Research

Gene expression programs underpin cell identity and their response to external cues. These signals from other cells may induce cell fate decisions, or change the way the cell responds to another stimulus. In my lab I combine experimentation and computational biology to study how genetic variation in our genomes regulates these cell-cell interactions and decision-making processes. We use a combination of in vitro cell co-culture and stimulation with quantitative genetics to identify the genetic variants that alter cellular function.

During my research appointment in Cambridge I developed a computational method, Milo, to identify perturbed cell states during a single-cell experiment, or between patients and healthy controls. This powerful approach blended graph theory (kNN-graphs) with statistical modelling (generalised linear models). I now extend this paradigm to model dependent observations using generalised linear mixed models which unlocks population genetic analysis of cell state perturbations. Therefore, we are on the cusp of identifying genetic variation that shapes both gene expression programs (eQTLs) and cell states (csQTLs) at an unprecedented resolution.