The ability of cells to sense their environment and respond to it is one of the most basic function common to all living organisms. Individual cells constantly integrate a large array of inputs such as growth factor signals, metabolic and mechanical status, to make decisions about whether to grow, divide, migrate, differentiate to a specific lineage, or die. Complex signaling networks allow to integrate these cellular inputs into clear-cut, robust fate decisions. While most of what we know about signaling networks comes from population average methods (western blots, proteomics, …), modern technologies now allow us to measure signaling dynamics in thousands of single living cells with unprecedented spatio-temporal resolution. This provides novel information that refines our current understanding of signaling networks.

One of the focus of the lab is to build novel biosensor systems to visualize single-cell signaling dynamics at second and micrometer time/length scales. We also engineer optogenetic/microfluidic actuator systems to manipulate signaling at these specific spatio-temporal scales. Modern live cell imaging technologies then allow us to measure/manipulate signaling dynamics in thousands of single cells. We are then confronted to large datasets that require modern computer vision tools for automated image analysis, statistic approaches to characterize the signaling time-series, and mathematical modelling to provide insight into about the underlying signaling networks. We use this multidisciplinary toolkit to study receptor tyrosine kinase signaling in a variety of fate decision model systems, as well as in cancer, and stem cell biology.

We have recently focused on ERK MAPK signaling and shown that different cells within a population can display significantly different signaling states that, for now, most often have been averaged out by population average measurements. These heterogeneous signaling states reflect the fate decision heterogeneity typically present in a population of cells. In addition to the measurements of these dynamic single-cell, signaling states, which is already highly informative, we have shown that temporally perturbing these signaling dynamics using optogenetic and microfluidic actuators, is highly informative about the underlying signaling network circuitry. This approach, when coupled to mathematical modelling, enables a quantitative understanding of the non-linear behavior of signaling networks. This can then provide insight about phenomena such as the emergence of robustness in these signaling networks. The novel insights we derive from our multidisciplinary approach provide novel opportunities to reprogram fate decisions in stem cell biology, or to measure signaling heterogeneity and network robustness that feed into mechanisms of cancer drug resistance during targeted therapy. Together, our approach provides new insight into pathologies such as cancer, inflammation and developmental disorders.