fluorescent reporter viruses.

在过去的三年里，我们开发了单细胞SLAM-SEQ(scSLAM-seq)和生物信息学工具Grand-SLAM来研究单细胞水平的实时转录反应。我们的方法使单细胞水平的剂量反应分析成为可能。我们利用scSLAM-seq以前所未有的分辨率详细描述了小鼠巨细胞病毒(CMV)裂解成纤维细胞过程中的转录调控和潜在的分子机制。在这里，我们的目标是扩展计算方法，开创基于液滴测序的高通量方法的先河，并充分开发其在功能基因组分析中的潜力1。在过去的几个月里，我们对scSLAM-seq进行了修改，使其适用于商业10x基因组学Chromium平台。这使得现在能够对数千个单细胞进行时间分析，并使这些技术得到广泛应用。因此，我们预计SCSLAM-SEQ将在许多研究领域中广泛使用。目前，还没有可用的计算工具来分析数千个细胞的此类数据。我们将把大满贯发展成为此类实验的标准工具。我们有对这些数据进行质量控制和探索性分析的原型实现。我们将对它们进行扩展，将它们简化为R包，使它们公开可用。在这里，主要目标是实现可重复的分析，并找出根据我们的经验，在此类实验中经常出现的陷阱。这对于本提案中的目标以及其他研究人员进一步使用我们的技术是必不可少的。我们将发展“异质测序”。SCSLAM-SEQ允许将每个小区在扰动之前的状态与扰动之后的状态相关联。这允许利用“之前”细胞的异质性，以便识别与任何“之后”参数的功能关系。我们将开发一个灵活的统计框架(并将其嵌入R包)，以执行此类分析，同时考虑到单个单元数据的特性(稀疏、丢失、过度分散)。我们将使用“异质性测序”来识别与裂解性CMV感染相关的促病毒基因和抗病毒基因。这包括在总体感染效率中发挥作用的因素，以及更细粒度的分析，以剖析对感染的特定阶段或细胞免疫反应的影响。候选人将使用基因敲除和荧光报告病毒进行验证。