Lygeri (Lily) Sakellaridi
I obtained my BSc in Biology from the National Kapodistrian University of Athens, Greece. During my studies, it became clear to me that the future of biology would be increasingly quantitiative and computational. The ability to work with large mounts of messy data and extract meaning from them was extremely appealing and fascinating to me, and I wanted to develop these skills.
After an internship that involved analysis of bulk RNA-seq data, I decided to continue with an MSc in Wageningen University, Netherlands. There, I honed my bioinformatics skills and explored my research interests. After my degree, I took some time off to think about what I really wanted to do, and stumbled across the field of viral bioinformatics: an intriguing but also vastly understudied combination that I had never considered before. I applied to ESR6, did surprisingly well in an interview that rigorously tested my understanding of Bayesian statistics, and now here I am.
In my project I will try to find out what happens when a virus enters its host: which genes determine the path of the infection? Why some cells immediately lysed, while others follow a lytic path, and yet others are not affected at all? I will use a combination of metabolic labeling, statistics, and single-cell RNA sequencing to try and answer these questions. I am very excited to participate in this consortium, and I’m sure it will lead to many exciting results.
Mini pronounciation guide for my name:
Lee (as in “Stan Lee”)
Ye (as in the first syllable of “yeah”)
Ree (as in “tree”; this is also the stressed syllable!)
Other things I like include: reading (magical realism, classics, fantasy, biographies), walks in nature (but no camping!), logical puzzles, and funny videos of ducks.
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.