I want to- Under the hot suit. You get the full position. Interesting. You get a bottle of water as your reward. Oh, super. Well, we're going to transition from that exciting AI panel to another exciting panel, the second of our two Expanding Horizons panels here at the Genomic Medicine event. This one has a bit of an ear towards, you know, expanding beyond a lot of the discussion today. We've had a lot of discussion around, you know, specific next-gen sequencing platforms and applications based off of those platforms. But the goal here is to have a much wider aperture, a much broader aperture, and focus on technology advancement elsewhere in the broader 'omics field and cellular analysis as well. So with that, we have a big, a big group and lots of diverse backgrounds. I will ask each one of you to introduce yourself, say a couple words about your company, and we'll dive into Q&A. So Rob, why don't you kick us off? Sure. Well, thanks for having me here, Dan. Rob Carson, I'm CEO of Ultivue. Ultivue is a think of it first as a precision oncology company. That's 90% of where—more than 90% of where our revenue comes from. We leverage spatial proteomics technology, in particular, a ultrasensitive chemistry that, I guess, appropriately, given the conference title here, leverages NGS-type amplification, and then we have an AI-driven image analysis platform. Perfect. Wenbin? Yeah. Wenbin Jiang. I'm the CEO of Cytek Biosciences. Cytek is a company based on full spectral profiling technology. It's a more advanced flow cytometry technology, and unlike many of our peers addressing a new market, a new technology, we are, in fact, addressing a known large market. Certainly makes life a lot easier than many of our peers here. Michael? Yep, and... Yeah, hi, everybody. I'm Michael Egholm, the CEO of Standard BioTools, and thank you for having us here. Through a couple of acquisitions, we have built a portfolio of translational medicine technologies. About 80% of our portfolio is in proteomics, and we have three distinct technologies there, highly differentiated. We have the only scalable true plasma proteomics platform. We do spatial biology, and we do immune cell profiling, where we not only enumerate the immune cells but also tell what state they are in, and focusing on pharma and the broader market here. Still very early on our journey here, two and a half years in, but exciting stuff we have there. Great. Maneesh? Thanks, thanks for having me, Dan. Maneesh Arora, I'm the CEO of Elephas, and what we have built is an advanced imaging platform to predict response to immunotherapy from live tumors. So immunotherapy has transformed cancer care over the last decade, and it's not going to stop. Unfortunately, from a diagnostic standpoint, we have no really good tools to help inform which immunotherapies to use, and what we're building is a platform to allow someone, a patient, an oncologist, to know which therapy to use before that patient has to go on therapy. Great. And then batting cleanup, Mark Munch. Yeah. Hi, I'm Mark Munch with Bruker Corporation. I'm group president there. I guess, Bruker's most, most famous for mass spec and proteomics, metabolomics, lipidomics, as well as NMR. In the Bruker scientific instruments, you know, there's three groups. I, I run the Bruker Nano Group, which is the part that you can think of as not mass spec and NMR. It's kind of everything else. It's where the spatial biology sits for us, microscopy and Bruker Cellular Analysis. Mark, I hate to put you on the spot, but you have a very broad portfolio, and even if we just narrow in on spatial, you have a broad portfolio in spatial. Can you help the audience better understand, you know, what you're most excited about within that portfolio? Where are the biggest opportunities? Yeah, yeah. We have this broad portfolio where, you know, the way I view spatial is everything needs to be kind of fit for function, designed for function. So, you know, we have the spatial, the NanoString GeoMx platform. We have the NanoString CosMx platform, and then we have the CellScape platform, which is a spatial proteomics platform. And each one does its own thing kind of perfectly well. And we will soon have a thing called Acuity Spatial Genomics, where in the not too distant future, we'll be looking at the 3D genome, direct visualization spatial. Now, kind of more to come on that. So things I'm most excited about, for example, there's some great things coming in the CosMx platform, that I'm sure we can elaborate on more. It's a great platform. I think it's got one of the best scalable technologies in terms of scaling with high fidelity, high detection efficiency, for high-plex. I think there's also some great things in kind of repositioning genomics. It's the only whole transcriptome and a high-plex protein platform out there, actually. And so, even though it's not a single cell, it's really got a lot to offer, and I think we need to rejuvenate that. And the CellScape, which is a very new product, it's got this high-fidelity proteomics. And so that is probably our best platform. And it's not there yet, but positioned for the clinic in the future. Very excited about that. Let's stick with that topic for a hot moment here. What are the different needs as you translate spatial into the clinic versus spatial as a discovery tool, for research purposes? Yeah, great question. I guess, let's start with the, just, you know, some of the main needs in discovery, right? High-plex, right? 'Cause you're trying to look really broadly. And so I think one of the most unmet needs is at a single-cell level to be able to do the whole transcriptome. And so that's key in the discovery segment, and so there'll be a lot more to come on that. I can get a lot into the data analysis part in discovery, too, 'cause it's a big problem in how you handle data, and I think there's a big need for standardization of data and actually standardization of quality of experiment and interpretation result. Then as you move to the clinic, I'm a believer that, you know, it's more—it's the proteomics end, which will move to the clinic. I'm sure Ben will, you know, he, he can give you his back, probably... But, there's a need to, you know, not get too crazy in plex there, but there is need because you want to, with one platform, be able to handle many diseases. You know, you can't—the pathologist can't switch from, you know, "Hey, I'm studying this sort of disease in this type of tissue type, and then this other one," and have, you know, have to switch platforms. You need a standard platform that can give you somewhat a range of plex, like 4-plex to 12-plex. And so the needs there are not about high-plex, but it's about very easy to use, very dependable, and then data presented in a way that pathologists is used to looking at it. So I don't know if that answers your question, but that's kind of what I see amongst some of the bigger unmet needs around these two ends of the spectrum. It does. So narrower plex level and proteins over genomics in terms of translating from discovery- Yeah. to the clinic. Yeah. Rob, would you agree with that? I would. Yeah, I would, I would echo practically all of what, Mark had to say. I mean, I think during the panel discussions I've heard today, we've heard, you know, the range of, from one end of the spectrum of untargeted analysis, you know, whether with, transcripts or otherwise, to targeted. And I think the, the closer to the clinic, that one gets... And, and I do think that, you know, spatial, we think of it at Ultivue as needing to build the bridge from spatial biology, as research, to, first of all, clinical oncology. And I think, you know, the conventional wisdom that one will deplex, one will standardize or harmonize the workflow and ensure that's very robust, and then have really robust informatics tools, and can really make sense of the data is, is super, super important. Mark mentioned the data analysis was a big problem, so how do you propose to solve that? Yeah, I mean, I think, I think there are nuances, depending upon which end of the spatial spectrum that you're playing. Again, sort of you can think of it as, untargeted to targeted and, across different therapeutic areas, specific applications. But I think to one extent or the other, image analysis, deriving insight from the image, is a challenge across that entire spectrum. You know, with high plex or ultraplex transcriptomics, you could be talking about a TB worth of data, I think, in a given slide. Even for lower plex proteomic readouts, you're talking about 10 GB. And so just that amount of data, even if you're accustomed to... If you're trained to interpret it, it's just such a high volume that you need tools that can get through it quickly. And so that's why, obviously, the prior panel was dedicated to AI. That's where deep learning-type models have a big role to play in spatial. What about the user base? How accepting or understanding is the potential user base of the importance of spatial context when trying to understand the, you know, protein markers? I think we're seeing increased recognition of that within biopharma in particular, which is our primary customer base. Like I said, more than 90% of what we do is oncology-oriented, and, you know, 90% of what we do is ultimately for a biopharma client, whether it's the biopharma directly or for a CRO that they sponsor. You know, yesterday, I think it was Maneesh who mentioned that existing diagnostic tools for immune checkpoint inhibition therapy are limited, and, you know, they're taking a live tumor analysis approach to that. You know, there was a paper in Nature Precision Oncology yesterday that really backed that up, just how limited power existing diagnostic or molecular profiling tools are. Primarily, or at least to a large extent, that's because they're single-plex in nature. And I think there's certainly growing recognition within biopharma that one needs to get higher-plex data to the table, and then be able to, again, translate that from just image to insight in order to ultimately have clinical trial success. Okay. Michael, you've had, you know, some shots on goal in this market for quite some time. You know, Fluidigm, before Standard BioTools, made that acquisition of DVS Sciences, which got you into the, the spatial market. Any, any learnings from, you know, that journey, and, and what, what gets you excited about the path forward? Yeah, so we took over Fluidigm a little more than 2 years ago, and part of the portfolio was mass cytometry, either as flow cytometry or imaging. In fact, our Hyperion system was probably arguably the first multiplex proteomics platform out there. It had some huge deficiencies. It was very slow and very expensive. After we came in, we actually thought of that as a niche technology. But in unlocking all the science here, we found out that we have a huge runway ahead. And so about 1 year ago, we launched an instrument called the Hyperion XGI, which is about 10x faster than our legacy system, much more robust, and really been propelling our growth. And we just unlocked new imaging modes, where we can do 10x faster than that. Again, just as an indication, we're the only platform that needs a slide loader. We have two 20-slide cassettes. All the other technologies on multiplex, whether it's RNA or DNA, proteins, do one slide a day, just as a balance, with the exception of rubs like low-plex, but high-throughput assay, where we found that this really will play long term. We see this huge excitement about discovery, the CosMx, the 10x, Xenium, and the Visium system. Thousands of transcripts in one slide a day, and lots of dollars. We do tens, maybe more slides per day, 40 proteins, and proteins are where pathology lives. And so we see that as our niche, and eventually all the stuff going on in discovery will go into translational research and be used on patients. So now we can image a tumor with 40 markers, or you can do it in multiple sections and in sets of 40 in just hours, and you get the entire tumor heterogeneity. So we believe that we'll get big penetration here going forward in pharma, but super early here. We just launched the newest imaging mode last quarter. So your technology will be a downstream beneficiary of the wider end of the funnel work that's happening with higher plex and- Oh, yeah. Got it. Understood. And actually, there are a couple of customers out now that are combining the 10x, the CosMx with our workflow. Just overlaying all the immune cell profiling that we are doing actually enhances the analysis. But ultimately, what you get out of these hypothesis-free screening technologies are set of markers on. Mm-hmm. Some will be small set. We actually believe there will be a fairly sizable niche that will need 20, 30, 40 markers, just because you wanna see what all the immune cells are doing, what the stromal tissue, what the tumor is doing, and you run out of markers quickly. I agree with Mark's point, how you actually need maybe a pan-cancer panel at the end, so you don't have to change for every subtype of cancer along the way. But it's early technology. It's not sort of ready for the clinic quite yet, but long term, I've no doubt that it belongs there. Okay. You know, these are all interesting markets with some market development required. Wenbin, you mentioned that you are pursuing an existing market, the flow cytometry market, with a different solution, multispectral flow. Does that then... I don't wanna trivialize it. Is that a lighter lift? But, you know, I'm curious what that looks like from a market development perspective. You know, on the one hand, there is some establishment of the use case, on the other, there are incumbents. So could you walk me through your thoughts, if you don't mind, on trying to penetrate that market with a nuanced different technology? Sure. And, as you know, flow cytometry by itself is a phenotyping tool, and it's a very fast single-cell analysis tool, looking at millions of cells plus in a very short period of time, right? And, now, the question is, this following panel is about spatial and how it's linked to the theme of this panel. In fact, we do have our ImageStream technology and called ImageStream flow cytometry tool. And, this is again, we call it single-cell and actually high-speed single-cell microscope. It can provide a very precise image on the single-cell level and provides a lot of applications which the typical flow cytometry may not be able to provide you. But this tool provide additional, basically, image information along with the phenotyping of the cell. Linking this to the clinical side of the application, in fact, this tool has been used extensively to support one of the FDA cleared applications, which is, it's actually the kind of CRISPR-based gene editing and for sickle cell disease. And so that basically pretty much is one of the very important applications what we see now based on this type of imaging technology in the flow cytometry space. And on top of that what we see is to link this technology together with the full spectral profiling tool we have developed.... Then in that case, not only providing the multiplexing and technology or capability available for the tools we are doing, and also along with that will be the additional high-resolution imaging capability that will enable us to look at the cell, look at the shape of the cell, look at inside the cells, as well as cell-to-cell interactions. This is what we feel, you know, important going forward. Well, two things I want to touch on there. One, the panel does have and beyond in the title, so don't feel- That's why I'm talking about the beyond as well. Like you have to be confined to spatial. And, you know, secondly, I wasn't aware that your technology was involved in that December approval of the sickle cell gene therapy, so that's exciting. And that was from a research perspective, or is there any, you know, companion diagnostic application? No, it's actually for the validation of the efficacy of the treatment- Got it. looking at the shape of the cells afterwards. Well, you know, no shortage of technologies to dive into, but one of the things, I'm curious, Maneesh, for you, you know, when you're thinking about approaching new market opportunities and, you know, meeting unmet need, how do you think about that through an application lens as opposed to a technology lens? No, thanks for-- I agree with everything that everyone has said. I think that what we are doing is completely approaching it, instead of from technology, from that application lens. We have built really, really innovative imaging instruments, but born of one thing, is that can you predict response to immunotherapy? So can you build a phenotypic platform if you're a cancer patient? Yes, the mechanism is important, but I really just want to know, is this going to work or isn't it? Something Michael said is really important, and that is, and I think both of you said, Can you do this label-free? Can you do this without hypothesis across tumor types, so that you can take a look with an instrument and say, single application, irrespective of what therapy you put on, this is working on this particular tumor. I don't know necessarily why. I know that I'm seeing cytotoxic T-cell killing. The Dynamic OCM instrument platform we just published on this year, brand-new technology that allows us to assess the entirety of a tumor fragment and assess the viability of those cells while those T cells are alive in the hands of a pathologist. If I take that beyond, the next instrument that we are developing is an integrated multiphoton Dynamic OCM instrument through the same objective, so that you can imagine a world where we are able to label-free identify cell types, identify what is happening in time. It's the temporal element, it is the live label-free element that allows us to bring the application. The application is straightforward for now. Our first application is, can you predict response to immunotherapy? But the power of that technology that we've developed could go much beyond that over time. We want to make sure we stay focused on the application because that's what we think is most important and why we were founded. But the tool we've built is actually pretty exciting and compelling. Does that mean, though, that you would source the instruments behind your platform from different vendors, and you would bring the application knowledge, or are you actually developing the instruments in-house? So we have designed our own and sourced components, and we would likely partner to manufacture them, but it is our design, our software, our instruments. Where have other methods for predicting tumor response fallen short? I know there have been different methods in terms of, you know, genetic testing methods for tumor burden and tumor fraction, and just other ways to try to interrogate whether or not there is a response occurring. You know, pseudoprogression is something that has challenged immunotherapy over time. Like, how do you solve those problems with your solution? Well, I don't know that we solve the problems. The challenge is huge, and the diagnostics that we have, whether it's tumor mutation burden or PD-L1, these are techniques that are the best we have, but they're just not very good. And as a result, the vast majority of patients that take immunotherapies don't... They don't work. And now, if we think about what's happening in the next 36 months, with the biggest drug in the world, Keytruda, going off patent and an enormous number of new therapies coming, we really don't have tools. So, in the case of TMB, there are a number of patients that are going to have high tumor mutation burden that are not going to respond, and by the same token, you're going to have low TMBs that would respond to immunotherapy. So, it's the best we have. I'm not advocating don't do it, because it's easy to do, but clinicians know that it doesn't work, and we need better tools. That's really what we hope to solve. So we're running trials now with Mayo Clinic, with other institutions, where we're getting samples to prove what the efficacy of the platform is, and that's something we hope to read out, in the first half of next year. Okay. Well, just to press that point for one minute longer. So TMB, it sounds like it's a sensitivity and specificity issue in terms of predicting drug response, but then in terms of follow-up and monitoring drug response, would you argue that there are similar issues with some of the genetic methods around tumor fraction or, you know, recurrence and those types of things? You know, we've seen with the new, circulating tumor cell tests, there's lots of them out there, and they're much better than watching and waiting. But I think, there isn't anything that has emerged to be the, you know, the, the silver bullet on this, and we hope to be able to address that, but still early days. Okay, great. Well, you know, I'm curious for the whole panel, what do adoption curves look like in these markets for some of the esoteric mixed technologies you're bringing to bear? Do you know, they reach maturity in 5 years, 10 years, 15 years? Like, what is the length of a product cycle, and just any insight you could offer in those dynamics? Maybe Mark, I'll start with you. We're early innings, right, in spatial, if that's your question. I mean, if you look at, like, the adoption cycle S curve, we're down at the first curve, in my opinion, of it, at the first bend. And that has to do with, you know, we're just, like I mentioned earlier in the discovery segment, pushing the whole transcriptome at a single cell level, and then adding multi-omic proteomics, and then standardizing the way you take sample, and then standardizing what do you do with all that data? And how do you interpret it? And are scientists gonna interpret it in a way that has high integrity because of, you know, the complication involved. There's so many... It's kind of back to your unmet needs, why we're so early in innings. There's so many things that have to be solved still, right? And then that's just, I just spoke about transcriptomics and proteomics, but then there's other modalities that you could add on to that spatial footprint, right? Epigenomics signatures on top, or, you know, so it's, we're really early, yeah, in terms of the overall spatial adoption cycle. Yeah. 25 year product cycles, or? So as far as the product cycle, you need to build platforms that have the legs, that you know... You know, what a burden, 'cause there's, you know, there's some capital outlay upfront to, to, you know, every time there's a new modality, you've got to come in with a new instrument. So that is- that would be a broken model. So you-- and it's kind of why you have to pick platforms that are scalable, that aren't dependent upon s- very specific kind of more esoteric protocols, right? So if you can develop these platforms where you can envision running many different modalities, different, many different protocols, then, you know, those same instruments can just run forever. So it's, yeah, in terms of the capital adoption cycle, I don't see it as the, you know... I see it honestly as, you know, 10-year, 12-year, 15-year- Okay Type usage. Yeah. Let me just tag onto that, if I can. Again, would echo, particularly in the translational and clinical research space, you know, that we're still in the bottom part of the S curve. I think there was a data point related to ASCO back in June, that of the, I don't know, 1,600 or so trials that were in some way profiled during ASCO, that 11% were biomarker driven, and about two-thirds of those, you know, were tissue based, i.e., you know, spatial biology oriented markers. And I you know, that's consistent with sort of the bottom part of the curve, and I think that's only going to go up. I think the, you know, there, there's so often talk about how close we are to the clinic, and I think the... An important thing to consider there, is sort of the, reflected in the types of companies you've had today. You know, there have been companies that are strictly instruments and consumables and maybe software. There have been companies that are strictly, you know, more service platforms. And, I think it's very conceivable that for spatial in clinical, whether it's clinical oncology or some other therapeutic area, that you'll see more of, service-type platforms that, that ultimately serve that. And that, I think, that will get us into the clinic sooner than maybe some conventional wisdom might suggest. So you productize the technology through a service offering, and the customers would send you samples, you would perform the work and send them back data? Yeah. Is there a way to accelerate adoption by drafting off of an existing installed base from an incumbent? I mean, Michael, you're developing a product that will draft off of Illumina's NovaSeq, right? What does that do for accelerating the adoption curve of SomaLogic, which you acquired only earlier this year? No, hopefully it would be fantastic, but, like, big limitation in, actually in all of the technologies that we have, is that they either require very expensive instrument purchase, a lot of training, or you can only use it as a service of... For SomaScan, we have this amazing relationship with Illumina, where we have this, for the first time, a scalable plasma proteomics platform, where we can really read through most of the proteome, get very strong biological signals. But, and then with Illumina, as they launched the solution with their strong technical support, marketing apparatus, and installed base of 2,200 NovaSeq, we expect this to dramatically increase the uptake. It also actually becomes a demand that the customers have, and it's obviously going to be at a lower cost than we could do it as a centralized service. So very excited to work with Illumina on making that a successful launch. ... Well, one question that comes to mind, which I would like each one of you to address, and fully appreciate that these various technologies are on the first part of the S-curve, but investors always or often struggle to size the eventual opportunity. So where are we getting to at the end of the S-curve? I'd love to hear your thoughts about how you're approaching that problem. And maybe Mark, we'll just go one at a time down the row here. Mark, you can start. Yeah, a lot of TAM numbers have been thrown out over the last few years, right? You know, you're not going to let me off the hook by just saying it's big, right? But, you know, 'cause I you know, I don't quibble about whether it's a $10 billion TAM or $7 billion. It's, it's just big, right? But I think, to me, what drives... So the, so the discovery segment and some of the translational segments are, are big on their own. I think where a lot of those really large TAM estimates come from, honestly, is then the, the vision of some of these, and I think mainly the proteomics pieces, moving to the clinic. And, and that's, I this is my take on what drove some of those, those really large numbers. You know, we see, you know, we're kind of taking more conservative numbers out there is what we saw the TAM, 'cause it's still big. We see it as a $5 billion TAM, you know, at Bruker, spatial biology, which is still quite large. Not as big as what others have thrown out, but that's kind of how we've sized it. Okay. So. Maneesh, how do you try to come up with a TAM for live tumor cell imaging? I mean, I'll just go where Mark started, which is, it's really big. So the way I'll dimensionalize it, the way we think about it is in what some call a crazy way. My board says it's a crazy way. Today, we are, like, lying on the ground with respect to how much progress we're making, predicting response to immunotherapy with all these great tools coming. If we are successful, and we are able to predict response to immunotherapy across tumor types in solid tumors, the TAM would be every... This isn't a device that's being built for the Western world. Every device in every hospital, a device for every cancer center in the world. So for every solid tumor, on any given immunotherapy, a patient would get tested before starting treatment. That's big. I don't know what that number is. It's probably bigger than 5; it's probably less than 50. It's crazy, and what we have to do is see how close we can get. Our efficacy, I'll go back to the prior question, what in diagnostics, the adoption curve is going to be, it can be really slow, but it also, also can be a NIPT. I remember when we came, you know, in diagnostics, NIPT testing, and the adoption curve went from, like, 5 to 15 to 80. And now you can't, no matter what age you are, as a woman, you're going to get that test. If we show efficacy in an area that is so problematic, we're going to see a fast adoption curve. It comes back to the trials, it comes back to the efficacy, and then we can use the tools to improve spatial and temporal and transcriptome. But it's big, and it can be fast if we can prove the data. You introduced an interesting concept, though, the global concept, and how would that apply here? And the reason I ask is because for a lot of the specialty diagnostics I follow, the market is, for all intents and purposes, US only. Yep. Why, when you're trying to frame your own opportunity, would you not just tell me, "Here's how many cancer patients there are in the United States?" So what makes you think more global than the lived experience in some of these spaces? Yeah, I mean, we've been in stealth mode for four years. We're just coming out. So the premise of our platform and what we've built and designed is the instrument has to be close to the patient, but the data gets processed back in the cloud. So think of a Tesla. So it's a doorstop if you can't connect it. You can't see the screen. And so the platform we're building is much simpler, but it relies upon, irrespective of where you are in the world, it's the same workflow, but that data comes back, and the report is a cloud architecture. So the way we think about this is we're probably going to commercialize ex-U.S. before inside the U.S., but it is, we're agnostic to where we go to generate those datasets. But the technology involved in Dynamic OCM, that doesn't come with a high cost point- No. the different imaging and No. Okay. No, the box will cost us under $100 to place. Oh! Okay. Michael, you have so many different technologies under the roof at Standard BioTools. I'm not sure how you'd even approach the question of, of TAM, but- I try not to contribute to the TAM inflation here. I don't want to at the moment, but let me try to be additive here. So think of NGS or genomics today, the sequencing market. I was there super early on. People couldn't wrap their head around that $1 billion opportunity. So look where it is now. I think we are, with plasma proteomics, which up until now had lacked a sort of systematic, scalable platform. I am actually now beginning to, in sort of my more wild moment, think that eventually that will get to the same size as sequencing is today, for the simple reason that you get 10, 100x more biological insights when you look at proteins in blood than when you look at DNA. Being originally a genomicist, it's a hard thing to swallow. But with time, that will come, and the solution that Illumina is launching here first half of next year is a good first step. Let me just make one more point, sort of grabbing a thread from before. So there's tremendous excitement about spatial biology. We don't know where it's gonna end up, but it really comes from single cell sequencing, all the success 10x had, and then suddenly realizing, okay, cells sit next to each other. There's now this leap that, oh, flow is out, and it's all gonna be spatial, which is a really, really silly notion. It's a lot easier to get a blood sample than it is getting a tumor sample, and hopefully we can pick up most of the stuff there before. So what Wenbin and I are both doing in high plex immune cell profiling, I think would actually be tremendous growth just from all the work they've done today. And then I actually believe spatial biology is gonna contribute further to that, but I won't, I won't put numbers on it. No inflation, especially not in this current environment, where, like, dampen inflation. Wenbin, how are you framing that for your offering? You know, flow cytometry is actually the tool is a basic life science tool needed for almost every lab today, right? And so it runs across from discovery to translational to clinical, and a broad range of applications that are supported by the flow cytometers. So in terms of the total TAM, and I just don't want to end up how big it is, depending on how you define it, right? And because it does work with many other tools available on the genomic side, on the spatial side, they all need in some way need a flow cytometer to work along. Globally, there are tens of thousands of those tools over there, out there. One of the requirements certainly is, and the technology continue to advance, the life science studies require more and more higher plex type of panels to support the applications, and this is where we come along. Starting from there, to support the needs and along with Michael's mass tools, and certainly we're together to support the needs. Of course, from one of the requirement everywhere you will see is always what's important for customer, one is the cost, which you need to address, and second is ease of use, another things you need to address. This is where we have been focusing on, especially on ease of use, especially for those high plex panel. Very, very difficult, sometimes taking lots of time and money to address. And recently we launched this automated panel design and to really support the technology for spatial technologies, to support those high-plex panel design, and basically to automate the process. So, user will be able to have a panel, a very large panel, complex panel, already pre-designed, optimized virtually on our platform. Then they can take right onto our instrument and to continue to optimize to add on top of what they need. That really to help, really to shorten the time and saving their development costs. This is what we feel is something needed for customers, for users, and to help to expand the market, expand the applications. Just quick conclusion here, Rob, would you concur with Mark's comments that the translational market for spatial proteomics is big? Yeah, just very briefly. We think of it in two categories: you know, translational and clinical research. There are varying estimates, but they're between 1,600 and 7,000, I think it depends on the methodology you use. Drugs in cancer trials going on in the world, including U.S., Europe and Asia. So that gives you a sense for, you know, what the total penetration opportunity is or denominator. And then, you know, it's better to listen to others on TAM than it is to oneself, and so I'd, I'd cite AstraZeneca, you know, on the clinical side, right? I mean, they- they've predicted that ultimately, tens of millions of patients will be eligible for, you know, antigen-directed therapeutics like ADCs, and you're gonna need multiplex tools to best guide, those therapies. Okay, well, I'll take AstraZeneca's word for it. And it helps you, at least put some clarity to an answer where we really didn't give you a quantifiable, you know, response. You know, because just as the investor analyst community struggles with, you know, how big the market, how big is the TAM, so do we, right? And that's just natural because it's so early innings. We don't know. So it's difficult to say what it is, but I can tell you one thing, and probably true for others, I don't struggle with it because, you know, I don't care if it's $2 billion or $5 billion, it's just big, and so it's a good opportunity, right? And good science to be done. So that's truly how we look at it. Okay. Well, that's a great note to end on. Thank you all for your time.