Cytek Biosciences, Inc. (CTKB) Earnings Call Transcript & Summary

Good morning, everyone. Welcome to our first Analyst Day. And many of you, we actually probably have met during the last 12 months, remotely, virtually through Zoom. This is the first time we will be able to meet in person. Really glad to be here. And last 12 months, and really, it has been an exciting journey for Cytek. We are very, very happy to have you together riding 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. And -- but on the other hand, so our communications, many of you have been asking us, what Cytek is? As to what kind of business are you having? Because you are all very familiar with the generic space, and there are many companies of our size in that part of the business. However, Cytec, 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. So 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 sales analysis. We have also invited 4 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. So 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 on to the next slide. And during this process, if there are any questions, you are encouraged to just to raise your hands to stop us. We can communicate. We can -- hopefully, 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 site leadership team. Many of you probably have already seen, and we have a few new faces here, and several of them are actually here. We have Todd, our Chief Commercial Officer, joining us from actually BD; and we have Mark, managing our marketing, Mark is going to present as well; and Todd, our Head of Investor Relationship; and Patrick, our CFO; and me in the back, our CFO (sic) [ CEO ], the founder of the company, co-founder of the company. I hope I haven't missed anyone. And the rest are all sitting here, you can see. Many of them actually are associated with the flow cytometry business for many, many years. So I think one of the objectives when you go back today to think, okay, what I have got exactly and what I've learned, these are the 5 bullet points, I would like you to remember before I forgot, actually. Let me show this to you. First is what Cytek have is really patented what we can transformative FSP, full spectrum profiling, platform. So what does this platform do? Deliver a deep insight, high throughput and ease of use for our customers, for our users. So that's first thing. Second part, our technology really is addressing the unmet needs from the scientific community. So 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 5 years and validated, our technology has been by the diversified customer base and also with accelerating publications. Cytek is not an U.S. -- 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. So just to show you exactly what we are talking about in terms of global scale. In North America, our headquarter is in Fremont. There, we do, of course, R&D, also manufacturing. We have 2 site offices here in the U.S., 1 Seattle, which does our informatics initiative development as well as the customer application support. San Diego is where we manufacture our regions. And we have assessed office right next to NIH actually to support our customers in the East Coast. Now we have our European headquarter 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 and we see another manufacturing site as basically backing up for the U.S. manufacturing. And then 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 and New Zealand and Southeast Asia, including Japan. As in the revenue in terms of revenue by industry, almost 50-50, 50% is academia, another 50% for commercial, including pharma, biotech, life sciences. And 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 and more than 150 just biopharma based in the field companies. And our products are into more than 40 different countries today. And 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 instrument maintenance. Just to summarize what we have generated. As you can see, and our technology has been already validated by more than 1,200 instruments as well as 740 publications by our users using our technology. And I already mentioned, we have a broad customer base and a global presence. And again, a uniquely, Cytek has a very strong financial profile today. Last 4 quarters, the trailing 12 quarter revenue $139 million and $18 million EBITDA. As you all know, in our space, not that many countries actually profit. That's how Cytek differentiate ourselves from many of our peers here. And of course, many of them are struggling to show a path to profitability, we are already there. And we have $362 million in cash, and we have no debt. Looking at our journeys. 5 years ago, we launched our Aurora product. A year later, we launched Northern Lights. Both Aurora and Northern Lights are cell analyzers that have driven our revenue growth over the last 5 years. And then towards the end of 2020, we launched reagents to capture the installed instrument base for recurring revenue. And last year, we launched the sale sorters. And in the same year, we acquired a [ton] biosensors, primarily for their region business to support our overall region initiatives. And then last quarter -- actually not last or this quarter, earlier this quarter, we have our regions clinically approved to support clinical applications in Europe. Now this is a summary of the overall market that Cytek is in, the Cytek supporting. There are 2 part, 1 is the initial terms the conventional for 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 accounting; $1.9 billion, cell proliferation; and the $3 billion, cell ID. And then because of Cytek's unique technology 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 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. And 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 interns today are being sold and used for those type of applications. Now our focus today, and starting from Mark, for you to capture is, we want to demonstrate, show you, present to you exactly where flow cytometry fits in the overall research space, clinical diagnosis and other areas, kind of educational. And give you further details around our technology in the market segment sizes and the overall growth rate for our technology and show why customers are choosing Cytek. And we will also look at region opportunities in the markets. 4 of our distinguished guests will talk about Cytek technology data usage -- data use for their applications, driving their applications with their research. And Patrick will talk about our financial profile. And before we summarize will go over Cytek's overall business strategy and our objective. And of course, lastly, and through all of those activities, we want to assure you the reasons why Cytek can become the #1 company in the cell analysis space. With this, I'll pass it on to Mark.

Thank you, Wenbin. Good morning. Mark Herberger, Director -- Senior Director of Marketing. I've been at Cytek now about 3.5 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. So 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? And so this diagram here pretty much covers it all in a simplified manner. But if you think about a flow cytometer, it's made up of fluid[ X], we could think of this funnel as the fluid [X]. It's made up of optics. If we think about the magnifying glass there, also a laser and detectors, it starts with some question around a particular disease area. These are just a couple of those application areas that are 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. And they all get put together, staying with CD markers out or tag 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. And 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. So it kind of moves this direction here. Other things that are mentioned -- or measured that you'll hear more about here are exhaustion. So an individual cell that, for some reason, loses its potency or the potency is regained once the patient undergoes therapy that would be exhaustion. And this senescence is as we age, our immune system overall loses its potency. And you'll hear some more about that later in today's presentation as well. So 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 self-order we're able to sort out these individual cells of interest to then be able to use in some downstream applications such as genomics, transcriptomics, proteomics, on and on and on. So that's just a basic overview of why flow cytometry and how it works. But we'll get deeper into the technology as we go through the presentation. So the market forces and the needs that are shaping cell analysis. If we look over here at the research, right, so 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%. So when you look at those larger numbers thinking that flow cytometry actually plays a very important role. It has been for a long time, very important role in cell analysis. The target customer is there, this is where a lot of our instruments are already placed in academic institutions. And then the R&D departments within the pharma and the biotech businesses. Then there's the translational. This is where the CROs 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 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. So that's a big need of the CROs of the pharma in this translational space. They need panel construction, optimization, the validation and the automation. And you'll hear as we go through this is that, that is something that we actually provide and support through our partnerships and collaborations. And 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 tire customers, of course, CRO, pharma and then specialty laboratories. They are helping us determine then which of these assays, which of these application areas should we then move into the clinical market segment. And 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. So 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. And it's something that is extremely important for a company to be able to adapt to. So for example, in Europe, just as last May, they have now put in place what they call the in Vitro Diagnostic regulation, replacing the original what was called the Vitro Diagnostic development. So we have to adjust to that. More clinical trials and things that are required there. Of course, they're still the laboratory developed tests. And so those are laboratories that basically buy the supplies and then self-validate in their own hands, a particular application. We support that as well. And then 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. -- will replace what's typically used now is the 510(k) and laboratory developed tests with a new category. That is their proposal. And there's a lot of discussion that goes around the Valid Act. It's important for companies to really pay attention and adjust to these changing regulatory requirements and needs. Target customers there, reference laboratories, hospital laboratories; in developing country, clinical laboratories. So 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. So we're building growing the pie, making it larger as well as getting a bigger slice of that pie. And again, just focused on these sort of 3 areas here. The research, again, the academic laboratories, right, applications, immuno-profiling immuno-oncology, immunology, the list goes on, but a lot of research being -- a lot of very good research being done in the laboratories. The competitors in that space that have been there, of course, is the familiar names to BD, Danaher. And we have now a very substantial portion of that laboratory space, Thermo, Agilent and others. That market CAGR is about 10% to 12%. The translational is really where the growth is going to be. So pharma biotech, CRO, working on applications in dendritic cells, CAR-T cell, so immunotherapy applications, vaccine development, receptor occupancy assays. A number of things where they're applying the lessons learned from research in their laboratories. Again, competitors, BD, Danaher, Thermo, Agilent. And we have now established a good footprint in this translational space. CAGR in that area, 12% to 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 blood cell disorder, immuno-oncology and then, of course, laboratory developed tests and lots of various application areas, including immunotherapy monitoring. So that's a big up and coming. The dominant players there, the dominant competitors 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% to 8%. It's steady. It's and goes for a long, long time. So here is just a chart of our customers' publication. So some pretty prestigious journals that were published in. And you can see the growth of these publications utilizing site technology over time, and there's a 740 publications. And 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 profile. I mean we were right there on the front lines with the laboratories as they were doing the early studies of coronavirus infections and looking at the immune response as well as in the vaccine development. Some other large areas, immuno-oncology, immunology that covers a lot of different things. But you can see a pretty evenly split around these application areas here towards the middle. So what I thought I'd do is just go over a couple of those. So selection of application areas for full spectrum profiling where our flow cytometers were used and then published on. So 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, December of 2020, published by Pathology Laboratory,[ Lutipy ] Hospital in China, one of our accounts there, one of our customers there. They're right spectral multicolor flow cytometry has shown an advantage over traditional flow cytometry, and that more fluorescent markers could be detected simultaneously. So giving -- 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 precise diagnosis of deep cellular level correlation. So looking at those various subtypes and the maturation pathways and so forth. So that was very beneficial for them. So there, we helped convert their existing multi-tube panel into 1- to 24-color panel, designed according to our experience and there's some recommendations from an organization in Europe called the Euroflow. And their conclusion offers more cellular information that's unmatched by traditional flow cytometry. So you'll hear more about some of these plots and things in the later presentations, but that was 1 early application of the technology in myeloid disorders in leukemia lymphoma, otherwise immuno-oncology. There's another one here. This was a poster presented by a large reference laboratory in China called [ Kinsar ]. So it would be the equivalent of a LabCorp or request. And there, they compare the antigen expression and diagnostic results obtained from our system in Northern Lights to the widely used be effects cancel the sort of an entrenched instrument that's been out there in the clinical market. And with the help of our scientists, R&D scientists, they were able to convert 4, 6, 7 collar 2s into 1, 23 collar 2s. So you think of the time savings and the efficiencies that were gained there. Found no significant difference between the platforms. That's great. However, we -- the NLCLC has many advantages, including more detection parameters in a single tube, lower compensation interference, easier processing with smaller volumes, so forth. So right, they claim to establish a new standard 23-color panel for the highly accurate detection of multiple myeloma. This is very recent here and flow cytometry can now analyze 50 parameters. So earlier, we were talking about 7, 8, 10, 23, scientists have now been able to apply our technology to be able to put 50 markers in and cover a lot of area with that, right, and cover these immune populations and associations with diseases. So very powerful tool. That would be in that sort of research to translational space is to take advantage of this year. Here, right, the conclusion is better, or flow cytometry can now analyze 50 parameters, a lots of different types of cells and components of the cells. A conventional flow data analysis, can't keep up with that. So because of the data that's generated from 50, better methods are, therefore, critically to take full advantage of the powerful technology. So in other words, some analysis software to be able to analyze all of the data, all the good data that comes from the analysis of using 50 parameters. There's one here, another one application of flow in immunology. This is a 43 color panel and again, a recent publication, being able to just look at all of these different cell types in the immune system. So that funnel, right, and all the mixture of cells that go into that funnel and outcome out of the bottom, right, being able to analyze these various cell types. Quickly, right, analyzed on a 5 laser Cytek Aurora, data analysis is not using -- this is a third-party software. And this panel can help make a thorough interpretation of the immune system. This is very, 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 And. And it's using flow cytometry along with some of these complementary technologies. And I know you're going to hear more about some real-life applications in the later presentations from our invited guests on this. But in this case here, it's right, analyzing or sorting as I showed earlier in that diagram, analyzer sorting 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. So all of them providing additional important information to the scientists or in fact, the clinician as they combine these technologies. And just to show an example of that here, publication from a few years ago, there was many out now that show the combination, and in this case, a combination of multicolor flow cytometry along with next-gen sequencing. So using those 2 technologies together, 73% of the time can predict the 4-year relapse of an acute myeloid leukemia cell blood cell disorder. Whereas, either of those technologies use individually, you don't get that kind of power. So there's always a question about what is the relevance of future relevance of flow cytometry now that there are other technologies out there, they're complementary. They're going to be used together. And in the case of like this, a clinician, is able to make very decisive answers or direction on a patient with the use of the technologies. So just to bring it all home here, wrap it up. Our commercial and reagent strategy over time over the 5 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 and as we been mentioned, over 1,200 instruments installed globally. And 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 -- the over 700 publications in those application areas. We've been positioning the platform then with the support of the lessons learned here into the farm and the biotech. We have instruments at the top pharma and CRO companies. And it's when we're now exporting them with our recently launched seafloor reagents in panels. So we learn here, we apply it. and then position the platform accordingly into the pharma and biotech. And now starting to translate those applications into the clinical space, all along with 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. So transforming our company into a just a solutions provider, a system of hardware, software, reagents, support everything that goes together, turnkey. And our path there has been IVD product registration is completed or are in process for in recent or current discussion with the FDA as we had been with the European authorities and the Chinese authority. So we're well on our way there into transforming to the clinical market. So the next 4 presentations, we've invited in guest speakers to talk about their application. So Dr. Bill Telford will talk about his application and use of Cytec technology in basic immuno-oncology. Dr. Kevin Welders here. He's going to talk about sorting in flow cytometry in oncology. Dr. Belkina is here, and she is going to be talking about the application and use of the Aurora in oncology. And then 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. And we're going to turn it over to our remote guest speaker. I'll move the slides. Bill? Okay.

Okay. Can you hear me?

We can.

You can. Fantastic. Okay. Well, thank you, Mark, for the introduction. And 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 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. So 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 and keeping a suite of advanced instrumentation is very important to us. It's critical for our mission. Next slide, please. So what do we work on? We do a lot of basic both 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. But 1 thing we do a great deal of this 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. So 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 20s for markers simultaneously and all the way up to 30s and even 40s. But 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. So 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 analysis, I know that in 3 months, they're going to want to sort those cells out and do something with them. So having cell sorters that match the capability of our analyzers is really critical to our mission. Next slide, please. So 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. And 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. So 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. So you're going to be hearing a lot about the technology today. Many of you already know a great deal about it. But the technology that I came up with in flow cytometer is the so-called traditional technology. where we have single lasers, we have single dichroic 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. So -- and you'll be hearing about this from Ming and many other people today. But rather than taking a single bandwidth of fluorescence from a single laser we take full spectrum. 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 bit Bioscience suspect Symphony A5. It's a very admirable flow cytometer, does a great job. But it uses traditional technology. And 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. So 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. And what Cytek has done and other manufacturers have done is now moved into the spectral space to allow us to expand that capability further. Next slide, please. So there are many advantages of spectral over traditional. I'm just going to highlight a few of them that kind of hit me when we started using the Cytek technology. We are -- we acquired our first Cytek instrument a couple of 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 nonidentical probes. So 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 is 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 1 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 of that today as well. Next slide, please. So 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. And what we've done in this panel is deliberately chosen fluorescent probes that are very, very close to each other spectrally. This is something a panel you probably wouldn't use in real life. It's almost too challenging in terms of the spectrum requirements. And yet because of the power of spectral cytometry, it works, a panel that there is no way you could do this by traditional flow. So again, it's a more forgiving technology in terms of the choice of fluorescent reagents you use in your experiment. Next slide, please. And 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 2 fluorescent probes that if you look at their spectrum, our practical 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. And 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 myeloid linear 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. But 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 going to be using this in a year or 2. Next slide, please. We can also employ many new fluorochromes. This is a poster we had in CITO at the CITO 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. Phycobiliproteins isolated from protozoans and other probes that are related to Phyco erythroid and some of the natural product fluorochromes that are out there. They weren't useful back then because they pretty much mimic 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 Phycobiliproteins that we're already using in flow cytometry. So we're able to tap into these chemical resources and add fluorochromes to our pallet as it were to the fluorochrome 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, Phycobiliproteins, these fluorescent molecules. This was done in collaboration with Columbia Biosciences. They turned out not to be useful 20 years ago, but we've fold them out and reassessed them and are now using them in spectral flow cytometry. There are now -- there are 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 want to sort it almost immediately. And this is the and that Mark is talking about and that some of the other speakers will bring up because once we do our flow cytometry, we want to do other things to those cell populations. We want to do PCR, we want to do RNA seek, we want to put the cells back into culture. And one issue that has traditionally sort of dogged us as shared facility operators is that cell sort of 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. But 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. So 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. So just to show you a little bit about our assessment of this technology. Currently, we have 2 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. And I won't bore you with the details, but the 2 instruments, our analyzer and our sorter, are essentially identical to optically. I cannot tell the difference between them unless I look at the header in the file structure. They are not only equally sensitive, but they are normalized to 1 another. So we can use our analyzer, collect our analytical data, then sort on that data and the results will be virtually identical. Next slide, please. So this is actually what -- just to show you what the level control panel on the instrument looks like. The instrument sales orders break the stream of cells into droplets and then the droplets are sorted using electrostatic plates to guide the sidestream into tubes. The Cytek system has a little camera down here that focuses on the collection tubes, the cells will ultimately land into. Next slide, please. And this is what the data looks like. And again, don't worry too much about the details here. But 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 color -- 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 cell -- 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. But the systems are working very well, and one of the sorters has now been put into regular rotation for our usage. So 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 end. We do a lot of things with these sorted cells in proteomics, genomics and other things. And it's going to be a critical tool for our research. So I will stop there, and thank you very much.

Thank you, Bill. So I'd 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. So that's going to help us get a larger share and has been a larger share of the pie. And then the power of and is actually growing the pie overall. So... 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 want to have, a few more clarification or anything else. So my role at Ohio State, I was actually brought in to be the Associate Director for a new immune monitoring lab. So we're fortunate enough there to have funding organization called Pelotonia, which isn't the Peloton Bike company, but it's a turbo bike ride that raises about $15 million a year, purely for cancer research. And we are one of the benefactors of that our Institute. So about 3 years ago, they've invested -- actually go this way. They invested $102 million to start this immuno-oncology Institute. So this was a purpose driven decision to try to focus on 1 of the most advanced areas of cancer research and to try to power it through as quickly as possible. So a big theme of ours is we want to rapidly make discoveries. There's an urgency in our group to do everything. So there's a picture of the [ Zhihe ] our Founding Director. He's a real dynamo in the immuno-oncology world. And so on the IND, the immune monitoring and discovery platform, I didn't pick that name, but that's the name of me monitoring lab. We are part of the bench when it comes to this previous description we were saying we want to have a bench to clinical research trial initiative. So right now, we're in a stage where we're building our resources and our abilities up we've rapidly accelerated through really the pandemic 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 it's kind of how it worked for everybody, I think. But we made some advancements we did some contributions to that research, and that's -- so that's where we fall. We're the bench. So we're building a leading immune monitoring platform to support immuno-oncology research. That's the goal. And to just start with discovery but also to power it through with the end goal, hopefully, in a couple of years here, we want to be able to do patient-based medicine based on the work that we're doing now. And we want to 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 their profile. And so we're a shared resource, but we're just focused on IO. So a lot of labs you'll hear, there's a core lab for flow cytometry. And I also run that for Ohio State, so I got kind of stuck running that as well, but my real focus is this immune monitoring lab, but we are resources really focused on immuno-oncology. And we're trying to get as much of the information as we can looking at the immune system for everything that we're doing. So we're not limited to flow cytometry. We have everything in my lab, but flow cytometry is a key part of what we do. So we are built for this. So we have right now over 100 members with broad specialization. So this is from basic researchers to clinician those active clinical trials. Every pharma has got a trial going on at high state for immuno-oncology at this point. And it's a really fertile ground, Ohio State is I think, the second largest NCI cancer center and when it comes to patients bed size essentially and they're racing the other cancer center to build more beds as we speak. So we just have an incredibly rich environment to do this research. So we really have a heavy emphasis on bioinformatics. That was one of the first questions I asked when they were recruiting me was how are you going to handle this? Because a lot of labs kind of think about that later. And we have that built into the front end with single cell specialization. And we've already got an IO database, specifically focused on IO cancer patients right now, and we have our whole plan is upon enriching that database to make it a minable resource for all IO. So right now, we're at the point where we have experiments plan based on the early work that we've done already with the Cytek. And the order, which I'm going to 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, and I started out at a little company called Systemics in California that was sorting hematopoietic stem cells to -- and we were -- it's actually reinfused into patients. It was the first time that was done in a clinical trial setting. But really old sort of technology, I've been back and forth between industry and academia. And 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 ahead of the sorters, we've been in the space now for many years where you could go to a single cell platform for flow cytometry and or mass cytometry, and you could see a lot of markers, but then you either ablated those cells in a plasma arc or you throw them in a bleach waste and you throw them away. And so you got that you had a limitation of what you could get sorting now at the level that we're able to do it with the Cytek's order has just opened up all kinds of new avenues. So with these other investigators that we have now, where these teams were developing these experiments where it isn't just 1 investigator doing 1 thing, focusing on one thing at a time, we're allowed to focus on many, many things at a time. So we have optimized panels and everything is for kind of every specialization. We have panels custom built. And we work actively with Cytek on this. So they're kind of a team member that developed 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. And again, we want to get as much information from these patient samples as we possibly can. And I think that's a responsibility, especially with these cancer patients. If you're getting a small piece of tumor or you're getting blood, some of these patients have been on 4 or 5 trials at this point. We owe it to them to get as much as we can back. So instead of throwing out the cells after we look at them, we can take those cells and go forward now. So the previous orders beat this because people have been discussing this. Really limited by the number of biomarkers that you could focus on, really 10% to 15% practically is what you could do. So that was enough to -- 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. And now by tripling in even more of that number, we can focus on many subsets of many other cells at the same time. So again, these previous platforms that we've had, they were really limited practically, and there's -- there's been a thing to -- and so I work for BD at one point in my career as well, again, back and forth in industry and academia. There's this sort of stated limit of what the technology can do, and then there's the actual like what's being practiced. And right now, that's early 25, not even 30 markers, is the highest anybody is really going with some of these other platforms, so it's still pretty limited. And one of the great things that we're seeing, like what Bill alluded to earlier and showed his data that the panels that we have developed on the Aurora analytical platform that doesn't sort, those are matching very well with the self sort of the CS. So we're getting a -- which is critical because we want to do our development on the on the analytical systems and then stop that. I don't want to keep running cells and patient samples on a nonsorting instrument. I want to sort everything. And if -- most of the time with a specific plan and we will sort the specific subsets they will go to downstream applications. But then if we don't do that, we can bank the samples. So we can bank them, we can save them for a further research down the road and we're not just discarding them. And so we're really trying to just build the pipeline for discovery right now. So 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. So if you're not familiar with that, yes, it's a powerful thing, and they're really passionate about it. It's very cool. So if you could do anything, what would you do? And our answer is let's do everything. Let's not limit ourselves in any way. And so the Cytek order is allowing us to do that better than we've ever been able to do this before. So in my lab, we'll take tumor, peripheral blood, whatever tissue, whatever is part of the specific trial, and this could be -- right now, we're primarily doing a lot of animal research, but we're also doing some human work as well. And then I have a team that will have to process these cells. So 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 going to depend on how big the tumor is, how cold or hot the tumor is as far as the infiltrating lymphocytes. You may have to culture of the aerated lymphocytes up. But at a certain point, you can actually process these samples and then sort them out. And then from there, so kind of everybody's seen these lineage panels, which are kind of hard to look at this all at once. But just to make the point of there are all these different cell subsets. And you can have the B cell, a T cell, but then there are how many different lineages going down and 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, major role, and we're trying to look at these patients so that with their exhaustion history, and as far as, hey, if this person's T cells are expressing certain markers, they might not be a good candidate for a certain part key or a certain drug trial. And this is -- the pharmas we work with, they want to know that. So we can screen patients. We can get people in the right trials depending on what their status is, is the goal. And so with all these different markers, depending, we don't have to just look at 1 of these lines of lineage, we can pick 6 of them. right? And we can focus in on what specialist area of individual PI might want to be looking at that's part of a larger team. So it's just an incredible opportunity at this point to do this. So downstream from that, we have the traditional single cell genomics platforms and transcriptomics platforms like 10X or Takeda Smart SE. 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. But we want to look at all the proteomics down to the phospho site-specific activation and everything to get as much information as we can and then look at single cell functional screening. So we've been -- there are multiple platforms looking at this. We've been dabbling with something called sex which single cells go into a microfluidic system, and you can measure their cytokine secretion and look at secretion of 35 cytokines and chemokines. It's not quite where I want it to be, but there are other platforms to do that. And then, of course, anything else that you can think of, this part doesn't really matter. This is -- this will change over time, and it already has. It will actively change as these downstream tools are becoming available. And so 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 -- get all this information for what's inside that tumor, really high dimensional. And then we can actually use that information to look at paraffin-embedded samples and look at a select group of markers that the flows to try gates told us, well, these are the cells most prevalent. And then we can say, okay, well, this is the actual tumor, let's say, and then we can look at 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. I can tell you a lot about it and then where they are in different parts of the tumor. So by combining all this information, you can just see you can get this incredible amount of data, first, 1 patient. You can just get all this information and learn as many things as we possibly can. So the whole sort of theme for us is the single cells and then team science. So we're designing experiments right now where it's multiple labs. It isn't 1 PI doing 1 experiment that's going -- that's burdening, all the costs are going into that on PI's budget. It will be 5 or 6 PIs. And so 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 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. So we get these groups together, and we're planning these experiments. So something that would be very costly for an individual investigator now becomes pretty affordable because you're splitting the cost by 5 or 6 ways. So with the funding that we have through the Institute and everybody's individual NIH funding, the sorter kind of democratize 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. So again, each cell population is kind of matched up with an expert that's interested in that. And there, we're all going in together when we're building these to decide how are we going to do this and of course, with bioinformatics the front end as well. And the goal is to have multiple publications from, 1 experiment could have 5 or 6 publications or a single high-impact publication or multiple high-impact publications because it isn't -- again, it's -- it isn't just 1 lab, it's a team. So it's a pretty fun environment to be in right now to be doing this kind of work. So in every data set now gets added to this IO database that we're building. So we already have all the patient information and we have specialists that have actually gone in and can even now mine the notes of the doctor. So they've gone in and build code to look at the scribbles the doctor made on a piece of paper and then get that into the record as well. And so combining all of that information with everything that we learn through the cell sorting and then the downstream applications. And again, we'll just keep adding things as we go. We have some limitations like I know the original 10X Promium can only do about 16,000 cells per sample. And that's again why it's really important to be able to sort out these different samples. And the new 1 that Chromiumx is about 1 million cells per sample, but we already have 1 in Ohio State, and we're working on that. But we can still compete add more and more samples and batch things you unique oligo label things and then contain them all in 1 sample, save even more money. So it's just giving us that much more ability to look at more things at once. Yes. And then if we don't use something, we can bank it. So that's the other great thing about this. So even if we're not -- 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, that we can go back later and new volatile experiments based on what we learned already. And then just -- yes...

Taking up on your offer for asking questions.

Go for it. It's just Kevin, by the way. Yes.

It's Kevin, all right. On your previous slide, you talked about team science. I know one of the what traditional challenges and conventional flow sectors, the way the instruments are built often leads to configuration, lactocormity, right? So in other words, you're building a high-parameter instruments usually in one by one, right? So 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.

Yes, absolutely. And I think the other thing is even if you want to talk about doing these kind of -- this kind of work across institutions because we already we're building an IO consortium in Ohio, so with Nationwide Children's and Shriners and Cleveland Clinic, Case Western. So we're building this kind of work now. And the cool thing about the Cytec 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. So that's one of the reasons why the data from the analyzers looks so much similar, almost exactly matching the order because all the instruments are the same. So it helps the end process of doing that. So if we have somebody and we do at Ohio State, people that have their not -- that might not set up their initial experiment on another Cytek instrument, well, that will translate very well to what we do on this order because it's the kind of the same. I mean nothing is exactly the same, but it's practically, yes. So it's -- yes, and having lived the other side of that, where we would buy very customized, very specialized instruments from another vendor, and no 2 instruments were the same. And it was really hard to take something off of 1 instrument, especially when you're going from analytical sorting. But then going from 1 institution to another, we used to have to do all these other things to try to make these things comparable. So yes. So this is just some example data and you guys have seen the stuff all day long. And I just put it up here to kind of make a point. So this is -- I've got a donor and patient here for something that we were working on. This panel is a T cell exhaustion-focused panel, so you'll see markers like TCF, CTLA-4 LAG 3, EMs. So these are markers that will help investigators determine at what stage of exhaustion T cells can be in. And that's even contentious. Everybody -- there's a lot of different mindsets about is exhaustion a one-way path or once T cells reach a certain point where they're expressing certain markers or they never going to be able to function in a tumor again. And it really helps us to identify things, but there's still, again, a lot of research to be done on this. And so this is, I think, a 34-color, I believe, example. And so 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 getting better every day. But you can see you're getting the separation that you need. And I think Bill mentioned this earlier, like you might not always get just an 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. So this is on the analytical platform. And then we took the exact same samples, and we just -- because those instruments are right next to each other. And we just put them on the other instrument, and I think it was like a couple of hours later practically. And then this is the same data on the cell sorter. So you can see there's no really major difference. You might -- we might have had a little bit of loss in fluorescence intensity in a couple of markets, but everything is still easily identifiable, and you can see it, but it's just over time, things might degrade a little bit. But -- and then again, this platform is the same, but it's good enough and more than you do enough because we can now see this translate. So we can develop something on the analytical system, walk into this order and then just keep going from there. Here's another just example of a patient their profile looked a little bit different. They had some history what they had already received certain cancer treatments. So their cells look a little bit different. And then this is -- you won't be able to see this, but we sorted the cells. We did a 6-way sort. We picked 6 different populations that were interesting. And this is just something that actually we were doing while we were there with Cytek trainers. And you can see, as Bill reported, here's a sort of population that's pretty big pretty easy to see you got a good purity hear. And then here's something that's a fraction of a percentage and the purity is exceptional. So we're able to get these really, 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. So it's just a game changer for, I think, immuno-oncology research and then research in general. Because my plan is to not have a bunch of analyzers. I want to have a bunch of sorters. And I want to rarely analyze. I always want to sort if I can. And then this is -- I just want to say thank you. The Institute is pretty exciting pay attention in the future. I hope you guys are going to hear good things about it. And that's my wife and portable children that are at a Pelotonia event in Columbus. So do you guys have any questions? Is there anything I can clarify or you want more information? All right. That's pretty quiet. Yes?

patient will exactly like you showed, it assumes 2 different sections consistent.

Absolutely, yes. So yes, it's going to be -- the goal is serial section. So I actually -- I didn't put it in here, but I have something I call the crazy hamburger or if you take like a tumor if it was like this and then you cut it and you took half of it and maybe you did 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. And so you could look at multiple sections of that. So yes, it's certainly not going to be the same tissue, but it's representative of the tumor. So you have a cold tumor and you don't have 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. So yes. Yes, sir. In the back.

So do you see that as a contango ? Or is that like how difficult is that...

It's extremely difficult, and it's something that you're not going to be able to do that with every patient sample. It's all going to depend on the size of the tumor. And then I think this is a challenge. I think every institution faces in every clinical trial phases is everybody needs a piece, right? So pathology is going to be -- clinical pathology is going to be the key. They're going to get what they need first, and then we're going to be able to get what we can after that. But it really depends. We 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. But the goal is to run -- we're kind of agnostic. The fact that Cytek has the cell sorter just, okay. Great. We just need something that in this and this company has the thing that does this. So -- but the other technologies will do whatever. And so we've already looked at many other things I don't want talk about here, but I think the imaging is going to be more important as we go forward. And I showed you an example of a platform that did about a 7- to 9-color platform, it's [indiscernible] System, blanking on the name of the imager. But I mean we're also looking at things that do 50 to 100 markers and to look at the spatial information on that, and that technology is now getting to the point where it's going to be usable. So I think we're always going to want to have that spatial information because they're so rich and it's so important and then combine that with this technology because you just get a lot of information. And I think the question that becomes probably get the flow data before 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 -- what panel we would use for the imaging? Because if we've already determined the flow data that certain markets just aren't there, then we would -- we wouldn't look at that for imaging. So it allows us to kind of curate our imaging a little bit better, make it more informative, I think. Yes.

We kind of hear that sometimes with people saying single cell sequencing can be used to inform what you look at with spatial. So is this, I guess, kind of an alternative to that and what you're saying?

I don't know that -- like sort of doing like 10X you're saying would be -- 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. So I think and 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 50 to 150 or 1,000 different markers. You can do that as well. But yes, that's one more technology. But I think with the other things that we want to 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 to you. You're never going to get that from these other platforms, I think. And then the proteomics as well, like the single-cell mass spec, which I think I'm most excited about that because nobody is really doing it at this level. So yes. Sure. Good? All right. Thanks. My contact information is up there. So call me, if you ever need anything.

Thank you, Kevin.

My pleasure.

And thank you for pointing out the ability to standardize the instruments across consortium of laboratories. That's extremely important. And I hope there are no Michigan fans in the audience. Okay. So next, we're going to have -- or hear from Dr. Anna Belkina, talking about the Aurora and powering immunological research.

Thank you. Right. Thanks for having me here. I'm Anna Belkina. I'm from Boston University. And I'm going to talk today about mainly my research and immunology field on how we employ platform for even. So I'm -- as this Professor of Pathology and Boston University, and I also had the flow cytometry core facility there, where we have the 5 laser side Aurora since 2018. So we're like early adopters. And yes, basically, I wear 2 hats. So 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 conduct of HIV, obesity, aging and other, like COVID, obviously, everybody knows COVID, right? And then also do some bioinformatic research. So bioinformatic approach is for single cell data. That's how you can visualize cytometry data to highlight different aspects of the data sets and also how you can combine the data from the single cell analysis with other readouts and like do some multivaried models and so on and so forth. So we know that the immune system is very complex, but basically, when you think about the single cell analysis, as we noted for centuries that Micros could be, right, is a single cell analysis. So we can see a lot of different structures, and that was driving biological research for years, right? Right in the -- with very simple tools, you can see different structures that the cells form in the body, but the immune system is actually very boring in that regard. So all our lymphocytes pretty much look. So the development of motor and immunology was linked to the flow cytometry. I will stand here because I think the micro in better. So was linked to the development of cytometry techniques all the time. So pretty much any advancement in the development of immunological studies, starting from the '70s was related to the advances in cytometry. So it was an integrated part of the immunological research. And the diversity of the cells in the immune system, we basically know everything about it because of the existence of the single cell analysis and cytometry techniques. So all these different types of cells that you've heard about and people got way more knowledgeable about this over the last several years. So after the pandemic, everybody knows that there are T cells and B cells and so on. So this is all supported by our labeling of the cells with this -- for the specific products that they express on their surface. So that's how we can distinguish them. It's pretty much not the morphology like to think about other tissues in the body and other cell types, right? So we create some kind of a protein fingerprints for every cell type that would define the diverse functions and types of the immune cells. And that's also because the immune cells, basically, they use all these molecules to talk to each other, right, to convey their function. So it's not just like where we have labels like posted notes on every cell. Each of these molecules have biological meaning. And this is why also, like we are talking about high parameter, and we think about, well, we want to reach like 40 or 50 parameters. And 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? But actually -- so 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, you kind of, yes, you get measurement of thousands genes, but you can't really trust any measurement as a single measurement. You will get the information as like networks, you get this beautiful graphs about all those inflammation going on, there's exhaustion going on, but all of that findings have to be validated by other techniques. And this is one of the ways people validate these findings, and that's why we're actually getting -- we were getting boiled down the specific molecules that actually execute the functions. And also, that is why the single cell analysis is so important for the therapeutical applications because in the end, you really don't target the network of genes that are activated in a certain cell type. You're actually targeting sometimes a specific molecule with your therapeutics. So it's really important to get that readout. So on the other hand, basically, if you measure something on a very crude level, like, let's say, you have 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 a bulk of different like lymphocyte count, right, you basically had very little correlation with the specific disease output. So that's not good enough. So you need to tease this lymphocyte, for example, population to specific cell types like with 1 or 2 or 3 or 4 markets just to boil it down, not just lymphocytes, but we go to T cells, not just T cells, we go to T regular cells, not just the regulatory cells, but the specific subset of them. So once we boil that down, then we can actually assay this specific sale. And that's why we actually need this not just like 5 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. And as Kevin was talking about, we're not talking now about, oh, I'm a T regulatory cell person, I just -- 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 interest and we have to share the platform to get the readout. So that's why we want to go for a multiparameter. And I'll -- right? So we assay all these different products in 1 single measurement and also identify different other biomarkers, not just for typing of the cell, but also to assay their functions. And that is how we use it for basic and translational research. This is how we use it in immune monitoring and clinical trials. And obviously, we use that for some clinical diagnostics, where 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 therapeutical or for a diagnostic outcome. So I'm going to just show several highlights from my research just to give you an idea how we use all these principles in real life. So 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 2 different cohorts: one is a younger cohort, and the other is an older cohort. So we know that there is our therapy that HIV is pretty well controlled in old 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. So they have -- they develop a lot of conditions that we usually think are associated with aging, with normal aging in humans, right? So -- but they have it earlier in life. So cardiovascular diseases, neurodegenerative disorders, diabetes, cancer and so on and so forth. So they have higher at every given age-matched category. This HIV-positive people will have this disease at a higher rate. So the question is, do HIV individuals just age earlier or they age in some different trajectory? And I mean it's a million-dollar question, multimillion-dollar question. Because it's -- 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. And we -- 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 and long lives that they are counterparts that are not HIV positive are expecting. So we had the study where we basically assay multiple cell types in the samples, in the blood samples of these individuals. And with the older traditional flow cytometry technology, we have 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. And there is a specific molecule on this call 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. So we found that this specific TIGIT expression on the gamma delta T cells were stratifying the individuals, being the HIV positive from HIV negative. So the amount of this molecule on gamma delta T cells was tracking with different plasma inflammatory markers in the subject. But again, we were looking at basically just a very simple panel with the traditional full cytometry platform. We still got a lot of interesting data out of that. So we were able to predict or whether the person is old or young, HIV positive or HIV negative just based on their TIGIT on their gamma delta T cells. But we wanted to see the -- look deeper into the biology of this. And I mean I don't have a pointer, but if you see this 4 red circles on that plot on the left. So that's -- we got 4 different types of gamma delta T cells in the cells. But basically, we want to go back and we wanted to implement the high-parameter data, high-parameter analysis to dissect this gamma delta T cells. So -- and with the high-parameter analysis, so we basically need the reagents to distinguish this analytes that we're studying, right? You need the instrumentation to detect it and you need data analysis tools. So we were -- we heard a little bit about the instrumentation already in the previous presentations. So we are using Cytek platform to do that. So that definitely allowed us to generate larger data sets with better signal resolution. We also were working on data analysis tools that would allow us to evaluate these results. And so we use basic software solutions that are provided by Cytek for so-called spectral unmixing and also the standardization that Cytek provides that gives us -- 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 those 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. So I have developed an optimization of the algorithm that people have been using for years for these data sets. But basically, the bigger data sets that we generate with Cytek platform require adaptation of this old algorithm. So the field is catching up with the algorithmic analysis to answer the need that these data sets provide us. So the algorithm that I have optimized, so we call it opt-SNE. So it allows us to basically find the populations of our interest instead of just seeing this big 1 blob of data, we're able to dissect it into multiple subsets of cells. And the -- it's important to know that the field is ready for this. So mass cytometry users kind of prepare the immunological community for using all these methods. So now we are kind of using the path that they opened and just providing them with the tools that they need now to explore the spectral data sets because their data centers are much smaller, so they didn't have those challenges that we have now. But this is kind of already resolved question. And all these large data sets are fully supported by the Cytek tools. So we don't have anything -- like any road blocks here anymore. So this is the analysis that we have done on the spectral platform. And these are just a few markers that we have in the panel. The panel was actually over 30 colors. But we were able to characterize all of these markers on just on gamma delta T cells. So this is a snippet of just gamma delta T cells from the subject. I think this is more than 90 individuals. They are combined. And so we can find 40 distinct clusters of cells in the gamma delta T cells as opposed to 4 that we saw before, right? So there's a huge diversity there. And if we look at 4 different groups that I was talking about, so we have younger and older individuals and some are infected some are not infected with HIV, so we can actually see a lot of clusters going up and down with the infection or with aging. So now we can actually sort all these subset and actually see what exactly they're doing and what exactly their profiles are in terms of transcriptomic analysis, so this is all down the road. So that's like a big possibility for us to look at the population that we only saw is like basically 1 unified speckle, okay? And then another project that we're doing, we're actually looking at the model of pneumonia, where we induced the streptococcus pneumoniae in mice. And we are actually getting a readout how the mice recover from pneumonia and develop immunity. So this is a very well-established model for pneumonia studies, murine model that has been used for multiple diseases, not only streptococcal pneumoniae, but also for flu and for COVID and so on. So the reason I brought this up is that we're not only looking here at the immune cells in this loan, 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. So you're kind of do one or another, either you do immune cell profiling or epithelial cell profiling. So we were able to combine all this data together and do like separate cluster analysis for epithelial cells and for lymphocytes. But also, this was all done on 1 single sample because, I mean, you can imagine the most longest, it's pretty small, and we can actually divide it the same loan between several applications and still get a lot of readout from that. So this was a study that was done, as you can see, with multiple time points. And as opposed to human blood, this is like -- there's much harder to reach standardization in this kind of study. So we were -- totally we had -- this was done back in 2019, even before the more modern Cytek tools were developed for the standardization of the platform. But actually, even back then, it was very, very stable. Now it's superb. And so 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. So that advanced the field a lot. So we published this in -- I think it was actually the very end of 2021, and we already had like, I think, multiple citations, and we already published some follow-up papers on this project. So just to sum it up, so the full spectrum cell analysis or a spectral analysis like people use these terms interchangeably. So this is our method of choice now absolutely for single cell characterization. And it's -- in the field, the site expected 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. And also, the reagents-wise, we are not using the kits that the Cytek provides yet. We have been testing them. They work really well. The -- we're trying to adopt them for our applications. But I -- as I'm changing my hat to, like as a head of the cytometry core person, I definitely highly recommend my users to use those kits if they have the application that this 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 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 are developing. And I can take any questions.

Curious for your -- relating a little bit to your HIV aging study, are you at all going to -- 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 we're seeing this phenomenon now, and we don't know how long it's going to go on.

Yes. We have a long COVID project. We're mostly looking at the B cells in that context and in the auto antibody profiles of the subjects. But yes, so we're definitely -- we're working with the long COVID patients as well.

Well, yes, looks like a no-brainer.

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 time frame, is it -- are you waiting for development from the side tech side? Or are you waiting for the right type of experiments in order to adopt those kits? How is that working?

It's basically the kits are smaller than my -- than the studies that my group is doing. So we're looking -- for example, with these gamma delta studies, we're looking at much more granular readouts than the kits support because we have a very niche application. So it's just because our specific projects were kind of not going the traditional routes. But 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. Yes.

I could just imagine the translational opportunities from the HIV studies as right the population ages and how it can be applied in other applications such as long COVID and so forth. So wonderful. That's great. Next, we're going to hear from Dr. Franklin Buddy Fuda, and he's going to describe a little bit about working in a clinical laboratory and oncology. Thank you.

Okay. Great. Okay. I'm Buddy Fuda. I appreciate the opportunity to come and talk about the Cytek system. And so I'm going to be talking about it from a perspective of a diagnostic flow cytometry lab, where we're basically immune phenotyping leukemia and lymphomas. So I just -- I have no actual or potential conflicts of interest in relation to this presentation on 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 2 clinical flow cytometry laboratories and 1 immunology laboratory. We have 1 of the largest university-based laboratories for flow cytometry in the country. We service 3 different unique type of large hospitals. And so 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 were set forth by experts in the field such as Louis Picker or Steven Kroft and Nitin Karandikar. It's a little bit of a unique way that we have approached flow cytometry. We have particular expertise in comprehensive and detailed analysis, and we use different various software programs to do that. including a cost analysis by Cytopaint software. So one of the things that we insist upon is that our actual hemopathologists analyze their own cases. So 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 flows cytometry experts on essential projects such as context flow and building standardized screening panel or tubes for high-parameter testing. And then I'm actively involved in -- an actively involved member, contributor inspector on international education committees, quality standard committees and regulatory committees for the clinical flow cytometry. Okay. So what exactly do we do in a clinical flow cytometry lab? Well, we're just going to do in immunophenotype. So immunophenotype is a fingerprint basically for cells. So if we have a cell sample and here, we have 3 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 population. So in this plot, you can see that there's 3 different cell populations. Now based on where the cell population sit on these different plots, you can see that this population sitting in this region here means that it's both CD3 positive and CD4 positive. So that tells us that this is an actual helper T-lymphocyte population. So our flow cytometry basically allows us to identify and name all the normal populations in a natural sample. And importantly, it also allows us to identify and name cancer cell populations in the sample. And that's how we're going to use flow cytometry in order to help diagnose leukemias and lymphomas. Okay. So how are we graded as a flow cytometry laboratory? Well, first and foremost, every flow cytometry, clinical flow cytometry laboratory is graded on a sensitivity and accuracy of diagnosis. So basically, you got to be able to identify the population and then once you identify this cancer, you have to be able to name it and say this is the actual cancer. So that's, first and foremost, the most important thing. And then beyond that, it's kind of, we're going to be graded on our operating expenses and on our turnaround time. Now for an academic institution, you're going to add on expertise in the field. So what does that mean? Well, basically, we're going to have to use our knowledge to put forth publications so that we can gain national prominence. So we want to become famous within the field. This is pretty much how we're going to set our goals in a clinical flow cytometry laboratory. So 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 because we have pretty tight budgets. Okay. So what does that mean? So in order to meet our goals, we're going to need 2 things. We're going to need basically correct personnel. So that's going to be your laboratory technologists, and then we're going to need correct resource. So as far as the resource go, that's where the vendors come into play. So for resources, the first thing we need are instruments, and they have to be good instruments. We've got to have instruments that perform well and are reliable. And I can tell you my many years of experience in flow cytometry if the instrument goes down, it creates a lot of problems for us. So we want 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, okay? And then we're going to need vendor application support. Every laboratory -- clinical laboratory needs this, especially when you're bringing up a flow cytometry laboratory. So what we want and 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. And then any changes in the flow cytometry lab, we're going to want support from our vendor as well. Okay. And then vendor customer service. So basically, when things do go wrong, we need the vendor to be there to support us to get the lab up and running again. So these things are going to have a big influence on quality of product and efficiency of operation, basically. And that's where Cytek comes in the play. So what the Cytek do as far as provide these kind of resources? Well, Cytek started off as a customer service company, all right? So I think their customer services, they've got a good history of excellent customer service. So that's checked off. Next thing, Cytek, is a flow-cytometry-focused company. So what does that mean? Well, it's kind of like if I'm a jeep enthusiast, right? And what I want is to go to a company that's going to match my passion for my jeep, right? So am I going to go to a Chrysler dealership basically to have my jeep worked on? Absolutely not. I'm going to go to a place like 4 Wheel Parts where they share the same passion that I do for my jeep, right? For flow cytometry, I want the same thing. I basically want a company who is focused on flow cytometry. And that company basically is going to share the same passion that I have for flow cytometry, and Cytek basically is that company. All right. So let's talk a little bit about the actual flow cytometry systems, the hardware and the instruments. So ultimately, in a clinical flow cytometry laboratory, it's just a numbers game. It's pretty simple, right? So markers matter. The more markers you can put per tube, the more powerful the test is going to be, the more cost effective the test is going to be. So 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? And that's not bad. It seems like this is all right. If we look over here, your conventional research flow cytometry, you get to 30, 40 markets, okay? And since we're a laboratory developed test, we can use these research flow cytometers rather than these clinical flow cytometers, right? So that sounds great. I mean, I think that, that's enough markers for us to get to where we need in the clinical lab. So what's the problem. They're dirty, all right? So they're not very clean as far as the data goes. The reality in your clinical labs and you diagnose the clinical labs, most labs are stuck at about 6 to 8 colors, and that's where they're at. Labs that have the highest clinical expertise or highest flow cytometry expertise are running about 10 to 14 colors per tube, right, depending on the actual system that they have. Now in order to get there, though, you really have to weed through a lot of the dirt, right? So you've got basically to set it up really well. You got to know what you're dealing with artifact, and then you can run 10 to 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. So in come Cytek. I say Cytek basically, you can do high parameter testing up to 40-plus colors, depending on how many lasers you actually use on the machine. And most importantly, they claim is clean, all right? So that's what we want to see. Is it truly clean? All right. So just trying to give you a picture of what's going on here. So here's a conventional flow cytometer. So I want to see what's in the actual sample. So here, I've got 3 tubes of a conventional flow cytometer, some 6 to 10 colors, whatever it may be. And each tube is going to basically be represented by a port hole window. So if I look at this, I can see that, okay, I've got -- this will take sky and maybe clouds. I don't know maybe these are barns. So here, I've got a farm. That's my diagnosis, right, okay? So I've got a farm. I'm happy. I did my 3 tubes. I figured out, I got a farm. All right. Now Cytek says, "Well, I can offer you a bay window." All right? And we say, "Oh, look at the cow, right? It's got a cow. I missed my cow over here. Well, my cereal wants milk in the morning. I don't want to miss that cow. That's an important cow. Now with expertise, when you're saying in a clinical flow cytometry lab, you have a lot of expertise, you'll go through here and you go, okay, well, yes, this looks like a cloud, it looks like some sky, it -- what is on? That doesn't really look like a cloud. All right. So what would I do in my lab, I'd say, okay, I'll add another port hole window. So I add another tube, and I find the cow. And so I've got the cow. But what did that cost me? It cost me time, resource, money, right? All right. So I don't want to deal with that, just show me the cow upfront. That's all I want. I want to 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 going to miss the cow all together, right? Even with expertise, it cost me a lot more. So I want a system basically that shows me that cow upfront. All right. So looking at that, we're kind of looking at the sensitivity and the accuracy. So theoretically, Cytek basically improve sensitivity and accuracy in that kind of scenario. And this is the center we have in clinical flow cytometry lab all the time. So now let's talk about operating expenses. Because ultimately, if we can't afford a system, it doesn't matter how good it is. And again, we're on tight budgets in a clinical lab. So we want to be able to afford the system that's best for us. So if I look over here, here's the peripheral blood screen that we do. So when we do a peripheral blood screen, we do 17 different unique markers. So what does that mean? The unique markers or the markers you can bill for. So -- in a private insurance setting. So I'm going to run 17 markers. My flow cytometry, which is a 10-color system, I'm basically going to have to run 2 tubes to do that. When I run my 2 tubes, I'm going to have to run a total of 19 different markers. I've actually got 2 redundant markers in there. So that means I've got 2 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. So I'm basically saving money. My operating expense is cut right? So 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 1 tube. 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. So let's just go down to this example 4. This is the most complex type of testing that we would do in a flow cytometry lab. So we're looking for acute myeloid leukemia. In order to do that on my system, my 10-color system, I'm doing 31 unique markets. I've got to run 6 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 2. Basically, I've only got a single redundant marker, right? And I'm basically cutting my operating expense by 200%, right? Again, that's incredibly significant for a clinical laboratory. So ultimately, fewer tubes means you're more, of course, cost effective. Okay. What about turnaround time, all right? So if we look here, we've got basically, if you're running routine sensitivity, it takes about 3 minutes to run on our tube. So is Cytek faster than the other machines? No, it's not. Not per tube. But the key is you're not running as many tubes on the Cytek. So in that sense, the system is faster. So just let's go back down to that acute leukemia panel. Here, I've got to run 6 tubes that takes 18 minutes on the machine, where the Cytek, I'm running 2 tubes, it only takes 6 minutes. That's a time saving of about 300%, okay? And over about 1,000 different patients, you're looking at 200 hours just for that kind of tube alone, right? That's significant. That's just the acquisition of the data. Then we got to go over to analysis. Okay. So analysis, again, on average, maybe it takes about 3 minutes per tube. And you can see you're going to get the same type of time savings when you're running a Cytek machine with less tubes. So again, about 300% time savings, about 200 hours over 1,000 patients. That's very significant. So bottom line, fewer tubes equals faster results, less resource consumed and then faster patient results. Okay. So faster turnaround times. So basically, that creates increased productivity for the lab. And it's not only the flow cytometry lab, it's the hematopathology workup. So what happens in hematopathology is flow cytometry is 1 piece of the puzzle, 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 a diagnosis. Well, if your flow cytometry is basically delayed, your hematopathology is delayed as well. So if we can get them our -- or we can get the flows commentary out faster, your hematopathologists then can direct their investigation in a more time-efficient manner. So it's really significant for all of your clinical operations in that regard. Improved patient care, faster turnaround time means earlier diagnosis. That means earlier induction of treatment basically, more cost-effective patient care. So with the faster turnaround time, 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. And then reduced duration of patient care. What do I mean by this? So Fridays, I always get a call from our hematology/oncology team. And they want to know what the flow cytometry show, basically. So they try to order a stat case. 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 them out of the hospital. Because if they're stuck in the hospital for 2 more days, that is a huge expense. So again, if we get faster turnaround times, then basically, we're going to have reduced duration of inpatient hospital care. All right. So ultimately, theoretically, just 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. For me as a clinical flow cytometrist, I want this system if that's true, right? But does it actually work? So 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. So theory, sounds great, but does it actually work? Particularly, the truth testament is not going to be on a peripheral blood because when I first started talking to the 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 want to see the data is in a complex tissue like a bone marrow. Why a bone marrow? Because bone marrow has self subset, like an entire B-lineage subset that's going to show similar expression of markers, but there's going to be subtle differences. So I want to be able to sell, can the system actually resolve those tiny subsets? That's going to tell me whether or not the system works. Okay. Two, can I identify a minute malignant population within that complex tissue? Those are the 2 things I really, really wanted answered. So they brought a machine in for us, and we spent 2 weeks basically throwing everything we could at it. Now if you remember, I said our institution covers 3 different large hospitals that have different demographics. So we have a lot of different types of diseases that come through in a 2-week period. Everything we threw at that Cytek machine, it worked beautifully. So I got my answer. The data looked fantastic. So this is just 1 example where I'm talking about. So this is the 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. So what we're going to do is we're just going to take the 19 positive cells. Those are all your B-lineage cells. And if you look here, once we do that, we get rid of all these great steps, so that's all the myeloid and T cells and so forth, so on. And then we just got B-lineage cells here. So 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? So if you look in here, there's no way to resolve certain things, no way you can kind of make out certain things not color them, so it makes it a lot easier to see. But 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. So you've got Stage 1, Stage 2, Stage 3 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? And the answer is a resounding yes. And not only that, but can I identify my cow, right? So in the background here, 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 negative 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. So basically, I can 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-colored system, it's because I have 21 colors in this tube, all right? So I was able to put in markers that I usually don't, and it pulled them out. Look, in 23 versus 43, they just sit, they come, they pull right out, right? So that's why this is so impressive. That's why this is the system that I want my clinical flow cytometry lab. So successful 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. So what happens? There's hematopathology, the flow cytometry and hepatopathology realm and then there's flow cytometry and the immunology realm. There's a lot of stuff in the immunology realm that basically hematopathologists don't know. When we are able to identify these things as when we can do more markers per tube, that opens up a lot of publication for us. And what you'll see is we'll start to publish things and you just dig into the immunology research, it's already been published. But because it's in a different realm, it's meaningful. And so by being able to do more parameters in a single tube, this is going to open up easier publication for us. So it's just -- this is just beautiful for us as clinical hematopathologists. And again, we'll hit that national prominence, but become a famous by publication and talking at national conferences. So achieves our -- 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. So what's that mean for flow cytometry. I think Cytek revolutionizes flow cytometry. We talked a little bit about artificial intelligence and automated analysis, right? So that's where we're moving in the diagnostic realm, too. We want to get there, all right? So 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 going to be recognized because we can do more parameters per tube. So that's what Cytek enables us to do on the clinical side. It simplifies the technical component. I think a couple of people have talked today about how it's simpler to get good data. So I showed you that slide earlier where we've got flow cytometers out, the conventional ones can do 30-plus colors, right? But if you cannot get good data off that, especially in the diagnostic realm, you can't use it. right? Here, you can. The Cytek machine in the system actually makes it a lot easier to get that good data. So it simplifies that technical component. It's going to open up new horizons for research and clinical practice in the hematopathology world. I've kind of mentioned that already. And it's going to meet new challenges brought about through advances in clinical therapeutic. So 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, does this thing express CD79b 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 upfront. When oncologist calls me, yes, it's there. So it really, really does meet those kind of challenges. So 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 going to want this, right? They're going to go and buy these other machines with this outdated technology that doesn't allow for this type of investigation. So the reference laboratories, university laboratories, they're going to switch the latest technology. No question in my mind about that. Okay. What about private practice and small laboratories? So what happens right now? They're going to end up establishing in-house labs. Why? Flow cytometry is a high revenue generator, all right? So what's that mean? Right now, these small practices in these small hospitals and labs, they capture the professional components. So what they do is they send off to a reference laboratory. They say, do the technical component for me, I'll make the interpretation, and I'll capture that. What's the problem? Well, the technical component is where all the money is. This professional components claim, 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. But the Cytek system that's going to simplify that, you're going to start seeing more and more labs pop up with this machine in this system. Out of the gate early, all right? So this is it, like Cytek is -- they're basically running with this right now. So what does that mean? So for years, we land on the FACS calibers, which is the BD machine. When we were going to 10 color, we had a couple of options, we stuck with BD. Why? Because we had a bond with BD, all right? What's going to happen since these guys are -- BD, nobody else has this technology right now. So you're going to get the switch. What does that mean? In the future, people are going to stick with Cytek. It just makes sense as long as you've got the agents with Cytek has a good -- I found that out by bringing in the machine for 2 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 going to move this way. We're going to basically develop that bond and stay with Cytek. So Cytek basically, it's just got good balance. Like I said, the most important thing in clinical diagnostic flow cytometry is health, right? That's what we need. But we also have to consider what's the cost of that, right? Cytek balances that out beautifully. So 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?

Very helpful talk here. I was curious as to the point you made about 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 approaches like immunosequencing from Adaptive. They have a immunoSEQ assay, sort of outperform flow on sensitivity. What has your experience been like? And does Cytek essentially enable you to bridge that gap to a point where the delta is perhaps not clinically meaningful?

Yes. So I think with some of your molecular testing, your sensitivity can go down to 10 of the 6 power basically. So theoretically, even practically, it is a more sensitive test. But what happens is there is a timing difference. So when you're doing MRDs right now, the molecular tests take a little bit longer to do. Flow cytometry is immediate. So you can get those answers right here right now, and that's what our clinicians are actually making their therapeutic decisions on. So 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 runs some of their tests, they're collecting 10 million cells on it. They're getting down to 10 to the negative 6 sensitivity. So I think with the advancements and with clean data, so 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 going to improve the sensitivity. So I think with a system like this, you're going to start to see the sensitivities in flow cytometry go up. And the advantages of flow cytometry, they both have their pluses and minuses. The advantages of flow cytometry, and I kind of mentioned it, you're getting a phenotype, right? So basically, you know what the cells are actually expressing. You can target therapeutically from that. With molecular studies, a good example is acute myeloid leukemia with an 821 translocation, right? So what happens? Well, that type of myeloid leukemia actually has maturation in the neutrophils. So on day 29, when they're looking for minimal persistent disease, you can use a molecular study, right, to look for that 821, and it's positive flow cytometry is negative. Why? Because you're seeing the neural residual disease in the neutrophils, not the blast. And that's significant as far as therapeutics go. And so that's where flow cytometry shined. We're actually showing you what cell is containing that molecular abnormality. So again, they have their pluses and minuses, but molecular is not going to replace flow cytometry in the realm.

We have another question here.

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 -- your slide about some of the smaller practices developing in-house labs, do you think they can benefit from those cost savings because they might not have the scale that you do?

Yes. Absolutely. So for us -- so for a state-run type lab, obviously, your budget is really tight. So you're always looking what's it going to -- can we stay above water basically. We don't have to make money. We just got to stay above the water. For these small laboratories that are actually private groups. For them, the money spent is going to be the money spent on reagents, right? So this system, no matter what scale you're looking at, this system is going to 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 1 tube instead of 3, you start to look at the cost savings for the reagents, the actual tech time, the over everything, the wear and tear in the machine. I mean, 1 of the big issues with flow cytometry is wear and tear on a machine. If you're only doing 1 tube instead of 3, then you have a lot less wear and tear. Yes, no question, small labs are basically going to 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.

So you mentioned leukemia, lymphoma and MRD as applications that benefit from higher parameter analysis, 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 a higher parameter analysis. Are there other applications that you see out there that you see obvious benefits from high dimensional biology or so companion diagnostics, stem cell enumeration, HIV, other areas of where flow is used in an application that you see significant benefit from higher parameter analysis?

Yes. I think the leukemia and lymphoma realm, definitely on the diagnostics side, where it's probably going to make the biggest splash. Some of the other diagnostic, I'm not -- because we don't do any like of the esoteric type testing. So I don't know how beneficial higher parameter is going to be in those. I mean it certainly can't hurt. And if you need to, so say you've got -- if you're running any kind of assay and you're running more than 1 tube and you can get down to 1 tube, it's going to be beneficial. So I think across the board in that kind of scenario, if they need to run more than 6 to 12 colors, this is going to 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 want to take a 10-minute break for everyone. But let's come back as quickly as we can and get started again. Thank you. [Break]

I am so excited to hear the KOL talk. I don't think I need talk anymore. So really was so energetic. But I see these things every customer we are visiting. I was made tour in Europe. Everybody is so excited about using on discovery. So I want to step back in time a little bit is how the Cytek makes it happen in the past. So we clearly see is the unmanned need. You'll be here all day long today with the KOLs, we see multiple things actually is really the market requires such high dimensional cell analysis, right, for [ immuno-characterization ] and functional assay and the clinical space, leukemia MRDs, and I will talk about a bit narrow particles actually recently. So as few KOL talked earlier, the flow cytometry more or less settled around 20 to 30 markets detection, conventionally. You can see from 2010 to 2020 is flattened, the field does not go further until flow cytometry coming to market, we made 24 color with 3 lasers in 2017, great, great market. In 2020, we've made omni paper for the color single tube. As you can hear today, there's a talk about 50 markets, so we're talking about 459 markets, all pushed the technology with our enthusiastic users and create helping to make the field advance. So I don't think this slide is needed, and Bill already showed the single conventional where detecting 1 [ fluorochrome ] a time, and the full spectrum give you a fingerprint where you have a mix the data, where you have all the different mark together and because you have each fingerprint, then you can call a mixing unravel so you can count on cells, right? And the important thing is on the right-hand side, you can see those 2 dyes Bill was talking about earlier, those 2 dies in conventional people never thinking can use together, never, I use the word never because you're either using this APC or [ Alexa 647 ]. And when you open the full spectrum, you say, "Wow, they're so different." So we will narrow laser zoom one of the windows. Now you suddenly open the window, you say, wow, future is impossible. So both dyes can be using -- used in the same assay and on co-express cell. So both dyes label on the same cell, you can mix them really well. And this general technology really opened up for advanced next-generation cell analysis. You already see the outcome for the last couple of years and full spectrum be will adapt and high sensitivity, high throughput and without compromise the data. That's the key, right? And now in the whole world, many talks earlier using all the dye together, people enjoying the research. And important is even for people doing few colors, because the full spectrum able to get autofluorescence of the cell, which cancer cell has a very high background, we're able to remove it. And even very few markets, we're able to deliver to resolve from the background. And that's another key thing I want to deliver because not only more markers, you're able to resolve the background with cancer markets, you see much better resolution. So all the technology is basically empowered by patent 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 get the inferred. So it's really maximize the resolution accuracy, the resolution in special, the resolution in data range. So both. That's why we're able to do 40 or 50 markers at the same time and optimalize the signal-to-noise ratio I was talking about earlier and really provides the insight for our users, as you can hear today. So this is our instrument platforms. So Aurora is analyzers. You hear many made talks and be capable to do 40, you can see more like 59 markets right now. And the Northern Light is the intra-level market and 1 to 3 lasers, and we think it's great for clinical lab, but clinical labs studying in [indiscernible]. So we see the market changing because I [Indiscernible] the Fuda today, more information is better, back to the bottom line, right? You can hear multiple talks because the sample is precious. So, [ Fuda ] you hear [ 2 talks ] today. I think we're very glad to helping transform the cell technology to the next adjacent market, which downstream analysis. So we're able to purify the cells. As this is the first time human being able to get those rare cell up for analysis you can hear from Kevin's talk. So I don't think I want to repeat all those, but it's our platform today. And Cytek provides end-to-end platform -- so from instrumentations and automatic sample loaders, we -- because automation is key for us to be successful. -- in more successful in the clinical space, 1 less user influences, right? So fully automatic. And we have the reagent kit and we're talking about early -- some are talking about it, and I'll have more detail later on. Service is important, uptaking changing support is super important. We're helping customers to get adapt technology, help them design their panels. And that's the way we get the region pull-through as well. So data acquisition and software and we're getting to advanced data software analysis. I will give a little bit more. But we provide end-to-end solutions to get the full solution of -- to our customers, as few of the KOL alluded earlier. At this the comparison which is a mass [ cytometer ] earlier opened the market for appetite to using a very high prime cytometry. However, you can see that the mini downside for sputter mass back, that's why we -- but they really get people realize multi-high dimension cytometry is important. And the spectrum cytometry, developed by [Indiscernible] also lead the way for us to see there is a possibility for that. But you can see the instrument has a lot of downside, but everybody now getting to the bank record and to the full spectrum. And we definitely exceeded in the convention optometry, and you can see the market adoption. So I would like to say is our technology really gives us a unique opportunity for reagents and because the new technology opens us the new requirements for the dyes and reagent because in the past, you have to be well suffer the peaks right, to using reagent. Now we're able to using our spectral overlapping dye. So which one we called spectral unique dyes. So those ones we call high prime enabler. So this get people from 20-color to 40-color range, right? So it enables them to do more colors. Right now, we have 28 unique cFluor dyes -- I mean, cFluor dyes and with high parameter commercialized. And we're making kit because our goal, the company goal is make easier to use from day 1. And so the kit and really is our focus, right? And then we can link our instrument to the reagent. So hopefully, it will save customers a huge time and they save the special into the instrument and be used again and again. So that was as a company instrument maker we're able to link them all together with the reagent instrument solutions altogether as a one-stop shop. So the kit actually, the kit is not just putting reagent together. We're developing in-house, we pre-titrated, save all the customer work to titration reagents, optimize that fluorochromes, and we provide them acquisition template and a large template. The whole goal for, you can hear a couple of talks earlier, just 1 button click, okay? That's the goal. So we have couple of kit in the market, and we continue developing kit. You will see more kit in the market in the near future. So our company strategy is developing kit. And also we're helping customers expanding their panel, right? The kits, and you can hear a couple of talks that Anna wanted to do a little bit extras, so we're able to allow them or help them to expanding the panel. And also you can make a big kit using sub cells. So this is really a game changer for our industry. We're also very actively working on the clinical market. So we have multiple, I cannot count anymore, it's a single-color reagent in China get certified as Class I. We're on the path for Class III registrations with very unique panels, and actually we got feedback from market now. And we also had the same one certified or self-certified in Q2 in EU customers, European customers. I just talked about earlier, I went to Europe, had a tour, our customers are really excited about our opportunity there, especially in the clinical space. So Bioinformatics is important. As you can see, some labs already doing by themselves, right? So we cannot lag behind. We need to catch up. We need to work with them together. So the Bioinformatics program, we established in-house. The goal is make it easier to do Flow, and accelerating the reagent and instrument pull-through, that's the key, right? Because you provide information that customers either use. Also, we allowed us better understanding what customers need, right? Because this become a hub, we're able to get all the data. And this also will greatly enhance and accelerate our product development path. And to just give you one example, I heard AI earlier. So we are just starting working on the AIs. And this is very encouraging data. Leukemia MRD detection is a collaboration. And you can see on the left side is manual gate, right, as now you've become pro-expert. So each slide is a cell, we lock the cell classifications. So within the gate is same type of the cell, we're able to tell how many cells inside the gate. But the AI software doesn't require the manual gates, automatically can pull out. Very exciting, right? And then this really provides our users seamlessly, because when I went to the lab, people told me, we don't want manually adjusting everything, right? We want to be automatic. So 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 to the clinician is the key, right? So we're working very hard for that. So with advanced technology, as you can see, when the instrument adoption will be really welcomed by our enthusiastic users. So we collaboration through worldwide. So on the top left is Dr. Sylvain Simon in Fred Hutch who talked just 2 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 to get more information from the same patient, right? Very excited. And the one down below is the multi-site collaboration with NIH, and there's another group, different one than Bill, and the University of Ottawa and the Cytek Bethesda office to really get together with nano-particle standardization. They are very important, nano-particle, I went to the meeting in Europe, and really see the trends how people look at [indiscernible]. So it's very important to standardize. So as I said, our instrument is the more scientific instrument actually for nano-particle as well. So we have global collaborations. And you heard about MRD -- I mean, leukemia talk today. Actually, we started in 2000 and earlier. So it's just the slide says right before pandemic, 2019, in China, had a whole session about leukemia diagnosis MRD. So the work was already going that time. So I think right now, we have so many collaboration sites. We learned from experts, right? We're all working together. And also collaborating on AI and clinical data analysis and this is just the very beginning. Okay. I will turn to Mark to go over more clinical market.

Yes, there won't be much to say here. Most of this has been covered. Thank you. Yes, when working with our clinical laboratory customers out there, we realize, and this essentially just summarizes all of what Buddy said in other laboratories that are doing exactly that type of work, more informative antibodies in 1 tube, we've heard that'll eliminate the redundant reagents. I had not previewed Buddy's slides before. Putting this one together here, but optimizes, uses smaller amounts of patient specimen. All the things that we've already heard about that improve laboratory efficiency overall. That's our value proposition in the clinical market. As Ming just mentioned, we have ongoing collaborations and we are helping laboratories. There are some examples here of the various projects that we have ongoing right now around the world in these types of panels here and just really helping through our technical application support team, through our R&D team, helping these laboratories convert over multi-tube panels into single-tube or two-tube panels using, in our case, using the Cytek cFluor reagent. So we're driving the customers that way and getting them familiar with our cFluors. The next-gen clinical flow cytometry, sort of key needs, key focus points here is we're striving to make it completely operator independent. So the one-button, the one-push button. So 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 working on the characterization initialization of the instrument, making it easier to set up. It's already pretty easy. The automation is important, especially on the reagent side, as we're going to be mixing now together the multicolor cocktails to be able to automate, that is extremely important. So that's next generation. The data acquisition. And then, of course, the data analysis, talking all the way to the AI. But cluster determination & visualization, which you've seen some of already. So those are our key focus points, things that we're currently working on in our programs. And then just the final slide I have here is right, it's all about the system solution. It's under development currently at Cytek, works the hardware, the software, the reagents, the support, and the automation. So we've already reached out to the FDA through a Q-Submission, asking them some questions about how to basically just get this approved. And that's it. Patrik?

Thank you, Mark. All right. Good morning, everyone. I'm Patrik Jeanmonod, I'm the CFO. It's going to be hard to go into the financial numbers when we see all the exciting slides that I've seen, colorful slides. Well, I'll walk you through my single-colored slide here. So this one, how can you get excited about this slide, right? So going backwards, I'm looking at 2018 when we crossed the first time 100 instruments. And today, we have 1,200 instruments. So the company has grown substantially, and we sold obviously a number of instruments. Obviously, the Aurora is a key instrument, top of the line. Then we have the Northern Light and now the Cell Solar that's coming up and will continue to help on the growth for the future. Now that's backwards. But looking forward, I'd say that it gives us an opportunity to build our reagent business. That's how we are -- I mean with all we've heard today, all the projects that we have ongoing, this will give us the future of Cytek as well. So a couple of numbers here. Key numbers going back to Q1 of 2020. This is revenue split by product revenue and service revenue. Key point here I want to just bring out, Q1 of this year, 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 obviously have 1,226 instruments. Our service revenue, we've continued to add discipline around the service. And we've also seen also more instrument come off warranty. So our instrument revenue actually doubled compared to a year ago. And that's a trend that we expect to see moving forward. The adjusted gross profit margin, one of the number that I like to look at quite substantially, is the 61% in this industry, which is substantial. And our goal is obviously to stay at a 60-plus percent range. So this slide is also a two-color side; revenue and adjusted EBITDA. So 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 costs, right? So it's top line growth, but also managing the cost and continuing to invest in the business. I'll talk more about that. But overall, the adjusted gross profit margin as a percent of total revenue in high 28% -- 27% 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 that number also go up. Operating expenses. So splitting down sales and marketing, the R&D expenses and the G&A. So if I go through the sales and marketing, so we're going to 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 going to be also a key investment area for us. We'll obviously optimize our footprint. I mean, we have offices here 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 going to have higher revenues. G&A, as you can see, I mean, we came out at a very low, 7% in Q3 of '20, but it's gone up substantially, and that's just a result of us becoming a publicly traded company. So we have to reinvest in some areas. And obviously, audit and SOX compliance are key investment areas for us. And we took that very seriously. So we're going to continue to see some G&A expenses up high. But over time that number should flatten. And say, from a dollar point of view, and the expectation is, as a percent of total revenue, it will come down. So some stats here. So the top line really is the gross profit margin, the adjusted gross profit margin. And that's a number that we are laser focused on. And really, it's building the top line, and at the same time, building the gross profit margin to remain at a 60-plus percent 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. So over time, as I said, I mean, we've invested in sales. This is our service transition year. We have added people in the sales and reagent group. But we're going to continue to invest also in instruments as we go into some new markets like clinical market as well. So 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. But this year is still going to be a transition year considering that this year is the first year after becoming a publicly traded company last year. So just to talk about where we are, where we want to go, right? So we're going to support the 4-pillar strategy, and I think Wenbin will talk more about that. So the focus remains on the top line. The focus remains on the gross profit margin. So obviously, we want to 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 going to also remain focused on the adjusted EBITDA. We want to stay positive. So we'll continue to invest, but in a friendly manner, considering our P&L. We'll be continuing our discipline on 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. And so far, we've done a very good job. And this is something we're going to remain focused on. So we'll continue to invest also in some CapEx as we continue to grow around the world. So 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 will be some CapEx. And finally, we are reaffirming our 2022 revenue guidance closer to the higher of the range of $160 million to $168 million. So we feel good about these numbers. We've looked at the numbers in many ways, and we feel pretty good of where we are at this point. And considering everything I've heard today, I feel even better. So with that, I'm open to questions. Otherwise, I'll just pass it all on to Wenbin.

Patrik, thanks for the update there. 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 '22 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 3 to 6 months for that to happen?

So the first comment I would say is, I'm expecting that there's no recession. So that's the first element. So I believe that the assumption at this point is it shouldn't impact us. So that's the first comment. I would say that the funnel that we have remains strong. And we also, as we roll out the new rating strategy, but also I think the cell sorter has shown to be very powerful, has strong demand. So I think these are the elements that put me in a good position about our numbers for this year.

Got it. And then 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 profitability? I mean only from an optical standpoint, I'm not saying sort of negative 30%, 40% sort of profit margin. And is that something that you've debated internally, like perhaps accelerating the investments to build on the momentum that you're clearly seeing on the top line here?

Yes. So I mean it's possible. So we had a discussion. But 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. And maybe at that point, we will consider. But for us today, the goal is to remain adjusted EBITDA positive.

And then final one for me. I mean, as you look at sort of 2025, with the business having scaled 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 gross margins look like and what sort of steady-state EBITDA margins would look like for Cytek?

Yes. So the expectation is that as we add products or instruments or reagent, to have a higher gross profit margin. So the expectation is the gross profit margin should improve. So just a little bit maybe some barriers here. I think 60%, 65% is a good number for us. And obviously, we want to remain positive on the bottom line. So 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 we'll less invest in R&D, but we'll continue to invest in sales and marketing as we continue to grow. So overall, I think the company should remain within these numbers.

Patrik, maybe just on the reagents. I think you said in previous forums that end of year probably single digits 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 see reagents maybe as a percentage of sales, like longer term, if you want to comment on that? Or just in terms of this year, is that single digit still what you expect?

Yes. So it's going to be mid- to high single digit for this year, right? Our internal goals so far have been met on our reagent goals. We're coming obviously from a -- this is a transition year, obviously. I mean, last year, we had almost no reagent revenue. On the going forward, I'm expecting the -- when I'm talking about ranges as a percent of revenue, it will be in the 28%, 35%, that's the goal today. I think there's a great opportunity for us to eventually go beyond. But I think this is a good number to start with.

Got it. And my last question, and maybe Wenbin will get into this, but just on capital allocation, just given the cash balance, given that you've done a small acquisition earlier on the reagent side, how are you looking to deploy capital in this environment?

Yes. So we're looking at a number of opportunities, right? But we don't want to hurry. We want to find the right fit for the company and for the long term, that fits the long-term strategy. So overall, I think we'll probably be acquisitive. So 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, and we are getting to the close of this event. In fact, I myself have 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. And along the line, and Patrik also mentioned about 4 business pillars. In fact, all of the presentations today earlier have built a foundation for us, for the company, to continue to grow, to expand. So that's what we said about the 4 business pillars here. That's where the company is going to be. And so if we look at what we have here, first, those 4 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. And on that aspect, we're going to continue to improve, to optimize our instrumentation from performance perspective, making it more intelligent and more easy-to-use, and also more compact. And we all know lab space becomes more and more precious and the size is important. And we have heard cost is important and that we would really like to have our instrumentation to be the lowest cost solution for everyone, for our researchers, for our clinicians. And applications. Today, we have talked a lot about applications and how our technology is enabling, broadening the applications. And here, when we talk about applications, that also includes the reagents. So what the company is going forward is to focus on field areas on the application side. The one is, we talked about our own cFluor reagents. Our intention is to make cFluor reagents as enablers that will work together with our partners, means other reagent companies together. Okay, we'll build on those type of partnerships along with our reagents to expand and enable us to focus on panels, kits. And those panels, kits will not only have what we call with functionality and purposes as well as the flexibility, means also for the research use, with kind of backbone to enable our users to add or deduct from the panels we have optimized. And again, our application is going to focus on the business with volume and that can repeat, okay? 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. And bioinformatics, we have heard quite a lot about it. Our unique technology has really enabled users to generate lots of data, okay? Then the question is how the company can help our users, our customers, to manage those data, okay, data management, and to store those data and to analyze the data and to optimize the panel, optimize the data. And again, lastly, is to enable our users to exchange information and to help each other, okay? 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 bioinformatics initiative. And lastly, clinical. We've heard very exciting stories how Cytek technology is enabling the clinical applications. And from company's perspective, this is 1 of the 4 business pillars here from a 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 becomes important. We want all the instruments 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 instruments pretty much is the same, almost the same, which other technology is not capable of providing. So with all of that, the 4 business pillars, that's what we see where the company is going to be as Cytek Innovation. That's eventually how the company will be, a comprehensive solutions company. Starting from our Full Spectrum Profiling technology, expand vertically and horizontally. From 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, autosampler, those type of directions. That's what we are going to continue to expand, to enhance. And then horizontally, how we expand the company. And that we include earlier mentioned about the Imaging technology, the spatial, mass spectral, microfluidics, all of those technology adjacent space, that's what we are going to continue to look at to integrate into our platform. And of course, there are other applications earlier mentioned about, for the marine biology, environmental sciences. So that's what you have to envision how the company eventually will become. So 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 companies have in mind. And we'll continue to maintain our operational excellence to ensure we have a high gross margin. And 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 are 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. And again, the excellent speed is an important part of it, okay? And as you can see, we actually developed all our technologies very quickly, in just few year, and thereafter, during the last 5 years, as you can see, we have Northern Lights, Aurora Cell Sorters, and all kinds of reagent panels. We will continue along that path and maintain the execution speed. And I heard a 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 licensing and joint venture aspects. All of those is to show our real commitment to our shareholders for the value generation. So finally, come down 2 years, why you should all invest in Cytek, right, that's impossible. And number one, all of the presentations have 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. We will continue that momentum going forward. And we have already validated Cytek is the most competitive innovator in the industry. We have really the fastest growth top line in the industry. We maintained 40% to 80% growth during the last few years. That momentum is going to continue going forward for the next 3 to 5 years. And as you can see, we have really a strong balance sheet. So lastly, why you should invest? Cytek really has a very attractive valuation today. It's a growth company with a value, pretty much is a value company, right? So that shows why it is really a good time for investing at Cytek, to grow, to ride together with us. That's the end of all of our events. I think now is the Q&A. I believe there are some questions in the...

Yes. We do have some questions from the Internet audience, but I want to open up the Q&A to the room here first. Or if there aren't any, we can go right to the Internet app.

Thanks, Paul. Wenbin, a question for you on, if you look at sort of that $8 billion plus $8 billion TAM that you've outlined, can you walk us through what percentage of the TAM today sort of is looking at high parameter and analysis 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. And if you look at the high-parameter cell analysis, basically Cytek generated basically the new front, generated new barriers. Before Cytek, high-parameter cell analysis, anything above probably 15-color considered high-parameter, right? And at that time, we see the technology from our friends like Tesla and Symphony all considered high parameters, but not anymore with what we have done. But 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% to 50% of the market is in that space, in the research market.

Got it. And then one question on just pricing dynamics. I mean, have you seen any shifts from the competition? I mean, clearly, at time of IPO, something that resonated well with investors is the fact that you're offering more sort of information at a significantly lower price point. I mean competitors obviously 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.

Clearly, you can see, because of Cytek, we have shifted the whole dynamics of the flow cytometry towards the spectrum. Today, if anyone is not talking about spectrum, probably they are not in fashion. The reason you can see every major flow cytometry companies are talking about spectrum. And we have heard the spectral sorters from 2 players and one is based on similar [indiscernible] prism-based spectral sorter technology from one of the, I don't call a competitor, our partners for this way. And we do work with them and together, right? And another technology just 2 weeks ago launched the imaging sorters with spectral functions. Those are -- on the sorting side, that's what we have seen. And then on the analyzer side, we have heard of course Sony and in fact, they preannounced the new spectral sorter like 3 years ago before the pandemic. And so that's the kind of landscape what we have seen, one spectral analyzer, two spectral sorters. However, what's important is exactly what our customers are going for, right? And where they are. And I can clearly say, of course, the latest imaging base -- so it's 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. But at least then in that case left with only 1 spectral analyzer, 1 spectral sorter. I can say at least I haven't seen that many from our customer side. And then from performance side, definitely Cytek continues to outperform them. Cost perspective, we continue to do a lot better as well. And so that's all I can say. Of course, probably, we have 3 other -- actually another remote -- so 4 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. So there is also -- like I think the most aggressive marketing that we've seen, and I'm talking like as a customer, right, was from the mass cytometry platforms that were basically blatantly saying well, our machine is now cheaper than 5-laser spectral machines. I mean -- and 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. And I mean maybe people are looking at secondhand machines for like -- to add to their existing labs because they already support the platform. But I don't know a single person who considers to start a mass cytometry suspension flow lab at this time of the day, yes? No, not happening.

I can expand a little bit on a few of those points. And regarding mass cytometry, I hosted a retreat for our immune monitoring labs last year, fall of last year. And one of the questions I had. So these are -- I'm not going to name names, but they're major immune monitoring labs around the country. And one of the questions I posed was, what technology do you think is going obsolete next? And everyone said mass cytometry, except one lab, I won't say which lab, but it's an obvious lab. I would disagree with it essentially. And 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 risk 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 is kind of narrowing a little bit. With 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. And 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 going to be easier and easier to do over time the way Cytek is 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 how you would get the overlapping signals out. And that was really hard for people to understand years ago, and now it's like people don't even think about it when it comes to how the end mixing is done. So I don't know, things are just changing to where it's getting easier for the end user. And Cytek is really leading that. The other companies just aren't kind of there when it comes to that. So I don't honestly see any competition with Cytek sorter or for any other platform. And I think it's at a time right now where all the pre-existing flow cores, they all have older generations of equipment that are all obsoleting out. So I know my lab, the flow core lab that I have, that's in addition to my immune monitoring lab, we received end-of-life letters from the companies saying, "Hey, we're not going to service this anymore." There aren't going to be a lot of options for service. A lot of labs are scrambling to replace this equipment now. And we just did. We just bought another spectral sorter for the flow core because there are no other good options at this point. So it's -- I don't know, curious to see what happens over the next couple of years with that.

And just one final one for me on Techne and their acquisition announcement this morning of Namocell. At least the press release says it's a gentler on the cells. And 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'll let Ming to comment on it. We haven't looked at it.

Yes, I think this is a very good question. So I think the news just announced today, I think. So this is a different type of cell, and this is mostly focused on individual labs, particularly I think the bridge op for linkage for the small volumes. So we play in different spaces. And actually, I should say this is another area we are looking to.

I just have 2 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?

Actually, that's a very interesting question. Firstly, on that initiative itself, we are expanding our Seattle office to focus on supporting that bioinformatics, so we're actually hiring people to help to support. However, the objective of bioinformatics itself is not try to terminate a kind of a 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 and the data analysis and the exchange of information. It's a tool, it's a product. Then in the meantime, they will come back 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. So bioinformatics matters basically across the rest of the 4 business pillars, support the rest 3 businesses we are building up.

Got it. And then just 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 it the performance? Is it the overall cost? I'm sure it's all of the above. But I 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 jab at that first. There is that -- it used to be coming up labs, Coulter or BD. And that's just whatever they started with, in institutions, they just kind of stuck with it. In BD, you had the gross -- like the vast majority of the market share, I think 95% of market share, I think, it was at one point. But over time, I think a lot of customers soured on a lot of the experiences they had, and they felt like they only had one option. And I know when I worked for BD, I heard customers just tell me flat out, I can't wait until I have any other option than this. So I think we're at a point right now where I don't think that matters. It's more about what can the actual technology provide. And I know my customer service experience has been phenomenal with Cytek, and 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. So my experience has been, even though some of the people are still BD people, we're not having those kind of issues. So I've had better service in the last 3 years with Cytek than the last 13 years I had at Vanderbilt prior with BD. And I used to work for BD and they used to just send me parts and we would fix stuff ourselves. So yes, it's not comparable. So I don't think that's really going to matter. I think there's actually a window right now where people are dying to switch, personally.

I totally agree. And 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 force people. Because the efficiency of using the instrumentation was very much like not stable. So I mean, you don't develop a relationship with your centrifuge, and it just works. And 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. And 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, 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 done hard, is that you had some super successful labs that published high-impact publications, but an average Joe, who got technically the same instrument, paid the same price, would expect sometimes pretty subpar performance. So yes, my Aurora'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. So I think that this all development -- like some people had very strong relationships when things worked well. Some people had very sour relationships when things didn't go well. So 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? So we just see that things work. That's how I want it to be. So yes.

I know this 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?

So I can do that. So 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 Patrik 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?

So we don't break out reagents today, not by customer, not by instrument. It 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% to 80%. So that's what I would say at this point.

And then 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, and it's labeled as ABCD, there's some alphabet out there. All of those are actually having Cytek patents covering. We have a patent cover our laser excitation side, covering all the sieving technology, covering how we do the mixing, covering how we do the full spectral technology, and also covering how we put all of those together as a contract flow cytometers. So it's kind of very complete. And then the patents, not including U.S. patents as well as international. And of course, many of the U.S. patents have already been approved; international, some are approved, some are ongoing. So I think we are very comfortable regarding our IP protection here. Of course, we continue to find more and more patents to surround our current and future technology.

We do have one more question also again on reagents. 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? I don't think we've disclosed as a company what that is. So all right. Anyone else have any burning questions you want to get answered? And with that, I'd like to give everyone a round of applause for their great performance today. Thank you.

Thank you.