Genomes are organised into gene circuits that control cell fate decisions and how cells organise themselves in tissues. We want to figure out how these circuits are wired and how the cellular microenvironment influences them during human development and regeneration.

To understand how the tissue microenvironment influences cell fate choices, we take a quantitative genomics approach. We profile multiple layers of regulation within individual cells using single-cell multiomics tools, and study their intercellular connections using spatial transcriptomics tools. We build reference tissue atlases that allow us to understand perturbations during disease and bioengineer cells and tissues in a dish.

Essential to navigate these atlases is the development of computational and statistical tools.  Our team has developed CellPhoneDB, which allows us to uncover the cell-cell communication processes driving differentiation using single-cell genomics approaches. Recent updates of this tool include the incorporation of (i) spatial data, to consider proximity between interacting partners, and (ii) multiomics data, to connect external and internal cellular circuits.

We also use advanced computational and machine learning tools to make accurate predictions from our datasets.

We use and develop robust, scalable in vitro cultures combining organoids and other supporting cells, such as fibroblasts or immune cells. These allow us to perturb gene circuits using gene editing tools or synthetic drugs. Combining all these tools we can mechanistically dissect biological processes and generate models for personalised medicine.

We are interested in leveraging our cellular maps to design more realistic in vitro models. For example, informed by our cell-cell communication predictions we can design targeted functionalized synthetic hydrogels that recapitulate the original tissue microenvironment. We use an iterative computational/experimental framework where each step is compared to the in vivo reference atlases.