Okay, hi.
Thanks, everybody, for joining us today. My name is Casey Woodring from the Life Science Tools and Diagnostics team. Welcome to the J.P. Morgan Healthcare Conference. Pleased to be joined today by the management team of 10x Genomics. As per usual, with these sessions, we'll do 40 minutes, about half of which will be the corporate presentation, then the other half will be Q&A. So with that, I'll pass it off to Serge.
All right, thank you. So first, this slide contains important information about forward-looking statements we plan on making today. So with that, yesterday we announced our preliminary Q4 and full year 2025 results. We had about $599 million in revenue for the full year and $166 million in Q4, exceeding the high end of our Q4 guide. We also generated about $40 million in cash to bring our balance sheet to over $500 million. I have to say that I'm really, really proud of the team. Last year was pretty crazy in many ways, major upheavals in a bunch of our end markets, and the team executed really well throughout the whole course of the year. Nice momentum and key metrics that are driving the fundamentals of the business, multiple new product launches, and really strengthened our balance sheet.
So, you know, as I stand here today, as I look to the future, I would say that we're probably in the strongest position we've ever been as a company. You know, stepping back, you know, from the beginning, when we started the company, we started with the premise that this is the century of biology, that there are multiple exponential processes happening that have the potential to transform human health, to really advance the human condition. The key challenge, what we're stymied by, is the lack of fundamental understanding of biology. Like what we don't know is much greater than what we do. Maybe we understand something like 10% of biology. Our goal at 10x, our mission, has been to accelerate the mastery of biology to advance human health. The key challenge in doing that is that biology is very, very complex.
Easy to state, but it's still a very underrated fact. You have to think of myriads of molecules and cells and tissues interacting with each other in all kinds of complex, dynamic ways, which means to address that complexity, you need tools that can measure it at scale and resolution. What that means is measuring lots of things, scale, and measuring the right things, resolution. And when we started the company, technologies at the time did not really allow you to do that. They were fundamentally lacking. So you would be missing most of the necessary biology, most of the necessary view of what is necessary to drive the understanding. And so we set up the company to become really good at building technologies, building tools to measure biologists for life sciences.
And that entailed building deep expertise across multiple distinct areas in a culture of tight multidisciplinary collaboration to move fast and rapidly develop products. Our philosophy is to think of the future, think about where the world is going, what are going to be the open questions, and then work backwards to figure out what products need to be built and what technologies need to be developed in the service of those goals, in the service of those questions. And so this, the innovation engine, this ability to build, break through products rapidly, and also know what products to build, we see this as the foundational capability of 10X. It gives us tremendous sustained advantage and sets us up for many opportunities as the century of biology unfolds.
You know, to start with, we focus very much on the research tools market, on basic science research in order to accelerate basic scientific discovery. This has served us really well and also has set us up really in a great position to take advantage of future opportunities that are now starting to emerge, especially ones having to do with advancing human health more directly. And so our innovation engine has been incredibly productive over the past decade. And the question is, what are the products, what are the platforms that it has delivered? And here, like an important thing to appreciate is that the key to understanding biology is to understand what is going on at the cell level. The cell is the fundamental unit of biology. And until recently, we didn't really have the means to measure biology at that level.
With previous generations of tools, you take your sample, you mix the contents of all the cells in there together, and you measure that mixture, which gives you an average profile, but doesn't really tell you what is going on in the underlying system. Sort of like the analogy I've used, like shown on the slide, is like trying to understand how cars work, but instead of actually looking at individual cars, you kind of, you mash them all together and then measure how much aluminum, how much glass, how much metal you have in total. But with our technology now, it has become possible to measure individual cells at really large scale, which effectively gives you the parts list of human biology.
And then more recently, with the emergence of spatial analysis, now you can see how cells and molecules are actually arranged with respect to each other in tissue. Essentially, it tells you how all the different parts actually fit together. And so this is actually, this is really, really foundational because if you think about diseases, almost all diseases, one way or another, stem from dysregulation of cell behavior. And if you look at all our medicine, just about all medicines ultimately act by modulating cell behavior. And so we brought forth onto the market these platforms. We invented, developed, and commercialized technologies that now allow researchers to do this highly scaled single cell and spatial analysis. We built instruments, consumables, software to provide customers with end-to-end experience, solutions for analyzing their samples to see biology they could not see before.
These platforms are also reinforced by really powerful capabilities we've built within the company in commercial and within operations. We have a large direct worldwide sales force. We have best-in-class customer support that's obsessed with customer success. We have really, which entails really deep expertise across a range of really sophisticated applications. We consistently hear really great feedback from our customers. Our NPS scores are off the charts. We also built out really sophisticated manufacturing and operations capabilities to drive quality, robustness, supply chain control, and really scalable cost structure. We have this awesome set of capabilities now that we have scaled up that we are now leveraging to drive growth and to drive expansion going forward.
And so what is one of the best ways to measure the impact that we have been making with all these capabilities we've built out and the platforms we brought to market is to look at the publications that have come out of our customers' labs. There's now well over 10,000 papers that in high-impact journals across just about every field of biology, every therapeutic area, every disease. It's actually hard to think of an area of biology where our customers have not made fundamental scientific discoveries using these products. And the reason for that, as I said earlier, just the cell is the fundamental unit of biology. Single cell and spatial are foundational to understanding biology. They are foundational to understanding health, foundational to understanding disease, and to ultimately how we're going to figure out how to cure disease. So now to go a step deeper into each platform.
So, first, Chromium. Chromium is the unambiguous leader in single cell analysis. It's the platform that catalyzed the single cell revolution. It works on dissociated cells, you know, again, parts list of human biology, and the system is well known for extremely high performance, data quality, ease of use, consistently wins on all the benchmarks, all the rigorous benchmarks out there by, I would say, by a long shot. Now, huge ecosystem of customer service providers, protocols, papers, and supports a wide range of applications and analytes. The platform has come very far. At the same time, in many ways, it's still just getting started. The vast majority of biological research that could benefit from single cell does not yet.
And in fact, over the past year, we've been seeing a new wave of interest in single cell emerging really powerfully, driven by new applications, large-scale AI projects, and expansion of translational research. And you know, to a big extent, this has been driven by new products that we brought to market recently. We had a whole set of big launches over the past 24 months that took the platform to a whole new level in terms of performance, in terms of scale, in terms of cost. And maybe one of the most emblematic examples of these innovations is our latest Flex assay, which delivers on some truly remarkable advances.
It's incredibly sensitive, flexible, robust, works across cell types, tissues, sample types, delivers massive scale, both in terms of cells and in terms of samples, and also delivers on cost, where the cost is actually getting close to the cost of bulk RNA-seq. And also, it allows distributed and longitudinal sample collection because it has this really robust, really elegant method of sample fixation. In fact, it works really well in FFPE samples, which is something that was not even possible to contemplate several years ago. It's interesting because until recently, I'd say that this product has been somewhat underappreciated, but last quarter, it became our most popular assay by volume. And it is now, I would say, without a doubt, a revolution in single cell analysis. Spatial is naturally complementary to single cell, right? Just the same, like I said, single cell tells you the parts list.
Spatial tells you how the parts fit together. For us, it comprises two platforms: Visium, which is based on next-generation sequencing, and Xenium, which uses direct imaging of single molecules directly in tissue. And both platforms have the strength, but increasingly, we're seeing that our customers prefer more and more Xenium. And you know, the number of times I've seen customers come up to me with just this visceral delight at the Xenium data they've been seeing is really, really striking. I'm not sure if I've ever seen it for any product, and we've had some really successful products in our history. As with single cell, there's a large ecosystem of customers, papers, resources. These products have the best performance, best quality of data. It's been shown time and time again in various benchmarks, support a wide variety of applications, and also outstanding workflows and ease of use.
The thing to appreciate about spatial is that if you step back, biology is ultimately about measuring molecules, cells, and tissues. And in that way, with spatial, for the first time, you can measure all three together, which means that it's really kind of a culmination and convergence of what were previously separate fields: molecular biology, cell biology, histology. And it's really the future of how we measure biology. And for many of our customers, the future is already here. They're running their Xeniums just around the clock continuously. It's just that the future is not evenly distributed yet. And we see that as our job, our responsibility to bring that future forward to all the customers, all the potential customers around the world. And we're excited in particular to keep pushing in our innovation engine. There's just so much more headroom for innovation in spatial.
And going forward, we intend to leverage our R&D capacity to deliver more and more value to our customers, to measure more biology, to increase scale, to increase ease of use, to keep driving down costs. Product innovation has always been and will continue to be the foundation of our market growth and expansion. And yeah, I would say if you look at the last two years, there's been big step change advances that we have made that have really positioned us well to drive democratization and expansion of our existing customers, existing products. Also positions us really well to drive expansion and translational research. And also, one of the most exciting areas right now of growth is to enable the generation of massive data sets for AI. Indeed, we all know, you know, the world is going through a massive revolution because of the advances in AI.
What is often less appreciated is that there is a parallel revolution that has been happening in our ability to measure, in our ability in technologies to measure biology. And the two revolutions are actually incredibly complementary. AI has this huge potential to transform the understanding of biology, but the key thing it needs is data. And the thing is, the potential data, scale of data in biology is essentially inexhaustible. You just need the tools to be able to actually get at that data, to gather that data at scale. And that's precisely what we have done. We've built tools to measure, to generate data, large amounts of data, and precisely the right data to measure the right biology. Whereas conversely, for us, the biggest obstacle when we build our technologies, build our tools, you can measure lots of critical underlying biology.
But going from those measurements to insight is precisely what advances in AI are really geared to do really well. For now, the greatest success, arguably, in AI in biology has been in the field of protein folding. And as we go forward, it's becoming clear that the next frontier is going to be cracking the cellular code to learn what determines how cells respond to changes in health and disease. This would be ultimately probably the biggest unlock in science and medicine and has the promise to compress that century of biology into just a couple of decades. Grand vision. How will this happen?
You know, now there have been a number of high-profile publications over the course of the past two years kind of laying out this vision for building foundation models for biology with the ultimate goal of fully simulating cells and tissues in silico, in a computer, and that's what the term virtual cells refers to, and you know, there's actually deep reasons to think that AI is the right representation for biology, and the fact, the reason we can contemplate these kinds of proposals, these kinds of visions, is exactly precisely the progress that we have made in technologies to increase scale, to decrease cost, to be able to generate very large data sets now of the right kinds of data, and I also would point out that this is actually a big shift in how we think about research.
For the first time, AI is actually being the driver of data generation. Like instead of being a tool you use at the end, once you kind of generate your data to finish your analysis, it is actually the demand generator in itself to drive science. A lot of the data that's feeding these models is observational in nature, measuring lots of cells, lots of samples, lots of populations. And that gives you a lot of critical information, and the models are quite powerful for a number of tasks. But what becomes a real game changer is data from perturbation experiments. And these perturbations can be anything that affects the state of the cell. And what's shown here is a particularly popular approach, CRISPR-based, Perturb-seq, where you target specific genes, you change their expression, and you see the effects of those changes in your whole system.
You can also use epigenetic modifiers, chemicals, drugs, combinatorial drug libraries. It's an incredibly powerful approach because it not only tells you what the cells are doing, but also tells you what causes what. Like it resolves causality. It's also incredibly useful practically from the industrial perspective because it enables rapid discovery of high-quality drugs, which is why these methods are also of really great interest to biotech and pharma companies, really hold the promise to transform drug development. And so we've been seeing recently this massive wave of these kinds of projects. They're increasing in number and in scale, going from profiling tens of thousands to millions to now soon billions of cells in these experiments. And you see that in publications. You also see that the investments that biopharma, biotech, and pharma companies are making.
And in fact, because of the power of these perturbation experiments, the term virtual cell oftentimes now refers to specifically AI models built specifically from perturbation data sets. Now, if you consider what you need to actually make all these efforts a reality, there's a number of things. First of all, you need really massive scale. That's kind of the name of the game in AI, right? Massive numbers of cells and many kinds of cells. And I would say nothing really matches the throughput of Flex and our other solutions. Second, you really need robustness and ease of use. You need to generate vast quantities of data across conditions, types, and workloads. And you can't afford to use something that only works kind of part of the time because it undermines the whole purpose of these projects. And third, you really need high-quality data. This is foundational.
High sensitivity to measure your perturbations, to measure the effects of those perturbations. High cell recovery to make sure you're actually measuring the cells you're putting in. And you know, as with so many things with AI, garbage in, garbage out. And these are large experiments. These are large undertakings. You can't afford not to have the very best quality of data coming out at the very end. And specifically, we have worked really hard to build products that emphatically deliver on these needs. Not just us saying it. We've announced multiple partnerships across this past year, and a lot more customers are scaling up, applying to do more experiments. The feedback has been phenomenal. And we know that more of the stuff is in the works is just sort of the wave that's starting up.
And in fact, today, we are announcing a partnership with the Cancer Research Institute to build a massive data set, what we call Discovery Engine, to accelerate progress in immunotherapy. So the idea is to create a well-controlled, robust, high-quality data set as a foundational resource to understand the mechanisms of drug response, to develop new therapies, to ultimately target the right drugs to the right patients. And so we're very excited about this partnership, and not just because it's going to enable really sophisticated AI, but also because it holds the promise of creating a foundation for materially improving patient care. And this, in fact, brings me to my next point and the next theme that we've been seeing in the market is the adoption of our products in translational research.
It has been a big theme over the past year and we expect to be a huge growth driver going forward, and that is for three reasons. Fundamentally, like in many areas like immunotherapy for cancer, we have an increasing number of therapies, but not a good understanding of which therapies to give to which patient when, and we desperately need biomarkers. We need signatures of response. We need to enable precision medicine. At the same time, there's increasing evidence coming out of scientific literature. You can also reason from first principles that single cell and spatial is the right technology to find the key biomarkers to develop these signatures, and three, our products have advanced tremendously over the past few years and are now really well suited for this purpose to run these large cohorts of studies in translational areas.
And you know, in principle, single cell and spatial have always been promising for translational research, but until recently, they've been largely confined in their capabilities to basic scientific research. And now a lot of those obstacles have been addressed. Our products work with FFPE samples on whole blood in a distributed sample collection mode. It's now straightforward. Importantly, they have huge scale and cost advantages. Workflows have become much easier. And all of this happened recently. It's now straightforward to run these large cohort studies to uncover drug targets, biomarkers, and signatures of treatment response. And that's precisely what our customers have been doing. Many translational projects across many different indications, scaling up numbers, scaling up number of studies, indications. They're looking to understand biology, discover biomarkers, and really enable precision medicine. Many with an eye toward developing clinical diagnostics.
Translational research is also an important driver of biopharma adoption. There's tons of utility of single cell and spatial across the entire drug development continuum, from target idea to drug discovery to preclinical to clinical trials. Lots of examples of amazing use cases, but so far, we've largely focused on the early stages of discovery, but there's a much larger opportunity to go downstream into later stages, translational stages of drug development, where biomarker strategy becomes important to tell you which patients will respond to the drug, which will have toxicities to dramatically increase the odds of success, and so this is where we see more of a focus going forward, both because there's a need and because the technologies can now address that need.
Now, as this translational wave has been picking up, we're starting to hear more and more interest from our customers in deploying these technologies ultimately for diagnostics, for routine patient care. We actually have physicians coming to us asking for single cell and spatial analysis to run on their patients. So from everything we're seeing, from first principles, we believe there's a huge potential for diagnostics applications in single cell and spatial in the future. For that, two key things need to happen. You need generation of robust clinical evidence, and you need deployment of those tools, technologies in the clinic. To make that happen, we will pursue two paths. One, we will keep supporting our customers as we've always been doing to help them generate their clinical evidence, and B, to collaborate with them on the future to enable clinical deployment of technologies in the future.
At the same time, we believe we're in a unique position to accelerate the arrival of some of the highest impact diagnostics ourselves because by virtue of our strength in technology development, understanding of the applications, actual position in the research market where we see where the future is going, and also the efficiency and the cost-effectiveness with which we can run these kinds of clinical studies, and also the ability to stand up a CLIA lab very cost-effectively and efficiently. And so going forward, we're going to embark on generating clinical evidence for specific applications and also stand up a CLIA lab of our own as well. In particular, there are two important diagnostics applications that we're really excited by that we're going to develop with partners. First, perhaps most naturally, in oncology.
Recently, more and more literature where people run single cell and spatial to analyze tumor samples to find biomarkers of drug response. So more and more we know that the signal is there, and the need is there and growing. We see an analogy to the world of oncology as it was about 10, 15 years ago with the appearance, the arrival of NGS testing modality as a way to support new generation back then of mutation-targeting therapeutics. Now we have this new wave of all these new modalities that target specific expression markers in cells and tissues: ADCs, bispecifics, T-cell engagers, immunotherapy drugs, radioligand therapies. And what is needed there is a new generation of tests that can comprehensively assess the levels of all these markers and the state of the tumor microenvironment. And 10x is ideally positioned to provide these tests.
So we see a future clinical workflow as kind of outlined here where patient samples are received at 10x, analyzed using single cell and spatial, and then a clinical report is generated to support oncologists in their optimal selection of these therapies. Now, of course, cancer is only one of many possible areas where there's potential to improve patient care. In particular, autoimmune disease is a huge cause of morbidity and mortality in the U.S. and around the world. Over the past 10, 20 years, there's been tremendous progress in delivering, developing therapies to the market targeting different biological pathways. But at the same time, there's not much in the way of precision medicine in tailoring these therapies to individual patients. There's a lot of guesswork. The physicians are just flying blind. And again, we see 10x is ideally positioned to address this gap using single cell.
And here the idea, again, as shown in the slide, you have routine sampling with single cell of patient blood. You can measure disease and drug target pathways using single cell during patient care, and clinicians would now be able to see the biological basis for what's going on with their patients. And now they'll be able to select the right therapy, see the effects of that therapy, and then change course as necessary. Our goal here is to bring forward a new standard of care for autoimmune disease where all this guesswork, all this uncertainty is replaced with precision and clarity. And of course, how do we make this future happen? Well, clearly the main driving factor will be clinical, robust clinical evidence generation. And so what we're doing is partnering with leading academic medical centers to generate that evidence.
Today, we're really excited to announce our first two collaborations, one to support each of these two applications. The first, we announced our collaboration with Dana-Farber Cancer Institute to work on oncology applications, and second, we announced our collaboration with Brigham and Women's Hospital to work on autoimmunity. We expect more collaborations in the future to continue to build out the programs across these indications. As I mentioned, we're going to be building out a CLIA lab to enable clinical deployment of the resulting tests that will come out of these kinds of collaborations. Now I want to close with two thoughts. First, as a company, we're in a really strong and really privileged position. We've got unrivaled innovation engine, really great portfolio of products, really exciting markets, and scaled organization and a really strong balance sheet.
And second, zooming out, the world is in a really unique position and point in time. There's been this rapid parallel progress in AI and in technologies to measure biology. This has created the conditions for rapid acceleration of scientific discovery and the deployment of those discoveries to radically advance human health. We started the company with the premise that this is the century of biology. And now we have an opportunity to compress the centuries' worth of progress into the next decade or two. Thank you.
All right, great. Thank you for that overview. Maybe we can dig into the pre-announcement from yesterday. You saw solid top-line growth driven by both instruments and consumables. So maybe can you just walk us through some of the growth drivers that you saw in the quarter, if you saw any sort of budget flush, and maybe just how customer purchasing behavior progressed relative to your expectations?
Yeah, I can at least kick that one off. We did see a bit of budget flush that was unanticipated. So I think when we had our call after Q3 and we gave the Q4 guide, we said, "Hey, look, we're not anticipating a big budget flush." We definitely saw some, mostly on the CapEx side of our business. So that was a big part of it. And interestingly, a lot of it came really in the last two weeks or so. I mean, so it was kind of, I guess, a later-than-normal typical flush. At times, we'll see signs in November, even early December. This was really later in the quarter where we started to see that uptick. So that was a part of it, like I said, predominantly on the CapEx side of our business, which grew close to 30% sequentially, which was in excess of our expectations.
The other thing I would just note is, and Serge just spent some time talking about the single cell portfolio, we're seeing really good customer momentum, really good uptake, particularly in the new Flex assay, but across that entirety of that portfolio. And then spatial consumables, which has been growing in the teens during the course of the year, also had a really strong quarter. And again, I think that's more sort of fundamental underlying growth, both there on the spatial side and even what we're seeing in single cell, augmented by some budget flush as well.
Okay, that's helpful. Maybe just double-clicking on the instrument replacement, or I'm sorry, instrument placement dynamic in the quarter. So I guess on the year, you've seen over 6,400 cumulative Chromium instruments sold and 1,500 on the spatial side. So maybe just what's the latest and greatest on customer appetite to add instruments now into 2026?
Look, the environment is still very challenging for instruments. I mean, I think we've sort of been talking about this for several years now, and I would say like last year, 2025 was substantially worse than the previous years. I think it's actually true kind of around the world. There has been a tightening of budgets for capital equipment. It's obviously very much true in the U.S. and academic markets. It's been very challenging. So the team has done a tremendous job of navigating this environment and being creative in kind of deploying these instruments. But I think the environment still remains very, very challenging in that sense when it comes to large CapEx investments for customers.
Okay. Maybe one on visibility, right? So in the first quarter of last year, you stopped providing full-year guidance and shifted to only offering guidance one quarter ahead, really on that limited visibility piece. When do you expect visibility to improve, and do you expect to reinitiate full-year guidance at any point this year?
So you're looking for guidance on guidance.
Correct.
I would say we're encouraged by what we're seeing right now in our recent customer conversations. I mean, I think it's one of the things that we talked about when we pulled the full-year guide and moved to quarterly. It was really just about the connectivity we have with our customers and sort of the input that we're getting from them. So it's our current plan to reinstate guidance, full-year guidance when we do our Q4 call. Now, of course, when we did that, I think last year was right when DOGE announcements came out, right? So I mean, of course, the intent is to provide full-year guidance, barring any sort of crazy circumstances like we saw early last year.
Okay. And then last quarter, you mentioned that you would expect the first half of 2026 to look similar to the second half of 2025. You kind of just walked through some budget flush dynamics that maybe you didn't expect when you gave that statement. So can you just provide more detail on what that means? And then I'd be curious to hear the difference between, were you referring to revenue there or more speaking broadly about the overall end market dynamics?
Yeah, go ahead. Yeah, I would say we're talking about the broader market dynamics, right, rather than just sort of first-half dollars looking like second-half dollars. So it's really just, I think it was our way of just saying, "Look, we're not seeing any fundamental shifts." Again, we'll see how everything sorts out with the NIH budget. Obviously, we've got a high level of exposure to U.S. academic and government accounts. So our anticipation, again, and you just sort of called out maybe the one exception, which would have been the budget flush that we saw in Q4. Absent that, we anticipate that the first half, based on the customer visibility, the conversations we're having sort of on a global basis as well as with our biopharma accounts will look pretty similar from a macro perspective in the first half of this year as it did second half of last.
Okay. On that U.S. academic and government piece, right, I think most people are assuming that the NIH budget will be flat, but there are dynamics with multi-year funding and grant disbursements that we saw this year kind of being frozen and held up. So just what's the customer conversation with academic and government customers at this point? Are you still seeing a lot of hesitation? Are things kind of looking brighter in the year ahead?
I hesitate to say that things are looking that much brighter. I mean, we've been sort of in this situation over the course of the past couple of quarters where things are not as dire as they were when you go back to kind of the first half of last year. But at the same time, there has not yet been sort of a return to stability and clarity with customers, right? So I think the general mood is better than it was before, but there's still a lot of uncertainty and kind of uncertainty around how the sort of policy environment and how the dollars will actually get dispersed.
Okay. Maybe a few on spatial here. So you've noted sustained strength in spatial consumables over the past few quarters. Sounds like that continued in 4Q. Can you just discuss the sources of this interest where you're seeing the most momentum, whether that's Visium or Xenium, and what's really driving growth there?
Yeah, I mean, for sure, Xenium has been doing really well. And I kind of specifically pointed it out in my remarks here. When we first got to market, kind of our philosophy was we want to make sure that customers have a full range of application technologies to support them in their spatial analysis. And the reality was no one really knew what is going to be best for what applications. It has become more and more increasingly clear that sort of the Xenium approach using direct imaging of single molecules and tissue provides the best kind of data for people to drive their research and definitely has been a big driver. And like I said, there's lots and lots of customers now that are just running these things at a really, really high velocity and generating amazing data made from it.
So very excited about Xenium, and it is really kind of the engine of spatial growth at this point.
Okay. At AGBT last year, you launched Xenium RNA and protein, which began shipping in 3Q. Just can you discuss the opportunity there with this product and any initial customer feedback so far?
Yeah, so it's sort of like our first foray into proteins and RNA on Xenium. We launched. Great feedback. We launched an immuno-oncology panel. It's a set of proteins, 28 proteins to be measured. Importantly, it's still in the same section, so something that was not really possible before, true multi-omics, and I think one of the things that we've been learning, again, great feedback, and what we've emphasized is multi-omics that's actually practical and robust, and especially when it comes to translation applications, because oftentimes when people talk about multi-omics, it ends up being sort of this grand kind of exciting, maybe scientifically kind of a set of experiments, but not really fit for practical use, so we've optimized really to do something that is fundamentally has streamlined workflow, gives you amazing data, but it is really practical and really scalable.
And so great feedback from that initial panel. And going forward, I think there's a big opportunity to expand sort of the range of markers and the range of panels that we can deliver with this capability. So excited about it. It's still just kind of the first step, but the initial feedback has been quite positive.
Okay. Maybe turning to single-cell, just one on competition here. So we've seen an increased amount of competition in that market and multi-omics markets for that matter, with more entrants vying for similar research dollars. Could you just share how you're addressing that competitive landscape? What gives you confidence in the long-term trajectory of your portfolio in both of those segments?
Yeah, so I mean, I would say, look, first, always been competition in single cell, and especially when you go back to sort of the beginning of it, it's easy to kind of forget that now, but it was actually really, really intense, and over the years, the competitors have come and gone, and our North Star has always been product innovation and delivering best value to customers, and that's why we won sort of at every iteration, and I would say that it's pretty clear now as you look over the last sort of the course of the last two years, we're clearly winning. Kind of there has been a bunch of new entrants that came to market with sort of big promises, and customers have trialed them and so on.
And by and large, I would say we're in a better position now than we've been at any point in the last two years when it comes to that sort of landscape in a competitive environment. Our products are materially ahead of competition along multiple axes, and there's really no, traditionally, there's been sort of a cost argument around our products, and that has been obviated with all the launches that we have had. So we feel really good about our positioning now. Again, product development innovation has always been our North Star, and I think we've got feedback from customers now that is deeply resonant with that strategy.
Okay. Maybe we have a couple more minutes here left. Just one, you've talked about how AI has the potential to unlock large projects in single cell research with a growing interest in AI modeling and building out large-scale models in biology. You have the collaboration with Anthropic. Maybe can you just elaborate on how you envision AI further increasing demand for single cell and how will this support the scaling of data and really drive the need for more advanced cell models?
Yeah, so there's actually two ways, and they're actually fairly distinct. In the long run, probably they will start converging, but kind of pointing to our partnership with Anthropic, specifically that's around building out an agentic AI to help with analysis, kind of downstream analysis. So you generate the data, and traditionally, I would say probably the biggest bottleneck in people running experiments and kind of going through that cycle, getting grants and all the rest of it is data analysis. You hear it over and over again. There's only so many bioinformatics people around in the world, and that's the biggest bottleneck. And agentic AI and what we're doing with Claude, with Anthropic, is kind of a first step in that direction, should actually very much solve that kind of bottleneck. I mean, you can see what these agents can do already. It's mind-blowing.
So we are pretty excited about the potential here. I think we definitely see that as the future where it becomes an integral part of going from data to insight to paper to ultimately the next experiment. And then separately, as I've touched on earlier, there's this new, it's a different kind of paradigm where you're now driving progress in science through the creation of these ever more large and more sophisticated AI models. And those models themselves become drivers of demand of doing science. And again, their appetite for data is essentially inexhaustible. And our tools tend to benefit tremendously from that because they're the means you can generate vast amounts of data and, importantly, the right kinds of data. So we definitely see this as being a big, not just sort of a current wave, but the start of a huge secular trend.
Okay. Maybe last minute here. I just wanted to touch on the Scale Biosciences deal. You noted the acquisition won't have a material impact on revenue or OPEX for the remainder of last year, but just any current expectations of how to think about Scale's contribution in 2026 and what excites you about the capabilities of that business?
Yeah, I mean, look, in terms of the capabilities we've talked about before, so much of what we're doing is about scale, ultimately, and kind of bringing out the scale of experiments. And the team at Scale, they've invented great technologies. There's some really great capabilities that very naturally belong and integrated into our Chromium portfolio. And so it is a key part of our product development strategy now. There will be more kind of that will unveil with time. As far as financial impact, it's minimal at this stage, yeah.
Okay. All right, great. It looks like we only have 30 seconds here left. Any closing remarks? What's most misunderstood about the 10x story, I guess, at this point?
I think the fact that it's important to appreciate that where we sit right now, in terms of our technology stack and in terms of our position in the market, but really sort of the intersection of the most important trends that are happening in biology and life sciences and ultimately in clinical care as well. When you look at the set of assets that we have and our ability to deploy them, it's a really, really privileged and unparalleled position. Like I said at the beginning, I do think we're in a stronger position now as a company than we've ever been in our history.
Great. Well, we'll have to leave it there. Thank you guys for joining us today. Thank you, everybody. Join us for the conference.