Recent advances in single-cell sequencing technologies have opened a unique opportunity to explore biological systems at a resolution and scale that seemed inconceivable only ten years ago. However, interpretation of the vast and complex data generated by these technologies remains a major challenge in the field. Our team develops open-source computational tools tackling some key challenges in single-cell data science. These include reference-based approaches to interpret single-cell omics data, data integration, gene signature scoring, cell type purification and web resources for single-cell data analysis ( https://spica.unil.ch/).

We mine single-cell omics data to dissect the diversity of immune cells in the tumor microenvironment. Our approach is based on the use of “reference atlases” which summarize current knowledge of immune cell diversity, and serve as stable, curated systems of coordinates for the interpretation of unknown data, such as from a cancer patient biopsy (Interpretation of T cell states from single-cell transcriptomics data using reference atlases | Nature Communications). We conduct large-scale meta-analysis of single-cell omics data to investigate how the immunological status of a patient, at the single-cell level, relates to its likelihood to respond to a specific therapy. 

Single-cell omics data including transcriptome, chromatin accessibility, T cell receptor and antigen specificity allows exploring the interplay between transcriptional and epigenetic programs, T cell affinity and clonotype. The aim of this project is to exploit single-cell multi-omics data from multiple timepoints and tissues to reconstruct T cell differentiation pathways in cancer and infection models. In collaboration with our experimental partners, we use CRISPR-based screenings to study the effect of targeting gene regulators on reprogramming T cell fate in vivo.