Good morning, everyone. Welcome to our first analyst day. Many of you we actually probably have met during the last 12 months remotely, virtually through Zoom. This is the first time we'll be able to meet in person. Very glad to be here. Last 12 months, really it has been an exciting journey for Cytek. We are very, very happy to have you together fighting with us over this path. Although the market has been up and down, Cytek has continued to grow, to expand according to what we have told you, what we are going to do, according to our plan. Company have been doing very well. On the other hand, through our communications, many of you have been asking us what Cytek exactly is. What kind of business are you having? Because you're all very familiar with the genomic space, and there are many companies of our size, in that part of the business. However, Cytek, in fact, is the only company in the cell analysis space, a public company, independent. All of our competitors in this business is something embedded in a large organization. One of the objective of this event is to communicate and to explain exactly what we are doing, how our technology is driving the advancement of the cell analysis. We have also invited four of the distinguished scientists in our space who have been using Cytek's tools, Cytek's technology in their own, advancement in their scientific discovery, for them to explain exactly what kind of applications Cytek technology is driving. As usual, that's how we are going to get started. You are all very familiar with this. I'm not going to get into any details. Here is the agenda. As you can see, quite a busy morning. So I think many of you probably have already seen it. To save the time, I'm going to jump onto the next slide. During this process, if there are any questions, you are encouraged to just raise your hands to stop us. We can communicate. We can hope we can answer your questions right away. If not, we can always move to the last minute, and before we close, we can have another Q&A session. This is the Cytek leadership team. Many of you probably have already seen, and we have a few new faces here. Several of them are actually here. We have Todd, our Chief Commercial Officer, joining us from actually BD. We have Mark managing our marketing. Mark is going to present as well. Todd, our Head of Investor Relations, and Patrik, our CFO, and Ming in the back, our CFO, the founder of the company, co-founder of the company. I hope I haven't missed anyone, right? The rest are all sitting here. You can see many of them actually are associated with the flow cytometry business for many, many years. I think one of the objective when you go back today to think, "Okay, what I have got exactly and what I've learned," these are the five bullet points I would like you to remember. Before I forget, actually, let me show this to you. First is what Cytek have is a really patented, what we call a transformative FSP, full spectrum profiling platform. What does this platform do? Deliver a deep insight, high throughput, and ease of use for our customers, for our users. That's first thing. Second part, our technology really is addressing the unmet needs from the scientific community. That really provides highly intuitive and flexible customer experiences. The third thing, our technology is enabling a broad applications in discovery, translational, and clinical science. Not only Mark, also our distinguished speakers will explain to you how the technology has been used in those fields today. Now, through what we have done during the last five years and validated our technology has been by the diversified customer base and also with accelerating publications. Cytek is not just a U.S. company. We have a global presence. In fact, our technology has been going to many different countries in the world from across all the continents today. Just to show you exactly what we are talking about in terms of global scale. In North America, our headquarters is in Fremont. There we do, of course, R&D, also manufacturing. We have two site offices here in the U.S. One, Seattle, which does our bioinformatics initiative development as well as the customer application support. San Diego is where we manufacture our reagents. We have a Bethesda office right next to NIH, actually, to support our customers in the East Coast. Now we have our European headquarters office in Amsterdam. There, through that office, we support our customers in the EMEA region, Europe, Middle East, and Africa. APAC region, we have our Shanghai office supporting our R&D as well as the China marketing. Wuxi, another manufacturing site basically backing up for the U.S. manufacturing. We have a JV in Tokyo to support our sales operation in that area. Looking at our revenue, 58% are North America-based, 25% EMEA, and APAC 17%, including China. The rest part of the business is mainly from Australia, New Zealand, and South East Asia, including Japan. The revenue, in terms of revenue by industry, almost 50/50. 50% is academia, another 50% for commercial, including pharma, biotech, life sciences. For the company now we have more than 900 customers, more than 500 employees. In fact, in China, close to 200, and the rest spreading across the world. More than 150 just biopharma-based in the field companies. Our products are into more than 40 different countries today. We have, of course, many of our employees in the field to support our customers, not only just sales, also including application support as well as the instrument maintenance. Just to summarize what we have generated. As you can see, our technology has been really already validated by the more than 1,200 instruments as well as 740 publications by our users using our technology. I already mentioned, we have a broad customer base and a global presence. Again, uniquely, Cytek has a very strong financial profile. Last four quarters, the trailing twelve quarters revenue, $139 million and $18 million EBITDA. As you all know, in our space, not that many companies actually profitable. That's how Cytek differentiate ourselves from many of our peers here. Of course, many of them are struggling to show a path to profitability. We are already there. We have $362 million in cash, and we have no debt. Looking at our journeys. Five years ago, we launched our Aurora product. A year later, we launched the Northern Lights. Both Aurora and the Northern Lights are cell analyzers that have driven our revenue growth over the last five years. Towards the end of 2020, we launched reagents to capture the installed instrument base for recurring revenue. Last year, we launched the cell sorters. In the same year, we acquired the Tonbo Biosciences, primarily for their reagent business to support our overall reagent initiatives. Early this quarter, we have our reagents clinically approved to support the clinical applications in Europe. Now, this is a summary of the overall market that Cytek is in, that Cytek is supporting. There are two parts. One is the initial TAMs, the conventional flow cytometry is supporting. That's the total TAM, okay? Not necessarily everything is captured by the flow cytometry. For that cell analysis, about $1 billion is for the single-cell analysis, and $1.6 billion for the cell counting, $1.9 billion cell proliferation, and $3 billion cell ID. Because of Cytek's unique technologies, that also drive us to another $8 billion potential TAM, okay? That we can serve. Again, not necessarily everything is by flow cytometry, by Cytek's technology, but that's just the overall cell analysis market. In total together, the cell analysis space is about $16 billion TAM and expecting to grow to $23 billion by 2024. In addition to those cell analysis market, our technology are also supporting other applications, including marine biology, water supply contamination, and alternative biofuels. In fact, quite a few of our instruments today are being sold and used for those type of applications. Now our focus today, starting from Mark, for you to capture this, we want to demonstrate, show you, present you exactly where flow cytometry fits in the overall research space, clinical diagnosis, and other areas, kind of efficiently. Give you further details around our technology in the market segment sizes and the overall growth rate for our technology, and show why customers choosing Cytek. We'll also look at adjacent opportunities in the markets. Four of our distinguished guests will talk about Cytek technology, their daily use for the applications, driving their applications, their research. Patrik will talk about our financial profile. Before we summarize, we'll go over Cytek's overall business strategy and our objective. Of course, lastly, through all of those activities, we want to show you the reasons why Cytek can become the number one company in the cell analysis space. With this, I'm passing on to Mark. Thank you, Wenbin. Good morning. Mark Herberger, Senior Director of Marketing. I've been at Cytek now about three and a half years, and for the last 20 years, I've spent the majority of that time in flow cytometry, mostly focused on the clinical application of flow cytometry. We thought it would be a good idea to start with just a basic overview, very basic overview of why flow cytometry, what is flow cytometry as a transformative platform. This diagram here pretty much covers it all in a simplified manner. If you think about a flow cytometer, it's made up of fluidics, and we can think of this funnel as the fluidics. It's made up of optics. If we think about the magnifying glass there, so laser and detectors. It starts with you know some question around a particular disease area. These are just a couple of those application areas that our customers are focused on, infectious disease, autoimmune disease, immuno-oncology. You start with a mixture of cells that are taken from a tumor, maybe it's blood or bone marrow, it could be a lymph node that the cells come from. They all get put together, stained with CD markers that are tagged with fluorochromes. You'll hear more about all of that. They go into the flow cytometer, and then one by one, they're essentially analyzed. With the help of the computer and the software and the analysis, the user is able to determine abnormal cells from normal cells or abnormal conditions from normal conditions, and then back to normal again, following some type of a therapy. It kind of moves this direction here. Other things that are mentioned or measured that you'll hear more about here are exhaustion. An individual cell that for some reason loses its potency or, you know, the potency is regained once the patient undergoes therapy, that would be exhaustion. Senescence is, as we age, our immune system overall loses its potency. You'll hear some more about that later in today's presentation as well. Exhaustion, senescence, describing normal from abnormal, all of that is being able to be done by this transformative platform technology. In addition to all of that now, with our cell sorter, we're able to sort out these individual cells of interest to then be able to use in some downstream application, such as genomics, transcriptomics, proteomics, on and on and on. That's just a basic overview of why flow cytometry and how it works. We'll get deeper into the technology as we go through the presentation. The market forces and the needs that are shaping cell analysis, if we look over here at the research, the emergence of high dimensional characterization and functional assays, the rise of immunotherapy, tumor microenvironment, infectious diseases, especially COVID, drove a lot of the discovery in immunology in the research laboratories. In 2021, flow cytometry accounted for the largest share of the cell analysis market at 29%. I mean, look at the, those larger numbers, thinking that flow cytometry actually plays a very important role and has been, for a long time, a very important role in cell analysis. The target customers there, this is where a lot of our instruments are already placed in academic institutions and in the R&D departments within the pharma and biotech businesses. There's the translational. This is where the CROs, you take lessons learned over here in research, right, and focus on cell markers, biomarkers, leading to new clinical applications that they put into use through building comprehensive panels, new fluorochromes that then they put into service for their customer base, usually pharma or any type of vaccine development. Important needs for that group are, right, the instrument characterization, the optimization, and the standardization, because typically they have laboratories around the world, and so they're doing similar kinds of work in those laboratories. These instruments need to be standardized. That's a big need of the CROs or the pharma in this translational space. They need panel construction, optimization, the validation and the automation. You'll hear, as we go through this, is that that is something that we actually provide and support through our partnerships and collaborations. Then, of course, the data analysis. As we're generating more and more data with our flow cytometers, there's a need for better data analysis, automated analysis, cloud-based. These are some terms that you'll hear. The target customers, of course, CRO, pharma, and then specialty laboratories. They are helping us determine which of these assays, which of these application areas, should we then move into the clinical market segment. There, the clinical diagnostic laboratory, it's all about performance, quality, pricing because of reimbursement, the in vitro diagnostic process with the regulators, the customer service. Those are all the fundamental needs of the clinical market segment. They need validated applications and assay kits ready to be used. We need to understand and adjust to the changing clinical regulations, and they're happening all the time. It's something that is extremely important for a company to be able to adapt to. For example, in Europe, just this last May, they have now put in place what they call the In Vitro Diagnostic Regulation, replacing the original, what was called the In Vitro Diagnostic Directive. We have to adjust to that. More clinical trials and things that are required there. Of course, there's still the laboratory developed tests, and those are laboratories that basically buy the supplies and then self-validate, in their own hands, a particular application. We support that as well. In the U.S., there's been discussion here, it's called the VALID Act, and it's actually proposed, and it's been in front of Congress now for a while. We'll replace what's typically used now as a 510(k) and a laboratory developed test with a new category. That is their proposal, and there's a lot of discussion that goes around the VALID Act. It's important, you know, for companies to really pay attention and adjust to these changing regulatory requirements and needs. Target customers there are reference laboratories, hospital laboratories, and developing country clinical laboratories. You've heard of Labcorp and Quest. Those are some big laboratories here that utilize flow cytometry in clinical diagnostic applications. We have been expanding the market and capturing share. We're building, growing the pie, making it larger, as well as getting a bigger slice of that pie. Again, just focused on these sort of three areas here. The research, again, academic laboratories, right? Applications, immunoprofiling, immuno-oncology, immunology. The list goes on, but a lot of very good research being done in the laboratories. The competitors in that space that have been there, of course, the familiar names of BD, Danaher, and we have now a very a substantial portion of that laboratory space, Thermo, Agilent, others. That market CAGR is about 10%-12%. The translational is really where the growth is going to be. Pharma, biotech, CRO, working on the applications in dendritic cells, CAR T-cell, immunotherapy applications, vaccine development, receptor occupancy assays. A number of things where they're applying the lessons learned from research in in their laboratories. Again, competitors, BD, Danaher, Miltenyi, Agilent. We have now established a good footprint in this translational space. CAGR in that area, 12%-15%. In the clinical laboratory, we're working on that right now, currently. Right, those are the reference laboratories, the hospitals, as I mentioned. Applications there, MRD, stands for minimal residual disease, typically in a blood cell disorder. Immuno-oncology, and then of course, laboratory-developed testing, lots of various application areas, including immunotherapy monitoring. That's a big up and coming. The dominant players there, the dominant competitors there have been BD, Danaher, and now we've established with getting clinical approval on our instruments in Europe and in China, that we start to have a foot- build a footprint there. That market hovers around 5%-8%. It's steady, and goes for a long, long time. Here is just a chart of our customers' publications. Some pretty prestigious journals that we're published in. You can see the growth of these publications utilizing Cytek technology over time. There's the 740 publications. The research areas span a number of application areas. The big ones, of course, COVID. That did a lot to drive immunology and the application of Full Spectrum Profiling. We were right there on the front lines with the laboratories as they were doing their early studies of coronavirus infections and looking at immune response, as well as in the vaccine development. Some other large areas, immuno-oncology, immunology, that covers a lot of different things. You can see they're pretty evenly split around these application areas here towards the middle. What I thought I'd do is just go over a couple of those. Selection of application areas for Full Spectrum Profiling, where our flow cytometers were used and then published on. The first one here was a publication in Blood, which is the Journal of the American Society of Hematology. This was back in, I think it was December 2020, published by a pathology laboratory, Ruijin Hospital in China. One of our accounts, they are one of our customers there. Spectral multicolor flow cytometry has shown an advantage over traditional flow cytometry, and that more fluorescent markers could be detected simultaneously. Providing the ability to put a lot of markers in a tube that would otherwise be spread across several tubes really does allow for more antigen combinations that they can analyze for precise diagnosis and deep cellular level correlation. Looking at those various subtypes and the maturation pathways and so forth. That was very beneficial for them. There we helped convert their existing multi-tube panel into a one tube 24-color panel designed according to our experience, and there are some recommendations from an organization in Europe called the EuroFlow. Their conclusion offers more cellular information that's unmatched by traditional flow cytometry. You'll hear more about some of these plots and things in the later presentations, but that was one early application of the technology in myeloid disorders and leukemia lymphoma, otherwise immuno-oncology. Here's another one here. This was a poster presented by a large reference laboratory in China called Kindstar. It would be the equivalent of a Labcorp or a Quest. There they compared the antigen expression and diagnostic results obtained from our system, the Northern Lights, to the widely used BD FACSCanto, the sort of, you know, an entrenched instrument that's been out there in the clinical market. With the help of our scientists, R&D scientists, they were able to convert four, six, seven color tubes into one 23 color tube. You think of the time savings and the efficiencies that were gained there. Found no significant difference between the platforms. That's great. However, the NL-CLC has many advantages, including more detection parameters in a single tube, lower compensation interference, easier processing with smaller volumes, so forth. They claim to have established a new standard 23 color panel for the highly accurate detection of multiple myeloma. This is very recent here and, you know, flow can now analyze 50 parameters. Earlier we were talking about seven, eight`, 10, 23. Scientists have now been able to apply our technology to be able to put 50 markers in a tube and cover a lot of area with that, right, and cover these immune populations and associations with diseases. Very powerful tool. That would be in that sort of research to translational space is to take advantage of this here. The here, right, the conclusion, right, is, you know, better. Our flow cytometry can now analyze 50 parameters, a lot of different types of cells and components of the cells. Conventional flow data analysis can't keep up with that. Because of the data that's generated from 50, better methods are therefore critically need to take full advantage of the powerful technology. In other words, some analysis software to be able to analyze all of the data, all the good data that comes from the analysis using 50 parameters. Here's one here, another one, application of flow in immunology. This is a 43-color panel. Again, a recent publication, being able to just look at all of these different cell types in the immune system. That funnel, right? All the mixture of cells that go into that funnel and out come out of the bottoms, right, being able to analyze these various cell types quickly, right? Analyze on a 5-laser Cytek Aurora. Data analysis is done using this third-party software, and this panel can help make a thorough interpretation of the immune system. This was very important in the early days of COVID, where the scientific community really did sort of band together to figure out what's going on with the infection and what's the immune system response. There's another sort of application area that we're also focused on, and that's what we call the power of N. It's using flow cytometry along with some of these complementary technologies. I know you're gonna hear more about some real-life applications in the later presentations from our invited guests on this. In this case here, it's analyzing or sorting, as I showed earlier in that diagram, and then using those sorted cells to be able to do downstream analysis and next-gen sequencing, high content imaging, molecular biology, all of them complementary. All of them providing additional important information to the scientists, or in fact, the clinician as they combine these technologies. Just to show an example of that here, a publication from a few years ago, there are many out now that show the combination, in this case, a combination of multicolor flow cytometry along with next gen sequencing. Using those two technologies together, 73% of the time can predict a four-year relapse of an acute myeloid leukemia, so blood cell disorder. Whereas either of those technologies used individually, you don't get that kind of power. There's always a question about what is, you know, what the relevance, the future relevance of flow cytometry now that there are other technologies out there. They're complementary. They're gonna be used together. In the case of, right, this, a clinician is able to make very decisive answers or direction on a patient with the use of the technologies. Just to bring it all home here, wrap it up, our commercial and reagent strategy over time, over the five years, has been really establishing the credibility in the academic laboratories. We have instruments at the top universities, research institutes across the world, around the world. As Wendy mentioned, over 1,200 instruments installed globally. Focus there has been on these cutting-edge applications in immunotherapy, immuno-oncology, immune profiling, CAR T cells, on and on, any immunology-related application. Demonstrated by over 700 publications in those application areas. We've been positioning the platform then with the support of the lessons learned here into the pharma and the biotech. We have instruments at the top pharma and CRO companies, and we are now supporting them with our recently launched cFluor reagents and panels. We learn here, we apply it, and then position the platform accordingly into the pharma and biotech. Now starting to translate those applications into the clinical space, all along the continuum here, immune monitoring, minimal residual disease, infectious disease. We have expanded KOL partnerships, collaborations to help them, the laboratories, develop their laboratory developed tests and support them in these clinical applications. Ultimately, all driving towards the solutions provider. Transforming our company into just a solutions provider, a system of hardware, software, reagents, support, everything that goes together, turnkey. Our path there has been IVD product registration completed or are in process. We're in current discussion with the FDA as we had been with the European authorities and the Chinese authorities. We're well on our way there into transforming to the clinical market. For the next four presentations, we've invited in guest speakers to talk about their application. Dr. Bill Telford will talk about his application and use of Cytek technology in basic immunology, immuno-oncology. Dr. Kevin Weller's here, he's gonna talk about sorting in flow cytometry in oncology. Dr. Belkina is here, and she's going to be talking about the application and use of the Aurora in oncology. Dr. Fuda is here to talk about his experiences in the clinical laboratory and the requirements and the needs, and how he's been able to address those with our technology. We're gonna turn it over to our remote guest speaker. I'll move the slides. Bill? Okay. Okay. Okay. Can you hear me? Can. You can. Fantastic. Okay. Well, thank you, Mark, for the introduction. I'm very sorry I cannot be there in person. It's great to see that everybody else is there in person. It's wonderful to be going back to real meetings again. So if you'll move to the next slide for me. Great. Okay. So my name is Bill Telford. I run a flow cytometry core laboratory at the National Cancer Institute at the NIH in Bethesda. I've been at the NIH for over 20 years, and I've been doing flow cytometry for over 30 years. I've been doing this for a long time. We are a shared resource laboratory, so we have investigators from many laboratories come in and use our equipment. Keeping a suite of advanced instrumentation is very important to us. It's critical for our mission. Next, slide, please. What do we work on? We do a lot of basic and clinical research projects within the NCI. Mark talked a lot about that, and you'll hear a lot about specific projects in the upcoming presentations. One thing we do a great deal of is clinical trial support. We're not a clinical diagnostic lab, but as a biomedical research institute, we support a lot of clinical trials, particularly in the area of allogeneic bone marrow transplantation, CAR T therapy, TCR-based immunotherapies, and other cell transfer therapies. We do a lot of high dimensional immunophenotyping, which you'll hear a lot about when Kevin and Anna and Buddy give their presentation. Where we're looking at least in the high twenties for markers simultaneously and all the way up to thirties and even forties. We also work in tumors. We do cancer cell analysis. We don't only look at cell protein markers, we look at fluorescent protein expression, physiological markers. We do a little bit of everything. Having flexible instrumentation is very critical to us. Finally, we do a lot of cell sorting. It's a major focus of our group. When people in my group do an analysis, I know that in three months they're gonna wanna sort those cells out and do something with them. Having cell sorters that match the capability of our analyzers is really critical to our mission. Next slide, please. As I said, we look at everything. We do a lot of immunophenotyping for intracellular markers, extracellular markers, transcription factors. There are thousands of different markers out there now and probably hundreds of different fluorescent probes that can be used to do this. We look at expressible fluorescent proteins like GFP. We look at physiological markers. As you'll see in the next few presentations, being able to do high dimensional analysis is very important to us. We need to look at many markers simultaneously. We need a technology that will give us the flexibility to do that and to combine many markers into a single panel. Next slide, please. You're gonna be hearing a lot about the technology today. Many of you already know a great deal about it. The technology that I came up with in flow cytometry is the so-called traditional technology, where we have single lasers, we have single dichroics and filters and detectors. We detect each of our fluorescent markers as a discrete entity, and then we use compensation to separate them. This technology has held us in very good stead, but it does have upper limitations. What spectral flow cytometry does is allow us to analyze many more markers simultaneously. We take entire spectra from all of the excitation sources in our system. You'll be hearing a lot about this from Ming and many other people today. Rather than taking a single bandwidth of fluorescence from a single laser, we take full spectra. The instrumentation takes full spectra from all lasers simultaneously. The data is far more granular and allows better spectral separation than traditional methods and traditional compensation. Next slide, please. So this is what we've been using up until this time. This is a BD Biosciences FACSymphony A5. It's a very admirable flow cytometer. It does a great job, but it uses traditional technology. As you can see, it requires a lot of detectors. The visible region of the spectrum is actually pretty limited and, through no fault of the manufacturers, we've sort of reached the upper limit of what traditional cytometry can do. We can only pack so many lasers and so many detectors into that space. It's turned out that really the upper 20s, perhaps low 30s, seems to be the upper practical limit of what we can do using a traditional cytometer. What Cytek has done and other manufacturers have done is now move into the spectral space to allow us to expand that capability further. Next slide, please. There are many advantages of spectral over traditional. I'm just gonna highlight a few of them that kind of hit me when we started using the Cytek technology. We acquired our first Cytek instrument a couple years ago, so we were not early adopters of the analyzer technology, but we are early adopters of the cell sorters, which I'll talk about in a little bit. You'll be hearing about this from the other speakers, but spectral does allow us to dramatically improve our ability to separate signals of fluorescent probes from similar although non-identical probes. We can pack a lot more fluorescent probes into a single panel, and Mark showed you papers where people have gone up to 50 markers simultaneously. Because the data's more granular as well, you're also, I think, able to get improved quality of signal separation. The sensitivity isn't necessarily better, but you're going to have better separation and better data. Some interesting advantages that we've seen is that the technology is more forgiving per se. Panel design is really critical in cytometry. Picking the right fluorescent probes that have minimal spectral overlap into one another. This is less of a problem in spectral. It's still something we have to pay attention to, but if your panel and my panel are not equally optimal in terms of the overlap of the fluorescent probes, we're much more likely to get similar data than if we were using traditional cytometers. We're also able to take advantage of a lot more fluorescent probes that are out there. Many older fluorescent probes that were not so useful for traditional flow are now finding new potential in spectral, and I'll talk a little bit about that. There are also a lot of mathematical tricks that you can do with spectral data. For example, subtraction of cellular autofluorescence, which can be very useful when looking at tumor cells, myeloid lineage cells, and cancer cells, and you'll be hearing some about that today as well. Next slide, please. Just to show you a little bit of data, this is actually a 12-color panel that we did as part of a study to compare spectral to another form of cytometry called lifetime cytometry. What we've done in this panel is deliberately chosen fluorescent probes that are very, very close to each other spectrally. This is a panel you probably wouldn't use in real life. It's too, almost too challenging in terms of the spectral requirements. Yet because of the power of spectral cytometry, it works. It's a panel that there's no way you could do this by a traditional flow. Again, it's a more forgiving technology in terms of the choice of fluorescent reagents you use in your experiment. Next slide, please. As was mentioned earlier, we can combine fluorescent probes and spectral that you simply cannot use together in traditional cytometry. Here I'm using Brilliant Violet 421 and Super Bright 436. These are two fluorescent probes that if you look at their spectra, are practically although not entirely identical to one another. They are at the upper limit of what the Aurora can do, but they can be used practically together. This allows us to build larger panels and to have a much wider selection in terms of fluorescent probes that we're able to use in our experiments. Next slide, please. We can build very large high dimensional panels. This is a panel from one of our groups here in the NCI, looking at B cells, T cells, and K cells, and myeloid lineage cells. Don't worry too much about this grid, but this is what we call a similarity index grid that the Cytek software provides, where a deeper red means greater overlap between fluorochromes. This is an entirely usable panel, and the ability to do this number of colors is not a theoretical consideration. When I tell my investigators that we can do 35 or 40 colors, they come back with 35 or 40 color panels. We actually have applications that require this, where we want to look at these markers relative to one another, so the need is absolutely out there already. This isn't something where people are gonna be using this in a year or two. Next slide, please. We can also employ many new fluorochromes. This is a poster we had in CYTO, at the CYTO conference last year, where we went back and reanalyzed a group of fluorescent probes that we had assessed 20 years ago for doing flow cytometry. Phycoerythrin proteins isolated from protozoans and other probes that are related to phycoerythrin and some of the natural product fluorochromes that are out there. They weren't useful back then because they pretty much mimicked things that we were already using, but their spectral properties are slightly unique, and we're able to use them in spectral flow now because they do differ very slightly from the traditional phycoerythrin proteins that we're already using in flow cytometry. We're able to tap into these chemical resources and add fluorochromes to our palette, as it were, to the fluorochromes that we can employ in our high dimensional panels. Next slide, please. This slide was actually out of order, but these are the fluorescent probes that we tried. This is a paper we had now over 20 years ago, where we assessed some of these natural product phycoerythrin proteins, these fluorescent molecules. This was done in collaboration with Columbia Biosciences. They turned out not to be useful 20 years ago, but we've thawed them out and reassessed them and are now using them in spectral flow cytometry. They're now new tools in our toolkit. Next slide, please. Okay, as I mentioned though, cell sorting is a major focus of our group. When our users analyze something, they wanna sort it almost immediately. This is the demand that Mark is talking about and that some of the other speakers will bring up. Because once we do our flow cytometry, we wanna do other things to those cell populations. We wanna do PCR, we wanna do RNA-Seq, we wanna put the cells back into culture. One issue that has traditionally sort of dogged us as shared facility operators is that cell sorter development typically lags behind analyzer development. Most companies put their energy into getting a good analyzer, then they build their sorter with the hopefully similar optical characteristics to the analyzer. Typically, our cell sorters don't have the fluorescence, the analytical capability that our analyzers do. Fortunately, that has not been the case with the CS. The front end of the CS, as I call it, the analyzer end, is exactly the same as the analyzer. It's 5 lasers, 64 detectors, 40-plus color analysis capability. What Cytek has done is build a cell sorter where we can take samples from our analyzer, immediately bring them over to the cell sorter and separate out those cell populations. It's a big need for our group. Next slide, please. Just to show you a little bit about our assessment of this technology. Currently, we have two CSs in our laboratory that were acquired last fall. We're now starting to put them into regular rotation, but we've done extensive testing on them both for their analytical capability and their sorting capability. We have a variety of tools for assessing sensitivity. These are cocktails of fluorescent beads that express extremely low levels of fluorescent proteins, of fluorochromes that we use in immunophenotyping. I won't bore you with the details, but the two instruments, our analyzer and our sorter, are essentially identical optically. I cannot tell the difference between them unless I look at the header in the file, in the file structure. They are not only equally sensitive, but they are normalized to one another. We can use our analyzer, collect our analytical data, then sort on that data, and the results will be virtually identical. Next slide, please. Just to show you what the little control panel on the instrument looks like. The instrument. Cell sorters break the stream of cells into droplets, and then the droplets are sorted using electrostatic plates to guide the side stream into tubes. The Cytek system has a little camera down here that focuses on the collection tubes that the cells will ultimately land into. Next slide, please. This is what the data looks like. Again, don't worry too much about the details here. This is our panel up here. This is a 15-color panel. Our unseparated cells are up at the top. These are human peripheral blood that have been labeled with 14 colors, sorry, looking at different T-cell subsets, and we can sort out fairly rare populations. Here we're targeting populations that are only about 3% of the total and are able to enrich to greater than 96%, 97%, 98%, using a single sort. We're doing a lot of assessment on this now. We are looking not just at purity, but yields and other characteristics as well. The systems are working very well, and one of the sorters has now been put into regular rotation for our usage. For us, this is very exciting. The spectral analysis has been terrific, but we really need to translate it over into the sorting world so that we can get at the and. We do a lot of things with these sorted cells in proteomics, genomics, and other things, and it's gonna be a critical tool for our research. I will stop there and thank you very much. Thank you, Bill. I would just like to say that, I mean, he's demonstrated there in that presentation a couple of things that, one is the power of Full Spectrum Profiling over traditional, being able to get more markers into a tube than otherwise could be done on traditional flow cytometry. That's gonna help us get a larger share and has been a larger share of the pie, and then the power is actually growing the pie overall. Next, we're going to hear from Dr. Kevin Weller talking about high-dimensional cell sorting. Hi, everybody. Feel free to interrupt me with questions at any time. I'd encourage any kind of discussion you guys wanna have, if you want clarification or anything else. My role at Ohio State, I was actually brought in to be the associate director for a new immune monitoring lab. We're fortunate enough there to have a funding organization called Pelotonia, which isn't the Peloton bike company, but it's a charitable bike ride that raises about $50 million a year purely for cancer research. We are one of the benefactors of that at our institute. About three years ago, they invested $102 million to start this immuno-oncology institute. This was a, you know, purpose-driven decision to try to focus on one of the most advanced areas of cancer research and to try to power it through as quickly as possible. A big theme of ours is, you know, we wanna rapidly make discoveries. We're you know, there's an urgency in our group to do everything. There's a picture of Zihai Li, he's our founding director, he's a real dynamo in the immuno-oncology world. The IMDP, the Immune Monitoring and Discovery Platform, I didn't pick that name, but that's the name of the immune monitoring lab. We're part of the bench when it comes to, you know, this previous description, we were saying, we wanna have a bench to clinical, research trial initiative. Right now we're in a stage where we're building our resources and our abilities up. We've rapidly accelerated. Weirdly, the pandemic, you know, slowed down some things, but it accelerated other things. When it comes to that, we were able to really focus on COVID research that we didn't really want to, but that's kinda how it worked for everybody, I think. We made some advancements. We did some contributions to that research. That's where we fall. We're the bench. You know, we're building a leading immune monitoring platform to support immuno-oncology research. That's the goal. To just start with discovery, but also to power it through with the end goal, hopefully, in a couple of years here, we wanna be able to do patient-based medicine based on the work that we're doing now. We wanna be able to have clinicians get information, make decisions about treatment, make decisions about who would be a good candidate for a different trial depending on their immune signature and their profile. We're a shared resource, but we're just focused on IO. A lot of labs you'll hear is a core lab, you know, for flow cytometry. I also run that for Ohio State, so I got kinda stuck running that as well. But my real focus is this immune monitoring lab, but we are a shared resource just really focused on immuno-oncology. You know, we're trying to get as much of the information as we can of, you know, looking at the immune system for everything that we're doing. We're not limited to flow cytometry. We have everything in my lab, but flow cytometry is a key part of what we do. You know, we're built for this. We have right now over 100 members with broad specialization. This is from basic researchers to clinicians with active clinical trials. Every pharma's got a trial going on at Ohio State for immuno-oncology at this point. You know, it's a really fertile ground. Ohio State is, I think, the second-largest NCI cancer center, when it comes to patient beds, you know, bed size, essentially, and they're racing the other cancer center to build more beds as we speak. We just have an incredibly rich environment to do this research. We really have a heavy emphasis on bioinformatics. That was one of the first questions I asked when they were recruiting me was, you know, how are you gonna handle this? 'Cause a lot of labs kinda think about that later. We have that built into the front end with single-cell specialization. We've already got an IO database specifically focused on IO, cancer patients right now, and our whole plan is upon enriching that database to make it a mineable resource for all IO. Right now we're at the point where we have experiments planned based on the early work that we've done already with the Cytek. The sorter, which I'm gonna really focus on talking about, is a key piece for us. I've been waiting for this. It's been a huge frustration in my career. I started out at a little company called Systemix in California that was sorting hematopoietic stem cells and it was actually reinfused into patients. It was the first time that was done in a clinical trial setting. Really old sorter technology. I've been back and forth between industry and academia. One of my big frustrations has been with these higher dimensional panels and the lag that the previous speaker, Bill, was talking about when it comes to the analyzers are always, you know, ahead of the sorters. You know, we've been in this space now for many years where you could go to a single-cell platform for flow cytometry or mass cytometry, and you could see a lot of markers, but then you either ablated those cells in a plasma arc or you threw them in a bleach waste and you threw them away. You had a, you know, limitation of what you could get. Sorting now at the level that we're able to do it with the Cytek Sorter has just opened up all kinds of new avenues. With these, you know, these other investigators that we have now, where, you know, these teams, we're developing these experiments where it isn't just one investigator doing one thing, focusing on one thing at a time. We're allowed to focus on many, many things at a time. We have optimized panels and everything is, you know, for kind of every specialization, we have, you know, panels custom-built, and we work actively with Cytek on this. They're kind of a team member that developed, you know, during the pandemic as we were rapidly trying to develop our panels, they were heavily embedded in our work, sometimes coming to the labs when everything else was shut down to help speed the research along. You know, again, we wanna get as much information from these patient samples as we possibly can. I think that's a responsibility, especially with these cancer patients. If you're getting a small piece of tumor, you're getting blood, you know, some of these patients have been on four or five trials at this point, we owe it to them to get as much as we can back. Instead of throwing out the cells after we look at them, we can take those cells and go forward now. The previous sorters, you know, beat this because people have been discussing this. You're really limited by the number of biomarkers that you could focus on. Really 10-15 practically is what you could do. That was enough. If you wanted to focus on, like a small subset of, like T cells, you could do that, and you still didn't get the whole view, but you could do that. Now by tripling and even more of that number, we can focus on many subsets of many other cells at the same time. Again, these previous platforms that we've had, they were really limited practically. There's been a thing too, and I, you know, I worked for BD at one point in my career as well, again, back and forth between industry and academia. There's this sort of stated, you know, limit of what the technology can do, and then there's the actual, like, what's being practiced. Right now that's really 25, not even 30 markers is the highest anybody's really going with some of these other platforms, so it's still pretty limited. One of the great things that we're seeing, like what Bill alluded to earlier and showed his data, the panels that we have developed on the Aurora analytical platform that doesn't sort, those are matching very well with this, the cell sorter, the CS. So we're getting, which is critical because we wanna do our development on the analytical systems and then stop that. I don't wanna keep running cells and patient samples on a non-sorting instrument. I wanna sort everything. Most of the time with a specific plan of we will sort these specific subsets, they will go to downstream applications. If we don't do that, we can bank the samples. We can bank them, we can save them for further research down the road, and we're not just discarding them. We're really trying to just build a pipeline for discovery right now. You know, we're in this kind of bizarre position where we have a lot of funding through this charitable bike ride that Columbus does. It's powered really by the fanaticism of Ohio State football fans. If you're not familiar with that, yeah, it's very cool. You know, if you could do anything, what would you do? You know, our answer is, let's do everything. Let's not limit ourselves in any way. The Cytek Sorter is allowing us to do that better than we've ever been able to do this before. You know, in my lab, we'll take a tumor, peripheral blood, whatever tissue, whatever's part of the specific trial, and this could be, you know. Right now we're primarily doing a lot of animal research, but we are also doing some human work as well. You know, I have a team that will have to process these cells. It can be very different depending on what your end goals are. If you're looking at peripheral blood, you can freeze that, you can bank that, you can run that later. If you're looking at a tumor, it's really gonna depend on how big the tumor is, how cold or hot the tumor is as far as the, you know, infiltrating lymphocytes. You may have to culture the infiltrating lymphocytes out, but at a certain point, you can actually process these samples and then sort them out. From there, you know, kind of, everybody's seen these lineage panels, which are, you know, kind of hard to look at this all at once. Just to make the point of, you know, there are all these different cell subsets and, you know, you can have, you know, these, a B cell, a T cell, but then there are how many different, you know, lineages going down. Exhaustion is a big thing that we look at in immuno-oncology because with checkpoint inhibitors and the therapies around that, exhaustion plays a major role, and we're trying to look at these patients so that, you know, with their exhaustion history and, you know, as far as, hey, if this person's T cells are expressing certain markers, they might not be a good candidate for a certain CAR T or a certain drug trial. This is, you know, the pharmas we work with, they wanna know that. We can screen patients, we can get people in the right trials, depending on what their status is the goal. With all these different markers, you know, depending, we don't have to just look at one of these lines of lineage. We can pick six of them, right? We can focus in on, you know, what specialist area an individual PI might want to be looking at that's part of a larger team. It's just an incredible opportunity at this point to do this. You know, downstream from that, we have the traditional, you know, single-cell genomics platforms and transcriptomics platforms like 10x or 10x Genomics. For single-cell proteomics, we actually purchased a mass spec, and we hired somebody to do single-cell mass spec, which is very rarefied. There's only a handful of people in the world doing that. We wanna look at all the proteomics down to the, you know, phospho site-specific activation and, you know, everything to get as much information as we can, and then look at single-cell functional screening. There are multiple platforms looking at this. We've been dabbling with something called the IsoPlexis. Which, single cells go into a microfluidic system, and you can measure their cytokine secretion and look at, you know, secretion, you know, 35 cytokines and chemokines. It's not quite where I want it to be, but there are other platforms to do that. Then, of course, you know, anything else that you can think of. You know, this part doesn't really matter. This will change over time, and it already has. It will actively change as these downstream tools are becoming available. The other great thing about this is if we have a big enough tumor, we can take part of the tumor, process it, and we can look at, you know, get all this information for, you know, what's inside that tumor, you know, really high dimensional. We can actually use that information to, you know, look at paraffin-embedded samples and look at a selected group of markers that the, you know, the flow cytometry data has told us, "Well, these are the cells most prevalent." We can say, "Okay, well, this is the actual tumor," let's say, and we can look at, you know, spatially where are the cells in next to each other, where are these markers expressing next to each other in the tumor, which is very informative. It can tell you a lot about it, and where they are in different parts of the tumor. By combining all this information, you can just see you can get this incredible amount of data for you know, one patient. You know, you can just get all this information and learn as many things as we possibly can. You know, the whole sort of theme for us is the single cells and then team science. We're designing experiments right now where it's multiple labs. It isn't one PI doing one experiment that's, you know, going and that's burdening, you know, all the costs are going into that one PI's budget. It'll be 5 or 6 PI's. We have people that specialize in T-cell exhaustion or myeloid cells like MDSCs or the B cells in tumors, which we're finding out more and more, you know, are playing important roles that we didn't even know about and then various subsets of all kinds of other lymphoid cells and myeloid cells. We get these groups together, and we're planning these experiments, so, you know, something that would be very costly for an individual investigator now becomes pretty affordable because you're splitting the cost by five or six ways. With the funding that we have through the institute and everybody's individual NIH funding, it, the sorter kind of democratizes science a little bit more, where some investigators that might not normally be able to do this kind of science can now do it, which is just fantastic. Each cell population is kind of matched up with an expert that's interested in that, and they're, you know, we're all going in together when we're building these to decide how are we gonna do this, and of course, with bioinformatics on the front end as well. The goal, you know, is to have multiple publications from, you know, one experiment could have five or six publications or a single high-impact publication or multiple high-impact publications because it isn't, again, it isn't just one lab, it's a team. It's a pretty fun environment to be in right now to be doing this kind of work. Every data set now gets added to this IO database that we're building. We already have, you know, all the patient information. We have specialists that have actually gone in and can even now mine the notes of the doctor. They've gone in and build, you know, code to look at the scribbles the doctor made on a piece of paper and then get that into the record as well. Combining all of that information with everything that we learn through the cell sorting and then the downstream applications. Again, we'll just keep adding things as we go. You know, we have some limitations, like I know the, you know, the original 10x Chromium can only do about 16,000 cells per sample. That's again why it's really important to be able to sort out these different samples. The new one, the Chromium X, is about 1,000,000 cells per sample that we already have one at Ohio State, and we're working on that. We can still add more and more samples, and batch things. You know, you can unique oligo label things and then contain them all in one sample and save even more money. It's just giving us that much more ability to look at more things at once. Yeah, and then if we don't use something, we can bank it. That's the other great thing about this. We don't have an immediate plan to do a 10x or to do single-cell proteomics, we can just bank these populations and then just keep adding that into that we can go back later and do follow-up experiments based on what we learned already. Yeah. Thanks. Sorry, taking you up on your offer to ask. Go for it. Yeah. It's just Kevin, by the way. Hey, Kevin. All right. Hey, on your previous slide, you talked about team science. I know one of the traditional challenges of conventional flow cytometry is the way the instruments are built often leads to configuration lack of uniformity, right? So In other words, you know, you're building a high-parameter instrument, usually in one by one, right? You'll get a lot of variability in how the instrument is actually produced. With spectral flow cytometry, does the limited configuration set and standardization of the manufacturing process help this whole team science concept you found so far? Yeah, absolutely. Okay. I think the other thing is even if you wanna talk about you know, doing this kind of work across institutions 'cause we already. Yes. We're building an IO consortium in Ohio, so with Nationwide Children's Hospital and Shriners Children's Ohio and Cleveland Clinic, Case Western Reserve University. We're building this kind of work now, and the cool thing about the Cytek, you know, instrument settings platform, how they did that, every single instrument uses the same quality control beads. Everyone has the same targets when it comes to setting up the instrument. That's one of the reasons why the data from the analyzers looks so much similar, almost exactly matching the sorter, cause all the instruments are the same. It helps, you know, the end process of doing that. If we have somebody, and we do at Ohio State, people that have their, you know, that might not set up their initial experiment on another Cytek instrument, well, that will translate very well to what we do on the sorter because it's, you know, they're kinda the same. I mean, nothing's exactly the same, but it's practically, yeah. It's, yeah. Having lived the other side of that, you know, we would buy, you know, very customized, very specialized instruments from another vendor, and no two instruments were the same. It was really hard to take something off of one instrument, especially when you're going from analytical sorting, but then going from one institution to another, we used to have to do all these other things to try to, you know, make these things comparable. Yeah. Thanks. Helpful. Thanks. Sure. This is just some example data, and you guys will see this stuff all day long. I just put it up here to kind of make a point. This is. I've got a donor and a patient here for something that we were working on. This panel is a T cell exhaustion focus panel, so you'll see markers like TCF-1, CTLA-4, LAG-3, EOMES. These are, you know, markers that will help investigators determine at what stage of exhaustion T cells can be in. That's even contentious. There's a lot of different mindsets about, you know, is exhaustion a one-way path or, you know, once T cells reach a certain point where they're expressing certain markers, are they never gonna be able to function in a tumor again? It really helps us to identify things. There's still, again, a lot of research to be done on this. This is, I think, a 34-color, I believe, example. We've gone up to about 40- colors in our lab, which is a huge challenge, and we're by no means perfect at this, but we're, you know, getting better every day. You can see, you know, you're getting the separation that you need. I think Bill mentioned this earlier, like, you might not always get, you know, just the unbelievable sensitivity that you might get in some other if it was configured with a different detector or something like that, but you're getting everything more than good enough. You can see everything separate apart, and the consistency is the key. Like, we have to be able to see the same thing every time we do it. This is on the analytical platform. Then we took the exact same samples, and we just 'cause these instruments are right next to each other, and we just put them on the other instrument. I think it was, like, a couple of hours later, practically. This is the same data on the cell sorter. You can see there's no really major difference. We might have had a little bit of loss in fluorescence intensity on a couple of markers, but everything's still easily identifiable, and you can see it, but it's just over time, you know, things might degrade a little bit. These platforms aren't 100% the same, but it's good enough and more than good enough 'cause we can now see this translate. We can develop something on the analytical system, walk it to the sorter, and then just keep going from there. Here's another example of a patient. Their profile looked a little bit different. They had some history with. They had already received certain cancer treatments, their cells looked a bit different. This is. You won't be able to see this, but we sorted the cells. We did a six-way sort. We picked six different populations that we're interested in. This is just, something actually we were doing while we were there with Cytek trainers. You can see, as Bill reported, you know, here's a sorted population, and that's pretty big, pretty easy to see. You know, you got good purity here. Then here's something that's, you know, a fraction of a percentage, and the purity is exceptional. We're able to get these really small subsets out of the patient samples and go on to use them for something else, depending on what we're trying to do. It's just a game changer for, I think, immuno-oncology research and then research in general. Cause my plan is to not have a bunch of analyzers. I wanna have a bunch of sorters, and I wanna rarely analyze. I always wanna sort if I can. I just want to say thank you for you know the institute is pretty exciting. Pay attention in the future. I hope you guys are gonna hear good things about it. That's my wife and horrible children that are at a Pelotonia event in Columbus. Do you guys have any questions? Is there anything I can clarify or you want more information? All right. You guys are pretty quiet. Yes. The spatial biology example that you showed, is it safe to assume it's two different tissue sections? Absolutely. Yeah. Yeah, it's gonna be. You know, the goal is serial section. I actually didn't put it in here, but I have something I call the crazy hamburger, where if you take, like, a tumor, you know, if it was like this, and then you cut it, and you took half of it, and maybe you did, you know, your flow cytometry on that, and then the other half, maybe you would take that, and you have a serial section that would be parallel to what you were looking at, and then you would do imaging on that. You could look at multiple sections of that. Yeah, it's certainly not gonna be the same tissue, but it's, you know, representative of the tumor. If you have a cold tumor and you don't have, you know, immune cells infiltrating, you'll see that. You won't see that with the flow cytometry, but you probably will see the exhaustion markers will be elevated because the cells aren't getting into the tumor. They're not actively fighting the tumor. Yeah. Yes, sir. In the back. Do you see that as, like, a common, like, complementary technique going forward or is that, like? How difficult is that? Well, it's extremely difficult, and it's something that, you know, you're not gonna be able to do that with every patient sample. It's all gonna depend on the size of the tumor. You know, I think this is a challenge every institution faces and every clinical trial faces is everybody needs a piece, right? So, you know, clinical pathology is gonna be the key. They're gonna get what they need first, and then we're gonna be able to get what we can after that. It really depends. We're actually setting up a biobank for this right now, so we can do that, and we can prioritize getting our samples to do this kind of work first. The goal is to run. You know, we're kind of agnostic. The fact that Cytek has the cell sorter is just, okay, great. We just need something that did this, and this company has the thing that does this. But the other technologies will do whatever. We've already looked at many other things that I won't talk about here, but I think the imaging is going to be more important as we go forward. I showed you an example of a platform that's a seven-nine color platform. It's an Akoya system, but I'm blanking on the name of that imager. I mean, we were also looking at things that do 50-100 markers, and to look at the spatial information of that, and that technology is now getting to the point where it's gonna be usable. I think we're always gonna wanna have that spatial information 'cause they're so rich, and it's so important, and then combine that with this technology, 'cause you just get a lot of information. I think the question then becomes, you'll probably get the flow data before you'll get the, you know, you get the imaging data, so you'll be kind of chasing it always. But the flow data can inform on what we would do for the imaging. Like, what panel we would use for the imaging. 'Cause if we've already determined in the flow data that certain markers just aren't there, then why would you know, we wouldn't look at that for imaging. It allows us to kind of curate our imaging a little bit better, make it more informative, I think. Yeah. We kinda hear that sometimes with people saying single cell sequencing can be used to inform what you look at with spatial. Is this, I guess, kind of, you know, an alternative to that in what you're saying? Well, I mean, you lose the information with 10x. Now, if you're doing Visium, which is another 10x platform, you can do spatial transcriptomics with that as well. I think that's something that we're looking at. There's multiple platforms, and actually we have a GeoMx DSP at Ohio State, which that's one of those, you know, 50-150 or 1,000 different markers. You can do that as well. Yeah, that's one more technology. I think, you know, with the other things that we wanna do, like things like looking at functional output of cells, like getting these cells out, sorting them out, and then seeing, well, what is their capability to make TNF or interferon gamma? Like, what is their capability to respond to stimuli? That's really informative too. You're never gonna get that from these other platforms, I think. The proteomics as well, like the single-cell mass spec, which I think I'm most excited about that because nobody's really doing that at this level. Yeah. Sure. Good. All right. Thanks. My contact information's up there, so call if you ever need anything. Thank you, Kevin. My pleasure. Thank you for pointing out the ability to standardize the instruments across the you know consortium of laboratories. That's extremely important. I hope there are no Michigan fans in the audience. Okay, next, we're gonna hear from Dr. Anna Belkina talking about the Aurora Empowering Immunological Research. Thank you. Right. Thanks for having me here. I'm Anna Belkina. I'm from Boston University, and I'm gonna talk today about mainly my research in the immunology field and how we employ Cytek Aurora platform for deep immunophenotyping. I'm assistant professor of pathology at Boston University, and I also head the flow cytometry core facility there, where we have the 5-laser Cytek Aurora since 2018, so we're, like, semi-early adopters. Yeah, basically, I wear two hats. I do my own research. I have my own research group, and I focus on studying different inflammatory conditions, like multi-chronic inflammatory conditions in the context of like HIV, obesity, aging and other like COVID, obviously. Everybody does COVID, right? I also do some bioinformatics research. Bioinformatics approaches for single-cell data. It's how you can visualize cytometry data, and to highlight different aspects of these rich datasets and also how you can combine the data from the single-cell analysis with other readouts and, like, do some multivariate models and so on and so forth. We know that the immune system is very complex, but, basically, when you think about the single-cell analysis as we knew it for centuries, that's microscopy, right? This is single-cell analysis. We can see a lot of different structures, and that was driving biological research for years, right? With very simple tools, you can see different structures that the cells form in the body. The immune system is actually very boring in that regard, so all our lymphocytes pretty much look the same. The development of modern immunology was linked to flow cytometry. I will stand here because I think the mic will pick it better. It was linked to the development of cytometry techniques all the time. Pretty much any advancement in the development of immunological studies, starting from the 1970s, was related to the advances in cytometry. It was an integrated part of the immunological research. The diversity of the cells in the immune system, we basically know everything about it because of the existence of single-cell analysis and cytometry techniques. All these different types of cells that you've heard about, and people got way more knowledgeable about this over the last several years. After the pandemic, everybody knows that there are T cells and B cells and so on. This is all supported by our labeling of the cells with this for the specific proteins that they express on their surface. That's how we can distinguish them. It's pretty much not the morphology like you think about other tissues in the body and other cell types, right? We create some kind of a protein fingerprints for every cell type that would define the diverse functions and types of the immune cells. That's also because the immune cells, basically, they use all these molecules to talk to each other, right, to convey their functions. It's not just like we have labels like Post-it notes on every cell. Each of these molecules have biological meaning. This is why also, like, we're talking about high parameter, and we think about, well, we want to reach like 40 or 50 parameters. People from other fields are usually surprised because they're saying, "Well, we're measuring like thousands of genes. Why are you talking about an advancement of having just 50 molecules? Why is it actually cool?" Right? "Why can't you do genomics and just do thousands of genes?" Actually, this is a very boiled down, very targeted studies where we're absolutely sure in the biological meaning of what we measure. Because with, for example, with genomic studies, where you kind of, yes, you get measurement of thousands genes, but you can't really trust any measurement as a single measurement. People get the information as like networks. You get this beautiful graphs about, oh, there's inflammation going on, there's exhaustion going on, but all of that findings have to be validated by other techniques. This is one of the ways people validate these findings. That's why we're actually we were getting boiled down to specific molecules that actually execute the functions. Also that is why the single-cell analysis is so important for the therapeutic applications, because in the end, you really don't target the network of genes that are, you know, activated in a certain cell type. You're actually targeting sometimes a specific molecule with your therapeutic. It's really important to get that readout. On the other hand, basically, if you measure something on a very crude level, like let's say you have the, like, CBC, right, like blood count from a person, and you try to correlate that with the disease, you usually get, like, if you have bulk of different like lymphocytes count, right? You basically had very little correlation with the specific disease output, so that's not good enough. You need to tease this lymphocyte, for example, population to specific cell types, like with one or two or three or four markers, just to boil it down, not just lymphocytes, but we go to T cells, not just T cells, we go to T regulatory cells, not just regulatory cells, but the specific subset of them. Once we boil that down, then we can actually assay these specific cells. That's why we actually need this, not just like five or 10 markers, but maybe 30 or 40, because once you get your cell of interest, you want to actually study and look specific products on that cell type. As Kevin was talking about, we're not talking now about, "Oh, I'm a T regulatory cell person. I'm interested in the T regulatory cells." That doesn't work like that. I have a team of people. One person is interested in B cell, another person is interested in T cells, and we work together, and we have a very small sample sometimes where we have to share our interests and we have to share the platform to get the readout. That's why we want to go for multiparameter analysis, right? We assay all these different products in one single measurement and also identify different other biomarkers, not just for typing of the cell, but also to assay their functions. That is how we use it for basic and translational research. This is how we use it in immunomonitoring and clinical trials. Obviously, we use that for some clinical diagnostics where if the biology of the process is already well-studied, but you still need to assay multiple parameters to get the specific readout that is relevant for the therapeutic or for a diagnostic outcome. I'm gonna just show several highlights from my research just to give you an idea how we use all these principles in real life. One of the projects that I've been working for years is a project in our institution where we're looking at the cohort of subjects that are HIV positive, and we divided them into two different cohorts. One is a younger cohort and the other is an older cohort. We know that there is antiretroviral therapy, that HIV is pretty well controlled in most patients, at least in developed world. But despite the successful suppression of the virus in these individuals, HIV positive people have an elevated risk of so-called serious non-AIDS events. They develop a lot of conditions that we usually think are associated with aging, with a normal aging in humans, right? But they have it earlier in life. Cardiovascular diseases, neurodegenerative disorders, diabetes, cancer, and so on and so forth. They have higher at every given age match category, this, HIV positive people have this disease at a higher rate. The question is, do HIV individuals just age earlier or they age in some different trajectory? I mean, it's a million-dollar question, multimillion-dollar question because this is not related to like so specific, like socioeconomic group or anything. It's true for every HIV positive individual that they have higher risk of certain diseases. As we pass the stage where we need to control the virus itself, we are basically seeing this population of individuals that we want to have healthy lives too, and long lives that their counterparts that are not HIV positive are expecting. We had this study where we basically assayed multiple cell types in the samples, in the blood samples of these individuals. With the older traditional flow cytometry technology, we had a 16-parameter phenotyping panel, and we identified that we have a specific subset of T cells that's usually ignored. That's called gamma delta T cells. There is a specific molecule in these cells that's called TIGIT, and you might have heard about it. It's one of the druggable checkpoint inhibitors that has been like a target of immunotherapy in several clinical trials. We found that this specific TIGIT expression on the gamma delta T cells was stratifying the individuals between HIV positive and HIV negative. The amount of this molecule on gamma delta T cells was tracking with different plasma inflammatory markers in the subjects. Again, we were looking at basically just a very simple panel with a traditional flow cytometry platform. We still got a lot of interesting data out of that, so we were able to predict whether the person is old or young, HIV positive or HIV negative, just based on their TIGIT on their gamma delta T cells. We wanted to look deeper into the biology of this. I mean, I don't have a pointer, but if you see this, four red circles on that plot on the left. So that's four different types of gamma delta T cells in this, in the cells. Basically, we want to go back, and we wanted to implement the high parameter data, high parameter panels to dissect these gamma delta T cells. With the high parameter analysis, we basically need the reagents to distinguish these analytes that we're studying, right? You need the instrumentation to detect it, and you need data analysis tools. We heard a little bit about the instrumentation already in the previous presentations. We're using Cytek platform to do that. That definitely allowed us to generate larger datasets with better signal resolution. We also were working on data analysis tools that would allow us to evaluate these results. We use basic software solutions that are provided by Cytek for so-called spectral unmixing, and also the standardization that Cytek provides that allows us to process multiple batches of samples, for example, over time, or actually implement our analysis on different instruments and different sites and combine all this data together, because that's a big problem of bioinformatics. Basically, if you have multiple batches of data, you put them all together, and all you get is noise because the data are not compatible. I have developed an optimization of the algorithm that people have been using for years for these datasets. Basically, the bigger datasets that we generate with Cytek platform require adaptation of this old algorithm. The field is catching up with the algorithmic analysis to answer the need that these datasets provide us. The algorithm that I have optimized, so we call it opt-SNE, so allows us to basically find the populations of our interest instead of, like, just seeing this big one blob of data, we're able to dissect it into multiple subsets of cells. It's important to know that the field is ready for this. Mass cytometry users kind of prepared the immunological community for using all these methods. Now we're kind of using the path that they opened and just providing them with the tools that they need now to explore the spectral datasets because their datasets were much smaller, so they didn't have those challenges that we have now. This is kind of already resolved question. All these large datasets are fully supported by the Cytek tools, so we don't have anything, like, any roadblocks here anymore. This is the analysis that we have done on the spectral platform, and these are just the few markers that we have in the panel. The panel was actually over 30 colors. We were able to characterize all these markers on just on gamma delta T cells. This is a snippet of just gamma delta T cells from the subjects. I think this is more than 90 individuals there combined. We can find 40 distinct clusters of cells in the gamma delta T cells as opposed to 4 that we saw before. Right? There is a huge diversity there. If we look at four different groups that I was talking about, we have younger and older individuals, and some are infected, some are not infected with HIV. We can actually see a lot of clusters going up and down with the infection or with aging. Now we can actually sort all these subsets and actually see what exactly they're doing and what exactly their profiles are in terms of transcriptomics analysis. This is all down the road. That's like a big possibility for us to look at the population that we only saw as like basically one unified speckle. Okay. Another project that we're doing, we're actually looking at the model of pneumonia where we induce the streptococcal pneumonia in mice. we are actually gettin`g a readout how the mice recover from pneumonia and develop immunity. This is a very well-established model for pneumonia studies, the Murine model, that has been used for multiple diseases, not only streptococcal pneumonia, but also for flu and for COVID and so on. The reason I brought this up is that we're not only looking here at the immune cells in these lungs, we actually can see all different types of epithelial cells, which was a huge challenge with traditional flow cytometry platforms because of the high background of the cells, different profiles of autofluorescence, and generally, like, the cells are much harder to work with. You kind of do one or another. Either you do immune cell profiling or epithelial cell profiling. We were able to combine all this data together and do, like, separate cluster analysis for epithelial cells and for lymphocytes. This was all done on one single sample because, I mean, you can imagine the mouse lung is pretty small, and we can actually divide it the same, the same lung between several applications and still, get a lot of readout from that. This was a study that was done, as you can see, with multiple time points. As opposed to human blood, this like, there's much harder to reach standardization in this kind of study. We had. This was done back in 2019, even before the more modern Cytek tools were developed for the standardization of the platform. Actually even back then it was very, very stable. Now it's superb. We could see a lot of interesting biology of the how the recovery from pneumonia induces the crosstalk between the epithelial cells and the immune cells. That advanced the field a lot. We published this in. I think it was actually very end of 2021, and we already had like, I think multiple citations, and we already published some follow-up papers on this project. Just to sum it up, the full spectral cell analysis or spectral analysis, like people use these terms interchangeably. This is our method of choice now absolutely for single cell characterization. It's in the field, the Cytek spectral platform became basically a default tool for spectral cell analysis. This is the state-of-the-art today, so people are using, like, they talk about spectral analysis pretty much all the time they're talking about Cytek platforms. Also the reagents-wise, we are not using the kits that the Cytek provides yet. We have been testing them. They work really well. We're trying to adopt them for our applications. As I change my hat to like a Cytek head of the cytometry core person, I definitely highly recommend my users to use those kits if they have the application that these kits would actually fit in well. Because it would cut a lot of time for the development of the assays because this kit is already pre-standardized, so they can actually go from the conception, like conceiving the experiment to actual execution in weeks as opposed to months, as it used to be, as it is for us, for example, for the applications that we're developing. I can take any questions. Sure. Curious for your relating a little bit to your HIV aging study. Mm-hmm. Are you interested in pursuing, like looking at potentially long COVID patients over time? I mean, probably not with the same panel, but you could apply that technique to look at, you know, we're seeing this phenomenon now, and we don't know how long it's gonna go on. Yeah, we have a long COVID project. Okay. We're mostly looking at the B cells in that context and in the autoantibody profiles of the subjects. Yes, we're definitely working with the long COVID patients as well. That makes sense. Cool. That makes sense. Cool. Yeah. Yeah, looks like a no-brainer for us. Yeah. In terms of the potential kit adoption, you mentioned that you're testing them and you're recommending them given the right applications and usage. In terms of like timeframe, are you waiting for development from the Cytek side, or are you waiting for the right type of experiments in order to adopt the kits? How does that work? It is basically the kits are smaller than the studies that my group is doing. We're looking, for example, with these Gamma delta studies, at much more granular readouts than the kits support because we have a very niche application. It's just because our specific projects were kind of not going the traditional routes. For example, for the long COVID projects, we're planning to adopt the Cytek kits for that because they give us a more broad phenotyping as opposed to like looking at a very specific subset. Yeah. Great. Thank you, Anna. Good. I could just imagine the translational opportunities from the HIV studies, as the population ages and how it can be applied in other applications such as long COVID and so forth. Wonderful. That's great. Next, we're gonna hear from Dr. Franklin "Buddy" Fuda, and he's going to describe a little bit about working in a clinical laboratory in oncology. Thank you. Do you have a pointer? There is. It's just. Thank you so much. I hit the button. Green button, yeah. Okay, great. Okay. Hi, I'm Buddy Fuda. I appreciate the opportunity to come and talk about the Cytek system. I'm gonna be talking about it from a perspective of a diagnostic flow cytometry lab, where we're basically immunophenotyping leukemias and lymphomas. I just have no actual or potential conflicts of interest in relation to this presentation or the program. A little bit about me. I've worked in clinical practice for approximately 20 years in hematopathology and flow cytometry at the University of Texas Southwestern Medical Center in Dallas, Texas. I'm the director of two clinical flow cytometry laboratories and one immunology laboratory. We have one of the largest university-based laboratories for flow cytometry in the country. We service three different unique type of large hospitals. We pretty much have a wide range of patient demographics, which gives us a high variety of disease that we look at. We practice in a tradition that was set forth. By experts in the field, such as Louis Picker, Steven Kroft, and Nitin Karandikar. It's a little bit of a unique way that we approach flow cytometry. We have particular expertise in comprehensive and detailed analysis, and we use different various software programs to do that, including a cluster analysis by Cytopaint software. One of the things that we insist upon is that our actual hematopathologists analyze their own cases. We're really good at detailed analysis for clinical flow cytometry. Our laboratory is used as a reference laboratory for regional laboratories in the North Texas area on particularly difficult cases. Myself, I've collaborated with other flow cytometry experts on essential projects such as EuroFlow in building a standardized screening panel or tubes for high parameter testing. I'm an actively involved member and contributor and inspector on international education committees, quality standard committees, and regulatory committees for clinical flow cytometry. Okay, what exactly do we do in a clinical flow cytometry lab? Well, we're just gonna do an immunophenotype. An immunophenotype is a fingerprint, basically, for cells. If we have a cell sample, and here we have three different cell types, we can use flow cytometry to identify what type of cells are actually in the sample. Here's an actual plot of the flow cytometry, and you can see that each dot up here represents a single cell. We're not really interested in single cells. We're interested in cell populations. In this plot, you can see that there's three different cell populations. Now, based on where these cell populations sit on these different plots, you can see that this population sitting in this region here means that it's both CD3+ and CD4+. That tells us that this is an actual helper T lymphocyte population. A flow cytometry basically allows us to identify and name all the normal populations in an actual sample. Importantly, it also allows us to identify and name cancer cell populations in a sample. That's how we're gonna use flow cytometry in order to help diagnose leukemias and lymphomas. Okay, how are we graded as a flow cytometry laboratory? Well, first and foremost, every flow cytometry clinical flow cytometry laboratory is graded on its sensitivity and accuracy of diagnosis. Basically, you gotta be able to identify the population, and then once you identify it as cancer, you have to be able to name it and say, "This is the actual cancer." That's first and foremost the most important thing. Beyond that, it's kinda we're gonna be graded on our operating expenses and on our turnaround time. For an academic institution, you're gonna add on expertise in the field. What does that mean? Well, basically, we're gonna have to use our knowledge to put forth publications so that we can gain national prominence. We wanna become, you know, famous within the field. This is pretty much how we're gonna set our goals in a clinical flow cytometry laboratory. Our ultimate goals are finding health for the patient and doing it in a cost-efficient manner. That's what we need to do in a diagnostic clinical flow cytometry lab 'cause we have pretty tight budgets. Okay, what does that mean? In order to meet our goals, we're gonna need two things. We're gonna need basically correct personnel, so that's gonna be your laboratory technologist, and then we're gonna need correct resource. As far as the resource go, that's where the vendors come into play. For resources, the first thing we need are instruments, and they have to be good instruments. We've gotta have instruments that perform well and are reliable. I can tell you that, you know, my many years of experience in flow cytometry, if the instrument goes down, it creates a lot of problems for us. We want reliable reliability out of our instruments. Next, our vendor has to be able to provide us with the correct reagents. Again, they have to be excellent reagents and perform well. They have to be able to supply the reagents and get them to us in a timely fashion. We're gonna need vendor application support. Every laboratory, clinical laboratory needs this, especially when you're bringing up a flow cytometry laboratory. What we want or what we expect of our vendor is that they're going to be able to provide training for our technologists to be able to get that flow cytometry lab up and running. Any changes in the flow cytometry lab, we're gonna want support from our vendor as well. Okay, vendor customer service. Basically, when things do go wrong, we need the vendor to be there to support us to get the lab up and running again. These things are gonna have a big influence on quality of product and efficiency of operation, basically. That's where Cytek comes in, into play. What does Cytek do as far as provide these kinda resource? Well, Cytek started off as a customer service company. All right? I think their customer service is, they've got a good history of excellent customer service. That's checked off. Next thing, Cytek is a flow cytometry-focused company. What does that mean? Well, it's kinda like if I'm a Jeep enthusiast, right? What I want is to go to a company that's gonna match my passion for my Jeep, right? Am I gonna go to a Chrysler dealership basically to have my Jeep worked on? Absolutely not. I'm gonna go to a place like four Wheel Parts, where they share the same passion that I do for my Jeep. All right. For flow cytometry, I want the same thing. I basically want a company who's focused on flow cytometry, and that company basically is gonna share the same passion that I have for flow cytometry, and Cytek basically is that company. All right, let's talk a little bit about the actual flow cytometry systems, the hardware and the instruments. Ultimately, in a clinical flow cytometry laboratory, it's just a numbers game. It's pretty simple, right? Markers matter. The more markers you can put per tube, the more powerful the test is gonna be, the more cost-effective the test is gonna be. If we look at a conventional flow cytometer, basically over here we've got conventional clinical flow cytometers. We can do up to about 12 markers in a single tube. All right? That's not bad. It seems like it's all right. If we look over here, your conventional research flow cytometers, you can get up to 30, 40 markers, okay? Since we're a laboratory developed test, we can use these research flow cytometers rather than these clinical flow cytometers. All right, that sounds great. I mean, I think that's enough markers for us to get to where we need in the clinical lab. What's the problem? They're dirty. All right? They're not very clean as far as the data goes. In reality, in your clinical labs, in your diagnostic clinical labs, most labs are stuck at about six-eight colors. That's where they're at. Labs that have the highest clinical expertise or highest flow cytometry expertise are running about 10-14 colors per tube, right? Depending on the actual system that they have. All right. Now, in order to get there, though, you really have to weed through a lot of the dirt. All right? You got basically to set it up really well, you gotta know what you're dealing with artifact, and then you can run 10-14 colors. But it's difficult. You need that high expertise, and that's not very common in most flow cytometry labs out there on the clinical side. In comes Cytek. All right. Cytek basically can do high-parameter testing, up to 40+ colors, depending on how many lasers you actually use on the machine. Most importantly, they claim it's clean. All right. That's what we wanna see. Is it truly clean? All right, just to kinda give you a picture of what's going on here. Here's a conventional flow cytometer. All right. I wanna see what's in the actual sample. Here I've got three tubes off a conventional flow cytometer, so I'm on, like, six-10 colors, whatever it may be. Each tube is gonna basically be represented by a porthole window. If I look at this, I can see that, okay, I've got, you know, this looks like sky and maybe clouds. I don't know, maybe these are, you know, barns. Here I've got a farm. That's my diagnosis, right? Okay, I've got a farm. I'm happy. I did my three tubes. You know, I figured out I got a farm. All right, now Cytek says, "Well, I can offer you a bay window." All right? Once I show. Oh, look at the cow. Right? It's got a cow. I missed my cow over here. Well, you know, my cereal wants milk in the morning. I don't wanna miss that cow. That's an important cow. Now, with expertise, like I was saying, in a clinical flow cytometry lab where you have a lot of expertise, you'll go through here and you go, "Okay, well, yeah, this looks like a cloud. It looks like some sky. Is it? What is it? that doesn't really look like a cloud. All right, what would I do in my lab? I'd say, "Okay, I'll add another porthole window." I add another tube and I find the cow. And so I've got the cow. What did that cost me? It cost me time, resource, money, right? All right. I don't wanna deal with that. Just show me the cow up front. That's all I want. I wanna see the cow up front. That is what Cytek can do for me. It just simplifies it. If I don't have expertise, I'm gonna miss the cow altogether. All right? Even with expertise, it costs me a lot more. I want a system basically that shows me that cow up front. All right, looking at that, we're kinda looking at the sensitivity and the accuracy. Theoretically, Cytek basically improves sensitivity and accuracy in that kind of a scenario. This is the scenario we have in clinical flow cytometry lab all the time. Now let's talk about operating expenses, because ultimately, if you can't afford a system, it doesn't matter how good it is. Again, we're on tight budgets in a clinical lab. We wanna be able to afford the system that's best for us. All right, if I look over here's a peripheral blood smear that we do. When we do a peripheral blood smear, we do 17 different unique markers. What does that mean? All right. The unique markers are the markers you can bill for, so in a private insurance setting. All right, I'm gonna run 17 markers on my flow cytometer, which is a 10-color system. I'm basically gonna have to run two tubes to do that. When I run my two tubes, I'm gonna have to run a total of 19 different markers. I've actually got two redundant markers in there. That means I've got two unbillable markers. Now, if you look at the Cytek system, I can do those 17 colors in a single tube. I have no redundancy whatsoever. I'm basically saving money. My operating expense is cut, right? Here, if you look at and when you start considering it's the markers, all of the reagents, the tech time, everything that goes involved, you're basically cutting your operating cost by about 50% on this one tube. All right. Now, that may not sound like much, but over thousands and thousands of tubes, it starts to be significant for a clinical flow cytometry lab. All right, now I've got a bunch more examples here. Let's just go down to this example four. This is the most complex type of testing that we would do in a flow cytometry lab. We're looking for acute myeloid leukemia. In order to do that on my system, my 10-color system, I'm doing 31 unique markers. I've got to run six tubes to get there. Okay? That means I ran 47 markers. I've got 19 redundant markers. That's pretty significant. Look what I can do on the Cytek machine. Instead of running 6 tubes, I can run two. Basically, I've only got a single redundant marker, right? And I'm basically cutting my operating expense by 200%. All right, again, that's incredibly significant for a clinical laboratory. All right, ultimately, fewer tubes means you're more cost effective. Okay, what about turnaround time? All right, if we look here, we've got basically if you're running routine sensitivity, all right, it takes about three minutes to run a tube. Is Cytek faster than the other machines? No, it's not. All right. Not per tube. The key is you're not running as many tubes on the Cytek. In that sense, the system is faster. All right, just let's go back down to that acute leukemia panel. Here, I've got to run six tubes. That takes 18 minutes on the machine, on the Cytek, I'm running two tubes, only takes six minutes. That's a time saving is about 300%. Okay. Over about 1,000 different patients, you're looking at 200 hours just for that kind of tube alone. That's significant. Now, that's just the acquisition of the data. We got to go over to analysis. Okay, analysis, again, on average, maybe takes about three minutes per tube. You can see you're gonna get the same type of time savings when you're running a Cytek machine with less tubes. Again, about 300% Cytek time savings, about 200 hours over 1,000 patients. That's very significant. Bottom line, fewer tubes equals faster results, less resource consumed, and then faster patient results. Okay, faster turnaround times. Basically, that creates increased productivity for the lab. It's not only the flow cytometry lab, it's the hematopathology workup. What happens in hematopathology is flow cytometry is one piece of the puzzle. All right? The morphology and the genetics, everything else is the other piece of the puzzle. You got to put it all together to come up with your diagnosis. Well, if your flow cytometry is basically delayed, your hematopathology is delayed as well. If we can get the flow cytometry out faster, your hematopathologist then can direct their investigation in a more time-efficient manner. It's really significant for all of your clinical operations in that regard. All right, improved patient care. Faster turnaround times means earlier diagnosis. That means earlier induction of treatment, basically. All right. More cost-effective patient care. With the faster turnaround, you end up with more specific therapeutic approach sooner because you're seeing what the actual molecules that the cancer is actually expressing in a more efficient manner of time. All right, then reduced duration of inpatient care. What do I mean by this? Fridays, I always get a call from our hematology oncology team, and they wanna know what the flow cytometry showed, basically. They try to order a stat case. All right. While it takes a little bit of time, we may or may not be able to get them an answer. Why is that important? They're looking to see if the actual patient is negative for cancer at this point in time, because they want to get him out of the hospital. Because if they're stuck in the hospital for two more days, that is a huge expense. Again, if we get faster turnaround times, then basically we're going to have reduced duration of inpatient hospital care. All right. Ultimately, theoretically, going through all those things, the Cytek machine seems to be, it should be more sensitive and specific, should have faster turnaround times, and should be more cost-effective. It's a no-brainer. You know, for me as a clinical flow cytometrist, I want this system if that's true. All right. Does it actually work? All right. We've been waiting around for a long time in the clinical world of flow cytometry for something that can do this type of high-parameter testing and do it well. All right. Theory sounds great, but does it actually work? Particularly, the true testament is not gonna be on a peripheral blood, because when I first started talking to Cytek people, they were showing me some data off of peripheral blood. Peripheral blood is easy. All right? It's clean. It looks really good. Where I wanna see the data is in a complex tissue like a bone marrow. Why a bone marrow? Because bone marrow has cell subsets, like an entire B-lineage subset, that's going to show similar expression of markers, but there's gonna be subtle differences. So I wanna be able to tell, can the system actually resolve those tiny subsets? That's gonna tell me whether or not the system works. Okay. Two, can I identify a minute malignant population within that complex tissue? All right. Those are the two things I really, really wanted answered. They brought a machine in for us, and we spent two weeks basically throwing everything we could at it. Now, if you remember, I said our institution covers three different large hospitals that have different demographics. We have a lot of different types of diseases that come through in a two-week period. Everything we threw at that Cytek machine, it worked beautifully. I got my answer. The data looked fantastic. All right, this is just one example of what I'm talking about. This is a bone marrow sample, and over here in this first plot, this has every single cell population or every single cell that's in the actual sample. All right. What we're gonna do is we're just gonna take the 19 positive cells. Those are all your B-lineage cells. If you look here, once we do that, we get rid of all this gray stuff. That's all the myeloid and T-cells and so forth, so on. We've just got B-lineage cells here. You can see that for every one of these markers. Each one of these B-lineage cells is going to show some expression of it, or for most of them. All right. If you look in here, there's no way to resolve certain things, no way you can kinda make out certain things, but I've colored them, so it makes it a lot easier to see. Basically, if you're going down here, you can see that here you have these are known as hematogones, or these are immature B cells. You got stage one, stage two, stage three, and then beyond. This is what I wanted to see when I was saying, can we resolve? Can we pull out the different subsets of these actual cell populations in complex tissue? The answer is a resounding yes. Not only that, but can I identify my cow, right? In the background here's my cow. See the red population? See it here? Okay. I was able to identify at a sensitivity of 0.001%. That's 10 to the -5. All right. That is MRD type sensitivity, as high as we can get, or minimal residual disease, as high as we can get right now in flow cytometry. Basically, I could pull that out, and it's easy to pull out. Why is it easy? It's not because it would be easy on my 10-color system. It's because I have 0.1 colors in this tube. All right? I was able to put in markers that I usually don't, and it pulled them out. Look at 23 versus 33, 43. They just sit. They come, they pull right out. Right? That's why this is so impressive. That's why this is the system that I want in my clinical flow cytometry lab. All right. Successful installation operations. Academic institutions. Basically, the Cytek machine hits the accuracy and the sensitivity. It hits the operating expenses. It really hits the turnaround time. This is a fast machine. We get results fast off of this. Okay, and then expertise in the field. The publications. What happens? You know, there's hematopathology, the flow cytometry in the hematopathology realm, and then there's flow cytometry in the immunology realm. All right, there's a lot of stuff in the immunology realm that basically hematopathologists don't know. All right. When we are able to identify these things is when we can do more markers, 40. That opens up a lot of publication for us. What you'll see is we'll start to publish things, and you just dig into the immunology research, it's already been published. Because it's in a different realm, it's meaningful. By being able to do more parameters in a single tube, this is gonna open up easier publication for us. It's just this is just beautiful for us as clinical hematopathologists. All right. Again, we'll hit that national prominence by becoming famous by publication and talking at national conferences. All right, basically achieves our goals. I'm a huge fan of this. I mean, a really big fan. We've been waiting, like I said, so long to get high-parameter testing in the clinical lab that actually works. All right. What's that mean for flow cytometry? I think Cytek revolutionizes flow cytometry. We talked a little bit about artificial intelligence and automated analysis. All right, that's where we're moving in the diagnostic realm too. We wanna get there, right? All of us, I mean, we're building our own AI at my institution. A lot of my colleagues are building their own AI. The full potential is gonna be recognized because we can do more parameters for two. That's what Cytek enables us to do on that clinical side. All right. It simplifies the technical component. I think a couple people have talked today about how it's simpler to get good data. I showed you that slide earlier, where we got flow cytometers out there, conventional ones can do 30+ colors. All right? If you cannot get good data off that, especially in the diagnostic realm, you can't use it. All right? Here, you can. The Cytek machine and the system actually makes it a lot easier to get that good data. It simplifies that technical component. It's gonna open up new horizons for research and clinical practice in the hematopathology world. I kinda mentioned that already. It's gonna meet new challenges brought about through advancements in clinical therapeutics. All right. What do I mean by that? Well, the more that we have immunotherapy being used, the more difficult diagnostic clinical flow cytometry gets, and the more questions they have. I constantly get called by my oncologist asking me on this B-cell lymphoma that we looked at, you know, "Does this thing express CD79B?" I said, "Tom, I don't know." I mean, because it wasn't in our tube. Or I don't have enough cells to actually run it and see. With this type of system, I don't have to do that anymore. I can just add the CD79 up front when my oncologist calls me, "Yeah, it's there." It really, really does meet those kinda challenges. All right. What's that mean for Cytek? To me, in my simplistic mind, I mean, it just, it means it takes the market share. I mean, there's nobody that I know of in the clinical diagnostic realm for flow cytometry that's not gonna want this. All right? They're not gonna go and buy these other machines with this outdated, technology that doesn't allow for this type of investigation. The reference laboratories, university laboratories, they're gonna switch to the latest technology. No question in my mind about that. Okay, what about private practice and small laboratories? What happens right now? They're gonna end up establishing in-house labs. Why? Flow cytometry is a high revenue generator. All right. What's that mean? Right now, these small practices and these small hospitals and labs, they capture the professional component. What they do is they send off to a reference laboratory, they say, "Do the technical component for me. I'll make the interpretation." I'll capture that. What's the problem? Well, the technical component is where all the money is. You know, this professional component is pain. Why don't they capture that right now? Because it's too complex for them with a conventional flow cytometer to actually bring on the technical component. With the Cytek system that's gonna simplify that, you're gonna start seeing more and more labs pop up with this machine and this system. All right. Out of the gate early. All right. This is it. Like, Cytek is. They're basically running with this right now. What's that mean? All right. For years, you know, we ran on the FACSCalibur, which is the BD machine. When we were going to 10 color, we had a couple options. We stuck with BD. Why? Because we had a bond with BD. All right? What's gonna happen since these guys are BD, nobody else has this technology right now. You're gonna get the switch. What does that mean? In the future, people are gonna stick with Cytek. It just makes sense. As long as you've got the reagents, which Cytek has. I found that out by bringing in the machine for two weeks. They've got an excellent catalog right now for the reagents. You have to have that, right? They have all the reagents we need. They've got the customer service, and they've got the machine. That means people are gonna move this way. We're gonna basically develop that bond and stay with Cytek. All right. So Cytek, basically. It's just got good balance. All right. Like I said, the most important thing in clinical diagnostic flow cytometry is health, right? That's what we need. We also have to consider what's the cost of that. All right? Cytek balances that out beautifully. That's my experience that I had with the machine, with the system, and I'm really excited about it. Thank you, Buddy. Do we have any questions for Dr. Fuda? Hi, Dr. Fuda. Very helpful talk here. I was curious as to the point you made about, you know, the much higher level of sensitivity that you get with flow. You described it as sort of best in class for flow. Specific to these lymphoid malignancies in the clinic, there is a perception out there that, you know, approaches like, you know, immunosequencing from Adaptive, you know, they have a clonoSEQ assay, sort of outperforms flow and sensitivity. What has your experience been like? Does Cytek essentially enable you to bridge that gap to a point where, the delta is perhaps not clinically meaningful? Yeah. I think with some of your molecular testing, your sensitivity can get down to 10 to the six power, basically. Theoretically or even practically, it is a more sensitive test. But what happens is there's a timing difference. When you're doing MRDs, right now, the molecular tests take a little bit longer to do. Flow cytometry is immediate. You can get those answers right here, right now, and that's what our clinicians are actually making their therapeutic decisions on. Flow cytometry, in that sense, is a little bit better. Is there opportunity to make flow cytometry even more sensitive and match that of molecular? Absolutely. You're starting to see some labs, like, I think, the Mayo Clinic run some of their tests. They're collecting 10 million cells on it. They're getting down to 10 - the - 6 sensitivity. I think, you know, with the advancements and with clean data, it's hard. The reason flow cytometry is so difficult is because the data is just not as clean as you want it to be. If you can get cleaner data, that's gonna improve the sensitivity. I think with a system like this, you're gonna start to see the sensitivities in flow cytometry go up. The advantages of flow cytometry, they both have their pluses and minuses. The advantages of flow cytometry, I think Anna kinda mentioned it, you're getting a phenotype, right? Basically, you know what the cells are actually expressing. You can target, like, therapeutically from that. With molecular studies, a good example is acute myeloid leukemia with a t(8;21) translocation. All right? What happens? Well, that type of myeloid leukemia actually has maturation in the neutrophils. On day 29, when they're looking for minimal residual disease, you can use a molecular study, right? To look for that t(8;21), and it's positive. Flow cytometry is negative. Why? Because you're seeing the minimal residual disease in the neutrophils, not the blast. That's significant as far as therapeutics go. That's where flow cytometry shines. We're actually showing you what cell is containing that molecular abnormality. Again, they have their pluses and minuses, but molecular is not going to replace flow cytometry in the realm. We have another question here. Thanks, Dr. Fuda. Just on the comparison you made on the cost savings and time savings, is that incumbent upon having large scale like your lab? Or do you think this, you know, your slide about some of the smaller practices developing in-house labs, do you think they can benefit from those cost savings? Absolutely. Because they might not have the scale that you do. Yeah. Absolutely. For us, a state-run type lab, obviously, your budget's really tight. You're always looking, what's it gonna be. Can we stay above water, basically? We don't have to make money. We just gotta stay above water. For these small laboratories that are private groups, for them, the money spent is gonna be the money spent on reagents. All right? This system, no matter what scale you're looking at, this system's gonna be cheaper to operate than a conventional flow cytometer. It's just, it has to be. I mean, if you're getting similar costs for your reagents, you start to look at, okay, so if I can run one tube instead of three, you start to look at the cost savings for the reagents, the actual tech time, the overhead, everything. The wear and tear on the machine. You know, I mean, one of the big issues with flow cytometry is wear and tear on a machine. If you're only doing one tube instead of three, then you have a lot less wear and tear. No question, small labs are basically gonna benefit financially as well from this kind of system. Any other questions? I don't know if I'm allowed to ask questions or not, but first, love the cow analogy, buddy. That was awesome. I'm from Texas. Yeah, exactly. Hey, you mentioned leukemia, lymphoma, and MRD as applications that benefit from higher parameter analysis. Mm-hmm. Right? I think you talked about how clinical labs are kind of coming out of the low parameter dark ages, if you will, and emerging into the need for higher parameter analysis. Are there other applications that you see out there that you see obvious benefits from high dimensional biology, so companion diagnostics, stem cell enumeration. Mm-hmm. HIV, other areas where flow is used in an application that you see significant benefit from higher parameter analysis? Yeah. You know, I think the leukemia/lymphoma realm definitely on the diagnostic side is where it's probably gonna make the biggest splash. Some of the other diagnostic I'm not, 'cause we don't do any, like, of the esoteric type testing, so I don't know how, you know, how beneficial a higher parameter is gonna be in those. I mean, it certainly can't hurt. If you need to, say you've got if you're running any kind of assay and you're running more than one tube and you can get it down to one tube, it's gonna be beneficial. I think, you know, across the board in that kind of scenario, if they need to run more than six-12 colors, this is gonna be beneficial. All right. Thank you, everyone. I think you can tell we've got some very energized presenters. We are running a little bit behind and, I do wanna take a 10-minute break for everyone, but let's come back as quickly as we can and get started again. Thank you. I was so excited after hearing the KOL talk. I don't think I need to talk anymore. I feel like it was so energetic. I see these things every customer we are visiting. I was making a tour in Europe. Everybody's so excited about new things and discovery. I wanna step back in time a little bit to how Cytek makes it happen in the past. We clearly see the unmet need. You'll be here all day long today with the KOLs. We see multiple things. Actually, it's really the market requires such high-dimensional cell analysis, right? For immunophenotyping and the functional assay in the clinical space, leukemia, MRDs, and I will talk about the bio-nanoparticles actually recently. As few KOLs talked earlier, the flow cytometry, they're more or less settled around 20-30 markers detection conventionally. You can see from 2010 to 2020 is flattened. The field does not go further until full spectrum cytometry come into market. We made 24-color with three lasers in 2017. We created market. In 2020, we made our 40-color paper 40-color single tube. As you can hear today, there's talks about 50 markers. We're talking about 40-59 markers, all push the technology with our enthusiastic users and helping to make the field advance. I don't think these slides are needed, and Bill already showed the single conventional was detecting one flow at a time, and the full spectrum give you a fingerprint where you have a mixed data, where you have all the different mark together. Because you have each fingerprint, then you can call unmixing, unravel, so you can counting cells, right? The important thing is on the right-hand side, you can see those two dyes that Bill was talking about earlier. Those two dyes, in conventional, people never thinking can use together. Never. I use the words never. Because you're either using this APC or Alexa Fluor 647. When you open the full spectrum, you say, "Wow, they're so different." We will narrow laser zoom one of the windows. Now you suddenly open the window, you say, "Wow, mixture is impossible." Both dyes can be used in the same assay and co-express cell. Both dyes label on the same cell. You can mix them really well. This generation technology really opened up for advanced next generation cell analysis. You already see the outcome for last couple of years, and Full Spectrum being widely adopted and high sensitivity, high throughput, and without compromise the data. That's the key, right? Now in the whole world, you many talks earlier using all the dye together, people enjoying the research. Important is even for people doing few colors, because the Full Spectrum able to get all the fluorescence of the cell, which cancer cell has a very high background, we're able to remove it. Even very few markers, we're able to deliver to resolve from the background. That's another key thing I want to deliver, because not only more markers, you're able to resolve the background. With cancer markers, you see much better resolutions. All the technology is basically empowered by patented innovative design from laser excitations, from the detector modules. We're using semiconductor detector modules and open up the sensitivity, also the wavelength range. We can squeeze more dyes actually than conventional yet get the infrared ranges. It really maximizes the resolution accuracy, the resolution in spectral, the resolution in data range. Both. That's why we're able to do 40 or 50 markers at the same time and optimize the signal-to-noise ratio, talking about earlier, and really provides the insight for our users, as you can hear today. This is our instrument platforms. Aurora is the analyzers. You hear many talks and able, capable to do 40, as you can see more like 59 markers right now. The Northern Lights is the entry-level market and 1 to 3 lasers. We think is great for clinical lab, but clinical lab study using 5 lasers. We're, we see the market changing because I learn from Dr. Fuda today, more information is better. That's the bottom line, right? You can hear multiple talks because sample is precious. Sorters, you hear two talks today. I think we're very glad to helping transform the cell technology to the next adjacent market, which is downstream analysis. We're able to purify the cell. This is the first time human being able to get those rare cell out for analysis. You can hear from Kevin's talk. I don't think I will repeat all those, but this our platform today. Cytek provide end-to-end platform. From instrumentations and automatic sample loaders, we, because automation is the key for us to be successful, being more successful in the clinical space, want less user interference, right? Fully automatic. We have the reagent kit that we're talking about, our customer talking about it, and I will give more detail later on. Service is important. Application support is super important. We're helping customers to adopt technology, help them design the panels, and that's the way we get the reagent pull through as well. Data acquisition and software, we're getting to the advanced data software analysis. I will give a little bit more. We provide end-to-end solutions to get the full solution to our customers, as few of the KOL alluded earlier. In this comparison, which just like mass spec earlier opened the market for appetite to using a very high parameter cytometry. However, you can see that there are many downsides for spectral, for mass spec. That's why we work. They really get people to realize multi-dimensional is important. The spectral cytometry earlier, developed by Sony, also led the way for us to see there is a possibility for that. As you can see, the instrument had a lot of downside, but everybody now getting on the bandwagon and to the full spectrum. We definitely excel in the conventional cytometry, and you can see the market adoption. I would like to say is, our technology really gives us a unique opportunity for reagents. Because the new technology opens up the new requirements for the dyes, reagent. Because in the past, you have to well separate the peaks, right, to use reagent. Now we're able to use all spectral overlapping dyes. Which we call spectral unique dyes. Those we call high parameter enabler. This gets people from 20-color to 40-color range, right? It enables them to do more colors. Right now we have 28 unique cFluor dyes with high-parameter commercialized. We're making kits. Our goal, the company goal, is to make it easy to use from day one. The kit really is our focus, right? Then we can link our instrument to the reagent, so hopefully it will save customers huge time, and they save the spectral into the instrument and be used again. That way, as a company, instrument maker, we're able to link them all together with the reagent, instrument, solutions all together as a one-stop shop. The kit. Actually, the kit is not just put reagent together. We're developing in-house. We pre-titrate it, save all the customer work to titration reagent, optimize the dye flow cost, and we provide them acquisition template, analyze template. The whole goal, you can hear a couple of talks earlier, just one button click. Okay? That's the goal. We have couple kit in the market, and we continue developing kit. You will see more kit in the market in the near future. Our company strategy is developing kit and also we're helping customer expanding their panel, right? The kit, you can hear a couple of times, it's like, "Anna want to do a little bit extras." We're able allow them or help them to expand the panel. Also you can make a big kit, you can using sub cells them. This is really a game-changing for our industry. We're also very actively working on the clinical market. We have multiple. I cannot count anymore. It's single color reagent in China get certified as a class one. We're on the path for class three registrations. With very unique panels. We've actually got the feedback from the market now. We also had the same one certified or self-certified in Q2 in EU for EU customers, European customers. I just talked about earlier, when Europe had a tool, our customers really excited about our opportunity there, especially in the clinical space. Bioinformatics is important. As you can see, some labs already doing it by themselves, right? We cannot lag behind. We need to catch up. We need to work with them together. The bioinformatics program, we establish in-house. The goal is make easy to do flow, right? Accelerating the reagent instrument pull through. That's the key, right? Because you provide information that customer easy use. Also, we allow this better understanding what customer need, right? Because this become a hub. We're able to get all the data. This also will be greatly enhance, accelerate our product development path. Just give you one examples. I heard AI earlier, so we are just starting working on the AI, and this is a very encouraging data on leukemia MRD detection is a collaborations. You can see on the left side is manual gate, right? As you now become flow expert. Each dot is a cell, we look the cell classifications. Within the gate is the same type of cell, we're able to tell how many cells inside the gate. The AI software doesn't require the manual gates. Automatically can pull out. Very exciting, right? This really provides our users seamlessly. It's because when I went to the lab, people told me, "We don't want manually adjusting everything," right? "We want to be automatic." That is the goal. This is clearly our goal. How can we make things easier for users, especially in the high parameter space? Seamless workflow vital to the clinician is the key, right? We're working very hard for that. With advanced technology, as you can see when the instrument adoption will be embraced, really welcomed by our enthusiastic users. We collaborate worldwide. Then, on the top left is the Dr. Sylvain Salmon at Fred Hutch talked just two weeks ago in Philadelphia, and he broke the ceiling of 59 markers in single tube for immunotherapy-treated patients. He really tried to achieve the goal, getting more information from the same patient, right? I'm very excited. The one line below is the multi-site collaboration with NIH and then another group, different one than Bill, and the University of Ottawa and Cytek. That's the effort to really get together with nanoparticle standardizations. That's a very important nanoparticle. I went to the meeting in Europe and really saw the trend, how people look exosomes. It's very important to standardize those. Our instrument is the most sensitive instrument actually for nanoparticle as well. We have global collaborations. You heard about MRD, I mean, leukemia talk today. We started 2000 earlier. This was a slide to 2009, right before pandemic. 2019 in China had a whole session about leukemia diagnosis MRD. The work was already going that time. I think right now we have so many collaboration sites. We learn from experts, right? We all working together and also collaborating on AI, on clinical, data analysis. This is just the very beginning. Okay, I will turn to Mark to go over more clinical market. Yeah, there won't be much to say here. Most of this has been covered. Thank you. Yeah, you know, in working with our clinical laboratory customers out there, we realize that this is essentially just summarizes all of what Buddy said and other laboratories that are doing exactly that type of work. More informative antibodies in one tube. We've heard that. Eliminate the redundant reagents. I had not previewed Buddy's slides before putting this one together here, but optimizes use of smaller amounts of patient specimen. All the things that we've already heard about that. Improves laboratory efficiency overall. That's our value proposition in the clinical market. As Ming just mentioned, we have ongoing collaborations, and we're helping laboratories. Here are some examples of the various projects that we have ongoing right now around the world, in, and these types of panels here. Just really helping through our technical application support team, through our R&D team, helping these laboratories convert over multi-tube panels into, you know, single tube or two-tube panels using, in our case, using the Cytek cFluor reagent. We're driving the customers that way and getting them familiar with our cFluor. The next gen clinical flow cytometry, you know, sort of key needs, key focus points here, we're striving to make it completely operator independent. The one button, the one push button. That removes the bias, the day-to-day variability, the tech variability, all of that, which overall improves results and data quality. We're doing that by, you know, working on the initialization of the instrument, making it easier to set up. It's already pretty easy. The automation is important, especially on the reagent side, if you're gonna be mixing them together the multicolor cocktails, to be able to automate that is extremely important. That's next generation. The data acquisition, and then, of course, the data analysis, talking all the way to the AI, but the cluster determination visualization, which you've seen some of already. Those are key focus points, things that we're currently working on in our programs. Just the final slide I have here is, right, it's all about the system solution. It's under development currently at Cytek, where it's the hardware, the software, the reagents, the support, and the automation. We've already reached out to the FDA through a Q submission, asking them some questions about how to basically just get this approved. That's it. Patrik. Thank you, Mark. All right. Good morning, everyone. Patrik Jeanmonod, I'm the CFO. It's gonna be hard to go into the financial numbers when we see all the exciting slides that I've seen, colorful slides. But I'll walk you through my single color slide here. This one, how can you get excited about this slide, right? Going backwards, I'm looking at 2018 when we crossed the first time 100 instruments, and today we are at 1,112 instruments. The company is growing substantially, and we saw, obviously, a number of instruments. Obviously, Aurora is a key instrument, top of the line. We have the Northern Lights and now the Cell Sorter that's coming up and will continue help on the growth for the future. That's backwards, but looking forward, I'd say that it gives us an opportunity to build our reagent business. That's how we I mean, with all we've heard today, all the projects we have ongoing, this will give us the future of Cytek as well. A couple numbers here. Key numbers going back to Q1 of 2020. This is revenue split by product revenue and service revenue. Key point here I wanna just bring out, Q1 of this year, our year-over-year growth 44%, which is a substantial growth. I think last year we finished the full year year-over-year at 38%. This is 44%. We are obviously at 1,226 instruments. Our service revenue, we've continued to add discipline around the service, and we've also seen more instruments come off warranty. Our instrument revenue actually doubled compared to a year ago. That's a trend that we expect to see moving forward. The adjusted gross profit margin, that's one of the numbers that I like to look at quite substantially, is the 61% in this industry, which is substantial. Our goal is obviously to stay at a 60%+ range. This slide is also a two-column slide. Revenue, adjusted EBITDA. This one shows a little bit our desire to stay profitable from an adjusted EBITDA point of view, and we drive that through management of cost, right? It's top line growth, but also managing the cost and continue to invest in the business. I'll talk more about that. Overall, the adjusted gross profit margin as a percent of total revenue and a high 28.7% in Q1. As expected, we continue to invest, and the expectation is that we will over time, as we continue to scale, we should see this number also go up. Operating expenses. Breaking down sales and marketing, the R&D expenses and the G&A. If I go through the sales and marketing, we're gonna continue to invest in adding salespeople, but we've now built our reagent sales group, and we'll continue to build that out as we expand and as we continue to grow into the clinical world. On the R&D side, we'll continue to invest in instrument, in software, in reagent. This is gonna be also a key investment area for us. We'll obviously optimize our footprint. I mean, we have offices in the U.S., but also around the world, and we expect that number as a percent to stay high, but also over time to come down just because we're gonna have higher revenues. G&A, as you can see, I mean, we came out at a very low 7% in Q3 of 2020, but gone up substantially, and that's just a result of us becoming a publicly traded company. We have to reinvest in some areas and obviously audit and SOX compliance are key investment areas for us. We take that very seriously, so we're gonna continue to see some G&A expenses up high. Over time, that number should flatten and stay from a dollar point of view, and the expectation is as a percent of total revenue, it'll come down. Some stats here. The top line really is the gross profit margin, the adjusted gross profit margin, and that's a number that we laser focus on and really is building the top line, and at the same time, building the gross profit margin to remain at a 60%+ range. As we roll out new instruments, new products, the expectation is that gross profit margin will continue to remain or build over time. We talked about sales, marketing, R&D, G&A, so 18%, 19%, and 18% for a 7% adjusted EBITDA. Over time, as I said, I mean, we invested in sales. This is a serious transition year. We have added people in the sales and reagent group, but we're gonna continue to invest also in instruments as we go into some new markets like clinical market as well. Over time, we'll continue to build our gross profit margin. We'll continue to invest in sales marketing, R&D and G&A, as I said, will remain flat over time. We'll this year is still gonna be a transition year considering that this year's the first year after becoming a publicly traded company last year. Just to talk about where we are, where we wanna go, right? We're gonna support the four pillar strategy and I think Wendy will talk more about that. The focus remains on the top line. The focus remains on the gross profit margin. Obviously we wanna make sure that when we introduce new products, new reagents, that they have a higher gross profit margin than the one we have today. We're gonna also remain focused on the adjusted EBITDA. We wanna stay positive. We'll continue to invest, but in a friendly manner considering our P&L. We'll be continuing our discipline of cost management. I think we talked about that. I think the company, since the very beginning, has had a goal to grow the top line, invest, but also have an adjusted gross profit margin that's positive. So far we've done a very good job. This is something we're gonna remain focused on. We'll continue to invest also in some CapEx as we continue to grow around the world. We should expect maybe some CapEx through the second half. But this is typical of a growing business and it wouldn't be major, but it would be some CapEx. Finally, we are reaffirming our 2022 revenue guidance closer to the higher end of the range of $160 million-$168 million. We feel good about these numbers. We've looked at the numbers many ways, and we feel pretty good at where we are at this point. Considering everything I've heard today, I feel even better. With that, I'm open to questions. Otherwise I'll just pass it on to Wenbin. Patrik, thanks for the update there. You know, just starting with your last comment there, that you feel a little bit better about sort of the prospects on a go-forward basis after hearing all the user presentations here. How do you think about sort of the high end of the guide for 2022 and sort of the potential to exceed that? What would be some of the drivers that need to sort of really inflect in the business on, say, over the next sort of, you know, three-six months for that to happen? Right. The first comment I would say is I'm expecting that there's no recession. That's the first element. I believe that the assumption at this point is it shouldn't impact us. That's the first comment. I would say that the funnel that we have remains strong. We also, as we roll out the new reagents strategy, but also, I think the cell sorter has shown to be very powerful, has strong demand. I think these are the elements that put me in a good position about our numbers for this year. Got it. You know, you're sort of, you mentioned investing in the business, but sort of being committed to staying profitable. I mean, is there a trade-off there at this stage at all? Like, could you perhaps grow even faster if you were to abandon, you know, profitability? I mean, only from an optics standpoint. I'm not saying sort of negative 30%-40% sort of, you know, profit margin. Is that something that you've debated internally, like, you know, perhaps accelerating the investments to build on the momentum that you're clearly seeing on the top line here? Yeah. I mean, it's possible. We had a discussion. I think for us, especially in this environment, it's critical that we remain EBITDA positive. We could foresee some increased growth down the road as we are more prepared to go into the clinical in some other areas. Maybe at that point we'll consider. For us today, the goal is to remain adjusted EBITDA positive. Final one for me, I mean, as you look at sort of 2025, you know, with the business having scaled on, in terms of the costs, et cetera, to where you need it to be to sustain top line growth, just paint us a picture for what, you know, gross margins look like and what sort of steady-state EBITDA margins would look like for Cytek. Yeah. The expectation is that as we add products or instruments or reagents that have a higher gross profit margin, the gross profit margin should improve. Just to put some barriers here, I think 65%+ is a good number. And obviously we wanna remain positive on the bottom line. The expectation is that the G&A will remain flat from a dollar point of view. At some point, R&D will also, I wouldn't say become flat, but will be less, we'll invest less in R&D. But we'll continue to invest in sales and marketing as we continue to grow. Overall, I think, the company should remain in these numbers, within these numbers. Thanks, Patrik. Maybe just on the reagents. I think you said in previous forums that end-of-year, probably single-digit percentage of sales. As you continue to develop and talk to more customers and the development happens and folks are testing it, like we heard today, where do you foresee reagents, you know, maybe as a% of sales, like longer term, if you wanna comment on that, or just in terms of this year, is that single-digit still what you expect? Yeah. It's gonna be mid- to high-single-digit for this year, right? Our internal goals so far have been met on our goals, reagent goals. We're coming obviously from a this year is a transition year, obviously. I mean, last year we had almost no reagent revenue. Going forward, I'm expecting the. When I'm talking about ranges as a percent of revenue, it'll be in the 28%-35%. That's the goal today. I think there's a great opportunity for us to eventually go beyond. I think this is a good number to start with. Got it. My last question, and maybe Wenbin will get into this, but just on capital allocation, just given the cash balance, given that, you know, you've done a small acquisition earlier on the reagent side, you know, how are you looking to deploy capital in this environment? Yeah. We're looking at a number of opportunities, right? But we don't wanna hurry. We wanna find the right fit for the company and for the long term that fits the long-term strategy. Overall, I think we'll probably be acquisitive. That's where we are. Let's get through our last few slides, and then we'll open it up again for questions. Thank you, Patrik. Okay, thank you. We are getting to the close of this event. In fact, myself has learned quite a lot from our guest speakers here, and many of those things are new to me and really exciting, and they are educating me and to be more aggressive since. Along the lines, Patrik also mentioned about four business pillars. In fact, all of the presentation today earlier has established, built a foundation for us, for the company to continue to grow, to expand. That's what we said about the four business pillars here. That's where the company is going to be. If we look at what we have here, first, those four business pillars are built on the instrument, applications, bioinformatics and the clinical. Now, instrument is our conventional strength. That's how the company started from. On that aspect, we're going to continue to improve, to optimize our instrumentations from performance perspective, making it more intelligent and more easy to use and also more compact. We all know lab space become more and more precious and the size is important. We have heard cost is important, and we would really like to have our instrumentation to be the lowest cost solution for everyone, for our researchers, for our clinicians. Applications. Today, we have talked a lot about applications and how our technology is enabling, broadening the applications. Here when we talk applications, that also includes the reagents. What the company is going forward is to focus on a few areas on the applications side. One is we talk about our own cFluor reagents. Our intention is to make cFluor reagents as an enabler that will work together with our partners, means other reagent companies together. We build on those type of partnership along with our reagents to expand and enable us to focus on panels, kits. Those panels, kits will not only have what we call with functionality and the purposes as well as the flexibility. Means also, for the research use, with kind of backbone, enable our users to add or deduct from that panels we have optimized. Our application is going to focus on the business with volume and that can repeat. This is how our application strategy is going to build upon and to enable us to really capture the recurring revenues. Of course, recurring revenue also including our service. The bioinformatics, we have heard quite a lot about it. Our unique technology has really enabled users to generate lots of data. The question is how the company can help our users, our customers, to manage those data. Data management, and to store those data, and to analyze the data, and to optimize the panel, optimize the data. Again, lastly, is to enable our users to exchange information and to help each other. This is how we are going to build upon and this is what we call the third business pillar of the company. That's what today we are investing to build the bioinformatics initiative. Lastly, clinical. We've heard very exciting stories how Cytek technology is enabling the clinical applications. From company's perspective, this is one of the four business pillars here. From regulatory perspective and also supporting the LDT applications and menu-driven and AI to enable the reporting and analysis. Of course, finally, standardization. For clinical, that become important. We want all the instrument to come up with the same data. We enable the data reporting from one lab, the same as from the other lab. This is in fact also the advantage of Cytek technology. We have heard earlier today, the data from Cytek instrument pretty much is the same, almost the same, which other technology is not capable of providing. This is with all of that, the four business pillar, that's what we see where the company is going to be as Cytek's vision. That's eventually how the company will be. A comprehensive solution companies. That starting from our Full Spectrum Profiling technology, expand vertically and horizontally. Vertically, as you can see, not only we are going to enable the multi-omic downstream analysis as what we have heard after the sorter with gene sequencing. As well as starting from the sample preparation, auto sampler, those type of directions, that's what we are going to continue to expand, to enhance. Horizontally, how we expand the company. That includes earlier mention about the imaging technology, the spatial, mass spatial, microfluidics, all of those technology adjacent space, that's what we are going to continue to look at to integrate into our platform. Of course, there are other application earlier I mentioned about for the marine biology environmental sciences. That's what you are going to envision how the company eventually will become. That's what in terms of how the company is going to operate. We will continue to pay attention to the capital efficiency. Return on investment is always part of the strategy company have in mind. We'll continue to maintain our operational excellence to ensure we have a high growth margin. Of course, we are going to maximize our free cash flow and maintain our positive EBITDA earlier mentioned. Not that many companies in our space is maintaining the kind of profitability and many companies are struggling to paint a path to profitability. Cytek is already there. We'll continue to maintain to make sure that we will stay being positive. Again, the execution speed is an important part of it. Okay. As you can see, we actually developed our Aurora technologies very quickly, okay, in just few years. Thereafter during the last five years, as you can see, we have Aurora, Northern Lights, Aurora sales orders and all kinds of reagent panels. We will continue along that path and maintain the execution speeds. Heard about question about the acquisition, and that's the area we'll continue to look at all the competitive technology out there available to us and take full advantage of the cash the company has to expand, look smartly and including not only acquisition as well as the licensing and joint venture aspects. All of those is to show our real commitment to our shareholders for the value generation. Finally, come down to is why you should all invest in Cytek, right? That's important. Number one, all of the presentation has shown, and just to validate, we do have a really transformative platform technology that we have already built, which has driven our high growth. Our expansion will continue that momentum going forward. We have already validated Cytek is the most competitive innovator in the industry. We have the, really the fastest growth top line, in the industry and we maintained 40%-80% growth during the last few years. That momentum is going to continue going forward for the next three-five years. As you can see, we have really a strong balance sheet. Lastly, why you should invest? Cytek really is, has a very attractive valuation today. It's a growth company with a value, pretty much is a value company, right? That shows why we should really, it's a good time for investing at Cytek to grow, to ride it together with us. That's the end of my call, all of our events. I think now is the Q&A. I believe there are some questions. Yes. We do have some questions from the internet audience, but I want to open up the Q&A to the room here first. If there aren't any, we can go right to the internet. Thanks, Paul. Wenbin, a question for you on if you look at sort of that $8 billion- +$8 billion TAM that you've outlined, can you walk us through what percentage of the TAM today, sort of is looking at a high parameter analysis, you know, over, let's say, 20, and how that number has evolved in your mind over the past couple of years? This is a great question. If you look at the high-parameter cell analysis, basically Cytek generated basically the new front, generating the new barriers. Before Cytek, high-parameter cell analysis, anything above probably 15-color considered high parameter, right? At that time, we see the technology from our friends like Fortessa and FACSymphony all consider high parameters. Not anymore with what we have done. If we still maintain the original the kind of criteria in terms of what is the high parameter. Let's say if any flow cytometer with 4-laser or 5-laser is considered as a high-parameter cell analysis tool, then I think roughly today we can say 40%-50% of the market is in that space, in the research yeah market. Got it. One question on just pricing dynamics. I mean, have you seen any shifts from the competition? I mean, clearly, you know, at time of IPO, something that resonated well with investors was the fact that you're offering more sort of information at a significantly lower price point. Now, I mean, competitors obviously, you know, will respond to that. Some of them are responding to it. Just walk us through sort of the evolution you've seen in the competitive landscape, perhaps over the last 12 months or so. You clearly can see, because of Cytek, we have shifted the whole dynamics of the flow cytometry toward the spectrum. Today, if anyone is not talking about spectral, probably they are not in fashion. Recently you can see every major flow cytometry companies are talking about spectral. We have heard the spectral sorters from two players, and one is based on similar screening or prism-based spectral sorter technology. From one of the, I don't call it a competitor, our partners, put it this way. Another technology just two weeks ago launched the imaging sorters with spectral functions. Those are on the sort sorting side. That's what we have seen. Then on the analyzer side, we have heard, of course, Sony and, in fact, pre-announced their new spectral sorter like three years ago before the pandemic. That's the kind of landscape what we have seen. One spectral sorter, one spectral analyzer, two spectral sorters. However, what's important is exactly what our customers are going for, right? Where they are. I can clearly say, of course, the latest imaging-based sorter is too early for us to say. I don't even know if it's a formal product or not. It's probably still just in the kind of pilot run stage, so hard for me to comment. At least then in that case, left with only one spectral analyzer, one spectral sorter. I can say at least I haven't seen that many from our customer side. Just from performance side, definitely Cytek continue to perform to outperform them. Cost perspective, we continue to do a lot better as well. That's all I can say. Of course, probably we have three other actually perhaps on the remote support guest speakers. Maybe they can provide a better answer than I do. Well, the quotes that reach the end customers show that the pricing of Cytek is way more competitive. Like I think the most aggressive marketing that we've seen, and I'm talking like as a customer, right, so, was from the mass cytometry platforms that were basically blatantly saying, "Well, we're our machine is now cheaper than five laser spectral machines." I mean, we all know what it means, right? Which is, first of all, it's not true. Second, I don't think that people are investing in mass cytometry units at this point. I mean, I definitely wouldn't. I think it's kind of a sinking ship thing. I mean, maybe people are looking at secondhand machines or like, you know, to add to their existing labs because they already support the platform. I don't know a single person who considers to start a mass cytometry suspension flow lab at this time of the day. Yeah. No, not happening. I can expand a little bit on a few of those points. In regard to mass cytometry, I hosted a retreat for our immune monitoring labs last year, fall of last year. One of the questions I had. These are, I'm not gonna name names, but they're, you know, major immune monitoring labs around the country. One of the questions I posed was, what technology do you think is going obsolete next? Everyone said, mass cytometry, except one lab. I won't say which lab, but it's an obvious lab that would disagree with it, essentially. It wasn't that people hated it or it was bad or anything like that. It was just they felt like, the juice wasn't worth the squeeze kind of thing. It's so hard. There's so many trade-offs. I do think there is a place for mass cytometry, but I think the market's kinda narrowing a little bit. When in regards to the cell sorting, there's a lot of talk, I think, from other vendors that just has never matched up to reality. There's a lot of vaporware selling going on. Again, the prices just aren't there when it comes to the competitiveness. It really just, you know, it just comes down to really what Buddy was saying about the cleanliness of the data. Like, we're just seeing the data is so much cleaner, and I think it's gonna be easier and easier to do over time, the way Cytek's going about it. If you've been around the flow world for a long time, you've seen things get easier and easier to do, and I think we're on that path. Like it used to be, people would talk about something called compensation, which was, you know, how you would get the overlapping signals out. That was really hard for people to understand years ago. Now it's like people don't even think about it when it comes to how the unmixing is done. I don't know. Things are just changing to where it's getting easier for the end user, and Cytek's really leading that. The other companies just aren't kinda there when it comes to that. I don't honestly see any competition with the Cytek sorter for any other platform. I think it's at a time right now where all the pre-existing, you know, flow cores, they all have older generations of equipment that are all obsoleting out. I know my lab, the flow core lab that I have, that's an addition to my immune monitoring lab, received end of life letters from the company saying, "Hey, we're not gonna service this anymore. There aren't gonna be a lot of options for service." A lot of labs are scrambling to replace this equipment now. We just did. We just bought another spectral sorter for the flow core because there are no other good options at this point. It's, I don't know. Curious to see what happens over the next couple of years with that. One final one for me on Cytek and, you know, their acquisition announcement this morning of Namocell. You know, at least the press release says it's gentler on the cells and, you know, I'm just curious, Wenbin, as to your take. Have you run into them in the field at all? Sounds like they have about a 200 unit install base. Curious as to your take. Actually, I think that I would let Ming to comment on it. We have looked at it. Yeah. I think this is a very good question. I think the news just announced today. I think. This is different type of cells, and this mostly focused on individual labs that particularly as in the great jobs for the linkage for the small volumes. Does it? We play in different spaces. Actually, I should say this is another area we are looking to. I just have two quick ones. One for you, Wenbin, just on the bioinformatics opportunity. When you think about that in terms of where you are in the process of developing that, and then in terms of the potential monetization opportunity for bioinformatics, how are you looking at the commercial opportunity, or is it sort of an added service for customers going forward? No, actually, that's a very interesting question. First is, on that initiative itself, we are expanding our Seattle office, to focus on supporting, that bioinformatics. We are also actually hiring people to, help to support. However, the objective of bioinformatics itself is not try to turn it a kind of very profitable business by itself. Okay. We see this as a supporting tool, to help our instrument, our reagent, to help our customers to come to Cytek, and, we help them to solve their problems on the data, management, on the data analysis, and, on the exchange of information. It's a tool. It's a portal. In the meantime, they will come back and to continue to work with Cytek for our applications that we have developed for our reagents, for our instrumentations. As well as actually the clinical side. Bioinformatics matters, basically go across the rest of the four business to support the rest, three businesses we are, building up. Got it. Ming, maybe one question for the doctors, customers in the room. As you think about flow cytometry analyzers and the sorters, and you think about sort of the loyalty and stickiness of that instrument category, maybe relative to other instruments that you use, where do you kind of see the most valuable aspects of that instrument in terms of your willingness and your loyalty to stick with it? Is it the customer service? Is the performance? Is the overall cost? I'm sure it's all of the above, but just would love to get a sense for you. Do you think that flow cytometry in general is a much stickier instrument within a lab, maybe relative to other instrument categories, and if there's a certain reason for that? I can take a shot at that first. There is that, you know, it used to be coming up, labs were Coulter or BD, and that's just whatever they started with, you know, in institutions, they just kind of stuck with it. BD had the gross, like, the vast majority of the market share, you know, 95% of the market share, I think, was at one point. Over time, I think a lot of customers soured on a lot of the experiences they had, and they felt like there was, you know, they only had one option. I know when I worked for BD, I heard customers just tell me flat out, "I can't wait till I have any other option than this." I don't think that. I think we're in a point right now where I don't think that matters. It's more about what can the actual technology provide. I know my customer service experience has been phenomenal with Cytek, and it's oddly, a lot of these people are ex-BD people that experienced that other side of it, and they were frustrated as well from what the customers were experiencing, and they're that much more passionate about that not happening. My experience has been, even though some of the people are still BD people, we're not having those kind of issues. I've had better service in the last three years with Cytek than the last 13 years I had at Vanderbilt prior with BD. I used to work for BD, and they used to just send me parts, and we would fix stuff ourselves. It's not comparable. I don't think that's really gonna matter. I think there's actually a window right now where people are dying to switch, personally. I totally agree. I also wanted to add that, I mean, I think that there's the whole nature of the technology of the flow cytometry kind of forced people because the efficiency of using the instrumentation was very much, like, not stable. I mean, you don't develop a relationship with your centrifuge, and you're just, you know, it just works. If you need to switch from an Eppendorf centrifuge to some other vendor, usually you're not expecting to see any difference. Now, with the flow cytometry, there was a huge variability in your success rate. Another thing was that, I mean, I think that it's not a surprise that Cytek, which definitely provides the exceptional service. On the other hand, your kind of your expectation is that my machine will behave as well as my neighbor's machine. They're all at very high rate of success as opposed to, like, former experience with BD instrumentation or Beckman Coulter, which is now Danaher. That's you had some super successful labs that published high impact publications, but an average Joe who got the, technically the same instrument, paid the same price, would expect sometimes pretty subpar performance. Yes, Mario Roederer's lab would publish 27 panels one after another, and you have technically the same instrument, and you paid, like, $1 million for it, but you can only make it to 18 colors, and then it becomes all garbage. I think that this all developed, like, some people had very strong relationships when things worked well. Some people had very sour relationships when things didn't go well. I think it's way more healthy relationship with Cytek, where on one hand, we love how they treat us, but on the other hand, it's like business, right? Yeah. Thank you. I know this was kind of, went over just not so long ago, but I just want to, go into a little bit more on the specifics of the revenue from the reagents. What percentage of the revenue do you expect to be coming from the reagents this year? I can take that. The information that's been given out is mid-single digit. All right. Actually, on that point, I do have a question from the Internet audience, and Patrick may already have answered this part of it, but, let me just read this for everyone's benefit. What are reagent gross margins? How quickly is the reagent business growing? And what portion of trailing 12-month revenue is reagent sales? We don't break out reagents today, nor by customer, nor by instrument. Doesn't say that we wouldn't do it down the road. What I can say, though, is the gross profit margin expectation for reagent is between 60%-80%. That's what I would say at this point. We have another question. This really would be for Wenbin. Can you discuss in more detail how broad the patent protection is for the instruments? How difficult would it be for a competitor to get around them to make a similar instrument? I think Ming has actually one slide on the hardware side. If you look at that slide carefully, it's labeled as A, B, C, D. There's some alphabet all there. All of those are with should have Cytek patents covered. We have a patent cover our laser excitation side, covering all receiving technology, covering how we do the mixing, covering how we do the full spectrum technology, and also covering how we put all of those together as a compact flow cytometers. So it's kind of very complete. Now the patents not including U.S. patents as well as international. Of course, many of the U.S. patents have already been approved, international, some are approved, some are ongoing. I think we are very comfortable with regarding to our IP protection, yeah, here. Of course, we continue to file more and more patents to surround our current and the future technology. We do have one more question, also again on reagents, and this may be for our scientists in the room. What is the average annual spend by a customer on reagents for an instrument? Any takers on this? No? We haven't even. Yeah. I don't think we've disclosed as a company what that is. Yeah. All right, anyone else have any burning questions you wanna get answered? We do have some hors d'oeuvres available now, and I'd like to invite everyone to participate. We also have a little parting gift, if you would like. It's a nice leather portfolio. One per firm, please. I think you'll enjoy it. With that, I'd like to give everyone a round of applause for their great performance today. Thank you.