All right. Good morning, everyone. Welcome to day one of the JPMorgan Healthcare Conference. I'm Julia Qin, Lead Analyst covering life science, tools, and diagnostics at JPMorgan. It's my great pleasure to introduce you to our very first presentation today by 10x Genomics. We'll do 20 minutes of presentation and 20 minutes of Q&A. Just a reminder for the audience, if you have a question, there's a function to submit your question through the digital conference book online, and hopefully we'll be able to address it over the Q&A portion. With that, let me turn it over to Serge. Thank you, Julia. Thank you. It's great to be back here in person. Before we begin, I want to point out our safe harbor statement on Slide 2 and invite you to visit our website for additional disclosures regarding forward-looking statements made today. As I stand here today, we're in a great position. We have a large revenue base, rapid growth to get here, thousands of instruments installed around the world, strong employee base, a formidable intellectual property estate, and thousands and thousands of publications that have come out of our customers' labs making fundamental scientific discoveries enabled by our products. We have come far, but it's still very early days because what we see in front of us is massive growth. We see a set of opportunities that are as large as anything in the history of life sciences. This is the century of biology. The progress in the life sciences has been on exponential trajectory, driven by the advances in miniaturization, computation, and compounding effects of biological knowledge. These advances have potential to transform the world, completely change human health. The main challenge, the main obstacle to overcome is that we still understand very little of the underlying biology. The amount we don't understand, don't know, is still much greater than what we do know. Our goal at 10x is to accelerate the understanding of biology, to accelerate the mastery of biology, to ultimately advance human health, to lead the revolution that we see unfolding over the coming decades across life sciences. The thing to appreciate about biology, what makes it so different from other disciplines, the most salient feature of biology is its immense complexity. Each one of us has close to 40 trillion cells. Each one of those has millions of molecular species interacting with each other in all kinds of myriad complex ways, always changing in a dynamic fashion. The only way to really address that complexity, to drive that further understanding of biology, is really to be able to measure all these objects at massive scale, at high resolution, in a systematic fashion. It wasn't really until the advent of genomics as a discipline that people have started looking at biology with this kind of a large-scale systematic mindset. The initial focus has been on DNA, with the Human Genome Project, followed by genome-wide association studies, followed by population sequencing. Essentially, what all these projects have been doing is compiling a parts list of human biology, all the genes, all the variants. Another question going forward is, like, what do all those genes, all those variants actually do? DNA is very static, and what needs to happen is for genomics, broadly defined, to expand downstream into epigenetics, into gene expression, into proteins, into cells, into tissues, the actual stuff of biology. To be able to measure all those elements at large scale, at large resolution, that's how you will drive the understanding. That's how you will ultimately drive the ability to manipulate biology and drive the cures. We made this point back when we were going public three years ago. If you look at the last several years of the industry, it's precisely the direction that things have been evolving in. With the rise of multi-omics, the interest in single-cell biology, the arrival of spatial biology. The challenge, of course, is that the conventional tools of biology, of life sciences are just not adequate for this job because they lack the necessary resolution, the necessary scale. To give you a sense, a reminder of what I mean by resolution and scale, on the left is how people used to measure biological samples. If you need to measure gene expression in your sample, you take all the cells in that sample, mix their contents together, then measure that mixture, which gives you an average profile, the average across all the cells in your sample. Now, for the past several years, researchers, you can see on the right, have been able to use our system, our Chromium system, to measure individual cells, thousands to millions of cells in a single experiment, measure the full transcriptome profile of each cell. What you see emerge is a vastly more complex picture that's vastly more informative than you could ever hope for when you're relying on previous generations of tools that were low resolution and low scale. The products that enable these insights, that generate these kinds of views, our products are comprised of instruments, consumables and software. We sell instruments, that's a source of upfront revenue for us. Consumables provide the recurring revenue stream. Software, for the most part, we deliver for free to enable the full end-to-end solution for our customers. We sell these products to researchers, whether in academia, in research hospitals or biopharmaceutical companies. They really enable scientists to measure biology, see things they could not see before. From the beginning, we set out our ambition was to build the next generation of technologies to kind of address the complexity of biology. We realized early on that that entailed becoming very good at a number of very disparate disciplines. That's what we've done from the beginning. We invested in developing deep expertise across all these different areas: hardware, software, chemistry, biology, and many others. Also invested very intentionally to develop the right processes, the right cultures to enable really tight multidisciplinary collaboration, to be able to innovate and develop products at speed. This really speaks to one of our core fundamental tenets about how we've built the company is to invest in foundational capabilities that then become an engine for generating innovation and sustained differentiation. This innovation engine, this ability to rapidly build products and knowing what products to build, we see as our core competitive advantage. We think really hard about where the world is going? What questions scientists are going to be asking? What are the big questions that are going to emerge in the future? And work backwards to determine what technologies, what products need to be built in the service of that? Once we gain conviction, we move rapidly to deliver awesome products to market. It took us less than three years to go from a standing start to ship our very first product. If anything, our innovation velocity has increased since then. In fact, last year was the biggest year of our product launches in our history. We are immensely proud of the team we have at 10x. At the same time, we realize we don't have a monopoly on smart people or a monopoly on innovation. M&A is a core part of our innovation strategy as well. As with everything else, we work backwards. We think about where the world is going? What technologies would be necessary to get there? We formulate a strategy based on that. If we see something outside our walls that could be helpful, that could be useful for that strategy to drive to our goals, we work hard to bring those technologies, those assets into the company. This is where the unique strength of our product development engine really comes in that we're able to take assets all across the spectrum from really initial embryonic ideas to more mature concepts, plug them into our product development engine, and build them into awesome products, and then leverage our commercial infrastructure to then build them into franchises. That's precisely what we have done across the years, and we take special pride in helping founders realize the full potential of their vision and their inventions. We have used these capabilities to build products to enable the future of biological research. It's also become very clear to us that preserving single-cell context is going to be critical across just about all biological measurements. At a high level, from first principles, there's roughly three fundamental approaches, technological approaches to preserving single-cell context. Those are approaches represented by the three platforms that we have developed. What that means is that by virtue of having them all under one roof, we have confidence that we have a full suite of solutions to enable the future of biological analysis. These platforms are also highly complementary in terms of the insights they provide for the customer. They by virtue of that, they provide a lot of value, the three of them together, a lot of value to our customers now. Our Chromium platform, this is the platform that catalyzed the single-cell revolution, created the single-cell market as we know it today. It has, it works with dissociated cells, so thousands to millions of individual cells. It allows analysis of broad range of analytes, from gene expression RNA to epigenetics, to proteins, to immune cell receptors, and also in combinations with multi-omic context. It's known for high performance, quality of data, ease of use. We've now sold thousands of instruments around the world. There is now this large ecosystem of customers, protocols, papers, datasets that has emerged. That ecosystem provides a lot of value to our customers because it allows them to get help, to collaborate with others, to put their results in the context of other publicly available data, and to learn from each other. We're continuing to invest, to make big investments in the Chromium platform, both in terms of the core capabilities of the platform and also in terms of the workflow around it to remove bottlenecks, whether it's upstream and sample prep, the core workflow, downstream and data analysis. Just last year, we launched several new products to help with workflow, to open up new applications, to answer, solve some of our customers' biggest challenges. Our Nuclei Isolation Kit is our very first sample prep product. It's designed to help customers extract clean nuclei from their samples. This is something that a lot of our customers have been really keenly interested in, but also had mighty struggles with trying to do this well. This kit allows them to do this very simply in a highly efficient, highly scalable workflow, opens up all kinds of new samples in a very straightforward fashion, opens up the possibility of working with frozen samples and many challenging cell types and tissues. BEAM is a product that's built on top of our immune profiling solution, which allows customers to analyze up to millions of individual B or T cells, measure the immune cell receptors, and also now with BEAM in parallel, at the same time, at scale, you can measure the antigens those receptors are binding to. This is a big, new, really exciting capability and particularly exciting to our biotech and pharma customers. Finally, Gene Expression Flex. This product, which we initially launched with the name Fixed RNA Profiling, allows fixation at the point of sample collection. By virtue of that, addresses probably the biggest logistical constraint that people have had running single-cell analysis, the necessity of working with live tissues. By virtue of now being able to fix, this opens up a whole new kinds of experiments, removes major constraints, and really addresses some of the biggest issues that people have faced with single-cell analysis. Flex, this is great, but Flex is much more than that. We see it as the new standard for single-cell gene expression. It's built on top of a new kind of chemistry that results in leading performance and really great sensitivity. It also has built-in multiplexing, which allows for massively scaled experiments. Many more cells, many more samples per run. It's also, we showed recently that it's compatible with FFPE samples, formalin-fixed, paraffin-embedded samples. This is how the vast majority of clinical samples are processed and stored. There's vast archives of biobank samples that exist out there, millions, hundreds of millions, probably more than that are all stored as FFPE. Until just a few months ago, none of them were accessible to single-cell analysis. This is a big development and will drive expansion of single-cell into translational and clinical applications. We have come a long way with Chromium, but it's still early days, and we continue to invest along multiple dimensions of product development. Certainly with sample prep solutions, and especially adding fixation to more analytes, more applications, and also continue to simplify and streamline workflows. We keep investing to drive more scale, both in terms of cells and in terms of numbers of samples, that entails addressing the total cost of the experiment and also removing any logistical barriers to running larger experiments that our customers might face. As we have always done, we keep investing in more core capabilities, more analytes, more performance metrics, and more multi-omic measurements. Now, while Chromium allows you to measure what is happening, to see what is happening in your samples at the right level of resolution, at single-cell resolution with the fundamental unit of biology, with Visium, our second platform, you can see where things are happening in your sample, how the molecules are arranged with respect to each other in tissue. It gives you an unbiased analysis of the entire tissue, the entire transcriptome, and as such, is really bridges the worlds of histology and molecular biology together, worlds that previously existed far apart. It's a relatively recent platform, already has established itself as the leading platform for spatial discovery. It's been used now in thousands of labs around the world. There's over 400 publications or preprints. Has vastly more customer data sets that's been generated with this platform than any other spatial platform. We're now especially excited by the recent acceleration of this franchise with the launch of our first Visium instrument, the CytAssist. CytAssist addresses the biggest challenge that our customers have faced working with Visium by allowing them to use standard histology slides and standard histology workflows in their work. In fact, what it does, it marries your typical standard glass slide with a tissue mounted on it with the special Visium slide and transfers the molecules from that tissue onto the Visium, onto the slide. By virtue of that, it greatly simplifies all the logistics around the Visium workflow. It results in a simpler and more robust workflow, significantly improves the quality of the data, and it also provides now access to all the archived samples that exist out there, where the slides are already pre-mounted with tissue. It's been met with great enthusiasm by our customers, and we see it as by far the best way to run Visium and is the future of the platform. We're really excited by how far Visium has come and also the, by the potential of it going forward and are investing aggressively to bring more capabilities to the platform. High resolution, expansion to more tissue types, more workflows, measuring more and more analytes, including with multi-omic context. We see Visium as really the ideal tool for unbiased spatial discovery. Once you know what you're looking for, once you have a set of genes you want to focus in on, Xenium, our third platform, becomes the ideal choice for you. Xenium allows you to measure large panels of molecules directly in tissue, in situ, in a fully integrated fashion. As we announced last month at our investor day, we have now officially started commercial shipments of the platform. This is something that would've seemed just like science fiction just a short while ago, you know, it provides this incredibly detailed view of your tissues of subcellular resolution, but it is now a reality. Even more so, we built a system for routine use, fast turnaround time. It'll be a workhorse in labs around the world. Produces very clean data. It works with custom panels. Importantly, you can also stain the same tissues with H&E, which makes it compatible with standard pathology. This is a very powerful platform, but this is just the beginning. In fact, what really excites us here is that this technology has tons of headroom to keep innovating for many years in the future. We are investing across the board to bring more analytes, more throughput, higher plexy, more content, more analysis to the platform. Really extensive roadmap to bring more future capabilities. In the longer term, we see there's great powerful clinical potential to this platform 'cause if you step back and think about the capabilities of these technologies, it has the makings of an ideal clinical instrument. It allows for high plex targeted measurements together with imaging, all in integrated fashion. In many ways, it's ideally suited to bridge the worlds of digital pathology and molecular analysis together, and with it, has the potential to transform medicine. Our three platforms are built on very different foundational technologies. At the same time, they have many technology components in common and share many workflow elements, share many software elements, and also importantly, provide complementary insights for our customers. There's this great synergy in having them all under one roof in terms of product development and also for our customers to use all three platforms together. We have built these products to enable fundamental measurements to drive deep insights. That's precisely what they have done. Thousands of papers around the world in just about every area of biology, answering a huge diversity of questions. It actually is really hard to think of an area of biology, field of the life sciences, where these technologies have not led to fundamental breakthroughs. The reason for that, and maybe one of the major findings of the last several years of biological research, is this pervasive cellular heterogeneity that exists in essentially all the tissues, all the biological system one looks at. Every tissue you look at, there's this large diversity of cells, cell types, cell states that lies underneath. That's where the crux of biology is. That what makes healthy tissue different from diseased tissue. We believe that over time, just about all tissues will need to be analyzed with single-cell context and at scale. This is true of just about any therapeutic area you would wanna look at. If you look at cancer, you have to consider tumor heterogeneity, you have to consider the microenvironment, the immune component. You have no hope of understanding any of these elements of how they interact without cell-specific measurements. If you look at autoimmunity, all of the diseases really, autoimmune diseases really come down to these complex cell and gene expression networks, and you have no hope of understanding or diagnosing or ultimately curing them without single-cell context. When you consider neuroscience, which is probably further behind in our understanding, oftentimes feels like a black box when we're dealing with these diseases. We have built these large catalogs of gene associations, but it's been a challenge to figure out how these genes actually impact phenotype. What do they actually do? With single-cell analysis, we have started to open up that black box. The brain has this very large number of cell types, and the effects of genes and the fundamentals of these diseases ultimately come down to what's happening in individual cells and individual cell types. Again, it's just really hard to think of a disease or the therapeutic areas where these products, these technologies have not driven fundamental insights, important discoveries. When you consider all the different applications, all the different analytes, all the different customer use cases where these platforms are used, they're really replacing the legacy toolkit of across the life sciences. Of course, this was by design. Our goal from the beginning was to bring a new generation of tools and technologies to the life sciences. This represents a challenge when it comes to estimating the size of the market opportunity here, because we're not replacing any particular technology. We're not restricted to any particular application. In fact, we're drawing dollars, we're drawing customers from all across the ecosystem. While there's not a very simple way to precisely estimate the market, there's different ways we have used to triangulate on the magnitude of this opportunity. In particular, if you focus on just the research markets and look at it through the lens of the kinds of questions that researchers are asking, where single-cell and spatial analysis can provide critical answers, four high-level categories of questions emerge. One, the first one is atlasing, which is basically the baseline characterization of your biological systems. Figuring out what cell types exist, and how they all interact with each other. This is where single-cell really got started, and there's still way much more to do. Researchers are keen to understand, to expand to more types of samples, more genetic backgrounds, ethnic backgrounds, different phenotypes, health, disease, longitudinal measurements. In particular, for these researchers, it's important to start to measure more and more analytes. To move beyond gene expression into epigenetics, proteins, spatial analysis, multiomic measurements. The second category is essentially functional genomics. These are, this is research that's looking to understand, to go from DNA, from genes, to understand how those genes actually function. This is where the focus of a lot of genomics research now is trending toward. For these researchers going forward, scale becomes particularly important. They wanna run larger and larger cohort studies, do more and more cells with the larger and larger perturbation experiments. This third category is really essentially mainstream biologists. These are the people that are like your typical biologists who's working on particular biological system, a gene, a pathway, an organ system. They typically use a collection of conventional tools to drive their science. Here, the big opportunity is for them to go upstream to gain all this genome scale and cell-specific context to truly illuminate the biology they've been seeking to understand. We've made some initial inroads with these customers, but it's still very early days. These are not technologies, technologists. For these customers, ease of use, ease of data analysis, and cost become particularly important. The last category is translational research. This is within, for clinical research or within biopharmaceutical companies trying to drive toward new diagnostics, new therapeutics. We've established a great beachhead with these customers. There's a lot more opportunity to expand going forward, especially with the arrival of fixation, which is really important for these customers, for these kinds of experiments going forward. For them as well, it's ease of data analysis and reproducibility of workflows become also critical. We have this tremendous set of opportunities in front of us, and we have the commercial scale to maximize the realization of these opportunities. We have a large direct sales force. We have best-in-class customer support people that are obsessed with customer success. We have marketing people who really seek to understand and inform the customer. We're building on top of a strong foundation to deliver the next level of commercial excellence and the next phase of growth. In fact, our commercial infrastructure, we see it as a core pillar of our competitive advantage. We strive to form deep relationships with our customers to really understand the questions they're asking, the direction they're going, what questions they're going to be asking in the future, and feed that information back into our product development engine to put out products that our customers love. That then further deepens the relationship, thus feeding the product development and the innovation cycle, which then also allows us to put out highly differentiated products with superior economics, high margins, which allows us to invest more and more into our innovation, which also feeds by allowing us to build out really exciting new technologies which attracts amazing people who want to work on hard problems to make a dent in the universe, thus adding to our innovation capabilities in this ever-growing virtual cycle. This innovation engine that really encompass the entirety of the company has been the foundation of our success and the source of enormous confidence for us going forward. Which is why, as I stand here today, I have full confidence to say, as far as we have come so far, we're still just getting started. Thank you. Thank you for a great presentation. Welcome, Justin, over to stage to join us Q&A session. Maybe let's start with a macro question. I know obviously you guys are not, you know, reporting 4Q until a month, but, you know, especially given the China reopening and, you know, the resurge of COVID cases there, I think a lot of people are interested in learning, especially given your China exposure. Are you seeing any meaningful impact in China, you know, over the last couple months? How do you see the China kind of demand situation evolving in 2023? Sure. Well, to start with your question on the macro environment, it has been a dynamic macro environment overall. As far as the quarter, I would say that, you know, overall, we're pleased with how the quarter shaped up. We're not doing a pre-announce, obviously we're not announcing the revenue, but we're pleased with how the quarter shaped up. As far as China specifically, you know, we called out some risks in on our Q3 earnings call. Across the world, there was puts and takes that would impact Q4. I'm sure you all saw the news, saw the level of disruption there. Yeah, there was a level of disruption there. Gotcha. How should we think about, you know, the puts and takes for now? Obviously not asking for any specific guidance as you'll wait for 4 Q to do that. Just qualitatively, you know, obviously there are a few moving pieces, right? There's the macro pressure that you mentioned, but you guys are also very excited about your new product cycles. I mean, is it fair to say that, you know, the two factors will largely offset each other, so you'd be looking at a growth rate in 2023 that's, you know, more or less similar to your initial guidance for 2022? Yeah. We'll get to guidance on our February earnings call. But I think we have a good setup going into 2023. It's the first full year of Xenium, Flex, CytAssist. You know, but overall, the macro environment does remain dynamic and difficult to predict. Mm-hmm. Gotcha. One of your peers pre-announced their orders for their in situ spatial platform this morning. I know you guys typically don't provide order book for any of your products. Any qualitative color you can share about Xenium, you know, how the initial launch is going and what the demand looks like? Yeah. Demand for the product is strong in Q4 for both orders and shipments. We exceeded our own expectations on those. Great. As we think about the launch, do you think, you know, the launch trajectory will kind of, you know, jump-start because, you know, there's a lot of pent-up demand there? Do you think kind of, you know, the order book will gradually build up over time as you get more publications and external validations? How do we think about the shape of the curve there? Yeah, I think you have to disaggregate the orders from the shipments. Like we've said many times in the past, we're focused on making sure that our early customers are successful and that we don't get too far ahead of ourselves with placements. The focus right now is on the customer and the early success of those first placements. I guess I'll just add that, in our experience, demand is not a problem here. We've said that before, and that has been continuously validated what we're seeing in the market. Mm-hmm. How about the supply side? Are you gonna be constrained in terms of, you know, manufacturing capacity? Well, we're doing everything we can to mitigate that. You know, just looking over the past couple years, not just that product, but all products, there's been things that have popped up that our teams have been able to manage. We've done a lot of things like, you know, buying ahead inventory. We've done that for Xenium as well. So, you know, we're controlling, what we can control. Again, the environment is still unpredictable. Great. A reminder for the audience, if you have a question, you can submit it through the digital conference book. There's also a mic going around the room, so don't be shy to raise your hand if you have a question. Maybe stepping back and looking at a bigger picture, you've highlighted the synergistic nature of having all three platforms in your portfolio, and you actually gave a very good example of customers using all three platforms in a very complementary way in your recent analyst day. I'm just curious, you know, as you look across your current customer base of, I think, over 4,000 customers, how many of them or what types of customers would have the kind of use cases that would require the deployment of all three platforms? And how many would, you know, more likely to focus on just one platform? I know obviously, you know, heme versus solid tumor is a clear distinction, but maybe you could give us more nuanced color regarding, you know, other factors like biopharma versus academic or other use cases? Yeah, I mean, we'll have to see how it plays out. Certainly our initial core customer base, a lot of our sort of genomics and technology-oriented people, I think they will be naturally predisposed over towards having all three because they can most naturally take advantage of all three platforms and can think of the use cases most readily. Kind of as we expand into more other kinds of customers, I do see there's some potential of sort of some amount of splintering where some customers, in particular on the spatial side, might be keen to, you know, kind of work with tissues and not have to go through the sort of the dissociation route. We'll have to see. It's not yet the case so far. We do think that, you know, spatial has a lot of potential to bring in new customers kind of into the fold as well. It has potentially sort of the effect of like, once you start measuring, we've seen this already happen, once you start kind of running your spatial experiments, you then actually naturally start asking questions like, "Well, okay, like what can I learn more about those cells?" That leads you down toward the single-cell route. I think it can sort of go both ways. Got it. Great. I know there's been a lot of question on, you know, whether Xenium is going to cannibalize Visium. I think I can fully appreciate that the two platforms will be more complementary, at least in the near term. I think the more interesting question is what is the relative relationship between Xenium and Chromium in the long term, right? Both can deliver, you know, especially with Visium HD, both can cover whole single-cell resolution, both cover whole transcriptome. How will customers choose whether to use Visium HD when it comes versus Chromium for that initial state step in that discovery process? Yeah. It's an interesting question, and some of the answers just, you just have to kind of admit that no one really in the universe yet knows the answers to those questions because it's an intersection of like science and technology with some un-fundamental unknowns over there. We do... I mean, there are some elements, some ways in which the Chromium approach is actually like really, really attractive 'cause it allows you to kind of achieve a census of your cell states in a very efficient way. Oftentimes, you don't need to sample, you know, 2 million cells to determine what cell exists in your sample, and doing 10,000, for example, or 100,000 is plenty, and Chromium is very efficient in doing that. If you think in terms of experiments where you need the census of the cells, and especially if you want to, you know, to go really deep in terms of profiling immunes, immune receptors or some other kind of really intricate features, Chromium can be really, really powerful, and is likely to stay here for a long, long time as the kind of workhorse for those kinds of experiments, very much complementary to what people are doing. Mm-hmm. -with Visium HD or other approaches. Mm-hmm. Okay. I know you're very excited about Xenium. As you launch and, you know, put your sales and marketing focus on that platform, how are you managing kind of, you know, the balancing of all three platforms in your portfolio? How do you manage the risk of, you know, not distracting your sales force away from, you know, sales of the Chromium platform? Yeah, it's a really good question and something we definitely have been very, very careful and intentional with. We've put together like a really thoughtful incentive structure for our commercial team going forward to kind of... I think we're doing a good job of balancing all of the incentives. It is definitely up until recently, obviously, Xenium, you know, wasn't there. Now that we have launched it's a core part of our offering. I think it is definitely we're balancing the incentives. At the same time, we strongly believe that our, you know, the size of our commercial force is a, is a big advantage here because these three platforms oftentimes are really go together and it helps, you know, going into a customer with sort of value proposition around one oftentimes helps to bring in the other platform into the mix, into the conversation. We're finding that consistently. Our goal is, A, kind of balance the, you know, the incentives appropriately, but also leverage the size of the sales force and the synergy between the platforms to drive sales of all three. Mm-hmm. Great. Sticking with spatial, any update on the timing of when we can expect Visium HD? Look, Well, as you know, customers are very interested in this platform. We are very strongly committed. We're working really hard. When we're ready to give an update, we will give an update for sure. Cool. Any questions from the audience? All right, let's move on to the single-cell side of the business. I'm a big believer in the long-term potential of single-cell. I believe there's a very long penetration runway there, but I think price elasticity is really the pivotal point of, you know, how to think about the revenue inflection going forward. Just curious, you know, can you talk about how you envision that price elasticity play out? Do you think the incremental demand will mostly come from existing users scaling up their studies or from, you know, more users joining single-cell studies? I mean, I think ultimately the answer is both. I do strongly believe in the elasticity of demand here, again, for various principles because, as I said in my presentation, ultimately all measurements have to go. If you start with tissues, if you start with cells, they have to go to single-cell context. There's huge amounts of dollars in the ecosystem that are being spent on all kinds of other technologies, and they should be available. They should to go into the single-cell realm. It's gotta be there. Cost is clearly one of the obstacles to get there, but there's others too, right? Like I talked about workflow and data analysis that need to be addressed in turn. For that reason, I think there's sort of a time component where price elasticity has a greater impact on a lot of our current customers because they already know how to use sort of single-cell. They sort of worked out all the different obstacles, and so they can scale. With a lower price, they can potentially scale to more experiments. With new customers, it is more of a timing issue where like once you kind of address that barrier, they still have to, you know, educate themselves and get up to speed and think about how to run these experiments. Over time, I think the answer is absolutely both. That makes a lot of sense. Obviously, we don't have a lot of empirical data to rely on for the price elasticity for single-cell. Is there any analogy or benchmarking we can draw from bulk sequencing? I think. Like, are there factors that make single-cell, like, the curve can look different from bulk sequencing? I know obviously, you know, the single-cell is addressing a much more fragmented customer base than, you know, bulk sequencing in its early days. we don't have much, you know, population scale projects for single-cell yet. on the other hand, you know, our science is more advanced. Researchers appreciate the value of single-cell analysis more. how are you thinking about that? Yeah. I mean, I think it's a very natural analogy and maybe the only, like, the closest analogy that exists historically in our industry. Certainly you saw the NGS market. If you think about it, the sequencing, NGS did not replace Sanger sequencing, right? It's a much, much larger market than Sanger ever was. It created all these kind of new markets that drew lots of new dollars into the sequencing ecosystem. Kind of analogously to what I've, you know, the, to what we're seeing with single-cell analysis. It is true that single-cell analysis by nature is more fragmented, but it's also more exciting because it just, it's applicable to so many more things. If you think about sequencing, certainly in isolation, you know, before the revival of technologies like ours, it's very much focused, it kind of limited the genetics and analysis of DNA. Whereas, with our technologies, we're really covering the full range of the life sciences. The potential there is much greater. I do think it's fragmented, and so it's not going to be necessarily just a continuous one curve. There's lots of different customer segments, lots of different applications, and they will kind of materialize in different timescales as we go forward. That is great. I know you're excited about the Fixed RNA Profiling. It's gonna be a really big needle mover for you guys. How long do you think the validation process will take and for us to see a more meaningful inflection? Yeah. With these technologies, people are always, kind of going through a process of initial kind of trying kit first to just see if it works, then starting to scale up to some initial pilots, and then starting to think about, well, maybe this becomes sort of my, more of my sort of default, way of doing experiments. In this case, the particular challenge is that they have to sort of like better than the Beatles problem in that, the existing products are already, really highly regarded. For the Fixed Kit to displace those products, it's a pretty high bar. There's a little bit of that, we should, we should expect some amount of time lag over, you know, over the coming quarters as people convert over. At the same time, we are hearing lots and lots of great feedback from our customers. We're certainly seeing them going that trajectory of transitioning from, "Hey, let me see. Let me try it. Let me run a pilot experiment," to where now people are definitely scaling up into thinking of this as now their default assay for some of their applications. Mm-hmm. We have one minute left, so I was just squeezing one question that our audience just submitted. How has the competitive landscape shifted across your three main platforms over the past 12 months, and how do you see that evolving going forward? Like on the, on the single-cell side, as we've talked about in the past, we've always had some competition. It's a big market that's obviously attractive. Over the years, people have come in various, using various approaches to try to compete against us, and our philosophy has always been invest in innovation, product development, and customer focus. That has been our north star, and we've consistently stayed ahead of competition. I think the dynamic, there was a little more kind of flux this year, a little bit more noise, but ultimately, it's turning out kind of the same way. I feel really good about where we are from that perspective. On the Visium side, a little more noise. I think especially this year, we've made a lot of progress in addressing a lot of the core challenges with our with the workflows and with the usage. We do see now we're like in a very good leading position when it comes to spatial discovery. When it comes to Xenium, well, it's, you know, like we talked about before, in situ is a bit of a, there's lots and lots of players right now, and lots and lots of interest. It's obvious to a lot of companies that it's going to be a huge, huge opportunity. There's definitely a lot a lot of action and, you know, not a lot of clarity yet for for even customers to understand what, how things are evolving. At the same time, you know, again, we have full confidence investing in innovation, investing in customer success, leveraging our three platforms. We intend to lead the way and to make this platform as meaningful as and as important as what we've done with Chromium and even beyond. Excellent. Well, let's wrap it up. Thank you so much for your time today. Thank you. Thank you.