All right. Good afternoon, everybody. I'm Tycho Peterson from the Life Science team. It's my pleasure to introduce our next company, 10x Genomics. Just a quick reminder, if you have questions, you can submit them through the website. With that, I'll turn it over to Serge. Thanks, Tycho. Next slide. Before we begin, I want to invite your attention to our safe harbor statement on slide two and to visit our website for additional disclosures regarding any forward-looking statements that are made during the presentation today. This is the century of biology. The progress in life sciences is in an exponential trajectory driven by advances in miniaturization, computation, and compounding effects of the accumulation of biological knowledge. These advances will transform human health because they will allow us to develop cures based on the actual understanding of the underlying biological systems. Everything from curing cancer to solving Alzheimer's, to getting rid of infectious diseases. The key here is that we still understand very little of the underlying biology. What we don't understand is much greater than what we do understand, and this is a surprisingly underrated fact. Our goal at 10x is to accelerate the understanding and mastery of biology to lead this revolution we see unfolding now and over the coming decades. We start by recognizing the most salient feature of biology. The thing that makes it so challenging and so distinct from every other discipline. It's the fact that it's incredibly complex. Every human is made of close to 40 trillion cells. Each of those contains millions of molecules, and they all change and interact with each other in enormously complex ways. To address this complexity, you need to be able to measure these different objects. You need to be able to measure them at the right resolution and at massive scale. From the beginning, we set out to build technologies to deliver these kinds of capabilities. To give you a sense of what we mean by resolution and scale, on the left is how the world has been measuring gene expression until recently. You would take your biological sample, take all the cells in the sample, and mix their contents together, then measure gene expression of that mixture. That gives you an average profile of gene expression across the cells in your sample, effectively hiding all the underlying complexity. Now with our Chromium products for the past six years, you can take your sample, separate out all the cells across thousands to hundreds of thousands, and measure the gene expression profile of each cell individually. Now you can see all of that underlying biology that was previously obscured. From the beginning, we built 10x to deliver the kinds of technologies that would address the complexity of biology, which entailed being good at a number of very different disciplines. This is something we have invested in and nurtured from the very start. We build deep expertise across a wide range of fields, from biology, chemistry, and microfluidics to hardware engineering, data analysis, and software development. We work hard to set up the right processes and culture to enable tight multidisciplinary work. The core of our philosophy is to invest in foundational capabilities, so that those capabilities become an engine for generating innovation and differentiation over the long term. We don't constrain our thinking to any particular technology or any particular platform. We start with biology. Think critically about where the world is going, what are the big questions, the big capabilities that the world is going to need, and we work backwards to figure out what technologies and products we're going to build. We see this innovation engine, this ability to rapidly build breakthrough products and know what products to build as a core competitive advantage. We have launched more than 20 complex products since we introduced our very first product less than 7 years ago. These solutions include a whole range of groundbreaking capabilities covering single-cell and spatial analysis, multi-omics, epigenetics, immune profiling, as well as others. We innovated across many different fields, pushed the state-of-the-art, and kicked off a revolution in life science research. Our products comprise instruments, consumables and software. The instruments provide a source of upfront revenue. The consumables include reagents, microfluidic chips, microarray slides, and are a source of recurring revenue. We provide the software for free to enable the full solution for our customers. We sell these products to researchers in academia, research hospitals, biopharmaceutical companies. They enable scientists to measure biology, see things they could not see before. We strive to delight our customers. One thing that we're particularly proud of is that our customers often tell us our products just work. That quality, that ease of use is actually a result of tons of innovations and advanced technology development that goes into making our products. We do the hard work on the back end so that to the customer it's easy. It just works. We're obsessed with providing a superior customer experience. That's why we have invested to build a best-in-class commercial team and a sophisticated operational infrastructure. We have over 400 commercial employees, including a large team of customer success specialists. We have established a broad commercial presence all around the world, supporting a diverse revenue profile with over 50% of our revenue coming from outside the U.S. We have also scaled both R&D with sites in California, Sweden and Singapore, and operations with sophisticated, highly automated manufacturing centers in California and Singapore. This infrastructure and our products have driven strong success in the marketplace. Last year, we crossed $500 million in annual revenue run rate. We have an installed base of over 3,500 instruments. Our customers have booked more than 750,000 reactions since inception. There are now well over 3,000 peer-reviewed papers involving our products. It's not just the number of papers, but also their impact and breadth. Our customers are making fundamental advances across oncology, immunology, neuroscience, infectious diseases. In fact, it's hard to think of an area of biology that has not been affected by discoveries made using 10x products. More and more, we see that single-cell methods are becoming the standard for a growing fraction of life science research, an essential element of many new grant applications, and are increasingly becoming a requirement for publications. We see these papers as both an indicator of past success and a foundation for the future. Essentially, they lay out the case, the recipes for why and how to use single-cell methods across all these different areas. In their totality, they make a very strong case that this is just the beginning for single-cell, that we're just getting started. One of the central learnings from all these papers, and perhaps the greatest revelation of the last several years of biological research, is the pervasive cellular complexity that underlies just about every biological system. It turns out that every tissue harbors much greater diversity of cells and cell types than we have thought. All of them interacting with each other in a complex interplay of massive gene expression networks. The implication of this is still very much underappreciated. We expect that in the future, just about all tissue samples, whether for basic research or for clinical diagnostics, will need to be analyzed at single-cell resolution and at large scale. That's because to understand normally functioning human biology and to understand disease, we need to measure and understand what's happening in individual cells. When you think about healthy tissue, you have to imagine a complex ecosystem of cell types and cell states enmeshed in complex signaling networks. In disease, you have a different ecosystem, different cells, different cell states, functioning as a different kind of network. To cure disease, you need to measure and understand these ecosystems, and then you need to figure out ways to shift them from disease to a healthy state. Ultimately, it's all about changing cell behaviors. For example, there have now been many, many studies that have delved into the biology of neurodegenerative diseases, with a particularly large focus on Alzheimer's. Neurodegeneration is an area that's famously lacking in therapies because we have limited understanding of how these diseases develop. We have gene associations, but we don't really know what those genes actually do. Well, we're now learning that many of these genes are expressed in very specific cell types under specific circumstances. Often, these are subtypes of cells known as microglia, whose job at a high level is to maintain a healthy environment within the brain. Changes in these cells are associated with disturbances in the cellular ecosystem of the brain, and thus, ultimately in the disease. On the one hand, Alzheimer's has been this incredibly depressing therapeutic area over the past few decades. On the other hand, there are reasons for optimism. Hard to cure something when so much of what you're dealing with is a black box. Once you start opening up the black box, you have many new avenues for progress. This is what single-cell analysis gives you. Take the example of autoimmune disorders. There have been many, many studies analyzing autoimmune conditions through the lens of single-cell and finding this intricate complexity underneath. These are big studies of Crohn's, psoriasis, rheumatoid arthritis, and many others. They all show that affected tissues contain very different compositions of cells and expression networks compared to the healthy counterparts. Also, these days, there are quite a few powerful therapies to treat a number of these conditions, but none of the treatments are perfectly effective and all leave much to be desired. Given what we've learned about these diseases, it's clear you need single-cell context to really understand how these therapies work, how they cause side effects, and why they fail when they do. For example, last year, there was this great study looking at eczema and response to Dupixent, which is a blockbuster drug that's used to treat this condition. The issue is that the disease comes back once you start treatment. Using 10x, the researchers learned that a special population of cells persisted through the treatment and now should be targeted for a more sustained response. Ultimately, we will need single-cell measurements in the clinic in order to make the best use of these therapies and the future ones yet to be developed. Perhaps the area where single-cell work has been particularly fertile is oncology. At a high level, there are two approaches to fighting cancer. You can make an agent that kills cancer and leaves other tissues alone, or you can help the body in its own fight against cancer. Single-cell is fundamental to both. When we think about the first approach, there has been lots of single-cell work to classify and categorize cancers to extract tumor-specific markers and signatures. Many significant findings across just about every cancer type that have described clusters, subclusters, pathways, and cluster-specific expression signatures. Finding genes expressed in cancer is only half the story. You also need to make sure they are not expressed elsewhere. Single-cell analysis is essential for that as well. There was a great recent study that explained the origin of neurotoxicity associated with common CAR T therapies that target B cells. It turns out that it's due to small populations of cells in the brain that express the same antigen as the B cells. In the future, we're going to need sophisticated combinatorial targets that are in contrast to the simple traditional characterization of tumors being positive for a single marker. The only way to find and measure those combinatorial targets will be through high-plex single-cell analysis. Now, when we think about the second approach to help the body's own fight against cancer, we're mostly thinking in terms of immunotherapies, which have now shown tremendous progress and huge potential. The key here is to appreciate that the tumor microenvironment is really a super complex dynamic system of different cancer cells, normal tissue, and various types of immune cells engaged in a multi-front struggle. The goal is to use therapies to modulate this ecosystem to help the good guys. We currently have two major agents for doing that, two types of checkpoint inhibitors, but many more therapies are coming. The future will be around combination therapies that will rely on sophisticated biomarker signatures to guide treatment, signatures that are hyper-specific to the particulars of the patient's cancer. Given the underlying complexity, these signatures would need to be based on high-plex single-cell measurements. Single-cell is proving to be foundational for driving new insights and discoveries across biology. One of the ways this shows up is that our customers keep getting more funding. Their overall funding has grown at a rate that's twice the NIH average. Their discoveries allow them to get more grants, which drive more discoveries, and so on in a virtuous cycle. Yet, on average, our customers still use single-cell for only a fraction of their experiments, and we expect that to increase over time while their total funding keeps growing as well. In the meantime, the overall funding for single-cell research has been increasing rapidly, averaging about 40% over the past few years. Now, this means that while we see a lot of growth ahead within our customer base, we also anticipate many new customers entering the ecosystem. In fact, our current customer base is only a small fraction of the overall opportunity. If we look at just within the U.S., there are over 90,000 labs who have applied for NIH funding in the last several years. Of those, over 50,000 can already benefit from single-cell and spatial genomics applications. Less than 5% of them are 10x customers. We're seeing more and more that single-cell is becoming a requirement for publishing, which helps bring new researchers into the single-cell ecosystem and drive more experiments with current customers. Whether we focus on our existing customers or the large universe of new potential customers, we really are just getting started with this opportunity. Now, while academia has been a big driver of single-cell adoption, the utility of our technologies is becoming increasingly clear for our pharma and biotech customers as well. Curing disease is ultimately all about changing cell behaviors. Single-cell has powerful applications throughout the drug development pipeline. Given what we now know about human biology, single-cell should be foundational to how therapies are developed in the future. Big pharmaceutical companies started trying single-cell almost from the very beginning and have grown their usage consistently over time. More recently, we're seeing increasing adoption by many smaller biotechs. There are already lots of use cases in early discovery phases for target identification and validation. As we look ahead, we see a big opportunity for single-cell to expand into later stages of drug development, which require more samples and entail larger budgets. There are already intriguing examples in lead identification and optimization, especially around antibody discovery and cell therapy development. Now there's increasing interest in preclinical translational work to understand the effects of therapies in model systems and on patients. Ultimately, all of this is headed toward the clinic with the goal of using biomarkers to help guide therapies. The key thing here is that this is all still very much early days. Probably the biggest challenge, especially in the later phases, is that single-cell analysis currently presents significant logistical hurdles, especially when it comes to clinical sample processing. Helping our customers solve these challenges is an important priority for us going forward. Now, as we look ahead, it's unambiguously clear to us that in the future, the vast majority of biological tissue samples will need to be analyzed with single-cell context and at large scale. This is going to be true for research, for clinical, and for therapeutic applications. From first principles, there are three fundamental technology approaches for delivering this kind of information, and these are represented on the slide by the three technology platforms that we have been building out. The Chromium approach allows you to work with dissociated cells. This has been the catalyst for the single-cell revolution that has been sweeping biology for the past several years. With Visium, you work with intact tissues. It allows you to measure in an unbiased way where molecules and cells are located. It gives you spatial information. Ultimately, it's the best approach for translational discovery. Once you know what you're looking for, In Situ presents an ideal approach for assessing specific targets at high-plex, high resolution with a fully integrated workflow. It's particularly great for downstream characterization, validation, and ultimately clinical applications. We have all three platforms, all three approaches under one roof and investing aggressively across all three to bring the future of single-cell and spatial analysis to our customers. The Chromium platform was launched in 2016, and as I mentioned, ushered in the single-cell revolution that has been affecting just about every area of biology. Since the initial launch of single-cell gene expression, we have released many new capabilities and versions of our products focused on expanding the range of applications on our platform, including immune profiling, epigenetics, proteins, and multi-omics. We have expanded the fundamental capabilities of the platform, highlighted by the release of the Chromium X instrument last year, which has made it possible for people to run up to 1 million experiments routinely. While Chromium is a go-to platform in many labs for some types of studies, it's still very early in its adoption. Our goal is to make single-cell analysis accessible to every biology lab in the world. With that in mind, we launched a series of products last year and are continuing to invest aggressively going forward. Our goal is to remove bottlenecks, enable broad adoption, and create more value for our current customers. Now, right now, one of the biggest obstacles to doing single-cell work is that it requires viable live cells. This poses a particularly big challenge for translational or clinical work. Imagine the kind of constraint that puts on a customer working with human samples. To maintain cell viability, you need rapid sample acquisition from the clinical team, pathology assessment, transportation to the lab, cell dissociation, sample resuspension, and then single-cell analysis, all within a day or less to maintain cell viability. You need this highly coordinated routine, which is especially challenging for staff-limited groups and for multi-site clinical trials, and even more so in a pandemic environment. To start addressing these logistical challenges, we're launching a Fixed RNA Profiling Kit later this year. It will allow people to fix their tissues at the time of collection so that the patterns of gene and protein expression are chemically frozen in place. This will use a common fixation technique, but we'll couple it with powerful new assay chemistry that we developed specifically for this purpose. Once the tissue is fixed, you no longer need to rush. You can store the tissues, ship them, and process them at a time and place of your choosing. This means you'll be able to collect samples from multiple locations and easily batch them for your control experiments. This kit will allow measurements of whole transcriptomes as well as proteins through our Feature Barcoding technology. It will also provide a streamlined approach to multiplexing samples. We expect this product to be a significant enabler, especially for translational and pharma customers. Now, there are many areas where single-cell approaches provide totally revolutionary capabilities. In particular, with single-cell, you can exponentially transform the process of antibody or T-cell discovery. The idea is that you barcode libraries of antigens, and then in a single run, you can analyze millions of B cells or T cells to determine which ones bind to which antigen. This application has been attempted by some of our most sophisticated customers, but it really requires additional capabilities to deliver a complete solution. We have built out these capabilities, in particular around technologies for barcoding antigens, as well as the software to process and analyze this incredibly rich data. Later this year, we'll be launching these solutions as our BEAM-Ab and BEAM-T products. With BEAM-Ab, you effectively get a general platform for antibody discovery. As a demonstration of the power of this platform, we ran a single experiment and in a week to find antibodies against SARS-CoV-2. We found 55 human broadly neutralizing antibodies that have picomolar affinity. Two of them, in particular, neutralize SARS-CoV-2 and other endemic coronaviruses. The point here is not that these antibodies are outstanding, although they are. It is that with these capabilities, anyone will be able to discover excellent antibodies with a minimum of effort or risk. Now, turning to the Visium platform. Launched two years ago, it is particularly early in our adoption cycle, both in terms of product capabilities and in terms of its customer impact. We made significant progress along both of these axes in 2021. In June, we launched Visium FFPE, which expanded the capabilities of the platform to formalin-fixed paraffin-embedded samples, which is the standard way that samples are collected and stored in pathology. This made Visium the only platform to enable true discovery in FFPE through delivering the full transcriptome across the entire tissue. We have hired a new team of dedicated tissue support specialists and are using customer feedback to optimize experimental protocols, enhance current offerings, and develop new products. The number of Visium publications and preprints is now at over 185, which is nearly triple the number from a year ago. This is all just the beginning for Visium. We're continuing to invest aggressively to build out additional core capabilities and to keep extending our leadership in spatial discovery research. We're really excited about our upcoming Visium product pipeline. This includes the ability to measure very high levels of proteins simultaneously with gene expression. It also includes the ability to go much higher resolution of Visium HD. With CytAssist, we're going to introduce an instrument to simplify the workflow and bring large numbers of new samples into the Visium ecosystem. The idea here is that one of the most exciting things about Visium and spatial biology more generally, is that it has this revolutionary potential. This technology brings together the previously distinct worlds of histology and molecular biology. That's very exciting. It's also a big logistical challenge. Histology people are not used to working with molecular biology protocols. They're also often uncomfortable adapting their tissue workflows to the requirements of Visium slides. On the other side, genomics people have no experience with tissues. To help bridge this gap and address these issues, we will launch Slide Assist later this year. The instrument will take tissues mounted on a standard glass slide and transfer molecules from that tissue onto a Visium slide, all while preserving the spatial location of those molecules. This means that histology people can simply do what they have always done, slice tissues and store them on regular glass slides. Separately, at the place and time of their choosing, the customer can preview and choose the best tissue section for their Visium assay and initiate Visium workflow through the Slide Assist. What's more, Slide Assist will open up significant archives of tissue samples that are already stored in preexisting slides. Overall, we expect that Slide Assist will really simplify the workflow for many of our customers and will significantly expand the number of samples that can be run on Visium. With all these new capabilities and its pace of adoption in the market, Visium is on its way to becoming an ideal platform for translational discovery. In addition, in parallel, we're also investing aggressively in developing our in situ platform. In situ is meant to help customers analyze their tissues at an incredible level of detail once they know what genes they're looking for. The in situ approach can be viewed as analogous to IHC and FISH, which are commonly used in pathology, but scaled to vastly greater multiplex levels with vastly greater amounts of information content. In situ should serve as a great follow-up validation tool for discoveries made using our two other platforms. As more research and more translational work get done with Chromium and with Visium, in situ will provide a natural format for many of these applications to be ultimately adopted in the clinic. This platform is based on technologies we have gained through the acquisitions of ReadCoor and Cartana, as well as a large number of internal innovations. Since acquiring Cartana, we have continued running their service as a special program to answer customer needs and to learn from the market. We have now run more than two dozen projects, which have given us a great perspective on customer needs, applications, and features they find most valuable. We're using these incredible insights as we work in developing our brand-new in situ product. We have made significant advances across many different areas, spanning hardware, chemistry, and software, pushing the state-of-the-art, and we will put these advances into the new platform we will bring to market. It will be called the Xenium platform, and it will be released in 2023. It will support high-plex analysis of RNA and proteins with single-molecule resolution. Most importantly, it will be designed for ease of use, robustness, and throughput. As with all our products, our overarching goal is that for our customers, Xenium just works. In the meantime, we're excited to open a technology access program later this year. Now with our three technology platforms, we intend to bring forth the future where just about every tissue is analyzed with single-cell and spatial context at large scale and with high information content, whether for research, therapeutic, or clinical use. As we have always done, we will keep investing in the business with a focus on foundational capabilities, innovation, and long-term value creation. Of this, the most important element, the core of our success so far, and the reason for all the success that is to come is our team. This is the ultimate source of our competitive advantage, and I'd like to thank our team for everything they've done so far and everything they're set to do in the years ahead. After all, we're just getting started. Great. Thanks, Serge. Great presentation. A lot to unpack there. I'm actually gonna start off with one, either for you or Justin, just on kind of what you're seeing in the lab market now, you know, given Omicron, given supply chain. We did get a good pre-announcement from one of your peers, so maybe just any updates on what you're seeing here in the near term, budget flush dynamics as well. Hey, Tycho, I'll take that one. I'll start with what we're seeing on lab activities, and then go on to supply chain and budget flush. Back in November, we called out continued first and second order impacts related to COVID-19 that were impacting our customers' ability to perform their experiments. By the end of November, case counts began to rise in Europe due to the spread of the Omicron variant. By early December, cases also began to increase in the Northeast. As those cases rose, we saw headwinds to our business in December, resulting from decreased activity in these regions, particularly with academic customers. Now in early January, case counts are still high, and we have yet to see a meaningful change from the situation in December. As far as supply chain issues, we're still seeing risk related to global logistics and the sourcing of key components. This is mainly electrical components for instruments, but we've generally been able to manage through it. We've been making spot purchases on key components where we can and also buying ahead and carrying more inventory where we're able to. We are seeing logistics providers being impacted, and we have seen significant decreases in shipping capacity over the last week due to Omicron. As far as budget flush goes, we did see some typical year-end budget flush within biopharma. They were less impacted overall by Omicron. This was offset by some cases of budget extension into 2022, but we didn't see a budget flush so much from the academic customers given the impact of Omicron. Can you remind us your current mix between academic and pharma? You know, obviously it should be a pretty good year for academic, you know, given the NIH budget. You know, another question we get is, as sequencing costs continue to come down and Illumina's gonna have a handful of new competitors coming out, you know, do those incremental dollars generally flow to single-cell and spatial? Yeah. On the first question, the mix between the biopharma and the academia. Generally it's been fairly consistent for us over the years, sort of in the range of around 20% or so biopharma, much of the rest going to academia. You know, there's definitely sort of puts and takes underneath. Like I mentioned, sort of biotech has been growing. Larger sort of companies, pharma, have been growing as well, but the academic market has been growing in parallel. It's been roughly there. We do see that there is a, there's significant potential for sort of the biopharma use to expand like I talked about in my sort of talk. I think there's a lot of interest to kind of start moving downstream in the drug development pipelines. You know, these things take some amount of time, especially kind of working through logistical hurdles, and such as people start kind of planning out those kinds of experiments. In terms of so on the sequencing question because yeah so I mean that generally has been certainly a tailwind historically for us as in some sense the company was. A lot of our technology was built on kind of the foundation of some of the sequencing advances and assuming that those will keep going, and that has really sort of proven out over the years. To some extent, yes, the incremental dollars oftentimes do go to us. At the same time, you know, for a lot of our customers, the ratio of 10x to sequencing kind of favors 10x. The amount of incremental changes happening on the sequencing side might not be as meaningful as they might have been in the past when the ratio was more equal. Got it. That makes sense. Why don't we talk about in situ and Xenium. You know, good to see the name and the form factor. Anything, Serge, you're willing to, you know, provide us with in terms of specs on throughput, price, what the early access customers might look like? We'll talk more about that throughout the year. This is really the year that we'll start answering those questions. As I said, you know, we do emphasize there's a core set of features like being able to do RNA and being able to do proteins is going to be important. Working FFPE is going to be important. One of the things that we've really focused on, we've emphasized from the beginning is, you know, the notion of like actually being sort of having a robust technology that works in people's hands robustly across labs, across tissue types. I think that's a particularly big challenge for this kind of technology for in situ because it sort of has to deal with the fundamental chemistry of tissues and that varies a lot more than any other technology. It's kind of exposed to that challenge. That's where we are particularly investing our attention and time to again, kind of like I said, make sure that once it actually gets to the customer, it just works. I know in the slide you talked about it can be used with pre-designed and custom panels. I mean, how do you think that plays out? Do you think a lot of users will want to do their own custom work? We'll see. I mean, there's definitely use cases where the panels. There's a fair amount of conversion, convergence in terms of like what kinds of genes people are going to be looking for. We do expect that to be reasonably robust in terms of customer demand. But yeah, there's definitely going to be a use case, quite a bit of interest in custom panels. We've seen that with our Cartana early learnings as well. Maybe shifting over to single cell, you know, we're still early in the adoption cycle there. Obviously a lot of use cases for explorative studies. You know, how do you think about the single cell research roadmap from, you know, basic cell characterization to deep gene expression analysis to ultimately functional studies? You know, what inning are we in now? Yeah. You know, I wouldn't say we're like we're no longer in the first innings. I think we're out from the very beginning. I think, you know, if I had to really characterize maybe like a bottom of the second, I would say, you know, if you think about like where single-cell started, the initial experiments were, you know, some sense kind of just proving out the technology. Does this thing work? Does it give you like data that's consistent with what we might have expected? Then there was a big sort of push, 2016, 2017, 2018 maybe, kind of cell atlasing, basically open-ended studies just to kind of map out what is out there, looking at, you know, going tissue by tissue, kind of just seeing the landscape of underlying biology. I would say over the last couple of years, people have gone further and actually starting to like predominantly, I think the bulk of kind of research has shifted towards, you know, concrete biological questions, right. Sort of downstream into sort of these functional analysis, functional studies. In particular, and I would say this is maybe even more recent, kind of more of the translational interest, where, you know, these specific, you know, people are kind of interested in working human samples more and more, more like clinically-oriented questions and disease-oriented questions. Still very much early days in the sense that, you know, single-cell tends to be outside of a few labs, more of an exception than the rule. People don't yet run, you know, too many replicates or like either entire campaigns using single cell. It's still kind of more of a, sort of a supplemental technology, although there's a lot of interest to kind of moving toward more standardization around this. You know, you've done a nice job stratifying the portfolio, obviously, with Chromium. You've got the Controller, you know, you've got iX and X. You know, how do we think about pull-through? You know, where current pull-through is today, where you think that goes this year? Yeah, I mean, how do you think about, you know, the X being at the high end, obviously driving it up, but then the controller at a lower price point. How do you think about the gives and takes between those platforms? Yeah. As you know, pull-through, it's an average of a wide range of customer utilizations, and it's really a simplification of a lot of different drivers, mainly the rate of new instrument placements and the growth rates of existing customers. In the short term, you know, pull-through is not a metric that we manage to. We focus more on the overall consumable utilization with increasing instrument placements being an enabler of that. We feel really good about the breadth of our offerings right now with the Chromium Controller, the Chromium iX and the Chromium X and the Chromium Connect. There's a wide range of choices out there for customers that are at different points in the single-cell adoption curve. Going back to pull-through overall, as our portfolio continues to grow, pull-through does become a less meaningful metric. In some cases, it can actually obfuscate trends in the business. As the number of new customers continues to increase, their ramp-up period can be a drag to pull through. And, you know, you'll see this with, you know, customers that are, you know, coming on board more recently and moving up that curve. More new customers coming on board in general is a great thing for the business overall. Placements overall have been higher than we initially expected. We're nowhere near the ceiling of opportunity here. Overall, we focus on the growth of consumables utilization and the number of reactions that run through our platforms in total. As far as iX and X, we're, you know, we're seeing this being more strongly biased towards the X. We're encouraged by the number of customers that are ordering high throughput kits, and the Chromium Controller still remains strong as well, particularly at the lower price point that we rolled out in Q2 and then made permanent in Q3. How do you think about the, you know, potential addressable market for X? I mean, is it dozens of customers, you know, hundreds of customers? I mean, how do you think about the overall opportunity for X at the high end? Yes. I mean, initially our thought was sort of this is for the high end, the top 10% of our customers. I would say that based on, still early, but based on the initial, like, you know, roughly two quarters or just a bit over a quarter that's been on the market, it looks to be exceeding those kind of expectations. I think it has potential to gain much wider adoption than that. I think people are. While the actual number of people who are looking to run these, you know, massive experiments is still fairly contained, I think a lot of people wanna be sort of future proof for actually being able to do this in the future. Even if they're not doing it just yet, they'd rather kind of get there in anticipation of those capabilities or all those needs. Maybe shifting over to spatial in the, you know, last couple of minutes here. Just talk a little bit about FFPE, that rollout and, you know, incremental demand for Visium. How has that kind of driven adoption? Yeah. I think that, look, FFPE has been pretty encouraging for us. We're definitely seeing a, like, substantial incremental growth on top of fresh frozen. Yes. There's like a good proportion of new customers in sort of the translational and pharma worlds. Roughly, yeah, it's opening up this kind of additional incremental opportunity, similar magnitude maybe to fresh frozen, I would say. You know, in particular, particularly on the translational side. I would say it's going kind of, it's been encouraging, kind of roughly proceeding along our expectations by opening up this new opportunity. It really kind of sets us up nicely for you know, for later this year with CytAssist to start kind of accessing all the archive samples as well. You know, ahead of the HD launch, is there any risk of customers kind of holding out on Visium, you know, before you launch HD? Yeah, I mean, so there's some of this dynamic, it's hard to quantify. People certainly kind of take that into account. At the same time, there has been a lot of research and a lot of tools people have worked over the past year or so, kind of combining Chromium data with Visium, with standard definition Visium, to deconvolve and get it the single-cell resolution by combining these datasets. In fact, that tends to be kind of the predominant way that all spatial technologies are currently being run, like where you kind of combine the datasets with single-cell analysis. From that perspective, the kind of value you get from standard Visium actually appears to be quite strong. You know, both dynamics are there, and we'll see how it evolves this year. With the launch of CytAssist and then FFPE, as you talked about, I mean, is that what it takes to open up, you know, the histopathology market or, you know, do you need to do more around educating customers? I mean, you talked about those customers not necessarily being used to interpreting genomic data. Like, how much friction is there still in that market, even though you've kind of evolved your product set? Yeah, I mean, there is still, right? I mean, you kind of go through and you knock down sort of the tallest nails as they appear in front of you. I think sort of the logistics around the workload, not so much the workload itself, but the logistics of coordination has been certainly a big challenge. You know, that's getting now resolved through all the various efforts that we're putting out, like this past year and this coming year. In terms of data, yeah, I mean, it's a new data type and it's like as with all of these technologies, it takes time for people to kind of really extract the most value from it. It requires building out all the sort of the tooling and analysis tools around it as well. We're certainly encouraged by the interest, but yeah, we also have to be mindful that those issues will be there as well. People will have to kind of work their way through the analysis and interpretation of the data. Last one. I know we're running out of time, but the protein menu on Visium, where does that stand? And you know, does that enable you to reach more clinical customers going forward? So we'll be releasing that kind of midpoint of this year. We'll be, you know, panels for oncology and immunology to start with, and there will be more coming. Yes. I mean, the answer is yes. There should be. Proteins are sort of the, you know, the traditional way that you look at tissues and having the ability to measure proteins at high-plex is very interesting to customers for sure. Now we do have immunofluorescence that you can use as well to combine if you only have one or two markers to combine with Visium. That has been available for, you know, just a bit over a year now. Certainly this, you know, kind of large scale panels will be quite beneficial ultimately later this year. Great. I think we'll leave it at that. Good to see you guys. Thanks for taking the time. Thanks, Tycho. Thanks, Tycho.