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

Hi. Good morning, everyone. This is Derik De Bruin, the Bank of America life sciences and diagnostic tools analyst. I'm here with my associate, Mike Ryskin. And welcome to the 2020 Bank of America Virtual Health Care Conference, virtual Vegas. Thanks, everyone, for being here. Thanks for adapting to this brave new world. We appreciate your support in this time. And our next company up today is 10x Genomics. And with us from 10x is CEO, Serge Saxonov; and Justin McAnear, Chief Financial Officer. Welcome, gentlemen. Thanks for being here.

Thanks, Derik. Glad to be here.

Thanks, Derik.

Great. So I think let's do a big -- Mike and I are both lab rats in the sense that when we were doing the IPO last year, we sort of looked at the technology and said, yes, of course, we'd use this. Why not? I mean this is like perfectly obvious to us as to why we would want to do single cell research. I think there's a lot of people probably that are listening to this fireside chat. They are not familiar with 10x. And can you just give a quick download on why single cell analysis is important? And what you're seeing that was -- particularly that was being missed previously? Because I still -- when I still talk about the company, I still think some people don't really appreciate exactly the sort of cutting-edge science that you guys are doing. So let's start with a nice general one like that.

Yes. So just to step back, the very highest level, kind of the premise of the company is to accelerate our understanding of biology. We're also understanding of biology. And the challenge that we see about fundamentally there is the biology is very complex. And I think that kind of goes to the crux of your question. All of us, like humans are made up of close to 40 trillion cells, and each of those cells actually, for the most part, do like very different things. And up until -- really we came along the way that people have analyzed biology is by taking your samples and mixing the contents of all the cells together and kind of measuring averages. And as it turns out, that just like -- that ends up missing a lot of the fundamental biology that is actually sort of driving human health and disease. The -- so the crux of it -- it's really kind of -- it goes to just about every sort of aspect of what you want to measure and understand about biology. The cell has been around -- single cell has been around since just about the start of life for billions of years, fundamental unit of biology. So it's almost like when you look at it from the perspective of actually being able to do this, not being able to do, it seems kind of crazy, like it was only been limitations of technology -- technologies that will prevent us from doing that, from measuring individual cells. And part of the -- the key here, too, is to not just measure things of one cell at a time, but being able to see, in a single experiment, look at thousands -- tens of thousands more. So also, you actually get a comprehensive profile of the underlying biology. And that sort of scale and resolution is what our products first started enabling back in 2016 or thereabouts when we first kind of came in the market. And the idea is to be able to measure all the different analytes within individual cells, whether it's gene expression, which tends to be the most interesting analyte or proteins or epigenetics or DNA. And that's what we built out. Now in terms of -- and that's sort of the fundamental rationale. Now you can see, empirically, now over the last several years, again, we haven't been in the market for that long. But now there's an order of 800 publication papers that have come out using this technology to look at many different biological systems, meaning different diseases, and these are very high-profile journals, Science, Nature, Cell, making fundamental discoveries, whether it's discovering new cell types we have no idea existed. And that turned out to be very crucial for understanding specific diseases to just gaining a view of what is the actual underlying kind of biological circuitry. And you can look at healthy tissue, you can look at disease tissues. And for the first time, you can actually see the difference. These are very complex networks that act a certain way in healthy tissue, and then act in very different ways in the disease tissues. And you see this across many just about every sort of disease, Alzheimer's, autoimmunity, certainly cancer. And once you understand that value, you can kind of make -- start reasoning about sort of the etiology of the disease, how it develops and also how you might want to treat it, what kinds of drugs in order to change your networks from the disease state into healthy state, and there's been a lot of papers on that coming out as well. So it ends up being kind of like a really foundational way to understand to do biological research and ultimately develop diagnostics and ultimately develop therapeutics as well.

Great. Great. Can you just give us an example of how your customers are using 10x technology to study COVID? I assume some people are doing a lot of host pathogen interactions and sort of like immune system profile, but just some examples of where you're seeing some of your customers reposition?

Yes. So there's -- I think some of the initial work was done -- was focused on teasing out antibodies from recovered healthy -- from recovered patients. And the idea there is to make these recombinant -- ultimately make recombinant antibodies that can access prophylaxis and therapy. So that has been going. We've got picked up by a number of customers and pharma companies. But then also just understanding the disease. Pretty quickly, people jumped in to map out all the cells sort of human tissues that express the right receptors to let the virus in to kind of -- to figure out what they can actually infect. And then there's been a lot of work looking at -- well, looking at the infections of various patients, mild cases, severe cases and comparing what is going on in terms of the immune system and the infected cells that makes the different types of patients different from each other, so that you can -- well, you can have better prognosis and also on start receiving about how to -- what kinds of therapies might work. And ultimately, helping sort of developing vaccines or at least testing to make sure that you have -- you can see that the given vaccine creates enough immunogenicity without unwelcomed side effects. So there's sort of a -- there's a full gamut of really profiling the immune system at different time points that we've been seeing people doing and also the actual infected cells and the bystander cells. But our technology is the only one that can really tell you -- allow you to see which cells are being infected, which ones aren't and what they're all doing, the underlying biology.

Great. You noted on the first quarter call that 75% of your customer labs were closed at the end of March. Can you sort of give us an update on what you saw in March and April as COVID spread across the globe? And this sort of gives way to the question on sort of your general expectations on how you sort of see the rest of the year unfolding at this point in time from your base case?

Yes. So the first kind of COVID we saw was in China. That was early in the year. And towards the end of the quarter of Q1, China actually started recovering. But -- so the big, big effect really started, I would say, well, early March, when people -- when we start hearing about labs shutting down first and sort of Pacific Northwest, but then quickly spread all around North America and Europe. And it kind of went from, I would say, the 2nd of March down to the end of the quarter, it kind of went down from there, as we said, down to 75%, basically labs getting shut down completely, people getting pulled out. And it was that way through early April as well. And then I'd say probably the last few weeks, we started seeing some signs of life coming back, just customer activities and reengagements, more questions, quotes, things like that coming up. So -- and it is -- there's a geographical distribution to that. So the -- in Americas, the Midwest, south sort of labs, they started to -- or at least are planning to open up sooner than others. And then we're also seeing some European geographies, in particular, Germany, kind of opening up sort of others. So we're watching that. I mean I think it's not going to be an immediate bounce back once labs open up. There still needs -- they kind of need to restart some of the research programs like if some of the cells or mice have now need to be those colonies need to be regenerated. But the level of interest that we know from our customers suggest that once labs are open and given some of the time, they'll be back up and running with enthusiasm. As far as sort of our baseline scenario, what we're looking at is -- again, there is more and more activity and interest that we're seeing from customers. And of course, we see the single biggest variable, the kind of determining variable is the openings of the labs. And roughly Q3 is when we see things coming back in -- to a significant amount, I would say, and Q4 being -- starting to get close to normal. That's roughly the baseline, and we have plenty of scenarios, pretty kind of different patterns in the coming quarters.

Serge, this is Mike Ryskin here. Let me hop in for a couple of questions just to follow up on that exact point you just touched on now. That's probably been the biggest area of incoming questions we've had from clients after your earnings call was just some more clarity on that 75%. Just because given some of the data points we've seen from the industry in terms of surveys on lab closures, that came in a little bit lower than, I think, some people were expecting. Just more curious, are you seeing any diversions in terms of who's still operational versus who's not based on whether it's a core lab? I know you've highlighted over the last couple of quarters, this move towards core lab facilities, where you've got one central lab at university, for example, University of Pittsburgh and others, they are running multiple Chromiums or that are running at a much higher capacity above your normal sort of average user. Are you seeing any divergence there in terms of who's up and running, who's not? And sort of just because the lab is open or close sort of what their level of operations is?

So it's a little hard to say. I would say like whether there's a clean break between, say, cores and noncores. I think some of the larger labs are open, but then they're only doing and running COVID-based research. So -- and we've seen that quite a bit. So even the labs are open, they end up transitioning completely to COVID research. So I wouldn't say there's like a very clean break. I would say on the biopharma side, those -- especially maybe smaller biotechs tend to be stayed open relative to the academics who a larger fraction closed down. I think that's the biggest sort of difference that I've seen. Otherwise, I wouldn't kind of bring out any core labs or any meaningful core labs on that.

Okay. And then just follow-up on that quickly on your other comment on expectation of a lot of these labs reopening in 3Q and more normalized in 4Q. There's reports in the last couple days, last couple weeks of some universities debating whether they would open back up in the fall or not. I think just a couple of days ago, University of California announced that they'll conduct most classes online in the fall. Sort of are you seeing any separation being made in terms of universities being open for general learning and for classes versus the labs being opened because, in some cases, they're tied together, in some cases, they're not? So are you seeing any potential risk to that 3Q, 4Q recovery from that aspect of things, if university stay closed longer than we expect right now?

Yes. I mean to our minds, these are pretty well disconnected because labs -- when I was in grad school, I remember that like we worked pretty well disconnected from how the classes are conducted then from whether undergrads were actually in section or not. The vast majority of all the work in labs just conducted with grad students and post docs, and they're operating on their own schedule. So we see those as being fundamentally pretty disconnected. And the scientific research is actually fairly competitive. Scientists -- the name of the game is to publish papers and to publish papers before others. And so once some people start coming back into labs and start running research, that creates pressure for others to figure out ways to do the same. And I think there's -- it's one thing for large classes of people kind of gathering together, it's another thing for researchers to be doing their research, sort of -- it's a lot easier to implement social distancing and protective measures in that context. So I feel like -- we feel pretty confidently we will start a little quite uncorrelated. And the -- so the researcher should be able to figure out ways to get back to work regardless of what happens with the large universities.

Great. It's Derik, again. So how are you adjusting sort of your spending resources and allocation of resources for the pandemic? You've launched some new products or discussed about launching new products at AGBT. Can you talk about where you're still investing? And then I'm going to follow this up with some questions on the Visium and sort of the outlook for that.

Yes. I mean so we certainly have to -- there's a lot of adjustments all over the place. Every part of the company has to be adjusted once the pandemic started hitting. So I mean, for one, we had to pull our -- the vast majority of our R&D folks out of the lab once shelter in place orders came in. And we've been slowly bringing them back, but that means that, our initial productivity took a hit. And there's a more limited number of people that we can have in a lot having. We're also, certainly, contemplating the fact all the different multiple scenarios, so we want to be prepared for those and pushed out. We've got a very aggressive ambitious hiring plan that we set out at the beginning of the year. It's still quite ambitious, but we have moved out some of the hires to the future, especially for people who can't realistically be in the lab anyway to begin with. So we've made some adjustments, still very much -- still investing very ambitiously, especially into our product road map. We have to also make some adjustments just because interactions with customers or physical interactions are more challenging right now. So a product that involve collaboration. So product development involves collaborations ends up being more challenging. But overall, the road map that we outlined at AGBT, that takes priority, and that's what we want -- we're pushing on forward. In fact, we have -- imminently, we're about to launch our targeting solutions for single cell analysis. That is one of the announcements we made at AGBT, and we're very excited about. So that's coming. And there's going to be more coming later in the year and next year. So yes, the bottom line has lots of adjustments, but we are prioritizing investment in R&D and product development and pushing forward on that.

Great. So let's talk a little bit about one of your most recent product launches, which is the spatial genomics platform, the Visium product there. You commented on the call that your users doubled to about 400 since the launch late last year. Can you discuss spatial genomics in the context of what it means for the business? And what people can learn on it? And there's obviously some competing products out there. Just how does your -- what does it -- what does it offer that the competition doesn't? How does this sort of the products compare between yours and sort of like NanoStrings and some of the things like ReadCoor and some of these other companies that are doing?

Yes. So our entrant to spatial field kind of happened a little while ago. We realized after launching our single cell products that people -- our customers, in particular, started asking questions around spatial context. Like once you know where all the single cells were all them -- what kind of cells are in your samples? The next question is like where are all of them? How are they arranged with respect to each other in tissues, how are the molecules arranged with respect to each other in tissue? And that's the question in spatial analysis, mapping all the molecular and cellular information onto an actual placement on a tissue. And we've thought about all the different approaches one could take. To get there ultimately converged on this technology developed by a company called Spatial Transcriptomics, which we acquired in 2018. And on top of which we've now built and launched our Visium platform as a result of the acquisition at the -- towards the end of November of last year. And fundamentally, as with all of our products, the big driver behind this platform and kind of how we see the world is to deliver the right resolution and scale, being able to measure lots and lots of elements that we see is really important. Because again, going back to the fundamental complexity of biology, and just the fact that we -- there's just so much we don’t know. To address the complexity, you need high resolution, you need scale. And that's what Visium delivers. And that's I think where it is fundamentally different from other technologies that are out there, whether it's NanoString, which doesn't have even in principle, anywhere near the kind of scale that we can deliver with Visium, the number of measurements you can take from a tissue in a single experiment. And when it comes to some of the other platforms like ReadCoor, and there's sort of things that are similar to what we do in tissue kinds of approaches. It’s also very interesting, we see them as largely complementary to what we're doing, and they're all very early. So it's a little hard to comment on them precisely without knowing the specifications. But if you imagine them kind of coming to a full kind of fruition in terms of what they can do, then they become very nicely complementary to what we're doing when you want to get a very granular view of a couple of genes, very -- really, really high sort of image precision. So yes, so that's how we see the market. We obviously -- we had great reception coming out of the gate with Visium now in over 400 labs in just a few months. It's -- runs the gamut in terms of applications. Certainly, our existing customers, just like we expected, picking up on this application and kind of seeing it as a complementary to single cell analysis, providing the where in addition to what is happening in the samples. But we're also seeing, interestingly, and that this was ahead of what we expected, significant interest from translational and clinical researchers as well. And we've been responding to that interest in terms of kind of how we prioritize our road map and in terms of sort of our marketing and customer service activities as well.

And I assume part of that is making sure that you've got an FFPE solution up and running as soon as possible. It's like where are you in that process?

Yes. So we've -- we announced at AGBT, we're going to be working a full FFPE solution for targeting the first half of next year. And that's what we're proceeding on. The existing Visium platform can actually be -- can be made to work with FFPE samples. We made it work with internally, and we've actually had some customers test them, produce some nice data. But really, we're focused on delivering complete FFPE solution in the first half of next year.

Got it. So you -- I think that you said at the end of 2019, you had about 1,666 Chromium systems in place. By our math, you've got something around over 1,800 systems by the end of the second quarter. I guess where are we in the placement curve in terms of this? I know you've historically used the PCR installed base comparison, but given that they're clear diagnostic applications for PCR that your technology doesn't really have, I don't know if that's really applicable. So what's the right number to think about an installed base number? Or potential installed base number?

Yes. So I would qualify this a little bit. So the way we look at it, in terms of -- if you look at our technology, is we see the potential for widespread adoption based on the number -- the specific number of use cases, they're all the different analytes that you can measure using the Chromium system, all the different areas of biology where it's being used. And so we think in terms of kind of the broad biological research market that's out there and taking the wallet share from across that kind of user base. And that gets into hundreds of thousands. That's the number of labs. And we've designed the instrument in terms of the form factor, in terms of the workflow and the price point to ultimately be able to go into every lab batch. So that's still -- I mean, that is our mindset, and that's -- that we see as the goal that we're driving toward. I would say, in terms of the PCR comparison, part of the point there is like the existence proof of -- yes, there has been a technology that has reached widespread adoption that gets to 50,000. I just want point of clarifications that we've been kind of triangulating on those number. I would say, 50,000 is actually specifically the research that's [ wide there of ours ], but that's specifically the research -- number of research instruments that's out there. And in fact, that’s potentially include biopharma in there that's likely an underestimate. So we feel pretty confident whether it's 50,000 or 100,000, there's like way plenty and plenty of room for us to go in terms of installed base for Chromium -- in base for Chromium.

So the -- your business, I think you were looking at around $180,000 in reagent pull-through per system at the end of 2019. Obviously, COVID’s going to create a hiccup on that. But how should we think about the trajectory of the consumable pull-through once things sort of normalize?

I'll let Justin answer that.

Derik, this is Justin. So our pull-through at the end of 2019 for the full year was $158,000. It's been gradually increasing over time. It was $148,000 in 2018, $140,000 in 2017. And this is pretty much in line with the analysis that we've done at the customer level, which shows that customers, basically the longer they have an instrument, the more they use it. There's some volatility on this from quarter-to-quarter based upon the seasonality of customer ordering patterns and also our new product launch timing. So really, it's best to look at this on an annual basis. It's also important to keep in mind that this is an average across all the instruments. So we have some instruments that are up near $1 million of annual pull-through, and we have some that are much lower than the average because they belong to new customers who are just ramping up. In some ways, focusing on this metric can be an oversimplification of some really complex dynamics. The lift provided by increases in usage has to offset the drag of new placements in order for this to increase overall going forward. Certainly, the bottom line and what we focus on is that we want application revenue overall to increase for each customer, whether they run their applications on a base of 2 or 20 instruments. New products, like the Connect, which has a higher utilization, and also Visium will contribute to pull-through increases. Visium itself doesn't go through an instrument, but the majority of Visium customers own or used an instrument for other applications. So from a business perspective, it make sense to include this in our metric. Visium helps pull-through at the end of the year. But primarily, the increase was driven by increased usage of our new -- of our existing products, so single cell gene expression, ATAC-seq and the immune profiling. Taking all of this into account, we've said in the past that around $150,000 annual, maybe slightly more is a good number to use for pull-through modeling. I think that still holds up. Pull-through really describes something that's already happened. And going forward, we're really focused on increasing the installed base with both new and existing customers and increasing our customer application revenue overall. And we'll see how the metric pans out in the future.

Great. How should we think about -- just got a question from a client, just wanting an update on -- you obviously had a number of developments in the legal realm last year with your Bio-Rad cases. I guess is there anything on pending on the legal front that we need to be worried about? And then the -- as you transition people from legacy to Next GEM products, the gross margin is improving, are you still thinking you'll have 100% of your customer base transferred over by the end of 2020?

Yes. So I would -- first of all, to answer the second question first. Yes, we’re well tracking toward that goal. And in fact, somewhat ahead of it, if anything. So that looks really good. And in terms of anything to watch out for, I mean nothing really anywhere near the scale of what was happening last year on the legal front. I mean there is motions around sort of the legacy lawsuits around legacy jumps that are happening and deals and such. But I don't think it's anything existential that's on the docket for us at all.

And Derik, as far as the Next GEM transition, we've laid out basically a linear transition from mid-2019 to ending 2020. That would put us at 50% transitioned at the end of Q1. So we're pretty much in line, slightly ahead of that, and we do expect to be fully cut over by the end of the year.

Great. And just one final question, I'm asking all the companies. It's like, what do you think are going to be some of the long-term implications from COVID-19? How are you going to change your business? How do you think your customers are going to change the business? I'm particularly curious on your point here, given that -- because you sell such cutting-edge research tools, there's a lot of like conferences and interaction with clients, and FaceTime and KOL in your interactions to sort of get your technology up there. So I'm sort of curious sort of how you see the brave new world unfolding? And what do you think changes?

Well, the -- so there's a couple of things. In terms of just the overall like macro investment in biomedical research, I think that's going to increase, and that's fundamentally going to benefit us. I think it's hard to argue with that thesis. I think what's also happening is that while we've got -- always had a very diverse sort of use cases, infectious disease research was generally more on the margins relative to some of the other areas like oncology or degenerative diseases and things like that. Now of course, everyone is focused on that and a lot of -- lots of the work that I sort of was pointing to earlier is getting published. All of that is kind of supporting and proving out use cases for using our technology on infectious disease research. And I think there's going to be a lot more of that, both from an investment side and now because of essentially developing the market for us. As far as like how is the world going to be different, yes, I think it's -- going to imagine it's now going to be more virtual. The whole idea of traveling to conference, spending time in hotels and going from one meeting room to another, probably not as efficient as what it's clearly happening now and people are learning that. So -- but also, I mean, from our side, we see that as also being quite efficient. We can touch a lot more customers a lot more quickly and virtually. We've been organizing webinars and kind of other conferences, virtual conferences with our customers and leveraging a lot of our sort of materials that we've built up now over the years, and that has been going great. I mean in fact, we're getting a lot more new people getting into our ecosystem that we had before when we have those physical conferences. So we actually feel quite optimistic about that. And I think there's a great sort of open field of experimentation that's ahead of us. Now that people are not wasting their time flying on airplanes and staying -- kind of going from -- to the hotels and commuting and all that, there's -- they're going to be spending their times in other ways and scientists like to learn. And we're here to educate them about the kind of science that can be done with our products and all the new technologies. So we'll all see, but it's actually kind of interesting and exciting in certain ways.

Great. We're out of time. Serge, Justin, thank you. Thank you to all the clients on the line listening. We appreciate your support. And good day, have a great conference. Thanks, everyone.

Thanks, Derik. Goodbye.