Time-resolved single cell genomics of human cytomegalovirus infection in myeloid cells

人巨细胞病毒（HCMV）持续感染世界上大多数人口。虽然健康个体的HCMV感染通常是亚临床的，但危及生命的HCMV疾病在免疫功能低下者中常见。在病毒进入细胞后，决定这是否导致裂解、潜伏或流产感染。这种决定是由细胞内在和外在因素之间的相互作用形成的。此外，HCMV表达一个免疫evasins武器库，以靶向宿主防御机制并促进生产性感染。详细了解定义不同感染结果的潜在分子机制，需要在单细胞水平上进行全局分析。此外，越来越清楚的是，体外HCMV潜伏模型中病毒基因表达的谱系比之前认识到的要广泛得多。这些研究挑战了限制性潜伏期相关转录程序的教条。 在这里，我们的目标是利用我们最近开发的一种称为单细胞SLAM-seq（scSLAM-seq）的新型单细胞RNA测序（scRNA-seq）方法的全部功能。scSLAM-seq采用代谢RNA标记，化学核苷酸转换和高级计算分析（GRAND-SLAM），为scRNA-seq增加了时间维度，并促进了单细胞水平的剂量反应分析。基于来自健康供体的HCMV感染的原代人巨噬细胞和单核细胞的时程scSLAM-seq分析，我们将利用存在于单细胞水平的广泛的细胞间异质性，并利用新型随机条形码报告病毒进行组合的功能丧失和获得筛选。因此，我们将确定新的宿主因子，以及它们在调控网络中的相互作用如何控制骨髓细胞中的感染结果。我们将使用功能测定来表征感兴趣的候选者，并破译真正的潜伏HCMV转录组。总之，这项工作将在单细胞水平上提供对HCMV感染结果的全面功能性理解，并开创令人兴奋的新方法，在生物医学科学中具有广泛的适用性。

The human cytomegalovirus (HCMV) persistently infects the majority of the world’s population. While HCMV infection of healthy individuals is usually subclinical, life-threatening HCMV disease is frequent among the immunocompromised. Following entry of the virus into a cell, a decision is made whether this results in lytic, latent or abortive infection. This decision is shaped by the interplay between cell-intrinsic and extrinsic factors. Moreover, HCMV expresses an armory of immune evasins to target host defense mechanisms and promote productive infection. A detailed understanding of the underlying molecular mechanism that define disparate infection outcomes, requires global analyses at single cell level. Furthermore, it has become increasingly clear that the repertoire of viral gene expression in in vitro HCMV latency models is much broader than previously appreciated. These studies have challenged the dogma of a restricted latency-associated transcription program. Here, we aim to exploit the full power of a novel single cell RNA sequencing (scRNA-seq) approach termed single cell SLAM-seq (scSLAM-seq), which we recently developed. Employing metabolic RNA labeling, chemical nucleotide conversion and advance computational analyses (GRAND-SLAM), scSLAM-seq adds a temporal dimension to scRNA-seq and facilitates dose-response analysis at single cell level. Based on time-course scSLAM-seq analysis from HCMV infected primary human macrophages and monocytes from healthy donors, we will exploit the extensive intercellular heterogeneity that is present at single cell level, and utilize novel random barcode reporter viruses to perform combined loss- and gain-of-function screens. We will thereby identify novel host factors and how their interplay in the regulatory network governs the infection outcome in myeloid cells. We will characterize interesting candidates using functional assays and decipher the true latent HCMV transcriptome. In summary, this work will provide a comprehensive functional understanding of HCMV infection outcomes at the single cell level and pioneer exciting new methodology with broad applicability in biomedical sciences.