I'm Manuel Leonetti, I'm a group leader at the Chan Zuckerberg Biohub in San Francisco. Yeah, we're a research institute that really focuses on building technologies and applying them to biomedical questions. We have two main focus areas. One of them is infectious diseases. And the other, which I'm part of, is called quantitative cell sciences. What we're really trying to understand as part of this project is how the human body is organized. And we're asking this question at a few different levels. But what we are looking at, in my team, is really trying to understand how a single human cell is organized, and especially how it's organized as an ecosystem of all the different proteins that make up our cells. Cell biology is being completely transformed by technologies that allow us to ask questions with precision, depth that was unthinkable even just a few years ago, right? And we also have all these new tools that not only allow us to look at what cells are doing, but also to manipulate the properties of cells. And of course, you know, genome engineering and CRISPR is one of the driver of all of this. So there are so many opportunities. Now, the problem, the challenge is that the cell is a very, very complicated system. And so essentially, there are so many things that we should be looking at, at the same time, so many measurements that we have to take. And so the main challenge here is really that, from my point of view, of scale. How are we going to invent technologies or workflows that are going to allow us to do all these super powerful experiments tens of thousands of time? So the key approach that we use, in my group, to understand how proteins are organized within the cell is to turn them fluorescent or using tools like GFP, for example. And cell sorting is really essential in our workflows because we need to be able to isolate cells that are fluorescent. And so, in particular, we're using fluorescence-activated cell sorting, FACs, and, you know, some products by Sony Biotechnology to be able to do that. One of the big challenges in our workflows is that we have to do these experiments thousands of times. And we realized, a few years back, that there was a real opportunity to kind of rethink how we're doing all of this and create automation to be able to really help us. And here, we actually partnered with Sony to solve some of these problems. Especially, I collaborated with some bioengineers at my institute to build robotic and software automation systems to really drive our cell sorters in a completely automated way. And it's been a lot of fun, and that's been making a lot of people happy. This is such an exciting time for cell biology in general. I mean, we have technologies now that have never been as powerful and as diverse as possible. And we can really look at what cells are doing in so many different ways and with such a precision and depth that, you know, there are really, really big opportunities in making real progress in understanding of diseases in the near future. One of the big challenges is the cell is very complicated. There are so many different moving parts. And so at the end of the day, that requires to do so many different experiments and measure so many different things to really understand what's going on. And so one of the challenges that we're facing, but we think of it as an opportunity, is how can we scale all of these different experiments to really be able to do them, you know, tens of thousands of time and kind of match the complexity of the human cell. That's going to lead us to generate a ton of data. You know, and then we have to think about ways to share this data with the community, curate it, standardize it, you know, build, for example, websites or databases so that everybody in the field can access it and think about it. Because, at the end of the day, you know, these are very complex questions. It's going to take a lot of different pairs of eyes to really understand what's going on there. And I think, from my perspective, as somebody who's really interested in, you know, the mechanism of diseases, one of the big challenges for people like me, while still doing experiments in vitro, is how can we use systems like stem cells and organoids to really recreate models that allow us to ask disease-specific questions. How can we use the right cell types and the right combination of cell types to kind of recreate in a dish something that's, you know, the best position to ask these kind of disease questions?
