Hi, my name is Yuko Oko, and I'm part of the Life Sciences Tools and Diagnostics Team at Morgan Stanley. Before we begin, I'd like to remind our listeners that important disclosure information can be found at MorganStanley.com/ResearchDisclosures. If you have any questions, please reach out to your Morgan Stanley sales rep. With that, it's my pleasure to host here and speak on behalf of the company, Seer, Omid Farokhzad. Thank you for joining us today. Thanks, Yuko. You made significant progress over the last couple of years in increasing awareness of the Proteograph Product Suite and highlighting its use cases via over 52 preprints and publications from the scientific community. To set the stage, would you reflect on how customer discussion has evolved over the last year? Of course. Yeah, yeah. Look, when we started here, the overarching hypothesis was that if we were right about it, we would be able to give access to proteomic information in an unbiased way at scale, speed, cost, and robustness that wasn't previously possible. We shipped our first instrument at the end of 2020, beginning of 2021. At the time, the biggest study ever done that looked at plasma in a deep way was 48 samples that was published. The deepest study ever published was from Broad, and it was about 5,000 proteins. We've now gone through three cycles of our product. We just released the latest, the third generation, Proteograph One, in June of 2025. With that, it's now possible to do really what we had predicted would happen, which is large-scale proteomics. In the first half of this year, we've already announced one corporate customer that started a 10,000-sample study. We announced in Q2 the second customer, Korea University, that announced a 20,000-sample study. We're in discussion with biobanks now and, also, to be announcing a third one, which has already happened, which is a, quote, "a pilot study of 10,000 samples," to pave the road to a 100,000-sample study. I think we now are able to impedance match proteomic and genomic at the same scale speed. The shift in conversations has been that we would have customers asking to do 10 or 20 proof-of-principle studies with us before they kind of adopt. I'm not seeing those anymore. Now customers are starting, you know, with our Seer Technology Access Center, which, by the way, has been a great asset. They're starting literally out of the gate with hundreds of samples at a time. Now we're in discussions with customers doing thousands or even tens of thousands of samples. I think the validation and the proof points are driving customers to kind of bypass those very initial skeptical view, which scientists should be skeptical, but kind of now just adopting like the way they would a proteomic solution to do for their applications. Great. That's a great overview into what we'll be digging more into. There are many emerging competitors in the proteomic space, including those with affinity-based proteomic platforms such as OLINK and SomaLogic, as well as those touting higher sensitivity like Alamar and Quanterix. Moreover, BGI and PreOmics are using nanoparticle-based platforms to improve proteomics workflow from MassTech as well. In the midst of so many options for proteomics, where does Seer's offering fit? Yeah. Look, I think I'll break up the offerings into really two buckets: targeted offerings and untargeted offerings. Before the world of untargeted at scale became possible, the only option you had if you wanted to do any study at scale was with a targeted offering. That was OLINK and SomaLogic for a long part. When we started Seer and we started becoming a commercial company, making it possible for the first time to do untargeted at scale, the conversations were always that the customer would say, "Well, you know, it's OLINK or SomaLogic, and then, you know, should we consider Seer?" I would say over the last four or five years, I've seen a shift where the conversations are more, you know, is it OLINK? Sorry, is it OLINK or is it Seer? I'm hearing a lot less SomaLogic out there in terms of customers' options. The wallet that the customer needs to kind of open up and share is really between the targeted approach and OLINK, let's say, versus Seer. I actually see both platforms as really very complementary to each other. They answer different questions, and the questions are both needed for the scientists to be answered. In fact, if I just look at a company that we spun out, Prognomic, from Seer and Fullerton Public, it was to look at leveraging proteomic information for liquid biopsy and early detection of lung cancer. They were our largest customer, and you know, we would report them on our earnings related to the transaction. They went from mid-30% of our revenue to mid-20%, you know, mid-30% in 2022 to mid-20% in 2023 to mid-10% in 2024. If I look at 2025, first half of the year, they were 6%. We'll probably finish the year with them being low single-digit percent. What that means is they started off using Seer for discovery, and then they shifted to targeted for their clinical and LDP. Both OLINK and Seer have value to the customer. I think for discovery purposes, you need an untargeted approach for content discovery. Once you know what you're looking for, a targeted approach is perfectly fine. You mentioned kind of other me-too follow-ons that came after Seer did. Frankly, it's flattery for me that our approaches are being replicated. Those me-too ones, they don't have the performance that Seer does. They do create confusion and distraction in the customer's eyes because they go to them and say, "Here's something that looks similar to Seer," and they offer it at, I don't know, one-third, one-quarter of the price. If you look at a lot of our customers, they have begun to actually publish comparative studies. The performance of those things is terrible in terms of depth of coverage, reproducibility, batch-to-batch variability, and robustness that one needs. If I look at a company like Prognomic or most biopharma companies, for that matter, the most valuable commodity they have is their biological samples. Prognomic probably spent $2,500 for every patient sample that they collected. If you offer Prognomic, run Seer for a price of X, or run this me-too follow-on for 30% of X, they will never choose an inferior product because their major commitment financially was on their sample. Seer is a very small part of that in terms of cost. More importantly, their lifeline is developing a test. If they use an inferior product and never end up with a test, they wasted hundreds of millions of dollars of investor capital. I am finding those solutions to be more of a distraction than relevant from a customer perspective. I always say if you get people on a rope that's long enough, they hang themselves anyway. What's happening is instead of us needing to do anything about this, me-too follow-ons, because we have a very robust IP portfolio, I am seeing customers beginning to publish comparative studies, and those comparative studies kind of answer the questions for most people. Those studies are now in the public domain by many customers that are unrelated to us as well. You mentioned complementary use cases using the unbiased approach for discovery purposes and then maybe going into affinity. Do you ever see it going the other way around where you see, maybe you see the affinity population scale discovery studies, using affinity-based approaches and then using MassTech-based Seer kind of approach to probe more deeply into the results, translational modification, or other protein-protein specific interaction? Yeah. Yuko, I guess there's always an outside case where somebody may do X. That X may be very different than what everybody else does. I'll never say never. I think that's kind of backward, meaning when you're looking at something like the proteome where the complexity is massive and we know a tiny bit about it in terms of its content, you need untargeted approaches for discovery, not the other way around. Targeted approaches have no discovery power because you're interrogating the same thing. If you look at the Prognomic test, that test will never exist without Seer. If you look at the biomarkers that were discovered for detection of lung, the majority of them were not in the public domain to go pick from. Once you found them, a targeted approach is a perfectly fine approach to utilize them. If you happen to like a targeted approach, sorry, a particular protein that you're interrogating with a targeted approach, and you now want to go look at PTMs of that or, well, I don't know, maybe protein interaction with that, sure, you can use an untargeted approach. That I would say is 1% of the value add of that approach, meaning the lion's share of value proposition when you look at a hypothesis-free approach, untargeted approach is seeing things that you were not seeing before. I don't see the customers going from a targeted approach backward toward an untargeted approach. I would say for every probably 1,000 people, I would see one in that direction. I might see one sinking in the other direction, because there's also other tools that one could use, for protein-protein interaction, etc., other than this approach that are easier for customers to adopt. I don't see that, Yuko, actually. Got it. Okay. Makes sense. I wanted to dig into your product offerings since you introduced a number of them this year. Starting with the Proteograph XT and cell lysate application, you launched this application early in the year. Could you provide some feedback on the application that you've heard so far? How much did the ability to look at cell lysate theoretically expand the application samples that could be analyzed from the Proteograph? Yeah. What we released at the ASMS Conference, American Society of Mass Spectrometry Conference, in June was two things. One, second generation of our instrument, it's the SP200. The second thing was Proteograph One, which is our third-generation assay. We introduced a new assay, which is the Proteograph Direct Assay, which is for cell and tissue. Let's break those three and say what each of them does. In the first iteration of our instrument, SP100, the key innovation was that Seer enabled nanoparticle capture of intact whole proteins by compressing the dynamic range. That protein capture happened at the beginning step. As the assay moved on, it would get washed, and then those proteins get digested into peptides. The peptide would be purified through a method that was not proprietary to Seer. That method required kits from Thermo Fisher Scientific for purification of the peptides, which would then go into the MassTech. With the second iteration of the instrument, the SP200, the workflow is now end-to-end Seer. At the beginning, the nanoparticles do whole protein capture. This is relevant if you're interested in PTMs, because you're capturing the whole protein, you would capture PTMs of that protein as well, or variants of that protein. It then becomes peptides on the back end. Instead of using that other kit for peptide purification, we now have proprietary engineered nanoparticles that now do the peptide capture. We have proprietary engineered nanoparticles for protein capture and for peptide capture. The total workflow is now end-to-end Seer. It's obviously from a proprietary and data generation and IP perspective much stronger. That's the instrument. The workflow also got compressed from about eight hours to four and a half hours in an automated way by optimizing some of the assay and eliminating the need for that additional peptide purification in the back end. We were able to compress the workflow. The other innovation was that the assay went from one run of the instrument being 40 to one run now being 80 assay because each sample is being analyzed through one well. A well of a 96-well plate has multiplexed nanoparticles that a single well does the job of multi-wells. That's the Proteograph One that enabled this assay to go through so that in four and a half hours, we can sample 80. Now, the direct product, the purpose of that was that customers were saying, "For the footprint that is occupying in my lab to run your assay, I would also like to be able to run other proteomic assays that may not require your particles but are helpful to me, let's say, in cell and tissue." Where Seer's value proposition is very distinct is when you have a high-complexity sample, by compressing the dynamic range before it goes into the mass, that you can see a lot of content. If you look at less complex samples like cell lysate or tissue, where the dynamic range is much, much narrower, the relative value add of using proprietary engineered nanoparticles for content is smaller. The real value proposition is the automation. The direct assay does not require our particles, but it runs the assay on our instrument so that for very small dollars, the customer can take away a manual workflow that with it comes inherent irreproducibility that happens with a manual assay, complexity of a workflow, and it lets you do cell and tissue on our instrument, broadening the utility of that instrument that occupies the footprint. That was really kind of reacting to the market and reacting to what the customer's needs were. With every new product offering, because you know it takes time for adoption, the adoption of the direct assay is foremost going to require an expansion of the install base of the SP200 because the direct assay only runs on the SP200. As the SP200 install base grows, my expectation is that Proteograph One will feed the folks that are looking at complex samples, and the direct assay will feed the folks that are looking at relatively less complex samples on the same instrument. Okay. Got it. Before we get in, dig a little bit more into Proteograph One, as we think about your workflow and menu broadening over time on direct, what are some other assays that you could offer that would enable customers to further their proteomics? Yeah. I mean, we've now done about 10 white papers in various different, kind of expanding low-volume animal, inner model organisms, cell and tissue, and various other ways of leveraging our platform, by the way, including also PTMs, in collaboration with Professor Kallenherz's group. My expectation is that over time, Yuko, you're going to see us addressing what the asks of the customers are. What are they? Number one, the customer wants to see content. The customer wants to understand the value of that content. That's different than seeing the content. The customer wants to do that efficiently. Every innovation and every advancement we make is along the line of one of these three axes in terms of delivering the value that the customer wants to see using the Seer platform. There's obviously interest in PTMs, and Seer's technology uniquely allows that because protein capture happens at the whole protein level where PTMs can be captured. We're certainly thinking about that. I think Proteograph One, being a new assay, has a lot of legs to run. You can see some almost like a label expansion, if you would, in that leveraging the Proteograph One in different ways for customers is probably the direction we're going to go in the next 12 to 18 months. Trying to increase the throughput and adding flexibility, like seeing PTMs that may be of interest to them, etc., should be forthcoming. Got it. Okay. Going back to Proteograph One, this workflow basically doubled the throughput to 1,000 samples per week, reduced runtime by 30% to four and a half hours compared to XT. How does the workflow, if any, differ from Proteograph XT? Yeah. Very similar workflow, Yuko, with the following two exceptions. One, XT required two wells per sample, and each well would be separately injected into MassTech. XT required two wells per sample plus two injections of MassTech per sample. Proteograph One is one well per sample, one injection of MassTech per sample. It halved the MassTech time. That was doubling the MassTech throughput while concurrently doubling the throughput of the Proteograph because instead of now running 80 samples, you run 40 samples. It also almost doubled the throughput in that the previous assay was eight and a half hours, is now four and a half hours. It became half the length, doubled the numbers. MassTech time went down by half. In terms of the chemistry differences, there's a difference in the engineered particles. We now also added our proprietary engineered nanoparticles for peptide purification on the back end as well in the Proteograph One. That all said, it's improvement in terms of throughput, speed, reduction in cost. In terms of content, the customer is not compromising because they're seeing the same number of proteins. They're seeing it with very similar reproducibility and precision. If you happen to be a customer that is not a high-volume customer, then XT is actually still a perfect assay for you. If you happen to be a high-volume customer in terms of your need, then likely you would shift to Proteograph One. If you're a population-scale customer, you want to be on Proteograph One just because over time, you know, in tens of thousands of samples, cost and time become very different between XT and One. If you happen to be running hundreds of samples at a time, and maybe you run a couple of thousand samples in the course of a year, two, three times, then it doesn't really make a difference to you overall. You may continue to be on XT versus going on One. We'll continue to support both customer types. There are customers that have now shifted to One, meaning they upgraded to the next instrument. There are customers that say, "We want to bring in One in addition." My expectation is, Yuko, that over the course of the coming couple of years, a lot of customers will probably shift to the One, because you're getting the same information just a lot faster. Why would you not do that? What proportion of customers do you think will stay on X? What do you think the steady-state mix would be? Yeah. I think it depends. I think if you happen to be a customer in the midst of a study, then you may not transition until your studies are done to the next assay, right? Just for steadiness of your data. We certainly are seeing customers saying, "Boy, that sounds great, but I'm really, really happy with the XT, and we're in the midst of a study." We will support those customers for the foreseeable future. Any new customer is going to get the One. A subset of the customers have already opted to upgrade. The sample size, Yuko, is too small for me to have a very precise number. The general theme is if you're mid-study, you tend to continue it. If you're starting from scratch, odds are you would pick the faster, more efficient system available to you. Okay. That makes sense. Are there any price differences between price per sample or margins with Proteograph One version on the assay versus the XT? Definitely, there is. Our cost of goods are significantly better with the Proteograph One versus XT. It's one well versus two for us, and we want a kit that is 80 samples versus 40. A lot of those reagents like enzymes and buffers are the same volume, but now it does twice as much. From the cost of goods perspective, we're better off, and that obviously helps our margin. That margin expansion can give us flexibility in terms of pricing if we want to. We have not found a need to discount because, if anything, we're giving a better product to the customer. The cost saving comes, for example, in the fact that they'll use half as much MassTech time. They're saving money because from an FTE perspective, running a Proteograph is half as much time of an FTE to run twice as much of the samples. There is definitely a cost saving in terms of operating the system and also running the MassTech. From a kit perspective, pricing is very similar, and the margins are better. Okay. Great. I wanted to move on to some of the population-scale studies that you announced. You announced that Proteograph was selected to run a 20,000 sample with Korea University. From the press release, it sounded like they evaluated several proteomics platforms before settling on Seer. Tell us key features of Proteograph that resonated with Korea University and ultimately led them to choose your platform over other proteomics solutions. Yeah. Yeah. No, thank you, Yuko, for that. You're right. They did kind of boil the ocean. I'll give you one other boil-the-ocean anecdote in just a second. In their case, they definitely boiled the ocean in looking at various different platforms. At the end of the day, the reproducibility of the assay, and by that, I don't mean if I run your sample over and over again, of course, we're reproducible there. By that, I mean I need to be reproducible in the long run, meaning if you're running a multi-year study and you're going to cross-manufacture lots of mine, that reproducibility needs to be high for a longitudinal long-term study. The study needs to be robust. Let's say you're a multi-site study and you may be running in Hospital A and they're running in Hospital B, different operators, different instruments. You need to generate the same data. The robustness of the Seer platform is fantastic. Nothing else comes close to that for unbiased proteomics. It does that, by the way, when you think of the CVs of Seer's, when you hear CV numbers, let's say on an ELISA assay, it's usually one ELISA, one antibody detects one amyloid. You look at the CV of that. You repeatedly mix that antibody with the antigen and you see how reproducible it is. When we talk about the reproducibility of Seer, the CV is being measured not across one amyloid, but across almost 10,000 amyloids. You're seeing CV reproducibility across 10,000 proteins that may be seen in plasma. The robustness of the CV, you have to also put it in the context of how much am I seeing. The reproducibility robustness was very important for the folks in Korea University. The automated system made it scalable for them. By the way, I'm very proud to also share with you, and I'll get into it in more detail over the course of the coming maybe weeks. We just had another customer, a government entity that wanted to fund a multi-institutional team, multi-tens of millions of dollars study. They boiled the ocean for proteomic solutions. This is the U.S. government. Many of these multi-institutions had come to comfort with the other proteomic platform, which I will not name because they've been around longer than we are. Yet the final solution that got picked for that gigantic study was Seer, which is fantastic for me to see, because that is a highly informed group of scientists with an extreme level of experience with the other platform, generating a lot of body of data on the other platform, and yet conclusively reaching a scientific answer, which is for our purpose, which is discovery of a large amount of content. When we're spending tens of millions of dollars, this is the right solution. Anyway, that's great. Great. Looking forward to hearing more from that. You're welcome. As you mentioned in the opening comments, it sounds like you're getting a lot of traction with this large-scale project. Are there any others that we should keep in mind and monitor as we go through the remainder of the year? Yeah, we're definitely discussing it with biobanks. We've already signed the one, which we'll discuss more. I mean, it's a, and we'll let the biobank themselves kind of disclose it. It's a 10,000-sample pilot to become a 100,000-sample study. We're also in discussion with others with multi-tens of thousands of samples. The first time I predicted that we would be at 10,000 samples plus was at your conference. In fact, it was exactly a year ago, at your conference where I said, "I think 2025 will be a year where we will see for the first time population-scale studies get done at 10,000 samples." I was thrilled to say that the first half of the year that we finished, three such customers were signed, in 2025. Let me be also here to say that I predicted just a couple of weeks ago at the Connecord Conference that I think 2026 is the year that we'll see the first 100,000-sample study, in terms of unbiased proteomic computation. Of course, a study like that is going to take time to finish, but I think studies like that will be initiated because the innovations that happen at Seer, but also improvements that our colleagues in the MassTech space are making, is really making it possible that a biobank can actually realistically choose to do this in an unbiased way because the speed, the depth, and cost all make it possible for them to do a 100,000, 20,000-sample study in an unbiased way using MassTech. The macro environment continues to be pretty challenging for instrument vendors broadly within life science tools. Could you provide some color around how the environment has trended since about a year ago in terms of sales cycle and funding availability? How does the dynamic differ between biopharma and academic customers? Yeah. I mean, the macro picture has been challenging for multiple quarters for us and our peers. I think that continues to be the case. There's volatility on top of that with tariffs and questions about the NIH budget. You know, you cut the budget, then it comes back, and then you know tariffs are there, and then they come back, and then they go away again. All of that makes any reasonable customer kind of pause or at the very least slow down in terms of their decision-making. We are observing that as everybody else is. I think what has helped us, and I want to be cautious because the path isn't going to be linear for us, and we'll go through ups and downs, no question about that. What has helped us is in the setting of that headwind that's been reasonably strong, our tailwind has continued to get stronger and stronger, which has helped us kind of maneuver this and push us through. We'll continue to have challenges, I think, over the course of the, you know, the coming, certainly the balance of the year, but maybe even into next year as we maneuver this, the macro picture. Great. You ended Q2 with a healthy balance sheet with $263 million in cash. Walk us through your capital allocation priorities between share repo, M&A, or internal investments. Yeah. Look, we've been very disciplined about the way we spend money. This part of our balance sheet, our cash burn, and I say that in the context of a free cash flow, has come down year after year. This year, it will probably do low $40 million, and my expectation is that next year will be even lower than that. It'll be somewhere in the $30 million, and this is in the context of heavily investing in innovation and product development. We launched our third generation of product just a couple of months ago. What's in our R&D roadmap is incredibly exciting. I'll make announcements about that over the course of the coming months. Given our balance sheet, and frankly, no debt, we are constantly being asked by others to look at opportunities for us to do inorganic growth. I have no ego in this game, nor do I think that we have a monopoly on innovation. We're always looking for other verticals to kind of add that are synergistic and valuable to our customers. I've seen a lot of things come up my way. We have not acted on any, and the reason for that is that I have not found any that we would want to deploy our own cash on, but we're always looking. What I think has been the most incredible value that I do want to invest in is us doing our own share. We've now authorized a $25 million buyback. As of the end of June, we had done $20 million of that. My expectation is that we'll try to finish the rest of that $5 million balance this year. If the stock continues to be where it is, I hope the board will authorize another $25 million buyback after that. We brought back about 10.8 million shares as of end of June, which is roughly about 14% of our stock, and we have more than enough cash on the balance sheet that I think we're going to get to a cash flow positive company with a significant cushion. We'll continue to invest internally, we'll continue to look outside, and we'll continue to buy back our stock. Great. I wanted to wrap up with a quick, bigger picture question. How do you anticipate proteomics to evolve in the future with new emerging technologies, improving scalability of proteomics, as well as increasing the number of targets that can be identified on the platform? What will become the key differentiating factor, in your view, for those that take the majority of the market versus those that are limited to niche applications? Yeah. Look, I break up this space in discovery, translational, and clinical. For discovery, you need to find new content. When you're starting in a space like proteomics, where we're at the tip of the iceberg in terms of content, the question is, what solution offers content discovery? I think the only solution that offers content discovery is untargeted approaches. To that, Seer is a leader, frankly the only organization that can deliver on that. When you shift on translational clinical, the need shifts a bit in that for those applications, you need to reproducibly and robustly interrogate a set of known proteins to do it efficiently. By the way, it isn't always that you need to be the most sensitive. Your sensitivity gives you an edge if you happen to be looking at something that is very low abundant. A lot of important proteins may not be low abundant, but you need to do it in a reproducible, scalable, robust way. You need to do it very cost-effectively. Those targeted approaches will dominate there. I think the real money, at the end of the day, is in the clinical part of it. The platforms that can grab clinical are going to take a significant part of it. If you happen to be a platform that can go all the way from discovery to clinical, then I think you will likely dominate that. Let me give you one prediction here, which is when content discovery velocity is slow, frankly, relatively speaking, at snail's pace, targeted approaches have the time to discover and analyze specific reagents for a particular protein of interest. When content discovery velocity picks up, as it now has because of technologies like Seer, you no longer have the time, the capability, or the dollars to begin to interrogate analytics-specific reagents for all that content that is forthcoming. I think, and that's a prediction, that we're going to see innovation come from the MassTech side, that it's going to leverage a lot of those targeted, specific targeted approaches, that they will actually effectively compete with the targeted panels today. I think the jury is out with who's going to own from discovery all the way to clinical. Certainly, discovery needs untargeted approaches. I don't think any of the targeted approaches are going to get there. The question is, would an untargeted approach get themselves all the way to clinical or not? I think time will tell. Great. Thank you so much. Thank you. Really appreciate it, Yuko. Thank you. Thank you.