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Science in Real Time (ScienceIRT) podcast serves as a digital lab notebook—an open-access, conversational platform that brings the stories behind cutting-edge life science tools and techniques into focus. From biologics to predictive analytics and AI-powered innovation, our guests are shaping the future of therapeutic discovery in real time.
Carli: Hey everyone, and welcome to Science in Real Time. This is the show where we sit down with the people pushing science forward and talk about what's really happening behind the breakthroughs. Before we dive in, make sure to subscribe so you never miss an episode. We've got some incredible conversations coming your way. Alright, let's get into it.
Carli: Your presentation really was showcasing projects ranging from everything to cell painting to even neuronal outgrowth assay, which was incredible. So I was wondering, how do these techniques expand what is already possible within biology?
Claudia: These are kind of like different complexities of screens, right? We can do a more basic kind of target screen, which, depending on the system, can be really interesting—especially coupled with different counter-screening techniques and specificity. Sometimes you don't really get that in some of the more basic screens, but we can kind of expand on it that way. But when you get into more complicated models like cell painting and neuronal outgrowth, you get a lot more data from those screens, and you can actually kind of answer more complicated questions. We're not just looking at, like, "is it there or it isn't there," or "the cells are alive or the cells are dead." We're actually looking at the effects that compounds may have in the cells. So you get a lot more information, and the goal in that case is to have something that will be more successful when it translates out of the lab into a more translational setting, such as a clinical trial, animal model, or larger-scale model that people use to take the leads that we identify and develop further.
Carli: As I was hearing in your presentation, the idea of miniaturizing cell painting to a 1536-well format is incredibly fascinating. I was wondering, what were the biggest challenges and breakthroughs in making that scalable?
Claudia: Typically, everything that we do is 1536, which gives us a lot of breadth, so it was important to try and attempt to do so with cell painting as well. Victoria was the intern who came in and did the work this summer, and she did a really great job. We did some optimization with our liquid handling. When you work in 1536, you can't manually do any of that by hand, so you have to use liquid-handling instruments in order to add liquid to plates, cells to plates, dyes to plates, and also remove them. So we do have a couple of techniques that we've adapted for these situations in order to work in 1536. Victoria did a bit of optimization with dye concentrations, as well as pressure on the liquid-handling system so that we wouldn’t wash cells off the bottom of the plate, along with other optimizations. She did a really, really great job.
Carli: And I've also seen that you worked very closely with Araceli's Endeavor platform. I was wondering, what did that integration enable that wasn't possible before?
Claudia: It's really a modern instrument, which is really nice. The software is very user-friendly, and it's easy to understand what needs to be modified in order to get an excellent image, and that makes things much easier. The speed at which things get done is also a huge benefit. For example, if you image a plate and think, "I don't really like that," it's not a big deal to just go back and do it again. You can also try multiple exposures if you're trying to find the best exposure time for a set of dyes. You can test different conditions and compare them later in analysis, which is really helpful and helps you get the best assay and best data.
Carli: Absolutely, especially with how quickly things are moving—being able to generate that data faster is key.
Claudia: Yeah, for sure. We move really fast with things, and we're trying to get things in and out so we can maximize capacity. The speed of imaging acquisition really helps with that, and it helps us optimize much faster.
Carli: Absolutely, and now I want to switch gears a little bit—from targets to phenotypes. I want to talk a little bit about the power of integrated screening, which was a big part of your presentation. You mentioned how HCS enables really information-rich assays. I was wondering, what kinds of biological questions can we now answer that were previously out of reach?
Claudia: It's really information-rich, so you can ask more complicated questions versus just "there or not there" or "live or not alive." You can look at localization, intensity of spots, number of spots, and morphological profiles—like whether cell painting shows changes in cell size or nuclear size. These are all more complex questions you can ask about your drugs. Ultimately, this leads to better drug candidates at the end of your screen. One of the challenges we face is non-specific hits or off-target effects, so we design screens to minimize those and focus on compounds that will truly be efficacious. High-content screening allows you to get more information about the compounds you're working with, which leads to better lead identification down the road.
Carli: For those building screening programs today, like you have been, what's most important when integrating imaging, automation, and data analysis?
Claudia: There are several important things. First, you need to consider your biological question when looking at images. It's easy to say, "We see fluorescence, so it's good enough," but that's not usually the case. You need to ask: what are we trying to learn, and does this make sense in our model? For automation, washing is really difficult, so choosing dyes that are compatible with automation is a game changer when transitioning from manual to automated platforms. And for image analysis, people underestimate how much data you generate. You need to plan how you'll manage it, what your analysis pipeline is, and whether you have the computing and storage capacity to handle it. Those are key considerations before generating large datasets.
Carli: Yes, and now I wanted to kind of dig a little bit deeper into this AI automation. In your experience, what do you see as the future of discovery? The first thing that I was seeing during your presentation was the remarkable examples you were giving of auto-HCS specifically identifying outgrowth modulators using AI. I was wondering, how does this approach change the pace or accuracy of discovery?
Claudia: Yeah, I think it does a really good job of taking some of the conventional rules away from things that maybe don’t fall into conventional buckets. We do a lot of cell segmentation, which is kind of the classic high-content way of doing analysis, but neurons specifically are really hard to segment. You can ask anyone at this conference how difficult it is, and they’ll definitely tell you. But with the work we’ve been doing with ViQi and their auto-HCS toolkit, they don’t necessarily need to segment. They can do more of a bulk comparison between wells, which removes that hurdle. I think that’s going to make a big difference in how we approach these problems and how we quantify different cell types—especially ones that don’t fit into traditional models.
Carli: I'm wondering, what excites you most, and what do you think still needs a little bit of refinement?
Claudia: I think it’s really exciting to be able to eventually do things in real time—where you take an image and immediately get high-quality segmentation or analysis without much tweaking. Right now, people spend a lot of time adjusting segmentation algorithms, trying to get them just right. There’s also a lot of user variability—like debating whether something is one nucleus or two. Taking that subjectivity out with AI would be really powerful. I’m not an AI expert, but I think making these tools more accessible is key. Right now, you often need some expertise in machine learning to use them effectively. More user-friendly tools will be really impactful for scientists.
Carli: Absolutely, I certainly agree with that, because it’s not always the most intuitive.
Claudia: Yeah, exactly. There can be a pretty big learning curve between being a strong cell biologist and being able to do advanced image analysis. Companies like Araceli are really helping bridge that gap and enable scientists to work at scale.
Carli: I’m wondering, how do you see technology partnerships—like the ones you’ve had with ViQi and Araceli—shaping the next generation of discovery?
Claudia: From the work we’ve done with Araceli, you all have been really good about taking feedback and improving based on it. That’s really exciting. When you have direct conversations with users, you can understand what’s actually needed and what’s practical. We’ve had discussions about small features—like “we really want this button”—and those things end up making a big difference. That kind of collaboration enables functionality that might not have been obvious initially, but becomes really impactful for everyday users. I think that relationship between scientists and technology developers is really crucial.
Carli: I agree. Ultimately, you’re the ones using the machines, so we want them to work for you as effectively as possible. Looking ahead, do you think data models or analytical algorithms will be the biggest drivers of innovation in the next few years?
Claudia: I think AI and analytical tools will definitely be big drivers, but we shouldn’t discount physical technology. Microscopes, cameras, lasers—those are all still incredibly important. We’re in the age of AI, but having a really good instrument that captures high-quality images will always matter. Engineers are constantly improving these systems, and those improvements are just as important. A great camera can really make or break high-content imaging.
Carli: I love that point—just because we have AI doesn’t mean we should forget about the fundamentals or stop improving them. Now I want to switch gears a little bit about yourself—your motivation and mentorship. I know that you have worn a lot of different hats in all the places that you’ve been, and that you’ve built a very successful career. I was wondering, what originally sparked your passion for discovery science?
Claudia: Yeah. My first kind of science experience was in undergrad. I joined HHMI as part of the SEA-PHAGES initiative, and that was one of my first real science experiences. It was really positive. I wasn’t planning on doing research as a career at first, but I really enjoyed it. I loved the idea that I could do something no one else had done before—that I could solve problems and think about things in a new way. I think programs like that are really important because they open up possibilities that you didn’t even know existed.
Carli: Out of all the projects and experiments that you have led, is there one in particular that taught you the most about perseverance or creativity in research?
Claudia: If I go back to my PhD work, that was really a game changer. It was basically the biggest logic puzzle you can imagine. Biochemistry is very different from high-content imaging—you can’t actually see what’s happening. You run experiments and interpret results indirectly. That required a lot of creativity. I had to imagine what was happening in the system and design experiments to test those ideas. It was challenging, but also really rewarding in the end.
Carli: I am very glad that you did it as well, because you’re very good at it. So I’m very, very glad. And how do you approach mentorship or inspiring young scientists to enter this rapidly evolving field?
Claudia: I think it’s really important for young scientists to feel excitement about science. That curiosity and excitement for discovery need to be there. If they have that internal motivation, they’ll do really well. I also really like the creative aspect of science. I think of science as structured creativity. In another life, I might have been an artist, but in this one, I like having structure. Science allows you to be creative within the laws of physics, which is really unique. Helping people find that joy is really important.
Carli: To wrap up, for listeners who are interested in learning more about your work, yourself, or UF Scripps, where can they go?
Claudia: Yeah, absolutely. You can find me on LinkedIn—Claudine McCown—and I share my publications there. Scripps also has a website, and the High Throughput Screening Center has a webpage that lists our recent publications and the assays we’ve been working on. Those are great places to start.
Carli: That pretty much concludes our interview. I want to thank you so much for joining us. Your background and the work that you’re doing are incredibly inspiring—you’re really pushing the needle of discovery. It’s been an honor not only to have you here, but also to see your presentation in action. Thank you.
Claudia: Yeah, thank you for having me. It’s been great.
Carli: Thank you so much for tuning in to Science in Real Time. We’re really glad that you were able to spend time with us. If today’s conversation sparked questions or ideas, we’d love for you to keep it going. You can connect with our guest and the Real-Time team using the links in the description below. And if you enjoyed this episode, don’t forget to like, subscribe, and share it with someone who loves science as much as you do. Until next time, thanks for watching!