Everyone, welcome to the 10x Genomics 2022 Investor Day. We're so excited for what we have in store for you today. To everyone here in the room and everyone on the webcast, thank you for joining us. For those of you I haven't met, my name is Cassie Corneau, and I'm the Head of Investor Relations and Strategic Finance here at 10x. While I joined 10x a little over 2 years ago, I was first introduced to the team almost 4 years ago while working on the company's IPO. It was through that process that I first got to experience how different 10x is. The company has evolved a tremendous amount since that time, that differentiation in our innovation, our people, and our scale is still at the heart of 10x today. We've built our event today to showcase this. Serge is going to kick off the day. There's no one better to outline the mission and vision of the company, discuss the opportunity ahead, and show you how we're just getting started. You'll hear from Ben and Mike, who will walk you through the 10x innovation engine and our leading products and technology. They will turn it over to Alex, who will discuss our software products and how we enable sample-to-answer solutions. Eric will then walk you through our strategy for protecting our innovations. You'll hear from two customers today about how they are using 10x products and where they expect to take their work in the future. In between the customer perspectives, we'll have our first Q&A session of the day and take a quick break. Following the break, we will focus on how we are delivering on the promise of our technologies. Bex will discuss how it's the people at 10x that make the magic happen. You'll hear from Jim, who will tell you how we're strengthening our commercial engine. Ben will come back up to talk about our operational scale, and Justin will discuss our plans to deliver on a best-in-class financial profile. We will end the day with another Q&A session and closing remarks from Serge. For those of you joining on the webcast, you can submit questions throughout the day virtually by clicking on the Ask A Question tab to the right of the video player. Before we begin, please take a moment to note the information on this slide about our forward-looking statements. With that, let's get started. The future can be way better than the present, and it's our job to take us there, to bring it forward, to bring it here. We tend to tackle the most difficult problems. 10x encompasses everything we're about. We don't work on incremental things. We work on things together that create orders-of-magnitude improvement over what anyone can imagine being done today. We enable finding new things, understanding a disease better, being able to find treatments, much faster. We were at a sort of standstill with our research, where we wanted to answer particular questions, but we just did not have the tools on hand. 10x Genomics has allowed us to answer unparalleled questions with respect to the role of the immune system in neurodegenerative disease. Being able to really see what's going on in that patient's tissue and then being able to see how a patient is responding to the treatment will really help us move precision oncology so that it's truly patient specific. The innovations change what's possible. The Human Cell Atlas wouldn't have been feasible to even think about 20 years ago. Having the ability to look at thousands of single cells and hundreds or thousands of genes is really revolutionizing immunology, and it's allowing us to ask questions and discover therapies faster than we would have ever thought. Every one of the products that we put out there enables the users uniquely to identify new features in their research, find new pathways, make new discoveries that were not possible with the other technologies that were available. We can look at individual cells and their DNA at the same time as you can measure the genes that they are transcribing at the same time that you measure the protein that's actually on the cell surface. There's hundreds of thousands of cells. It will allow us to identify all the parts and start addressing pathologies, how basic cellular functions go awry in infections. Now we have the ability to integrate both fields, right? Tumor science, tumor biology with tumor microenvironment science. I think it's beginning to usher us into a new era of molecular profiling. The data that we now have, it's like science fiction. This was just not possible to think 10 years ago. Our technology lets you see everything that's in your sample, individual genes, gene pathways, cell types and frequencies. You see it all at once. We help every customer work so much faster. Faster answers means a vaster impact on health and saving lives. We can delete genes or turn genes on or off, or rewrite DNA sequences in individual cells, and then read out the consequence with incredible resolution, which will give us new opportunities to come up with drug targets. We are on this exponential curve of understanding biology. That, in turn, will drive massive improvements in healthcare and just transformation in how we view health and disease. We strive for excellence in no matter what we do. Our customers rely on us. Eventually their patients will rely on us as well. Lives will be saved because of the work that we are doing, and that is very inspiring. Please welcome to the stage CEO and Co-founder, Serge Saxonov. Thank you. Thanks, everyone, for coming here. This is our very first investor day, so really great to see you all. This is the first time we're actually seeing many of you, maybe all of you since the IPO. Of course, it's great to be here and to see you all in person. We have a lot to show you, and I realize it's going to be a lot. If you remember these five key themes, you'll be in pretty good shape. First, 10x. At 10x, we have a long-term orientation and ambition to take on the hardest problems. That's how we make decisions, that's how we think about the future, that's how we think about value creation. Our innovation engine is the foundation of our competitive advantage, our competitive differentiation. We have multiple massive market opportunities ahead of us, we're just getting started. We're an established leader. We have commercial scale infrastructure and a really strong financial profile to be able to win in all the markets we enter. The ultimate source of our success, the ultimate source of the confidence in our future is the team we have at 10x, and I hope you get to see that throughout the day going forward. As I'm standing here, we're in a great position as a company. We have over $500 million in trailing revenue. We got here really fast. We have thousands of instruments around the world, a strong base of employees, really formidable IP estate, and thousands of publications that have come out of customer labs that have used our products to make fundamental scientific discoveries. We have come far, but it's still really early days, and we expect to continue to go on the trajectory that we have set for ourselves. To give you a sense of that trajectory, this is the entirety of the company, of 10x, less than 10 years ago. This is in a garage in Pleasanton, just a few miles away from here. We were really fortunate to have a really great group of people at the very beginning. We had ambition to take on really hard problems, and we had a general direction of where we wanted to go. We knew roughly what we wanted to do, but we didn't really know how we were gonna do it. In fact, when we first started fundraising, turning to some initial friends and investors, pulled together a few slides, one of the slides kind of outlined the key technical challenges we would need to overcome. One of the investors asked, "Well, how are you guys gonna do this?" We said, "We don't know. That's why we need the money." The reason I tell the story is that 10x is not about any particular product. It's not about any particular market or any particular technology. 10x is about, fundamental about capabilities. It's the ability to innovate, ability to build things, ability to problem solve. The company is built to be a problem-solving machine, that's how we invented our first and built our first gem platform, the a-architecture, which answered that investor's question. That's how we commercialized and built the market for single-cell from nothing to a massive franchise. That's how we invented new microfluidics capability for Neogeo that others thought was impossible. That's how we brought FFPE compatibility to our Visium platform that others thought was unachievable. That's how we solve problem after problem after problem, whether it's technology, business or commercial. Propelling us through our history and going forward is our mission. That's what motivates me personally. That's what motivates all of us at the company. This is the century of biology. The progress in the life sciences has been an exponential trajectory, driven by advances in miniaturization, computation, and compounding effects of accumulation of biological knowledge. These advances have the potential to transform the world. You can start imagining tangibly curing cancer, solving Alzheimer's, getting rid of infectious diseases. It's not gonna happen tomorrow. It's gonna take time, but you start seeing this becoming tangibly possible. The key challenge, the key problem to overcome, is we actually understand very little of the underlying biology. The amount we don't understand is vastly greater than what we do understand. Curing diseases right now oftentimes feels like trying to fix a car when on your own when you don't know how cars work. Our goal at 10x is to accelerate the understanding, the mastery of biology to lead this revolution we see unfolding in the life sciences to advance human health. The crux, the important thing, the most salient feature of biology to understand what makes it so different from all other disciplines, it's inherent immense complexity. Each one of us has close to 40 trillion cells. Each cell has millions and millions of molecules and molecular machines interacting with each other in complex ways and changing all the time. These cells form tissues. Tissues are organized in organs. For us to understand biology, we need tools to be able to measure all of this complexity at the right resolution and the right scale. It wasn't really until the advent of genomics as a discipline that researchers started looking at biology at the right scale in a systematic fashion, starting with the Human Genome Project, followed by genome-wide association studies, followed by population sequencing. The goal here, very much focused on DNA, to essentially compile the parts list of human biology. The next question becomes like, what do all those parts do? DNA itself is static. It doesn't really tell you much. Genomics as a field needs to expand beyond DNA to encompass all of biology, move downstream to encompass epigenetics, gene expression, proteins, cells, tissue, and all the ways that these objects are all organized. In order, you need to be able to measure them, you need to be able to measure them at high resolution at high scale. That's ultimately how we're going to understand the underlying biology, and that's ultimately how we're gonna arrive at cures. We outlined this vision during our IPO process three years ago. Now if you look back over the last several years, that's precisely how the field has been evolving given the explosion of interest in multi-omics, increase in single-cell and spatial biology. The challenge, of course, is that the conventional tool, the legacy toolkits of the life sciences are just not up to the task of being able to enable this because they lack the right resolution, the right scale. To give you a sense of what I mean by resolution and scale, to remind you, this is. On the left is what the world looked like before 10x. When you take your sample, you take all the cells in your sample, you mix their contents together, and then you measure gene expression of that mixture, which gives you an average profile of gene expression in your sample. Now over the last six years, with our Chromium platform, what researchers have been able to do is take their samples, separate out all the individual cells across thousands to millions of cells, and measure each individual cell, the full transcriptome, full gene expression profile of that cell. Now you see this really complex picture that represents the true underlying biology that was previously obscured when we were relying on low resolution, low scale tools of conventional biology. From the beginning, we set out to build this new toolkit to lead the revolution in life sciences. We realized from the beginning that entailed being really, really good at multiple, very different disciplines. From the beginning, we invested, we thought really deeply invested to build deep expertise across all these areas, hardware, chemistry, biology, software, data. Paid particular attention from the beginning of investing in software and computation, given how important data is in the age of genomics. We invested in the right culture and the right process. We nurtured that across the years to create the infrastructure to have this engine for generating new technologies and new products. Really speaks to the core of our philosophy of investing in foundational capabilities. This capability, this ability to rapidly develop new breakthrough products and also knowing what products to develop, we see that, we call this our innovation engine, as our core competitive advantage. We think deeply about where the world is going and then work backwards to figure out what technologies, what products we need to develop to get there. Once we gain conviction, we move rapidly to bring those products to market. Took us less than three years to go from a standing start to launching our very first product. Since that point, our innovation velocity has only accelerated. In fact, this year is shaping up to be the biggest year of product launches in our history. We're immensely proud of the team we have at 10x. At the same time, we recognize we don't have a monopoly on innovation or on smart people. Because of that, M&A is a core part of our innovation strategy. Same as with everything else, we think deeply about where the world is going, we work backwards to figure out what technologies, what products we need to take the world there. When we see technologies and products outside these walls that can fit within our strategy, within our goals, then we work hard to bring them into the company and build on top of those technologies. That's what we've done consistently across the years. In fact, we have a unique advantage here. Because of our product, the strength of our product development engine, we can take assets from all across the spectrum, from very initial ideas to raw technology concepts to pretty mature technologies and bring them, plug them into a product development engine and turn them into awesome products with massive commercial potential. We take real pride in helping founders realize the full potential of their inventions. We have used these capabilities to build the products to enable the future of biological analysis. From first principles, and also based on thousands and thousands of publications that have come out over the past several years, it's very clear to us that the ability to preserve single-cell context is going to be absolutely foundational, critical across just about all measurements of biology in the future. From first principles, roughly, you can determine that there's three general approaches to be able to preserve single-cell context for biological measurements, and those are represented precisely by the three platforms we have developed inside the company. That means two things. First of all, together, given that we have them all under one roof, we have the full complement of solutions to enable the future of biological research, biological measurements. Also, these platforms are highly complementary in terms of the kinds of questions they can answer right now. Having them all under one roof provides tremendous value to our customers who can now ask complementary questions and use these platforms synergistically. First, the Chromium platform. This is the platform that created the single-cell market and really catalyzed the single-cell revolution. It works with dissociated cells, so you can take cells, thousands to millions of cells in a single experiment and measure a broad range of analyses within each one of them, whether it's gene expression, epigenetics, proteins, immune receptors, or multi-omics. It's applicable to a wide range of applications, lots of products that we've built across the years, and lots of capabilities. It's known for high performance, high quality of data, and ease of use. There are now thousands and thousands of instruments installed in labs around the world. There is now an extensive ecosystem of customers, data sets, papers, protocols, and that creates real value for our customers, both for new customers when they're entering the ecosystem. They have a lot of existing colleagues that they can rely on to help them get started. Also for existing customers, because it allows them to collaborate, to place their own data in context of all the other findings that have been made, and also to learn new applications as those emerge. We're continuing to make big investments in Chromium, both in terms of the fundamental capabilities of the platform, of the assays, you know, whether it's pushing throughput, pushing sensitivity, pushing more analyte or investing around the platform to remove customer bottlenecks to improve ease of use, whether it's investing in sample prep, workflow optimization, or data analysis. The platform has come a long way. It's a big market, but it's still very early relative to its potential. With Chromium, what it allows you to do is measure what is happening in a sample at the right level of resolution, the fundamental unit of biology, single cell. With our second platform, Visium, you can tell where things are happening, where the molecules are arranged in your tissue. It's the only platform that allows unbiased analysis of your tissues, meaning you can measure the full transcriptome across the entire tissue section. As such, it bridges the worlds of histology and genomics, the worlds that previously existed fundamentally far apart from each other. The Visium platform is early. It's only been around for a few years, but it has already established itself as the leading platform for spatial discovery. It's used in thousands of labs around the world. There's over 350 publications that have come out of customers' labs using Visium, and there's vastly more customer datasets using this platform than any other platform. We're especially excited by the recent acceleration of this platform, of this franchise, driven by the by our introduction of a new instrument, CytAssist, to address some of the major bottlenecks and some of the major challenges our customers have faced with running the Visium workflow. Visium is an ideal tool for unbiased spatial discovery. Once you know what you're looking for, Xenium, our third platform, becomes the ideal platform of choice because it allows you to look at large panels of molecules directly in situ in an integrated approach. Today, really happy to announce that we have officially started commercial shipments of Xenium. This, only a few years ago, felt like science fiction. In fact, I was at one of the investor conferences when someone asked me the question, I said precisely that this technology feels like science fiction. It's incredible to me to now think about the fact that it is actually becoming a reality. More so, we build a system for routine use, for fast turnaround time to be a workhorse for labs around the world. Provides very clean data, customizable panels. Importantly, it's compatible with H&E, which makes it particularly suitable to work with standard pathology on the same tissue section, H&E and the high-plex molecular analysis. It's a very powerful platform, but this is just the beginning. There's tons of headroom in the Xenium technology, and we have an extensive roadmap for many years ahead to keep adding new features, new capabilities to the system, to keep driving more and more applications and more use cases with our customers. Also, in the long run, this platform has great clinical potential. If you think about it, the Xenium platform, the Xenium technology, has the makings of an ideal clinical instruments. It allows you to make high-plex targeted measurements together with imaging, all in an integrated fashion. It has this potential to really bring together the worlds of digital pathology, and genomics, and molecular analysis, and with it, the potential to ultimately revolutionize medicine. We build these platforms, we build these products to enable fundamental measurements, which end up driving deep insights across biology. You can now see, as I mentioned, there's over 4,000 papers that have been published out of customers' labs using these products. Thousands of discoveries, many different areas of biology. In fact, it's hard to think of an area of biology where these products have not driven fundamental insights, fundamental discoveries, major breakthroughs. One of the major learnings from all these papers, all the science, is the pervasive cellular heterogeneity that underlies just about every biological system. Every tissue you look at, you end up seeing a very large diversity of cells, cell types, and cell states. That's where the crux of biology lies. If you try to understand what makes healthy tissue healthy and what makes a disease disease, you really need to be looking at the level of single cell, because that's where the fundamentals of biology are. That's why I like to say that over time, our vision is that just about every time you start with tissue, you'll need to analyze it with single cell context at large scale, with spatial context where that's appropriate. It's true in just about every therapeutic area you can think of. If you look at cancer, consider tumor heterogeneity, tumor microenvironment, the immune system. You can't understand any of those elements, and certainly not how they interact with each other, without single-cell context. If you look at autoimmunity, there have been many studies with single-cell looking at different autoimmune diseases. The consistent theme, the pervasive theme, is that when you look at healthy tissue, you have this really complex network of cells, cell type, cell states interacting with each other using these complex gene expression programs. When you look at disease, you also have this complexity, but it's a different kind of network. You only can understand that when you're looking at these systems with a single-cell approach. That's the only way you can understand them, that's the only way you can diagnose them, that's ultimately the only way you'll be able to treat them consistently. When you look at neuroscience, this field is arguably behind some of the others in terms of our ability to understand these diseases. Oftentimes, when we look at neurodegeneration, it feels like you're operating with a black box. That's why we don't really have any great treatments. We don't really have a lot of understanding. We have catalogs of gene associations, lots of genes associated with these diseases, but we don't really know how those genes impact phenotype, how they actually relate to the disease. Now there's been a lot of single-cell research that's been done on these diseases and we've started opening up the black box. You see it turns out, you know, the brain has this insanely large number of cell types. When you look at any given gene association and any fundamental disease, it really comes down to what is happening in any given cell, any given individual cell types. It's just really hard to think of any therapeutic area where single cell is not driving important discoveries. When we consider all the different application areas, all the different analytes that their platforms are addressing, we see that our customers using our products are using them in place of all kinds of other legacy technologies. We see that over time, our products, our platforms are going to be replacing much of the legacy toolkit of across the life sciences. This is by design. We specifically set up the company, we developed these platforms to deliver the next generation of technologies. It also presents a challenge when you're trying to estimate the size of the market opportunity because we're not displacing any particular technology, we're not limited to any particular application, not limited to any particular customer segment. We're really drawing dollars and customers and applications from all across the ecosystem. While we can't make a precise measurement based on a specific formula, there's different ways we use to triangulate on the size of the opportunity here. First, you can look at the total dollar spent within the life science tools market. In total, that's about $67 billion growing at about mid-single digits year-over-year. Within it, if you just focus on technologies, the tools that are most amenable to be replaced by the 10x platforms, we estimate the size of that opportunity to be about $16 billion, that should be available to us in the near to medium term. We also anticipate that as the world of research kind of moves forward to more researchers, we conceptualize the experiments, we conceptualize the experimental programs to take a greater advantage of this new generation of tool of high resolution, large scale, that more of that $67 billion could become available to us over time. In the long run, we also anticipate there's going to be massive opportunities in the clinical, in the clinical arena. Taking some initial conservative assumptions, just focusing on cancer diagnostics, making assumptions around incidence of disease and reasonable pricing per sample, we estimate the size of this opportunity could be $10 billion with large upsides beyond that as you imagine moving to other disease areas and to other modalities as well. Now, focusing back on the research market, you can also look at it or estimate it through the lens of the questions that the researchers are asking, the application areas. At a high level, there's four categories of research areas of questions where our tools are being used. First, in atlasing. This is cell atlasing, this is where single-cell started. This refers to the baseline characterization of your system. Researchers started looking at every tissue by tissue and try to understand what are the cell types that exist within each tissue and how they're all organized with each other. We established a strong presence, and this market has been growing well, but there's a huge opportunity, there's a huge expectation that it's gonna keep growing because all these researchers are looking to do more and more combinatorially more studies because they're looking to do now moving from just a single organ, single tissue, now looking at multiple people with different genetic backgrounds and different ethnicities, different phenotypes, different time points. It is also important now for atlasing to move beyond just gene expression into epigenetics, into proteins, into multi-omics, and also into spatial analysis. There's tremendous potential for this to keep growing. To us, just we estimate this opportunity to be about $2 billion a year. A lot of it is going to be enabled by the capabilities that we have released, the technological capabilities we have released recently or will release in the future. The second category centers around the question for understanding genes and their function. This is really kind of what the genomics people, where they have been migrating, where the focus has been shifting, going back, starting with DNA, but now trying to understand what do all those elements, all those variations, all those cell types they've learned, what they actually do. Going downstream for there. For these researchers, now there's been great work done, especially recently around these kinds of study. There's a lot more that's coming. These researchers are most importantly looking for scale to do more larger cohorts, larger and larger perturbation experiments. Scale, more cells, more samples. This is something that we're also investing in and enabling going forward. The third category is really the easiest way to think about this mainstream biology. This is your typical biologist typically working in a particular specific system, question around the gene or pathway or cell type of organ. For them, the big opportunity here is for them to go upstream and to gain genome-scale and cell-specific context around their particular system to truly illuminate the biology they've been, like, yearning to understand. These people we have made inroads, but it's still very early for us because these people, the biologists by definition are not technologists. It's really important for them to have the ease of use, ease of data analysis, and cost is particularly important for them. Again, areas that we're investing on the product development side. Finally, translation applications. This is, these are researchers, whether within clinical, for clinical research, within research hospitals or biopharma companies that are looking to understand working in human tissues to understand, to find drug targets, biomarkers with the goal, more direct goal of impacting human disease. We established strong beachheads in these areas, but it's still very early. In particular, the arrival of ability to fix your samples from us over the course of the past year is going to be a tremendous enabler for these research questions, for these researchers going forward, because it's going to allow people now to do batch processing of their samples, to do distributed sample collection, and to be able to look at archive samples to enable retrospective studies, something that's not been possible up until very, very recently. For these questions, for these researchers, ease of data analysis in particular is going to be important. Is consistency of workflow and all the other innovations we're bringing to bear as well. We have this tremendous market opportunity, and we have multiple forces that are going to be driving our growth going forward. Underlying all this, of course, for 10x, is the technology innovation. We have many products, both recently launched and that we're going to be launching to deliver better insights, the core capabilities of our platforms, improve workflows, and just remove bottlenecks to increase adoption, both with existing customers from new applications and to enable more customers to enter the ecosystem. Because single-cell is critical for understanding biology, we expect to keep expanding into more and more areas of academic research in something that we're certainly leading into both technologically and with our commercial strategy. For the same reason, a single-cell is critical. Single-cell is fundamental for understanding human samples, and we see it as inevitable that it will gain great traction in translation and biopharma applications. Fourth, we see spatial biology now, having just starting to establish itself, but still very much in an embryonic state relative to the massive potential going forward. We see it as poised to accelerate and transform multiple fields going forward. Finally, we anticipate we're already starting to see this early evidence of clinical utility of these approaches, which will, A, set us up well for future clinical opportunities down the road, but also feed into these other market drivers, especially in translation application, but all across biology as well, as people see the endpoint and the value of these approaches more and more tangibly. We have a great set of market opportunities, huge tailwinds behind us, and we have the commercial infrastructure, the commercial scale to maximally take advantage of these opportunities. We have a large force of sales force of direct sales professionals all across the globe, augmented by great distribution partners in some territories around the world. We have a best-in-class customer support organization that's obsessed with customer success. We have a great marketing organization to drive adoption of our products. Most importantly, we see our commercial organization as one of the core pillars of our competitive advantage. 'Cause we strive to form deep relationships with our customers, which allows us to see what is next, what they're thinking, what questions they're asking, we use that information to feed back into our product development to know what products to develop next. In fact, we see our commercial infrastructure as a core part of our innovation engine. Through our customer relationships, we get an early view of where they're headed. That allows us to develop great products that they love. That customer success leads to further deepening of the relationships into ever-growing cycle of growth. That also, that innovation engines also allows us to build highly differentiated products, which allow us to command superior economics and high margins, which then allow us to invest back in innovation to keep increasing the flywheel. Fundamentally, underlying all of this is the innovation engine allows us to build really exciting technologies with massive impact and a really powerful mission, which helps attract amazing people to the company that want to work on hard problems and make a dent in the universe, leading to the ever-widening cycle, virtual cycle of innovation. With that, I'd like to now invite Ben Hindson, our Co-founder and Chief Scientific Officer, to the stage. Ben will be joined by Michael Schnall-Levin, our CTO, Chief Technology Officer and Founding Scientist. Ben and Mike will talk about our product development engine, our innovation, and all the portfolio of great breakthrough products we have at the company. Thank you. Thanks, Serge. Well every day when I come to work, I have the privilege of working together with a phenomenal R&D team. We're a 450 person strong team, and we're comprised of the brightest minds. We love to be challenged with hard problems, and not just because they are hard, but because when we solve hard problems, we unlock tremendous value for our company, for investors, and most importantly, for our customers. Now, cells are the fundamental unit of biology that define health and disease. We need to know what cells are present in any given tissue, what state they're in, where they're located, and their regulatory relationships. Our team builds the most impactful technologies, and we put them into the hands of researchers to unravel these hidden complexities of biology. We're continuously pushing the limits of what's possible. We have our Chromium, Visium, and Single Cell spatial products respectively, which allow you to go broad, hypothesis-free, and make unbiased discoveries. We have our newest offering, Xenium, which allows you to look at biology with unprecedented resolution. We're obsessed with innovation to bring more capabilities and assays across all platforms, and I couldn't be more proud of our team for what they've accomplished this year, our biggest product development year in our 10-year history, and with their passion and determination to continue to make it happen every day. Over the past 10 years, we've come a long way, but we have far more in front of us, and it starts with the application. We think about that deeply. We find the right combination of technologies that, in combination, ultimately provide turnkey solutions to our customers. Staying close to our customers is one of our best sources of truth, and we learn about important applications, what their needs are. We also learn about how we're doing, how our products are performing, and get their insights into the direction of their science, and that in turn informs our roadmap and prioritization of our team's efforts. I'm really grateful also to our customers for driving science forward and their continued partnership with our team. We do take a truly multi-disciplinary approach. It's really easy to say on a slide, it's harder to do in practice, but we do think deeply about how we tackle any given problem. We do it with high velocity and focused execution. We set ambitious goals, and then we execute against them. I believe that we've built this company for the genomics of tomorrow. The status quo is never satisfying, hence the name 10x. Over the years, we've figured out how to innovate on multiple projects in parallel. It was nice when we were a one-product company, but over the years, through our product development process, we've, in our collaborative culture, we've seen increasing benefits of our ecosystem. These synergies, both internally for the company, create leverage, but also importantly for our customers. The Chromium, Visium, and Xenium platforms are all absolutely essential tools. Together, all three platforms have the benefit of this ecosystem and with significant interplay between them. We have shared technology across platforms, for example, proprietary enzymes coupled with novel and innovative assay schemes that make the data learnings in one platform valuable for the others. The shared workflows, so we can take even the same tissue section and analyze them on one platform and then get different data from the second platform. Connecting these assays with the high-dimensional assays provide a seamless transition from the now to the future. By design, our analysis and software tools are at the heart of what we do. Computational biology and software tools are our superhighway that connects both our teams and the data generated by our products. This ecosystem and interplay was showcased in a recent preprint from our development and applications teams. For the first time, we showed the power of all three platforms to look at a serial sections of the same block of FFPE cancer tissue. This was really well received by the community, and we'll discuss the highlights of this in a later section. From the company's inception, we knew that single-cell analysis at scale was a tremendous opportunity, and we're proud to play an important role in the single-cell revolution. Our innovations on Chromium should provide confidence in our team and to those out in the field and our customers that we're continuously continuing to push the boundaries, not only on Chromium, but also on Visium and Xenium. Customers expect our innovation to produce on all axes of depth, breadth, and scale, and our goal is to continue to meet and exceed these expectations. Chromium portfolio pictured here is comprised of our instrumentation, the Chromium X, which is supported with a whole range of different application kits and consumables, together with the Chromium Connect, which automates some of the more manual processes, freeing up precious personnel resources. The computational pipelines and visualization tools that accompany each product are essential in the acceleration of our adoption of our products. The data sets that we produce, they're of ever-increasing scale, different modalities, and complexity, and we require software to accelerate customers' transformation from data into insights. Many of our assays are offered individually or in combination, for key research areas shown here, and true multi-omics solutions are offered in many of these application segments. They allow researchers to take different viewpoints into the underlying biology to enable researchers to challenge the status quo, make unbiased discoveries, and ask new questions. We love to see these charts where we can see the impact of what we're contributing to research, and it's best showcased with the cumulative number of publications for Chromium in just a short time. Over 3,800 publications in some of the top-tier journals. The biological impact is shown in some publications just highlighted here, where we're looking at ever-increasing scales. Thinking about thousands of samples by thousands of patient samples for this research study to really look at the underlying cause of complex disease that can only be enabled by single-cell analyses. Our customers are extremely innovative. They take our on-market products, and they tweak them. They figure out new applications, which also, you know, is a guide point for us to see which one of these is exciting and which ones should we consider bringing in-house and providing a full turnkey solution. Just to highlight a few advancements on the Chromium front. We have our Nuclei Isolation Kit, which was launched earlier this year. This is intended to replace some of the complex homebrew protocols that customers had spent up to a year developing in their labs. This is a consumable that enables to take complex tissues like brain tissue, for example, generate high-quality nuclei, which can then be as an input into our different assays. Our BEAM assay, I'm happy to say it's shipping now as well. It's been a really nice week for us, for this new capability to be available to customers as well. Our Gene Expression Flex. Now when we launched this capability, what we're calling now the Gene Expression Flex, just to keep you orientated, we called it Fixed RNA Profiling. The more that we've worked on it, the more that we've seen that there's exciting capabilities beyond what we initially thought, including the ability to get single-cell data from FFPE tissues. To reflect that, we're calling this new capability in our Chromium portfolio Single-Cell Gene Expression Flex. You can just call it Flex for short. Our BEAM assay is a new capability that builds on our immune profiling solution. BEAM will play an important role in the fields of antibody discovery and cancer immunotherapy. The reagents come with customer loadable content, whether that be antigens or peptides, paired with fluorescently labeled and uniquely barcoded complexes that are now available together with fully supported protocols and software. For BEAM-Ab, we're excited to see that the potential of screening B cells against multiple antigens to increase the chances of finding the most potent neutralizing antibody that can recognize epitopes with very highest binding specificity and affinity. What makes BEAM-Ab stand out is the optimized multiplex antigen screening approach that we provide that lets you interrogate the genetics and binding specificities of thousands of such immune cells simultaneously. For BEAM-T, we strive to be at the forefront of cell immunotherapy innovations and to help render the adoptive T-cell therapy and our contributions there to be more effective and accessible as treatment, a treatment modality for patients. We hope that the community will learn to match the uniqueness of individual tumors with the appropriate immunotherapies. To Flex. We think of this as the new gold standard for gene expression. It's our highest performing expression assay to date. Depending on the sample type, we've observed up to 2-3 times more genes detected from any given sample. It works on FFPE, which is notoriously challenging to analyze, it is the standard for how most biospecimens are treated, stored, and biobanked. For FFPE, we've developed streamlined new protocols for processing FFPE and getting single-cell data out of that. We see a boost of our initial protocol, which we provided to early customers in the middle of this year with new protocols that were developed in-house that also offer a streamlined experience together with a boost in performance. This is an example of that performance from a human glioblastoma FFPE sample. Access to samples will be opened up dramatically with the Flex assay. We also hear these examples of customers where with our current assays, you get a call on a Friday night. There's this precious sample coming in. Would you mind staying and making sure that sample gets processed? It's 5:00 on a Friday afternoon. Well, with Flex, we're able to change that and give customers the alternative option of taking that sample, fixing it with our proprietary fixative, and then wait till next week or the week later. Wait till you've collected a batch of samples, and then process those and get very high quality gene expression data across all your different cell types that are comparable with what you would have got if you processed those samples immediately. We really think this will unlock the power of single-cell analysis for larger studies and where the logistics are now freed up. We'll continue to innovate on this. When we think about scale on the top right, with multiplexing that's inherently built into this assay, we're able to run from anywhere from just a single sample to up to 128 samples in a single run. That can get you to the capability of million-cell experiments from a single chip. Many years ago, that would have been just unimaginable, but today it's possible. Finally, we think about the total cost of experiment. With the Flex Assay, we've seen that we're able to reduce our recommended sequencing per library by a factor of 2, that provides valuable savings for customers to spend less money on sequencing and free that resources up to do, for example, more studies, more samples. Our early customer success has been demonstrated on, for example, there's examples here from the Fred Hutchinson Cancer Center up in Seattle. Sanjay Srivatsan's team showed that the benefit and sensitivity of our Flex Assay on matched samples versus our 3 prime V3 assay. Luciano Martelotto in Australia was quick to test our Flex Assay on archival FFPE samples and put out a bioRxiv reflecting the data that he was able to generate, and that generated a lot of excitement within the community. Just to show you the power of scale, we performed an internal demonstration, here's a recent example where we took 32 clinical samples. They're clustered by donor. We processed them in 1 Chromium chip over a period of about 1 week, and what's shown is the UMAP in the sensor there for close to 1 million single cells. This was done with about 50% savings on the sequencing, and these are from 32 different lung cancer samples all on 1 single chip, it's incredibly powerful. As we've mentioned, we're not done yet. We're gonna continue to innovate. Here are some themes about the areas of what we're going to innovate on. Obviously, fixation capability is really exciting. We're gonna open that up to a larger range of analytes. What's available on fresh samples could one day be available with FFPE. We're thinking about dedicated sample preparation and preservation solutions to make our Chromium assays available and suitable for a wider adoption on larger scale studies, especially thinking about clinical trials in these translational applications. We're going to continue to drive down the total cost of experiment. An all-in cost, including sequencing and other factors, to where we can get to one center cell and beyond. Together, we're thinking about how to free up the logistics of single cell genomics, and that's through innovative solutions, including automation and dedicated sample preparation solutions. With our new Flex Assay and our innovations on the underlying assay schemes which are shared across our three platforms, we're able to look at new assays, so customizable content. We provide some panels and customers are able to add to that with their own content, looking at things like, in the future, single-nucleotide variants and fusions. Additional sequencing technologies enable us to look at single-cell isoform analysis, which is pretty exciting, potentially a new forefront in single-cell genomics. Ultimately, we're gonna push on multi-omic analysis. The ability to get more samples, more information from a single run is essential, and we're gonna continue to push the limits on that on our Chromium platform. We've come a long way. We've further expanded the adoption of single-cell, but we have a long way to go. With that, I would like to welcome Michael Schnall-Levin, our Chief Technology Officer and Founding Scientist to the stage. Thanks, Ben. Thanks all of you for coming. I'm really excited to talk to you more about our Visium and Xenium platforms, as well as do a little bit of a deeper dive on that preprint that Ben mentioned briefly that our team released a few months ago and which really showcases the power of using all three platforms combined on the same sample. Visium is the discovery spatial platform of choice, and it really has been since late 2019 when we introduced the platform and the first product in the platform. Since then we've been really excited about the reception that the platform has had and the traction it started getting, as well as the key capabilities we've been able to add over the last few years, and we're really just getting started. We're also extremely excited about the desire of customers for new capabilities and what we think we can deliver in the coming months and the coming years on the platform. Visium's already had great impact. It's been used in over 350 customer publications and preprints, but even more important than just the raw number is the breadth of applications it's been used in. When you look at some of the biology that's been used, and it really covers the gamut of all different areas of biology, and that's from looking at the remodeling that's happening in the heart from before and after a heart attack to measuring details about key genes as they're expressed in human pain receptors with implications for developing drugs for treating pain to understanding the infiltration and maturation of immune cells within tumors with obvious implications for immunotherapies. As Ben went over in Chromium, for Visium, we're also excited about not just the biological papers that come out on the platform, but also the key technological developments that our customers have been able to do on top of the platform. We've seen things like querying of non-polyadenylated mRNAs, looking at copy number variation in addition to gene expression, and most recently, querying the T and B cell receptor sequences with their spatial context on top of the Visium platform. This really gives us a view into what is possible and where customers want us to go and helps us prioritize our future product roadmap. We've been hard at work on the Visium platform, and we've introduced some key additions this year, and we'll be introducing additional technologies in the coming months. In about the middle of this year, we introduced the CytAssist. This is the first instrument for the Visium platform, and we've been really excited about both the customer reception as well as the feedback we've been getting from the first people who have had this in their own hands. I'll talk more about some of the benefits in the coming slides. Along with the Visium CytAssist, we also introduced a version 2 of our FFPE gene expression Visium solution. This came with core improvements to the key technical performance, as well as the addition of additional SKUs to measure tissues of different sizes. Again, I'll talk about some of these advancements that come along with Visium CytAssist in the coming slides. This assay was exclusive for the CytAssist. In the first half of next year, we'll be launching our Visium gene and protein multi-omics solution, again, exclusively on the CytAssist. Our Visium FFPE version 2 came with key improvements. Some of these are really driven by the capabilities of the CytAssist itself. Improvements to the workflow and robustness of the assay are inherent to the instrument, as is access to archival samples, including tissues that have been stored on glass slides. Additionally, we've baked in many key technological improvements into the assay. There's a completely optimized and updated human probe set, which now completely matches the probe set used in that single-cell gene expression Flex product that Ben talked about. That allows customers to seamlessly go back and forth between the data in the two platforms. There's also improvements in spatiality, gene sensitivity, and specificity in the core assay. This is evident across all samples that you look at, but it's most evident when you look at some of the most challenging human samples. Here, we're showing that on a challenging tissue section from a human brain run side by side on the version 1 and the version 2. In particular, you can see that some of those very low expression genes are showing up very clearly in version two when they were absent or nearly absent in version one. It's for some of the key technical improvements we've seen internally as well as the excitement from customers that we've really made the decision that CytAssist is the future of Visium, and we'll be focusing our future technology development and new products on the CytAssist. The CytAssist offers the highest quality of data. It offers streamlined logistics, in particular, is fully compatible with standard histology and tissue workflows because it operates on tissues that have been sectioned on completely standard glass slides before the assay has even begun. It offers the broadest sample access, including a broad range of tissues and archival material. The R&D team has been hard at work adding capabilities on top of the CytAssist. One of the top ones that the customers have been asking for is the ability to look at fresh frozen tissues in addition to FFPE, and we have a protocol that enables that on the existing reagents, which is coming out in the next few weeks. Here, I'm showing some data from that on 2 mouse lung replicates at the top. The next capability that we'll be releasing in the first half of next year, as I mentioned, is the gene expression plus protein multiomic solution, and here I'm showing an example of a T-cell marker and the T cells identified by that from the protein and RNA side by side on the same tissue section using the CytAssist and this capability. As I said, we're really just getting started with the Visium platform, and we have a number of key directions that we're gonna focus our future product development in. One of these is obviously resolution. We've talked about Visium HD. Customers are extremely excited and clamoring for it, and we've been making great progress and are fully committed to this solution. The second is that some of the earliest customers of Visium have been having great success, really see the power of the platform, and now wanna apply Visium to a greater range of tissues and at higher scale. As we've done with the CytAssist and the introduction of the XL reagents on Visium version 2, we're going to keep increasing both the sets of tissues that can be assayed with Visium and the automation capabilities and scalability of the platform. Finally, as we've done with Chromium, we're going to be adding additional analytes and additional multiomic solutions that can be queried in a spatial manner. We're starting with that with the gene expression plus protein multiomic solution that I mentioned, we think that's just the beginning. With that, I wanna switch gears and start talking about Xenium. As Ben and Serge mentioned, we're really excited because Xenium started shipping this week, and we're getting the first commercial units out there to customers. The goals are now really clear. We have to make the first set of customers extremely successful. We have to scale up production and introduction of this product into a broad set of users' hands, we have to execute on an extremely ambitious roadmap that we've set for ourselves. We spent a lot of time talking to customers over the last few years as well as learning, using the learnings from the companies that we acquired to understand deeply customer needs. We think that the version 1 product that we're releasing is resonating extremely highly with customers, generating really intense demand. We're also excited because we think this is very, very early days for this platform and for this technology, and we can foresee years to a decade-plus of intense technological innovation. We've built the capabilities to do that in this platform with some of the key architectural decisions we've made. One of the things we're really excited about, in particular for Xenium and Visium and our spatial approaches, is the ability to take 2 worlds that have largely been separate and start bringing them together. This is the world of imaging and molecular analysis. Imaging is really the first way that people understood biology using tools like the microscope, and it's a real standard in things like pathology and diagnostics of disease. Molecular analysis has obviously been absolutely critical in truly understanding the complexity of biology. With spatial technologies, you can now start bringing these two worlds together and taking things like digital pathology and genomics and pulling them into one. We think we're just at the cusp of starting to be able to do this, and we're really just scraping the surface, but it's very at the forefront of how we think about things and some of the decisions we make in how we architect these platforms. Xenium's built on powerful foundations from three companies. Of course, there's been tremendous technology development at 10x Genomics going back many years, and there's been a very intense product development effort on the Xenium platform in the last few years in particular. We also looked at the entire landscape as we thought about going into in situ, and we bought two companies along the way to enable us to build the solution we wanted, not just for our version one, but for the next 10 years of development. These were ReadCoor and CartaNA. From these acquisitions, we got access to core technology and IP that we believe will fuel the key innovations customers want in the coming years. We got key early technologies and prototypes of products that allowed us to execute quickly on building the Xenium platform. We got key talent and expertise, including teams of people who had been thinking about these problems for years. We've also been really excited because through the initial service provided by CartaNA when they were a startup and then through subsequent continuation of that under 10x, the CartaNA product has been used in over 70 customer studies and has now been adopted in over 15 publications and preprints. We've made huge technological advancements in going from CartaNA to Xenium. We see tremendous performance gains. Even from that early version of the technology, you could see the power of application to many different areas of biology and the excitement from customers. Now I just wanna walk through some elements of the Xenium technology and why we're excited about them and where we see tremendous power. One of the first of these is the Xenium chemistry. One important component of the chemistry is the ability to probe individual mRNAs through the use of these padlock probes. A key aspect of this is the ability of these probes to very specifically interact with RNA through 2 hybridization events that then have to go through an enzymatic series of steps, including an amplification. The end result of this is that you have a probing of the underlying RNA molecules that is both highly specific and also generates really strong signals so that a 1 probe is enough to actually detect an underlying transcript. This is highly differentiated from some of the approaches that have been out there in the literature for more traditional academic FISH-based methods. What this means for customers is that there are very minimal limitations on gene length, that you can address the broad range of genes from low to high expression, and really importantly, you can go beyond just gene expression counting into totally new areas, including isoforms and expressed SNPs, both of which we see really great exciting proof-of-concept data internally and a ton of excitement from customers. Finally, we've thought from the beginning of the ability to do high-plex protein multiomics on the same tissue section, not serial tissue sections, with our Xenium assays. We've built that into some of the core architectural decisions we've made on the RNA side. The end result on the gene expression side is a highly performant assay on both sensitivity and specificity, which I'm showing here in the middle on the right on a coronal section of a mouse brain. We think that both aspects of this performance are gonna be really important for customers. Xenium is compatible with a broad range of fresh and FFPE tissues and sample types, and here we're just showing that across a range of human FFPE tissues, both normal and cancerous. Importantly, for this set of data, we've generated this with a 350-plex panel used internally, the same one, as well as the exact same tissue preparation protocol that we've used for the first set of tissues we're supporting and that went out with the product this week. This leads us to believe that we can get this product into a broad set of customers' hands for a broad set of applications with minimal to no specialization and optimization on their part for different tissues. One of the really interesting things about in situ technology is that it's a targeted technology and you have to start with some notion of the content and the genes that you wanna measure. When you look and you talk to early users of the technology and how they're going to select that gene, the answer comes back very, very strong. You essentially have to start with single-cell data. That has been baked into the very way that we think about these panels and the way that we've built design and software tools for generating panels. Our overall content strategy is to have a mix of fixed panels that we've highly curated and taken the time to fully suss out all the biology of, but then to allow customers to add in custom genes on top of those fixed panels on every single run. With the initial product, this will be 100 custom genes, and we've taken care to make sure this is both economical as well as has streamlined logistics, so this is something that's easy for customers to do. We've built in the expertise and the large sets of data that both we have access to internally and the community has generated on Chromium into the design tools that have informed our fixed panels as well as the design tools that we'll be providing to customers for generating their custom content. Another key area of differentiation for Xenium is the throughput. We've made certain key architectural decisions in both the instrument, and we've been able to build on the power of the chemistry to generate far higher throughput than what we're seeing coming out with other in situ solutions. We think this is really important because it's one thing to generate one nice figure, but it's another to actually support large-scale studies and the kinds of scale that we think customers really want to do on this product. We're hearing from cores that they're really interested in streamlined logistics on this product. We're hearing from early customers that they want to do large-scale studies, things like case controls, to really understand different aspects of biology. You see the power of this throughput when you really want to look at an entire tissue section, which is what we believe most customers will want to do. We're showing that here with a 2-by-1 cm section breast cancer FFPE sample that's been run on one of the 2 slides that comprises a Xenium run. You can see all the molecules that have been captured for the entire tissue section there. On the right, a zoom-in, and really the power of this exquisite resolution of the Xenium technology, where you're seeing a blood vessel that's either surrounded by cancer cells at the top or T-cells at the bottom in two different parts of the tissue. One final capability of Xenium that we wanted to highlight is that Xenium leaves the tissue morphology intact so that at the end of the run you can take that exact tissue section that you've run on Xenium and run a whole host of different characterizations. This really means that Xenium's compatible with standards in pathology, and we think this will be one key important part of bridging that world of pathology with genomics that we've been talking about. You can do a number of things at the end of the Xenium run, including H&E staining. This is something we do on nearly every single run that we do in R&D in 10x when we run Xenium. You can also do things like protein multiomics immediately on the introduction of the Xenium platform with immunofluorescence, where you're doing any set of antibodies that you've already been able to do immunofluorescence on and do that on the exact same tissue section. That's been highlighted in the preprint that we've just released and I'm about to talk about. Finally, you can even do things like run Visium on the exact same tissue section, again, because the CytAssist allows you to take a tissue section that's on a standard glass slide and apply the Visium assay to it. Now I just wanted to go through a few of the results that we put out in the preprint that Ben and I have mentioned. In this preprint, we took a block of human breast cancer FFPE, and our team took adjacent curls and tissue sections and ran them through 3 products on each of the 3 platforms. First, they ran a set of curls on the Chromium Single Cell Gene Expression Flex product using the FFPE protocols that Ben mentioned. Then they took adjacent tissue sections and ran them on Visium using the CytAssist and Visium FFPE v2, and using Xenium with the development version of the breast panel. First, what do you get in Chromium? Because of the power of the ability to actually do single cell on dissociated FFPE for the first time, you can now take this human cancer block and fully understand all the cell types and characterize them deeply in this sample. What we see here is the clustering and the representation of 17 different clusters, which represent a wide range of both stromal, infiltrating immune, and cancer cells. In particular, there are three different subclusters, major subclusters of the cancer cells, two different subclusters of ductal carcinoma in situ, and one of invasive cancer. Chromium really allows you to tell what's in the sample and to go and deeply characterize that, Visium and Xenium allow you to take those cells and understand how they're localized with respect to each other and how the tissue architecture looks like. Here we see on the left the pre-CytAssist H&E that customers can take on every single CytAssist run, and on the right, the spatial localization of key marker genes that are markers for those clusters in Chromium that I described on the slide earlier. Now, Xenium allows you, in a targeted fashion, to go at the highest resolution possible, and in particular, to unambiguously map transcripts back to individual cells and start classifying individual cells by their nature in, fully in their spatial context. Here we see that on the left, where we've colored all the different cells by those different clusters that were present in the Chromium data, and you can see both the tremendous heterogeneity, but also there's real structure in the data and real interesting locations. Because you have both the molecular data and the H&E side by side, you can start going back and forth between these. Here we've shown selecting regions of interest driven by the molecular data, where you've selected 3 regions of interest, 2 for the 2 subsets of ductal carcinoma and 1 for the area of invasive cancer, and then being able to look at the morphology for those 3 regions. You can also imagine people going in the other direction, starting with the H&E, selecting regions of interest, and doing differential expression on that exact tissue section. I wanted to highlight 1 result that we're really excited about and we think shows the power of single-cell and spatial analysis that's really lacking in some of the standards in bulk. Here we're showing a really classic way of typing breast cancers by 3 markers: HER2, estrogen receptor, and progesterone receptor. When we purchased this block, it was labeled as double positive, meaning it was HER2 and estrogen receptor positive, but progesterone receptor negative. When we ran it on Xenium, we found that that was actually largely the case, so we agreed with that, except there was a very tiny cluster of cells that were actually triple positive within this tumor. What we found subsequent to this study is this is not an uncommon occurrence. We think this kind of heterogeneity, where there's a bulk characterization that is largely right, but there's very subtle details within the tissue that will likely have huge implications for the course of treatment, is going to be the rule and not the exception.Because we were able to find this tiny cluster of cells in Xenium, and we had run adjacent tissue sections on Visium, we were now able to take those Visium data and use the correspondence to find that same cluster of cells present in the Visium data as well. Now because Visium is whole transcriptome, we were able to go very deep on that cluster of cells and really characterize it and ask what is different about the overall gene expression and not just the targeted set of markers about those cells. We're just showing some of the most differentially expressed genes here. Hopefully that gives you a little bit of a taste of why we're really excited about the power of combining all three of these platforms. It's something that's really early days, but we're hearing from the earliest adopters of Xenium that they're excited about this too. One of the key areas for putting all these platforms together is going to be the software, of course. With that, I'm going to let Alex talk about that as well as a lot of other exciting parts of our software, and I'll introduce Alex Wong. Thank you. All right. Thanks, Mike. I'm Alex. I'm head of our software, product and infrastructure groups, and I'm gonna be talking to you about software at 10x. Our high order bit perspective on software is its power as an accelerant. In our view, that means accelerating not just our customers, with software as part of our products, but also accelerating ourselves, with software and computation powering our product development process. Also, we'll talk about the marriage of those two vectors of acceleration as well. Now, when I was joining 10x around eight years ago, I asked Mike to send me an Illumina NGS dataset to look at, because I wanted to really understand and feel what the status quo, experience of analyzing data was like, at that time. For software to analyze this file, he points me to a source code repository, okay? I clone that repo, of course, the code doesn't even actually compile. I'm sitting there doing stuff like fixing a make file, tracking down a bunch of library dependencies, and doing all sorts of gymnastics just to get the software to build. For a software engineer, while this is annoying, I can deal with it, but there are two really important questions here. The first is, why doesn't it just work? The second question is, even if it did work, are we seriously asking researchers, I mean, immunologists, oncologists, neuroscientists, to compile their own software just to be able to do their jobs? Now, my dad is a structural engineer, and I think about how absurd it would be if he had to compile AutoCAD to do his job or if all of you guys had to compile and debug Excel, to do your jobs. There was a Harvard study earlier this year that found that the vast majority of code that's bundled with research, published research, and this is across all fields, not just life sciences, simply failed to execute. So the quality of software is really so essential to the speed of research because it not only governs how fast our customers move, but it also governs how fast our internal research and development moves as well. When you think about the cumulative effort and investment in engineering that's gone into something like making mobile games easy to install on your phone, you would think that maybe we can invest even a fraction of that effort into making software easier to use for people who are literally trying to cure cancer. With that thought as a backdrop, we made a conscious decision at the very beginning of the company to make significant investments in world-class software and computational biology. We think of ourselves as an applications company, which means that our product is actually scientific insights and value, and not just chemistry, hardware, and software. That means that we don't fully deliver on that value until we incorporate data analysis. We think of that as being the essential last mile of our products. It's an integral part of a full vertical stack of capabilities. Bridging that last mile becomes even more critical when you think about the nature of the products that we build. As you saw from Ben and Mike, we don't make incremental products. We enable completely new capabilities, so by definition, our products tend to introduce new and novel data types, either categorically or by virtue of scale. Typically, existing software and analysis methods just can't handle the data that's generated by our products when we first introduce them, which makes it incumbent upon us to not just toss a few MATLAB or R scripts over the wall and hope that our customers can fend for themselves, but instead to build and ship scientifically sound, high quality, production quality, easy to use software so that our customers can go immediately from raw data to biological answers. Now, having that software available day and date with each of our product launches, has the effect of shifting the adoption curve of our products to the left, making that curve steeper and also raising the ceiling for those curves. That speed up might be 6, 12, or 18 months, however long it would take for the community normally to develop tools in the absence of that software. Software also expands our user base by helping us penetrate faster into a larger variety of customer types. It accelerates the utilization of our products over time, and reliable and scalable software that can actually handle large datasets, lets customers plan and execute larger, more ambitious experimental designs. All of these factors together, we think just sort of generally increase the area under that curve, which ultimately represents accumulated revenue over time. This is what Serge means when he talks about removing bottlenecks and barriers to adoption. Finally, we feel that... Sorry, got a little glitch here with the clicker. There we go. Finally, we feel that strong software and support really completes and strengthens the overall customer experience as well, which helps to build loyalty with our customer base. It's really great to see our customers reflecting that sentiment over time. Now, our software spans the full workflow in each of our application areas, and I'll take you through each of these pieces. Starting with core analysis, which is commonly referred to as our analysis pipelines. What these do is that they take you from raw sequencing reads to mapping to the transcriptome, unpacking barcodes, and ultimately giving you individual cell or spatially mapped analyte profiles and much, much more. We've also designed our software to be scalable and adaptive to the full diversity of customer compute environments, ranging from individual machines scaling to high-performance compute clusters as well as public clouds. This is something that we've done since the start of the company for every single one of our products, shifting all of those adoption curves to the left for each product and across the entire range of analytes and modalities and use cases that we enable with over 14 different software products and over 60 major software releases. Going beyond core analysis, we branch out into downstream paths that our customers can take, and we enable that in multiple ways. The first way is with rich, first-party, intuitive visualization software. For our single-cell and spatial products, that's our Loupe family of browsers. For in situ, this is Xenium Explorer, which just launched this week. Second, we also do this by establishing de facto standards for data structures and file formats for these new data types. We work actively with the academic and research communities to make sure that when they're innovating on new analysis methods and approaches, that our data types are always easy to import and ingest into their software as well. Starting with our Xenium platform, our scope now includes on-instrument analysis as well. Much is made about just sort of the sheer amount of data, raw data that's generated by in situ and the challenges of managing that. And we can definitely attest to that. This is exactly why we've built all of the computational power and algorithms that are required to handle that directly into the instrument itself, so that when your run is complete, you've already got biologically relevant results that come right off of the instrument. Cloud is gonna be a great additional piece for longer-term storage and sharing and collaboration, but it's not a required part of making the instrument run, which ultimately gives our customers more flexibility in how they deploy Xenium. The introduction of Xenium also highlights the power of using our platforms together, which Mike and Ben both talked about. The software is gonna play a key role in letting customers use our discovery platforms like Visium and Chromium to identify targets of interest and then drill down further with Xenium. That's what software does to accelerate our customers. The other side of that coin is what software does to accelerate us internally by us consciously focusing on building the machine that builds the machine. For us, the connectivity between all of our different technical disciplines within the company is really at the core of our strength and a big factor in our success, especially the connectivity between assay development and computational biology. Because that's so important to us, we augment that with a lot of investment in software infrastructure and tooling that we build internally to give us a faster time to insight and also reduce our iteration cycle time in our R&D cycle. On top of that, much of the tooling was intentionally designed from the beginning to ship as part of our products that go out to our customers. When we and our customers end up using the same software, that means that we're evaluating the performance of our products in the same way that our customers do, which keeps us closely aligned with their goals. This universal tooling also helps us in ways that you might not be thinking about, for example, in simplifying how we diagnose or root cause issues in the field, which, again, improves the customer experience and speeds up our development. Finally, to reiterate a theme that you've heard elsewhere, and maybe most importantly, by deeply engaging with downstream analysis, this gives us much better insight into not just how our customers are using our products, but also how they're innovating around our products, which gives us the ability to predict the future better. The third piece that I'll talk about is cloud, which started, again, for us, as internal acceleration. From the time that we got started, our on-premises computational infrastructure had grown by two orders of magnitude, in 2015, we started looking for ways to augment that with public cloud, elastic resources. We found, though, that existing software frameworks that existed, to manage high-performance compute was just not adequate for developing production software and were also not originally designed to work with the cloud. We invested in developing our own infrastructure to bring us into the cloud in 2016, and since then, that hybrid infrastructure has really powered our research and development with tens of petabytes of data managed. As far as compute, which you would normally denominate in terms of core hours of compute, today we now measure at 10x in units of core millennia. After several of those core millennia of hardening the platform with internal use, we decided to make that same infrastructure and technology available directly to our customers, and we launched 10x Cloud in 2021. This platform enables our customers to run our core analysis pipelines typically about five times faster than they would on their own, and we make this capability available to all of our customers at no additional cost, for every sample that they purchase. Since launch, we've seen very strong sustained growth in adoption and utilization across a wide variety of use cases, cloud is also an integral piece of supporting our instrument fleet as well as the Xenium platform going forward. Now, as we look to 2023 and beyond, we'll continue to drive this approach, which we've held as a guiding principle since the beginning of the company, which is accelerating both ourselves and our customers in tandem with software. I wanna note here that iteration speed compounds over time, which is why we think it's so important for us to keep focusing on this. We're also gonna push forward on how our customers collaborate with each other because the pace of data analysis is really both a software and a human problem. Finally, we're really excited about opening up the future for our Xenium platform with the capabilities that we have. We think that we're very well set up with the combination of software engineering, computational biology, image processing, machine learning, and combining that with our strengths in molecular assays. With that, we talked a lot about all the deep technology that we've developed. Another key part of accelerating into the future is being able to protect the value of those innovations. With that, it's my pleasure to introduce Eric Whitaker, our chief legal officer, and I promise you guys, he's gonna make intellectual property more exciting than you ever thought possible. Excellent. Some but not all. By way of introduction, I've done this for more than 2 decades. I've been a chief legal officer at different startup companies, mainly in the consumer electronics, the electric vehicle space. In 2017, I met Serge convinced me that the action was in life sciences. I did not have a life sciences pedigree at the time, he convinced me that there was a wave of innovation coming, it was a chance to work on hard problems, Serge delivered in every single way. Let's talk about another lens of innovation, which is IP. If there was an audible gasp when I showed this slide, there ought to have been, because this is an incredible quantification of the things that 10x has done in terms of patents. We've got 700 patents that have been issued or allowed in 10 years of existence. I really challenge you to show me another company that's had that velocity of innovation on the hard patent side. This is an incredible level of innovation. We've got over 1,000 additional patents pending on a worldwide basis. If you break this down on a product-by-product alignment, you look at Chromium, where we have over 900 patents, Visium with over 500 patents, and then this is truly remarkable, a product that went out for the first time this week, Xenium. We've got the Xenium already before shipment. It's an absolutely incredible level of innovation that takes place here. The reason for that, someone asked me the other day, "How does 10x think about intellectual property? Give me one word." I said that one word is intention. This company thinks deeply about intellectual property. They think deeply about intellectual property at every step of the product development process. More than that, when we're looking at new products and thinking about new innovation, we survey the landscape to make sure that we own or control or have licensed the necessary IP to build the very best products with the very best features for our customers. That's something that I don't think other companies in this space have done. We've got a really formidable IP portfolio overall. It reflects the amount of creativity and innovation that has come out of the R&D teams you've been hearing about this morning, that Mike and Ben talked about and Alex talked about with the product development. We were surprised and honored earlier this year to get the LexisNexis Top 100 Global Innovators. This is a geeky award for the IP folks amongst us, but it's an award that goes out to the top 100 companies in the world based on patent quality, and they try to identify in an unbiased manner which innovators have outperformed their peers. They have two companies that were given this award in the biotechnology space, us and Illumina. We didn't expect it, we didn't know about it, but I do think it's reflective of the incredible innovation that's been happening here. Let's turn now from the overall numbers and look at this on a product-by-product basis. Starting with Chromium, we've got 900 patents issued or allowed pending in the Chromium area. Fundamentally, at the platform level, that intellectual property enables single-cell resolution of biological molecules at a massive scale. This is what the company first was created upon, the foundational product. It covers DNA barcoding for tracking an analyte cell of origin, but also it also tracks UMIs, unique molecular identifiers, for ascertaining the origin of an analyte to allow true molecular quantitation, especially important for transcriptomics and other applications like that. I also wanna make clear that these innovations are agnostic as to the techniques used. We obviously use a droplet technique, but whether you use droplet or microwell or split pool, the IP applies equally to all of those things. If you look at the assay side, and this is really critical because it's our heritage, it's where we come from, it's where we've catalyzed a new industry really. The assays enable genome-wide readouts of biological information. They cover reverse transcription-based and RNA-templated ligation-based assays for probing RNA in fixed, fresh, and FFPE tissues. There's a ATAC-seq IP for probing the underlying structure of DNA as it is folded into chromatin. There are Feature Barcode assays for probing single-cell proteins and other non-nucleic analytes. Assays for interrogating immune cells, that's the original VDJ product, the five-prime product that was so important. Also now the BEAM product for mapping the antigen specificity of those immune cells. It's also critical, it's really critical as we go forward and look at product features and differentiation, that we assay multiple classes of analytes simultaneously. There's early and broad intellectual property coverage of multi-omic assays for a wide variety of analyte classes. As a whole, we think this creates a real differentiation for our products and something that our competitors have not invested equally in. Frankly, it's because they haven't had the innovation that we've had over the years. If you look at the spatial side, again, 540 patents issued, allowed, pending in this area alone. They cover core aspects of spatial arrays and barcoding methods for high-throughput spatial readouts used in the Visium products. They also cover different classes of high-resolution spatial arrays and the methods used to manufacture them, including photolithography fabrication techniques, formation, and decoding of random spatial arrays. On the assay side, you'll see a lot of parallelism to what we've done on the single-cell side. We've got assays that cover a wide variety of spatial interrogation of biological molecules, including fresh frozen, FFPE, RTL spatial biochemistries, including spatial protein and Feature Barcoding workflows, and spatial epigenetics, including ATAC-seq, immune profiling, and again, multi-omics assays. These are going to be the key ways that one has to engage in to offer compelling product with best-in-class features. On the Xenium side, again, the product shipping just today, just this week, we went out before we introduced this product, and we surveyed the landscape to say what IP is necessary to build a best-in-class product. Mike referred to this, not just today, but if we look out over the next decade, what intellectual property do we need to own or control to make sure we have a freedom to operate to offer the best features in the best product? This is highly differentiating. Companies do not do this in the level of detail that we do it, and it's resulted in a formidable IP estate already for a product that's just launching. We built off the ReadCoor CartaNA IP based on early in situ work from pioneers Mats Nilsson, George Church. This IP goes back more than a decade in some cases. Before anyone was really thinking in situ was more, to use Serge's words, than a science experiment, right? Before it was science fiction, these folks were thinking about what would be necessary to create incredible in situ products. We own, control, license that IP. The platform relates to core technologies for in situ detection of biological molecules. It covers temporal barcoding, detection of biological molecules in situ, a critical paradigm used in high-flexi FISH and in situ sequencing applications. Also, we cover the paradigm of embedding molecules in a 3D matrix and clearing away extraneous molecules for improved signal for higher resolution and sensitivity. The portfolio has broad and diverse coverage of critical instrumentation and hardware necessary for high-throughput in situ analysis. On the assays, we've covered rolling circle amplification for single-molecule RNA detection, the improved chemistries necessary for in situ SIPP detection, and combining in situ and spatial array analysis on a single sample for tissue sections. We've also got assays for additional analytes for open chromatin conformation, DNA methylation, et cetera. This is a critical differentiating feature in the Xenium product because the innovation has happened here and in the companies that we acquired to accelerate the technology development. Getting back to the numbers, and I'll leave you on this, the innovation engine is really, truly reflected in this level of IP. If you think about we have 225 patents issued alone this year in 2022. That really, if you think about how the patent office works, it takes 2 to 3 to 4 years to get a patent through the patent office. That work is the effect of what happened if you look at the right side of the slide in 2019 and 2018. As you can see that curve on the right side increasing, you can think about how this portfolio is gonna develop and how it will create additional protections for the innovations that we've made. We've had over 567 patents applied for in 2022. That's almost 2 a day, every day of the week. That's the velocity at which 10x is creating innovations and investing to protect those innovations. We mentioned earlier, we've invested almost $1 billion in research and development. We've equally invested in a patent portfolio to protect that research and development, to differentiate the products. There are companies that are free-riding on this, and we will defend our intellectual property against the companies who infringe our technology without compensation. Who are free-riding on the developments that we have created. It's our general policy not to out-license our patents, we've said this many times, but to protect our sole right to own and practice them. The reason all that's important at the end of the day, from an economic point of view, from a business point of view, is that we recognize litigation outcomes are not always predictable. We also strongly believe that by protecting these inventions, we will create the incentives that allow us to reinvest. This company is about reinvesting, about the next generation of technology. As we reinvest in the next generation of products, those are ultimately going to lead to the development of new tools that will fundamentally change the way we think about biology. This is the story in the IP side. It's the story on the R&D side, the commercial side, the HR side. The company is all aligned around these goals, and I hope that some of this data shows you that we have invested very seriously in it, and we are all the way behind it. Now let's take another look at innovation because there are many ways to measure this. At this point, for you to hear directly on how we push science forward, in this case, from Sam Behjati from the Wellcome Sanger Institute, moderated by Abby Cutchen from 10x Marketing. Let's go ahead and play the video, and you can hear his thoughts on our innovation. Dr. Sam Behjati is a pediatric oncologist at Addenbrooke's Hospital in Cambridge and a group leader at Wellcome Sanger Institute in Cambridge. His research sits at the interface of cancer genomics and single-cell transcriptomics and seeks to unravel the identity and origin of cancer cells with a particular focus in childhood cancer. Sam, I thought we could get started with you sharing an overview of some of the research themes your lab focuses on. Thank you, Abby, and thanks so much for having me. What I'm interested in is where childhood cancer comes from. The reason for that is that if we understand where something comes from, we might be able to do something about it. In the context of childhood cancer, that's really about the fetal, the embryonic origins of childhood cancer. What we need to understand is what is that fetalness? Is there something very particular about it that is different from sort of postnatal from adult cells that we could perhaps target with drugs. That's great. Thank you. Your lab was one of the earliest adopters, I think, of single-cell technologies. I'm curious, when you think about, you know, the world using single-cell and spatial tools versus what was previously possible with other legacy tools like flow cytometry or bulk technologies, can you share some examples of questions that you're now able to address that, you know, potentially weren't possible before? Cancer at its core is caused by mutations. In that sense, it is a genetic question that we have about cancer. What we then need to understand is what mutations do. We talk about function and phenotype, and that really at its core is a transcriptional question. Now, before single-cell RNA sequencing, we hadn't actually been able to address that question in any meaningful way. The problem, say, the way we would have done it is we would have taken a chunk of, you know, tumor tissue. We would have done bulk RNA sequencing, where you get a few cells out and look for your favorite markers by flow. The problem there is you can't actually tell what the cancer cell does because there are lots of different cell types in a chunk of tumor. For me, cancer transcription was not a thing that one could possibly study before the advent of single-cell RNA sequencing. What is so exciting to me about single-cell RNA sequencing is that actually now I can study the thing I had always wanted to study but couldn't do before because of our inability to interrogate single cells. That's wonderful. Maybe as you think about your research now or in the future, starting to incorporate spatial technologies, how do you imagine that changing the level of questions that you can ask or layer in? Cancers like every tissue, they are essentially organized as organs, and they're quite successful organs. They're not just sort of random, randomly stitched together cells. The cells are put together in a certain order to achieve that awful function of cancer spread and proliferation. What spatial technologies enable us to do is to understand cancer as an organ, as it were, and that then tells us fundamental things about cancer. I think more importantly, it then highlights this whole set of new vulnerabilities that we might be able to target. That it's really quite beautiful and powerful, this interplay of using our single-cell technologies to define the different cell types of the cancer and then go back into that spatial space and see how is the cancer actually organized as an organ. It's a completely... I mean, it's a very, very novel level of understanding. You sort of always tried a little bit with, you know, this stain and that stain. This is a different level. This is 20,000 readouts per cell, it is just phenomenal. That's wonderful. We're really excited about the deployment of those technologies together too. I wanted to maybe dig into that a little bit more. You know, we see our 3 core technology platforms, single-cell, spatial and in situ as being incredibly complementary and synergistic, especially now that, for example, you can perform all 3 platforms on the same FFPE block from the same patient. You know, we've engineered a lot of elegant kind of cross compatibility between the assays from probe design to shared chemistry, to similar data output. Can you share a bit more about, you know, how you use single-cell and spatial integration? Then I think also, you know, your perspective as a clinician scientist working with clinical samples, can you share more about the impact of enabling all 3 technology platforms on FFPE tissue? Yeah, absolutely. In terms of discovery science, the beauty of having those single-cell technologies or single-nuclear technologies and spatial at the same time is that we generate these data sets which then cross-validate each other, and we can generate hypotheses from one dimension and then go into the other and vice versa. That is just a really powerful way of doing discovery science. As a clinician, you know, the excitement of FFPE is difficult to overstate because really what I want to understand is, and this is where all my research is heading towards, how can I use single-cell transcriptomics to treat my patients better? In order to do that, I need to be able to correlate the biology, the single-cell signals with clinical outcome. The currency for that is FFPE tissue. no clinical trial will amass frozen tissue in any meaningful way. I can do it on a small scale for my research, but that is not the key material. What I need are really very large cohorts, I'm talking, you know, 1, 2, 3, 4 thousand patients perhaps with clinical outcome. the only thing I have available to study the biology of disease is FFPE tissue. clearly, the excitement there is that we have got these technologies available now and we can interrogate FFPE, and that's just awesome. I mean, that's it's gonna be a game changer over the next few years. Yes. I think you read my mind in terms of questions that I wanted to go to next, maybe we could talk a little bit more about scale. You mentioned scale in the context of retrospective studies with FFPE tissue. We think a lot about scale around our tools. You know, you can think about scale in multiple dimensions from population scale studies to deep multi-region, multi-tissue profiling in individuals. Can you share a bit more about your outlook for single-cell and spatial tools being deployed at scale? What are the biggest enablers, and where do you see the technology driving the greatest impact? The biggest enabler, the single most important thing is ease of technology. You know, at Wellcome Sanger Institute we can manage any technology. That's really no problem. We've got technical staff who can. They can just about do anything. That's not the issue. The issue is to have a kit or an equipment that anybody can use. The way that I do my research is that I actually put my Chromium controllers into the clinical laboratories of my collaborators. Whatever technology one has, it has to be usable by not the layperson, but by, you know, just a normal expert, not some super specialized single-cell transcriptomic technical whiz, the sort of people that we have in our own confines. That I think is the key thing. The key thing for enabling this clinical science that I'm envisioning is ease of technology, and then the other is throughput. You know, it's of no use to anybody if we do these things and it takes like one day or one week to process a sample. We need to be able to do lots of things in parallel at a rapid speed. Staying on the idea of clinical applications enabled by single-cell and spatial, you know, I think we're in this world now where therapies are more complex with immunotherapies. You know, combination strategies are becoming the norm. In light of this kind of complex therapeutic backdrop, where do you see single-cell and spatial and, you know, these high resolution tools having the greatest impact in terms of, you know, ultimately in years to come impacting patient care? That's an interesting question, Abby. I think it really comes down to the disease and the question that you have. In my head, if you think about a hospital, where do we interrogate cells? It's basically the clinical flow cytometer. You could envision a future where all of leukemias will be interrogated by single cell RNA sequencing instead of a flow profile. That's one dimension. The other is clinical cytology. Clinical cytology are the people who look at cells spun down from a urine sample or CSF sample. It's those sort of people where they sort of just look at it and do a couple of stains. You know, that is completely okay. You can just see that these anything that you yourself could be replaced by single-cell RNA sequencing and probably will make more fundamental, meaningful clinical statements. As for tissue, it is very clear that, I mean, you should think about the workflow at the moment. We get like a little piece of tumor, and then we do this stain, and then, oh yeah, let's do this other stain, and then you waste another day, and then this panel, and then that panel. You know, hopefully in the future we can just say, "Forget all of that. Let's just do one single spatial transcript readout," and then we can ask any question we have about any marker, and there's not gonna be this sort of uncertainty. Have we captured everything? Should I be doing this? Should I be doing that? That's really, I think, that would be the most beautiful future because, what single-cell RNA sequencing does and what spatial transcriptomics tools can do is to provide us a comprehensive readout of everything that we want to know about cells and tissue. Yes. No, I agree. That's a really profound observation. I think the power of these multi-omic high dimensional technologies is that you can really consolidate a lot of these serial assays into a single multi-omic readout in a very tissue-sparing manner. When we think about, you know, maximizing the learnings from these really precious patient samples, I think it's, you know, an incredible opportunity. Sam, maybe I'll leave us with an aspirational question, which is, in the next five years, where do you hope this field will be? Where do you see single cell and spatial approaches having the biggest impact in shaping the field? From a cancer point of view, my hope is that from a basic science point of view, that in the next 5 years, we will have an understanding of the different fundamental cancer cell types, as it were. From a clinical point of view, I hope that someone will, in their disease, demonstrate that actually this is meaningful for patient outcome. The bit that I'm interested in at the moment is I'm doing a large cell study in childhood leukemia, where we are doing the experiment of taking every single leukemia sample in the hospital. Our 10x is right next to a clinical flow cytometer. We flow the sample for clinical purposes, and then we just, you know, suspend it on our 10x. Run it at the same time. What I would hope is that in five years time, Abby, you will read a paper from me to say that single-cell RNA sequencing has improved our ability to treat children with leukemia. I don't think it's, it is beyond the realms of possibility. I actually think it may well happen. That's so wonderful. Hopefully sooner than in five years. Thank you so much, Sam, for all of those thoughtful responses and for spending the morning with us. We really appreciate it. Thank you, Abby. Okay. We will now move into our first Q&A session of the day. We ask that you please keep your questions to one question and one follow-up. For those of you in person, please raise your hand if you would like to ask a question, and for those of you watching on the webcast, you can submit a question via the Ask a Question tab to the right of the video player. To start, we'd like to take our first question in the room. Let's go with Dan at Stifel. Yeah. Thanks for the presentation today. Wanted to just talk a little bit about your point on removing bottlenecks to the single-cell workflow. If I think back on the history of the industry here, you guys have basically allowed it to happen by scaling on cost and making these large experiments possible. If you do talk to researchers, they will tell you that the single-cell kit is still the largest part of the expenditure when it comes to the experiment. How do you see costs coming down further from here? Is it CellPlex? Is it reagent pricing schemes akin to what Illumina is doing with elasticity? How do you see cost from here, and how do you make bigger experiments more affordable? Maybe I'll start. Yeah, a couple of things. I mean, over the course of the past couple of years, we released a number of products, starting with the Chromium X and the HT capability that helped to drive the cost per cell down. That's 1 axis that we're going to be pursuing. That's something that Ben emphasized as well. We have a long roadmap to keep driving those costs down. That's 1 element of some of the pricing equation. Now, for a lot of people, of course, they focus on price per sample. That's been important on there as well. We have introduced products to help there. CellPlex is 1, as it, you know, it works in some application. It is an extra step in the process that makes it somewhat constrained in terms of where it can be used. It's not a universal solution, but we do see usage in some customers that is pretty intense. There's also some customers that are particularly sophisticated that can do multiplexing using their own methods, especially using genetic multiplexing. That's something that has been increasing quite a bit. When you have a large cohort of samples from multiple individuals, you can actually combine those samples together. That requires some amount of sophistication and also only applicable to some types of experiments, but that's also been driving the cost per sample down over the course, especially over the past year or so. Of course, with the Flex Kit, which we've been talking about, that actually has multiplexing capability built in. We are really optimistic about what it can do for customers now going forward, because now it's a straightforward, it's as easy to multiplex as not. If you have multiple samples, you can actually drive the cost per sample down pretty far into a few hundred dollars a sample all in. I think that's going to be a tremendous enabler. Now, it's still early. The kit has only been launched recently, people are going through their similar paces of, you know, piloting and comparing the data, but we're hearing really good feedback. I think that's going to be at least in the near term, a big driver of the pricing equation. I would add to that, if I can. Yeah. I mean, the cost of logistics is significant as well. The ability to fix your sample at the point of collection and not have them rush shipped to wherever you need to be or have a courier take them there, is a significant cost that we've heard about from places like, and people like Sam, for example, where they're planning these larger studies. I think also on the technical detail, our assays have typically been we load cells, and we don't use all of our gems, for example, and people they push the limits on loading. You pay the price on extra sequencing because you have to sequence through the doublets. With our FLEX assay, we have the ability to load more cells per gem, and then we can recover those multiplex in each gem, which not only recovers more data, but it makes full use of the amount of money that you're spending on your sequencing. Okay. Just maybe as a follow-up, in order to sort of think about this as being a technology that's used industry-wide, to what extent do you think data standardization really needs to improve over the next couple of years? I mean, to Sam's point, if he's trying to correlate instrument data or outputs with clinical outcomes, it seems difficult to do if you've just got these platforms and technologies all sort of giving their interpretation of what a biological situation might be. How much do you see data standardization as being critical to the next 1, 2, 3 years of just advancement of the platforms and technologies? Thanks. Mike, do you want to? Yeah. I'll say that I think it certainly is important. As I mentioned in my talk, a lot of the data file formats and the data structures that are currently used are essentially de facto standards that we've set. To the extent that there are other technologies that are being blended in together, I think that's gonna be part of our software roadmap to make sure that different modalities are able to be integrated and aggregated together. Of course, our primary focus is integration of data sets that come from 10x first, and we'll look at other data sets as sort of customer demand and customer interest in other areas folds together. You're absolutely right. Scale comes with standardization, and that's something that's pretty top of mind for us. Maybe we'll go to Matt at Goldman Sachs. Great. Thanks for taking my questions and thanks for putting this on today. It's kind of similar to Dan's question about software, and I'm just wondering if there's a difference, likely a difference in standardization, but difference in the ability to understand and the compatibility across different customer segments. Meaning, do you get more traction with academic given the software compatibility relative to biopharma, relative to clinical? Are there bottlenecks because of the compatibility of analytics in each end market, or is it relatively you're solving similar problems for each end market? Yeah. I think that really, you think about it as a path from the raw data all the way out to further downstream, where there's a greater variety of different paths that customers will take. We start at the root, which is the raw sequencing data, or in the C2 case, this is handled on board the instrument. As you go out, there's a greater variety of different paths that customers wanna take. Our approach is, at the root, we start by making sure that those initial steps that are common to all the different workflows are well taken care of by our software. As we move out, we start to incorporate more of the third-party solutions, particularly new methods and analysis approaches that are coming from the academic and research community. There's certainly also different segments of customers and different needs that they have. We might have super KOLs who are very sophisticated, have bioinformatics expertise. A lot of them continue to use Cell Ranger because that first step is essentially de facto standard. Some of them are exploring other methods, but then they'll take it on their own with their own expertise. We also have the other end of the spectrum, where we've got customers who don't have a lot of bioinformatics expertise, or they're looking at a case where they can't find a collaborator. In those cases, our first party, easy-to-use out-of-the-box tools either help those wet lab folks answer their questions directly or make it easier for them to collaborate with bioinformaticians and make more effective use of the pool of resources that are out there. Got it. Just a quick follow-up, just on CytAssist, it sounds like that's gonna be a key driver for spatial going forward. Obviously, it's very early on in the launch, but, you know, based on feedback you've received, you know, I guess if you could talk about like what portion of CytAssist are you effectively utilizing at this point in terms of what else can you drive through there and what kind of, you know, driver can CytAssist be for that overall spatial and how unique is that in, within the competitive landscape? I mean, I think, again, it's really early days, but we're really excited because, you know, fundamentally, we took at a technical level, we took a lot of what we were laying on Visium, which is you have to do all the molecular biology and all the sectioning on that same surface, and now we've completely teased that apart and we said, "You can now do everything tissue-wise you've been doing in your completely standard way. You don't even have to teach the people who do the tissue sectioning anything about Visium. You can go back in a lot of cases into the archive, and now you can apply Visium in a, in a central place where the genomics expertise is there." I think it's got some real parallels to Flex in that sense. And we think there's a lot of potential. That's why we said CytAssist is the future of Visium. We think it makes it easier to develop new capabilities. We think it makes it more robust and higher performance in customers' hands, and we think there's opportunities in the future to really expand on that. I'd say we're really excited about it, but it's still early days. Why don't we take our next one from Mike at Bank of America? Thanks. Thanks for the presentation, guys. That was great. I wanna start with CytAssist and just Visium in general. You know, Xenium versus Visium, Serge, I think you and your remarks and some of the other speakers, you really talk about, you know, all three platforms, all three verticals and how they can be used across each other. There is also a scenario where you could see cannibalization between Xenium and Visium, given, you know, just given the spatial nature of the data you're getting and you're looking at whole transcriptome versus targeted and different level of resolution. Still, can you just give us a little bit more on your thoughts on that? Sort of what are your anticipations for how customers would use them, you know, one after the other concurrently, or whether there would be cannibalization between the two? Just expectations there. Yeah. Maybe I'll take that one. First, at the highest level, I think, right now we see them as highly complementary. I think they're both very early in their technology development, and what we found with these different technologies is sometimes it's hard to predict years ahead of exactly how technologies are gonna develop. Some of the real, nitty-gritty details can really matter about how things are gonna go. It is possible that, you know, the boundaries of what you can do with different platforms is gonna shift over the years, and that's part of our strategy here, that we have both in-house. We're gonna push both technologies as far as they will go, and we're sort of agnostic if one does better for some set of applications than another, or if they become sort of competitive with each other in that, you know, technical application area, that's fine because we'll push the better solution. We don't just have a hammer that we're pushing. When you take a step back and look at the core capabilities of a Visium-like approach, where you barcode data and then run next-generation sequencing or a Xenium-like approach, there are right now pretty big categorical differences in... things you can do. For instance, a lot of the types of assays you can pretty readily do on Visium, and we've seen in some of these customer technology demonstrations would be extremely challenging to do in an In Situ format, and also some of the throughput that you get from next-generation sequencing when you're doing really large-scale molecular collection, I think is pretty unique to Visium. Obviously, Xenium is extremely high resolution, you know, tens of nanometers, and it's also substantially higher sensitivity per gene right now. They're kind of sitting in different spaces, and we'll see how it plays out. Okay. Just a quick follow-up, sticking with the CytAssist actually, there was a lot of focus on sort of what's new and upcoming as being exclusively available on CytAssist, so pretty clear you see that as sort of being, you know, the future of the Visium platform, like you said. Any, you know, given you guys gave that 100 shipments number last quarter on 3Q, any early thoughts on the CytAssist install base potential over time? You know, should we use the Chromium install base as a template for that, as a way to think about it? Just as a clarification on that, you said $40 million in Visium revenue trail in 12 months. Is that inclusive of CytAssist? Yeah. It is inclusive of CytAssist. In terms of the install base, we certainly the early signs are, give us grounds to be optimistic, but it is also really early days, and every time you launch a new platform that you've talked about previously, there's some amount of pent-up demand, so we want to be cognizant of that as we see it progressing. You know, the instrument itself is really enabling. You know, we are personally, we've been excited about it as we've been developing and the feedback we've gotten from customers, once A, like they kind of get the concept, they get excited, 'cause it is a really elegant solution to all the critical challenges. B, like once they get the data, that's also been really positive to see. I think there's grounds to be quite optimistic, but it's early to make a sort of precise prediction. Okay, let's go to Dan Brennan next. Great. Thank you, Dan Brennan from Cowen. I guess the first question would be on Xenium. Can you just discuss maybe the customer interest in Xenium? Are you willing to provide an order number or a backlog number? Like, how is the interest stacking up versus initial expectations? Kind of related to that, I think during the presentation you mentioned thousands of Visium customers, how do we think about the opportunity for those customers for Xenium? Would it address all of them or some of them? In terms of just pre-orders and bookings, we've never disclosed that for our products. We're not doing that now. The interest has been tremendous. Demand is not an issue at all here. Lots of interest from our customers. Certainly, a lot of our existing, whether it's single cell or spatial customers, are very interested in this platform. No, I mean, I think both the interest right now and the potential feels very broad as we sit here today. Okay. Maybe just sticking with Xenium on my follow-up. Just could you give us, you know, you had the presentation, like we didn't see maybe like a side by side and how we think about Xenium comparing to some of the peers? You highlighted throughput, you highlighted, you know, I think the capability to do RNA and protein. Just could you give us a sense on what is it launching with plex? Is it capable to do RNA and protein out of the gate? On the throughput side, you know, you had that one slide showing maybe triple the throughput of the other players. Maybe could you just speak a little bit more to that? 'Cause, you know, what we understand on throughput, it depends how you define it, and, you know, different players define it differently. Just kind of wondering on an apples-to-apples basis how you stack up there? Thanks. Sure. I think first of all, at a high level, I would just say we're in this kinda weird state right now, where a bunch of these products have not been out in the market at all, and there's a lot of like PowerPoint wars going on. We don't really know the state of some of these other technologies. We know various marketing materials. Our general approach right now is really focus on making Xenium amazing and also making the trajectory of innovation far higher than anyone else's and not, you know, worrying exact details of who's saying what on specs. Out of the gate, the initial plex levels on the first panels that we'll be offering is up to about 400 plus plex. Those are base panels of around 300 plex, the 100 plex customization on top of every run. We don't see any fundamental ceiling there from the approach that we're taking. RNA plus protein, initially, as I mentioned, I think at least from what we can tell, Xenium's gonna be relatively unique in the ability to do this immunofluorescence at the end, and that allows you to do RNA plus protein on the same exact tissue sections. We are also working on, we've talked about this, the ability to do much higher plex protein 'cause that immunofluorescence will be kind of a handful of markers. That is not out of the gate. That is something that'll be coming in the future. But that'll be on the same tissue section, not separate RNA and protein solutions, which is what some people have talked about. On throughput, you know, really the way we think about this is kind of whole tissue sections. There is kind of some muddling out there where people will talk about samples, but then they're doing small ROIs per samples. We really think about doing things like kind of 2 square centimeters per slide, which would be 4 square centimeters per Xenium run. Again, people can do lots of different things, but that would be one kind of representative run, and then being able to do about 3 runs per week on the Xenium instrument. There may be some small burn-in to that in the first few months of the platform being out, but yeah. Has been helping with the total cost of experiments, and like Ben mentioned, we've also leaned into that as well with the increasing efficiencies of our products. That's all been kind of driving in the right direction. Now, somewhat depends, like, the pricing per sample and per cell, it gets fairly complicated pretty quickly, depending on the configurations that people are using and how much they're loading into the system. The X initially enabled high throughput HD kits, which essentially increased the number of cells you can put into a single lane by 2x and also drove the price down per cell by a factor of about 3x compared to the previous platform. In that kind of like Ben mentioned, the Flex Kit actually leans into that further because it allows you to load up a lot more cells per gem. Because now, like, again, this is a technical detail, but you can multiplex a lot deeper. We still have, like, the sort of goal of $0.01 per cell, all in, including sequencing. You know, materially far away from there, but headed in that direction. Got it. That's helpful. Perhaps one for you, Mike, on that earlier comment you made about sort of being agnostic, you know, between Visium and Xenium, just responding to customer demand rather than, you know, forcing your agenda. I guess the corollary to that is as you look to, you know, frame your next year's outlook or your near-term outlook, clearly there are implications to sort of, you know, consumables-only solution coming out, being replaced perhaps by a more hardware-focused, sort of mix here, that taking time to ramp, especially on the Xenium. As you look at your sort of base case assumptions, what will that be? Sort of on a related note, perhaps for Alex, on your point, is there anything that you can do on the software side to perhaps, give you better real-time sort of visibility on runs or, throughput that can help, you know, Justin and his folks? You wanna go first? Sure. Yeah, go ahead. The first part of the question, sorry, I'm, 'cause I thought it was more about expectations. Well, no, it's more about sort of what your baseline assumption will be as you frame your, you know, outlook for next year, in terms of that Visium versus Xenium sort of, you know, preference for customers. I mean, I'll let the surgeon, Justin, answer the framing or outlook to you guys. I think from a product development standpoint, we are working hard and putting a lot of resources into both, and there's a lot of capabilities we're working on both sides, of course, and Chromium as well. There's a huge amount of development. We're constantly trying to balance the prioritization there but, you know, we're pretty equally split among those three platforms, outlook-wise. I mean, we're investing. Right. We're investing in all three. I mean, that has been the, you know, the sort of the story and the rationale from the beginning. They are pretty different, and both kind of are in the early, you know, stages of the trajectory and the takeoff, so very hard to kind of predict the precise breakdown, like you're saying. They're very different in terms of the sales mechanics and the adoption mechanics for these products. I'll answer the other part of that question. Our instruments started to be connected starting with the Chromium X, and that applies to CytAssist as well, and obviously Xenium and Connect. We think for us internally, certainly, that's really important for supportability of the instruments, and also giving us visibility, so we're working on driving the adoption of that connectivity. Obviously, it's the customer's option, but we think that's gonna be pretty powerful for us going forward. Thank you. We're actually at time for this Q&A session, but we'll have another one after, you know, the next presentation. We're gonna go into the break, and then we'll be back in about 10 or 15 minutes. Thanks. Please make your way back to your seats. The program will resume in three minutes. Thank you. Please welcome back to the stage, Serge Saxonov. All right. Welcome back, everyone. It was great to dedicate the morning to looking at our innovation engine and precisely why it's such a fundamental differentiator and a competitive advantage for us. Of course, like Eric mentioned before, like we all say, ultimately, we measure innovation, our innovation by its impact and by its impact and success of our customers. So, it's my pleasure to introduce our next customer perspective, Dr. Shyam Prabhakar from the Genome Institute of Singapore. One interesting thing is that Shyam was actually here recently, a few weeks ago, visiting the headquarters here, and always loved talking to him. He's a phenomenal researcher, a thought leader, and he thinks really deeply about the data. His background is in computational biology, but also has a really long-term perspective about where the field is going and how ultimately it's going to impact healthcare and medicine. I think Shyam's talk is, in particular, is an excellent example why I, you know, why we firmly believe that in the future, just about all tissue samples should be analyzed using these kinds of methods. I hope that after watching the video, you'll share more of our conviction about why this endpoint is clear and our belief in it is stronger than ever. All right. Dr. Shyam Prabhakar is a senior group leader at the Genome Institute of Singapore, or GIS, where his lab focuses on both basic science and translational studies to uncover markers and mechanisms of human diseases. Shyam heads the Singapore Single-Cell Network and the GIS spatial and single-cell genomics platform, which is an open facility for all researchers in Singapore. He co-leads many exciting global and multi-institutional initiatives, some of which I hope we'll discuss today, including the Genetic Diversity Network within the International Human Cell Atlas and HCA Asia's Immune Diversity Atlas initiative. Shyam is a longtime user of both our Chromium single-cell and Visium platforms, We're so excited to showcase his work today. Shyam, I thought we could get started by you sharing an overview of some of the research themes your lab focuses on. Sure. We use spatial and single-cell technologies to understand disease mechanisms and disease markers, disease diagnostics, primarily. Along the way, we always realize that we need new algorithms or newer technologies for generating the data, and so we try to stay cutting edge in those domains. Really, our ultimate goal is to understand disease. You know, your lab was definitely an early adopter of our single-cell technologies. Can you share what types of questions these tools have enabled you to ask in your research that perhaps weren't possible before? Sure. I think perhaps one nice example is a recent paper on colon cancer, where we used the 10x Genomics Chromium to generate a large single-cell RNA-seq data set from colon tumors and nearby normal, integrated it with 10x data from 2 other countries, 180 tissue samples, mostly tumor, but also from nearby normal colon. We used it to answer the question of, what are the cancer cell types? The first thing that popped out was actually something different. First thing that popped out was that there are 2 classes of patients. Those 2 patients had, you know, the tumors were on the different side, parts of their intestine, of the large intestine of the colon. They had different survival based on whether they had fibroblastic tumors or not. They have different mutations, different cell signatures. That was quite remarkable actually, and that we found two very, very distinct subtypes of colon cancer, which none of the previous bulk-based studies have found, and to be frank, none of the previous single-cell studies had found either. Perhaps because they were not large enough or they were looking in a different direction. That's the kind of thing we like to do, you know, take the tumors apart or take samples apart one cell at a time and say, what are the subtypes we're seeing? Can we use that to learn something about the variations or the flavors of this particular disease? That's one example, and then we're applying spatial omics technologies also to colon cancer. Very excited to have the Xenium soon. We don't have it yet, but should have it in a few weeks, I think. I think that'll be an amazing tool for seeing how these different colon cancer subtypes are distributed in space. Are they all come from one common progenitor? Do they arise in a slightly spatially variable branching pattern? Do they have different interactions with the stromal cells in the tumor? Are some cell present at the invasive edge and some present at the core? So on. Those are all the kinds of questions we can ask once we have the spatial data. Already with some preliminary spatial data, we're seeing that we can identify cell states that we did not know about before from single-cell data. That's quite surprising. That's very exciting. I think you anticipated my next question, which is around spatial, maybe we can talk a little bit more about single-cell and spatial integration since you'll have both Chromium Visium and Xenium in your lab soon. I think we see single-cell and spatial platforms as being incredibly complementary and synergistic, especially now that you can, you know, for example, perform all 3 assays on the same FFPE block from the same patient. So I'd love to hear your perspective on how you imagine using the platforms together and how them together might be, you know, greater than, you know, than by themselves. Certainly. Initially, right, the dogma in the field is that you'll discover cell types using single-cell analysis, and then you localize them in space using spatial omics. We're finding that it actually goes in both directions. You can discover cell types with spatial data and with single-cell data. The great thing, say, in 1 Xenium run, we can profile half a million or 1 million cells from 1 sample, which you'll never do in single-cell data, right? You never profile 1 million cells from 1 sample. Obviously we have power, so you have fewer genes on these cells in the Xenium than you have in, say, a Visium or single cell, but you have an incredibly large number of data points. I think we can discover cell states that we never saw before. The other thing is intra-tumor variability. Again, if you're looking at only a few thousand cells from one sample, you're not necessarily gonna find all there is to find about the intra-tumor variability in one patient, right? Mostly what people do is they collapse that intra-tumor variability and try to see what's shared across patients. Our hypothesis is that actually each patient is a different animal, right? It's not like you have 50 cat image tumors, one cat image, and then you use AI to align all the cat images together, but they're really all the same cat. It's not like that, right? What we feel is that the intra-tumor variability is different in each patient. Each patient is a different animal. We can align them all together and say, "This is the face of the animal, and the animal has two eyes," and all that, but that is very crude, right? What we wanna do is analyze each animal in itself to say what is it and how is it different from the others. That's what I think the Xenium can allow us to do. They all have different strengths, I would say. Each technology does one thing that the other technologies can't do. That's great. I think we're so excited to have, you know, labs like yours outfitted with all three platforms and to see the discoveries that you'll make. You know, we've been really excited to see that, you know, much in the way that institutions and consortia made large scale investments in exomes and genomes 5, 10 years ago, we're starting to see an acceleration in institutional funding in single-cell population scale studies. I think you're kind of uniquely equipped to answer this question. My question for you is why now? What are some of the biggest enablers unlocking population scale in single-cell studies? That's a great question. Yeah, I said I was surprised that it has not happened in the 13-year history of single-cell, and it's only starting to happen. There are actually some reasons for that. They're not sufficient, but there are some factors. One is single-cell is more scalable now. Costs have come down. Library generation costs has come down per cell, and DNA sequencing costs have come down. Suddenly these cells, these studies are affordable, and earlier they would have cost way more. That's one major factor. Another enabling factor I would say is just the data quality and robustness of single-cell platforms now, and we are most familiar with the 10x Genomics Chromium controller and our Chromium X platforms. It's just been incredibly robust in our hands, minimal batch effects, when we do everything side by side in our lab. That also makes it a lot more exciting to do these cohort studies because if you have profound technical variability across batches, you won't learn very much about the actual biological variability in your samples. Thank you. You know, you mentioned some of the basic research questions that we can answer from generating an atlas like the Asian Immune Diversity Atlas. I think also the translational applications are tremendous in terms of discovering biomarkers that could be indicative of, you know, so many diseases from immunological disorders to cancer. Also I know that your group works closely with translational and clinical scientists in Singapore and abroad. I'd be eager to get your perspective in two areas. The first is, you know, how do you think single-cell and spatial technologies are changing the way that we think about translational research? The second is, what do you think lies ahead in terms of moving some of these biomarkers and insights discovered in single-cell and spatial into the clinical realm? I do believe that sooner or later, single-cell and spatial technologies will be in the pathologist's clinic. The next logical step is single-cell, right? For cancer, for example, it's obvious. You want to treat the most dangerous cell in the tumor. You don't wanna treat the average cell in the tumor. The average cell in the tumor is mostly the older cells. The most dangerous cells are probably the newer cells, which are rare, right? And they've evolved far from the average cell, actually. I do believe that single-cell diagnostics will help us identify the dangerous cells in tumors, and so it'll become essential. Again, the next logical step would be spatial omics in the pathologist clinic. Initially, when I tried to pitch single-cell omics to clinicians, the default was, "How is this useful? Tell me what it'll tell us that single, you know, bulk omics won't tell us. I can learn everything I want by PCR. If you learn everything from single-cell, it's not actionable. It's too fancy schmancy." Right? That thinking is changing, right? It took a long time. Remarkably, somehow with spatial omics, that hesitancy is not there. You know, I go in fully expecting them to say, "There you go again. You're Mr. Fancy Schmancy. You're telling us about these sci-fi things that don't connect to our lives, right?" They actually coming to me saying, "Can we please do some spatial omics on my samples?" I was like, "What? You know, this is really new." Like, now the tables have turned, right? I'm saying, "Wait, wait. It's all very new and fresh, and we have to work out the methods." They, "No, no, I want the spatial omics data now." Right? I don't know what it is. I don't understand why the psychology is different, spatial omics and single-cell, except to say perhaps that spatial omics gives you images and views of the tumor that clinicians and pathologists are used to seeing. There's an immediacy about that. You know, they can immediately see they're not abstract UMAPs. Like, you can immediately see a distribution of the cell types they're familiar with in the space of the tumor, and suddenly it's meaningful for them, right? I'm fully confident that spatial omics will... it won't replace H&E staining and IHC that pathologists use, but it'll become an essential adjunct to that in the pathologist clinic. They will still be the ultimate decision makers, but they will have decision support from spatial omics. I think it's just inevitable, and I hope to be among, in the vanguard of that transformation. Yes, I love that. What a profound observation. I think, you know, this whole field is so ripe for disruption in a way. If you think about the way that therapies have evolved, you know, we have complex immunotherapies, combination strategies, we really need kind of diagnostics and the tools to catch up to the complexity of the therapies and to be able to provide, I think, the situational awareness for oncologists and pathologists to better treat patients. I think, you know, it's a really exciting opportunity ahead. Sham, maybe I'll leave you with one final question. Thank you so much for the really wonderful discussion. In the next 5 years, where do you hope this field will be? And where do you see single-cell and spatial approaches having the biggest impact in shaping the field? Great question. I'm part of the Human Cell Atlas Consortium, huge international consortium. I think 2,000 researchers or something like that, all over the world, 80 countries. Using single-cell tools and now spatial tools, we're building an atlas of healthy cell types primarily, okay? That's really important for understanding diseases. To understand disease cell types, you have to understand the healthy cell types. In 5 years, I think the Human Cell Atlas will have many atlases within the Human Cell Atlas. We'll really have a fantastic compendium or you can say, periodic table of cell types in the human body. We will also understand to a substantial extent how these cell types are transformed in various diseases, right? There are already early stage efforts at doing single-cell RNA-seq, for example, as a clinical diagnostic. I think in 5 years that'll become common, at least at the academic medical centers. The price will come down. The, you know, the acceptance will go up. Single-cell will be in the clinic in 5 years as a diagnostic. Spatial will also be in the clinic as a diagnostic. 5 years or 10 years, it's hard to say, but it won't be much more than that. Then we'll also have a better biological understanding of what these cells do, right? When I say in the clinic, what does that mean? It means we'll use these technologies to not just diagnose patients, but also to stratify them. You mentioned immunotherapy. To stratify patients for cancer immunotherapy. We're doing autoimmune disease single-cell analysis, and we're finding that we can stratify autoimmune disease patients based on solely on peripheral blood, single-cell omics, right? I think it'll be huge for patient stratification. It'll be huge for diagnosis. It'll be used as a companion diagnostic also, again, to give the example of immunotherapy to say, in these tumors, immunotherapy will work because the immune cells have already infiltrated the tumor but they have this exact marker that this immunomodulator can activate. Yeah, based on what you see in the single-cell space moments, you'll decide what drug to give. I think the possibilities are tremendous for having this guide clinical trial. Thank you for that. That's, you know, I think an exciting place to close, so many opportunities ahead. Thank you so much for taking the time, to walk us through how your group is using single-cell and spatial. I'm so excited to see what you do next. Thank you so much. My pleasure, Abby. Good talking to you. All right. I hope you found these customer perspective as inspiring and as motivating as our team does here. I certainly did. It is why we do what we do. Would like to really thank here both Sam and Shyam for the wonderful discussions, as well as Abby Cutchen from our marketing team for moderating them. For the next 30 minutes, we're gonna talk about the key differentiators that make 10x 10x. Jim, our new Chief Commercial Officer, will share what we're doing to get the most out of this enormous set of opportunities we have ahead of us. Ben will talk more about global manufacturing and operations and the scale that's ready to deliver. Rebecca Port, our Chief People Officer, will talk about our mission and results-driven culture, and why I'm confident we have the team and the culture to win. With that, let me turn it over to Bex. Hello. I'm Rebecca Port. I'm our Chief People Officer. I joined 10x almost two years ago from Netflix, and I joined for the very reason that, or the very thing I'm gonna talk to you about today, our people and our culture. All companies have cultures, but we have been incredibly intentional about the environment that we want to create. Whilst our culture is neither fixed nor flawless, it is how we aspire to operate. Our goal is to create an environment where our employees can innovate, thrive, ask questions, take risks, and ultimately do the best work of their lives. Many great companies start out with great cultures, but as they scale, there is a tendency for that greatness to erode. We have prioritized taking the time and the rigor to dive deep into our culture and to articulate it in a fully written out culture document. Our culture is not just words on a wall. It is how we think, how we plan, how we make decisions, and ultimately what we do when no one is watching. There are five core components to our culture that we think is the 10x magic that makes things happen here, and I'll take you through each one. Let's start with our mission. To us, our mission is everything, and it is the bedrock to our culture. Over the coming decades, there will be a revolution in biology. Cancer will become a thing of the past. We will dispose of the threat of infectious diseases. Crucially, this revolution is dependent on the advancement of technologies in life sciences, and we have built 10x to excel at building those technologies. Our employees recognize that through working here, there is an opportunity to have an impact on the world and to leave a legacy. The second part of our magic is our people. If we are to succeed in that mission, we need the best people on the planet. We need them to invent, work hard, work smart, and work together. Without our people, there is no mission, there is no company, and there is certainly no value. We hire the best of the best. Over the last three years, we've over doubled in size. We've established ourselves as an employer of choice in the market with over 37,000 applicants to our open roles in the last 12 months. Our scaled leadership team is experienced and ready to solve some of the world's hardest problems. Many of them have experience of scaled public companies, and we've hired them from places like Tesla, Johnson & Johnson, McKinsey, and SpaceX. Our workforce is highly trained and diverse. It's almost 50% women, and we have over 330 PhDs. 10xers around the globe are exceptional at what they do. They're always top of their game. In addition to this deep technical expertise, there are three fundamental qualities that we hire for because we think that they make the difference. Firstly, intellectual horsepower. We will be more successful if our employees can figure out what the world needs and how to get there. We screen for people who can absorb all necessary information, prioritize, and create a plan. Secondly, drive and passion. 10xers are doers. We never settle. We push past the easy answers. We make things happen. We break through walls, and we never say, "It is what it is." We screen for people who are self-driven, move with a sense of urgency, and follow through on their intentions and commitments. Lastly, collaboration. If we are to succeed, we need to work together as one team. We need to solve problems together, give each other feedback, and learn from each other. We screen for humility and the ability to collaborate. The culture of collaboration is a core pillar of the 10x culture. We put we before me, and we operate with mutual respect, empathy, and humility. Ideas here are not judged on where their proponent sits in the organization. Instead, they are judged based on their quality. Even during difficult conversations, like performance management conversations, we treat people with respect. Those people who are unable to treat others with respect or collaborate have no place at 10x. The culture of collaboration is also a key part of our diversity and inclusion strategy. Like many other organizations, we have fair pay, promotion, and hiring practices that aim to remove bias and create a diverse workforce. To us, inclusion is about creating an environment where everybody can do the best work of their lives, regardless of their background. We strive for a sense of belonging because we know that that's what brings the best out in everybody. During our engagement surveys, our employees tell us, 87% of them tell us that they feel like they belong at 10x, and 93% of them tell us that they work with other amazing colleagues. 20% higher than the benchmark for other biotech companies. This demonstrates the virtuous cycle that is created when exceptional people are energized by the opportunity to collaborate with other exceptional people. The last two components of our 10x culture are really about how we approach our mission. We are forging our own path, we cannot follow conventional wisdom or a path that has been set by others. We simply must act and think and behave based on first principles. This is coupled with our unbridled ambition. We look for opportunities that have exponential impact, not just because it's in our name, but because these are the opportunities that create the most value. We take a long view, and we invest in the foundations. This is the 10x magic. We hire great people, and we empower them to do great things. We don't look to control our people with process. Instead, we look to inspire them to help them understand the contribution that they make to our mission and strategy. Don't just take my word for it. I'm gonna play a video, and we can hear it from the team. Thank you for listening today. What makes the magic happen? I think it's the people. The people. The people. The people, obviously. Without any doubt, the people. The people make the magic happen. The magic happens at 10x because of the people. Surrounding yourself with people with complementary skill sets is critical. We've assembled a really incredible collection of scientists and non-scientists alike. Being able to leverage all the different approaches and backgrounds and technical abilities that we have. That was an incredibly important element. 10x has a history of doing the near impossible and doing it with a lot of joy. It comes down to getting the right people, with the different skill sets and maybe even the balance of the kinda more practical-minded people with the hardcore dreamers. People who are very analytical and look at an incredible amount of data and debate decisions, and yet really open-minded. 10x is a collection of the greatest talent on Earth. Everybody is at the top of their game. Get those talented, curious, hardworking people in all these different disciplines, and you get them together in a room and give them a mission to go and achieve something, and great things come out of that. I think the magic is collaboration and communication. I think it's the snacks and something they're putting in the snacks. Probably something in those LaCroix. It's probably partly caffeine and partly collaboration. Really bright people working on difficult problems and focusing on finding those solutions. Everyone is constantly learning from one another. It's not about who comes up with the idea, but what's the best solution. The messaging from top down. We're gonna do the hard things. We're gonna provide new tools for scientists to answer questions that they've never been able to answer before. I just love the fact that that message never changed, not once. This collection of people this talented, this egoless, this driven towards mission is really rare. We all care deeply about our customers' success. It is an environment where the best talent on the planet can come and thrive and do the best work of their lives. We've got the best people, the smartest minds working on the hardest problems. We envision what the future can be. We have people who have the tenacity and the inventiveness to actually bring it to life. Please welcome to the stage, Jim Wilbur, Chief Commercial Officer. Hi, everyone. I'm Jim Wilbur, the Chief Commercial Officer of 10x. I actually think it's really fitting that I follow Becks today. As many of you may know, I'm new to the company. I joined the company about four months ago. And during the interview process, Becks and Serge shared the 10x culture document with me, and I read it, and I was incredibly impressed by it. And it had a big impact on my intention and decision to join the company. What I loved about it was what it said. I loved the idea of mission and impact and people, but I also loved that the leadership of the company took the time to write it down. They had the rigor and the commitment to stand behind that culture. What's been great is since I've come to the company the last 4 months, I see that it's not just a document. Our team at 10x lives by the ideas in that culture document, and its principles embody everything we do. I just think that's a great framing for my talk today, particularly the focus on mission and impact, as I talk about what we're gonna do with our commercial engine. Today I'll be talking about how the commercial organization furthers the 10x mission and will create impact going forward. We've got an amazing organization in place, a talented team that's serving customers throughout the world. This team's been incredibly successful. They've driven market-leading growth. They've driven adoption of complex products, and particularly driven Chromium and single cell in the academic markets. There's still great potential for growth in our current markets, bringing in new customer segments and bringing new people to our ecosystem. I'll share how we're gonna optimize the commercial organization to take advantage of that growth opportunity. Before we talk about our plans, I wanna establish where we are today. We have a really powerful base to work from at 10x. We have a large worldwide organization of experienced professionals in sales, marketing, support, and service, and these people have deep relationships with their customers in academia, top research institutions, pharma companies, and they are absolutely been driven to deliver a superior customer experience, mostly direct but also through our hybrid and distribution channels. As Serge said, you know, we are obsessed with serving our customers. Having an organization with this scale gives us the breadth and reach to support our customers at every step of the way, from the moment they first hear about the technology until they become an experienced advocate like some of the customers you heard from today. It isn't just that this team goes out and has sold products. They've delivered groundbreaking technologies, and they've transformed the scientific landscape. Through their dedication and support, they've actually built loyalty and success with their customers. You really see that in the feedback we get from our strong NPS scores and the types of things our customers say about 10x. If you look at some of the comments that are on the screen here, what you see is it's just not about the commercial organization. They really reflect the full integrated 10x package, from the fundamental technology, the incredible products, and the dedicated support that they have received. The remainder of my talk is gonna focus on how we're gonna change the commercial organization as we optimize for this next stage of growth. I first wanna emphasize something. Sometimes when you hear about change, you think, "Oh, that means something is wrong." We are not fixing something that is broken. We're building a commercial organization from a position of strength. What we recognized is, as much as 10x has accomplished, what got us to this stage is not gonna get us to the next stage of growth. What I wanna tell you as someone who's new to the organization and new to you, I am super excited for that work. It's really the reason I came to 10x. I love building organizations. I actually love building commercial organizations and bringing process and systems and culture together to create an awesome place to work. I think it's a once in a lifetime opportunity to be at a place like 10x, to start with something that's already so successful and has great products. It's got leadership in the market, incredible people, but yet there's still so much to do, and I think it's a once in a lifetime opportunity to be able to go after something with so much potential. Let's talk about what we're gonna do. Bex talked about a culture of a company, and I wanna talk about the importance of culture in a sales organization, and it's sort of an interesting situation. Most commercial organizations would say that their sales team is one of the most important assets that they have, yet often sales teams struggle with culture even inside the organization or perception inside the company. It's a little bit strange, but if you think about it makes sense. Almost every other function in the company, finance, legal, engineering, HR, management, communications, people go through degree-granting institutions at university and get trained in their discipline, and most of those disciplines have graduate programs. They have long-standing career development. I don't know about you, but I don't know of a single university that offers an undergraduate degree in sales, and I certainly don't know of one that offers a graduate degree. For some reason, we take the sales profession, which is incredibly complicated and important, and we tell ourselves, and we tell our salespeople that it is somehow special. Sales is an art, and it's really some mix of personality and hustle. The reality of it is great salespeople and great sales organizations have the same kind of rigor and complexity that you see in engineering or law or surgery, and there are essential fundamentals that drive performance and efficiency. The best salespeople can't just go out and hustle. They have to learn and study their craft, develop themselves, and become purposeful and intense about what they do. When organizations adopt this mindset, what you see is you enable an elite professionalism from the sales team and everything changes. It doesn't just improve performance and efficiency, but the morale of the team changes, their attitude about themselves changes, and their loyalty to the company increases dramatically. The same thing is true for marketing and support. You know, you need a culture that focuses marketing the right way. Here we have to focus on solving customer problems. You've already heard that our culture of support places support as a top priority for the company on equal footing with getting sales. Now, like any discipline, you can have a great culture, but you also have to create the right environment. For any discipline, that begins with training. Training is essential to develop the skills and efficiency through a common language and best practice. It's not just about telling people what to do. You have to put the right infrastructure in place so they can be successful in a team environment. That's about creating integrated systems and tools that reinforce and support what you train so you have adoption and you have success. I really love what Bex said about our culture. It's not about enforcing compliance for its own sake or telling them what to do. It's about having an attitude where you provide the training and process and tools so the individuals can focus their time and energy on high-value activities to do it with maximum effectiveness, to be the best that they can be. We've already started implementing changes in culture, changes in process here at 10x. We're seeing improvement. It's early, but we expect an acceleration of impact as the pieces fall into place. I don't have time to talk about each of these things today, but I'm happy to answer questions if you want in our Q&A session. There's certainly work to do internally to get the right culture, to get the right processes in place. We also have to focus what we're going to do externally. We have to focus our external commercial efforts in the right places to maximize our opportunity. We have to grow our expanding applications and use with our current customers. We have to find ways to bring new customers and a broader base of researchers into the current 10x ecosystem. We're really focused on 3 key priorities for doing it. First, we have to accelerate adoption by driving more education, more awareness, and increased funding for these amazing technologies. Second, we have to drive growth by recognizing that removing bottlenecks advances things, and we have new technologies that can remove those bottlenecks. Third, we have to be superior in our commercial execution. We gotta do things at scale. We have to make sure that we are absolutely committed to get every single thing we can out of this tremendous opportunity. As you know, 10x really created the single-cell market and catalyzed a revolution in science. It's amazing to think about that it grew revenue to half a billion dollars in just a few years. Yet despite that amazing accomplishment, it's really early days and things are just getting started. In the morning session, you heard the team talking about the technology developments that will remove our largest bottlenecks that impede our customers. Those are about workflow, sample prep, improving, more accessible pricing, and data analysis and insight. As R&D continues to drive these amazing innovations and deliver on the new products, we on the commercial side have to be ready to get these new launches into the hands of their customers. As you heard from some of our speakers, some of it is simply about awareness, and we are highly focused on customer awareness to reach a broader group of researchers. There's sort of a natural curve that takes place as technologies gain traction and move through early adopters, and we're really working to accelerate that to get to broad awareness. We have a high-touch commercial organization with global scale, and that allows us to drive 10x user groups, key opinion leader engagement. You know, we participate and sponsor all kinds of industry and customer symposiums. That increased awareness and education drive interest, but it also drives funding, and funding is a key enabler of adoption and growth. We're seeing that the overall funding landscape for single-cell is increasing rapidly. It's been averaging about 40% growth over the past few years. We also see evidence that single-cell technologies are gaining traction beyond those early adopters with a broader group of researchers. One interesting statistic we saw is that the funding for new academic laboratories who want to use single-cell or spatial methods has more than doubled in the last couple years. That tells us that more high-potential early career investigators who've used or learned about these tools during their education are putting in and seeing these 10x tools as essential when they start their labs and they build their own careers. Now, sometimes the bottlenecks are just related to specific problems. It's not about awareness. You have a problem that you need to solve. If you can solve that problem, you can really drive growth. I thought earlier, you know, hearing Mike and Ben talk about the 10x innovation, they really explained how those products are transforming biology. I wanna talk about how they transform things commercially. The launch of our gene expression Flex product, it is a huge commercial opportunity, and together with the Chromium X, it really changes what's possible with our Chromium system. Flex opens access to vast repositories of samples in different formats. It changes the workflow, particularly the ability to collect and batch samples. It just unlocks what a customer can do with Chromium. It changes where a customer can work. Visium CytAssist solves a different practical problem. It greatly simplifies the handling and processing of tissue samples for spatial analysis. It sounds simple, when I first joined the company, I did not appreciate how profound the CytAssist instrument is. If you listen to our customers, the feedback from our customers is just overwhelmingly positive. It really solves a profound problem. That's driving growth with our Visium market, both our existing customers, but accelerating the use with new customers in several markets. Commercially for us, it's about focus and activity on these products which are going to unlock essential bottlenecks. We've been aggressively training our commercial organization on these products. We keep iterating on our position in marketing. You heard about the renaming of Flex, driving awareness through sales campaigns, outreach, and events. As you heard before, it is early for both of the products, but the feedback and adoption has been really strong. As a commercial organization, we're really excited to see what these products are gonna do in 2023. Now, the improvements in workflow and sample prep ought to be really impactful in our pharmaceutical and biotech companies. There is every reason to believe that single-cell and spatial measurements should be big in pharma. The ability to resolve information from individual cells is just transformational in understanding disease, and it's transformational in developing new therapies. It's not that we haven't gotten started in pharma. Our beachhead in the pharma market is about 20% of our current revenue, and we've gained a lot of valuable commercial experience. We have a focused team of reps that are out there. They have relationships in pharma, and we have penetrated, you know, a lot of the top pharma and biotech companies. It's a small fraction of what's possible because most of our installed base is in discovery, where that limitation to a fresh or frozen sample isn't a showstopper. Our penetration in the translation with clinical segments, where access to fixed repositories of FFPE samples or being able to do batch workflows is absolutely essential, our access there has been really limited. The release of Flex Kits expands access to those things. We can get to those large FFPE sample repositories that our customers talked about for translational studies. It removes the major bottleneck to bringing these type of technologies into clinical trials. What's great about that is the translational and clinical trials market is actually quite a bit larger for us than discovery. The breadth of applications is larger. The number of samples, as you heard, can be really large. As we expand our pharma business into these areas, it should grow significantly with the potential to become comparable to our academic research business. We're also driving growth and adoption by improving our commercial execution. Today, I'll talk about three key tactics that we're focused on: cross-functional selling, e-commerce, and inside sales. Commercially, there is a strong synergy between our instruments and our consumables. As we grow our installed base of instruments, we naturally drive growth in consumables. It is really the breadth and the capabilities of our applications that drive the sale of instruments. That's just an ideal environment for cross-functional selling, where you have a single team that can sell all of your products. It's just so much more efficient than having to have individual teams for individual products. It's a better experience for customers who really appreciate the simplicity of a single team that can sell and support their full suite of products. We're also focused on driving growth by increasing touch points and improving access. We recently launched an e-commerce platform, improving the customer experience at the transactional level. It's early, but the customers have loved it, and we are continuing to add features and plan to expand that channel aggressively. We'll also expand our inside sales efforts. Inside sales increases customer touches dramatically. That's critical both for account management and prospecting, which drives consumable sales and drives instrument sales to new customers. Like cross-functional selling, both e-commerce and inside sales drive efficiency for us at 10x in the organization and create an optimized experience for the customer. Before I finish, I want to share our commercial strategy for Xenium. I think as you've heard, the Xenium product's incredible, the market is enormous, and growth is gonna come from everywhere. We've really thought hard about how to best sell and support Xenium to deliver its full potential. For a lot of reasons, we've decided to deploy our full global team to sell Xenium, supplemented by some Xenium specialists. I think it's a great decision. It leverages the scale and experience of our existing team. It gives us an immediate global reach for Xenium, and that gives Xenium the attention that it deserves. It's also the best experience for our customers. As you heard, they greatly value the integrated platform between all three of our products, having a single team that can sell Xenium to them is gonna be a great experience for them. We're really confident the existing team is capable of selling Xenium. We have great sales and technical functions. They have the experience and the science, they have the expertise, and they're experienced at selling capital equipment. We've piloted this strategy in Q4. We did a bunch of training. We've done a ton of customer events. We've deployed our global team out talking to customers, we see it's working really well. Support is also important, and we have a large field applications team that is trained and ready to support our customers. We are expanding our field service team to support instruments in the field, and we can leverage our global support team in-house for applications in the experimental design of custom panels. I think one of the things that's also important but also near and dear to me is our commercial team is building a strong integration with our R&D team. That provides an immediate feedback from the field for our Xenium launch. It allows us to get expert solutions back to our customers, and I think that's super important. You heard the exciting news that we started shipping Xenium this week, and I think it's an important milestone that really marks the next stage of this commercial launch. As Serge Saxonov said, demand's not an issue, we have a team ready to support our initial customers, I'm really excited to see what happens as they have success and we ramp up production. Bringing things to a close, I wanna reiterate how thrilled I am to be part of the 10x team. If you think about what makes a great commercial organization, the most important thing is actually to have amazing products and to be able to impact huge markets. I mean, that's what really makes a great commercial team. Here at 10x, we have it all. There's just no company in our space with our combination of single-cell and spatial platforms, our breadth of applications, and our large global commercial scale to go out and provide it to customers. For our commercial team, it's just a great privilege to bring 10x products to the world. With that privilege comes a responsibility. It is our responsibility to make sure that we can extract the maximum potential commercially from these great products, but even more importantly, to make sure they reach their full potential to impact the world of biology and human health. Going back to Bex's talk, I can't think of a better mission, and I can't think of a better place to be. With that, I'm gonna introduce Ben, who's gonna come up again. Ben will talk about our partners in operations who do the amazing work of making and delivering the products that we're so lucky to sell. Thank you. Ben? Thanks, Jim. Well, I do have probably one of the best jobs anyone could ever wish for. Not only do I get to work with our phenomenal R&D team, but I have the pleasure of also working together with our operations team. I think from my point of view, this organization has built a world-class operational organization, and it's built to scale. Our mission is to continue to deliver high-quality products to our customers on time, every time, and make sure they work out of the box. That's the expectation that we have. Our team is comprised of 200+ employees. Our manufacturing locations are based here in Pleasanton and also in Singapore. It is built for scale. We also have distribution centers which, after we've manufactured the products, kitted them up, we ship them to distribution centers, whether it be in the United States, APAC, EU, and in the UK. When that customer places that order, they can get their products as soon as possible, oftentimes the next day. We've heard about company-wide partnership and collaboration. That's true of our operations team. When we conceive a product, we don't just think about the cool technology and the cool biologic and unlock. It's also thinking about how can we build that product in a way that is quality, but also has a good business with high margins and works out of the box. This is really done in partnership with our organization, within the operations team, but also in deep partnership with the R&D, when products are conceived, all the way through when they are launched and through their lifecycle management, et cetera. Operational efficiency is something that we are keyed on constantly improving, and this is in partnership with our finance organization. We have business partners that work closely with us to help evaluate decisions and make sure we're making the right ones for the company long term. Ultimately, the customer experience is essential, and working together with Jim's team, when we do hear about things that from the field, we are able to work closely to resolve any particular issues, and also keep in touch with what the demand looks like, make sure we can supply those products, have them positioned at the right locations. This is another example of the great company-wide partnership that enables us to have built this capability, which is ready to scale. A little bit more detail here. Based here in Pleasanton, you've heard about some of the products, the new products that we've been developing. Our focus here in Pleasanton going forward is on new product introduction. Complex systems where a lot of the development is done fairly recently with R&D, it's really important that NPO activities are done close to R&D. You may have heard about our new forever home for U.S. operations is just a 2-minute walk across the parking lot here. We're gonna consolidate our U.S. manufacturing operations team here in this great new facility, which is gonna come online in March of next year. That integration is essential, and it was by design, and we're gonna see the benefits of that in terms of developing products and making sure that we can scale them. Together with just building products, there's a lot that happens behind the scenes. We talk about when products were released. From the very inception, for example, on our Chromium platform, we spent a lot of time thinking about how we're going to make gel beads, other things, chips, at scale and with good margins and at quality that's expected. That requires a lot of R&D that customers don't see that's behind the scenes that enables us to produce these reagents, kits, instrumentation at scale. Coming out of the IPO, we had a mandate that we, and even just a little bit before that, we had a mandate that we wanted to be less reliant on certain suppliers. Certain things led us to have to figure out how to make our own microfluidic chips, for example. This was not an easy thing. We figured it out, and now we have capabilities to make microfluidic chips at high volume internally. That's just one example. Vertical integration is key for us to have the stability of our business going forward. We have a robust supply chain that think about where we can source components, and all our sites are under a quality system, ISO 9001. When products get more mature, we're able to move that capability to Singapore. Singapore was brought online throughout COVID by sort of remote interactions between our two teams based here in Pleasanton and also in Singapore. It's quite remarkable. That facility is up and running. It's world-class. It produces our reagents for our Chromium business and others. They do formulation and kitting. They source components, and they distribute those products through the APAC region. We believe that there is substantial capacity here to continue to scale and utilize and leverage the capabilities that we have in Singapore. We don't do this alone. We have partners, obviously, where we have partners in USA, in Europe, in APAC. These partners complement our capabilities for what we can do internally as part of our vertical integration capabilities, whether that be on instrument systems to certain components, submodules, reagents like enzymes, oligos, for example, and also consumables. We believe that this network capacity is really built for growth multiples based on the investments that we've made. It's really these embedded teams that accelerate our innovation and execution. We design and develop. Operations is heavily involved there. For NPI, again, just a 2-minute walk over here, we're able to integrate the teams, come up with elegant solutions, and then once we're ready to scale, we can transfer those to our colleagues in Singapore. There's lots of behind-the-scenes here. In lieu of being able to give you a tour, We generated a short video which I'd like to play for you right now. Thank you. Quality is always at the forefront of our manufacturing processes. 10x's manufacturing is unique because our products are unique. What we work on is extremely complex. What we make are really system-level products. We have invested in capabilities to make them in-house. Our scientists are able to work alongside the manufacturing staff on the floor. One of the core strengths of our manufacturing and also process development is being able to work with R&D and assay development to really understand how these parts interact with each other. We have a manufacturing group that are experts in manufacturing. We have R&D experts that really understand the science behind our products. When we can get these people together, you can make really great things happen. The equipment to manufacture those reagents is entirely bespoke. It's designed and developed over years of investment of R&D research with an eye towards volume manufacturing. We've built with a mindset to try to have the ability to scale even as we become bigger. The Singapore site has the capability to grow up to at least 4x using the current space that we have. We're able to quickly scale to meet the capacity and the product maturity requirements. By understanding the chemistry and the requirements for the system, we're able to develop automation. Leveraging automation enable us to scale very quickly and still ensuring the quality of the product to the customer. That's really where we differentiate ourselves, that we can manufacture very complex products on a very quick timescale and put them into customers' hands. We have an extremely capable team of engineers, scientists, and production staff who are able to make products that will work. That's what we really strive for, and I think we've been able to deliver. Please welcome to the stage Justin McAnear, Chief Financial Officer. Hello, I'm Justin McAnear, and I've been the Chief Financial Officer at 10x Genomics for over four years. Previously, I've worked for Tesla, Apple, Johnson & Johnson, and also served in the US Navy. It's always been important to me to be a part of organizations with some higher purpose that serves some greater good. That is what drew me to 10x Genomics. We have enormous potential to provide the tools that drive the discoveries and insights to help cure many diseases and enable transformational advancements in human health. It's my and the finance team's focus to ensure that we allocate our resources effectively to create the highest possible value in delivering on this promise. I call this section Delivering a best-in-class financial profile because we are in the process of doing just that. We have the right pieces in place due to scaling rapidly over these last few years. Our expansive, market-leading, and truly market-creating products will continue to fuel high growth rates at high margins while we begin to drive leverage on our operating and capital expenditures. You will start to see this in 2023, and we will be very focused in driving towards free positive cash flow. This focus on cost, efficiency, and execution without sacrificing growth or margin will set us up for high EBITDA beyond that on our way to delivering a best-in-class financial profile. I'd like to start with announcing some changes we plan on making to our quarterly and annual financial reporting starting next year as we report 2023 financial periods. Our business continues to evolve with new product introductions and increased adoption, and we will be sharing more information in the future to increase your understanding of the business. First, there will be no changes in how we issue guidance. We plan on issuing annual revenue guidance with a range for total revenue. For our quarterly actual revenue reporting, beginning with the first quarter of 2023, we'll begin to break out instrument revenue and consumable revenue by Chromium and Spatial. We'll report service revenue in aggregate for all product lines. Finally, we will continue to disclose total instruments sold annually, beginning with our full year 2023 results, and we'll also break out instruments by platform, Chromium, Visium, Xenium. Annual average instrument pull-through is becoming a less meaningful metric due to, first, our driving of an instrument replacement cycle, where right now about half the half the Chromium X Series instruments are going to new customers and about half are going to existing customers. Second, the high capacity of our instruments, which allow many users to leverage a single instrument. Third, the fact that some customers are buying multiple instruments, sometimes for convenience, and the wide range of utilization per instrument we see across our customer base. Finally, we've driven expansion of our instrument portfolio and now sell many different kinds of instruments, some with common reagents, and some where the use of an instrument is optional but not required to run the consumable. For these reasons, we'll be replacing average annual instrument pull-through with total reactions sold by platform. This will be a more meaningful representation of the activity taking place in the market. Reactions are the physical product that we sell, and they make up about 85% of our revenue. These changes will go into effect in reporting 2023 revenue starting with Q1. Now, moving on to our financials and starting with revenue. Our revenue growth rate over the last 6 years is striking. From just $27 million in 2016, our first full year of commercial operations, to this year, where we are expected to deliver over $500 million in revenue in 2022. At the midpoint of our guidance range, that's a 48% five-year CAGR and a 28% three-year CAGR in challenging macroeconomic conditions. We've done this at a high gross margin, which has provided us the ability to reinvest into the business and fuel our R&D innovation engine and commercial expansion. This high revenue growth rate combined with a high gross margin is a key differentiator and key strength of the company. It has provided us the flexibility to invest much more heavily into supporting our growth and allowed us to scale all parts of the company very quickly. Drilling further, looking at our last 12 months revenue ending in September, you can see that about 85% of our revenue comes from consumables and is recurring in nature. We are well-diversified geographically with about 45% of our revenue ex-U.S. Also, there is growing diversification in our product lines with Spatial, which solely consists of the Visium platform in this chart, and now will include Xenium, making up 8% of our revenue base. All right. Speaking of Visium, you saw the cumulative publications and preprints earlier today, and here is the cumulative revenue. Cumulative revenue from Visium is over $75 million since launch in Q4 of 2019. The recent launch of our CytAssist instrument is accelerating the platform, and Visium has been growing as a% of our overall revenue. In this most recent quarter, we sold over 100 CytAssist instruments and more than $10 million in Visium consumables. We're excited about what the future holds for our Spatial platforms and what Visium coupled with Xenium will drive in 2023 and beyond. Moving to OpEx and CapEx. We've been investing heavily over the last few years to quickly scale all aspects of the company with focus on growing our commercial and R&D teams. We've more than doubled the headcount in each since the end of 2019. In the near term, 2023 in particular, there will be minimal headcount increases across the company. We feel that we've built these teams out sufficiently to deliver on our near-term objectives. In addition, we are nearing the end of a heavy phase of capital expenditures as we've built out significant additional operational capacity to support our future growth and do not anticipate undertaking additional large-scale CapEx projects for at least the next couple of years. I'd like to bring this all together now and share with you how I'm viewing 2023 and our objective to become free cash flow positive by the end of next year. We'll initiate our annual guidance in accordance with our normal practice when we do our Q4 earnings call in February. In the meantime, here are some contours that will help shape your view of our expected financial performance. Revenue. We'll continue to drive a high revenue growth rate through expanding existing customer usage, driving new customer acquisitions, and through new product introductions. It will be the first full year of some really exciting new products, Xenium, CytAssist, Single Cell Gene Expression Flex, BEAM, and others. Gross margin. We're focused on preserving the gross margin on existing product lines. There will be some near-term headwinds as newly built capacity comes online, but we'll realize economies of scale over time as we grow into that capacity. Xenium instrument gross margin will be a headwind to company margin, but the consumables gross margin will be much more comparable to our existing product lines. For OpEx, we'll add minimal headcount in 2023 over 2022 and begin to leverage our OpEx. There will be continued increases in our annual non-cash stock-based compensation due to the vesting schedule of multi-year grants. Capital expenditures. As mentioned, there's going to be a significant reduction in CapEx in 2023 after the completion of our new facility in the first half of that year. Last is working capital, specifically inventory management and our cash cycle. We've been in an environment where we've been buying ahead for crucial components and raw materials to mitigate supply chain risk and also to prepare for a number of complicated new product launches. There is the opportunity here to pull back on that as the macro environment improves. In closing, I'll leave you with the key takeaways on this slide, and I would like to say how honored we are that you've joined us here today. I hope that we've strengthened your appreciation for what a truly unique and differentiated company 10x is, and that you understand better how we'll attack the vast opportunity in front of us. It's been an exciting time for 10x, and in 2023, the pace and intensity will only increase. There's no place I would rather be, and I'm looking forward to providing you with future updates as we execute on these plans. Now I'll turn it back to Cassie as we move into our final Q&A session. Thank you, Justin. We'll give everyone a minute to get seated and situated. As we go to this second Q&A session, I just wanna place a reminder that we ask you please keep it to one question and one follow-up. For anyone on the virtual webcast, you can submit a question via the Ask a Question tab to the right of the video player, although we do have plenty of questions coming in in the room, so. Okay. Our first question, we're gonna go with Patrick. Thanks, guys. Patrick Donnelly with Citi. Justin, maybe just to pick up on the financial side there, 2023, appreciate the high level commentary. I guess when you think about 2022 versus 2023, you guys had some headwinds this year, whether it was China or COVID lingering at the early start of the year. I guess when you think about just the moving pieces for next year, you have Xenium coming in, obviously. Do you see any headwinds lingering? Maybe just talk about, again, the moving pieces as we work our way into 2023. Understand it certainly doesn't seem like you're gonna give hard numbers, but, just maybe some of those push and pulls, as we think about the next year's setup. Yeah. There were some definite macroeconomic factors that impacted primarily the first part of the year. You saw currency impacts outside the U.S., primarily in Europe. There were issues with disruptions in China. You know, the currency impact, I mean, we were just below parity at the beginning of this quarter. It's improved slightly from there hasn't been a big swing, you know, going back to where it was. I think we're all following the headlines every day in China. You know, things look a little bit better from, you know, what we've seen this past week. I think the one thing that we've learned and how we look at the future going forward is, you know, we feel great about the new products. We feel great about what we're launching right now and how that's going to impact the future. As far as forecasting future growth rates, not getting too far ahead of ourselves and, you know, sticking to forecasting, you know, based upon what we're seeing. I think with the dynamic environment that we're in, there's a lot of moving factors like you called out, you know, we'll see how they, how these play out, and they will feed into the view that we'll share with you in February. Okay. Maybe on Xenium, maybe just for Serge and Ben, just in terms of that ramp. You know, you kind of mentioned you wanna get it in the right customer's hands, kinda make sure you control a little bit. You know, it sounds like a pretty, let's say, measured launch in terms of, you know, making sure the data is good as you get it out there. Ben, just in terms of the production scale, how do you think about. Again, that's a moving piece for 2023. Just in terms of what the potential contribution could be. Serge, you mentioned demand is not an issue, presumably the only issue would be some of the production sides. can you just talk about, I guess, what that scale looks like currently, where you want it to be in terms of that measured launch just as we work our way into next year? I guess to start with, like, I think the emphasis we're placing is really customer success more than anything else. These are complex technologies, right, that have not really existed before. We have to learn from our customers, our customers have to learn from us as well. This is where we're placing emphasis in our initial rollout, and that's where it's going to be a focus. More than anything else, that's sort of that's the thing that's sort of regulating the ramp that we're going to be driving Xenium. Yeah. The demand is certainly there for sure. We have to be careful as we go through, like, the first quarter of shipments and the first half of the year. I feel good about where we're going to be, especially as we're looking to the second half of the year, and beyond that. Yeah. Okay. Anything else to add? Maybe- Yeah, as far as Justin mentioned, we bought ahead a lot of the components, we believe we have the inventory to make a lot of Xenium instruments. We wanna make sure they're high quality, so we're doing a significant amount of testing before they leave our building so that when they get to the customer site, they're gonna have a good experience. Our field service engineers are also being trained up and scaled to be able to support the customers in the field. We're excited. It's a complex product. It's not like the little Chromium instrument. It has way more capabilities built into it. We are focused on customer success, and we're scaling the team to be able to produce more Xeniums throughout the year. Maybe for next question, we'll go to Kyle. Kyle Mikson from Canaccord Genuity. I wanted to just talk about the long-term growth rate of the company. Justin, I know it's tough to kind of talk about, but, you know, 48% CAGR for the past several years and 28% for the past 3 years or so may work, but could the next 5 years or so kind of fall with respect to those numbers? I know it's again kind of tough. Maybe with the markets itself, spatial, single-cell, how could those maybe kind of grow relative to the past or what we're kind of used to seeing with competition increasing, things like that? Just again, like, with the kind of long-term rate, just think about, like, thinking about how these new products kind of fold in and the manufacturing, things like that. Yeah. You know, when you look across our different product lines, you know, starting with Chromium, although that's our Of our product lines, that's the one that's been around the longest. It's still very early days in Chromium. You know, growth rates for, you know, for new products aren't typically, aren't typically linear. It's even earlier for Visium and, you know, of course, Xenium, which just launched this week. When you look at this, when you look at going into 2023, you know, we still expect healthy growth on Chromium. You know, there is a, because Visium hasn't, you know, shipped until now, that could be a big factor in or that's gonna be a big factor in 2023, like how many of those we place and, you know, how the consumables utilization on those ramp up, what the acceleration on the Visium side is gonna be with the new CytAssist placements and all the new products that we're that we're launching out. You know, we're incredibly excited about what the, what the future holds. You know, we haven't put out a, you know, specific number for a long-term growth rate. You know, we have an internal view, with, you know, it's, you know, a range that we're planning around. We will share 2023 with you in February. Okay. Thanks for that. The reaction metric, I think that's been talked about in the past, how do you, like, evaluate that given that it's gonna be pretty important going forward, like per customer, per instrument, like, you know, per, like, reaction itself? Like, the type of reaction is pretty important. How do you kind of, like, gauge these things going forward? Yeah. For the reaction metric, I mean, it is, you know, we'll share more information as we get closer to reporting that. You know, it is the physical thing that we sell. It's the best approximation that we have for samples. Like, there is the multiplexing impact between samples and reactions, and that has been increasing over time. We think that talking the number of reactions, the physical piece of consumables that we sell, and then looking at trends and growth rates there, you know, by our different platforms, that is a more helpful metric for understanding and modeling the business than an average instrument pull-through. You know, in our initiative to give you more useful information and to help with modeling, I think that that's a, that's a more useful way to think about the business and model the business going forward. Okay. Maybe Madeline in the back. Hi, this is Madeline from Matthew Lare from William Blair. One thing that we've been hearing a lot about is biotech funding. As biotechs sort of become more cost-conscious and start laying people off, one thing we're hearing is that there's a large reluctance to adopt novel technologies. Is that something that you're seeing as you roll out your products? Is it something that you think is going to sort of impact how you consider product rollouts going forward? Maybe I'll start. I mean, we've been watching this carefully, there are some dynamics you definitely see in the data, the fact that biotechs have become somewhat more cost-conscious relative to the previous year where money was basically free. At the same time, when we look in our data, I don't think there's material effects that we're seeing, I think the adoption of new technologies, I think there's so much upside in terms of our technology and how much value they can drive. I think some of those macroeconomic effects are probably going to be somewhat attenuated. I think there's going to be some impact on biotech, less so probably on the pharma companies that sort of make decisions based on other criteria. Great. Thank you. Dan Arias, go to you. Yeah. Hi, guys. Maybe a question for Jim. Jim, you had mentioned that one of the things that you need to address is this idea that the things that have gotten you or had gotten you to the past several years of growth aren't necessarily the things that will take you forward. Serge has said similar things to us in the investment community. Can you put some context to that? I mean, what exactly is it that drove growth that may not work going forward? I don't know that I fully appreciate the change that you're talking about here. I'd be happy to talk about it. I mean, it's difficult because you're talking about a commercial organization that experienced remarkable success, and so it seems strange to say something like, well, you know, what worked before wouldn't work now. It's not like we have to stop and start everything new. If you look at how that organization grew over several years, it was really just growing, you know, rapidly in a context of enormous explosive growth and didn't really have the time and the bandwidth to put in place the sort of process and system and tools and culture that you need to sustain the next log of growth. It's kinda like what Alex says about software, right? You can have sort of run-of-the-mill software. You can have stuff that's new or novel, but it's when you put in place really good structure, really good systems, and a rigorous approach, that will allow it to scale. To some extent, you can say it's blocking and tackling. There's a lot of mechanical things to do in an organization that make it scale. It also really affects the people. You know, as you grow an organization and you're sort of doing things, you know, the way you've always done it, that organization starts to get in trouble. What happens is you change it, you make it more positive, you maintain retention, you keep your best people, you create that great work environment, and allows you to continue to grow and scale. Is that a change at all in selling tactics? I mean, you mentioned cross-functional selling. Does that mean that guys were selling to a certain type of customer before but now need to sell to multiple types of customers? What exactly is going to change inside the commercial organization? I put for some of the things we're gonna do and some of it is internal stuff like how we're gonna train and how we're gonna manage our process and our systems. Functionally, obviously, with new products coming online, we made the decision that we think the team will do well to sell all of them together. That is a change, but I think that's a natural and a good change for the team. I definitely think it will need additional resources as we see markets open, like as there's more demand in pharma from Flex and from this change in the ability to get into pharma, absolutely could see needing more directed resources to go after pharma. I think the inside sales organization is another great example. As you get larger, it's not just about going out and visiting customers, it's about finding efficient ways to have lots of touch points to increase both account management and to increase new prospecting. Some of it is sort of mechanical stuff. I wouldn't look at it like as a wholesale change. I think we have a great group of people. They're highly capable. Frankly, I think they welcome these changes, that they're ready to have a more supportive environment that allows them to be more successful. I think they're super excited by the opportunity to go out and talk with customers about these new products and leverage those new products to also drive interest in our existing products. You know, I think there's a lot of mechanical things that change. We'll obviously have to change some of our resources as things grow. Maybe Dan Brennan, we'll go to you. Great. Thank you. Dan Brennan from Cowen. Could you talk a little bit more about Flex? You know, Jim, you highlighted that as a key growth opportunity, one of the three, and obviously it seems like the product has a lot of advantages. In terms of the pharma, pace of pharma penetration, how do we think about, you know, they're obviously primed, I assume, to, you know, be ready to go ahead 'cause, you know, they've been unable to tap into the FFPE blocks. Now they can, so they're probably accustomed to using the product, but. Just any color about like the size of the opportunity and how we think about the pace and the uptake and maybe what do some of the early adopters think of the product? I can start there. One thing I would just emphasize about Flex, it's, you know, we talked about the fixation capability. It It has other advantages, like Ben talked about, you know, superior sensitivity, greater sequencing efficiency, straightforward multiplexing ability. It has a lot of things that it's bringing to bear that are pretty awesome. As far as the ability to fix it is really something that people have been asking us for, especially, you know, going back years now, you know, you go to talk to pharma, this is. I've heard that personally. It's like, "Hey, like, we love the single-cell data. Like, we'd love to be able to run it, but it's a really, really huge problem for us because we can't really do any kind of distributed sample collection. We can't include it realistically into our established experimental workflows inside because of the lack of fixation, because you have to run, like, analyze the sample as soon as you get it. I would emphasize it's not just FFPE. It's actually the fact that you can fix the sample and now batch process samples and now do this distributed sample collection. On top of that, you know, we have shown now that this kit works with FFPE samples, which allows you now to go back and do retrospective studies on archive samples. I think it's huge. I think that has been the main, the main or a main, like, certainly like the top sort of limiter for pharma adoption, to, you know, to drive it to the next level. We definitely see that. As we rolled out the product, you know, we were somewhat deliberate in how we positioned it. Because we wanted to see the customer feedback. We wanted to see how it works out there in the field. Kind of more recently, we're now putting more intentional kind of need behind it to lead with this product for a lot of applications. I think, and, you know, the name change is, sort of one aspect of that, and, kind of putting in more resources into marketing this product more explicitly, educating the customers more explicitly into biopharma and translational elements is definitely part of what we're seeing or, unfolding over the coming quarters. Maybe just as a follow-up, just back on the single-cell side, like the LRP, which, you know, it's over 90% of your revenues and the growth slowed materially over the course of the last 12 months. It creates a lot of uncertainty for investors. You've highlighted some factors that kinda contributed. Nonetheless, what could you speak to, obviously, the presentation today was, you know, very positive about the opportunity, but what could you speak to about any kind of tangible signs that, you know, something hasn't changed, whether it be the market getting saturated or maybe a lot of these smaller customers who bought boxes not, you know, really not using them? Any color that'll give us some comfort that, you know, the past 12 months aren't gonna be really a, you know, predictor, if you will, of, you know, what's to come. Yeah, I mean, it's really hard to imagine where the situation would be coming from because every customer we talk to has an orientation toward running more samples, more applications, more things with sort of more experiments with single-cell. This is something we certainly pay attention to. We don't expect that to be, you know, for our first principles to be saturation at this point. We obviously, you know, like Ben says, customer is the ultimate source of truth, and we spend a lot of time talking to our customers. Whether it's the earliest cohorts of customers, I mean, Sham and others are kind of in that category. You can see that they're like only expanding and going almost exponential expanding what kinds of things they want to do, or like the huge opportunities out there that we haven't really tapped into materially. I think there's a lot of Certainly this past year, lots of moving pieces, lots of factors, and to some extent, even the customers themselves have been somewhat confused to understand kind of the different bottlenecks that they face. We don't see any signs of anything approaching market saturation. If anything, all the conversations we have suggest the opposite. maybe Tejas Savant, we'll go to you for the next one. Thanks. Just following up on that question, Serge, I mean, is it fair to interpret what you just said about, you know, the market being far from saturation to say that you're very confident that 10x can continue to grow and that, you know, 15%-20%+ CAGR versus a low double-digit sort of CAGR? Because the question that we get is, you know, there's a lot of life science companies that scale really rapidly to $200 million in revenue. You were the ones who got to half a billion. Is this sort of now just a function of how the industry works and you managed to get a lot further than anyone else, and that's great, but going ahead, it'll be a lot harder? Do you still feel confident that you can scale up to, you know, become a billion-dollar-plus sort of top-line franchise over time? Can you guess my answer? Look, we constructed the company to be a high-growth company, right? That's why we invested in innovation. That's why we spent so much time working with our customers and really thinking about kind of where the world is going, where the biggest opportunities are here. All of us are here for that reason, right? This, if we were saturating, if there wasn't, you know, much material growth ahead of this, it would not be nearly as motivating to us as what we see unfolding. No, for sure, the company, I'm not going to comment on the precise numbers, but again, we've built this company to be a high-growth company. I think a lot of. A couple of things to kind of, maybe appreciate. One is, we do see a lot of product development and features that we have launched recently or are about to launch or going to be, this is zeroing on the single-cell market right now, are important for kind of unlocking more adoption, more broader adoption, which is, you know, puts us in somewhat different category relative to a lot of other companies that tend to kind of put out a box and then just kinda focus on selling that box. We really focus on applications, and we kind of build out the ecosystem, which should remove the bottlenecks. The other thing is, on, you know, what Jim was alluding to on the commercial side too, like the way that we were selling, when you sell in a kind of a genomics market is different relative to when you go out into a broader customer base where you're now focused on, you know, leading with applications fundamentally. It's not about, like I talked about mainstream biologists, talked about pharma customers. They don't care about what kind of box that you have, what kind of a solution. It's really about what problems you're solving. This is the, you know, the focus of a lot of what Jim is thinking about and preaching within the company is really now kind of flipping that. It's not a given that companies do that, but for us it's a huge opportunity, obviously, going forward. That's helpful. Just two quick follow-ups. One for Jim then one for you, Justin. Jim, on that point that you mentioned earlier about sort of a more process-led approach to sales as, you know, the portfolio gets complicated, as, you know, customer complexity is also on the rise. You know, do you think you have the right personnel in place? Because the kinds of people who can thrive in a personality-led sort of hustle-based sale process may not necessarily be the same who thrive under a process-led process. Mm-hmm. Then, Justin, one for you. On the Chromium sort of install base, I think you laid out sort of 4,150 units so far, but can you give us a sense for what fraction of those are perhaps not really being used anymore? You have an upgrade cycle underway, so I'm assuming some of the base controllers are not really sort of in that sort of active unit install base. I guess maybe I'll start and. You wanna start and I'll follow up? Go ahead, yeah. You know, so what we're able to track through our customer data is for those Chromium X and iX placements, you know, what's going to new customers and what's going to existing customers. Like I said, for the last few quarters, it's been about half and half. Unfortunately, we aren't able to tell for the original Chromium controller whether that was still being used, kept as a backup, passed to the lab down the hall. You know, there's not telemetry on that product. There's no way of telling. Great. In terms of our sales organization, I've been here a little over four months. One of my missions in the first 90 days was to do a lot of listening and learning. I have traveled with and talked to and met with and really started to dig into our sales organization and other parts. I don't wanna only focus on sales. What I'd first say is we have great people. I wasn't trying to characterize them as sort of just hustlers. They are really substantive people. They're technically capable. They are skilled at selling. What I have heard from them, and of course, it's great for me to hear it, is they really want to see us put tools in place that are more universal, more organized, take the burden off them from trying to run themselves, provide them with an environment where they can be successful. What I often say is, you know, we don't wanna create a bunch of robots. The brilliance is in the individual, and what we have to do is create an environment where that individual's brilliance can come out. When they're spending their time, each trying to do things differently, each having to sort of make up sort of the mechanical and systems functions for themselves, we're not getting the best out of them. I have very high confidence in the quality of people we have. They have great relationships. What I wanna do is make a better world for them. They're excited about that, I'm excited about that, and I actually think it makes a huge difference. You know, of course, there's other things like what Serge talked about. I think our focal point for selling when you have a new technology, particularly in the academic market, sometimes you can just show them something that's really interesting. They go, "Oh, I know what to do with that." As we get into pharma, as we get into translational science and other markets, we really have to be application-focused. I'm a really technical person, and I really care that when we go talk to a customer, we present a problem and an application, a solution that lets them know we understand them. That, I think our team is absolutely capable to do. As a commercial organization, we got to give them that ecosystem so they can do it well. Those are the types of things that I'm really focused on. Good. We're at time for Q&A. I'll invite Serge back up for some closing remarks. We've covered a lot of content today. Hopefully, you guys appreciate it. Hopefully, you enjoyed seeing the team, seeing all the data that we have shared and all the different aspects of the company. Just coming back to my first slide, if it's hard to remember all of this content, well, first of all, it's available online if you want to go back to it. But really, if you remember these five things, I feel like we've accomplished our goal for the day. First of all, 10x is built, I hope you appreciate it, with a long-term orientation. That's how we think about our investments, that's how we think about, the kind of work we do, and that's how we think about, investing fundamentally in the foundational capabilities. Our innovation engine, which covers many aspects of the company, certainly R&D, but also other functions, is the fundamental competitive differentiator for us. We are very much in the early stages of these multiple big market opportunities. We're just getting started. We are now already an established leader in life sciences. We have big, large commercial scale. We've got large operational infrastructure and a great basis for a strong financial profile. Finally, hopefully, you appreciate the quality of the team and ultimately, the source of our success going forward. Thank you all for coming and spending the time with us.