All right. Good morning, everyone. I'm Julia Qin, Lead Analyst covering life science tools and diagnostics at J.P. Morgan. It's my great pleasure to introduce you to our next presentation this morning by Akoya. With that, let me turn it over to Brian. Welcome. All right. Well, thank you, Julia Qin, and thank you to the team at JPMorgan for your continued support and the invitation. It's great to be back in person and soggy feet and all. I appreciate everybody's time today. Again, I'm Brian McKelligon. I'm CEO of Akoya, and I've been with the company since middle of 2017 as whopping employee number 3 at the time. And I'm excited to share with you a little bit about our journey and where we stand today and how we think about our progress going forward into 2023 and beyond. Prior to that, our friends in legal would like me to give you the appropriate disclaimers, and I invite you to look at that at your convenience. It's posted on our website. Maybe just one real sort of high level slide to summarize kinda who Akoya Biosciences is and kinda what we do again, really in one slide. We're leading kind of what is known as a sort of spatial biology solutions. I really would call it a spatial biology revolution. I think what's unique about this concept of spatial, which I'm gonna spend a few slides to define to make sure we're all sort of level set, is that it's one of the few new technologies in the life sciences market that has immediate, and I'd say simultaneous, and I think that's the key phrase, simultaneous impact from discovery to translational and ultimately to diagnostics. As a company, what we're really cognizant of and what we're providing to our customers are really best-in-class solutions that provide this spatial capability, and again, I'll define what we mean by single cell, but at single cell level, at subcellular resolution. If you're looking at a tissue on a slide trying to understand that biology, looking at it, that entire tissue sample in every cell on that slide, millions of cells. We do so by providing complete end-to-end solutions, not solution, but solutions that are purpose-built for these different market segments. The needs of a discovery scientist are very different than those of a translational scientist. I think we've done so with some real success over the last five years or so. We have about 860 instruments in the field across these various market segments. You know, one of the great byproducts of that, and I would say leading indicators and endorsements of that install base is the nearly 700 publications that have been put out to date. That's who we are. Maybe I'll take a minute to go sort of from the top down to define spatial. It's an incredibly simple, powerful, and I'd say inevitable concept in life sciences. As everyone in this room well knows and this whole building well knows, technologies like genomics and proteomics and single-cell analysis and flow cytometry and mass spectrometry have been fundamental to doing biology at scale and to giving us a really profound understanding of the advancement of disease and of response to therapy. Now, while those technologies are incredibly powerful, and they will be for decades to come, the power of spatial is an attempt to address those same questions, get those same answers while leaving the tissue on the slide and maintaining that tissue integrity, and really getting an unbiased map of those biomarkers within their broad tissue context. Hopefully the so what is obvious. It is the environment, it is that tissue architecture, it is sort of location, location that are the drivers of biology. Where your T cells are, where your B cells are, where the tumor stromal boundaries are, where those key checkpoint proteins like PD-1 and PD-L1 and LAG-4 and IDO. What we're attempting to do at Akoya Biosciences is deliver solutions that allow you to map that entire tissue architecture to understand those biomarkers in context, to get a much richer understanding of disease biology, disease progression, and response to therapy. How we do that, kind of a high-level summary of our workflow, again, is fairly straightforward. We take a tissue on a standard slide, FFPE, fresh frozen human mouse, whatever it is, we probe that with a large number, in this case, perhaps antibodies. It can be tens, it can be 100. We probe that tissue simultaneously with all those antibodies, and we detect those with fluorescence at single-cell, and you can see at subcellular resolution. Then what we do is we informatically identify every cell through a process called cell segmentation. We know where every cell is, we know where every biomarker is present, and as a byproduct, we create this massive computable map that helps you understand informatically and in an address-driven mode where the key cells, where all the cells are, where all the key proteins are, and where all the cell types are. A couple of very simple practical examples in the case of discovery biology, if you're interested in colon cancer and you've got 10, 15, 20 different diseased colon tissue samples, you can look at those in really rich context and really high plex to get a much better understanding of the progression of colon cancer and some novel drivers. I didn't just make that one up. There's plenty of publications in colon cancer and others where our customers are using this in that discovery mode to really understand diseases like colon cancer, non-small cell lung, and others. Another very practical example is you may have hundreds of samples of patients treated with therapies where there's outcome data. While you're not maybe interested in broad exploration, you have a specific thesis in mind, where you want to understand the specific biomarkers that might be driving response to therapy. In this case, rather than 20, 30 samples where you want to do a deep exploration, what you have is a large number of samples with a specific question that you want to get through in an expeditious manner of time to understand response to therapy in a more translational mode. Those are two very common examples of how customers are using our platform, and that latter example is actually true to life. In fact, that concept in immunotherapy, I think has been one of the main drivers from the clinical need down on some of the drivers of spatial biology. I think as we're all well aware, IO has had a tremendous impact on cancer treatment. One of the challenges that we all face, as indicated by the data on the left, is understanding who will and who will not respond to these therapies is a challenge. Whether that biomarker strategy is single marker PD-L1 immunohistochemistry, which is the current standard of care for current diagnostics or co-diagnosis, or if it's a tumor mutational burden, we're really nowhere near the sort of ideal predictive power that we need for the patients and broader health economic needs. One interesting, and I would go far as to say seminal publication in the early days of spatial, and for spatial, early days was way back in 2019, I say that somewhat facetiously, was this paper driven by the team at Yale and Hopkins, and in fact we participated in as well. What it was, it was a retrospective analysis of some 40 different clinical trials around 8,000 patients and 10 different indications that ask a very fundamental question which was, we understand the response that these patients had to these treatments. There were accompanying biomarker strategies with those therapies. Did those biomarker strategies predict the response to therapy? What you see on the left, for those of you that may not be aware of this, it's a standard area under the curve plot, which attempts to show you computationally the predictive power of that biomarker. What this publication showed is whether it's standard immunohistochemistry, gene expression analysis or tumor mutational burden, an ideal biomarker would be go up to that star and over, flipping a coin is that diagonal. What matters is the area under that curve or AUC. They all had similar AUCs. For those biomarker strategies that went beyond a single biomarker and looked at three, four, five different protein biomarkers and did, I guess, what we would call a spatial phenotyping or multiplex immunofluorescence or immunohistochemistry, those biomarker strategies were much more predictive than some of these other classic approaches. It is publications like this, drivers like this that are giving spatial biology a lot of energy and momentum in the market. What we do, Akoya as a company, is we're delivering solutions that span this continuum. We have solutions for the discovery market, our PhenoCycler-Fusion that do really high-plex. We have solutions for the translational clinical market that focus more on lower-plex and high-throughput. What we have in the case of our discovery platform, what's very unique about that is we really sort of deliver a 2 for 1. We enable our customers to do really high plex with our cycling instrument, which moves reagents on and off the slide, and it's that iterative approach that allows us to get to ultrahigh-plex. While at the same time, then they can use that microscope as a standalone instrument to do smaller panels, but 100 samples per week, for example, for much higher throughput. We're delivering platforms to the market that span this continuum from discovery to translational and ultimately clinical. Doing so with underlying technologies that use the same imaging methodologies and in many cases, the same reagent approaches, so we can sort of own this journey as a company, from discovery to translational and ultimately clinical. Those are the end-to-end solutions that we have on market today. I noted earlier, we've done so with some success, seeing rapid growth of that install base, and I think by the time we get to, say, Q2 or so of next year, it'll probably be a doubling over that 2020 number, well over 1,000. We initially launched this PhenoCycler, that discovery platform, with a third-party microscope, and we launched at this conference last year the Fusion to preferentially be paired, as I noted in the prior slide, with that cycler as the discovery tool. Our high-throughput system has been on the market for a couple years, and about half of those are this HT system, the remaining half are our legacy systems. A very large install base, and as I noted, delivering publications that continue to ramp and accelerate, not just in oncology, and not just in translational, but in discovery, in transplant medicine, and across a number of other different therapeutic indications. In terms of the markets, I showed you discovery, translational, and ultimately clinical. There's been a number of assessments of the TAM, and right now, directly, Akoya directly participating, as I noted, in these discovery and translational TAMs. As I noted at the outset, what's unique about spatial is this is a market that is immediately applicable to discovery, translational today, and I think our instrument placements prove that, but ultimately clinical. As you go from discovery to translational, about a $7 billion combined to clinical, which is also $7 billion, your sample numbers grow, but the value of those samples also grow. What we've learned over the years is there's some base requirements that we've learned that the marketplace needs. For discovery, what's the requirement is we need whole slide for spatial. Single-cell whole slide analysis is a hallmark of spatial, and that's why we do an imaging-based approach. As you get to translational, you still need this whole slide, but the example I gave with hundreds of samples, you need to get that done quickly. Your platform needs to be able to do, in the case of a translational study, 100, 300 samples on a weekly basis. As you approach the clinical market, not only do you have to obviously establish that clinical utility, but your platform has to have the clinical robustness, reproducibility, clinical standards, but frankly, so does your organization have to have the robustness to meet that clinical market. That's how we look at TAMs. I think for at least for me personally, I think a more practical description of the market recently done by the team at DeciBio, is looking at the actual realized TAM. The data that they showed was a 30% CAGR over the next 5 years and a realized TAM of $300 million-$700 million in 2022 and 2025, and up to $1.2 billion in 2027. I think what's interesting, and I don't think it's surprising, at least to Akoya, but it may be to others, it is that translational and clinical research market that is the largest component of this TAM growing at about a 29% CAGR. The largest growth and ultimately greatest value is going to be impacting patient lives with the fastest-growing TAM, again, but farther out of about 100% for the $1.6 billion. Because of this dynamic, at least their conclusion, is the biggest driver in terms of the analyte that's gonna drive this business is multiplex protein detection, multiplex immunofluorescence, multiplex IHC. Both protein as a main driver and RNA, particularly in discovery markets, are seminal to the needs of the spatial market. As we look at our journey, and I'm gonna start kind of pivoting to the product portfolio as we move forward to the future really been three phases of our product development. Building the systems first, that PhenoCycler working with third-party scopes, launching the HT system for single-cell whole slide analysis. The company began with in situ imaging as the foundation of our solutions. Then we recognized what our customers need to really do discovery at scale is a much faster solution. So we built this Fusion, it's sort of the little brother of the HT system, the same underlying capabilities. What that gave us is about a 10x increase in speed. Now, as we look forward, the priorities for the company is really to simplify that workflow so that every researcher, every technician can have a simple workflow that's accelerated in terms of the discovery market, 30 samples in a week, doing high-plex, the translational market, 300 samples a week at the clinical robustness. That's the focus of our portfolio kind of going forward. As I now talk a little bit about the investments in 2023 and beyond, those investments are really at different stages of that workflow. You know, investment number 1, to enable our customers, those 1,000 customers, to get real value out, is make it simpler and easy for them to build and leverage large high-plex panels. You'll see us talk, I'll talk in a minute about our launching of many high-plex panels to enable both our discovery and translational customers to get those panels up. In the case of the discovery customers, increase that plexing. We'll get into new applications like RNA and the byproduct of the investments here, it's gonna be an increased dollar per sample. That dollar per sample is gonna drive pull-through. We're also investing in the platforms themselves with continuous workflow and speed improvements, and that's gonna be more samples per unit time, also a big driver of pull-through. On the analysis side, these are large files, and so we invest in data compression algorithms and software partnerships to give our customers the ability to leverage our platforms in a more accelerated manner and get time to answer a lot quicker. Those are the investments. Let me dig into each one of those a little bit. On the menu expansion in terms of content, we are coming out with these ready-made panels for our customers on the discovery side, these Discovery Panels I'll talk about in a minute. We're coming out with ready-made antibody panels, we call them Signature Panels, for both the Fusion and the HT to allow our customers not only to validate, but have ready-made panels to serve specific applications in the translational and clinical market. Across all of these, the byproduct of that is higher ASPs and higher pull-through. With 1,000 instruments on market, that really is a strong strategic intent for us in 2023. Part of the menu expansion is not straight organic. Part of that menu expansion is through partnerships. One of the immediate partnerships that's gonna deliver application expansion for our discovery partners is that with ACD and the Bio-Techne team. What we have done is we have automated that RNAscope application on the PhenoCycler-Fusion for our discovery customers. For those of you that are not aware of RNAscope, it really is the gold standard, sort of like the qPCR of spatial transcriptomics. Most of the customers that buy RNAscope, and there's thousands of them, 5,000 publications, a huge amount of revenue. Most of those customers are doing 1 or 2 marker at a time. What ACD can do is they can build these probes really on demand very quickly. What we're doing is we're automating that RNAscope application on the PhenoCycler-Fusion to allow those ACD customers to quickly go up to a 12-plex in an automated manner. Those are really for validation studies. We have a very specific question in mind. I'll talk in a minute about our own RNA technology that's much higher plex designed for discovery. This is part of our rollout of improvements being made on our platform for content. Now let me spend a little bit of time talking about the panels that we'll be delivering for both this, our discovery and our translational partners. These panels are under a brand name of PhenoCode. And they're both discovery panels, again, run on our PhenoCycler-Fusion or translational panels run on the HT system. The PhenoCode Discovery Panels are really focused on a high-plex, protein first and then RNA, across a number of disease areas. They're going to be delivered as modules of 10, 20 proteins at a time that our customers will combine together to enable them very simply to do really high-plex. Doing routinely a 60, 70, 80 plex. The panels that we have already frankly launched on the HT system are really on the lower-plex called the 6-plex side that while these are spanning many indications, these are really focused on IO. They are fully automated, they're validated, and what they're doing for our existing and new customers is reducing that development time by about 3-fold, and for us, increasing that ASP also by several fold. Those are the content introductions. Very quickly on each one of those individually, the discovery panels, again, we're rolling out the panels focused on immune cell profiling core. Those are coming out in first half. Additional panels in neurobiology, for example, will be coming out in the second half along with our RNA panels. This just gives you a snapshot of the modules of 10 or 20 proteins each across our immune cell profiling core panel that our customers will buy several of these and combine them together in groups of 10 to 20 to give them a really high plex understanding of analyzing the tissue samples on the PhenoCycler-Fusion. Those are the panels for discovery. The panels on the HT system, as I noted, they're really designed for high automation. Those in fact have already launched. These are focused really in IO and understanding that tumor microenvironment. The content here comes out of hundreds of publications and hundreds of panels we built, and these are the panels that we know our translational clinical customers are most interested in. Those are the investments on content, and they're part of this roadmap of the PhenoCycler-Fusion, where we launched it with third party. We increased the sample throughput from a few to 10 samples per week while we increase plex. We look at 2023, what our customers can expect from us is getting that to 100 plex, enabling 20-30 samples per week with a multi-slide automation tool. The RNAscope is gonna get launched that I just spoke about, and the panels that are coming out on protein and RNA. What we're providing our customers is continuous improvement on the PhenoCycler-Fusion, and the byproduct for them is a more powerful system and more publications. The byproduct for us, frankly, is increased pull-through. On the HT system, this journey is really about establishing the clinical capabilities of our system with ISO, quality systems, publications that show the robustness. Now with the launch of these PhenoCode Discovery Panels, we're now at a place where we're really directly serving the clinical needs of the IO partnerships and several catalysts in the clinical market that I'll talk about in a minute that are helping drive our translational work to the clinic. That is a quick summary of a lot of the work that we're doing on the content side. On the software side, the spatial market serving these customers is not a monolithic solution. We have varying needs in terms of questions that our customers need to ask in discovery and translational. We also have varying preferences for platforms. One of the things that we've decided to do as a company is focus on building an ecosystem. Before you can do that, you have to create standardization. One of the opportunities with these spatial data is it's incredibly rich. One of the challenges is these files are enormous. These files are terabytes in size, and that's a challenge for our customers. What we've done on all of our systems, we've rolled out a file compression algorithm that takes these high complex images that are terabytes in size and reduces those to gigabytes in a standardized format. When we forge these partnerships to serve these various applications, we have done so in a standardized format. I think what we've seen in arrays and NGS and flow, when markets start to come into place, you have this ecosystem of software solutions that bloom, whether it's freeware or commercial, and that's in fact what we've done, is we've already forged an ecosystem of partnerships to serve the various needs of our customers, whether it's a cloud-based solution from our partners that enable or an open source free solution where we have workflows for those customers that like an open source and freeware. We have established players like Visiopharm and Indica. These are standardized formats, they support both. Emerging AI players like PathAI or bespoke solutions like Oracle. What we have now in terms of our portfolio is a growing capability to do more samples faster, a growing capability to get to the answers faster with our software partnerships. That's sort of our priorities for the entire portfolio into 2023. Now, as we look at our clinical opportunity, what we have to do as an organization, recognizing that we're evolving as a company marketing from scientists to clinicians. We have product platforms that can't be RUO anymore. They have to be IVD-driven platforms. We have to commercially be able to close CDX deals and deliver commercially on those. As a company that has clinical aspirations, we have to evolve to serve those customer needs and some catalysts along the way that are allowing Akoya to evolve from an RUO life sciences tools to a diagnostic company. There are some meaningful catalysts that have already occurred. On the product side, the most recent catalyst was our first companion diagnostic deal with Acrivon. What this is, it's a partnership with Acrivon where they are funding-Us taking the HT system through the regulatory authorities to have it IVD approved with this as the first assay across multiple indications, as the first clinical use, the first spatial signature CDX assay on our platform with Acrivon. This is the compound they in-licensed from Lilly, a signature panel they developed on our platform. First patient in was last summer. If successful, the on-market CDX will exclusively be run on our platform where if you're positive for the signature, you'll receive their compound. If you're negative, you'll receive it for the signature with the compound with a low dose of chemo. This is really the first foray for Akoya into the clinical market, which many thought was much farther off. That is really one very important catalyst for us to go from a research and a translational company to a clinical company. The second big catalyst is our partnership with Agilent that we announced end of last week. Agilent really is the leader in companion diagnostics in IO with their franchise in PD-1 immunohistochemistry, their Ki-67 with Eli Lilly. What Agilent recognized and why they partnered with us is that multiplex-based clinical tests as cDXs are coming and are required. They have an established workflow, a powerful workflow with their autostain and their content, and what they have with Akoya is really the industry's best multiplex imager with already established clinical and translational expertise in the clinic. We're combining with Agilent to work with our pharma partners with this workflow to be leveraged across clinical trials, primarily in IO, and that'll drive multiple instruments, that'll drive more service revenue for a CLIA lab. It will ultimately drive more cDXs. Two very important partnerships that are part of our journey in serving this translational market to also make that leap to becoming, over time, a clinical company. Two slides in closing. The two very important strategic priorities for us as a company. 1,000 instruments on the market with huge headroom to increase pull-through, high-margin reagents, driving margins and dollars to the bottom line along our path to profitability. Doing so by expanding our menu of applications, continued platform improvements, and investing in software and data analysis to streamline that time to answer across RNA and protein. Partnering to drive and accelerate our clinical journey with partners like Agilent and Acrivon, with the immediate byproduct being more instruments and more services and more reagents in this translational market, raising the probability of more and more CDX and menu on that IVD-approved system over time. Those are our priorities as a company, perhaps the most important slide is, well, how have we done? We pre-announced our Q4 earnings and our full year on Sunday, $21 million for the quarter. That's about a 30% growth over prior year. For the full year, about $75 million, that's about a 36% growth year-over-year. The strategy that I talked about building out a portfolio of products to serve this continuum has proven true, and we believe that the strategy around driving pull-through, further advancements in the clinical market are gonna continue this growth driven by recurring revenue. Again, every quarter since our IPO in April of 2021, we've been ahead of consensus. Really consistent performance as a company, we believe we have a solid strategy going forward, we're really well capitalized as a company to fund this business for several years on our path to profitability in 2025. At the end of Q3, $82 million in cash, we had a renegotiated debt facility with our partners at MidCap that give us access to another $20 million in capital. Sufficient cash to continue to fund the business. That's where we are as a company. A lot of success historically, a very trackable strategy going forward in the discovery, translational, and clinical markets with continued solid, predictable performance above consensus estimates. With that, I think I went a little bit long. Sorry, Julia. I thank you for your time. Thank you, Brian. That was excellent. For the audience, if you have a question, feel free to raise your hand, and we'll get them back to you. Perhaps I can get us started with an easy one. Yes. Congrats on the 4Q pre-announcement. Thank you. Are there any notable trends and developments to highlight? Based on 4Q trends what are the puts and takes you are thinking for 2023? I don't think, and Joe can chime in, I don't think there's any trends that are different from prior quarters. I mean, we didn't give the number, but it was a record number of instruments across the portfolio in aggregate. Reagents, in aggregate for the full year are gonna grow about 40%, and that's an important driver for us. No trends on the positive or negative side that are, I think, any different from prior quarters. Joe, I don't know. No, it's just another record quarter for us and good solid performance across all of our different product lines. Our customers were strong. No funding issues at our customers. It was a really good quarter for us and a really good 2022. Awesome. Then on the Agilent partnership, that you announced, that was very interesting and encouraging to see. Could you elaborate what specific capabilities are they bringing to the table, and in what sense would that accelerate your commercial position? Yeah, I appreciate you asking that. You know, if you just look at Agilent, and I kind of quickly alluded to, they've had tremendous success in IO in building out CDXs, and they have a really successful pharma CDX team that works across a large number of biopharma to secure these partnerships on their Omnis and with their reagents. They bring sort of a wealth of experience, contacts, connections to really partner with big pharma on CDXs. They have an IVD-grade autostainer, which we need to be in the clinic. We need an IVD-grade autostainer, an IVD-grade HT. They bring the workflow, they bring the expertise. Ultimately, what they bring for us as a company where our channel, I point to Niro as our commercial lead, where our channel is really focused on our RUO, what they ultimately also bring is a clinical channel. With subsequent successes in CDXs, we have that capability. Lastly, they bring tremendous confidence to our biopharma partners because when they make decisions on CDXs, they look at the finish line back. Can this platform, can this company support a CDX on our drug? 'Cause it's dependent on it. The partnership with Agilent and the Acrivon deal give our biopharmas even more confidence that we can support an on-market CDX for their drug. There's a lot of components to that, some quantitative and some quality. Excellent. Thanks. Touching on the PhenoCycler-Fusion 2.0 that you announced recently at your spatial day, how's the early customer feedback been? Have you started taking pre-orders? Maybe talk about the early demand funnel that you're seeing. Yeah. In terms of the PhenoCycler-Fusion 2.0, as Brian pointed out, what that involves is upgrading the system to a multi-slide carrier, so the throughput's much higher. It's also got a software upgrade that allows customers to now start running RNAscope in an automated workflow up to 12-plex. For that, what basically happens is that it's a field upgrade to the existing install base, so customers don't have to place an order for a new instrument. What we plan to do in the middle of H1 is schedule out a series of customers who have great interest in the capabilities and then prioritize them and go out into the field and be able to upgrade them to have the multi-site capability in the RNAscope. It's more of a scheduling exercise than anything else. Gotcha. Are you offering, like, trading arrangements for- That's right. customers who recently purchased? Yeah. It was not a trade-in, it's the same instrument. Same. Okay. What we're trying to do for our customers is show that buy our system because it's sort of future proof. We're investing heavily in the system that you buy, increasing capacity, layering in RNAscope, layering in our own RNA technology, showing them a powerful solution today that we're gonna continue in investing tomorrow. This 2.0 is part of that increased productivity and throughput improvement. Yeah. Gotcha. Very helpful. With the 2.0 upgrade, and there was a big highlight on translational clinical use as well. We can all hear the excitement about scale up and moving spatial technology down to more translational clinical uses. Maybe help us think about the big picture what kind of sample sizes are we seeing for these translational uses, and how to think about the upside to utilization and pull-through and consumable revenue stream for you guys beyond what is enabled by the enhanced system spec? Right. On the PhenoCycler-Fusion, which we look at as a discovery platform, 'cause that's designed to do 50, 100 targets and so on, whether it's RNA or protein, currently customers run 20-30 targets, about 30 samples a year. We think with the increase in throughput, speed capabilities, simplified workflows, simplified data analysis, on average, that's probably gonna move up to 50, 60 targets. And from a sample perspective, it's also probably gonna go up some number, probably 40 or 50 samples over time. It won't happen immediately, but that's sort of where we want to get that platform to. When I think about translational, it's largely HT platform, and the HT platform pushes through about 1,000 samples per year. It's a pretty high throughput system, highly utilized. I think what we're trying to do there is more provide high-value content, which is what the PhenoCode Translational Panels that we launched at SITC here are all about, which is highly validated, customers use them. That creates more value and increases the amount of dollars they spend per sample by about five to tenfold than what they do today. I think that's how we're gonna increase the pull-through on the translational side. Very helpful. ABS, that's an important engine for you guys to continue to advance the CDX developments. You're expecting 40% growth, for that business. Are you capacity-constrained in any way? Yeah. Can you speak to the pipeline of biopharma trials? Yeah. in the spatial market? ABS is our Advanced Biopharma Solutions CLIA lab out of Marlborough. While we don't call out that revenue line specifically, the capacity is set, and it can scale modularly. I think we have right now a capacity that probably can support a doubling without a lot more investment. I think the CLIA certification, the Acrivon deal, and I think we expect the Agilent partnership is gonna continue a really rapid expansion of the service projects that we go through. And our goal is working with these key IO biopharma to participate in more and more projects to become an indispensable central component, like we talked about with AstraZeneca, across their IO franchise, giving us an increased probability with that partner of a belief in and a realized CDX. We don't call out the numbers specifically, but it is. It's probably one of, if not, our highest growth areas on a percentage basis. Gotcha. Yeah. There is a kind of a long roadmap in terms of the boxes you need to check before a. Yes. -spatial-based CDX come to market. Help me understand what's required along this roadmap, and how should we think about the potential timeline? I like how you phrase that. The boxes to check are you have to have the throughput. The PhenoImager HT has that. The next box to check is it better be reproducible. I think there's plenty of publications, and in fact, our customers do those reproducibility studies. It has to be built in ISO, deployed in a CLIA environment, and those have all been checked. It has to be regulatory approved, and I think that's what the Acrivon deal does for us. You need to find signatures that have potential clinical utility tied to a drug. That's the phase we're in right now. We've checked all those boxes, and with our CLIA Lab ABS, with the Aslyn partnership, there are more and more of those clinical trials that are being used with our platform to establish the utility of our biomarker as predicting patient selection. It's that last box that you sort of check kind of an N of 1 at a time to build that menu. There, there's a series of foundational boxes, and then that gives you the opportunity to hit a bunch of big different clinical studies, to build out menu. Excellent. Yeah. As we think about the eventual, clinical use, of spatial, how do you envision it play out? Like, will we see a massive upgrade cycle from a traditional microscopes to multiplex immunofluorescence, or is it more of a stepwide approach? I think it's gonna be stepwise. I think it's gonna be tied to compounds. It's gonna be an IO, and it's going to be largely driven by the massive number of common control trials. There's 700 common control trials with Keytruda. There's something like 4,000 IO trials right now that all want biomarker strategies. It's gonna be tied to drugs, tied to compounds. I think it's similar to how we saw NGS evolve in targeted therapies with clinical NGS. I think there's a similarity there. Great. Yeah. Any questions from the audience? All right, last one. In terms of the competitive landscape, right? I think we can all appreciate that Akoya has historically focused more on proteins while some of your peers are focused more on RNA, although the two Venn diagram circles. Mm-hmm have moved closer to and closer to each other over time. Yeah. In the long run do you envision a complete overlap of these circles, or do you always see a differentiated angle? Yeah. In the 50 seconds, I'll tag team with Niro. I think first principles, as we look at our performance, the competitive landscape that most speak about is really on the discovery side. I think as a business, our growth drivers span all of these market segments. I think that's part one. With respect to the discovery segment and the competitive landscape, I'll let Niro in 29 seconds speak to that. All right. When we look at the discovery segment, I think the first is the perspective. I think most companies that are entering the spatial and spatial imaging are looking at placing their very first instrument this quarter or last quarter. For us, this quarter, we're hoping we place our 1,000th instrument out there. That's a fundamental difference in how we approach the business, because our line is set to figure out how do we get to the 10,000 instrument. The type of customers we're looking for require simplified workflows. The kinds of products we have to think about are not necessarily based on plex, it's about how simple the workflow is. Is it affordable to them? Is there simple data analysis solutions? That's sort of the competitive landscape we look at into the marketplace we're going after. The type of customers we're going after may be a little different than the customer, the folks that are trying to get in on day one to spatial imaging. With that said, I think with the portfolio and things that Brian talked about, that product profile is designed exactly to go after that 10,000 customer. Excellent. With that, we're out of time. Big thanks to the Akoya management team. Thank you. Thank you. Thank you, everyone.