Hello. Good morning. We are about to start. Great. Thank you. Great. Good morning. Welcome to the UBS Precision Medicine Frontier Summit. My name is Lu Li. I'm the Life Science Patient Diagnostic Analyst at UBS. So happy to kick off our first panel today, which is a new dimension in proteomics and cellular research. With me today are Dr. Wenbin Jiang from Cytek Biosciences, and then Jeff Hawkins, CEO from Quantum-Si. Great. I guess the theme today is talking about new dimensions in proteomics and cellular research. We're thinking about the new tools, new applications in the space. Both of your firms bring in new technology to the space, either full spectral profiling flow cytometry or protein sequencing. I wonder, with your new technology, what kind of new applications that we are able to do now that we were not able to do in the past? Sure. I'll start. Quantum-Si offers what we call next-generation protein sequencing, reading out individual amino acids. What we're seeing customers want to do with our technology, obviously, there's a lot of technologies in the proteomics space typically fairly dedicated to some sort of set of applications. What people tend to want to do with a new tech like ours is study things that have either been very difficult to study with current technology, maybe requires really expensive equipment or bespoke sort of bioinformatics pipelines, or they just couldn't do it at all. Single amino acid variants, literally wanting to see if there's a change at one position. They want to look at something called an isomer that they can't detect on mass spec, but they could see it with our tech. They want to look at post-translational modifications, these small changes to individual amino acids that happen when the protein is being expressed. It's those types of things they want to look at because they believe they're going to be very important in the context of response to therapy or prediction or progression of disease, those types of things. That's what we're seeing people want to do with the technology. When you're here in the U.S., I'd say maybe a little different outside the U.S., where maybe there's less access to proteomics tools in general, and you'll see people wanting to do more basic protein characterization and quantitations. That's what we see in the two different markets. Sure. We are a company, a life science tool company, and we do flow cytometer, and clearly it becomes more well-known these days because of the BD sales, BDB Life Science business to water recently. Flow cytometer, by look at the name, basically, it's to count cells. It's a [meter], right? It looks at the phenotype of the cells, then looks at the population of different types of immune cells. The conventional technology started quite a few years ago, many years ago, actually. It had a bottleneck because of the limitation of the conventional technology with the number of parameters it can look at. This is where we come from. We realized the conventional technology actually threw out lots of information when they detect the cells, capture the signals. What we come up with, what we call the full spectral profiling technology, basically, we capture all the information coming out of the detections and from the cells. From there, that enables us to expand the number of parameters that can detect using a flow cytometer, tremendous. By doing this, that enables many of the applications which conventional technology wouldn't be able to. For example, one of the examples is because it's a spectrum, right? With spectrum in the conventional sense, many of the signals, for example, in the cancer cell studies, the autofluorescence from the cancer cell is considered as a noise, which causes problems with the typical detection. Now with our full spectral technology, we treat those autofluorescence from the cell as one of the parameters that enable us to drive the application, improve the sensitivity of the detections substantially, that enable us to drive into application, for example, like MRD, right? Of course, MRD typically is one of the applications for the conventional flow cytometer, but the sensitivity is very low, a reason why you need to go to a different tool like PCS and or sequencing. With the flow cytometer, our technology that improves the sensitivity tremendously to orders of magnitude. That can really drive one of the applications conventional flow cytometers is not being able to. That's one. The second part is in the drug discovery. As you see, the more and more new studies, new drugs being developed, they want speed. That means they need to look at many different types of parameters. With our technology, that can really help them to look at lots of parameters very quickly and really speed up the kind of pharmaceutical early discovery work for the new drug development. Those are two typical applications. Of course, there are many due to our technology that have been enabling. Yeah. Great. That's very helpful. I think both of you mentioned that there are conventional technologies out there, and then you bring new ones. How do you really drive the new adoptions, right, for your technology? What will be like kind of like the key factors out there, and what are kind of like the challenges that you have seen? I mean, for us, we're truly something new that has never been in the market. It goes through the challenges, I think, anybody who's ever been a part of bringing a new tech. You have the initial, you know, sort of skepticism of, does this work, at what fidelity, with what accuracy, how broadly applicable is it? I think especially in proteomics, people recognize this is not DNA sequencing where you can use PCR and just amplify the low abundant thing and see it. People understand how hard the problem is. I think a lot of our early work has been getting the instrument into those leading research centers, whether academic or in pharma biotech, generating the data, proving that it works. Academically, it's probably a little more focused on the outcome is the publication. I think with our experience in pharma and biotech, they're often looking to just prove out it's going to work in their system, whatever their workflow is. They don't necessarily make the data public, but they do all the work to prove it's going to improve their workflow or improve some attribute that they're looking to improve. For us, it's really that like prove it is a big chunk of it. That's where we've been focused with the tech. For us, certainly, we know the market demand, the unmet needs, right? We found the key pinpoints. We started with those key opinion leaders and worked with them and developed new applications that really enable them to solve their issues, their problems. Suddenly, they realize, yeah, this is a tool I have been looking forward to for a long time, and now it's right there. Clearly, they become so enthusiastic to become your supporter, your endorser. From there, we move actually into one of the key critical applications and for CRO. Clearly, you know, CRO has been working with many pharma, solving their problem, doing work. Once you start to convince the CRO to adopt your technology, of course, you solve their issues, which is to provide solutions for almost all pharmaceutical companies with various different needs, as well as the cost associated with supporting those customers. If you solve those two problems with CRO, they will jump onto it. This is where we start from as well. From CRO, that gets us into pharma the other way. Got it. We all know that the market has been pretty challenging this year, either from the academic side or maybe the biopharma market. I wonder, in this environment, how do you really manage your business? I think both of the firms are also launching new products this year. I wonder, how do you actually identify the funding opportunities and go after those very limited amounts of budget? Yeah. I mean, obviously, the academic market's been very challenging this year. I think for us, we're fortunate that we've, despite being not, we're not a profitable company, but we have, we just talked about in our earnings call, we have a balance sheet that supports us out into Q2 of 2028, which in this market is almost unheard of for companies at our stage. I think because of that, we have the privilege of being able to stay on strategy. We're obviously very focused on only really investing in the right programs, really managing our expenses so they're not growing and burning that cash faster. We don't have to do the big pullback. We're able to stay focused on, OK, as an example, in academia, harder to get capital. We're offering people other ways to acquire the platform. They can rent the platform. They could lease it. We might place it in certain places. We have some optionality there with a good balance sheet and a fairly low cost to produce our device. Pharma biotech, we haven't seen a big drop-off, but it is a longer sales cycle. We can stay with it and keep working it because we have the balance sheet to support that. I think R&D-wise, everything we do is really based on people who have been using the tech. What are they saying they want to do next? What are they saying they'd like to be able to do that they can't do? We just factor that into our pipeline and continue to focus on delivering that pipeline, stay on the strategy, don't get distracted by other things. We're fortunate to have the balance sheet to be able to do that. Yeah. Flow cytometry is a basic life science tool, and you can find it in almost every lab. Now, if they want to do studies in that area, they need a flow cytometer to come along. There are lots of flow cytometers in the field, and many of them are up for replacement. We want to, even though, yes, indeed, the market is tough and the capital expenditure is kind of tight, as long as they want to do experiments, they want to work, they need it. We need to grab every opportunity with the privacy and the tools. Clearly, if they can perform and with our advanced features, high performance, we want to outperform all the older technologies out there to replace them. That's one thing. The second part is we are global. Clearly, when we see an opportunity in other parts of the world, we want to drill down to it. Hopefully, from there, we will be able to capture those businesses to supplement certain weaknesses in other territories. From there, that enables us to maintain our business, make it continue to grow. As you can see, even though under today's very tight environment last quarter, we continue to manage to grow our deployment quantity instrument, our full spectral core technology continues to grow in both the number of counts as well as the revenue side. Great. Since you mentioned the replacement opportunity, I wanted to stick with that concept, right? I think there are probably around 50,000 flow cytometers out there. How do you frame your opportunity in that? How many can you replace, and what would be the key opportunity, key factors to really capture those opportunities? There are two aspects. One is replacement. The older tools, typically, those tools run seven to 10 years, and that's the time they need to be replaced. We look at them clearly. When they replace, a few aspects. One is the new technology and the new performance. The second part is backward compatibility that needs to be ensured. Third is we want to make sure it costs lower. The cost is not only from the instrument acquisition perspective. That also includes the maintenance usage as well as the recurring consumption, all those aspects. With regarding the cost, that's where we provide them the solution to enable them and to lower the overall cost. Through Cytek Cloud, which is a very popular platform now, to help our users, once they get onto Cytek technology, they will be able to reduce their overall cost in designing panels, maintaining their operation. That's a very important aspect, especially for many farmers. That helps them to reduce the overall operation cost. That's one. The second part of the business, of course, is to enable, earlier we mentioned about new opportunities, new applications which cannot be done with the old technology. That actually helps them to drive towards Cytek, right? Because now you are having a tool not only to support your existing needs, but also the future. You mentioned we have some market disruption here, given that you mentioned the BD deals. Why not go after that? What do you think will be your opportunity with the potential disruption? Any share gain that you can frame about? Any color will be great. As you can see, we come from the high end of the technology, and clearly, we are leading in the industry. Any disruption from our competition clearly provides us an opportunity because Cytek has already been very well established in this market as a leader. It is not about competing against regarding performance or technology side. It is about whether you can ensure you can continue to support them going for long term and from an operation perspective. Even on the recurring revenue side, the reagents, panel design, all those things, Cytek has been working very hard to help them. As you can see, our reagent revenue has been growing and actually outpaced our instrument growth. That is good because leveraging upon our great installed base and our service revenue is growing. This is two areas from our business, reagent services plus the instrument continued additional installment, that drives our business to grow. I think anything happening out there is going to help us clearly. Great. Maybe lastly, on some of the, I think last time when we talked about, we mentioned, I think you mentioned some of the clinical opportunities, and you just mentioned MRD will be one of the examples, right? Maybe can you just give us a little bit of update in terms of like where Cytek is within the clinical markets? Yeah. You know, clinical clearly is depending on geographic locations, and you need to go through the clinical clearance country by country. We started with China first. In fact, two of our tools are clinically approved over there, not just tools, also including the reagent panels. That part, we are well covered. From there, we moved into Europe right now. In fact, our tool has gone through the IVD-R clearance, and we have partnered with one of the premier clinical providers over there to drive our clinical instrument adoption in Europe. We have seen some early traction and progress also, using our tool to drive the application earlier mentioned, like MRD and those kind of new panel designs with customers in Europe. Of course, coming back, always we talk about the U.S. and FDA. We continue to work through the process right now. It's going to take a while because Europe, China, the clinical approval process is different from the U.S. FDA, and we need to drive this based on the U.S. process. Jeff, I think for someone that really is not really close to like protein sequencing story, people will always look into, well, DNA sequencing, which we have seen in Illumina growing, right? I wonder, can we really copy that adoption curve of the DNA sequencing to your story? What would be like kind of like the common threats and opportunities that you have seen so far? Yeah, I think the number one thing you have to talk about is sort of when can you sort of overlay those stories. What I mean by that is, a lot of the team at Quantum-Si came from the DNA sequencing world. In DNA, you have an alphabet of four letters, and the properties of that are all fairly similar. It's all negatively charged. In protein, the alphabet's 20 letters. There's a tremendous level of sequence context. Without PCR to amplify the low abundance things in proteomics, you have this dynamic range of 10 or 11 logs of dynamic range. The problem is extraordinarily hard. I think today our technology is used in some of these more targeted applications. As it scales over time, it will become a de novo sequencing platform as we bring out our new next-generation platform and continue to improve the level of coverage. I think there will be a point in time when those will mirror more. It's quite a bit more difficult to get from zero to what you might think of in terms of being able to just drop in a sample and do a whole genome in DNA. I think that day will come for proteomics. I think it's just we're not quite to the beginning of that run. Our belief is when we get to that level of performance, we'll see something very similar to what we saw in DNA, which is when that capability was there, and when it was there at a declining cost, it opened up lots and lots of applications and capabilities. We view it similarly. We just think the journey is a little bit longer to get to that ubiquitous level of capability that we see today in the DNA world. Got it. Sticking with that point, what will be the cost point that we're able to kind of unlock the market demand? I don't know that anyone knows that cost point. Proteomics is sort of a fascinating market, having spent a lot of time in the DNA world. In the DNA world, you can walk in and talk to a customer and talk about their cost per G or their, you know, obviously now some companies want to talk more about the total cost of a workflow. In proteomics, it can be all over the board. You could have somebody in a small, basic research lab maybe doing Western blots and spending $50 or $100. You could have somebody running a big panel of, like an affinity-based platform that's spending several hundred. You could have someone who owns a million-dollar mass spec and then only spends a couple hundred dollars in reagents. There is very different, there's not like one uniform business model in our space, I think is the more concise way to talk about it. There are some areas that are more reagent-heavy and others that are more capital-heavy. I don't think right now pricing is what's holding the market back from that ubiquitous run like what DNA had. I think it's more about the technologies available to do this aren't yet as ubiquitous to do whole proteome or sort of do de novo in the proteome. Got it. Maybe talk a little about your pipeline. Anything that you get very excited in the next 12 -1 8 months from your pipeline? Yeah, over the last two years that I've been here at the company, with the management team that's in place now, we've really been iterating and improving upon our technology at the clip of about every six to nine months, improving the sequencing coverage, the sequencing depth, the number of amino acids we can see, also the amount of sample you need. We have programs across all these different areas. I think the big program that is slated for a second half of 2026 launch is a platform we call Proteus. The easiest way to think about it is our current platform, essentially the optics are in the chip. It's CMOS-based, small benchtop instrument, pretty simple in terms of what the instrument does, and the brains are in the chip. The Proteus platform flips that architecture. We go to a very simple consumable. We put the optics in the machine. Just as one example, our current chip has 2 million of these nanowells. You need more and more wells to take on more and more complex samples, very similar to DNA. The Proteus platform, a chip of the same size, first generation, will have 80 million. It will put us on an architecture that we can scale that through both the consumable size, but also going from point and shoot to scanning over time. We'll be able to scale that up into the billions of wells. This architecture change is key to not only unlocking some opportunities that people would use the tech for today if we had that, but also putting us on a clear path to be able to scale to that de novo level of output that we're going to need. It's a key program for us. We expect to show data toward the end of the year on our prototype machines and then launch in the second half of next year. Great. The next question, kind of like a mandatory, AI is kind of a mandatory question for every panel now. Same for you guys. What's the role of AI in your companies right now? How important is that? How will they evolve in the next few years? Yeah, sure. I think there are two aspects of the AI. One is on the technology side, how it drives our tool, drives the application. The second part is on the operation side, how AI can help improve our overall efficiency of the company management. We are doing both. A year ago, we launched one of the software tools to support our image stream. On the data analysis side, because AI really is a great tool to help improve the analysis of imaging and simplify the process and make it very efficient and fast. We are continuing to drive this application from there across our own platform on the data analysis side. In fact, another tool earlier I mentioned about Cytek Cloud and very popular within the Cytek Cloud, there's a tool called panel design. In fact, it has implemented the AI features. Typically, before, it takes about weeks or months to design a panel and to use a flow cytometer. Now, with our panel design, with AI implemented, it can automate the process, make it very fast. Pretty much in a few hours, you can have a kind of optimized panel out there. The second part is help to manage the operation overall. We are working on that. Across all our organizations, we are looking at using AI to help improve the operation and help to scale up our operation and management. I'd agree. I think operationally, if you haven't implemented it in your company, you're probably over-resourced in some areas. Everything from efficiencies of how you run meetings, convert that into meeting minutes, into a timeline. These things can be sort of automated through pipelines. Market research departments should be able to be automated into custom GPT. I think if you haven't done an enterprise-level adoption of AI for business process and business systems and different tools, you're probably overspending on some of those resources. I think when we talk more on the product side, we talk about the tool, but then also when do we have a proprietary set of data to train on? I think that's an element that not everybody has completely understood yet at the investor level. What I mean by that is we've historically used off-the-shelf AI tools to design our recognizers that bind to those amino acids, right? These are engineered proteins. They do not exist in nature. We've used classic tools, computational tools, directed evolution. What we've done more recently, we just talked about on our call and expect to release some more info. We have a collaboration with NVIDIA in this area. What we've done more recently is actually train those AI design tools, but on our proprietary database of over a million different candidates where we have inserted mutations. We have information about binding kinetics, about how they sequence, about how pure they are when you make them, how stable they are. We did that, and we did a cycle on that recently. In one cycle of AI design, we saw a 2x improvement in the coverage of an amino acid that historically would have taken many cycles. We think the tools, it's not just the tool. It's also when do you have proprietary data to train it on? We use it similarly on the analysis side. Our entire kinetic model is AI sort of derived. We can take in all the sequencing data happening in the field and internally and retrain that, sort of regenerate that database on a frequent basis. We do that a few times a year. It's a way to continuously improve the product just by learning about all the data that's been generated. We use it in sort of an external way, but also in an internal way trained on our database. Got it. That's very helpful. We only have a few minutes left. Any questions from the audience? Great. If not, we're going to take a break. Thank you so much for joining me on the panel. If you guys have any questions, we can certainly do it off stage. Thank you so much.