Cell scientist to watch – Julien Berro

2019 Journal of Cell Science  
in 2017. His laboratory is interested in force generation during endocytosis, and the underlying molecular mechanisms controlling membrane deformation and tension sensing. What inspired you to become a scientist? My mum was a psychiatrist and my dad was a journalist and I didn't grow up with a lot of mathematics, physics or cell biology around me. My grandparents had a butchery in a small town in France. My granddad was from a poor family and left school at 12 to do an apprenticeship, just like
more » ... ticeship, just like his brother who became a baker. The rationale was that they would always have something to eat. However, my grandfather was a very curious person and had he been born in a different time, I think he would have become an engineer. He was always trying to fix things; people would bring him electronics and things that he didn't really know so much about, but his inquisitive mind drove him to try to figure out what was wrongsometimes he would manage to repair it and sometimes he would irreversibly break it [laughs]. I remember him making a rake to collect shellfish, which was a bit of a contraption, but he liked this kind of thing. Despite no formal training, he had an engineer's and scientist's mind-set and I grew up with this inspiration. So as a child, my dream job was to be a doctor and car mechanic at the same time ('médecin-mécanicien' in French), because I was convinced that it was pretty much the same thing! Funnily enough, I feel like I achieved my dream job -I do biology and look at the mechanics of the cell! However, your studies and initial training were of a mathematical nature, right? I got into mathematics in college because the French educational system pushes you mostly towards maths and physics when you're good in science. Afterwards I attended a school of engineering for computer science and applied mathematics. I think it was a good choice and don't regret it, but I always felt there was something missing and I thought I would do a master in something more physics related. In the end, a master degree in mathematical modelling in biology in Grenoble caught my eye and I really liked it; so that's how I switched. What questions are your lab trying to answer just now? We'd like to understand how forces are produced in the cell and how forces are sensed. Our favourite case study for these questions is endocytosis, specifically, how cellular mechanics and biochemistry work together to deform the membrane. I started to look at endocytosis because I was interested in the actin cytoskeleton. It's a challenging system, because endocytosis is a very transient process involving proteins and lipids, and the process is diffraction limited. I feel that with our quantitative and modelling background, we can make the biggest impact and have a unique angle to address some of the open questions in the field. Most of the projects we are working on now came from discrepancies between the conceptual models that the field thought about in terms of mechanism and the numbers that we measured or the mathematical models that we made. The quantitative mismatches forced us to revisit the mechanisms and perform new experiments to test them. What are the techniques you are currently using in the lab? We use a lot of quantitative microscopy and our workhorse is still the spinning disc confocal microscope. In addition, we've developed single-molecule methods to get a sense of how dynamic the process is and how fast proteins exchange during endocytosis. We also aim to get a sense of nanometer resolution of the protein movements. Several labs are looking at the overall super-resolution organisation of these structures, but we think that by looking at the movement of single molecules, we can learn a lot about how forces are produced. We developed these methods in collaboration with David Baddeley at Yale before he moved to the University of Auckland, New Zealand. What do you find more fulfillingimaging a single-molecule assay or to see that the data fit the prediction from the model? Many people get excited by pretty images, but I prefer to see when an analysis comes together well. It's fun when you make a prediction from modelling and then the data work with the prediction. Actually, I'm also excited when the model doesn't work because it raises more questions about the mechanisms. The postdocs or students in my lab tend to get depressed when the
doi:10.1242/jcs.233296 fatcat:3wd7c23i3zc7xcfwiexrlhybie