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State of Biotech 2022: What's Next for NGS
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State of Biotech 2022: What's Next for NGS
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JULIANNA LEMIEUX: Hello, everyone, and welcome to our next session, The State of NGS. I'm Julianna LeMieux, science writer at GEN. It's an exciting and busy time for next generation sequencing companies. At least three new companies have entered the US market in the past year, which has for the past decade been largely dominated by just one company, Illumina. So today, we're going to explore what impact these new platforms and technologies could have in this space, what their priorities are, and how they plan to carve out a niche for themselves.
JULIANNA LEMIEUX: Some questions, we'll ask are, what does it take for companies to compete in this space? Is long-read sequencing poised to overtake the utility of short-reads? And what other applications are these new platforms going to address? Our guests are Eli Glezer, who became the CSO and co-founder of Singular Genomics in 2016. Before that, he spent two decades at Meso Scale Diagnostics where he led the design and development of their multi-array electrochromic luminescence technology.
JULIANNA LEMIEUX: And Shawn Levy, senior vice president of Applications and Scientific Affairs at Element Biosciences. Shawn recently joined Element from his position as CSO at Discovery Life Sciences. He maintains a position as an investigator at Hudson Alpha Institute for Biotechnology in Huntsville, Alabama, where he has been since 2009. Thank you both for joining us today.
ELI GLEZER: Thank you. Nice to be here with you.
SHAWN LEVY: Yeah, thank you.
JULIANNA LEMIEUX: So let's start with each of you introducing your companies. Shawn, why don't you go first and tell our audience about what's going on in Element?
SHAWN LEVY: Sure. So Element Biosciences just celebrated its fifth anniversary. It was founded in 2017 by three co-founders, Matthew Kellinger, Michael Previte, and Molly He. And Molly currently serves as our CEO, Michael is our CTO, and Matthew is vice president for Biochemistry. So Element was founded on the premise of essentially reinventing short-read sequencing and looking at the technology stack that underlies the types of assays that are performed in the pursuit of genomics and the pursuit of high throughput biology.
SHAWN LEVY: So it began with a close evaluation of what was in the field as far as existing players and technologies and methodologies and then kind ask the question, can relook at these technologies and methodologies to both embrace the successes that those technologies have had as well as provide a new beginning and a new beginning to head in novel directions and with novel resolutions, both from a quality perspective and performance perspective.
SHAWN LEVY: And I think most importantly, Element recognize that while we had been going downward in sequencing costs for the better part of a decade, one of the challenges with those sequencing costs is the cost of the equipment and the cost of each run was still going up substantially. So the accessibility and the value to-- in a democratized manner was still quite challenging. And so that was another strong premise for the company was to enter the market in a way that allowed that democratization and access to be kind of re-established.
JULIANNA LEMIEUX: OK. Great. So Shawn, I mean you've been in genomics a long time, you've been a longtime customer and spokesperson for Illumina. What made you join Element?
SHAWN LEVY: Yeah, I think it's a question I've gotten a lot. And it's been an interesting time and the time I was able to spend at Hudson Alpha was, of course, an amazing several years that we were fortunate to be very successful in a number of programs, both with Illumina where we were a HiSeq X site. Then we transitioned from HiSeq X to 10 NovaSeq. So we had quite a large capacity on the national or world stage. And then over the last several years, we did the same thing with PacBio where we currently have 20 Sequel IIe's.
SHAWN LEVY: So it was a pretty substantial investment and really embracing the different technologies as they apply to different areas in genomics. And so I think maybe the important thing is like any toolbox. There are so many interesting questions in biology, and that it's almost a disservice to the science to suggest that one tool fits all of those applications. What made me join Element was there really the recognition of a couple of things.
SHAWN LEVY: One primarily was the team, and the philosophy and the mission and the vision of the company was something like I had not seen in other locations and was very excited to join that team. But perhaps more importantly, and this probably comes as no surprise, I certainly didn't necessarily join Element for the sole purpose of launching a sequencer into a market dominated by a single player, as you already mentioned.
SHAWN LEVY: It was really for what the platform has the potential to do. And sequencing is, of course it's not to suggest there's a lack of focus on sequencing it's that sequencing is number one. It's kind of product number one, but the roadmap and the foundational technology and methodology that exists within element will extend well, well beyond sequencing.
SHAWN LEVY: And so it was just an opportunity to join at the time that I joined that just couldn't pass up.
JULIANNA LEMIEUX: OK, terrific. And I think we're going to get into exactly what you were just talking about a little bit later. Before that, Eli, can you tell us about what's going on in Singular Genomics and a little bit about your background and what attracted you to the company?
ELI GLEZER: Yeah sure. So we started in earnest in late 2016. And I started the company together with Drew Spaventa, who's our CEO, and David Barker, who is on our board and also on our scientific advisory board. And David used to be a chief science officer at Illumina back in the formative days. We really set out to improve on the state of the art in sequencing, in some ways similar, I think, to what the mission of element is as well, to offer some competition to improve on aspects of performance, speed, versatility.
ELI GLEZER: And so we started actually by visiting first maybe a dozen labs and then a lot more after that and trying to get a firsthand sense for what mattered to people, what they were able to do currently, and where they saw opportunities for improvement. And so people were looking for faster results, they were looking to avoid batching, cost as a factor. There's definitely a desire for competition in the field. And so I think we got a pretty good sense for where there were opportunities for improvements.
ELI GLEZER: And then at that point, we invested a lot of time in the fundamental basic R&D, so novel chemistry, novel enzymes, materials, nanofabrication, kind of all of the things that go into the heart of sequencing. And then on top of that, all the engineering that goes into it. I think sequencing technology has really pushed the limits of a lot of modern engineering technologies, in particular high-speed imaging.
ELI GLEZER: And so we invested a lot of time in that as well as rapid and flexible fluidity. So that's kind of in a nutshell where we started. And then since then, as you might know, we went public about a year ago. And since then, have commercially launched our first instrument are in that stage of new product introduction and excited to offer our tools to a wide range of customers in both research and clinical applications.
ELI GLEZER: OK. Terrific. Thanks. It's funny. I mean Element, Singular, and Ultima Genomics all started in 2016, 2017, and all launched in the last year. Was something in the water back in 2016, 2017? What do you think it is about that timing, because it seems coincidental?
LEVY: I think we're maybe not capturing a large number of other efforts that have existed in the field, other companies that have-- either are still more in their stealth mode or have launched perhaps just with more very specific applications or very specific areas. And we've even seen some mild attrition in the field as well. And it's kind of in different companies, in different ways. I think in this case, maybe the timing that was coincidental was the commercial launches around the three companies all happened at about or right around the same time.
LEVY: I think Element being originally was planned for AGBT but with AGBT moving from-- its usual time in February till June. We decided to move forward with a virtual launch on March 14, which was just after I joined the company. And as Eli mentioned, Singular had gone public and so had an opportunity to become a little bit more well known in the market prior to their commercial launch. And then I think, as we know, Ultima was very stealthy until their launch at AGBT.
LEVY: So I think although the timing was around the same time of the year, I'd still say the three companies took pretty different approaches to how they went into that launch mode, I think, where maybe it was more of the fact that the field is quite enthusiastic about learning more about these new opportunities And we had seen a little bit of, I think, a lack of comprehensive information coming out on platforms for the last several years.
LEVY: It was kind of a bit of a celebratory time of the field of saying, hey, there's now multiple new options hitting the market with probably, most importantly, with accessible data with clear specifications, clear pricing. And I think that was something that seemed to be lacking for a while.
JULIANNA LEMIEUX: That's a really good point. Yeah, there was a lot of information coming out over the last 6 to 12 months, so. Terrific. And I think both companies, Element and Singular, have or will be moving into new headquarters in San Diego this year. What's the significance of that?
SHAWN LEVY: Yeah. So Element just, in May, began its move into its new headquarters. So there were multiple sites in San Diego as well as we still continue to maintain two sites in the Bay Area of California . So Element continues to have multiple sites. But the headquarters in San Diego moved from grad labs near the UCSD's campus, which was where it was founded. And as the company grew, we were spread across a couple of different laboratory and office space areas within that building.
SHAWN LEVY: It was, as you mentioned, fantastic space. It was an amazing place to incubate the company. But a number of years ago, Molly worked with some of the existing staff of the company as well as some of the developers within San Diego and designed a new building. And so that building just completed so certainly, something that was in the works for a few years. And so we're fortunate to move into that new building.
SHAWN LEVY: It's an amazing space that allowed us to consolidate everything except for warehouse space into that one location, which helps with the culture. It helps with efficiency. And I think, most importantly, it gave us the space to have a-- for the long foreseeable future, we'll continue to do all of our own manufacturing, both on the reagent and instrument side in that headquarters, in that location.
SHAWN LEVY: So the new Element has every intention of keeping everything US made and within the United States under tight control within that space. And so that space was designed and outfitted to allow that with the exception, as I mentioned, of warehousing, which is done just a little bit inland from the new location.
JULIANNA LEMIEUX: I also heard there's meditation gardens, which I feel like would be necessary if you're entering into the NGS market. [LAUGHS]
SHAWN LEVY: Yeah. Certainly, the amenities in the area are impressive. And so it's definitely a place that's-- I'd say it was good timing as we transition out of the pandemic with people coming back into the lab, back into work, having the timing of the facility coming online is really helpful with re-establishing that collaborative culture and giving people a place that they really enjoy coming everyday.
ELI GLEZER: And we've been looking to gradually grow all within a couple hundred of yards of where we are today, I would say. We started off with half a lab bench at JLABS which is in about a couple yards from where I am right now and then move into some older buildings and gradually expand past that. And then earlier this year, we moved into a really nice new 80,000-square-foot facility that was custom remodeled for us right up here on Torrey Pines Mesa next to the future One Alexandria Square.
ELI GLEZER: That's being built. Actually, a lot of heavy construction going on right now. And we will be expanding further into that space down the road. But right now, we have a great facility here. And we also have a manufacturing facility in Mira Mesa where we build our instruments.
JULIANNA LEMIEUX: So now, both of your companies make benchtop mid-throughput sequencing platforms. But there are already a lot of those in circulation out there. So I'm curious how your companies are differentiating themselves. So is there something unique that sets your technologies apart from the other NGS platforms on the market? And Eli, let's start with you.
ELI GLEZER: Sure. Yeah. I would say fundamentally, it's speed, power, and flexibility. Those are the three things that we feel we offer something that doesn't exist today. So it's based on full four-color chemistry so kind of state-of-the-art accuracy. And then on top of that, offers advantages in terms of speed so a typical runtime on the bulk of the instruments out there. Of course, they're made by Illumina, the NextSeq instruments.
ELI GLEZER: Those run 29 to 48 hours if you're going to do your standard paired 150 based sequencing. And on our instrument, the G4, that's 19 hours. So much faster you can basically run the system overnight and have results the next day. In terms of power-- and really, by power, we mean data output per hour. It's two to three times more output than what you can get out of a NextSeq.
ELI GLEZER: And so that's a substantial advantage just in terms of capital investment. And then finally, flexibility. This is kind of a unique feature that we have. So on a NextSeq, you can run one flow cell, or you can run no flow cells. And those are your options. Whereas we have up to four flow cells, completely independent. So you can run one, two, three, or four flow cells to scale your throughput to your day's needs.
ELI GLEZER: So you don't have to worry about batching samples over specific period of time or not being able to run some samples because you haven't filled up a larger flow cell. So there's that flexibility. And then there's an extra level of flexibility. And that's that each flow cell runs four independent lanes. So it's not an afterthought like it was on some earlier systems, where you could load those manually.
ELI GLEZER: You can simply load the samples, and they'll be delivered into those lanes. So in a single run, you could run 16 different samples, load them in, and they'll all be processed in parallel. So you don't have to worry about any kind of problems in one library prep affecting another. And that's particularly nice for core labs where one investigator may not want to share their space on the flow cell with another investigator.
ELI GLEZER: So I think those are some of the critical features.
JULIANNA LEMIEUX: OK, terrific. And Shawn, what about the AVITI?
SHAWN LEVY: Yeah. I think there's a couple of things to touch on. The primary one is the AVITI system was developed to embrace the state-of-the-art of genomics where it existed and really all the way up through the original launch. What I mean by that is the recognition that the last decade or so, the number of applications and library preparation options, and even data analysis options, were substantial. But we also really carefully looked at some of the error modes in the data and really got an understanding of what some of the limitations of sequencing by synthesis is, particularly with the use of modified nucleotides.
SHAWN LEVY: So you get a scarring effect in the data, phasing and pre-phasing errors. The fact that it's very difficult to optimize enzymes for incorporation of those nucleotides when you're essentially doing the synthesis step with those modified nucleotides and trying to keep that accuracy very high. Plus then, even the amplification strategies on the flow cells themselves that have either high optical duplication rate, index hopping issues, all of those things.
SHAWN LEVY: So those were all really carefully looked at. And there were some underlying principles which went into the instrument as it exists today. And those principles are full compatibility with the ability to efficiently and without changing the library complexity or index bias or anything else that you can. And we have, on our website, we've listed approaching 40 different catalog numbers that have been empirically validated as well as a number of applications to essentially take almost anyone's library prep that's used on an Illumina platform and make it compatible with Element and be able to do so in a high-performing and very efficient manner.
SHAWN LEVY: And same thing with the downstream data analysis, we made sure that our output file formats could go right into existing bioinformatic workflows without change. And again, that was to really recognize and embrace the innovation and state-of-the-art of genomics as a whole, independent of the platform. But beyond that and more focused on the instrument-- in the interest of time, stating it very simply-- it's an entirely novel surface chemistry, exceptionally low background surface chemistry.
SHAWN LEVY: But when we develop things to stick to that chemistry, they stick with high specificity and very high performance. And what Element refers to that as a very high contrast-to-noise ratio rather than signal-to-noise, meaning we keep background very, very low to allow signal to be differentiated at a very high degree. And then the sequencing chemistry is fundamentally different. So if we think of Illumina, Singular, or Ultima, even Ion Torrent, are all in the same family of sequencing by synthesis, although the detection methods are different.
SHAWN LEVY: It may be a direct detection of fluorescent nucleotide in multiple-- all the way up through four colors, as Eli mentioned, or a side product of that synthesis step in the case of Ion Torrent. Whereas, I think, Element is in a little bit of a different family, probably belongs more in the same family of, say Omnium, which is a sequencing by binding event. So Element has split the synthesis step away from the detection step.
SHAWN LEVY: And so we use a unique molecule that we call an AVITI molecule, which has a very unique chemistry. It's very tunable. It's very flexible. And that chemistry allows us to separate the synthesis step. So we use only natural nucleotides in the synthesis step, but do our detection with an apatite molecule which senses the next base coming in. That gets detected, it gets washed away.
SHAWN LEVY: And what that allows us to do is use a much, much smaller amount of fluorescent dye compared to other platforms. And that's been one of the key areas of recognition, that the dyes are quite expensive. And they're a key component into the reagent costs that go into the sequencing platforms. They're a key factor in many of the other parts of the biochemistry process. So it really was a fundamental reinvention of the sequencing process to avoid some of the limitations of sequencing by synthesis that we had observed before.
SHAWN LEVY: And then the platform itself, similar to Eli's points, it's quite flexible-- two flow cells completely independently controlled in terms of not only when you start them, but also the conditions they run under. Now, our speed is similar to NextSeq and in the current iteration. Although, we do run the two flow cells. So it's two independent flow cells, and it's a paired 150 with a maximum data output, which is 800 million reads specified.
SHAWN LEVY: And I think in the field, we're seeing closer to a billion reads per flow cell. So 44 hours of runtime between whether it's one or two flow cells run at the same time. So not quite as fast as what Eli had indicated, but I think we wanted to keep some of the same flexibility. Capital equipment costs are relatively low in the field. Our pricing has been very transparent and very consistent.
SHAWN LEVY: And I think one of the unique things that Element recently announced was that we are holding reagent costs fixed for the life of the platform. And that was just recognition that research and development doesn't happen on a month-to-month or quarter-to-quarter basis like many businesses operate. It really is a many months to years basis. And as investigators are planning grants and planning long-term projects, the surprise of reagent cost increases or changes can be pretty detrimental to that research.
SHAWN LEVY: And so we really wanted to get in front of that, as well as get in front of any comments or concerns in the market that the pricing is introductory or that the platform is unstable or anything like that. And so we wanted to be sure we put that out there in a very transparent and very clear manner where expectations can be set, and we can focus more on the science and more on the data quality rather than the logistics around getting the data run.
JULIANNA LEMIEUX: OK. Thanks, Shawn. Yeah, you just anticipated a couple of my questions, actually. One was definitely going to be about what we call Elements' "forever stamp" of reagent costs, because that's just something that-- I mean, I don't know what from gas to my favorite takeout is going to cost tomorrow. So the reagent cost sticking was definitely an interesting announcement.
SHAWN LEVY: Yeah. I guess, just briefly, I'd say, it probably comes as no surprise. I already mentioned that Element has a very healthy roadmap in front of it from a company perspective and a technology perspective. So honestly, us deciding to put out that guarantee was not particularly difficult or a stretch. I mean, we already have significant data on where we're heading with our reagent efficiencies and costs, et cetera.
SHAWN LEVY: And this really was more in response to some feedback we were getting from the field of people just saying, oh, I heard this is introductory pricing, I heard there's no way that Element can keep this pricing, or that you're losing money on each box, just comments like that as we entered the market new. And we thought, what's the easiest way to alleviate those concerns? And the easiest way to do it is just to put a guarantee behind it.
SHAWN LEVY: And so that's what we decided to do. But just to stress, it wasn't-- it's not something that has high risk. It's not something that we are honestly particularly concerned with. Frankly, we were a little bit surprised that it got as much positive attention as it did.
JULIANNA LEMIEUX: OK, great. So for new NGS customers, I guess what I'm wondering is, how do you think about why-- how do you convince them that they should turn to Singular or Element rather than their more established platforms that they're used to? And I just want to preface this by saying that-- I mean, I talked to a lot of NGS customers. And I had a conversation with one Illumina customer who told me that they have no plans to switch to a new company.
JULIANNA LEMIEUX: And when I asked why, they said because I know that what I have works. So what do you say to that potential customer?
ELI GLEZER: Yeah, I think you have to offer some strong advantages, particularly initially, right? There are some people out there who are just not risk takers. And until a company has been commercial for a couple of years, they're not going to buy an instrument. And so that's not the initial set of customers. I think the initial set of customers are people who are willing to be early adopters who see some particular advantages, whether it's cost, or speed, or throughput, or flexibility, or something particular about the data quality.
ELI GLEZER: And so that's really where we're seeing a lot of interest is people, let's say, in a core lab who really appreciate the ability to run multiple samples independently, or people who really need faster results or want to avoid the batching. Those are strong factors. Cost, certainly-- we don't talk very much about costs, but we're cost-competitive with everything that's out there. And we haven't had a lot of questions come up in terms of longer term pricing.
ELI GLEZER: But certainly, the intent is to keep driving that down by increasing throughput, for example, and decreasing manufacturing costs and so on. So I think I think all those factors contribute. And then also, sometimes if you can offer something really unique and new-- so we have a couple of things along those lines that have generated a lot of interest. One we call high-definition sequencing, and that's a way to read the original double-stranded DNA.
ELI GLEZER: So when you think about where accuracy really matters, it's really in rare variant detection. If you're doing something that is germline, well, you sequence a little bit more and you get to good enough quality. But if you're looking for that needle in a haystack in liquid biopsy, that's where that accuracy is very important. And people have come up with very clever ways of labeling the double-stranded molecules and running it through the sequencing and then reassembling it.
ELI GLEZER: The problem is that that's quite inefficient, it requires a lot of sequencing. So we've come up with a way that you take the original double-stranded molecule and link it together into one, and then take it through all of the steps of targeted capture, clustering, and sequencing as a single molecule. And that's allowed us to get to error rates that are below 100,000, so Q50 kind of accuracies.
ELI GLEZER: And these are kind of holistic accuracies, so from start to finish. Because even if your sequencer itself is really, really high accuracy, makes no errors, you'll still get some errors from something as DNA damage or something that happens in the library prep steps. So the beauty of linking those two strands together is that you read both of those strands, and you can differentiate between something that appears on one strand and is an error versus something that appears on both strands and is a true genetic variant.
ELI GLEZER: So that, and then more recently, we've also introduced the M series flow cells. And those are a way to get the really high output for short reads. So as much as 4 billion reads per run, so kind of getting to NovaSeq S4 types of levels in terms of cost and throughput but on a mid-throughput sequencer. And that's not for all applications, but it's particularly powerful for counting applications or anything that's sort of in the hundreds base read range or shorter.
ELI GLEZER: So I'd say, for us, those have been some of the factors driving customer interest.
JULIANNA LEMIEUX: Eli, just to clarify, when you were talking about finding variants that are low frequency, were you talking about the collaboration with TwinStrand and the Duplex Sequencing Technology?
SHAWN LEVY: That's right. That's right.
JULIANNA LEMIEUX: OK. Great, perfect. Yeah, I just wanted to clarify for our audience. Got it. OK. And Shawn, how about yourself? How do you answer that question about customers that are happy where they are?
SHAWN LEVY: It's a fair question, right? The reality of Illumina sequencing, proven by the number of publications and the impact it's had in so many areas, is it works, right? There's no argument. There's no argument that it works. But I think we've demonstrated with data sets that we've made available on the website with many third-party data analysis, some of the things we presented at AGBT, say Andrew Carroll's analysis out of Google DeepVariant, that shows that, for specific applications, there are some very significant differences.
SHAWN LEVY: I think what Element has done in the market so far is set a new bar for sequencing quality and accuracy, particularly on a bench-- certainly on a benchtop sequencer, and arguably, on any commercial sequencer. With the acquisition of Loop Genomics, we also have an ability to do directed long reads. Now, I don't want to give an incorrect impression that this is a direct competition, say, to Oxford Nanopore or PacBio.
SHAWN LEVY: That's not the intent. But it gives us opportunities to cover other application areas with long reads on a short read platform, extending the flexibility and capabilities of the platform with the Loop Genomics. So again, specific areas, but there's a roadmap there as well for the company. But focusing on the question when somebody says, what can your instrument do that I can't do with Illumina, right?
SHAWN LEVY: And the answer is, well, there there's a number of things. I think Eli touched on a number of great points. And I think Element and Singular share some of those capabilities and some of those opportunities. But we've really focused on the quality of the sequencing. And as Eli mentioned, when your sequencing is accurate enough, you really start seeing error modes that come from either input DNA quality, library preparation errors, et cetera.
SHAWN LEVY: And those are very real phenomenon. And I think they've been very underappreciated in the field. We've often introduced band-aids to allow the state-of-the-art technology to get better, but incrementally better, and things like the use of unique dual indexes to get around index hopping and things like that. They were just-- the reality of those things is they were necessary at the time. And one of the opportunities that comes with the ability to bring out a new platform later, learn from the field, is, well, you can address those things.
SHAWN LEVY: And so that's what Element has done very effectively. So we're often, certainly in almost all runs greater than 80%, Q40. Our Q tables go out to Q45 as a routine. And in a PCR-free condition with high-quality input DNA, we see greater than 90%, 95% of data even exceeds that Q40 number. What that translates to from a results perspective-- and this is, again, data that was developed by some of our customers and analyzed by third parties-- is on a coverage-dependent manner, we see roughly 50% fewer errors in Illumina at low coverage.
SHAWN LEVY: That coalesce is down to a slightly lower number as we get to higher coverage. But when you look at the number of false candidates and things like that-- and again, this is data that we've made available on the website and that Andrew's presented on in a couple of different forms-- you see just a much lower rate of false candidates. And as we extend that to other applications, both in the single-cell space as well as in the somatic space, we're observing higher and higher sensitivities.
SHAWN LEVY: So I think the real direct answer to those customers is it really depends on what they're doing, and it really depends on what quality is acceptable for their applications. And in cases where exceptionally high accuracies in the raw sequence data is helpful, then it absolutely makes a difference. We can demonstrate that in many, many data sets that we've released, and even customers that are early commercial adopters have demonstrated as well.
SHAWN LEVY: In counting applications where those qualities aren't quite as important, well, then we may not be able to provide that answer as much as someone else. But I think we've also embraced the complementary aspects. And one specific example, before I joined Element when we beta tested the platform, as I mentioned, my lab continues to have a large number of NovaSeqs. And so we actually began using the Element platform as a top-up option for NovaSeq platforms.
SHAWN LEVY: Instead of having to run another S4 flow cell, we could top-off existing data on an Element platform running roughly 2 billion reads across the two flow cells. And since the cost models are quite similar-- not exactly the same, but they're in the same ballpark-- and especially when you factor in instrument costs, labor, service contracts, et cetera, they actually normalize very quickly. That flexibility and the different error modes in the higher quality data that we were seeing on the Element platform really made that top-up flexibility pretty important.
SHAWN LEVY: So what that enabled even a larger genome center to do was recognize, we can still use the factory sequencers but just kind of bring on a different type of benchtop sequencer fully compatible with the library types, fully compatible with the downstream data analysis, and just have that flexibility. So like I mentioned earlier, it's really making sure that the tool that you're taking out of the toolbox really fits the application.
SHAWN LEVY: So I'm certain that-- or I would just say, we really look forward to educating the field as to where we can make those differences. And we're doing so with releasing as many data sets as we can. We have a number of instruments in the field that, knock on wood, we've not yet seen a field-related failure on our commercial instruments.
SHAWN LEVY: And so we continue to model the 30 instruments we have in house that have run over 15,000 runs inside of Element and continue to run on a daily basis. We've been able to leverage those machines to a very reliable platform in the field. And that's how we know we can keep our service contract cost low, because we know the instrument's very reliable. So I think we're just going to have to continue to educate the field with real data and real results to bring and open up those opportunities of bringing somebody who maybe service very well with Illumina, right?
SHAWN LEVY: We certainly can't say anything negative about the successes that Illumina has had in this field and the transformative effects they've had in sequencing. There's absolutely nothing to take away. Really, the way Element looks at it as there are-- we can look beyond some of the limitations of SBS and really start looking at where you can develop a different tool and make sure we fit that tool in the places where it belongs.
JULIANNA LEMIEUX: You actually just, again, teed up two more questions that I have, Shawn. I can tell this is not your first time doing this. So one is about cost and the other is about data quality, basically. So let's start with cost. So I mean, earlier this year-- in fact, I'm surprised that we have not yet said $100 genome because it's said very frequently. Earlier this year, Ultima genomics came out talking all about $100 genome.
JULIANNA LEMIEUX: And this is not even that new. I mean, two years ago at AGBT, I wrote a story on MGI saying $100 genome. I mean, we know cost is incredibly important. Is $100 genome even a meaningful term anymore? And are we there? Are we going to get to $10 genome? How meaningful is this right now, do you think?
SHAWN LEVY: Yeah. I'll give a brief answer, but I'll preface it with, I think we've gotten comfortable with using the word genome too interchangeably, right? So my lab was very fortunate to be part of the All of US Research Program as the pilot long read center. And that was the emphasis behind bringing on the number of PacBio instruments we had.
SHAWN LEVY: And I'll just say that a genome from a telomere-to-telomere perspective, the amazing work that Karen Miga and colleagues and Evan Eichler and others have done, has really showed that's a very different genome than a $100 genome that you'll generate with short read sequencing, right? It's to two completely different perspectives. And certainly, if you could wave a magic wand and have a technology that allowed you to uncover all of the intricacies of the genome at $100, by all means, you would see a wholesale switch in the field.
SHAWN LEVY: I don't think Karen, or Evan, or anyone would disagree that if they could do telomere-to-telomere like genomes at $100, they would absolutely do it. But the reality is that we pick and choose our technologies and capabilities based on the state-of-the-art. And so I think the $100 genome, from the way we're talking about here, you have to first appropriately account for the compromises and where they exist.
SHAWN LEVY: And then you have to ask, well, that $100 a genome, how impactful is that, and where does it fit, and what are the limitations? And I think that's something we've been a little bit slow to come around on is being very honest about limitations of $100 genome. One of the founding principles of Element particularly driven by Mike Previte has been not doing things by brute force, right?
SHAWN LEVY: Not just saying, if you throw a massive amount of data at a wall and do it cheap enough, you can pull out a $100 genome. It's not the company's philosophy. It's not a direction we're going, which is why we very intentionally stayed away from any announcements around higher throughput sequencing, because we are more focused on the data quality and the performance of the platform and where the real science roadmap is driving us.
SHAWN LEVY: But I would say, as a scientist, I'm very interested as the field develops into moving in that direction-- how low can cost go? And I think having those metrics, like Ultima has done, is wonderful. It's great for the field. It's great to set a new stake in the ground. And what I'm most interested to see is not when somebody can do $100 genome once, it's when they can do it 100,000 times and do so with the same reliability and performance across 100,000 of those $100 genomes, not 1 of them, not 10 of them, not 40 of them.
SHAWN LEVY: And I think that's got to be probably the appropriate metric.
ELI GLEZER: And I would just add that I think the real application matters as well. So today, it maybe costs $400 or $500 to do whole genome on a NovaSeq S4 flow cells just in consumable costs. But you have to bundle together 25 samples, and so that's good for population genomics kind of studies. But when you look at the cost of, let's say, rapid whole genome, like done for babies in the NICU when you're trying to diagnose a rare disease, it's much, much higher than that.
ELI GLEZER: I remember when we first actually started, one of the places we visited was Rady Children's Hospital. And they were pioneers in doing this. And I asked them what it cost them just in reagents to do whole genome. And it was $6,000 for the baby, and then $6,000 more for each of the parents if they were doing a trio. And this is in the age of a sub-$1000 genome.
ELI GLEZER: So again, you have to look carefully what the particular application is. If you can have a high throughput enough system and pool enough samples together, certainly there are cost efficiencies obtained by that. But there are many applications that require faster results. And for us, for example, we size the flow cell to be able to do a 30x genome in one flow cell. So you don't have to batch multiple samples together, and you can get those results in an overnight kind of run.
ELI GLEZER: And that scale, to get to that at $100, that's still a long ways out, I think. But then even if it's considerably higher than that, the clinical value you provide is tremendous.
SHAWN LEVY: Yeah, Eli raises a good point. And I think that's where some of the benchtop sequencers really can shine is in that democratized approach. And maybe using Element as an example, we've released some data sets that show per flow so we can do a trio. And granted, it's not 24 hours. It's closer to 44 hours. It's probably 48 hours when you wrap in library preparation and some data analysis.
SHAWN LEVY: But I think that's a very important point of having that flexibility of turning a trio very quickly in a smaller laboratory benchtop-type format is exceptionally important in the field today, just as important as somebody's alternative perspective on population genomics where that $100 is more impactful. And I think what Element's done from that cost and value perspective-- and you mentioned some of the cost discussions-- is we're in the list price around $7 per gigabase of data at that 3-- we're in that $500-ish a genome ballpark at 3 genomes per flow cell.
SHAWN LEVY: And I think we're hopeful to see that, although the platform is a research use only platform. But we've been seeing some good excitement and momentum in the laboratory-developed testing space. And we're certainly working with partners on where things go next.
JULIANNA LEMIEUX: So also, over the years, some have grown skeptical of NGS companies presenting their own data and the quality of those data. So how can we trust that your claims are backed up by evidence, especially given this very competitive market?
ELI GLEZER: Yeah. I think, for us, we started early in getting external partners, customers to run things. So we started with a beta program with a couple of sites, and then we moved on to an early access program with six different institutions, kind of span research to commercial labs. And those have come out as posters and presentations with their data.
ELI GLEZER: Some of those data sets are available online. And that will continue to grow as we build. But yeah, absolutely, people need to see the data, see independent runs, and get comfortable that the system works, it's reliable, and so on. And some of that just happens over time.
SHAWN LEVY: Yeah, I agree completely. And I think what-- you absolutely need data in the field. And I think the concerns about claims and specifications, et cetera, are well founded. And I would say there's, maybe, two extensions to what Eli said I would offer. And one is Element's made all of the Q table training data that we've used as well as the base recalibration software, which we've kept everything in the open source-- used GATK and other tools-- so we could be transparent about all of that data and released all of that data.
SHAWN LEVY: And we continue to release data sets to, again, provide that confidence that Element can provide the sequencing data, and then somebody else can provide that independent analysis. And that's one of the reasons, if you look at whether it be our indel error modes that we work with Fulcrum Genomics or Andrew Carroll's work from Google DeepVariant, we've really tried to be very proactive in developing relationships, have that analysis done by a third party, so that we can hopefully engender some of that confidence in the field.
JULIANNA LEMIEUX: OK. And another big topic in NGS right now is that it's not just about DNA anymore. So both Element and Singular are collaborating with Olink for proteomics. I've also heard talks about transcriptomics on sequencing platforms. So is the future of NGS really multiomics?
ELI GLEZER: Well, I think it certainly goes beyond just the germline genome or maybe some of the earlier envisioned applications. I think, certainly, RNA sequencing is a huge application, right? When you look at core labs at academic institutions, there's a ton of RNA seq going on. And a lot of that has now moved to single-cell RNA, some of it has moved to spatial. So that's all typically done at the RNA level and as well as at the protein level.
ELI GLEZER: And so that's very much in line with the applications we're targeting basically being the sequencing readout for those. And then I would say, longer term, we also have a second platform in development where we're bringing sequencing directly into spatial biology and single-cell analysis, basically doing NGS directly in cells and tissues. And there's a number of companies in that space.
ELI GLEZER: And I believe all of them are using basically labeled probes as readout. Nobody's doing direct sequencing. So it offers us a unique advantage in terms of being able to not only look at levels of gene transcription and protein expression but actually come in and sequence variable gene regions. So in terms of where that's headed, I think that's going to be a really powerful way to look at many cells, many samples, and then directly look at tissue and better understand the context for things like immuno-oncology, for example.
SHAWN LEVY: Yeah, I think Eli said that very well. I think the flexibility and tunability of the platforms is very important from a data accuracy and data quality as we look at sequencing. But I think any of the newcomers to the field, and certainly being with Element and speaking specifically about them, that flexibility into multiomics and that flexibility into the detection of other analytes, or just being able to be creative. And I think one of the things that we have absolutely embraced is the idea of having a more open platform, in the sense that we have a few relationships where somebody has said, hey, I've got an idea that I want to try on the platform.
SHAWN LEVY: I think the optics, I think the chemistry, I think the fluids can do x, y, or z. And while it's things-- we're allowing our collaborators to be the ones to release it, rather than Element. We have a number of projects in that spirit moving forward. And I think your direct question about the multiomics, it's certainly-- I think today's platforms to remain competitive or to gain a competitive edge have to be able to embrace where the biology is going and where the science is going.
SHAWN LEVY: And we're trying to do that as robustly as we can and as cost effectively as we can. So I would just say to stay tuned for some further announcements on the roadmap in the near future.
JULIANNA LEMIEUX: So my last question in our last couple of minutes is just addressing-- I mean, obviously, Illumina is not going to let its market dominance go unanswered. At JPMorgan this year, they talked about a new chemistry called Chemistry X. Does the imminent arrival of this much anticipated chemistry keep you up at night?
ELI GLEZER: I think Shawn has more experience in that world, so I'm going to let him go first.
SHAWN LEVY: Yeah. No, as I said, Illumina has been a source of innovation and a driver of great success in biology. And it's hard to fault them for what they've done to the ability of scientists to access genomic technologies, right? Even if Eli and I or you and I were to have a conversation in the early 2000s and say, hey, in 20 years from now, we'd be doing routine genomes and talking about $100 genome, it would be almost laughable at that stage.
SHAWN LEVY: Now, granted, somebody would say, sure, 20 years is a long way, and a lot's going to happen there. But if you look at how quickly things have changed in terms of the output and the cost per output that's happened in genomics, it's been pretty amazing. That's going to have to continue to be innovative. And I think that's the open question, right? The field is moving to greater and greater degrees of transparency and clearer and clearer specifications.
SHAWN LEVY: And so I think the rather nebulous discussions around Chemistry X that have now been going on for going on nine months of just saying it's twice as fast and three times better, frankly, it's fairly unquantitative. And so I would say I'm very much looking forward to seeing what that means. As you said, Illumina is not going to stand idly by. And I'm sure Singular doesn't expect them to.
SHAWN LEVY: We don't expect them to. But we are, I think, quite prepared for the challenge. Because as I've discussed, and as Eli has discussed, I think each of our respective companies have clear areas of differentiation relative to sequencing by synthesis. And so, as a scientist, I say I'm very much looking forward to the field continuing to advance, both with Illumina's pushing.
SHAWN LEVY: And what I'm hopeful for is that we continue to take a science-forward approach rather than anything else.
ELI GLEZER: Yeah. I guess as a general perspective, I would say competition is really healthy for sequencing, just like other fields. And it will continue to drive innovation and deliver better products for both research and clinical applications. I don't know if our companies have inspired any additional innovation at Illumina or not, but I imagine it's probably good for the whole ecosystem.
ELI GLEZER: And then getting sequencing used more widely in medicine is still something that's coming, still mostly on tap for areas like infectious disease. We mentioned newborn testing for rare diseases. That's only done in a few leading hospitals. Even in oncology still, sequencing is not used nearly as widely as it could be to help diagnose and guide treatment and follow up minimum residual disease.
ELI GLEZER: So I think there's tons of opportunities out there. Illumina clearly dominates this field. And in the mid-throughput sequencer range, they sold something like 1,200 systems just last year. So there's still a vast opportunity out there. And I'm excited to see what they come out with.
SHAWN LEVY: Yeah. I think this is a field that we're not going to be successful if there's one option. There's just too many applications. There's too many nuances. Again, the reason I say the science-forward approach is we just really need-- what's best for the biology should be the driver of the question. And the challenge to the field, as Eli mentioned, competition is a good thing.
SHAWN LEVY: And I think that competition exists to make who can come out with the better tools. And what we need to rely on is that the field remains open-minded in selecting those tools. And I think we've seen that, right? We've seen that with the evolution of the space. And we've seen that with even the success that Illumina has had relative to some of their early competitors, is that they outcompeted them.
SHAWN LEVY: And we fully expect that Illumina is going to continue to be a formidable competition. And what we hope is that by the contributions from Element, and the contributions from Ultima, and Singular, and Omniome and everyone else, that it just absolutely drives the field faster and further than we would have seen if those companies didn't exist. But there's absolutely room for more than one winner.
SHAWN LEVY: And I think it's-- I hope, as leadership among these companies, that we don't get distracted with being a winner rather than helping the field win.
JULIANNA LEMIEUX: Yeah. And also, we would be remiss to leave out-- and I noticed just yesterday that MGI is now selling the G400C in the United States as of yesterday. So as if there weren't enough competition already, there's more players, established players, probably more players to come, as you said, Shawn. So-- well, it definitely keeps my job very fun and interesting. And at GEN, we will continue to follow all of this in the future.
JULIANNA LEMIEUX: And with that, I think we'll end our conversation. But thank you Eli from Singular and Shawn from Element for a very fun conversation. Good luck to you both, and we'll be back shortly with our next session on the state of biotech brought to you by GEN Edge.
ELI GLEZER: Thank you, Julianna. Pleasure to talk to you. Thanks, Shawn.
SHAWN LEVY: Yeah, thank you. Good to see you. [MUSIC PLAYING]
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JULIANNA LEMIEUX: Hello, everyone, and welcome to our next session, The State of NGS. I'm Julianna LeMieux, science writer at GEN. It's an exciting and busy time for next generation sequencing companies. At least three new companies have entered the US market in the past year, which has for the past decade been largely dominated by just one company, Illumina. So today, we're going to explore what impact these new platforms and technologies could have in this space, what their priorities are, and how they plan to carve out a niche for themselves.
JULIANNA LEMIEUX: Some questions, we'll ask are, what does it take for companies to compete in this space? Is long-read sequencing poised to overtake the utility of short-reads? And what other applications are these new platforms going to address? Our guests are Eli Glezer, who became the CSO and co-founder of Singular Genomics in 2016. Before that, he spent two decades at Meso Scale Diagnostics where he led the design and development of their multi-array electrochromic luminescence technology.
JULIANNA LEMIEUX: And Shawn Levy, senior vice president of Applications and Scientific Affairs at Element Biosciences. Shawn recently joined Element from his position as CSO at Discovery Life Sciences. He maintains a position as an investigator at Hudson Alpha Institute for Biotechnology in Huntsville, Alabama, where he has been since 2009. Thank you both for joining us today.
ELI GLEZER: Thank you. Nice to be here with you.
SHAWN LEVY: Yeah, thank you.
JULIANNA LEMIEUX: So let's start with each of you introducing your companies. Shawn, why don't you go first and tell our audience about what's going on in Element?
SHAWN LEVY: Sure. So Element Biosciences just celebrated its fifth anniversary. It was founded in 2017 by three co-founders, Matthew Kellinger, Michael Previte, and Molly He. And Molly currently serves as our CEO, Michael is our CTO, and Matthew is vice president for Biochemistry. So Element was founded on the premise of essentially reinventing short-read sequencing and looking at the technology stack that underlies the types of assays that are performed in the pursuit of genomics and the pursuit of high throughput biology.
SHAWN LEVY: So it began with a close evaluation of what was in the field as far as existing players and technologies and methodologies and then kind ask the question, can relook at these technologies and methodologies to both embrace the successes that those technologies have had as well as provide a new beginning and a new beginning to head in novel directions and with novel resolutions, both from a quality perspective and performance perspective.
SHAWN LEVY: And I think most importantly, Element recognize that while we had been going downward in sequencing costs for the better part of a decade, one of the challenges with those sequencing costs is the cost of the equipment and the cost of each run was still going up substantially. So the accessibility and the value to-- in a democratized manner was still quite challenging. And so that was another strong premise for the company was to enter the market in a way that allowed that democratization and access to be kind of re-established.
JULIANNA LEMIEUX: OK. Great. So Shawn, I mean you've been in genomics a long time, you've been a longtime customer and spokesperson for Illumina. What made you join Element?
SHAWN LEVY: Yeah, I think it's a question I've gotten a lot. And it's been an interesting time and the time I was able to spend at Hudson Alpha was, of course, an amazing several years that we were fortunate to be very successful in a number of programs, both with Illumina where we were a HiSeq X site. Then we transitioned from HiSeq X to 10 NovaSeq. So we had quite a large capacity on the national or world stage. And then over the last several years, we did the same thing with PacBio where we currently have 20 Sequel IIe's.
SHAWN LEVY: So it was a pretty substantial investment and really embracing the different technologies as they apply to different areas in genomics. And so I think maybe the important thing is like any toolbox. There are so many interesting questions in biology, and that it's almost a disservice to the science to suggest that one tool fits all of those applications. What made me join Element was there really the recognition of a couple of things.
SHAWN LEVY: One primarily was the team, and the philosophy and the mission and the vision of the company was something like I had not seen in other locations and was very excited to join that team. But perhaps more importantly, and this probably comes as no surprise, I certainly didn't necessarily join Element for the sole purpose of launching a sequencer into a market dominated by a single player, as you already mentioned.
SHAWN LEVY: It was really for what the platform has the potential to do. And sequencing is, of course it's not to suggest there's a lack of focus on sequencing it's that sequencing is number one. It's kind of product number one, but the roadmap and the foundational technology and methodology that exists within element will extend well, well beyond sequencing.
SHAWN LEVY: And so it was just an opportunity to join at the time that I joined that just couldn't pass up.
JULIANNA LEMIEUX: OK, terrific. And I think we're going to get into exactly what you were just talking about a little bit later. Before that, Eli, can you tell us about what's going on in Singular Genomics and a little bit about your background and what attracted you to the company?
ELI GLEZER: Yeah sure. So we started in earnest in late 2016. And I started the company together with Drew Spaventa, who's our CEO, and David Barker, who is on our board and also on our scientific advisory board. And David used to be a chief science officer at Illumina back in the formative days. We really set out to improve on the state of the art in sequencing, in some ways similar, I think, to what the mission of element is as well, to offer some competition to improve on aspects of performance, speed, versatility.
ELI GLEZER: And so we started actually by visiting first maybe a dozen labs and then a lot more after that and trying to get a firsthand sense for what mattered to people, what they were able to do currently, and where they saw opportunities for improvement. And so people were looking for faster results, they were looking to avoid batching, cost as a factor. There's definitely a desire for competition in the field. And so I think we got a pretty good sense for where there were opportunities for improvements.
ELI GLEZER: And then at that point, we invested a lot of time in the fundamental basic R&D, so novel chemistry, novel enzymes, materials, nanofabrication, kind of all of the things that go into the heart of sequencing. And then on top of that, all the engineering that goes into it. I think sequencing technology has really pushed the limits of a lot of modern engineering technologies, in particular high-speed imaging.
ELI GLEZER: And so we invested a lot of time in that as well as rapid and flexible fluidity. So that's kind of in a nutshell where we started. And then since then, as you might know, we went public about a year ago. And since then, have commercially launched our first instrument are in that stage of new product introduction and excited to offer our tools to a wide range of customers in both research and clinical applications.
ELI GLEZER: OK. Terrific. Thanks. It's funny. I mean Element, Singular, and Ultima Genomics all started in 2016, 2017, and all launched in the last year. Was something in the water back in 2016, 2017? What do you think it is about that timing, because it seems coincidental?
LEVY: I think we're maybe not capturing a large number of other efforts that have existed in the field, other companies that have-- either are still more in their stealth mode or have launched perhaps just with more very specific applications or very specific areas. And we've even seen some mild attrition in the field as well. And it's kind of in different companies, in different ways. I think in this case, maybe the timing that was coincidental was the commercial launches around the three companies all happened at about or right around the same time.
LEVY: I think Element being originally was planned for AGBT but with AGBT moving from-- its usual time in February till June. We decided to move forward with a virtual launch on March 14, which was just after I joined the company. And as Eli mentioned, Singular had gone public and so had an opportunity to become a little bit more well known in the market prior to their commercial launch. And then I think, as we know, Ultima was very stealthy until their launch at AGBT.
LEVY: So I think although the timing was around the same time of the year, I'd still say the three companies took pretty different approaches to how they went into that launch mode, I think, where maybe it was more of the fact that the field is quite enthusiastic about learning more about these new opportunities And we had seen a little bit of, I think, a lack of comprehensive information coming out on platforms for the last several years.
LEVY: It was kind of a bit of a celebratory time of the field of saying, hey, there's now multiple new options hitting the market with probably, most importantly, with accessible data with clear specifications, clear pricing. And I think that was something that seemed to be lacking for a while.
JULIANNA LEMIEUX: That's a really good point. Yeah, there was a lot of information coming out over the last 6 to 12 months, so. Terrific. And I think both companies, Element and Singular, have or will be moving into new headquarters in San Diego this year. What's the significance of that?
SHAWN LEVY: Yeah. So Element just, in May, began its move into its new headquarters. So there were multiple sites in San Diego as well as we still continue to maintain two sites in the Bay Area of California . So Element continues to have multiple sites. But the headquarters in San Diego moved from grad labs near the UCSD's campus, which was where it was founded. And as the company grew, we were spread across a couple of different laboratory and office space areas within that building.
SHAWN LEVY: It was, as you mentioned, fantastic space. It was an amazing place to incubate the company. But a number of years ago, Molly worked with some of the existing staff of the company as well as some of the developers within San Diego and designed a new building. And so that building just completed so certainly, something that was in the works for a few years. And so we're fortunate to move into that new building.
SHAWN LEVY: It's an amazing space that allowed us to consolidate everything except for warehouse space into that one location, which helps with the culture. It helps with efficiency. And I think, most importantly, it gave us the space to have a-- for the long foreseeable future, we'll continue to do all of our own manufacturing, both on the reagent and instrument side in that headquarters, in that location.
SHAWN LEVY: So the new Element has every intention of keeping everything US made and within the United States under tight control within that space. And so that space was designed and outfitted to allow that with the exception, as I mentioned, of warehousing, which is done just a little bit inland from the new location.
JULIANNA LEMIEUX: I also heard there's meditation gardens, which I feel like would be necessary if you're entering into the NGS market. [LAUGHS]
SHAWN LEVY: Yeah. Certainly, the amenities in the area are impressive. And so it's definitely a place that's-- I'd say it was good timing as we transition out of the pandemic with people coming back into the lab, back into work, having the timing of the facility coming online is really helpful with re-establishing that collaborative culture and giving people a place that they really enjoy coming everyday.
ELI GLEZER: And we've been looking to gradually grow all within a couple hundred of yards of where we are today, I would say. We started off with half a lab bench at JLABS which is in about a couple yards from where I am right now and then move into some older buildings and gradually expand past that. And then earlier this year, we moved into a really nice new 80,000-square-foot facility that was custom remodeled for us right up here on Torrey Pines Mesa next to the future One Alexandria Square.
ELI GLEZER: That's being built. Actually, a lot of heavy construction going on right now. And we will be expanding further into that space down the road. But right now, we have a great facility here. And we also have a manufacturing facility in Mira Mesa where we build our instruments.
JULIANNA LEMIEUX: So now, both of your companies make benchtop mid-throughput sequencing platforms. But there are already a lot of those in circulation out there. So I'm curious how your companies are differentiating themselves. So is there something unique that sets your technologies apart from the other NGS platforms on the market? And Eli, let's start with you.
ELI GLEZER: Sure. Yeah. I would say fundamentally, it's speed, power, and flexibility. Those are the three things that we feel we offer something that doesn't exist today. So it's based on full four-color chemistry so kind of state-of-the-art accuracy. And then on top of that, offers advantages in terms of speed so a typical runtime on the bulk of the instruments out there. Of course, they're made by Illumina, the NextSeq instruments.
ELI GLEZER: Those run 29 to 48 hours if you're going to do your standard paired 150 based sequencing. And on our instrument, the G4, that's 19 hours. So much faster you can basically run the system overnight and have results the next day. In terms of power-- and really, by power, we mean data output per hour. It's two to three times more output than what you can get out of a NextSeq.
ELI GLEZER: And so that's a substantial advantage just in terms of capital investment. And then finally, flexibility. This is kind of a unique feature that we have. So on a NextSeq, you can run one flow cell, or you can run no flow cells. And those are your options. Whereas we have up to four flow cells, completely independent. So you can run one, two, three, or four flow cells to scale your throughput to your day's needs.
ELI GLEZER: So you don't have to worry about batching samples over specific period of time or not being able to run some samples because you haven't filled up a larger flow cell. So there's that flexibility. And then there's an extra level of flexibility. And that's that each flow cell runs four independent lanes. So it's not an afterthought like it was on some earlier systems, where you could load those manually.
ELI GLEZER: You can simply load the samples, and they'll be delivered into those lanes. So in a single run, you could run 16 different samples, load them in, and they'll all be processed in parallel. So you don't have to worry about any kind of problems in one library prep affecting another. And that's particularly nice for core labs where one investigator may not want to share their space on the flow cell with another investigator.
ELI GLEZER: So I think those are some of the critical features.
JULIANNA LEMIEUX: OK, terrific. And Shawn, what about the AVITI?
SHAWN LEVY: Yeah. I think there's a couple of things to touch on. The primary one is the AVITI system was developed to embrace the state-of-the-art of genomics where it existed and really all the way up through the original launch. What I mean by that is the recognition that the last decade or so, the number of applications and library preparation options, and even data analysis options, were substantial. But we also really carefully looked at some of the error modes in the data and really got an understanding of what some of the limitations of sequencing by synthesis is, particularly with the use of modified nucleotides.
SHAWN LEVY: So you get a scarring effect in the data, phasing and pre-phasing errors. The fact that it's very difficult to optimize enzymes for incorporation of those nucleotides when you're essentially doing the synthesis step with those modified nucleotides and trying to keep that accuracy very high. Plus then, even the amplification strategies on the flow cells themselves that have either high optical duplication rate, index hopping issues, all of those things.
SHAWN LEVY: So those were all really carefully looked at. And there were some underlying principles which went into the instrument as it exists today. And those principles are full compatibility with the ability to efficiently and without changing the library complexity or index bias or anything else that you can. And we have, on our website, we've listed approaching 40 different catalog numbers that have been empirically validated as well as a number of applications to essentially take almost anyone's library prep that's used on an Illumina platform and make it compatible with Element and be able to do so in a high-performing and very efficient manner.
SHAWN LEVY: And same thing with the downstream data analysis, we made sure that our output file formats could go right into existing bioinformatic workflows without change. And again, that was to really recognize and embrace the innovation and state-of-the-art of genomics as a whole, independent of the platform. But beyond that and more focused on the instrument-- in the interest of time, stating it very simply-- it's an entirely novel surface chemistry, exceptionally low background surface chemistry.
SHAWN LEVY: But when we develop things to stick to that chemistry, they stick with high specificity and very high performance. And what Element refers to that as a very high contrast-to-noise ratio rather than signal-to-noise, meaning we keep background very, very low to allow signal to be differentiated at a very high degree. And then the sequencing chemistry is fundamentally different. So if we think of Illumina, Singular, or Ultima, even Ion Torrent, are all in the same family of sequencing by synthesis, although the detection methods are different.
SHAWN LEVY: It may be a direct detection of fluorescent nucleotide in multiple-- all the way up through four colors, as Eli mentioned, or a side product of that synthesis step in the case of Ion Torrent. Whereas, I think, Element is in a little bit of a different family, probably belongs more in the same family of, say Omnium, which is a sequencing by binding event. So Element has split the synthesis step away from the detection step.
SHAWN LEVY: And so we use a unique molecule that we call an AVITI molecule, which has a very unique chemistry. It's very tunable. It's very flexible. And that chemistry allows us to separate the synthesis step. So we use only natural nucleotides in the synthesis step, but do our detection with an apatite molecule which senses the next base coming in. That gets detected, it gets washed away.
SHAWN LEVY: And what that allows us to do is use a much, much smaller amount of fluorescent dye compared to other platforms. And that's been one of the key areas of recognition, that the dyes are quite expensive. And they're a key component into the reagent costs that go into the sequencing platforms. They're a key factor in many of the other parts of the biochemistry process. So it really was a fundamental reinvention of the sequencing process to avoid some of the limitations of sequencing by synthesis that we had observed before.
SHAWN LEVY: And then the platform itself, similar to Eli's points, it's quite flexible-- two flow cells completely independently controlled in terms of not only when you start them, but also the conditions they run under. Now, our speed is similar to NextSeq and in the current iteration. Although, we do run the two flow cells. So it's two independent flow cells, and it's a paired 150 with a maximum data output, which is 800 million reads specified.
SHAWN LEVY: And I think in the field, we're seeing closer to a billion reads per flow cell. So 44 hours of runtime between whether it's one or two flow cells run at the same time. So not quite as fast as what Eli had indicated, but I think we wanted to keep some of the same flexibility. Capital equipment costs are relatively low in the field. Our pricing has been very transparent and very consistent.
SHAWN LEVY: And I think one of the unique things that Element recently announced was that we are holding reagent costs fixed for the life of the platform. And that was just recognition that research and development doesn't happen on a month-to-month or quarter-to-quarter basis like many businesses operate. It really is a many months to years basis. And as investigators are planning grants and planning long-term projects, the surprise of reagent cost increases or changes can be pretty detrimental to that research.
SHAWN LEVY: And so we really wanted to get in front of that, as well as get in front of any comments or concerns in the market that the pricing is introductory or that the platform is unstable or anything like that. And so we wanted to be sure we put that out there in a very transparent and very clear manner where expectations can be set, and we can focus more on the science and more on the data quality rather than the logistics around getting the data run.
JULIANNA LEMIEUX: OK. Thanks, Shawn. Yeah, you just anticipated a couple of my questions, actually. One was definitely going to be about what we call Elements' "forever stamp" of reagent costs, because that's just something that-- I mean, I don't know what from gas to my favorite takeout is going to cost tomorrow. So the reagent cost sticking was definitely an interesting announcement.
SHAWN LEVY: Yeah. I guess, just briefly, I'd say, it probably comes as no surprise. I already mentioned that Element has a very healthy roadmap in front of it from a company perspective and a technology perspective. So honestly, us deciding to put out that guarantee was not particularly difficult or a stretch. I mean, we already have significant data on where we're heading with our reagent efficiencies and costs, et cetera.
SHAWN LEVY: And this really was more in response to some feedback we were getting from the field of people just saying, oh, I heard this is introductory pricing, I heard there's no way that Element can keep this pricing, or that you're losing money on each box, just comments like that as we entered the market new. And we thought, what's the easiest way to alleviate those concerns? And the easiest way to do it is just to put a guarantee behind it.
SHAWN LEVY: And so that's what we decided to do. But just to stress, it wasn't-- it's not something that has high risk. It's not something that we are honestly particularly concerned with. Frankly, we were a little bit surprised that it got as much positive attention as it did.
JULIANNA LEMIEUX: OK, great. So for new NGS customers, I guess what I'm wondering is, how do you think about why-- how do you convince them that they should turn to Singular or Element rather than their more established platforms that they're used to? And I just want to preface this by saying that-- I mean, I talked to a lot of NGS customers. And I had a conversation with one Illumina customer who told me that they have no plans to switch to a new company.
JULIANNA LEMIEUX: And when I asked why, they said because I know that what I have works. So what do you say to that potential customer?
ELI GLEZER: Yeah, I think you have to offer some strong advantages, particularly initially, right? There are some people out there who are just not risk takers. And until a company has been commercial for a couple of years, they're not going to buy an instrument. And so that's not the initial set of customers. I think the initial set of customers are people who are willing to be early adopters who see some particular advantages, whether it's cost, or speed, or throughput, or flexibility, or something particular about the data quality.
ELI GLEZER: And so that's really where we're seeing a lot of interest is people, let's say, in a core lab who really appreciate the ability to run multiple samples independently, or people who really need faster results or want to avoid the batching. Those are strong factors. Cost, certainly-- we don't talk very much about costs, but we're cost-competitive with everything that's out there. And we haven't had a lot of questions come up in terms of longer term pricing.
ELI GLEZER: But certainly, the intent is to keep driving that down by increasing throughput, for example, and decreasing manufacturing costs and so on. So I think I think all those factors contribute. And then also, sometimes if you can offer something really unique and new-- so we have a couple of things along those lines that have generated a lot of interest. One we call high-definition sequencing, and that's a way to read the original double-stranded DNA.
ELI GLEZER: So when you think about where accuracy really matters, it's really in rare variant detection. If you're doing something that is germline, well, you sequence a little bit more and you get to good enough quality. But if you're looking for that needle in a haystack in liquid biopsy, that's where that accuracy is very important. And people have come up with very clever ways of labeling the double-stranded molecules and running it through the sequencing and then reassembling it.
ELI GLEZER: The problem is that that's quite inefficient, it requires a lot of sequencing. So we've come up with a way that you take the original double-stranded molecule and link it together into one, and then take it through all of the steps of targeted capture, clustering, and sequencing as a single molecule. And that's allowed us to get to error rates that are below 100,000, so Q50 kind of accuracies.
ELI GLEZER: And these are kind of holistic accuracies, so from start to finish. Because even if your sequencer itself is really, really high accuracy, makes no errors, you'll still get some errors from something as DNA damage or something that happens in the library prep steps. So the beauty of linking those two strands together is that you read both of those strands, and you can differentiate between something that appears on one strand and is an error versus something that appears on both strands and is a true genetic variant.
ELI GLEZER: So that, and then more recently, we've also introduced the M series flow cells. And those are a way to get the really high output for short reads. So as much as 4 billion reads per run, so kind of getting to NovaSeq S4 types of levels in terms of cost and throughput but on a mid-throughput sequencer. And that's not for all applications, but it's particularly powerful for counting applications or anything that's sort of in the hundreds base read range or shorter.
ELI GLEZER: So I'd say, for us, those have been some of the factors driving customer interest.
JULIANNA LEMIEUX: Eli, just to clarify, when you were talking about finding variants that are low frequency, were you talking about the collaboration with TwinStrand and the Duplex Sequencing Technology?
SHAWN LEVY: That's right. That's right.
JULIANNA LEMIEUX: OK. Great, perfect. Yeah, I just wanted to clarify for our audience. Got it. OK. And Shawn, how about yourself? How do you answer that question about customers that are happy where they are?
SHAWN LEVY: It's a fair question, right? The reality of Illumina sequencing, proven by the number of publications and the impact it's had in so many areas, is it works, right? There's no argument. There's no argument that it works. But I think we've demonstrated with data sets that we've made available on the website with many third-party data analysis, some of the things we presented at AGBT, say Andrew Carroll's analysis out of Google DeepVariant, that shows that, for specific applications, there are some very significant differences.
SHAWN LEVY: I think what Element has done in the market so far is set a new bar for sequencing quality and accuracy, particularly on a bench-- certainly on a benchtop sequencer, and arguably, on any commercial sequencer. With the acquisition of Loop Genomics, we also have an ability to do directed long reads. Now, I don't want to give an incorrect impression that this is a direct competition, say, to Oxford Nanopore or PacBio.
SHAWN LEVY: That's not the intent. But it gives us opportunities to cover other application areas with long reads on a short read platform, extending the flexibility and capabilities of the platform with the Loop Genomics. So again, specific areas, but there's a roadmap there as well for the company. But focusing on the question when somebody says, what can your instrument do that I can't do with Illumina, right?
SHAWN LEVY: And the answer is, well, there there's a number of things. I think Eli touched on a number of great points. And I think Element and Singular share some of those capabilities and some of those opportunities. But we've really focused on the quality of the sequencing. And as Eli mentioned, when your sequencing is accurate enough, you really start seeing error modes that come from either input DNA quality, library preparation errors, et cetera.
SHAWN LEVY: And those are very real phenomenon. And I think they've been very underappreciated in the field. We've often introduced band-aids to allow the state-of-the-art technology to get better, but incrementally better, and things like the use of unique dual indexes to get around index hopping and things like that. They were just-- the reality of those things is they were necessary at the time. And one of the opportunities that comes with the ability to bring out a new platform later, learn from the field, is, well, you can address those things.
SHAWN LEVY: And so that's what Element has done very effectively. So we're often, certainly in almost all runs greater than 80%, Q40. Our Q tables go out to Q45 as a routine. And in a PCR-free condition with high-quality input DNA, we see greater than 90%, 95% of data even exceeds that Q40 number. What that translates to from a results perspective-- and this is, again, data that was developed by some of our customers and analyzed by third parties-- is on a coverage-dependent manner, we see roughly 50% fewer errors in Illumina at low coverage.
SHAWN LEVY: That coalesce is down to a slightly lower number as we get to higher coverage. But when you look at the number of false candidates and things like that-- and again, this is data that we've made available on the website and that Andrew's presented on in a couple of different forms-- you see just a much lower rate of false candidates. And as we extend that to other applications, both in the single-cell space as well as in the somatic space, we're observing higher and higher sensitivities.
SHAWN LEVY: So I think the real direct answer to those customers is it really depends on what they're doing, and it really depends on what quality is acceptable for their applications. And in cases where exceptionally high accuracies in the raw sequence data is helpful, then it absolutely makes a difference. We can demonstrate that in many, many data sets that we've released, and even customers that are early commercial adopters have demonstrated as well.
SHAWN LEVY: In counting applications where those qualities aren't quite as important, well, then we may not be able to provide that answer as much as someone else. But I think we've also embraced the complementary aspects. And one specific example, before I joined Element when we beta tested the platform, as I mentioned, my lab continues to have a large number of NovaSeqs. And so we actually began using the Element platform as a top-up option for NovaSeq platforms.
SHAWN LEVY: Instead of having to run another S4 flow cell, we could top-off existing data on an Element platform running roughly 2 billion reads across the two flow cells. And since the cost models are quite similar-- not exactly the same, but they're in the same ballpark-- and especially when you factor in instrument costs, labor, service contracts, et cetera, they actually normalize very quickly. That flexibility and the different error modes in the higher quality data that we were seeing on the Element platform really made that top-up flexibility pretty important.
SHAWN LEVY: So what that enabled even a larger genome center to do was recognize, we can still use the factory sequencers but just kind of bring on a different type of benchtop sequencer fully compatible with the library types, fully compatible with the downstream data analysis, and just have that flexibility. So like I mentioned earlier, it's really making sure that the tool that you're taking out of the toolbox really fits the application.
SHAWN LEVY: So I'm certain that-- or I would just say, we really look forward to educating the field as to where we can make those differences. And we're doing so with releasing as many data sets as we can. We have a number of instruments in the field that, knock on wood, we've not yet seen a field-related failure on our commercial instruments.
SHAWN LEVY: And so we continue to model the 30 instruments we have in house that have run over 15,000 runs inside of Element and continue to run on a daily basis. We've been able to leverage those machines to a very reliable platform in the field. And that's how we know we can keep our service contract cost low, because we know the instrument's very reliable. So I think we're just going to have to continue to educate the field with real data and real results to bring and open up those opportunities of bringing somebody who maybe service very well with Illumina, right?
SHAWN LEVY: We certainly can't say anything negative about the successes that Illumina has had in this field and the transformative effects they've had in sequencing. There's absolutely nothing to take away. Really, the way Element looks at it as there are-- we can look beyond some of the limitations of SBS and really start looking at where you can develop a different tool and make sure we fit that tool in the places where it belongs.
JULIANNA LEMIEUX: You actually just, again, teed up two more questions that I have, Shawn. I can tell this is not your first time doing this. So one is about cost and the other is about data quality, basically. So let's start with cost. So I mean, earlier this year-- in fact, I'm surprised that we have not yet said $100 genome because it's said very frequently. Earlier this year, Ultima genomics came out talking all about $100 genome.
JULIANNA LEMIEUX: And this is not even that new. I mean, two years ago at AGBT, I wrote a story on MGI saying $100 genome. I mean, we know cost is incredibly important. Is $100 genome even a meaningful term anymore? And are we there? Are we going to get to $10 genome? How meaningful is this right now, do you think?
SHAWN LEVY: Yeah. I'll give a brief answer, but I'll preface it with, I think we've gotten comfortable with using the word genome too interchangeably, right? So my lab was very fortunate to be part of the All of US Research Program as the pilot long read center. And that was the emphasis behind bringing on the number of PacBio instruments we had.
SHAWN LEVY: And I'll just say that a genome from a telomere-to-telomere perspective, the amazing work that Karen Miga and colleagues and Evan Eichler and others have done, has really showed that's a very different genome than a $100 genome that you'll generate with short read sequencing, right? It's to two completely different perspectives. And certainly, if you could wave a magic wand and have a technology that allowed you to uncover all of the intricacies of the genome at $100, by all means, you would see a wholesale switch in the field.
SHAWN LEVY: I don't think Karen, or Evan, or anyone would disagree that if they could do telomere-to-telomere like genomes at $100, they would absolutely do it. But the reality is that we pick and choose our technologies and capabilities based on the state-of-the-art. And so I think the $100 genome, from the way we're talking about here, you have to first appropriately account for the compromises and where they exist.
SHAWN LEVY: And then you have to ask, well, that $100 a genome, how impactful is that, and where does it fit, and what are the limitations? And I think that's something we've been a little bit slow to come around on is being very honest about limitations of $100 genome. One of the founding principles of Element particularly driven by Mike Previte has been not doing things by brute force, right?
SHAWN LEVY: Not just saying, if you throw a massive amount of data at a wall and do it cheap enough, you can pull out a $100 genome. It's not the company's philosophy. It's not a direction we're going, which is why we very intentionally stayed away from any announcements around higher throughput sequencing, because we are more focused on the data quality and the performance of the platform and where the real science roadmap is driving us.
SHAWN LEVY: But I would say, as a scientist, I'm very interested as the field develops into moving in that direction-- how low can cost go? And I think having those metrics, like Ultima has done, is wonderful. It's great for the field. It's great to set a new stake in the ground. And what I'm most interested to see is not when somebody can do $100 genome once, it's when they can do it 100,000 times and do so with the same reliability and performance across 100,000 of those $100 genomes, not 1 of them, not 10 of them, not 40 of them.
SHAWN LEVY: And I think that's got to be probably the appropriate metric.
ELI GLEZER: And I would just add that I think the real application matters as well. So today, it maybe costs $400 or $500 to do whole genome on a NovaSeq S4 flow cells just in consumable costs. But you have to bundle together 25 samples, and so that's good for population genomics kind of studies. But when you look at the cost of, let's say, rapid whole genome, like done for babies in the NICU when you're trying to diagnose a rare disease, it's much, much higher than that.
ELI GLEZER: I remember when we first actually started, one of the places we visited was Rady Children's Hospital. And they were pioneers in doing this. And I asked them what it cost them just in reagents to do whole genome. And it was $6,000 for the baby, and then $6,000 more for each of the parents if they were doing a trio. And this is in the age of a sub-$1000 genome.
ELI GLEZER: So again, you have to look carefully what the particular application is. If you can have a high throughput enough system and pool enough samples together, certainly there are cost efficiencies obtained by that. But there are many applications that require faster results. And for us, for example, we size the flow cell to be able to do a 30x genome in one flow cell. So you don't have to batch multiple samples together, and you can get those results in an overnight kind of run.
ELI GLEZER: And that scale, to get to that at $100, that's still a long ways out, I think. But then even if it's considerably higher than that, the clinical value you provide is tremendous.
SHAWN LEVY: Yeah, Eli raises a good point. And I think that's where some of the benchtop sequencers really can shine is in that democratized approach. And maybe using Element as an example, we've released some data sets that show per flow so we can do a trio. And granted, it's not 24 hours. It's closer to 44 hours. It's probably 48 hours when you wrap in library preparation and some data analysis.
SHAWN LEVY: But I think that's a very important point of having that flexibility of turning a trio very quickly in a smaller laboratory benchtop-type format is exceptionally important in the field today, just as important as somebody's alternative perspective on population genomics where that $100 is more impactful. And I think what Element's done from that cost and value perspective-- and you mentioned some of the cost discussions-- is we're in the list price around $7 per gigabase of data at that 3-- we're in that $500-ish a genome ballpark at 3 genomes per flow cell.
SHAWN LEVY: And I think we're hopeful to see that, although the platform is a research use only platform. But we've been seeing some good excitement and momentum in the laboratory-developed testing space. And we're certainly working with partners on where things go next.
JULIANNA LEMIEUX: So also, over the years, some have grown skeptical of NGS companies presenting their own data and the quality of those data. So how can we trust that your claims are backed up by evidence, especially given this very competitive market?
ELI GLEZER: Yeah. I think, for us, we started early in getting external partners, customers to run things. So we started with a beta program with a couple of sites, and then we moved on to an early access program with six different institutions, kind of span research to commercial labs. And those have come out as posters and presentations with their data.
ELI GLEZER: Some of those data sets are available online. And that will continue to grow as we build. But yeah, absolutely, people need to see the data, see independent runs, and get comfortable that the system works, it's reliable, and so on. And some of that just happens over time.
SHAWN LEVY: Yeah, I agree completely. And I think what-- you absolutely need data in the field. And I think the concerns about claims and specifications, et cetera, are well founded. And I would say there's, maybe, two extensions to what Eli said I would offer. And one is Element's made all of the Q table training data that we've used as well as the base recalibration software, which we've kept everything in the open source-- used GATK and other tools-- so we could be transparent about all of that data and released all of that data.
SHAWN LEVY: And we continue to release data sets to, again, provide that confidence that Element can provide the sequencing data, and then somebody else can provide that independent analysis. And that's one of the reasons, if you look at whether it be our indel error modes that we work with Fulcrum Genomics or Andrew Carroll's work from Google DeepVariant, we've really tried to be very proactive in developing relationships, have that analysis done by a third party, so that we can hopefully engender some of that confidence in the field.
JULIANNA LEMIEUX: OK. And another big topic in NGS right now is that it's not just about DNA anymore. So both Element and Singular are collaborating with Olink for proteomics. I've also heard talks about transcriptomics on sequencing platforms. So is the future of NGS really multiomics?
ELI GLEZER: Well, I think it certainly goes beyond just the germline genome or maybe some of the earlier envisioned applications. I think, certainly, RNA sequencing is a huge application, right? When you look at core labs at academic institutions, there's a ton of RNA seq going on. And a lot of that has now moved to single-cell RNA, some of it has moved to spatial. So that's all typically done at the RNA level and as well as at the protein level.
ELI GLEZER: And so that's very much in line with the applications we're targeting basically being the sequencing readout for those. And then I would say, longer term, we also have a second platform in development where we're bringing sequencing directly into spatial biology and single-cell analysis, basically doing NGS directly in cells and tissues. And there's a number of companies in that space.
ELI GLEZER: And I believe all of them are using basically labeled probes as readout. Nobody's doing direct sequencing. So it offers us a unique advantage in terms of being able to not only look at levels of gene transcription and protein expression but actually come in and sequence variable gene regions. So in terms of where that's headed, I think that's going to be a really powerful way to look at many cells, many samples, and then directly look at tissue and better understand the context for things like immuno-oncology, for example.
SHAWN LEVY: Yeah, I think Eli said that very well. I think the flexibility and tunability of the platforms is very important from a data accuracy and data quality as we look at sequencing. But I think any of the newcomers to the field, and certainly being with Element and speaking specifically about them, that flexibility into multiomics and that flexibility into the detection of other analytes, or just being able to be creative. And I think one of the things that we have absolutely embraced is the idea of having a more open platform, in the sense that we have a few relationships where somebody has said, hey, I've got an idea that I want to try on the platform.
SHAWN LEVY: I think the optics, I think the chemistry, I think the fluids can do x, y, or z. And while it's things-- we're allowing our collaborators to be the ones to release it, rather than Element. We have a number of projects in that spirit moving forward. And I think your direct question about the multiomics, it's certainly-- I think today's platforms to remain competitive or to gain a competitive edge have to be able to embrace where the biology is going and where the science is going.
SHAWN LEVY: And we're trying to do that as robustly as we can and as cost effectively as we can. So I would just say to stay tuned for some further announcements on the roadmap in the near future.
JULIANNA LEMIEUX: So my last question in our last couple of minutes is just addressing-- I mean, obviously, Illumina is not going to let its market dominance go unanswered. At JPMorgan this year, they talked about a new chemistry called Chemistry X. Does the imminent arrival of this much anticipated chemistry keep you up at night?
ELI GLEZER: I think Shawn has more experience in that world, so I'm going to let him go first.
SHAWN LEVY: Yeah. No, as I said, Illumina has been a source of innovation and a driver of great success in biology. And it's hard to fault them for what they've done to the ability of scientists to access genomic technologies, right? Even if Eli and I or you and I were to have a conversation in the early 2000s and say, hey, in 20 years from now, we'd be doing routine genomes and talking about $100 genome, it would be almost laughable at that stage.
SHAWN LEVY: Now, granted, somebody would say, sure, 20 years is a long way, and a lot's going to happen there. But if you look at how quickly things have changed in terms of the output and the cost per output that's happened in genomics, it's been pretty amazing. That's going to have to continue to be innovative. And I think that's the open question, right? The field is moving to greater and greater degrees of transparency and clearer and clearer specifications.
SHAWN LEVY: And so I think the rather nebulous discussions around Chemistry X that have now been going on for going on nine months of just saying it's twice as fast and three times better, frankly, it's fairly unquantitative. And so I would say I'm very much looking forward to seeing what that means. As you said, Illumina is not going to stand idly by. And I'm sure Singular doesn't expect them to.
SHAWN LEVY: We don't expect them to. But we are, I think, quite prepared for the challenge. Because as I've discussed, and as Eli has discussed, I think each of our respective companies have clear areas of differentiation relative to sequencing by synthesis. And so, as a scientist, I say I'm very much looking forward to the field continuing to advance, both with Illumina's pushing.
SHAWN LEVY: And what I'm hopeful for is that we continue to take a science-forward approach rather than anything else.
ELI GLEZER: Yeah. I guess as a general perspective, I would say competition is really healthy for sequencing, just like other fields. And it will continue to drive innovation and deliver better products for both research and clinical applications. I don't know if our companies have inspired any additional innovation at Illumina or not, but I imagine it's probably good for the whole ecosystem.
ELI GLEZER: And then getting sequencing used more widely in medicine is still something that's coming, still mostly on tap for areas like infectious disease. We mentioned newborn testing for rare diseases. That's only done in a few leading hospitals. Even in oncology still, sequencing is not used nearly as widely as it could be to help diagnose and guide treatment and follow up minimum residual disease.
ELI GLEZER: So I think there's tons of opportunities out there. Illumina clearly dominates this field. And in the mid-throughput sequencer range, they sold something like 1,200 systems just last year. So there's still a vast opportunity out there. And I'm excited to see what they come out with.
SHAWN LEVY: Yeah. I think this is a field that we're not going to be successful if there's one option. There's just too many applications. There's too many nuances. Again, the reason I say the science-forward approach is we just really need-- what's best for the biology should be the driver of the question. And the challenge to the field, as Eli mentioned, competition is a good thing.
SHAWN LEVY: And I think that competition exists to make who can come out with the better tools. And what we need to rely on is that the field remains open-minded in selecting those tools. And I think we've seen that, right? We've seen that with the evolution of the space. And we've seen that with even the success that Illumina has had relative to some of their early competitors, is that they outcompeted them.
SHAWN LEVY: And we fully expect that Illumina is going to continue to be a formidable competition. And what we hope is that by the contributions from Element, and the contributions from Ultima, and Singular, and Omniome and everyone else, that it just absolutely drives the field faster and further than we would have seen if those companies didn't exist. But there's absolutely room for more than one winner.
SHAWN LEVY: And I think it's-- I hope, as leadership among these companies, that we don't get distracted with being a winner rather than helping the field win.
JULIANNA LEMIEUX: Yeah. And also, we would be remiss to leave out-- and I noticed just yesterday that MGI is now selling the G400C in the United States as of yesterday. So as if there weren't enough competition already, there's more players, established players, probably more players to come, as you said, Shawn. So-- well, it definitely keeps my job very fun and interesting. And at GEN, we will continue to follow all of this in the future.
JULIANNA LEMIEUX: And with that, I think we'll end our conversation. But thank you Eli from Singular and Shawn from Element for a very fun conversation. Good luck to you both, and we'll be back shortly with our next session on the state of biotech brought to you by GEN Edge.
ELI GLEZER: Thank you, Julianna. Pleasure to talk to you. Thanks, Shawn.
SHAWN LEVY: Yeah, thank you. Good to see you. [MUSIC PLAYING]
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The cadmore media player is a professional video and audio player experience designed to deliver media as well as relevant transcript, segmentation, and metadata. On the screen is a traditional html video player with buttons such as play, pause, forward, captioning, language and play speed selection and more. Adjacent to the video player are generally tabs, one of which shows a scrolling transcript of the video and the other shows any segmentation of the media, sometimes referred to as chapters. At the top of the video is a menu bar containing an Explore button and Tools button. The Explore button allows you to dig deeper into the video by opening up an explore dialog which contains tabs for a visual navigation of the video using thumbnails, an about tab which contains rich metadata about the media, a segments tab which contains further information about the video, and a related tab which contains links to other media related to this media. The tools button opens a menu with options for sharing the media on social media, creating bookmarking links, creating embed codes for your website, generating citations, and more.
Keyboard Shortcuts
Keyboard shortcuts only work while you are in forms mode. By entering forms mode, you will be able to quickly access portions of the media player with single key commands.
Spacebar: Play/Pause Toggle
I: Info Menu,
O: Tools Menu
T: Transcript Tab,
S: Segments Tab
F: Forward 15,
R: Rewind 15
L: Languages,
P: Playspeed
M: Mute Toggle,
V: Volume Bar
N: Video Only Toggle,
B: Full Screen Toggle
C: Captions Toggle
Escape: Closes dialogs and menus / Exits Player
Z: Next Transcript Chapter
X: Previous Transcript Chapter
Q: Next Transcript Paragraph
W: Previous Transcript Paragraph
Entire Media
Segment(s)
Custom Clip
Start At:
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Embed Size
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You Size - Responsive
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We Size - Responsive
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Picture Overlay Popup
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400 x 225
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512 x 288
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400 x 700
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768 x 1024
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1024 x 512
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1280 x 608
Form Title Cite
No citation provided.
Info
Segments
Related
Thumbnails
Exit Clip Mode
You have requested to see a portion of the video outside of the clip.
Click OK to switch to the full video or click if you would like to stay inside the clip. You can always go back to the clip with Tools and Clip Mode