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spk02: Good afternoon, ladies and gentlemen, and welcome to the Singular Genomics first quarter 2022 earnings conference call. At this time, all participants have been placed on a listen-only mode, and we'll open the floor up for your questions and comments after the presentation. It is now my pleasure to turn the floor over to your host, Philip Taylor. Sir, the floor is yours.
spk05: Thank you, Operator. Presenting today are Singular Genomics founder and chief executive officer, Drew Spaventa, and head of finance, Dalen Meter. Earlier today, Singular Genomics released financial results for the three months ended March 31st, 2022. A copy of the press release is available on the company's website. Before we begin, I would like to inform you that comments and responses to your questions during today's call reflect management's views as of today, May 10th, 2022 only, and will include forward-looking statements and opinion statements, including predictions, estimates, plans, expectations, and other information. Actual results may differ materially from those expressed or implied as a result of certain risks and uncertainties. These risks and uncertainties are more fully described in our press release issued earlier today in our filings with the Securities and Exchange Commission. Our SEC filings can be found on our website or on the SEC's website. Investors are cautioned not to place undue reliance on forward-looking statements. We disclaim any obligation to update or revise these forward-looking statements. Please note that this conference call will be available for audio replay on our website at singulargenomics.com on the events page of the news and events section on our investors page. With that, I will turn the call over to CEO, Drew Spaventa.
spk06: Hello, and thank you for joining Singular Genomics first quarter 2022 conference call. My prepared remarks today will touch on a few key areas, a brief reminder of our mission, commercial progress and opportunities, partnership activities, and our newly published sequencing data and applications note and progress on our product roadmap. Lastly, I'll provide a brief preview of what you can expect from Singular at AGBT. Here at Singular Genomics, our approach has always been to prioritize the needs of our customers and to advance sequencing to meet their needs. This philosophy remains at the forefront of how we think about our technology, our products, our business, and how we pursue our mission to accelerate genomics for the advancement of science and medicine. The incredibly talented team of scientists, engineers, operators, and commercial staff at Singular are more focused than ever to execute on this mission as we are about to commence our first customer shipment later this quarter. The G4 is the most powerful, fastest, and most flexible benchtop sequencer available today. It can produce more data output per hour than any other competitive system by a significant margin. The G4 offers unmatched speed, also leading peers by a significant margin. The speed of our novel rapid SPS chemistry developed in-house is a fundamental differentiator of our technology today and our technology roadmap going forward. The G4's flexibility is unmatched. Each of the four flow cells offers four individually addressable lanes. No other offering comes close to providing this level of flexibility and modularity, which allows customers to scale up or down to optimize their sequencing run capacity as they need. In addition to these fundamentally differentiated performance metrics, it is imperative to deliver gold standard accuracy and data quality. Now with seven external pre-production units placed in third-party labs over the past year, the G4 has consistently demonstrated industry gold standard accuracy levels of up to 99.9% or Q30 for 75 to 90% of base reads. Core to our mission is to provide this industry-leading performance at attractive pricing. We provide direct cost savings across the pricing spectrum in terms of cost per gigabase cost per read, or cost per experiment, depending on the setting and customer profile. To sum it up, compared to other competitive systems, the G4 can achieve a data output rate that is two to four times higher, over two times faster run times, unmatched four flow cell and 16 lane flexibility, gold standard accuracy, and highly competitive pricing. For customers, this combination will enable new, more desirable workflow patterns, through daily runs, flexible run sizes, and simultaneous run type variation. It can also replace several alternative or redundant sequencers and enable applications limited by pooling or batching. This is a value proposition we are proud of and excited to bring to market. Turning to our commercial progress, we continue to build our sales funnel and have added to our system order book since the last earnings call held in March. Shipments of the G4 system and F2 flow cells remain on track for this quarter. Interest and interactions with prospective customers is accelerating. Our robust engagement with potential users across end markets reinforces the exciting opportunity in front of us. We are actively scaling our commercial team, adding experienced sales reps and customer support personnel in key regions around the US in anticipation of serving our customer and future install base. Offering a premium user experience is a critical component to our growth strategy. We are continuing to push forward with our plans to scale the team through 2022. We are also offering customers the ability to engage with our technology through our in-house customer care lab. This offering to prospective new customers is a courtesy service to help validate customer applications on a G4 via sample optimization and testing in advance of purchasing. We intend to increase the capability of this lab and offering over time in line with customer feedback and demand. I would now like to share more information on how we are qualifying the commercial opportunity in the field and describe how differentiated G4 attributes allow us to segment major customer groups and key applications to best serve their unique needs. As we think about our target customers, three profiles stand out, academic core labs, clinical and research commercial labs, and emerging growth labs. I'll go into detail on how the G4 positions well within these three profiles. Number one, academic labs. These labs are often serving multiple PIs and run a wide range of applications with different requirements, including RNA-seq, single-cell, targeted panels, exomes, and whole genomes. They often don't have a steady flow of experiments and are more highly price sensitive given grant funding constraints. The flexibility of one, two, three, or four flow cells, each with individually addressable lanes, coupled with a 19-hour or less runtime is ideal for their needs. The four flow cell flexibility scales up or down to accommodate changing volumes and allows them to optimize sequencing runs based on the needs of their applications and experiments. The significantly faster turnaround time alleviates the requirement to wait and batch like size samples or runs. And we can offer favorable pricing across all G4 consumable kits down to single digit dollars per gigabase for many of these users. Number two, clinical and research commercial labs. These labs consist of small and large commercial organizations utilizing both clinical and research sequencing applications such as targeted panels, RNA-seq exomes, and rapid whole genomes. Clinical applications in this setting are often in the form of validated lab-developed tests or LDTs in a CLIA lab. The G4 fits well into these routine sequencing environments where turnaround time is constrained. The flexibility of the platform alleviates the need, once again, to weight and batch like-sized samples or runs. The power, in terms of gigabase throughput, combined with attractive pricing, provides a more scalable solution relative to other benchtop offerings in this segment. For a number of clinical and commercial labs, the G4 serves as the first alternative option for those currently using high-throughput sequencers. Number three, emerging growth labs. As we have noted on prior calls, the amount of capital that has flowed into healthcare over the last two years is more than the previous 10 years combined. A significant portion of that capital is making its way into new company creation and businesses that are built on NGS. These companies are running both research and clinical LVT-based applications. They are looking for a sequencing provider that can scale up with their needs over time in a cost-effective way. They don't want to spend $1 million on a piece of capital equipment. The G4 can scale up with their sequencing needs over time, providing scalable throughput options comparable to instruments ranging from MySeq through NextSeq all the way up to NovaSeq, SP, or S1, ultimately providing high throughput level volumes and pricing on a benchtop sequencer. We realize there are considerations beyond the power, speed, and flexibility that factor into customers' purchasing decisions. For some labs, largely academic core labs, costs are among the most important consideration given funding limitations. Our pricing strategies provide attractive economics for all users across the pricing spectrum, cost per gigabase, cost per read, and cost per experiment. We believe it's important to look at pricing beyond simply cost per gigabase. Oftentimes, pricing comparisons are quoted solely in the context of the highest number of cycles on a 100% fully utilized flow cell. This is often not what happens in customer labs. Full flow cell utilization is not always practical based on customer sequencing and application needs, required turnaround times, and sample flow. As a result, customers end up paying more for their run. Alternatively, they wait days, if not weeks, to batch samples and run only after a flow cell is more fully utilized. This is not practical for many customers. The G4 scales down cost efficiently due to the four flow cell modularity. This alleviates inefficiencies from both ends, reducing the need to wait to pool samples and reducing the need for low-flow cell utilization. Because of this, all customer types, low-, mid-, and high-volume users can realize cost savings with the G4 through operational efficiencies. In summation, we believe the G4 is a superior sequencing platform in terms of core KPIs with favorable pricing and strong value propositions in some of the largest and fastest-growing markets. Now, I'll turn to our recent highlights and partnership activities. Integrating with customers' existing workflows as a plug-and-play solution has been a priority. We have already made significant progress with nine partners previously announced, including some of the most widely used library prep providers. We continue to expand our partner network to work with innovative and collaborative companies to accelerate our mission. As such, we are excited to announce that we have entered into two new partnerships with library prep solution providers, Bio-Rad Laboratories and Quantibio. With Bio-Rad, we are validating its Sequoia library prep kits for RNAC. With Quantibio, we are validating its Spark kits, a product line for both DNA and RNA sample prep. In addition, we are proud to announce our partnership with market-leading bioinformatics solutions provider, NVIDIA. With NVIDIA, we are validating the G4's workflows with their Parabricks secondary analysis platform with the goal of providing accelerated secondary analysis and data handling. Next, I'll provide a few updates on our recently disclosed G4 sequencing data and application notes. If you recall from our last quarterly earnings call, we highlighted a technical report where the G4 demonstrated state-of-the-art accuracy in whole human genome sequencing. We are pleased to expand the portfolio of available data and share some of the highlights from our most recently published application note to further demonstrate the performance and versatility of the G4 in one of the most widely run applications, RNA-Seq. The study covered RNA samples on the G4 compared to Illumina NextSeq 2000. Samples were individually loaded onto different flow cells on the G4 to highlight the sequence's reproducibility, both within the system and compared to the NextSeq. The G4 processed these RNA libraries using 2 by 100 base pair sequencing. Each sample produced 25 million reads for RNA-seq analysis for both the G4 and the NextSeq 2000. The overall comparison was nearly identical across all secondary RNA-seq analysis metrics, substantiating that the G4 meets customers' needs on quality and accuracy, coupled with the potential to produce results at a much faster rate given the shorter sequencing runtimes. In addition to our progress on standard sequencing applications, we continue to make headway on our specialized applications kits, HD-Seq for variant detection and extended rate sequencing or XR-Seq. We continue to advance our development of HD-Seq and are now demonstrated greater than Q50 accuracy impaired read 150 base formats with greater than 100 million reads per flow cell. We recently published a technical paper on XRC novel library prep method to enable longer molecule reconstructed from standard paired read 150 base cycle data. The study demonstrated how this technique can be used to reconstruct one to three kilobase long fragments from short read technology. We believe it has the potential to fill unmet needs in areas such as immunology for therapeutic antibody and T cell discovery, protein engineering, and vaccines for infectious disease. We expect the XR-Seq method to aid in the characterization of complex DNA libraries with high accuracy and throughput, ultimately expanding the utility of the G4. Transitioning now to the PX system, we are assembling our first beta instruments now. Later this year, we are planning to open a technology access program, or TAP, for early PX collaborators. The TAP will offer early customers and thought leaders the ability to collaborate with us to develop assays and applications in advance of the commercial launch in late 2023. Turning to AGBT, we are looking forward to meeting in person at the conference in June. We anticipate having a G4 system onsite for demos, hosting KOL discussions and networking events, showcasing new technical developments and application notes, and providing additional data and highlights from our specialized application kits. In addition, at AGBT, we are excited to provide a sneak preview of one of the disruptive innovations that our development team has been working on, the max reflow cell. We believe the max-read flow cell will redefine how customers think about cost and flexibility for short-read applications in the 30 to 100 base pair range, a configuration that is applicable to applications such as short-read counting for NIPT, proteomics, and single-cell RNA-seq. And one final note. As highlighted last quarter, we launched our scientific advisory board. We recently issued a press release noting the distinguished group of academic and industry experts on the SAB, David Barker, Lawrence Fong, David Ledbetter, Elaine Martis, and Daniel Shoemaker. We are thrilled to work with this exceptional group of industry thought leaders who advise in the company's product and service offering and R&D pipeline. With that, I will now turn the call over to Dalen to go over the details of our first quarter financial results and operational progress.
spk08: Thank you, Drew. I'll start by covering the Q1 2022 financials and then provide brief remarks on our operational progress, including our infrastructure build out and manufacturing capacity planning to support future growth. Operating expenses for the first quarter of 2022 totaled $22 million compared to $10.3 million for the first quarter of 2021. These totals included non-cash stock-based compensation expense, $3.6 million in Q1 2022 and $1.1 million in Q1 2021. The year-over-year increase in total operating expenses was driven primarily by our product pipeline and R&D roadmap, scaling headcount and infrastructure to support the G4 launch and the costs associated with being a public company. Net loss for the first quarter of 2022 was $22 million or $0.31 per share compared to $23.9 million or $2.05 per share in the first quarter of 2021. The year-over-year decrease in net loss and net loss per share was driven primarily by the change in fair value of convertible notes and warrant in Q1 2021, which were converted to common stock with the IPO and no longer outstanding in Q1 2022. This was partially offset by higher operating expenses, as previously noted. In addition, the year-over-year decrease in net loss per share was driven by the increase in weighted average share count used to calculate net loss per share because of the common stock issued in connection with the IPO. Our weighted average share count for the quarter used to calculate net loss per share was approximately 71 million. And in cash, cash equivalents and short-term investments, excluding restricted cash, totaled $316 million. Looking ahead through 2022, we continue to expect investment across the business to increase as we scale up manufacturing, ad headcount and sales, customer service and support, and marketing, and progress the product roadmap and future innovations in R&D. We expect our Q2 weighted average share count used to calculate net loss per share to be approximately 70 million. Turning to commercial, we are encouraged by the progress our sales team is making in developing the sales funnel. We're still early in our launch, and we will plan to share more information after we've begun to ship products and have more visibility into how the sales funnel is transitioning to orders. We are scaling our manufacturing headcount across both instruments and consumables in line with anticipated production volumes. We believe that our dedicated manufacturing site in San Diego is sufficient to meet our capacity needs for the near and medium term. We expect our longer term manufacturing and lab space needs will be accommodated with our new 78,000 square foot headquarters facility. And finally, we are pleased with our history of execution and progress to date. We've always been capital efficient. Since inception, we've raised approximately $450 million and have spent less than $140 million. We remain well capitalized to support the G4 launch activities. With $316 million of cash and investments on hand, Our existing resources are forecasted as sufficient to support our activities over the next three years. With initial orders in, we are ramping up our commercial organization and will continue to make the necessary investments to support our commercial goals and scale the business. Thank you and back to Drew for closing remarks.
spk06: Thank you, Daylon. We continue to advance the technical capabilities of the G4 with expanded library prep provider integration, improved accuracy, and advanced specialized applications to further differentiate it as the most versatile and powerful benchtop sequencer in the market. Interactions with current and potential customers across end user profiles indicate the G4 provides compelling value beyond the alternatives. We are excited to leverage the customer care lab to advance our increasing funnel opportunities and continue to grow the order book. The G4 is positioned favorably in this large and growing market. Singular Genomics is in a great position. We are well situated with two differentiated technology platforms, well funded with $316 million of cash and investments, providing roughly three years of runway for us to scale our operations and products, execute on our exciting technology roadmap, and generate valuable customer relationships. In the near term, we are laser focused on getting G4 manufacturing scale and identifying market segments and customers where the G4 offers a differentiated value proposition. Our exceptional team continues to execute across the organization. We are on track to commence our first customer shipments later this quarter, and we are confident that those early customers will get real value for their investment and will have an exceptional customer experience as they put our platforms to work. Joining me for Q&A, we have Eli Glesser, founder and CSO, and Dalen Meter, head of finance. Now let's open it up for questions. Operator.
spk02: Thank you. Ladies and gentlemen, the floor is open for questions. If you have any questions or comments, please press star 1 on your touchtone phone. Pressing star 2 will remove you from the queue should your question be answered. And lastly, while posing your question, please pick up your handset if listening on speakerphone to provide optimum sound quality. Please hold while we poll for questions. Once again, that's star one if you have a question or a comment. And the first question is coming from Matt Sykes with Goldman Sachs. Your line is live.
spk07: Hey, good afternoon. Thanks for taking my questions. Drew, maybe I'll start with where you just finished on the customer service part. Just given the critical nature of service and support in the early portion of your launch, could you maybe talk a little bit about the infrastructure that you've put in place to from a service and support standpoint? I mean, needing to properly balance in terms of the headcount and resources you put towards it, but knowing that the service and support element is so important at the beginning part. Can you just talk about kind of the investments you've made and the plan that you have for that?
spk06: Yeah, absolutely. Happy to. So, you know, early on with an initial launch, it really is, you know, bringing multiple parts of the organization together. So we've tried to get ahead of it by hiring and building out a dedicated CS&S team, but largely it's a collaborative effort with R&D to really bear hug the customers, the early ones. And another component of that is the sample testing, and what you heard in the call is we're setting up a customer care lab where we can scale that. So for your first handful of customers, you really want to make sure you understand their assays, you understand how they're going to use the sequencer, You do ample testing in-house first, and you work with them to make sure that once you drop a box in, they're going to get the expected type of data quality and results that they're expecting and that you've already done yourself in your own lab. And within that whole process, you're building out the customer sales and support infrastructure, which is FASs, technicians, to really shadow closely the first few units to make sure that we can absorb all the learnings we need to kind of learn and iterate forward. Taking a step back, you know, this is something that has been on the forefront of our, you know, kind of mind for quite some time. Doing the last year of EAP sites and placing these units gave us a number of really good learning experiences, and we're taking those learnings forward to build out dedicated personnel to kind of handle and scale that. But it's very much something that you have to take head on, and it's going to be, you know, more work for the first you know, few up to dozens of systems, you know, as we start to learn, you know, kind of what install looks like, what customer service looks like, understanding the nature of the early technology in third-party hands. So we feel like we're very prepared, but it's definitely a focus area for us.
spk07: Got it. Thanks. Very helpful. And then just two quick follow-ups. I'll ask them both up front and get back in the queue, but it sounds like we'll get more color later, but just Anything you can give us on order backlog and sort of the early stages of this quarter would be really helpful, or at least maybe some feedback you've gotten from customers. And then secondly, Drew, you talked about the emerging growth labs and totally appreciate the level of funding that we've seen over the past couple of years. But looking forward, it seems like a different environment and maybe cost consciousness might be a little bit more on the minds of these types of customers. How does the cost element and value proposition of G4 resonate with these customers? And has that come up in the conversations with those customers? Thanks very much.
spk06: Yeah, those are both good questions. I guess I'll take the first one, which is kind of, you know, the short answer is we, you know, we added to the order book. We're not providing guidance yet, but, you know, at the time and point when we're able to, with confidence, provide more color around things like order book and installs, we absolutely will. It's just still early for us. In terms of the emerging growth segment specifically, we directly haven't heard or seen too many people that at this point are rethinking purchasing decisions or rethinking their investment into their own businesses to advance their science. And I think that it's going to take some time. I mean, you know, taking a step back, the figure that we quoted on the earnings call, you know, just a few moments ago, the number or the amount of capital that's flown into the space over the last two years, you know, a lot of that's going to need to be put to work. And, you know, people are going to need to spend that capital to advance, you know, their efforts internally. That being said, I think we're going to have to be adaptable to how customers want to step into a new technology or spend money And that might be different solutions. It might be, you know, reagent rental. It might be a capital lease. It might be just having to walk people through and show them how there's real cost savings holistically with one of our sequencers in their specific use case over the alternative. But the need's still there, and these companies are well capitalized. They're going to have to invest in it, and a lot of that investment's going to need to be in sequencing to advance their own efforts.
spk09: Great. Thank you.
spk02: Okay, the next question is coming from Michael Oresken with Bank of America. Your line is live.
spk04: Great. Thanks for taking my question, guys. Following up on the previous question, I want to start with the evaluation process for some of these customers. There's been some preliminary data published. You've got your own data sets out there comparing the G4 with some comparator systems. I want to go through the process that you're your customers and your potential customers are going through, you know, when they're making that purchasing decisions? Can you just walk us through the process? What data points are they looking at? Sort of how long does that validation process go? And sort of just, you know, if you could give us some case studies of that. Thanks.
spk06: Yeah, so it really does vary by customer type. If you're talking about kind of academic labs, it's going to be a longer sales cycle in general, and there's probably going to be more back and forth over a longer period of time to really make sure that you have buy-in. You know, academic cores typically have budgeting cycles, and you really want to start conversations early and get ahead and know who has budget for a new system and really have a more tailored approach to, you know, engaging and bringing them forward. And for the academic cores, cost is really a big part of it. So you have to understand the nuances of their applications. You have to walk them through you know, how they would use the applications on your system. You're going to have to provide data, whether it's publicly available or other data that's not publicly available yet that we would engage with the customer and really get them confident that the system can perform. And once they're confident the system can perform, they're going to have to see a cost savings relative to alternatives typically. Now, there's some academic cores that have specific value propositions where the speed of the flexibility specifically makes it a very different dynamic for them to purchase. And again, those are where we want to, for a large part, focus our efforts, places where our competitive advantage really highlights the need to adopt our system versus others. As we're thinking about other customers, whether they're emerging growth customers or commercial labs, Typically, with a private entity, it's a quicker buying decision, but there's still that hurdle where you need to get them comfortable. The sequencer is going to meet their needs. So, we've offered and will continue to offer sample testing as needed, and that's something that, again, was not a surprise or a reaction. We're kind of building in that infrastructure to have capacity to test samples at high volumes for a high number of customers. Now, of course, we hope that you're qualifying your customers and understanding who the a high potential buyer if you do spend the time and work to validate their samples versus somebody that just wants to see free data. But that's part of the engagement process that, you know, we're trying to make sure we improve at. So, you know, long story short, it really varies. Some customers are very quick. There's a new technology. I've seen the technical paper. I know the team. I want to buy. And then there's people all over the spectrum from that very quick decision that doesn't need much all the way to the customer that needs many, many months of education, of interaction, of testing samples to the point when they get to a buying decision.
spk04: Okay. All right. That's helpful. And a follow-up on that, if I may, you know, in the last couple of months, obviously there's been a lot of noise by some existing competitors, by some new competitors with product entry to the market. I'm just wondering, you know, in some of this products that are already on the market and some of it is products, you know, for example, like chemistry X that's projected to be a future launch at some point, right? and you have your own, you know, F2 flow cell at launch, and then there's the F3. So I want to ask about, you know, future innovation. How much does that come into conversations? You know, when you're discussing the G4 with customers, you know, is the selling point on the F2 flow cell, or is F3 something that has to come into play? You know, is the timing on that still unchanged by end of year? And again, sort of, you know, as you're going through the comparisons that you're comparing with existing staff, with future staff, just sort of,
spk06: know could you level set where the discussions are from a chemistry and from a consumables perspective thanks yeah no it's it's a a good topic there were a lot of some questions in there i would say that again it varies based on customers some people uh look at the the g4 and the f2 right now and their comparator uh is a p1 or a p2 and if you're looking at an f2 versus a p1 or a p2 it's a very simple buying decision. It's going to be much cheaper on a cost per reader, cost per gigabase. It's going to be faster and more flexible when it scales down. There are other users that are using P3s and they're really at the high volume usage end of the NextSeq or users or potential customers that are using the low end of the NovaSeq. For those customers, they really do want to wait and understand, you know, is the F3 going to be on time? That's really how I want to use this sequencer. And then within that subset of customers, some are willing to, you know, get in line early and know that the roadmap's going to be there, and others are candidly saying we want to wait until we have an F3 and then we're a customer. I think the roadmap's actually very important. There's going to be continued competition in the space. In terms of your question on Chemistry X, we still don't really know enough to kind of handicap where or what that means, especially for our segment, which is really bench top, kind of mid to low end of the high throughput. I think the rumors that we've heard, and again, there are simply rumors, is that Chemistry X most likely makes its first appearance at the very high end, which, again, is not our target market. But back to the question on roadmap, you know, absolutely, you know, the ability to continually push our cycle times faster and our run times faster and to push the density or the throughput of our system higher are top of mind. And we certainly tend to continue to improve on those metrics And then the last part is, you know, we still are on track for an F3 launch in Q4 this year. So everything's on track in terms of the product roadmap for the near term.
spk04: Great. Thanks so much. Appreciate all the details there, guys.
spk02: Okay. Up next, we have John Sauerbeer with UBS. Your line is live, John.
spk03: Good evening. And I appreciate the update on the sales funnel. The company had previously highlighted that revenues aren't recognized until after G4s have completed installation and validation. It sounds like you're going to be shipping units in the second quarter. Should we expect revenues in 2Q, or is this more of a second half event?
spk08: Yeah. Hey, John. This is Dalen. Yeah, like Drew highlighted, the primary focus right now is shipping, placing systems in customer labs, getting them up and running. Just really trying to get as many units out into the field and start driving that consumable pull through. Like you highlighted on the last call, the revenue recognition is going to follow the specific terms and conditions for each of the orders. Those initial shipments, there's going to be a customary acceptance or validation process that we're going to engage with the customer on to complete, and that's going to be a gating item for revenue recognition. That's not uncommon for a new product launch, right? Longer term, we're going to structure the T's and C's to allow for revenue recognition upon shipment. But assuming we're shipping here in June, that acceptance process we're targeting is going to take about 30 days or so. It could take a little bit longer, and revenue recognition will follow that. So that could end up triggering revenue recognition on Q2 shipments. in Q3.
spk03: Yeah, appreciate that. And I guess just, you know, on the continued build-out on the commercial organization, any update on the search for head of chief health officer, head of the commercial organization, and this continued build-out on that team?
spk06: You know, no specific update in that position. We did initiate a search quite some time ago, and we're in the process right now, you know, hoping to have more information on that soon, but very encouraged by the existing team. As we've previously noted, we do have a very strong set of sales leaders across the country at this point, and the team continues to grow. In terms of a leader, we've been very happy with the quality of potential candidates out there and expect to be moving forward with the CCO position fairly shortly.
spk03: And then lastly, strong cash on hand, and companies have been very prudent on the cash You know, any changes just from the update that was provided last quarter and, you know, on the burn for this year and outlook for the next year?
spk06: Yeah, I'll let Daylon comment on any changes for this year, but I guess I'll just add one thing in. You know, we were very fortunate to access the market and put a very strong cash position on our balance sheet. And while we absolutely want to, you know, not ignore the macro situation and make sure that we have as much flexibility as possible to extend runway, very much the next 12 to 18 to 24 months are an opportunity for us to invest that cash into R&D, into product, into commercial, and to really grow our business and further advance our technology. So there's a lot of thought that's going into this, and we're trying to be very you know, dynamic and really maximize flexibility. In terms of the rest of this year, I'll let Dalen talk about kind of how we're thinking about cash.
spk08: Hey, John. Yeah, on the last call, you know, we talked about 2022 operating expenses, you know, could potentially be about double 2021. You know, we're probably tracking a little bit below that, but, you know, nothing materially different from our comments there. Yeah, I would just say, you know, we've all been really capital efficient, like we said in the prepared remarks. We're going to continue to be very disciplined in terms of, you know, how we invest our capital. We feel really good about our position, you know, and our forecasts, you know, are basically using our existing resources, you know, through the next three years. So we feel really good about the position we're in.
spk09: Thanks for taking the questions.
spk02: Okay, up next we have Tom Stevens with Cowan.
spk09: Your line is live.
spk02: Okay, looks like Tom's line has disconnected. If there are any further questions, please indicate so now by pressing star 1 on your touchtone phone.
spk09: Once again, that's star one if you have a question or a comment. Okay, up next we have Tom Stevens with Cowan.
spk02: Tom, your line is live.
spk01: Hi, is the line working now? You are coming through. Yeah, just to follow up on John's question. So I believe you said you exited last year with about 40 sales people on staff. What's your target for headcount this year? And then if you could maybe give some color on target headcount for your kind of services team, given your kind of ramping into this year.
spk06: Yes, sure. So I'm not sure I'll have to punt over to Dale on what we've said for total headcount. I know, We have said we plan to grow the operations team to about 100 by the end of this year, and we plan to grow the commercial team, and that's sales support and marketing, to about 40 by the end of this year. Outside of that, I don't know if we've guided further on the headcount, Dylan.
spk08: Do you know? No, that's it, Tom. We ended the quarter at a total headcount of about 240.
spk01: Got it. That's useful. And then just to kind of follow up on your kind of high-accuracy kit coming towards the end of the year. So, naturally, there are some competitors out there who have also kind of gone after this route. How does your kind of Q50 kit compare to kind of coming competitors in the high-accuracy arena? And where is your kind of key differentiator there?
spk06: Yeah, so Eli is with us, and I'll have him answer that.
spk00: Yeah, Tom, the key differentiator there is that we sequence both strands of a double-stranded DNA molecule. And that allows us to achieve not only inherent high accuracy of sequencing, but also get around other error modes that can be introduced by DNA damage, by polymerase miscopying, and so on. And so it really provides a unique advantage for applications where you want to detect rare variants and really get a true variant that are genomic in nature as opposed to just resulting from library prep steps or just general DNA damage.
spk01: Got it. Got it. Thanks for that. And then just the last one. So given you kind of wrapped up your AI access program, I was wondering if you could kind of give an overall kind of spread of the metrics you saw. So I believe there were seven in total. We know about five of them. If you could give any like metrics overall, so maybe the range of accuracy across the AI access partners and kind of the key learnings from the final two would be really helpful.
spk06: Yeah, so maybe I'll provide kind of a few overall metrics, and then I'll let Eli talk about any additional learnings. It was a very broad set of applications, so I would say if you think about kind of the core performance metrics of sequencing, You start with accuracy, and we were consistently at our target spec of above 75% Q30 for all of the different applications. In fact, for a number of them, we were quite a bit above Q30. And I think you have to remember that Q30 is a prospective estimate. What really matters is retrospective accuracy. And if you look at the accuracy metrics we had, For each of those sites, and I believe there's a table on the slide on our website, we were consistently above 99.7 and even at 99.8, which is higher than Q30 for all of the EAP sites. In terms of cycle time, there's actually a nice progression. We improved our cycle time throughout the year last year. So whereas we started with our first few sites, we were closer to four minutes, we were able to get the cycle times down to our target specs, which under three minutes And that's also on that table. So accuracy and cycle time or run time consistently within or beating spec. And then the last one would probably be the throughput or the number of reads. And I actually don't have this in front of me. I'm talking from memory. But I believe that we also demonstrated pretty consistent improvements actually getting above our spec on the F2. So the F2, you know, we set our specs rather conservatively at 150 million reads per flow cell, so across four flow cells at 600 million reads. And what we were showing on the EAPs is that we were actually able to achieve not only above 150 million reads, but on some of the EAPs we were getting over 200 million reads on the F2 flow cells. And I think that that really, you know, probably concludes the core KPIs. There are other you know, minor things like insert length and GC bias and other things like that that we worked on with the individual partners. And I think the fact that we were able to advance several of those to purchasing decisions speaks to the quality of the data and the experience and the confidence they have that it meets their need. In terms of learnings, you know, there were a ton of learnings. And, you know, one of the initial questions on the support component of it was a big one. You know, what is it going to take to make sure that the customer experience for you know, our initial users is really gold standard. And it's understanding the way the sequence is going to behave. It's understanding, you know, what onboarding is going to look like and all of those things we've now had, you know, experience with over the last year. There were definitely some learnings on the actual user experience. There were some learnings on the robustness, reliability of the machines. There were learnings across the board. I don't know, Eli, if you have anything specific that you would want to say that is a key learning that we're bringing forward on the machines.
spk00: You know, I would say we had kind of general progression development as we were going through the two beta sites first and the five early access sites. So we started off with relatively easy applications like RNA-seq and then progressed to full paired N150 reads with index reads additionally. So kind of working through that, and putting us to position where we're comfortable about hitting our commercial specs, like Drew mentioned, at 150 million reads per flow cell in the F2 flow cell. The F3 flow cell will have double that, 300 million reads. And then overall, you know, run time, accuracy, and some of the secondary metrics that Drew mentioned in terms of GC coverage and insert length that we can handle in the sequencing. Yeah, lots of good progression and confidence building throughout that early access period.
spk01: Great, thank you. And just one more if I could tie up. So given the recent kind of IP battle between Illumina and BGI, I just want to make sure you guys use four-color chemistry and not the two-color. And then on that kind of MAC-seq product you have being announced at AGBT, that is for the G4 and not for the coming PX?
spk06: Yes, to the first question, yes, we are a four-color novel chemistry that was developed from the ground up internally, so very different composition of matters, different polymerases than anything else out there. And we actually call it rapid SPS chemistry, given the fact that we're able to push the cycles so fast, and we actually have a roadmap to go even faster. And then on the second question, max read, yeah, max read is something that really, again, was born out of you know, deep understanding of how a lot of customers are using sequencers. And one of the pain points, and one of the pain points is essentially that, you know, the cost per read doesn't get that much cheaper for short reads versus long reads. And we saw that as an opportunity. If you can deliver lower cost or a higher number of short reads at a lower cost, you're really offering something differentiated that's a pain point. If someone's doing a 50 or 70 base read, versus a 300 base read, they're not getting, you know, a third of the price for that read. It's not even close. So how do you, you know, allow people to see real cost savings for short reads? And it's a technology we're working on. And there's actually a configuration that we're looking to share in a technical paper where we can produce up to about a billion reads on a single flow cell for short reads. And that dramatically will increase the number of short reads that you can do on a G4, which we think will be a big differentiator for a lot of the short read applications.
spk09: Good stuff. Thanks, Will.
spk02: Okay. If there are any final questions, please indicate so now by pressing star 1 on your touchtone phone. Okay. We have no further questions in queue. Thank you, ladies and gentlemen. This does conclude today's conference call. You may disconnect your phone lines at this time and have a wonderful day. Thank you. for your participation.
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