10x Genomics, Inc. (TXG) Earnings Call Transcript & Summary

All right. Good morning, everyone. Welcome to day 1 of the JPMorgan Healthcare Conference. I'm Julia Qin, lead analyst covering life science tools and diagnostics at JPMorgan and it's my great pleasure to introduce you to our very first presentation today by 10x Genomics. So we'll do 20 minutes of presentation and 20 minutes of Q&A. [Operator Instructions] And 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. So 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 of 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 an 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. But 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. And 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. And the thing to appreciate about biology, what makes it so different from other disciplines, the most salient feature of biology is an 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 the 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. And 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 was -- has been on DNA focused on -- with the human genome project, followed by genome-wide association studies, followed by population sequencing. And 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. But 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. And that's how you will drive the understanding, that's how you will ultimately drive the ability to manipulate biology and drive to cures. We made this point way back when we were going public 3 years ago. And if you look at the last several years of the industry, it is 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 -- for this job because they lack the necessary resolution, the necessary scale. And 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 your measure gene expression in your sample, you take all the cells in that sample, mix their contents together and then measure that mixture, which gives you an average profile, the average across all the cells in your sample. And now for the past several years, researchers, you can see on the right, have been able to use our system a Chromium system to measure individual cells, thousands to millions of cells in a single experiment measure the full transcriptome profile of each cell and now what you see emerge is a vastly more complex picture that's vastly more informative than you could have ever hoped for when you're relying on previous generations of tools that were low resolution and low scale. So the products that enable these insights that generate these kinds of use, our products are comprised of instruments, consumables and software. We sell instruments. That's a source of upfront revenue for us, and then consumables provide their recurring revenue stream. And then 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. And they really -- they enable scientists to measure biology, see things they could not see before. So from the beginning, we set out our ambition was to build the next generation of technologies to kind of to address the complexity of biology. And we realized early on that, that entailed becoming very good at a number of very disparate disciplines and 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 and 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. And this really speaks to one of our core fundamental tenets about how we've been -- we've built the company is to invest in foundational capabilities that then become an engine for generating innovation and sustained differentiation. So this innovation engine, this ability to rapidly build products and knowing what products to build, we see it as our core competitive advantage. We think really hard about where the world is going, what question 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. And once we gain conviction, we move rapidly to deliver awesome products to market. It took us less than 3 years to go from a standing start to ship our very first product. And 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 realized we don't have a monopoly on smart people or monopoly on innovation. And so 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 our strategy based on that. And then 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. And this is where the unique strength of our product development engine really comes in, in that we are 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. This 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. So we have used these capabilities to build products to enable the future of biological research. And it's also become very clear to us that preserving single-cell context is going to be critical across just about all biological measurements. And at a high level, from first principles, there's roughly 3 fundamental approaches -- technological approaches to preserving single-cell context. And those are approaches represented by the 3 platforms that we have developed. And so 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. And so they -- by virtue of that, they provide a lot of value, the 3 of them together, a lot of value to our customers now. So our Chromium platform. This is the platform that catalyzed a single-cell revolution, created the single cell market, as we know it today. It has -- it worked with associated cells, so thousands to millions of individual cells. It allows analysis of a 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, and there is now this large ecosystem of customers, protocols, papers, data sets that has emerged. And 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 capability 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. So our nuclei isolation kit is our very first sample prep product. So 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 mightier struggles with trying to do this well. So 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, allows -- opens up the possibility of working on 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 and -- 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 binding to. This is a big new really exciting capability and particularly exciting to our biotech and pharma customers. And finally, Gene Expression Flex. This product, which we initially launched with the name Fixed RNA Profiling allows fixation at the point of sample collection. And 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. So by virtue of not being able to fix, this opens up a whole new kinds of experiments, removes major constraints and really the rest is some of the biggest issues that people have faced with single cell analysis. But Flex -- this is great, but Flex is much more than that. It's -- 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 skilled experiments, many more cells, many more samples per run. And it's also we showed recently that is compatible with FFPE samples, formal and fixed paraffin-embedded samples. This is how the vast majority of clinical samples are processed and stored. There's vast archives of bio-bank samples that exist out there are millions -- hundreds of millions probably more than that, that are all store that's FFPE. And until just a few months ago, none of them were accessible to single cell analysis. So 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'll continue to invest along multiple dimensions of product development. So 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 and that entails addressing the total cost of the experiment and also removing any logistical barriers to running larger experiments that our customers might face. And 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, my 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 but 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 that's been generated with this platform any other spatial platform. And 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 the 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. And 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 premounted with tissue. So it's been met with great enthusiasm by 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 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. But once you know what you're looking for, once you have a set of genes, you want to focus in on, then Xenium, a third platform becomes the ideal choice for you. Because Xenium allows you to measure large panels of molecules directly in tissue, in situ in a fully integrated fashion. And as we announced last month at our Investor Day, we have now officially started commercial shipments of the platform. This is something that would have seen just like science fiction just a short while ago. And it provides this incredible digital view of the tissues of subcellular resolution, but it is now a reality. And even more so, we build a system for routine use, fast turnaround time, it would be a workhorse in labs around the world, produces very clean data. It works with custom panels. Importantly, you can also stay in 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. The fact, what really excites us here is that this technology has tons of headroom to keep innovating for many years in the future. And we are investing across the board to bring more analytes, more throughput, higher plexy, more content, more analysis to the platform, really extensive road map to bring more future capabilities. And in the longer term, we see there's a great powerful clinical potential to this platform. Because 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. So in many ways, it's ideally suited to bridge the world of digital pathology and molecular analysis together and with it has the potential to transform medicine. So our 3 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. So there's this great synergy of having them all under one roof in terms of product development and also for our customers to use all 3 platforms together. We have built these products to enable fundamental measurements to drive deep insights and that's precisely what they have done. Now thousands -- 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 life sciences where these technologies have not led to fundamental breakthroughs. And the reason for that, and maybe one of the major findings of the last several years of biological research is this pervasive cellular had originated that exists in essentially all the tissues, all the biological system one looks at. Every tissue you look at, there is this large diversity of cells, cell types, cell states that lies underneath. And that's where the crux of biology is that what makes healthy tissue different from diseased tissue which is why we believe that over time, just about all tissues will need to be analyzed with single cell context at end of scale. This is true of just about any therapeutic area you would want to look at. If you look at cancer, you have to consider tumor heterogeneity, you have to consider the microenvironment, the immune component. And you have no hope of understanding any of these elements of how they interact without cell-specific measurements. If you look at autoimmune team, all of the diseases really -- autoimmune diseases really come down to these complex cell and gene expression networks. And we have no hope for 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? And 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, individual cell types. So again, it's just really hard to think of a disease out of therapeutic areas where these products, these technologies have not driven fundamental insights, important discoveries. And so 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. But 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. And 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 those opportunities. In particular, if you focus on just the research markets and look at it through the lens of the kinds of questions that people -- researchers are asking and where single cell and spatial analysis can provide critical answers, 4 high-level categories of questions emerge. One is -- the first one is atlassing, which is basically the baseline characterization of your biological systems, figuring out what cell, what cell types exist and how they will interact with each other. This is where single cell really got started, and there's still way much, much more to do. Researchers are keen to understand -- to expand to more types of -- 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. The move beyond gene expression into epigenetics, proteins, spatial analysis, multi-omic 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. And for these researchers going forward, scale becomes particularly important. They want to run larger and larger cohort studies, do more and more cells with the larger and larger perturbation experiment. The third category is really essentially mainstream biologists. These are the people that are like a typical biologists working on a particular biological system, a gene, a pathway, an organ system. And they typically use a collection of conventional tools to drive their science. And here, the big opportunity is for them to go upstream to gain all this genome scale and cell-specific contexts to truly illuminate the biology they've been seeking to understand. We 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 the analysis and costs become particularly important. And the last category is translational research. So this is within -- for clinical research or within biopharmaceutical companies trying to drive toward new diagnostics, new therapeutics. And we've established a great [ beachhead ] with these customers. But 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 workloads become also critical. And so 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. And we're building on top of the 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 that are going, what questions are 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 a universe, thus adding to our innovation capabilities in this ever-growing virtuous cycle. And so this innovation engine that really encompass the entirety of the company is -- has been the foundation of our success and the source of enormous confidence for us going forward. And 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.

A great presentation. We welcome, Justin, over to stage to join a Q&A session.

So -- maybe let's start with a macro question. I know obviously, you guys are not reporting 4Q until a month, but especially given the China reopening and 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 over the last couple of months? And how do you see the China kind of demand and 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 overall, we're pleased with how the quarter shaped up. We're not doing a preannounce. Obviously, we're not announcing the revenue, but we're pleased with how the quarter shaped up. As far as China specifically, we called out some risks on our Q3 earnings call. Across the world, there was puts and takes that would impact Q4 and so I'm sure you all saw the news, saw the level of disruption there. And so yes, there was a level of disruption there.

Got you. And how should we think about the puts and takes for obviously, not asking for any specific guidance as you await for 4Q to do that. But just qualitatively, 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 cycle. So I mean, is it fair to say that the 2 factors will largely offset each other? So you'd be looking at a growth rating in 2023, that's more or less similar to your initial guidance for 2022.

Yes. So 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. But overall, the macro environment does remain dynamic and difficult to predict.

Got you. One of your peers preannounced 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. But -- any qualitative color you can share about Xenium, how the initial launch is going and what the demand looks like?

Yes. So 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 it will -- the large trajectory will kind of jump-start because a lot of pent-up demand there? Or do you think kind of 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?

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. And so 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 with what we're seeing in the market.

And how about the supply side, are you going to be constrained in terms of manufacturing capacity and...

Well, we're doing everything we can to mitigate that. But just looking over the past couple of 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 buying ahead of inventory. We've done that for Xenium as well. And so we're impacting -- we're controlling what we can control. But again, the environment is still unpredictable.

[Operator Instructions] So maybe stepping back and looking at a bigger picture. You've highlighted the synergistic nature of having all 3 platforms in your portfolio, and you actually gave a very good example of customers using all 3 platforms in a very complementary way in your recent Analyst Day. I'm just curious, across -- 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 3 platforms and how many would more likely to focus on just one platform? I know obviously, heme versus solid tumor is a clear distinction, but maybe you can give us more nuanced color regarding other factors like biopharma versus academic or other use cases?

Yes. 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 are -- they will be naturally pretty dispersed over towards having all 3 because they can most naturally take advantage of all 3 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 kind of work with tissues and not have to go through the other association route. But we'll have to see. It's not yet the case so far. We do think that spatial has a lot of potential to bring in new customers kind of into the fold as well. And 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 can I learn more about those cells and that leads you toward a single cell route. And so I think it can sort of go both ways.

Got it. Great. Now I know there's been a lot of question on whether Xenium is going to cannibalize Visium. I think I can fully appreciate that the 2 platforms will be more complementary, at least in the near term. But I think the more interesting question is, what is the relative relationship between Xenium and Chromium in the long term, right? Both can deliver, especially with Visium HD. So both can cover single-cell resolution, both cover whole transcriptome. So how will customers choose whether to use Visium HD when it comes versus Chromium for that initial step in that discovery process?