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

Good morning, everyone. Welcome to the 2022 Bank of America Healthcare Conference live from beautiful Las Vegas. Thanks, everybody, for being here. Good to see so many people in person again. Our next company up presenting -- by the way, I'm Derik De Bruin, the Senior Life Sciences and Diagnostic Tools Analyst. Thanks, in case you don't know me. Our next company is 10x Genomics. With us is Serge Saxonov, Co-Founder and CEO; and Justin McAnear, CFO. Gentlemen, thank you for being here.

Thanks for having us [ live here ].

So when we did the -- when we did the IPO a few years ago, I mean 10x was certainly -- having been in the lab for so long, I mean, certainly, the appeal of 10x and, I think, a sense of biology sort of really spoke to the scientist in me. And it's been nice to see the progress in sort of like the growth in the field. It's still really early, though, in this market, and I think that's something that may not be fully appreciated, the fact that it still has a lot of science that has to get done. So when you look at where we are in the development of the single cell market, and then we won't even -- and then spatial is even further behind that. But can we look at it -- that was like what -- I hate to use baseball analogies because I suck at sports, but what inning are we in on sort of like thinking about the single cell market and the runway?

Yes. So it's an interesting question. I think my sense is we're past sort of the earliest innings, right, as we're kind of coming out with the first -- with initial adoption, whether it's like the early adopters or technologists were oriented when you think about like the first inning, the second inning of the adoption curve. We're definitely getting out to like a more general awareness and broader adoption with people, kind of mainstream biologists. And so if you think about sort of that early majority starting to kind of starting on trade there -- and so if I -- but like you're alluding to, I think it's very clear to us that it's very, very early relative to where this is headed, which is some measure of ubiquity across biology, across experiments. So third inning, or something like that, where like the game is on. It's kind of -- it's gotten into some kind of a rhythm, but it's very, very early.

So when you look at some of the -- when I sort of look at the single cell market and sort of think about the science, I mean, it's not like -- it's not a market where like, okay, Illumina launches another sequencer. People know what to do. It's like, costs go down, thank you very much, we're going to sequence some stuff. Great. I mean, this is -- you actually have to do real biology. There is a learning curve. It's not off the shelf. The workflows are still complicated. I mean, has the pandemic been a bigger headwind to you than some of the other companies because you just sort of haven't fostered those scientific interactions, you haven't had the ability to go out and do it? I mean, you've launched a ton of great new products. But one would argue that the utilization of these products has been slower, and is it partly due to the pandemics or like dynamics going on? But the fact is, there still is this big learning curve.

Yes. So a couple of things to point out on this. First -- so first of all, our market, our concentration is very much concentrated with an academic market, right, relative to a lot of other companies, relative to Illumina, which is -- has a big clinical component. Ours is like 80% or so academic. So you have to focus in on, like what is happening? How do researchers behave? How have they sort of behaved in the last couple of years? And so the way we look at it, there's sort of first order and second order types of effects from the pandemic that we have seen. And it's something that we had to learn kind of empirically as we went through. It wasn't something that was easy to predict a priori. So on the first order effect, the thing to appreciate is that, with single cell, you work with live cells, right? That's sort of the core component of what you're doing. Like when you extract a sample, you have a very limited amount of time before you actually need to process those cells. Because by the time they sort of -- if you take too long, then their expression changes. So they start dying, and you do want to capture the biology of what's happening there. And that means that there's like pretty substantial constraints around the logistics of doing these experiments, around doing these workflows. You extract your sample, whether it's a tissue biopsy or even if you're working with mice, you have to examine, move that sample, do the -- dissociate the cells, do the QC and run it through the single-cell workflow, and all has to be done within 24 hours or so. So in the conditions of the pandemic, when there is constraints around when people can be available at what time, the introductions are constrained, and people just don't want to launch into something, unless they have certainty that they can actually run through these experiments. That has been certainly a constraint. And we've seen that then. It's kind of a first order effect, has certainly dumbed down usage, especially during the sort of the outbreaks and the waves of variants, or what have you. And then the other thing that's been -- become apparent to us, too, is that a lot of science right, happens through -- well, a lot of science is based around collaboration, right? People going to conferences, learning new techniques, they're talking to each other. And there's papers and also great validating tools, but the real, like, the crux of how people learn and adopt new technologies, that's through that interaction people. That's why there are so many conferences every year, like people go there for a reason, right? And for the last 2 years, that has not happened. And not just conferences, there's also sort of interlab interactions. That's a lot of how our market was growing. It's like not so much us teaching customers, but customers teaching each other how to do his workflows, how to run these experiments. And that all has been sort of dampened down throughout the last 2 years at pandemic. So that definitely has been sort of -- has been in effect. And I guess the last point I would make is all relevant for that transition to that sort of early majority, right? That's how they learn new techniques, that's how they kind of adopt. And so, yes, that has been a challenge, but the fundamentals of the premise of the usefulness of these technologies is certainly unarguable. And I guess, the last point I would make is also -- it's particularly relevant for that transition to that sort of early majority, right? That's how they learn new techniques. That's how they kind of adopt. And so, yes, that has been a challenge. But the fundamentals of -- the premise of the usefulness of these technologies is certainly unarguable.

And what are you doing to sort of improve the workflows? I mean, can you -- does -- I mean, obviously, you need to work on the fresh tissues. But I mean, what about FFPE and preserved samples and sort of your take on that? I mean are the sensitivities there for those sorts of samples?

Yes. So traditionally, we've not -- so all of our products have relied on fresh tissue. On the spatial side, we released the FFPE-compatible product close to a year ago, and that has been getting a nice ramp. But specifically on the single cell side, we are just about now coming up with a product that works with the -- specifically with fixed tissue. So you can fix your sample. And that gives you now, instead of having to them like run through the gauntlet very quickly to do your analysis, now you can kind of wait, time shift, and they analyze a sample at the time and place of your choosing. And so in many ways, it relieves that logistical constraint and that logistical sort of challenge of being -- having to line up your experiment precisely. And so that's big deal that we expect to be to quite help on our account. And there is another product that we're releasing that works with nuclei, which is -- which allows people to freeze their tissues and work with them again kind of at the time of their choosing and the place of their choosing.

So is there any -- is there any sort of consensus on how many single cells you need to analyze for an experiment, right? I think this is sort of the question of elasticity in the market and this goes down. The fact is, as sequencing costs go up, will 10x's sample volumes go up because people are going to be able to run more samples, right? So is it -- what's the equation there in terms of thinking about what's the right number of cells for an experiment?

Yes. So there's actually 2 variables, right? There is the cells per sample, and then there is like how many samples do you want to run per experiment. And on cells per sample, there's -- again, it depends on what you're trying to ask. A lot of people think that getting to sort of roughly 10,000 cells is sort of is a good -- is a good number. For a lot of experiments, that is definitely enough. I think there is -- we're seeing this. There's definitely interest in growing much higher numbers for specific applications, whether it's combinatorial screens, kind of this CRISPR-based perturbation experiments or looking for -- especially in immunology, looking for kind of getting immune repertoire profiling done, or looking for more rare cells. And there's like, you can get them to hundreds of thousands of millions of cells. So there definitely -- we released a new high-throughput kit earlier, last -- towards middle of last year or so. And that has been getting good pickup, where lowering price per cell has been driving extra interest and demand. But the biggest thing, I think, is also we're just running more samples. And I think there is a huge, huge potential there. Because it is still the case that single cell outside of the sort of the most -- sort of visionary or forward-looking labs is run as a kind of an experiment on top of your sort of basic biology, where you got the key samples and around those deep -- analyze those deeply on single cell. But a lot of people and our view of the future is, really, this should be kind of the default method where you run all your samples for a single cell, you do all the replicates using single cell analysis. And I think from that perspective, there's huge elasticity of demand to the market. It's hard to say what precisely is the sort of the equation there, but the potential is certainly there. And -- but it's not just the price, right? It also has to be sort of the workflow, which -- what we're doing there is going to help a lot. And -- but we're certainly encouraged by the fact that sequencing prices are going to be -- should be on a -- sort of the trajectory down.

So -- now I'll go actually to my prepared questions before [ Cassie ] yells at me. So when you look at the Q1 results, I guess, what were you sort of most encouraged about in the quarter? I mean are you starting to see this, where like the Omicron impacts wear off? Are -- what is sort of like -- is that finally starting to come off?

Yes. So there's -- I mean, like we talked about it before. Like at the start of the year was really dead for us. Kind of in January and February, people were just really not doing much in the way of experiments. So -- and it has been -- things have been coming up. So it was -- for me, personally, I mean, kind of sort of an intangible thing. I went out and like as soon as sort of restrictions started lifting, so a lot of customers in person. And that's probably, for me, like personally, is one of the most encouraging things. Because you see there's a lot of just pent-up interest, a lot of the trajectory of new studies, new experiments. New interest is there, and it's very palpable. It does take some time to -- what we've learned is for like for things to come back in a sense when people are not in the lab. It's not just a matter of coming back and picking up and now running the experiments we would have run like 6 weeks ago. It was like -- actually, there's a whole initiation -- reinitiation process of like you have to conceive a project, you have to collect your sample, you have to line up your collaborators and all that. And so that's -- I think we've consistently -- in the world, like you -- everyone wants to move faster, but you realize there's actually kind of -- it doesn't just like snap back. It takes some amount of time for things to come back over time. But overall, again, my feeling being other is like it's very encouraging in terms of what plans people have and what is the trajectory of the underlying demand.

Yes. Now you've got to take your cells out of the freezer. You've got to grow your cells. You've got to expand your cells. You got to...

Mice. You have to grow the mice.

Got to grow your mice, right? Those -- they don't grow on trees.

Yes. No, they don't.

So yes. No, it just -- I think, once again, I think sort of like the timing element and the complexity of the workflow is underappreciated for -- I mean, what you guys are doing is not easy, right?

Yes, there's definitely a greater complexity than most other techniques. It's a new technology and, again, we're having to work with live cells, live experiments, and its biology. You're dealing with biology. And there's a huge -- I mean, the positive is there's a huge number -- universe of different experiments that people are interested in doing and want to run. That's also a downside because it takes a substantial amount of time to think through and plan those experiments.

So you've reiterated your 2022 guide, $600 million to $630 million, and that's despite sort of like some of the China dynamics and some of the cold chain logistics and some of the other things coming out. It's like, what is sort of embedded for like the core business versus like new product introductions? So how would you think about the -- how should we think about the pace of new products that are coming out?

Yes. So we talked about -- like there's lots of new products coming out this year, like possibly the most exciting sort of year of product introductions this year. We also introduced a number of products last year. And so the way that we see these things tend to evolve is that initially, again, especially because our revenue, like our base revenue, is now substantial enough that any new product doesn't have like a huge effect right out of the gate. There's usually some amount of pent-up demand and then it sort of like it kind of goes through a bit of an initial curve and then it slows down and picks back up again and starts ramping. So we always try to caution against sort of over interpreting, right, sort of effects right out of the gate. Having said that, we do have products that have been ramping, like -- so the X -- Chromium X Series that was launched last year. It has been ramping, and now we're sort of leaning into that ramp. And I think that's going to be a material sort of contributor as we go into the second half of the year. The Fixed RNA Profiling is also -- is a kit that's only available in the Chromium X Series. And it's a new product, lots of -- certainly lots of demand that we're seeing out there. And I think, again, the caution was like right out of the gate, we're going to have to see. But I think as we move into the back half of the year, though, it will sort of amplify the fact we expect to see with the Xs and the rest of our portfolio. So yes. And I think a lot of these, again, I would emphasize these products that were coming out, they are meant to accelerate the sort of the adoption of the wider portfolio as well, especially looking at things like the nuclei kit and the CytAssist, they are amplifiers.

So you -- I'd be remiss if I didn't ask the China question. I mean, you actually have a fairly big exposure to China, about 15%, of which 30%, 40% of that is in Shanghai, I believe. So what are -- anything that you're seeing on the ground in China right now? Any sort of like -- any sort of sign of progress there? Anything -- restrictions lifting?

Yes. So it's -- It's difficult to predict when the restrictions in China are going to be lifted. But what we did on this last call was to lay out the math so that we could quantify what the impact is for each week that we go on with additional lockdowns. So as you said, in 2021, China was about 15% of the overall revenue. In Q1, it was about 18%. The Shanghai area is about 30% to 40% of China revenue. And so if you do the math on that, you can figure that for each additional week of lockdown in the Shanghai area, it's somewhere north of $0.5 million a week.

Got it. So one of the questions that we've -- well, a couple of questions that we got. So I think is -- the market has always been sort of hard to define, right? And so when we sort of talk about the single cell TAM, there's a part of you that can go to, it's like, well, why wouldn't everybody do a single cell experiment, right? It just makes sense. You don't want to -- once you sort of see what the data can give you, why would you go back? Then there's the -- because it's expensive, and it's sort of hard to answer. So how do we sort of think about the market and the TAM like that? And also, where in the TAM conversation is the clinical component of this? Or is -- I'm sure we're going -- we're going to go to spatial and in situ in a minute, which is sort of along those lines, but just from the single cell component, how should we think about the TAM?

Yes. So I'll set aside clinical sort of applications and just focus on research applications for the single cell, which is where that has been what we've focused on since the IPO. And I mean, between the 2 choices that you kind of -- the other 2 maybe extremes that you outlined, I'm very squarely on that one, the first extreme. I do think that very -- if anything, my conviction on this has grown because there's so much more empirical evidence. Now like everything should go to single cell, why wouldn't you do it? For every experiment, you should do it. This is where the biology is. That's where the value is, in fact. And I made a statement, I think, ultimately, all tissue -- if you start with tissues, if you start with cells, you need to really analyze them with single cell context. Again, whether you use spatial techniques or dissociated single cell, that's a separate question, but it has to be a single cell context. And I have not heard like anyone at all ever argue currently the opposite, right? It is a very clear argument. Yes, there's lots of -- there is bottlenecks and sort of obstacles to overcome, whether it's pricing, whether it's workflow, whether it's informatics, but that's all solvable. So it's pretty clear to me, and I think, again -- it's a growing consensus there, talking to scientists and so on, that that's where things are headed. So from that perspective, the TAM -- back in the IPO days, we talked about just kind of looking at the research market and looking at -- everyone who's doing sort of reasonably high throughput-ish kind of things, whether it's NGS or flow cytometry or other like high-throughput experiments, and you add up to the total spend over $10 billion. And that -- certainly, that has not changed in our view as the market -- if anything, the evidence of the last several years has reinforced that trajectory of that goal.

So what about the competitive dynamic? I mean, you do have companies like Mission and Parse and some of these other ones that are out there. I mean -- and we've got HBT coming up. I'm sure we're going to see a whole spatial and single-cell explosion of people coming into it. But so what's -- so how should we sort of think about the competitive dynamic?

Yes. I mean look, we had -- from the very beginning in the very first days when we launched our Chromium single-cell approach, we had competitors. And it's always been -- there's been a constant flux starting with the Bio-Rad and Illumina collaboration that went straight up against us. Becton Dickinson was there from the very early days, and lots of start-ups that would come and go over the years. And that's -- that -- like, the overarching dynamic has not changed that much. You do have -- there's a bit more noise these days from some companies, but not like -- I don't think there's a material difference relative to how it's been over the last -- since 2016. We have invested from the beginning of product development, that's why it's been challenging for anyone to compete against us. We keep doing that. So in terms of performance, there is no one out there that comes in close. I can talk about Mission, which has focused on G&A. I think that's so far has been a very small market. But certainly, on anything that actually goes head to head with us, our performance and the -- just product specs are like way, way better. It's also -- we have a diversity of applications that we have built out over the years. So it makes it very challenging for any new competitor that just has the one to return to the space. We have -- obviously, we have scale that we have built out now. And then the other thing to appreciate here is that we've talked about this before, too. Life science tools markets are very sticky fundamentally. Once we just adopt the technology, it's like -- it's a huge risk to change it because, again, biology is unpredictable and people tend to stick with things. And we certainly want to make it as attractive to people to stick with us, and we've invested in that across the years. So yes, these things fluctuate, the competitive dynamic. But the overarching trajectory, I don't think, is really changing.

So we'll shift topics a little bit and talk about some of the spatial and some of the in situ opportunity. So the spatial market is clearly not as advanced as the single cell market, and people are still there. So what are the customers -- how does -- I guess, the question is like how -- how does that market evolve, right? It's still more of a translational market right now, I would argue, to a certain extent. And with single cells sort of driving discovery, and then that's where -- being more the translational. So like how does that market evolve? And also it's the same thing on the competitive dynamic there, where there's a little bit more -- the technology is not as firmly established.

Yes. So it's -- on the spatial side, it is much more, I would say, embryonic. There's still -- there's lots of technologies, lots of approaches, lots of -- I think there's a greater maybe a recognition of priority. There is going to be a very big market, even relative to like the single cell days. So there's a bunch of different players kind of coming in with different approaches. I think there is definitely -- I think you're right, there's a -- relative to a single cell, there's a -- there's sort of this additional group of people who are like tissue people and more translational that are part of the market. I mean there is also the same sort of core of genomics early adopters that are also interested in that. So it is kind of -- there's the 2 different kind of categories of customers out there for spatial. It is very early on. People are still kind of trying to wrap their heads around what -- the customers are trying to wrap their heads around what they actually need because there's not necessarily a deep understanding of like sort of what the science will give you and what the different technological approaches will give you. So we -- we've made the investments early on in our platforms, and are making them very aggressively going forward to make sure that we cover the full range of potential applications and a full range of potential like technological approaches between our Visium platform and between the Xenium that we're developing. And so as these things evolve, our expectation and our goal is to kind of deliver the full spectrum of necessary solutions.

Got it. And on the Xenium, how do we think about how that sort of fits in the picture? I believe that's still on track for year-end launch?

Yes. Yes. So we talked about the commercial launch by year-end. We're on track. It's -- the way that we think about Xenium is this technology we're talking about, like the whole CMC approach is very, very attractive. It's been sort of in the literature for some number of years now. The big like, wow, with these approaches, you could generate like a beautiful picture for a PowerPoint or a publication. What's challenging is to make a robust system that works kind of routinely in the lab and making the commercial product. And so our view is, we have a lot of -- obviously, a lot of exposure to our current customers, and we've been talking through the CartaNA acquisition. We've had a lot of kind of interactions around the service, CartaNA service. So I think we have a pretty good view of what the market needs are there. And one of the core things, right, is to be able to like sort of the core use of this -- of this technology is to be able to give them a tissue, to be able to tell what cells are in the tissue, what they're doing. So type of cells, type of their cell states and determine the pathways. And so you need to have like a sufficient level of multiplexing to be able to do that. You need to be able to work with the types of tissues to be able to do that, and you need to have the right sensitivity and specificity. But most importantly, like once you have those things, is to be able to run it in sufficient throughput to again to be able to do this in a routine way. And that's where we've been optimizing and driving towards. We also feel like our -- us having sort of all 3 platforms under one roof, which you mentioned to like a lot, a lot of these in situ experiments, actually, just about every one of them, if you look at the papers, make use of single cell data. So there is Chromium or Visium that's run in conjunction with that. And we see that there's a -- when we talk to customers, we see that as a big advantage because, again, there's probably no one in the world that has sort of more tangible experience and expertise around single cell than we do. And providing kind of an end-to-end sort of solution is very attractive. And then the third thing around the platform, too, is that we intend to do this is like we're going to launch, but that's only kind of the start. We're going to keep investing in the platform, just like we have in the past with others. And Chromium, when customers see that, that is not just the version one, there's going to be a lot more investment coming. And I want to be investing in the platform that will involve with them.

So any questions from the audience before I continue?

I just wonder [indiscernible] what really [indiscernible] theoretically, which is [indiscernible]?

Yes. No, I mean, there's definitely a lot of very clear things like tangible things that's on our to-do list and for the next several years, for sure, to work through it. In many ways, we are resource constrained. We are a substantial company, but nevertheless, just like on our own product development, there is -- we've got the 3 platforms that we're working on, and we always have to make some of those decisions and trade-offs. You see us coming out with some lead solutions around kind of upstream workflow and logistics and, certainly, a lot more than we're thinking about and a lot more sort of on our to-do list on that side. Same thing on the informatics. You -- I mean, the nice thing about the single cell market now and academia over the last several years is that there's been more and more standardization roughly of the kinds of analyses that people do, the kinds of algorithms. And so you can sort of -- you can cover 80% of use cases on this stage with fairly well-established algorithms, which wasn't the case 4 years ago when things were sort of -- was still kind of a wild, wild west. And so it's pretty clear what needs to get done to kind of start addressing a lot of those use cases. And again, you're not going to solve the long tail of all potential experiments and use case, but you can certainly address like 80-plus percent of it.

So you sold a lot of Chromiums last year. You lowered the price on that. And they're not being fully utilized, right? There's clearly not -- people are not running them 24/7 and doing all that because, otherwise, the consumable revenues would be a lot higher if that was the case. So I mean, really, what is sort of like the secret to getting utilization higher or driving utilization higher? Because I mean, I think, at the end of the day, you've got 2 inputs to the model: boxes, consumables, right? And so how do you sort of like drive the -- just like how do you drive that utilization, that demand curve higher?

Yes. Well, maybe to start, and Serge, if you want to add. But we're -- we're still very focused on placing instruments. So you're right, we placed 1,100 last year. We ended the year with over 3,500 placed. We don't focus so much on utilization per instrument. It's more just driving overall consumables revenue. And so that really comes from the instrument owners and then the halo users as well that we've talked about in the past. So as far as instrument owners, it's really -- the growth there is coming from new products. And from halo users, they're more of a dabbler, trying it out, but they're a great source of leads for future instrument owners. So we're focused on 2 things: continuing to increase the usage of instrument owners overall, whether they own one instrument or 10. And then noninstrument owners, supporting them as well, making sure that they're successful in their experiments and that eventually we'll convert them over into instrument owners. Because we have seen that instrument owners ramp at a much greater usage than the halo users who are more sporadic in their ordering.

Got it. Any other questions from the audience? So as we're sort of coming to the end of the half hour together, my standard closing question is like, what's underappreciated or misunderstood about 10x?

Well, so one of the things is that, I mean, it's an earlier thing we talked about. The fact, I think, that people have some amount of uncertainty again around the market. And like what is -- there's a lot of talk about like what is the single cell market? And is it like limited to -- what are the limitations? And again, I'll come back to those underlying thesis, the fact that, ultimately, everything should be run through a single cell. And I think that thesis, even though it's sort of becoming -- is becoming sort of an underlying consensus on a lot of researchers. I don't think it's appreciated as widely outside. And the implications of it, I don't think they're necessarily being appreciated. So I think that's the big -- probably the biggest thing, or at least the most relevant thing right now, as we think about where this whole business is headed.

Right on time. Thank you, Serge, Justin. Thanks for being here. Thanks, everybody, for listening, and I appreciate your support. Thank you very much. Have a great day.