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

• Applicants: Professor Dr. Lars Dölken; Professor Dr. Florian Erhard
• Subject Area: Bioinformatics and Theoretical Biology ; Virology
• Term: since 2020
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 438122098

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.

• DFG Programme: Research Grants