The human cytomegalovirus (HCMV) is a herpesvirus prevalent worldwide responsible for life-threatening infections in individuals with an impaired or immature immune system. During the first 1-2 h after virus entry into a cell, a decision is made whether this results in lytic, latent or abortive infection. Previous single-cell RNA-seq (scRNA-seq) approaches did neither provide the necessary temporal resolution nor sensitivity to analyse this crucial phase of virus-host interaction. To overcome these limitations, we recently developed single-cell SLAM-seq (scSLAM-seq), which combines metabolic RNA labelling with thiol-(SH)-linked nucleotide conversion sequencing (SLAM-seq) and single-cell sequencing (scRNA-seq). scSLAM-seq visualised dynamic changes in transcriptional activity during the first 2 h of lytic murine CMV (MCMV) infection. By inferring expression profiles prior to infection for each cell, scSLAM-seq provides unique opportunities to identify factors that influence on the infection outcome. As a proof of concept, we predicted the infection efficiency in murine fibroblasts based on cell cycle and dose of infection with unprecedented accuracy. We have now made scSLAM-seq compatible with high-throughput 10x Chromium sequencing. We will use 10x-scSLAM-seq to investigate three experimental model systems that result in either lytic (primary human) or latent (primary human monocytes) or both (primary human macrophages) HCMV infection. While the lytic infection model is established in our lab, latent infections will be performed by the Stern-Ginossar lab (ESR 15).
These data will enable us to
- identify (viral and host) key factors that determine the infection outcome,
- infer the context-dependent regulatory network of the host cell and its changes induced by CMV infection,
- analyse the evolutionary conservation of these mechanisms between MCMV and HCMV and
- assess silencing of residual lytic and establishment of true latent viral gene expression.
This will clarify how HCMV reprograms the latently infected cell while avoiding detrimental immune recognition by cytotoxic T cells. To pursue these goals, the student will continue developing our established software tools for (sc)SLAM-seq data analysis. To gain experience in sequencing data analysis outside the academic field and with industrial infrastructures and processes, the student will be hosted for 6 months by InfectoGnostics to setup analysis pipelines in virus-related applications.