This is not common after sort of post-COVID to have all these things in person, so this is a good event for us. It's our first Berkeley Lights Investor Day, and for all of you attending online or in person, we intend to sort of tell you the story of where the company has been, where we are today, and where we are heading. Before we get into that, perhaps I'll start with sort of, you know, just making sure that we have a forward-looking statement here. I'll start with sort of why I came to Berkeley Lights. It sort of goes back to kind of why I came to the United States in 1990s, actually. I came here, obviously, from India, with a dream to participate in what I thought was going to be a century of biology. Even though I was an engineer, I decided to pursue biological sciences studies and have been, you know, incredibly privileged to have a front seat in the industry that has been driving a tremendous amount of discovery and innovation. My prior history was being a scientist for a while, working at a consulting company, mostly serving life sciences industry, and then being an operator, a large part of it at Life Technologies, which was built from a series of acquisitions and organic growth over a period of 10 years. Then managing a private equity-backed services business. For the last three, four years, actually, before I joined Berkeley Lights as the CEO, I'd been an investor in this space and, you know, mostly focused on what I thought was the next frontier of biology, single cell biology, gene therapy, cell therapy, and gene editing. In March 2022, so earlier this year, I stepped into the CEO role after being on a board for two quarters. The main reason I did that is when I sat there listening to what the company has accomplished in a very, very brief history the company has been in existence, I was truly impressed with the breadth of what this technology can do. I'll spend a little bit of time today just talking about sort of why that is that this technological foundation is so powerful. As many of you know, you know, in March 2020, just from the first set of U.S. patients who got COVID-19, the company's technology was used at Vanderbilt University, you know, where we had sent a platform and our person to run it because at the time the company was still new in its evolution that the university did not have any resources to use our technology. We were able to find, you know, with the partnership with Vanderbilt University, the COVID antibody that actually right now is still in use in the hospitals for, you know, severely sick patients. To me, that was very interesting that in a very short timeframe, it was a matter of weeks after the first few patients came to the U.S. that this had happened. As I was sort of reviewing how the company was doing in the first two quarters of me being on a board and being part of the governance structure, I realized that we also have something very unique. It was at the time, very early stage, in the AAV manufacturing area for undoing the bottlenecks of gene therapy manufacturing, which I know and had been studying for the last two, three years, has been one of the most important bottlenecks from a manufacturing standpoint the industry has had. You know, my belief that the tools that you have that unlock the acceleration of century of biology are incredibly powerful, and we are still in very early innings of that tools industry. This second thing really kind of drew my attention. The other thing that, you know, we have done in the company is a lot of focus on the T-cell work and finding the TCR receptors in some cases for specific patients or populations. You'll hear about that today from one of our customers, how that was used, and this has the potential to actually transform a whole another area of cell therapy where the company's tool could be useful. Then finally, I think the most important thing, if I can sort of ask you to take one thing out of this discussion today, is like, what is truly differentiating? I think everybody has to kind of think about, like, what mountain are they sitting on as an asset in this industry. I think one thing that is really differentiating about this company is our ability to manipulate cells at will, do a whole range of biological experiments with them at times in tens of thousands of cells at the same time, while keeping them alive, and even more importantly, at the end, to be able to retrieve them. There is no other technology that can do all these things. Now, I think we'll go into how the company has focused all of these core competencies into a couple of applications that have been successful, mostly focused on industrial applications. I wanna go to this chart here that sort of explains the journey of life sciences and innovation in biology and the human health impact. There's a lot of applications of this outside of human health, but if you just sort of take the chart here, you know, I was born in 1969, so this is, you know, goes the journey of my life. As you all know, 1957 is when structure of DNA was first discovered. In 1971 was when we first started to study pieces of DNA when restriction enzymes were, you know, discovered by actually an accident in a lab. That was the era of sort of, you know, chemistry being the main element of finding cures for human illnesses. Small molecules were the main thing. You fast-forward by 20 years, and you can see that the second wave of innovation focused on peptides and monoclonal antibodies. That wave of innovation is now fully matured, and there is a significant amount of, you know-how in the world, significant amount of, you know, products on the market, and even as of today, there are still thousands of clinical trials going on that focus on using monoclonal antibodies for a lot of things, including infectious diseases like COVID-19. The third wave of innovation, which is really starting in late 1990s and early 2000s, it has had its stops and starts, is around the gene and cell therapy. Interestingly, our company has already played role in both the second and third innovation, and actually could be used in high-throughput screening for the first wave of innovation that we talked about earlier. We are very much in the early inning of sort of what is going to happen here, and a tool that can keep the cells alive, can retrieve the cells, and those cells could be either studied in the future for further experiments, or those cells could be used for a therapeutic use, are some of the most fundamental things about this technology. That's the foundation on which we are building the strategy for the company. I also wanna talk about sort of my time in the industry. I joined the industry, you know, with a service provider at first in 1999 sort of late 1990s and early 2000s. Then I joined the industry of tools specifically because I thought that, you know, whether or not the people who succeed in sort of finally finding the cure or the final big blockbuster outcomes, the people who make the tools are always gonna be successful because we enable them to do what they do. Most of the focus in the, in the nineties was in sequencing. Because there was this idea that the human DNA is a secret to everything. As a result, if you figure out how the sequence of the genome of the humans are actually structured, we'll know lots of answers. It has been partially true. We have found lots of answers by just studying the sequencing of humans and other organisms. What happened in 2012 was our ability to edit the DNA, and actually with so much efficiency and at will, that it has really transformed, we can now engineer this DNA. What has been left behind in this is our ability to then study the function. 'Cause at the end of the day, the main organism unit for whether that's an illness or a cure is a cell. Cell biology is an incredibly important part of how the cures actually get created, how they are implemented, and it's the cells that actually decide whether something is foreign or not. Even if you have a fantastic cure, if you implement that cure in your body, our body is gonna decide whether it should accept it or not. As a result, the studying of the function of the cell is, I would say, the next big thing in the industry. I would like to assert that the next century will be the century of cell biology, sort of, you know, taking all the great learnings from molecular biology and moving to cells and systems biology that they study not just an individual cell, but also multiple cells. This is where Berkeley Lights is really uniquely positioned. As I said, the main differentiating thing, the hill that we stand on, is actually the hill which allows us to study functional biology at scale while keeping the cells alive. This live cell biology is a differentiator for us. You're gonna hear a lot of other technology today. As you hear, I ask you to, you know, pay attention to sort of what the company has focused on and where it has been successful. The two most important areas that company's first eight years of history stayed focused on are in two applications, antibody therapeutics and cell line development. This is a very unusual company in sort of history of life sciences tools, where it did not start with the first few applications for academic research. It started with the first few applications to really solve clinical problems, by the way. It has been very successful. However, it has also, as a result of not having the academic engagement upfront, missed opportunities, and we'll talk about how we're gonna change that going forward. Recently, we've had significant success in gene therapy, TCR discovery, and we are making a lot of progress in supporting our clients in agriculture. Company just at sort of a point of like just simple facts. We are about 285 employees. A significant amount of IP protection. The IP portfolio grows, and it's been building for last 10+ years. Originally started with a set of IP acquired from, you know, a couple of universities. Then built on that as the company started innovating. We have a significant IP about this ability to keep cells alive, ability to move cells at will, not just by the light, but by a couple of other methods, and then being able to retrieve these cells while we keep them alive. This is a core foundation of the company's IP. The business is global. I would say we are more successful in North America and Asia, and we have some work to do in Europe. The core of the company's technology, and a few minutes later, you'll hear from Troy, who will talk about the main functional unit is not that big machine that you saw. The main functional unit of a technology actually is this chip. The chip that allows us to actually compartmentalize cells into different small, you know, we call them NanoPen, but you can call them, you know, the jail cell for cells or something that allows us to like partition cells into, you know, tens of thousands of different, you know, separate units. We are able to grow the cells on this chip, which is again one of the unique things that we can actually culture the cells in a microfluidic environment, which makes it very efficient to grow cells, uses a significantly less media. Things that we haven't fully taken advantage of in the industry. As this industry scales, those basic components like cell culture media, how fast things grow, you know, can all become advantages for, you know, using the cell biology at large scale. Company was founded in 2011, 2012. The first technology launch actually of the machine that you saw and associated chips were in December of 2016. 2017 is the really first full year of launch. In my experience in life sciences tools, for a company to go from 0 to 100 in revenue, it is, you know, if it is a single product company, it takes a while. It has never been that successful for most companies to go from 0 to 100 in a very fast pace. This company is following, I would say, actually a normal path in that 0 to 100. It is doing it in a way that is actually significantly profitable on a per unit basis than how the industry usually evolves. We are actually at a scale in terms of our gross margins, and we'll keep coming back to that. We have a unique strength in our gross margins in the business, and we intend to retain that. In a post-IPO history, as a public investor, you know that its history much better than perhaps even I do. I've only been associated with the company for last year or so. The history of the company has been a significant success upfront. Obviously, with respect to the rest of the growth-oriented stock, the company ran into challenges in 2021. The leadership changes were announced in 2022. Since then, we are actually stepping back and saying, "Okay, what can we do with this asset? What kind of leadership do we need?" Today, most of the day, we'll talk about sort of what we've done in the last six months and what we intend to do in the next three years. Our customers are leaders in their field. We have 128 placements of our machines globally, and lots of customers are using this tool. I've had the privilege to spend time with customers face-to-face, understanding some customers are actually, you know, incredibly fond of tool. They have not one, two, three, sometimes they have four or five tools. Some customers are brand new who are just starting to get used to it. It is a complex tool, and it does take time for people to get used to how to run it fully. It is not a simple device. It is meant to be a simple device in terms of push button, but because of the number of things that involve a functional live biology and experiment, which is not controlled by the company's machine, it's controlled by somebody who's doing an experiment, it does take time for people to get used to a different way of doing things. The company is supported by an incredible board. I think I'm really privileged to represent the board here today. The industry experience, the scientific experience building companies, you know, from nothing, as well as experience taking companies that are worth $100 million of revenue to building them to be a few billion dollars in revenue. There is a very good balance on the board to be able to advise the management on all these aspects of, you know, running a life sciences tools company. I would argue that for the size of the company we are, our board is, you know, we are very privileged to have that. Finally, on the management team. As I said, in the last six months, we've brought on a tremendous amount of leadership to the company. I'm really honored and privileged to have had, you know, been able to retain all the executives who are meaningful for the company's future, who are here at Berkeley Lights, who created Berkeley Lights from zero to what it is today. They are super excited about the future we are creating. I'm also very proud to be able to bring on some real experienced people. Most people I brought on recently in the leadership team, each of them had 25 to 30 years of experience in life sciences tools or the function in which they are actually leading the function for the company. We'll talk about sort of, you know, the pieces of the leadership team throughout the day, but very proud to have that team. You know, I can say that, you know, to do something great in this industry, it does require a lot of experience in dealing with what is truly a unique industry. This is life sciences tools is a blend between innovation industry and a business-to-business industrial services kind of play. It is not a simple, "Hey, you make a drug," and 10 years later, whether you are successful or not. This is a fast-paced tools industry, a very fast-paced industry, and this experience is tremendously valuable for us. With that, I'm gonna actually, you know, make sure that, you know, people who are presenting the core of the technology have enough time to take you through. I'll ask you to pay specific attention to sort of unique three or four applications that Troy is going to talk about today. There is some recent advancements in the technology itself that have been made to enable the new applications. I think these are the applications that we haven't started selling as a tool, because we realize it's a much more complex biology. We will not sell them as a tool, but we would actually partner with people in form of licenses and partnerships to be able to do that work with them, for them, in our laboratories or sometimes in their laboratories. With that, I'm gonna invite Troy Lionberger to come and speak about our technology. Thank you. Right now I'm the General Manager for our Partnerships and Services business unit. I came to the company about eight years ago as one of the early scientists responsible for inventing some of the core technology that we've been commercializing to date. I'm very happy today to talk to you about both some of that older technology, but also, where it's going. With that, just some key messages I'm hoping to get across in this presentation. You know, Berkeley Lights at its core is really accelerating the discovery and development of therapeutics, and you know, I think it's important to keep in mind, I am a cell biologist by training. Prior to that, like Siddhartha, I was also from the engineering sciences. If you ask a cell biologist what it is that they ultimately care about in studying cell biology, it's, I mean, the idealized experiment would be one where you could actually get to the level of single cells to understand them and characterize them and profile them. I think biologists have spent, you know, the last few decades trying to find methods that allow you to make observations of cell biology, using surrogate methods. They aren't really the direct observation of a single cell, but rather an interrogation of a large population of cells. That has limitations. Particularly, the biggest limitation that we solve at Berkeley Lights is the ability to find rare cells within that population. If you have a superstar that is producing, for example, an antibody that protects patients from COVID infection, that may be one in 100,000 probability. If you were studying 100,000 cells and only one of them was acting the way you want it to, you would have a hard time finding it. That's really what Berkeley Lights is trying to solve. We're doing this by minimizing the number of cells that you actually need to make those measurements. We bring the level of experimentation for the first time down to the level of single cells in a way that allows the cells to, as Siddhartha mentioned, stay alive. There are single-cell technologies out there. I think what you'll find is a common theme of many of them is that they are often destructive to the cells. To study a single cell, you're oftentimes forcing the cell to be killed in the process so that you could ultimately understand, for example, how certain genes are turned up or down. One of the big advantages Berkeley Lights has is the fact that that is not a requirement of the system. We can actually keep cells alive while we interrogate them, and we can ultimately pull them off the system at the end. What we're able to do is because the experiments can be limited to a single cell, we can do this on very fast timescales because we don't have to wait for cells to divide. For anyone in the audience who's not a cell biologist, these cells will typically divide every 24 hours. If you want to take a single cell and grow it up to a shake flask, and you have to wait 24 hours for those cells to divide, it can oftentimes take you weeks to months to ultimately get enough cells to make a measurement. We're miniaturizing the experiment so that you don't have to wait for those cells to grow to a large enough volume. As I said, really the goal for us is to ultimately enable an ability to profile enough cells, typically in the tens of thousands range, to find those very rare cells that you care about. You know, the title of this particular slide is a bit of a mouthful, but it really encapsulates what we're trying to do. Beacon System enables rapid high-throughput polyfunctional screening of live primary biology with single-cell resolution. We'll go through this throughout the presentation, but rapid means we're doing this in a few days instead of months to weeks. High-throughput means we're looking at thousands to tens of thousands of cells as opposed to one shake flask, for example. Polyfunctional screening means that you're not, you know. There are again some technologies that can give you one measurement of a cell, but what we're uniquely able to do is to bring many, many different observations to cell biology, which is ultimately the goal. If you want to fully understand how a cell behaves, it's not just gonna be looking at how it grows or how it produces a certain compound. You want to look at all of those phenotypes at the same time to get as reflective, representative behavior of that cell as possible. As Siddhartha mentioned, the heart of the technology is shown here. This is our microfluidic device. I'm sorry. What I would just ask you to pay attention to is there are two small pinholes in opposite corners of the device. In every one of our workflows, a user will put a diverse pool of B cells into the machine. A syringe pump will draw those cells into the microfluidic channel, and then we will use light to take those cells and drag them into these observation chambers that we call NanoPens. To show you what this looks like in practice, here you're looking at a single field of view. The white dots you see in the center that are being dragged by these boxes of light are actually drawing those single cells, so those white dots are cells, into these observation chambers. When Siddhartha mentioned that we can interrogate cells at will, that's what we're talking about. We can position cells on demand in whatever location we want through this microfluidic device. We'll talk about why we drag them into this microfluidic chamber in a second. It's not just limited to a single observation. As I mentioned, we're looking at single cells. You can notice there are a couple cases here in this video where the system sees there are actually two, not just one cell, but two cells. You don't want that for these single-cell observations, so we can do something about it now for the first time. We can actually remove them, and we can try a second time. We can focus only on those single cells. What you're seeing here is a simple illustration of what we call determinism. Most cell biology right now is limited to random probabilities. You're, you know, you get what you get. Sometimes it's a single cell, sometimes it's two cells. This is the first system that allows you to do something about it in real time so that you can really focus on a single cell observation. Yeah, these NanoPens are much smaller than a typical well plate, which allows us to miniaturize these experiments. We're controlling the surface chemistry and temperature to make it a perfect culturing environment for cells. You can see in this video on the right-hand side what I would argue is actually a really important thing to understand about the system. This is an experiment we did very early on in the company's history, and all you're seeing here is a solution of beads, which just simply allow you to see how fluid is behaving on the system. What we're doing is flowing a solution through the chip. What you notice is within that NanoPen where we're typically doing the observations of cells, all these beads are just diffusing. They're completely unperturbed by the swept region that is above this NanoPen. What that practically translates to is when you put a single cell into this NanoPen, it is in a perfect culturing environment, so the cell is perfectly happy. It's being fed nutrients that keep it alive. It's in this unique environment that we can perform all these different assays that tell us how the cell is behaving. What you're looking at here is a single week of culturing. We're taking single cells, and what you're seeing here is them growing over time. As I mentioned, each single cell divides every 24 hours. We're observing that growth rate over time. This is one of many examples where you can take advantage of the live biology to make observations, in this case, about how cells are growing. You know, we're gonna take you through a couple applications now because what I've shown you so far is that we can isolate single cells, and we can ultimately grow them. But that's not what we ultimately wanna do as cell biologists. We ultimately wanna characterize the cells. I'm gonna give you several examples of how it is that we can use the technology to ultimately uncover rare cells that give us unique abilities to, in these cases, find therapeutic antibodies. There's an assay that we can do called the blocking assay on the chip. I'm showing you here this illustration, this graphical illustration, showing what a blocking antibody does. When a blocking antibody correctly interfaces with a target cell, it blocks the receptor from binding ligands. That ligand, as I'll show you, it can be many things, but one of which it can be COVID, for example. Looking at the spike protein interacting with the ACE2 receptor. In the image that you see on the right-hand side, I'm showing you how that assay works. We have these observation chambers we call NanoPens. You can see that in the middle of those chambers, you see single cells. Those cells are B cells that were just taken from an animal hours before this image was taken. In the bottom of these pens you have target cells, and you're trying to look for antibodies secreted by these B cells that block the binding of your ligand to these cells in the bottom of the pens. This is what the image looks like over time. On the very top, you see this fluorescence signal. This fluorescent signal tells you that the antibody is capable of binding an antigen of interest. However, what you see in the bottom here is that the cells in the bottom of the pen actually don't get bound by the fluorescent ligands. This is an example of an antibody that is successfully blocking a ligand from binding the target cells in the bottom of these pens. This is an example of what we call a functional assay. This is saying there are many technologies that can take a single antibody and find an antibody that will bind a target antigen. But to show that binding of the antibody is actually physically blocking the function, this is an assay that lets you actually confirm that. This was done, in fact, at Vanderbilt University, as Siddhartha alluded to. We had early on in 2020 sent a Beacon with one of our people to help Vanderbilt identify and find these antibodies. I believe it was March when this happened in 2020. What I'm showing you here is I think this the unique power of this functional assay that I just described. The very large blue bar that you see here is looking at the. You're looking here at the number of antibodies that were found that just simply binds to the protein target that you care about. In this case, it's the RBD domain of COVID. You can see that we found 886 antibodies that could actually bind to that target. However, only 10 of them were actually successfully blocking the RBD interaction to ACE2. That is to say that these are 10 antibodies that not just bind the target, but actually successfully interfere with the interaction with COVID and its target cells. Of those 10, two of them were ultimately promoted into the AstraZeneca drug known as Evusheld, which is still on the market. It's also one of the only antibody cocktails that's long lived enough in patients to act as a prophylactic. People who are immunocompromised, can't receive a vaccine, can actually receive this antibody drug to protect them from COVID. It's not just a therapeutic, but also a prophylactic, so we're quite proud of that. My point though is if you had to manually go through those 886 different antibodies to find those 10 and ultimately the two that are the ones you care about, especially during a pandemic, months would've been required of time, if not longer. This ultimately ends up, you know, speeding up this entire process by being able to directly observe when you see a blocking antibody on chip. That is the power of integrating this live cell functional screening. You know, this technology is not just used for finding antibodies against COVID. That's actually a serendipitous artifact of the antibody discovery work that we've been doing. Here I'm showing you data that was collected from our colleagues at Genovac, where you're looking at comparisons between antibody discovery assays using our technology in that light blue box versus the more conventional approach using hybridomas. You can see hybridoma screens yielded far fewer different antibodies against what we call hard targets. There are oftentimes targets like certain receptors on cell surfaces like ion channels or GPCRs that are quite difficult to find antibodies that actually recognize. You know, one of the unique opportunities of Beacon is it allows you to ultimately screen very quickly, but also you can find more antibodies that are ultimately the ones that you are looking for. This is one of the key features of the system as well. I just described antibody discovery and how we're using the same technology that I introduced you to at the beginning. Once you've found those antibodies, the question is how do you actually manufacture them? This is the second application of the Beacon platform that I'm gonna introduce you to today, and this is called cell line development. This is formally known as finding cell lines that produce the most therapeutic antibody that you're looking for. From a manufacturing standpoint, you're trying to reduce the cost of goods. You're trying to reduce the cost of goods by increasing the amount of antibody that your cells can actually produce. It's actually a very simple assay in this case. I'm showing you another image here on the right-hand side. That green signal you see against that blue background is actually corresponding to the amount of antibody those cells are actually producing. It's actually really straightforward. You find the brightest green signal, and that tells you those are the cells that actually are producing the most antibody. Oftentimes, you can see 2x-3x more antibody coming off of cells from this system than you would from conventional screening. You can imagine the cost savings for a biologics manufacturer if you can just magically produce twice or three times as much antibody. That is really, you know what cell line development allows you to do is to find out of tens of thousands of observations, you can find those cell lines that are ultimately producing the most therapeutic antibody. I will say we're also extending this to a number of different applications like bispecifics and multi-specific antibodies, so more complex antibody formats that are heavily engineered. This technology is showing great promise in accelerating those workflows as well. Now, you know, pretty pictures aside, I mean, it wouldn't matter if it wasn't meaningful. This is just showing the correlation in this case between our on-chip assays on the y-axis and those same cell lines after scaling them up and reassessing them on the x-axis. This is just simply saying that if you see a measurement on the system, you can trust it. It is actually a meaningful observation. It's not just random noise. This is just a measure of performance. I've taken you so far through really the first two applications the company began commercializing, and that's primarily in the antibody therapeutic space. I'm now gonna explain two new applications. The first one is looking at rare T cells, so finding T cells that are actually capable of killing a cancerous cell. This has applications in, you know, personalized vaccine work, TCR discovery, and others. I mentioned that, you know, cell biologists wanna be able to study single cells at the level of single cells. I'm gonna show you now how we actually do this, at a very high scale. Here, this video I'm showing you first, what you're seeing is a bead that's being dragged into these NanoPens. It's not a cell, it's just a little plastic bead. It has an antibody on the outside of the bead that lets us basically see, you know, a molecule that's ultimately produced by T cells called the cytokines. Cytokines are produced by T cells when they're actually engaging with a tumor cell. This is really gonna be an assay that we want to see positive results for. You know, T cells that are interacting should show cytokine secretion. That bead is gonna let us do that measurement. We then bring in single T cells, in this case, from a patient. Now these are very precious samples, so you'll notice that second NanoPen in this video didn't receive a bead. It didn't. The system is smart enough to know that that's not gonna be a useful experiment, so it didn't even try to put a T cell in that NanoPen. It actually is smart enough. It has intelligence. It says, "We're not going to try to load a T cell into those NanoPens that are not gonna be productive." Very similarly, you'll notice we just ran out of T cells, so the middle pen here didn't receive a T cell. It just has a bead, but no T cell. Now when we say bring in antigen-presenting cells or cancer cells from a patient, and in this case, we want not just one, but we want two cells because we wanna see if a T cell cannot just kill one cancer cell but also a second one. That's called serial killing, which is still my favorite description of cells. Here you're gonna look at us basically bringing in exactly two tumor cells into each of these observations. Those tumor cells are gonna be the targets for T cells, so we can observe now not just is this T cell going to kill, but how long does it take to kill. We can look at whether certain markers are expressed on the surface of those T cells, and we can look at cytokines that are secreted by those T cells during this engagement. When I said polyfunctional screening, this is what I'm talking about. That for a single T cell, you can now get, you know, seven, eight different parameters that are all relevant to understanding how a T cell actually functions. Just to show you what this looks like in practice, these purple dots you see here let us discriminate. I showed you in this previous video how we make a measurement with one bead for one cytokine. There's no limit to how many beads you wanna use. In this case, I'm showing you three beads for three different cytokines, so it's not just a single cytokine. You can choose which ones you want to measure. This purple fluorescent signal you see in this dark image lets you identify which of the cytokines you're actually measuring. Once the experiment is done, we switch over to this yellow fluorescent signal that you see here that actually tells you how much of each cytokine was actually being produced by these T cells. You know, we're generating massive amounts of data from tens of thousands of T cells on three different cytokines that it's secreting, whether those T cells are actually killing the tumor cell. Very basic questions, believe it or not, about, you know, is it an obligate requirement that T cells secrete certain cytokines in order to kill tumor cells? These are not really well appreciated or understood, and we're for the first time starting to uncover that because now we have a tool that we can use to measure it. This is an example of a rare T cell observation. I'm gonna show you this video. The green signal that you see here will eventually turn red. That's the T cell killing the tumor cell, so you can get a sense of what that actually looks like in practice. You can also see on the right-hand side, we can look at which cytokines are actually secreted in that process. This experiment basically allows you to do, you know, severalfold. We can use this for vaccine validation work. If people want to make personal cancer vaccines, you oftentimes will, you know, want to know if your vaccine is going to work on this N of one patient. Now you have a way to take T cells from that patient and ask the question, you know, is the vaccine we're about to administer actually going to raise a T cell response that we're looking for, right? Against that patient's tumor. That's what we're using it for personal vaccines. There's also just like antibody discovery, finding antibodies that are, you know, freely floating in solution, TCRs or T cell receptors are very similar in that sense. But TCRs are unique in the sense that they're not freely floating in solution. They're bound to a T cell's membrane and, you know, to say that a TCR is actually working requires you to make an observation like what you're seeing here. It's a cell-based observation. So we have this unique ability to also find what we call functional T cell receptor sequences. So you may be hearing more and more about TCR discovery. That's these are the applications that we're using this technology for. The very last example I want to share with you today is something you'll be hearing more about from Rolando later. This is how we've really hacked the cell line development technology that allowed us to find high producing cell lines that produced antibodies to now find cell lines that produce the most virus. Why would you want cell lines that produce virus? It's for a field called gene therapy. There are, you know, a small handful of FDA-approved gene therapies that cure things from genetic forms of blindness to hemophilia. Diseases that were previously devastating to patients are now seeing, you know, single-dose curative options. In order to actually administer those gene therapies, you have to produce the virus that ultimately injects those patients' cells with the missing gene or the correct gene that ultimately was, you know, required to solve that disease state. The biggest challenge that this field has right now is producing enough virus. You know, if you took the entire world's manufacturing capacity of, you know, that's available in pharmaceutical developers and ask the question, you know, could we actually produce enough virus to go after diseases that have a very large patient population? That's what you see on the x-axis here, left to right. If you start seeing patient populations in, you know, the millions for a given disease state, we can't manufacture enough virus today. On going top to bottom in this plot that you see here is the dose that's required to cure that patient. As I mentioned, you know, Luxturna, which is in this upper left-hand quadrant on this plot, is a direct injection into the eye that cures blindness. You don't need a lot of virus if it's gonna be just a direct injection. There are a host of disease states that are called systemic disease states, like sickle cell anemia or type 1 diabetes or rheumatoid arthritis, for example, where you need very large doses to you know a virus to actually you know re-deliver those genes throughout the body. You know, in both those axes, whether you have diseases that impact a lot of patients or diseases that require very large doses, that's putting a strain on the manufacturing issues that exist today. You know, we won't get into why that's limiting. I think Rolando will talk about that more. The basic point is we're not manufacturing gene therapies the same way we are antibodies today, and that's what we're trying to solve. We're trying to bring a technology that allows you to begin to manufacture virus with the same level of quality and safety, and cost of goods as antibodies. Right now, that's not the case. The last application I'll show you on the technology shows you how we've, you know, hacked our cell line development technology to do this for virus. Really what we do in this case is we purposely choose to only put single cells in every other pen on chip. That allows us to then expand those cells just within that one NanoPen. We can then do what we call a subcloning operation. We can literally transfer with our light half the cells from one NanoPen to the empty NanoPen next door. Now you have two NanoPens with genetically identical cells. We put them back into culture, and the technology that we had to invent for this workflow is we can actually use light to photopolymerize a barrier. This plug you see on the left-hand NanoPen is a physical barrier that's actually gonna protect the cells below the barrier. The cells below that barrier are protected from a lethal condition, in this case, triggering viral production. One of the things I didn't mention is when virus is produced within the cells, it kills those cells. You don't want to kill every cell because you ultimately want the goal is to retrieve a live cell population that I can scale up into a bioreactor and manufacture. We can actually do the subcloning step, protect half of the population while destroying the other half and subjecting it to an assay that tells us that it's what we're looking for. This red signal you see here only happens of, you know, one in 100, one in 1,000, you know, observations we'll see, right? That's hunting for the rare cells that we're talking about. But that's okay because we can do this at scales of 2,000 to 3,000. We'll usually find, you know, cells that we care about. We'll actually then finish the process by opening up that pen and retrieving the cells that remained alive that were previously protected. This is now taking, you know, what was a relatively simple experiment where you're just looking at a single fluorescence observation in the antibody applications of this. Now we're doing experiments where, you know, you're able to actually destroy half of a clone and keep the other half protected from a destructive condition. This is really opening up doors for applications out, you know, even beyond, you know, the viral vector manufacturing that we'll be talking about today. We're really excited about this new technology coming online. Look, at a very high level, we're trying to give you access to not just a single observation, but many different observations of cells to let you convince yourself that you found the cells you're looking for. We do this on both a timescale but also a throughput level that just hasn't been, you know, seen before. You can rank order all of these cell lines with whatever different parameters you're looking for, whether it's growth rates or secretion or cell-cell interactions. The system knows the physical address of each and every one of those cell lines, and you can come by, retrieve them one by one. You're looking at us using what we call a conveyor belt to just nudge those cells out into the main channel, and it's gonna shoot off to the right-hand side. That cell is being destined for a well plate that's waiting on the system with media ready to receive it. That basically concludes a workflow on the Berkeley Lights system. I hope this was a helpful overview. I'll hand it over back to Siddhartha. Thank you, Troy. Troy is one of the best communicators about how our technology works and, in spite of having some challenges with his throat conditions, he was still here today and, so round of applause. Listen, I think we can tell you a lot about what the company's platform does, but I think it's even more powerful to hear that from people who use it and use it to actually do things that allows them to be successful with their work. With that, I'm gonna invite Janet Lambert to lead a panel of esteemed customers who've used our platform in various different capacities and have a dialogue with those individuals. It's some of the sort of a double click down on sort of how the platform is used from an industrial application perspective, how the user experiences it, and any recommendations that they've had for us for sort of improvements and future workflows on the device. Janet, if you could please join us with the customer panel, please. Thank you. Sure. I'm gonna ask you to have patience for a couple of minutes as I set up the chairs for the panelists. That's good. Good. An all-purpose CEO. As Siddhartha said, I'm Janet Lambert. I've had the pleasure of working in the life science tools space for probably 20 years now, but have spent the last five years leading the International Trade Association that represents cell and gene therapy companies, the Alliance for Regenerative Medicine. It's my pleasure to have the chance to talk to some of these folks who are using the Berkeley Lights technology. As Siddhartha said, Troy's given you a view of how the technology works, but there's no substitute for actually hearing from people who are using it to solve problems. That's what we hope to do with this panel for you. Let me start first by asking our panelists to introduce themselves, the company they work for, and a little bit about their Beacon experience. Brian, can I start with you? Yes. Good morning. My name is Brian Walters. I serve as the CEO of Genovac. We're a contract research organization headquartered in Fargo, North Dakota, with a facility in Freiburg, Germany as well. We acquired our first Beacon in 2019, and we now have three, and primarily on the antibody discovery therapeutic application. Greg? I'm Greg Bleck. I'm Vice President of Research and Development for Catalent Biologics. We have one Beacon instrument that we acquired pretty early on after the product went commercial. We use it predominantly for selecting high expressing antibody-producing cell lines and other therapeutic producing cell lines. Will? I'm Will Hoos, President of Jaime Leandro Foundation, and we have been partnering with Berkeley Lights on some of the TCR work that Troy was just describing. Great. Okay, so I think we'll do kind of a deep dive into three different applications and then have some general conversation after the fact. Brian, if I can again start with you, could you say a little bit more about the work that Genovac is doing and the application for which you're applying the Beacon? Yes. Again, our company was primarily in the antibody discovery field and primarily serving the therapeutic part of the market segment. We were founded in 1999 around genetic immunization as a core technology to get an immune response in an animal. Coupled that for many years with the classical hybridoma technology, our spot in the marketplace was a company would come to us who's developing an antibody-based therapeutic or a cell therapy with a target. We would immunize an animal and then recover those cells and deliver a broad set of antibody candidates to those clients. Primarily again in that discovery space and historically serving clients who have a therapeutic need, and our specialization was difficult and hard targets. As you're trying to identify these difficult and hard targets, talk to us a little bit about why you decided to pick Beacon to try to solve that problem, and were there other kinda competitors that you considered at the time? Yeah. Yeah, absolutely. Yeah, so basically, companies like Berkeley Lights change the toolset from which we can execute our trade. Hybridoma is again a technology that's brought many antibodies to the market. It's a relatively inefficient technology, so especially for the class of targets that we're working on, these new tools that allow us to have success against these really challenging GPCR ion channel targets like Troy had referred to just changed. It was a combination of the toolset change. We can better achieve the results that our clients are paying us to deliver for them. There's definitely demand there. As these technologies were deployed, that's what the market wanted, that's what our clients wanted, so we shifted in that direction, based on market need and the, you know, passion we have to deliver success for our clients. We evaluated other technologies. By far, the Beacon was the most advanced in its combination of throughput and the elegance of being able to do sequential assays with it. Other platforms have been used, and we actually use some as well, but require a lot more downstream work. There's definitely a timeline and cost reduction dynamic that is associated with the Beacon as well as it's a very sensitive instrument, so that's a very desirable trait for the targets that we're working on. Can you say a little bit more about the sequential assay capability and the practical impact that has for the work you're doing? Yeah. We're able to do a primary binding assay, flush the channel and bring another cell line through and either look for a cross-reactive set of hits within that group or, as Troy mentioned, functional assays is another assay that you can run. You can do within a day many months' worth of experiments that you would typically have to have your clone produce it, then test it over the course of, again, many months. It's a massive time saver and cost saver. You've been using both the kind of capability and the throughput of the Beacon, but still you've acquired more than one, and I think have been described as kind of a Beacon super user organization. Talk about that, about kind of why you decided to deploy additional ones and the whole capacity that that's given you guys. Yeah. It's all about throughput and market demand. Demand our clients have for the capabilities. We're working on hard targets, so the more cells that we can screen, the better the chances that we have for success against our targets. It's based on demand and the success, which is driven by the number of cells that we can screen. In the prep call that we had, you told a story about a project you had worked on, forgive if I'm putting words in your mouth. The number of targets you actually identified on Beacon versus a previous approach was pretty significantly different. Could you explain that to the folks in the room? The class of targets, again, that we historically work on are very challenging multi-transmembrane proteins, that GPCR ion channel class. There's a lot of talk about yield increases and with the number of hits that you get from a campaign using a technology like the Beacon, diversity that you gain, but sometimes it's just success or no success. These are hard targets. The lower hanging fruit, I would say, has been picked by the industry over the years with the technologies that have well served those drug discovery efforts. This enables success where we didn't have success before. Sometimes it's just that big. It's the old technologies didn't deliver. This combination of the Beacon with our immunization technologies worked. In some cases, it's 10-50 times yield increases, which is very significant. Our goal is to deliver the most diverse, the highest number of hits possible against the targets that our clients are paying us to work for. GPCRs are a pretty significant target in the world of sort of biomedical discovery these days. Can you just say a few words about that, about the significance of GPCRs as a target? It's a class of targets that, again, I think I'd put in the higher hanging fruit, that the older technologies just didn't enable success. There's not a lot of cell surface for the ability to get an immune response and recovering the cells is very much a needle in the haystack exercise. This combination of technologies that our company has is which is a sort of a two-part equation. You immunize a host species, get an immune response, that's challenge number one. Challenge number two is recover those antibody producing cells. It's that combination of technologies that we found a really sweet spot that really lines up well to again, our niche, our spot in the marketplace, and what our clients want us to do for them. Yeah. Thanks. Willy, you have a kind of equally compelling but pretty different story to tell about your use of the technology. Before I ask you to describe that, can you tell everybody a little bit more about the foundation and the work you guys are doing on neoantigen vaccines? Sure. Thanks. Jaime Leandro Foundation was formed out of the request of a patient who was looking at the data coming around personalized vaccines and wanted himself and others to be able to access these personalized vaccines while they were moving through clinical trials. The compassionate access regulations allow that, but it's not always easy to do. We set up Jaime Leandro Foundation to essentially enable that access to these vaccines that have shown some promise but are not yet proven and, you know, appear relatively safe. Exactly what combination, the exact way to make them, the exact regulatory environment for getting them approved is all evolving. We basically set up the foundation to enable that access. The patients pay for the vaccine to be made for them, and we enable that through expanded access, or we also have a research protocol going now. You know, just briefly on personalized vaccines, what allowed the world to create the COVID vaccine within essentially two months of understanding that the virus existed was sequencing, designing vaccine and rapidly manufacturing it. When it comes to a cancer, to do a vaccination against cancer, there's unique parts of the cancer that are unique to every patient, and those are targets for an immune response, but it's personal to every patient. There are common targets, and people are doing vaccines there. In general, having things that are more personalized are more likely to do the job for a given cancer. We're running basically the same type of process that was done to create the COVID vaccine, and there's lots of different technology ways. There's definitely companies working this through the clinical trial process. While that's all happening, the foundation is set up to enable access for other patients who may benefit, who can't get into those trials. Really important work, obviously. Very, I'm sure, profound in its experience of working with individual patients and being able to help them. You used the Beacon technology in collaboration with partners at WashU to try to benefit a pancreatic cancer patient recently. Can you tell that story? Absolutely. I think broadly how the foundation works and how the vaccine process works. When we enabled how to do this, we worked with WashU in St. Louis. They have been running NCI-funded and other trials on, and we basically have taken the same approach and technology they're doing and are moving it into this program that we make available to patients. It actually was prior to the vaccine program being set up, but a patient who had a complete. A pancreatic cancer patient with multiple lines of therapy, metastatic disease, had the vaccine and then did a couple other things that complement the vaccine and has had an ongoing two and a half year complete response to that disease. That's exciting. That's a nice proof of concept. We've continued to work with other patients in pancreatic and other diseases and still looking for repeating of those stories and going through the process. That's kind of an experience of a patient and a result, but also just our process and our partnership with WashU. The next piece of that is, so what is driving that response? Can we understand it? Can we partner with these patients who are trying to do exceptional things and partner further to understand and contribute to the science and possibly develop more personalized cell therapies? That's where the Berkeley Lights partnership came in. Is there any more you wanted to say on the on the- Well, maybe just a sort of follow-on. Part of your process is, as I understand it, mapping TCRs to the relevant neoantigens, which is relevant in your personalized vaccine work, but I would think in immuno-oncology efforts broadly. Is that right? Right. The way I think of it is the vaccine, the primary goal of the vaccine is to create a T-cell response to multiple targets on the tumor to have a poly response, a multi-target response to the tumor. One of the critiques, the vaccines may not be able to produce a sufficient amount of that response. What are the ways to understand if the vaccine's working as well as it needs to or ways to boost that? That's really where the partnership with Berkeley Lights came from, one of the ways to potentially boost that is to understand exactly which cells are doing the job, which T-cells are doing that, and then find ways to either engineer or amplify or modify those to further the therapy. That could be a personalized or it could be discovery of TCRs, that could be a generalized therapy if it's a common target. That was kind of the root of where we started working with Berkeley Lights. As you move forward, are there particular kinds of patients and projects related to those individuals for whom the Berkeley Lights technology will be applicable, or do you see it being applicable for the reasons that you described to really all of the personalized vaccines you're likely to wanna develop? I mean, I think if we look at what's happening in the field, the gap between what can we manufacture, validate through typical clinical trial drug development process and get approved and scale. The assumptions of what can get through all those hoops is dependent on the tools you have. I think everyone would largely agree that the more personalized something can be, the better. Yeah. The question is, can you scale that cost effectively and get throughput reproducibility? Yeah. If there are tools that can enable that personalization, then that's likely to be the best possible solution, and somewhere in between is better. The more personal the better and the more targeted. The couple of things that I think of is, you know, there was just a paper in the last week that came out of using CRISPR to engineer personalized TCR therapies. That part of the manufacturing scale, but you still have to find them. That way to find them and that side. Then there's been a pancreatic cancer engineered T-cell that showed a profound response. That's a single target. There's another patient who had that same TCR five years earlier that was just a natural T-cell, and that developed resistance because there wasn't a second or a third target. Mm. To be multi targeted to prevent that resistance. Mm-hmm. The signs that this works and the signs that driving it more personalized and more detailed and better understanding of what the targets are and how to find them, all that points to, you know, why we partnered to do this. Yeah. Great. Thank you. Greg, if I can turn to you. Catalent, obviously a big global CDMO, involved in solving lots of problems for lots of customers. You described that you have one Beacon that you have been primarily using for cell line development. Can you say a little bit more about that? Yeah. Yeah. At Catalent, customers come to us for and have us use our proprietary technology called GPEx Lightning to genetically engineer their cells. Then we use the Beacon after we engineer those cells, as Troy mentioned, to take millions of cells that have been genetically engineered in different ways and to select those ones that are unique and high expressing, grow well, and perform well. One of the nice things about the Beacon system is that you constantly are feeding those cells. How we do big, large scale production of pharmaceutical proteins is we grow them in huge vats, you know, 1,000-20,000 L of cells growing in these vats. They produce material over a span of usually 10-18 days as they're growing in those vats. What's nice about the Beacon system was we can use the same media that we use in those vats to perfuse those cells, starting out with one cell going to one to four to eight over the span of a few days, to basically mimic a little bit what's occurring in those reactors. We can select the ones that are performing best in what we call those fed-batch conditions, and then pick those lines to move forward and scale up. What the Beacon's allowed us to do is to basically, because our engineering technology works so well, we can do a quick cut with the Beacon to select those really high producing ones and then move those directly at scale to larger and larger scale and help our partners get into the clinic faster and get products moving forward in development faster. It's really enabled us to move process development of these drugs much faster from sort of inception to clinical trials. Yeah, that ability to really kind of know that you're scaling up the high producing cells reduces a lot of trial and error, I guess, and allows you to have a faster, more effective process, more reliable process from start to finish. That sort of allowed us to reduce the risk of development for clients that want to move faster. There's still other things that we can do. Some of our clients want to go really fast, and so we'll pick the best cell line right off the Beacon and move that forward, and that's the cell line we're going to take forward in development. Some of them are still, you know, have a few more extra months, and so we'll take the best 12 cell lines or 10 cell lines off the Beacon, analyze those in a little bit more detail in equipment that Troy talked about, sort of smaller scale bioreactors to see which one performs best and then move it forward. We're still getting the best cell lines coming off the Beacon instrument that allow us to not look at as many at that scale. Previously, we'd look at a lot more cell lines at that scale in order to find that one that was the best one to move forward with. Can you say a little bit about what the end customers for this cell line development are working on? Is it a pretty broad-based set of therapeutic developers that are coming to Catalent for this improved cell line development? Yeah. It's basically any DNA recombinant protein. A lot of the molecules we work on are antibodies or antibody-based molecules. Troy mentioned bispecific molecules, and so we're seeing a number of products where they maybe target two different antigens or three different antigens. We're seeing a number of products that are maybe a antibody fused with a cytokine. They have two moieties that are two different functions that are moving forward. Another class are Fc fusion proteins. And then sort of the traditional recombinant protein kinds of therapies, virus-like particle molecules, so making vaccines that are not viral-based. You're just making some of the components of those viruses are sort of the classes and molecules that we're seeing coming through the pipeline and being developed and moving into clinical trials. Yeah. Again, you have a pretty broad view from where you sit in Catalent. Can you imagine uses of Beacon in the R&D work that Catalent is doing outside of cell line development? Yeah. We have cell and gene therapy parts of the company, and we're looking to potentially utilize some of the Beacon technology in those areas moving forward. We're still in early stages of some of the development we're doing in making stable cell lines for gene therapy. We have some unique things that we think that the Beacon could help with in that area and potentially in the future. Yeah. Well, we started out with basic sort of cell line development uses, but it got us a good handle and sort of have expanded our view on potential other uses that we could move it into on the cell and gene therapy parts of the business. Yeah. Yeah. Thank you. One of the things that, Siddhartha and Troy highlighted, and this is a question for all of you, is the kind of technology's unique ability to really characterize single cells without killing them and then with the ability to export them. Can you just talk maybe. Oh, I'll start at the beginning again with you, Brian, about, you know, what is so significant about that? For us in our application, that's the crux of the value of the technology that we're immunizing an animal, getting antibody producing cells, removing those cells from the host. They won't live long. The classical hybridoma technology to immortalize those cells, when you remove them from the host, kills 99.99% of the cells. So many times we're looking for a needle in the haystack. If you can imagine only having access to less than 1% of the haystack to do your work, your chances of success are, you know, quite a bit lower than if you had access to the full haystack, which this technology allows us to do. That's the crux of the value to us, that we can now screen the full repertoire of whatever species that we're working on and we find these real needle in the haystack technologies. Also lets us, for easier targets, do the functional assays that were also mentioned and let us do down selection, cross-reactivity, things that save many months of development time and expense. Yeah. I would imagine really in oncology applicationTs too, you often, I mean, there's only so many cells one has to work with. Is that? Yeah. the case in your work? Absolutely. I mean, some of those numbers that Troy mentioned, you know, that was exactly what led me to start talking with Berkeley Lights, is that if there's a few million T cells in a couple mL of blood, you know, a tube of blood you can draw, and you have one in 10 or even 100 in that million that are the ones you're looking for, if you're just looking for a signal to pop out of that background noise of a million cells, you're just not gonna see much. If you're looking at filtering through a set of, you know, that million and can see the ones you're interested in and then more clearly understand multiple ways they're responding. 'Cause that's one of the things we were able to do is some of the testing, you put it in a well, and you hope that it grows to a spot you can see. Then you don't know that much about what's even there, and it's kinda hard to manipulate that. You only kinda get one signal. You only get one color, you can't resolve it because it's just a mass of cells. By moving it into these pens, you can get multiple signals, I think. We use three, and we're looking at doing more. If it's one, it may be at a threshold that's kind of noise. If it's two or three, that tells you it's really something happening. That's an example. Now it's not just. I mean, you know, in one of the patients we did, there was one clone that we've identified what the target is, had one single cell that we found. Hmm. Another had three cells. That's enough to do the work. Yeah. Do the validation. It's kind of exciting. Yeah. Greg, anything you'd like to add to that? Yeah. For us, the cells we're selecting are the ones that are gonna be scaled up to 20,000 liters for production. The ones we're selecting are the highest, best producing ones. We need the instrument to be gentle in the way they get exported off the plate or off the chip into a well plate to start them expanding. We can't afford to lose any of those cells. We need each of those clonal cell lines that get exported off to grow up, so we can use them for further development. Got it. One of the things that Berkeley Lights team is gonna talk a little bit more about later today is kinda their product plan and efforts in some ways to democratize the technology. All of you are obviously sophisticated users of it. But they're interested in getting, of course, this exciting technology into the hands of more developers. Can you talk a little bit about, and Greg, maybe I'll start with you, about what you think the implications of that might be if the Berkeley Lights technology were democratized, if you will, or able to be put in the hands of more users? Yeah. We at Catalent, we've had a few experiences where we've developed technologies that we're then able to allow other folks to utilize. It's just, you think you know everything that can be done with that technology, but you're just scratching the surface. Getting it out there in the hands of researchers around the world just, you know, there's gonna be so many new applications that they're gonna come up with that, I'm sure Berkeley Lights maybe thought of a little bit, but really hadn't been able to pursue. I was first exposed to. This is an area I know they haven't gotten into. I was first exposed to microfluidics about 30 years ago, and we were actually looking at in vitro fertilization and embryo growth. Mm-hmm. I think the technology could be used for that on. With, I'm sure, a lot of research and development. That's just one example of something that it could be used for that I'm sure once it gets in the hands of more people, they'll be trying all sorts of different things. Any thoughts on that, Brian Walters? Yeah, I think it's the impact it'll have on medicine and the human population is profound. I mean, how we're applying the technology to have success against targets that people haven't had success on in the past. I mean, you combine these technologies that enable this, and you go after a class of targets that have eluded researchers for decades, that we can enable a new era of drug discovery. I think in that sense, in terms of the power of finding these rare antibodies, I think it's profound to have that more widely distributed and empower the field in that way. I think the other thing that we don't talk about as much for our applications, but is speed. I guess we did in terms of reducing development time, but we're looking at some biodefense applications. We're combining our technology. If we get a sequence, because we're unique in that we immunize with DNA versus protein and other things, antigens that take longer periods of time to make, we can get a sequence of whatever pathogen is of concern. Yeah. Be in a transgenic animal for human antibody generation in a couple weeks, use the Berkeley Lights technology to screen it in a day and do functional assays. You can't develop a therapeutic antibody against a biodefense threat or a pandemic threat faster. Yeah. I think that in more hands as we get into the whatever's in front of us in the future, whether that be protecting war fighters, future pandemics, I think that sort of infrastructure is also profound. Yeah. Yeah, I think, you know, obviously more and more attention is being paid to those kind of biological threats, from a national security point of view. Anything you'd like to add to that? Sure. I mean, I'll kind of talk on the Catalent side, you know, having done that side on the drug development. You know, when you're trying to get a drug into the clinic, you have to do all this manufacturing work under GMP, and you have to lock all that down, and the capacity in the industry for doing that, especially on all these cell therapies, is relatively limited. You know, you talked about that example of some people wanna go straight into manufacturing 'cause they don't have time to do that optimization cycle, right? So you get a lot of these processes where you spend all that money, you spend all that time, which is even more, and then you go into a manufacturing and you only get one-tenth or one-fiftieth of the yield that you want. Now you've gotta take another 18 days of that limited capacity in the industry. If you can get 10x or more x or 2x or 3x, the yield out of that, you can run larger trials, you can do fewer batches. I mean, it's a... And it and you can do it shorter and faster. You know, faster. I mean, the impact on that is just, like, it's very real. Like, because that's the thing that's preventing a lot of the early stages of development advancing. You know, I could talk more about the personalization, but I think I've covered those points. Yeah. Well, I can certainly, you know, vouch for the sort of need in the cell and gene therapy space. I mean, I think we at ARM are tracking almost 50 phase 3 gene therapy trials, and as Siddhartha and Troy noted, you know, a number of those are moving out of ultra rare and rare diseases into much more prevalent diseases. Certainly the manufacturing need on the AAV side is very real and then clearly we're trying to tackle a bunch of cell therapy production issues on the immuno-oncology side of things. Well, I know we just have a couple minutes left, but the benefit we were supposed to give to you, or one of them for being here is to give you the chance to offer any requests or advice to the Berkeley Lights team about whether there's something you'd like to see in the product roadmap, as kinda super users of the technology. Greg, anything on your wish list for Berkeley Lights? Yeah. I think a lot of it is going on now. The products we're seeing, and I mentioned a few of them, are getting more and more complex. Instead of just expressing one gene, or in the case of the antibody, two genes, a heavy and light chain, they're just getting to be much more complex. We have products, we're expressing four different genes, five different genes that come together to form the product that gets produced. Just having good ways to screen those products, to be able to select the cells that are making the best quality of protein coming from that. Troy mentioned the bispecifics are a way they're looking to do that, where you can look at different binding of the two cells producing the bispecific that binds to two different things to make sure what they're producing actually binds to two different things. Mm-hmm. Be able to quantify that is a step towards being able to handle those more complicated molecules that we're seeing being developed to treat a number of different diseases. Brian? I have to say we're well supported and engaged with the team. There are certainly things that we wanna do. We wanna screen more cells, so we've started with one instrument and a lot of our workflows were one or two chips, so we were doing 10-20,000 cells per screen. We have since acquired multiple instruments and doing 80-160,000 cells per project and then we want that number to continue to go up. I got a question about how many cells we've screened to date. It's 5.3 million. If we have started on the throughput that we have today, it'd be probably four times that number. We continue to wanna work with the Berkeley Lights team to be able to screen more cells. We work with cell-based assays and genetic immunization, so we're maybe a little bit different customer than some of the other people in the antibody therapeutic field. The wish list from the team, Troy and team, is I think smaller input volumes to enable higher numbers of cell screen, like on the 20,000 K chip. I think that's on their wish list. Okay. I think again, that's well-known. Then we wanna be in more species too. We're in mice, rat, recently established in collaboration and support from Berkeley Lights rabbit, that we've done human cells workflow that's been discussed. Going forward, the camelid program we have under development in alpacas and in a transgenic llama mouse, so we Wow. We need support there. Some of that's on us, some of that's we need support from the Berkeley Lights team and eventually some non-animal options that are pretty interesting as well. Being in more species enables us to deliver more success against a wider variety of targets for a wider variety of modalities for our clients. Last word, Willy? I think just more. More? There you have it. Okay. Thanks very much. We really appreciate you taking the time to. Thank you. Talk about how you're using the technology. Well, thank you so much, and thank you, Janet, for moderating this. Janet has a tremendous amount of experience as CEO, you know, shepherding a whole industry of gene and cell therapy companies, and very thankful to you to be here today. My pleasure. Thanks all of you as well for spending precious time here. We have 10 minutes of break now. Come back at 10:45 A.M here. Hopefully, there's some coffee and refreshments and some mingling to do. Thank you so much. Thanks. Get settled back into the room here. Hopefully, people had couple of minutes of break, both online and in the room. Get a cup of coffee. You know, for the last 1 hour and 45 minutes, we've spent a lot of time, you know, talking about the technology, its power, the possibilities. The next half of the session actually is about, you know, the intersection that I've always been drawn to in my life, which is I was drawn to science as a young man. I realized that I'm actually a better businessman than science. The combination of the science, business, and how is the money made. The next section is about that combination. You know, I think the people who are in your chairs are, you know, thank you so much for your engagement on the technology and its potential, but this will be even a more exciting part to listen to. I'm gonna introduce Rolando Brawer. I'm really privileged to have him as my colleague. We've worked together before in our previous versions of Life. Rolando is probably one of the most accomplished business development executives in life sciences tools industry, having worked at, you know, pretty much everything that is known in the industry as the large players, and knows where all the intellectual property and business development, you know, the good bodies and the skeletons are hiding in the place. Rolando, please. Thank you. Thank you, Siddhartha. Oh, yeah, it's working. All right. Thank you all for joining us for Investor Day. My personal thanks for coming over here. So today I'm gonna cover a bit of the intersection of what was presented previously this morning, and what does it mean from a market opportunity point of view. I'll start by highlighting that, you know, I'm gonna talk about SAM, Serviceable Addressable Market, not TAM. We wanna cut that to the customers that we can actually serve, that can afford our platform, and we'll talk about affordability throughout this second half of the session. The common message both for our antibody therapeutics and for the upcoming AAV workflow and T-cell receptor applications is that we're absolutely under-penetrated in the context of SAM. Here's, you can see our total SAM is $3.1 billion today. About actually in 2025 it's expected to be. $1.1 billion is where we mostly play today with our platform, so selling Beacons, Opto Chips and reagents for antibody therapeutics. When you add $410 million, sorry, $0.4 billion, $410 million to that SAM, when you add the academic market, that amounts to $1.5 billion. On the partnership and services side, it's another $1.6 billion, so it's pretty balanced, where AAV is expected to be the largest application within that market, followed by TCR discovery, gene editing, and then ag. You'll see that in more detail in the next slide. Like I said, the opportunities are pretty split between platform, so product sales, instruments and consumables, recurring revenues and the services side. Here are the build up. Instruments, it's in 2025, it's expected to be, so consumables is expected to surpass the instruments placements, so $350 million-$750 million to make up that $1.1 billion. Academic market of 410 to round for the $1.5 billion for platform and partnership and services. AAV, we're estimating that for 2025, and we'll go into a lot more detail on that in the presentation. It's $750 million, followed by TCR discovery at 450, gene editing $200 million, ag and crop protection $200 million, total services $1.6 billion, and the total of $3.1 billion. I'm gonna go into a bit of a explanation here of market penetration. Like I said, we're very early in penetration both in academia, industrial market for our platform business, so the antibody therapeutic space, as well in partnership and services in agriculture, which is where we have meaningful business. AAV starting really early with, in reality, one customer. The other amounts is $650 that we haven't penetrated at all so far. Like I said, I'm gonna spend most of my time allotted to go over the AAV space and why this matter to us and, more specifically, why it matters to our customers. As Siddhartha went over, AAV is part of this third wave of innovation in therapeutics that follow the first wave of small molecules and the second wave of antibody therapeutics. AAV is a subset of that. AAV is the virus that carries the payload that is delivered to patients. There's almost 1,000 therapies that are expected to be in the clinical pipeline in 2025 and with an approximate risk-adjusted value of $11 billion by 2025. What problem are we solving in gene therapy and specifically in AAV. The current manufacturing methods in AAV, which is transient expression, are not scalable for the wave of therapeutics coming, as also Janet mentioned during the customer panel. What we solve is that we can rapidly screen clones with our platform, and we'll go into more details. We'll provide access to our beacons not through a sale of the platform, but through partnerships with our clients. Let's start by going over a bit of what is gene therapy. Gene therapy is the delivery of genetic material that is packed into a vector, a viral vector in this case, and injected into a patient. These are two examples of commercial products. ZOLGENSMA from Novartis is delivered systemically to deliver a wild type gene to patients that have a mutated gene that confers this spinal muscular atrophy. Luxturna is a Spark product, it's also an AAV vector that's delivered in the eye of patients with a type of blindness. Both these AAV vectors, viral vectors are manufactured in transient systems. Let's define what a transient production system is. A transient production system is when the viral genes and the gene of interest that wants to be delivered to a patient is produced in a plasmid and inserted into a cell line to make viruses. The problem with this system is that it's not scalable because the genes that code for the virus, so the Rep and Cap gene and the helper plasmids are toxic. You need to grow the cells to a very large volume and then transfect those cells once you have a very large volume with large quantities of plasmids. You need large quantities of transfection reagents that are very expensive, and large quantities of plasmids, which are also very expensive. Every time you need to manufacture another batch, you need to repeat the same experiment and buy new plasmids and buy new transfection reagents. More importantly, the end product is very unreliable because every time you have new batches of every component, and also the resulting viral production contains a lot of empty capsids. You can see in this depiction here, a few of these capsids have the payload that you wanna deliver to the patient and which is typically after a processing is about 50%. 50% of the capsids are empty and 50 are full. It's worthwhile mentioning that antibodies, which it's a $300 billion business, are all manufactured in a recombinant fashion, very similar to this, but not in a transient system. They're all manufactured in stable cell lines. The gene that brings the message from the information to manufacture that antibody is inserted in the chromosome of the cell. It's not in a plasmid, it's not in a separate chromosomal form, but it's inserted in the chromosome of the cell that is producing that antibody. The vision is that AAV eventually will be manufactured in a stable system where all the genes that are required to make the viral particles are in the genome of the cells. That way you can bank that cell, and when you need another batch, you just take it out of the freezer and make it again. This way, plasmids are not required. We actually do require very small amount of plasmids for the first experiment, but they're not the same quantities or qualities that are needed for transient production. More importantly, it's a scalable and more reproducible manufacturing process. We also envision that eventually, the FDA would not allow, the same way that today you cannot manufacture monoclonal antibodies in a transient system, you will not be able to manufacture AAVs in a transient system. Today, this is the only way, but our vision is that that's not gonna be the case going forward. If we think about other ways or look into other ways to produce viral vectors, this is a study that was done by McKinsey & Company looking at three systems of Baculovirus, which is an insect virus that infects insect cells, two different transient transfection systems with a suspension or an adherent, and a producer cell line, which is where we're talking would be the gold standard. It's the gold standard for monoclonal antibodies, but it's not today employed in commercial products. Baculovirus is the most scalable, and the flexibility is decent, but the quality of the product is quite bad. Primarily, the resulting capsids from baculovirus are mostly empty. It's large quantities, but not a good quality product. Transient and suspension or adherent, as we explained, the quality is okay, but the scalability is really bad. The flexibility is great because you can insert plasmids, you can modify those plasmids at will, and it gives you flexibility in the manufacturing process, especially in the development phase. An inducible stable cell line is the least flexible today of all of these methodologies because again, you need to modify the actual cell line and that's again back to what the whole presentation was this morning from Troy is about finding a needle in the haystack. That's a very infrequent event, and that's why it's hard to do. I'll explain that in more detail. With the introduction of our inducible cell line selector, we would overcome that flexibility issue with something that may not be as flexible as transient, but quite flexible. In terms of the workflow of comparing developing a stable system in our platform versus a standard protocol of finding a needle in a haystack. In a standard protocol, because this is possible, it's just like the panel explained, you can do antibody discovery, you can do cell line development without the Berkeley Lights' platform. This will be the description of an inducible system without the Berkeley Lights' platform. You start in both cases with a polyclonal producer pool. You have a cell line that you modified and that's a heterogeneous pool of cells, some with the capsid genes and helper genes and the gene of interest that you're putting for a specific indication. You need to find. That's the haystack, and you need to find that best clone out of thousands, tens of thousands or hundreds of thousands of cells. In the traditional workflow that takes eight weeks, and you can screen around 400 cells, and whereas in our system, you can do that in two weeks, screen 3,000 cells, and it takes two weeks instead of eight. When you take this as a tenfold change in the number of clones that you can find, right? If that's an infrequent event, if that needle is buried in that haystack, you have 10 times more chances of finding that, and you can do that four times faster for a total effect of 40 times better. Again, the challenges in the current state of manufacturing of AAV is batch-to-batch variation, high COGS, and that makes the introduction of a stable cell line in a flexible workflow extremely valuable. I said in the prior slides that we estimated the market to be $750 million. The estimate is between $550 million and $1.25 billion. Like I explained, we would make this platform available through licensing to CDMOs and therapeutic companies alike. What are we solving? Why do we think it's so valuable? We estimated... How we estimated that $750 million market for viral vector CDMOs, we will increase their profitability 50% because of this introduction of this system, which will yield approximately $1 billion of value to a CDMO. To the biopharma innovators, the gene therapy therapeutics company will accelerate their launch, their product launch, which obviously for such terminal diseases that are addressed by gene therapy is extremely relevant, but also resulting in 70% COGS reduction. Finally, for cell line development service providers, they will be able to, because of the shorter timeframe of the workflow, increase their throughput, their ability to capture new clients by three to four fold. Excuse me. Here's a description for different disease types. You have indications where you have a trial batch for a clinical study of phase I. You have between 30 and 50 patients for a rare disease. In this case, you have a commercial batch of about 200 to 2,000 patients. This increase of 70% or decrease rather of 70% COGS results in the case of a phase I study of cost savings of $7 million-$12 million and $50 million-$500 million a year in the case of a commercial stage product. It's a tremendous value. Finally, I'll just make it full circle. How do we do this? Troy showed this in the actual slide. I have a graph showing how we do this. How this inducible producer cell line selector works. It starts with a pool of cells that has that is this haystack with a needle in it. Cells are loaded into our chips. One pen is left empty. Cells grow, continue to grow, and then this clone is split into the next pen that was left empty. This case is a description of an adherent cell line, so there's trypsin here and readherence, but if this was stable, these two steps can be avoided. There's an induction phase. The induction in this case, it's an adenovirus. This is what we did with Thermo Fisher Scientific in our collaboration. If the induction system could be a small molecule. We would introduce the small molecule that would activate the production of a virus. Again, as I said earlier, the viral genes are toxic. That's why you need to grow the cells to a very large volume and only then introduce the plasmids. In the case of a stable cell line, the genes from the virus that are inserted in the chromosome are silenced or regulated, so they're not expressed while you're growing those cells. That's why it's a complex engineering exercise. Ultimately, to produce virus, you need to induce the production of virus. This is the induction agent that triggers the virus to be produced. In this case, before we need to cap that pen that we wanna potentially retrieve, if this is the clone that we selected, then induce, produce the virus, cap again so that the induction agent doesn't flow and triggers the production in the next pen, and then retrieve the clones that produce the most virus. This will be, like I said, not only a tremendous benefit to our customers in terms of cost savings and time to market, but also to patients that need these therapies. As Troy went over before, the requirements in the industry of more systemic delivery for diseases that affect large number of patients. Because currently, AAV, and I gave the example of two drugs that are orphan diseases, so there are fewer patients and require small amounts of viruses. As gene therapy moves into larger indications and require systemic delivery so that the virus travels through the blood system to get, for example, to muscle cells to transform muscle cells, you need larger quantities of virus. Not only this will help the industry, but also patients that are in dire need of this type of therapies. With that, I'll pass it on to Siddhartha to go over our strategic operating plan. Thank you, Rolando. I think so far we've talked a lot about sort of the company's technology and the far-reaching impact it has on the applications. Largely, so far we've focused on industrial applications, both supporting the second wave of innovation and the most recent wave of innovation in providing therapies and solutions for mostly human health, the way we've described this. The company's platform and technology actually go way beyond that. I think the plan that we've designed here for the next three years is really grounded in some very basic facts and grounded in reality of what is achievable. We kind of call it a base plan for us to dedicate our resources. The company's in a tremendous state fiscally, and a very achievable and simple plan is what is needed right now given the external market conditions. The key messages in this section that you'll see are, you know, sort of how we are transitioning from being one technology platform company that sells, you know, each machine for $2 million to a diversified tools and services company that makes access available for broader sets of tools and services to clients. Second, we are focusing on dedicating our resources to the highest value projects and services. The company has had, you know, lots of ideas. I mean, today we have few more ideas from our clients, but we have to focus. The key to success is actually to do a few things and do them extremely well, so we can dedicate our mind share and our time and resources for that. Resource allocation, both capital and human, is a key job of management, and we are ruthless about that at Berkeley Lights. Number three, you know, we'll talk a lot about this because there's a lot of interest in understanding sort of how our product portfolio and pricing strategy will evolve. You know, is this gonna be a $2 million Beacon for the rest of our, you know, sort of next 10 years of history? No, it is not. We'll talk a lot about sort of how we are evolving that to a more flexible approach. Then finally, I think the most important thing here is that, you know, as I said, the company, for the right reasons in the history, went straight to industrial application because it had a tremendous impact on antibody discovery, cell line development, and increasingly so in cell and gene therapy space. However, it did miss something, and that is to not put this device and its core technologies into hands of thousands of academic scientists. Almost everything life sciences tools actually starts from academia and then migrates to industry. This has been a kind of a reverse story here. I've had a lot of experience, first as a bench scientist and then for, you know, 15, 20 years working in industry, trying to bring and build a whole set of reagents and bench top devices and tools to academic community. I found this. It's tremendously powerful in unleashing not just what we can imagine and conjure up, but what tens of thousands of scientists can co-create with us by using our tool. That's how the life sciences tools companies are built, by democratizing the platform. We'll share with you today the plans we have to democratize over the next couple of years with the resources we have today, as well as, you know, longer term plans for what we are doing with that. I think it's important to you know, pause here and acknowledge that, you know, creating a high-value technology platform like the one that we have, and, in fact, it's one technology platform with a whole range of capabilities. I wanna simplify again at the very highest level. This is the only technology that is able to manipulate cells at will. It is able to keep the cells alive. Now you saw a recent development where we can perform destructive assays on half the cells and keep the other half of the cells alive, and our ability to export and retrieve those cells for either further characterization, so we can understand what was unique about these cells that survived them or made lots of, you know, whatever output we are looking for, or use them in a therapy. This is a core capability. Now, not all of the core capabilities need to be in a technology. These are like five or six, I would say, breakthroughs that industry haven't really seen yet. One of the questions we have asked as a new leadership team is: how can we defragment all the capabilities we have, take it into pieces, and see which part of it could be valuable to academic researchers, which part of it could be valuable to a small biotech company, and which part needs to be put in. You know, the whole package doesn't need to go to everybody. That's our sort of simple approach to, you know, defragmenting our technology. Then at the same time, not ignore the fact that cell biology community at large, there's a whole range of different instrumentation that is available. We are also looking at figuring out, can we put one or two of our technology stack components into an existing tool and make that tool much more powerful than it can be? Taking example of our ability to take and extract the cells, the live cells that can be extracted, it's an incredibly powerful tool that could be deployed in many other bench top devices today, and we own the technology and IP for that. Our approach and our journey for the next three years actually is to become that broader tools company, more focused on recurring revenues. You know, a company that only sells very expensive instrument can only do well this year, and then next year, I have to sell 10% more instruments that are more expensive, okay? The only way the companies in life sciences tools actually become self-sustaining, profitable, growing businesses is by focusing on recurring revenues. Another thing that you'll see here today is our relentless focus on recurring revenues as our growth driver for going forward. How are we gonna do that? We laid out the five pillar plan, approximately three months ago, and we are now relentlessly executing against this plan. Number 1, and I wanna focus on this a little bit, so we're building the world-class leadership team, and I wanna talk about what that means actually in this industry. It is still a very young industry. The understanding of the commercial aspect of this industry and operational aspect of the industry is not something that's been mastered by many organizations. Most of these have traditionally been boutique shops that have been opened up by one or two product lines. Turning them into companies is actually only a few have done very successfully. We happen to have a leadership team who've done this before in the industry, and we intend to do this again. We think the timing is perfect for us to come into this industry right now with both the innovation that is happening, and frankly, we think that the market dislocations are actually attractive because it allows us to be on aggressive forefront of being the consolidator of choice when it comes to putting more technologies together, because we know how to do that, we know how to scale that, and we know how to commercialize that combination. The second is, you know, most of the life sciences tools companies, because innovation has been the main driver and originator of the company creation, oftentimes, and I've seen this in my experience many times. Oftentimes, R&D projects mushroom into a whole range of projects, and many of them are fantastic ideas. One has to apply the business filter on what is the return on investment of those R&D dollars. I believe the team that we put together is actually extremely good at being, again, ruthlessly good at figuring out what to focus on and what not to do. We made some very tough decisions already in the last six months to walk away from, you know, large revenue numbers where we knew that those revenue numbers weren't supported by margin profile that we are interested in. Number three, deliver commercial but consistent commercial execution. This requires us to understand how to commercialize life sciences tools not just in North America, but in the rest of the world. I personally have experience living in both Japan for two years, running life sciences tools company and in China for two years, running a life sciences tools company. I can tell you that the approach of commercialization in all of these different geographies is extremely different. You will see that our early success in North America has just started to become replicated in other geographies. Fourth, evaluating the opportunities to, you know, find tuck-in technologies that can go with our box. Also find opportunities to out-license our technology instead of just holding everything for ourselves. Continuing to look at the industry at large with a very different angle of like, "Hey, if this tool or parts of this tool were in thousands of people's hands, what could it do? And how can it enable acceleration of science?" Finally, and I think this is the true north for us. We are in an economic climate where cash is king. Every single investor, including myself, is focused on, do we have enough cash to achieve the plan that we put forward, and how are we gonna consume that cash? We intend to become free cash flow positive by 2025, and our plan is our base case is built around some very simple ways to get there. Starting from leadership team, I wanna just highlight a couple of people. Rolando came recently to the company. You heard from him. Rolando's first 15, 20 years were spent at Life Technologies, beginning from his journey as an Invitrogen employee to all the way to when it was sold to Thermo Fisher. Subsequent to that, he's worked in Genomic Health and Exact Sciences, and then subsequent to that, he was working at Danaher before he joined us. This is a very strong depth of bench experience in industry. Lucas Vitale used to be in human resources at Life Technologies, responsible for integrations, and he's our chief of, you know, human resources officer. You'll hear from Mehul soon. He's worked for two other companies where he's taken the company from $100 million-$200 million in revenue to multiple billion in revenue, both in medical technology industry. Again, a seasoned CFO with 25+ years of experience behind him. Our legal counsel, Scott Chaplin, has been public company general counsel. Again, this is a technology company that's focused on intellectual property. George Fox is one of the best IP minds in the industry. He's actually been with Berkeley Lights for eight years and is responsible for creating the platform of intellectual property that we've created here. We brought in Harjit Kular from again, previous experiences in Life Technologies, Thermo Fisher, Millipore and a lot of industry experience there. We also are complementing that new technology and leadership with the existing people in the company. Very privileged to say that, you know, I've had a great partnership with Eric Hobbs and the rest of the team who are here. You know, look, I think this is one of those examples of the company that was created by these great people who are scientific, you know, I wanna call them scientific geniuses. I often like to tell people it's not rocket science, but in this case, it was close to rocket science, what they did. But they also grew up at this company, and they haven't had the luxury of experiences that some of us recently who came to the company have had. Blending the two together is really important from culture perspective and from success perspective. We bring something to the table. They already have had a lot at the table. Having that good blend is the right approach for the company. In terms of the investment prioritization, you guys have seen that we've actually walked away from, you know, partnerships which weren't actually profitable, even though they were actually, in some cases, tens of millions of dollars of revenue. Sometimes it's important to do that. Our time when we first started as a new leadership team, it was important to do that to make sure that we are focusing on the most highest value returns, not only from a short term, but from a longer term perspective. We focused on the two areas right now on partnerships and services. AAV, which, we believe is a game changer for the industry. We know it because we've talked to 100+ customers who are involved in this ecosystem, and they all call this a game-changing thing for them as they evaluate our technology further and deeper. On TCR discovery, because it is one area where we believe that the impact on human health is the highest, and our platform brings an incredibly differentiated approach to be able to use the population of cells based on their characterization of which cells are the most powerful to be able to use in therapeutic setting. We are also seeking out commercial and technology partners, so we don't do everything ourselves. This industry is a great example of a whole range of partnerships created for people to work together. I believe in the power of working together and not holding everything inside the company. I think the innovation is it demands from the leadership teams that we work together with others. We are in active discussions with others to enable them to use our tools and actually enable us to use their tools. We talked a little bit about the consistent commercial structure. I'm gonna spend a bit more time today about the pricing strategy and the portfolio, 'cause that is where I think some of the biggest changes we are making. Part of it is just blocking and tackling, making sure that we have the right resources doing the right jobs, having the regional leadership that matter. Experience in that region matters. Making sure that we have enough sales resources is important. Also, because our technology is very consultative and technology-oriented, having a field application specialist team that is trained and engaged, and engaged with our values and the mission for what we are trying to accomplish with our clients is important. Also having, you know, lower cost resources that are enabling all those field-based resources is the best way to utilize our sales and commercial expenses for the next three years. Before I go into the product platform itself, I just wanna address a couple of things about the partnerships and services business. A lot was talked about it in the previous management discussion about what that business is. We've really taken a very simple approach to this. We're gonna take three types of business model. We're gonna provide fee for service, but we're not just gonna provide fee for service to everybody. Frankly, we can do it lots and lots of clients and create revenue like now. It will not be productive for the company's long-term roadmap. We are focusing on things that are truly differentiating that only we can do, and only we can do in partnership with clients, which creates this large, you know, going after billion-dollar markets versus going after a smaller market. Number two, we design the asset development and then transfer the technology to our clients. That's the second approach we are taking. A third approach we are taking is actually to have partnerships where we, you know, focus on programs that actually have milestones, and we participate in the value creation with our partner. The success examples so far have been, we have ongoing partnership with Thermo Fisher, with Bayer, as well as Visterra, and then we are in very active discussions with a range of partnerships in TCR discovery, as well as in personalized vaccine development. I won't belabor a lot more about that because they're a very small part of our revenue today, growing modestly over the next three, four years. I'll spend more time on the platform part of our company, which is the traditional life sciences tools business. I think one thing I wanna sort of highlight here is that the success that the company and its leadership team has already had, even before many of us arrived on the scene here. This platform actually was launched in December 2016, and it has, in spite of its price point being $2 million, we sold 128 different units. Now you start to see that the consumption of those units actually has really started to kick in gear, and a lot of the recurring revenue is actually becoming part of our workflow. Because many of these took last two, three years to install, and it does take our clients an amount of time to get used to using our tool and be efficient with using our tool because there's a training of personnel involved, there is training of their protocols and tweaking their protocols. It takes one or two years before people actually become a full consumer of our consumables. That tailwind that we have on our recurring revenue is because of all the great work that's been done until now on our platform. As I mentioned earlier, we have had mixed regional success. Strong success in North America as well in Asia-Pacific, specifically in China. We are still building our business and its strength in rest of the Asia-Pacific markets and in Europe. Our segment mix actually is nicely split between pharmaceuticals, the CDMO and CRO service providers, about 25%, and then the academic and other, which is about 25%. You'll see we have early success in academia, and we wanna actually double down on that by using more creative approach to pricing. Let's talk about the Beacon product roadmap. This is an important chart, and I'm gonna spend a little bit more time going through each of that. Our current Beacon system is. It involves what we call four nests. Actually, it's the four places where you can put those chips that you saw that Troy described very eloquently. Most of the applications for that workhorse, which is what I would call the Mercedes-Benz S-Class of Beacon, is antibody discovery and cell line development. The customer segment is mostly large biopharma and CDMOs and CROs. Consumables pricing is, you know, it's appropriate for the cost of the value that it brings to our clients. The purchase options right now, you know, we have introduced both the capital as well as sort of rent-to-own, so lease-to-own and lease and rental business models. Mehul, our new CFO, has a lot of experience in, you know, medical technology industry with rental businesses, and we are implementing a more robust approach to kind of being in a classic kind of leasing business where it's more profitable to us while providing access for this workhorse for those clients who want that. We are introducing in early 2023, in the first half of 2023, a platform called Beacon Select. That is gonna be application-specific tool for cell line development. What we found is in a cell line development, our price point for capital access is not affordable for all the clients who want to do cell line development as a service. We will lower the price of the capital that one needs to have to access the technology. This will be approximately half the lease price than our current lease price for the Beacon system. Instead of having four places to put the chips, we'll have two places to put the chips, so we'll only have two nests for that. We are specifically targeting midsize biopharma, as well as CROs and CDMOs who have a lot more volume of work. You heard from a few people today that they wanna buy more systems. They're already used to our systems. They may not need a full four nest workflow system. They might need half of the volume. We wanna enable that, you know, improvement in their workflow and efficiency by offering this model. The only switch would be that the consumables pricing for this system, because the cost of capital is lower, the consumable pricing will be higher. We found through our research that consumables for this application were already significantly discounted compared to what other resources people use and how much capital and human labor goes into doing the cell line development workflow. We believe the market actually will appreciate lower capital to access the technology in exchange for a higher consumable. Since our entire model is built on recurring revenue in the future, this is actually a win-win for all of us, for customers, for us, and it becomes a net neutral or slightly positive for the company, and it allows the customers to access the technology. The next thing we are launching again in 2023, and we are way ahead in our advanced planning to be able to launch this technology because we are not over-engineering a whole bunch of new device. We're actually taking functionality, reducing it, and making it application-specific for different applications. Beacon One is our academia-oriented tool. It's gonna be priced even lower. For that, we're gonna focus on immune oncology, broad functional biology, as well as gene editing applications. We've done a significant amount of market research to appreciate the fact that actually academic customers would love to have their hands on this tool, would love to tinker and find new applications on it. There is a whole range of translational academic medical centers who are focused on immune oncology or gene editing core labs that are doing a lot of genetic experiments right now. They would love to have their hands on a tool. Our original sort of the first two years approach is to focus on a few very well-funded institutions and place our tool and co-create with them the next generation of our tool. That last version in 2025, a future system, is to be focused on putting this tool in as I described earlier, in a deconstructed fashion, putting components of our capability into hands of thousands of researchers and make that tool affordable. Our target actually is to be less than $250,000, which happens to be somehow a magic number under which, you know, academia tends to buy devices at a higher frequency. Making that capability available, instead of having all the capability into one box, you know, be flexible with what we offer for what box for what application. We will use the time that we spend with Beacon One and working with key opinion leaders that have been identified by our team, and we continue to engage with further set of people who want to utilize the Beacon, and we'll use their experience to design a much smarter device. A whole lot of effort has gone on in improving our supply chain, so we can actually get to that future system that's lower cost. I wanna point out something here. The original components that were designed for Beacon were actually designed in 2016. We as a company have learned a lot on how to reduce the cost of everything. We haven't put all of those improvements into a new system because it was not warranted until. You know, we don't have a timeline to keep changing the cost structure just by a little bit for each component. We wanna have a wholesale reduction in the cost and a whole lot of improvements on, you know, putting a different set of components in 2022 versus what was a supply chain that, frankly, our team actually was sourcing for all this stuff in 2014, 2015 timeframe. This is eight years later, a lot of the components are actually better, more efficient, and we have an approach to actually make a lower cost tool happen in the next couple of years. We don't wanna just launch something without focusing a lot on applications, 'cause one of the things we wanna do actually is to not just have a narrow set of applications, no matter how fancy a tool is. It has to be democratized in a way that a broad set of applications can be addressed. This is just the workflows and chips that we've actually introduced until now. In addition to the tool, the machine itself, a whole range of workflows and chips have been introduced in the last six years. Yesterday, we actually announced another workflow that we launched for rabbit, the species extension that earlier Brian was asking for. You know, we've engaged with many of the clients, like Brian and his team, to extend the species, and we continue to do that for other species. We also have application-specific chips that only can be used on a tool that it is designed for. We'll have you know, a reader in the machine that actually allows us to only use those chips for the application it is specific for. They'll be able to control the pricing of the consumables versus the machine price in the system by managing that combination in a smart way. I've already gone through most of the Beacon Select and its description, but I want to you know, focus on it's a lower platform price upfront, higher consumable pricing point. In two years, it's a net even for the company. Reagent rental option is available for this tool. For Beacon One, it is specifically designed to engage broader academic community at a price point that is still profitable from a gross margin perspective for the company, but it's more affordable. We are focusing on well-funded institutions, and we don't anticipate a huge amount of revenue from that, but we anticipate a significant amount of new application development from this work, where we don't spend all of our R&D resources to the application development. We engage broader community to do that. In fact, today, we already have 28 instruments placed in academia. One of the sort of irony is actually even though we haven't really focused our applications for academia, many academic clients actually have requested and actually even paid sometimes $2 million. You know. We believe that there's a significant opportunity here to expand the number of people who access this technology by lowering the price significantly for them. Allowing us to actually finally get that 2025 version that we are very keen to launch. Let me pause here for a second and talk about the industry at large in life sciences tools space right now. From 2018-19 to 2021-22, in some ways have been an incredible time of innovation of life sciences tools, where a lot of investment went into this industry. Not all the companies have been successful in the public domain, and not all the companies that started, in spite of their technology strength, will succeed in the private market. We believe that we have a leadership team and a vision to continue to use M&A as a way to consolidate technologies that are adjacent to what we are bringing to the table, so we can use the leverage of our commercial as well as our SG&A structure that we have in the marketplace. We've gone onto this journey in a small cap, mid-cap growth in life sciences tools much faster than anybody else. We've been more aggressive on our cost restructuring. Within the first 45 days of new leadership team coming on board, we decided to, you know, focus on a very simple plan that focuses on cash flow positive as fast as possible as the main true north that guides all of the company's decisions, especially as it relates to financial resources. That progress is already showing in our results. Our cash flow negative free cash flow actually bottomed out in 2021. We are making progress in 2022 already. In 2023, with a very simple plan we have in place, we'll be able to actually reduce our cash burn, quote unquote, in half. By 2025, we'll be breakeven. Even at that point, our cash balance will be still strong. Mehul will go into more detail about all of that as well. With that, I'm gonna invite Mehul to talk about the financials for the next three years. We'll have some time for the questions at the end, and I really appreciate everybody coming in person here. For those of you who are engaging on the screen, I look forward to the questions. Thank you. Thank you, Siddhartha, for the introduction and the presentation. Today I'm gonna just briefly talk about the financial outlook relative to our strategy. I'm really gonna focus on how the outlook relates to the, you know, the five pillar strategy that Siddhartha talked about. From a key message perspective, you know, our financial goals are, you know, to achieve those goals through organic growth, and which include, you know, driving $150 million in revenue by 2025. With a significant shift to recurring revenue model, as Siddhartha highlighted. Attain significant leverage through lots of operational excellence initiatives, throughout all our functions in operations, sales and marketing, and then SG&A. Achieve positive operating cash flow in 2025. That you saw was the fifth pillar of our strategy. Again, I would say that this is our base plan, for the next three years. Our experienced management team is building an execution plan, to drive towards, achieving these goals. Before we get into the forward-looking, projections, I just wanted to highlight, you know, where we've come from. From a total revenue perspective, you see growth, from 2018 to 2021, and the CAGR over the forecast period is around 29%. As we reset the company, many of you have heard that, you know, our 2022 guidance is approximately in line with our 2021 revenue. As we reset the company, you know, we're continuing to evaluate that. The chart in the middle is our, you know, gross profit, gross margin chart. Again, you can see the history. As the company launched and saw higher, you know, gross margins in 2018 and 2019, it was predominantly driven by instrument sales and consumables. When we got into development agreements that were low margin in 2020 and 2021, our gross margins came down a little bit. As you heard earlier, one of our strategies is to focus on high ROI R&D projects. You know, we've eliminated some of those agreements and contracts along the way. The final chart on the right-hand side is our cash balance and our free cash flow. You know, from a cash point of view, you can see a significant increase in 2020 as the company IPO'd in July. You can see the utilization of that cash as the company made significant investments through to 2022. You can see the complementary free cash flow impacts along the way. The one thing I would like to point out here is both on the gross margin slide as well as the free cash flow line is, as we've implemented our strategy, and it's just been, you know, a little bit over a quarter, we are starting to predict a turnaround, and you can see, gross margins tick up, a little bit as well as, free cash flow gets a little bit better in 2022. We'll talk about the forward-looking numbers in a few minutes. This next slide is very detailed, but it really links our strategy to execution. You know, as we created the strategy, we wanted to ensure that we could execute on that strategy and achieve, you know, the milestones that you see there on revenue, gross margin, and free cash flow. The strategy again is high return on investment, specifically R&D, commercial execution, all driven by an experienced management team who has done this before. The outcome is, you know, hitting free cash flow positive by 2025. I don't know if you can see all the bullets underneath it, but from a revenue point of view, we're going to achieve 20% revenue CAGR between 2022 and 2025, and that's gonna be driven by our platform product portfolio as well as our consumables, partnerships and services revenue in gene therapy and TCR discovery, our pricing strategy, which you just heard about, as well as, you know, commercial execution. Our growth margins are going to be around 70%, and the key point here is that the mix is going to be driven towards recurring revenue, which will help us maintain that 70% margin. You know, from an operations point of view, we have a strong outsourced supply chain, and we have numerous continuous improvement initiatives around our manufacturing processes as well as our supply chain and component pricing. We're in the process of driving an ISO 9001 certification, and we're implementing lean inventory management, all of which drives you know, margin and free cash flow through you know, cash management initiatives. Then finally, on the free cash flow side, we're going to, you know, get to cash flow positive with focused R&D investment on high ROI opportunities, SG&A leverage through scale and efficiencies, and then working capital management initiatives as well as optimal CapEx. Now I'll just show you some numbers around the platform business. On the left-hand side, you can see where we've come from an installed base point of view and where we're projecting to go. The installed base is going to grow pretty significantly between 2022 and 2025, and that's really, again, driven by our product portfolio as well as pricing. You know, as we remove the barriers to purchasing instruments, we think our installed base will increase substantially over the next three years. From a revenue point of view, we're also seeing pretty significant growth between 2022 and 2025 in the platform business. Again, that's driven by our you know, growth in our installed base, but also by our consumable revenue, right? The quality of revenue in 2025 is going to be significantly improved because the mix of revenue is going to be, you know, much higher on the consumable side. I'll show you that in the next slide. This slide shows how recurring revenue has evolved between 2018 to the present, and then where it will be in 2025. The goal is to have our recurring revenue at 67% of our platform business versus instrument sales of about 33%. How will we do that? There will be significant consumable volume growth based on application-specific workflows as well as instrument sales and install base increase. ASPs for application-specific workflows will be higher than you know they are currently, as Siddhartha mentioned. We'll also have revenue growth from subscriptions as well as warranty-related revenue from you know the higher install base. The reason I talked about the quality of revenue is this will help us sustain and you know achieve our 70% gross margin you know goals. Instrument margins may come down a bit, but you know consumable margins will creep up pretty significantly. Here's a view into our partnerships and services business and how we're thinking about execution on some of the things you heard about earlier. On the upper left-hand side, you can see a few of the business models we are in the process of implementing and have implemented. The first one is around screening services, where we'll provide screening services to, you know, select optimal stable cell lines. You can see the type of customers we're addressing, and the value capture there will be per program. Fee for service type of business model. We'll also have a platform license business model where we'll license our Beacon platform to a similar set of customers, and the value capture will be, you know, per customer per year in license fees and consumables. We're evaluating, you know, other business models that'll enable us to capture downstream economics as well. All these business models will support our gross margin goals as well as scale, you know, as our business grows. On the right-hand side, which is a very exciting part of the partnerships and services business, it's showing our, you know, revenue growth from 2022 to 2025 with a 38% CAGR. You can see the mix shift in that revenue, which is going from development projects to screening services and licenses and royalties. You know, you can consider the licenses and royalties very much like recurring revenue, right? Very important for this business model. Okay. This slide is the Berkeley Lights base case and, you know, sums up the outcome of our, you know, four-pillar strategy. M&A is not included. As I alluded to earlier, it's all driven by organic growth. Total revenue, as we've highlighted, is going to be approximately $150 million in 2025, with a revenue CAGR of approximately 20%. Gross margins will be at 70%, with a significant mix shift, as I talked about. The chart on the right where we show cash and free cash flow, you can see cash balances, you know, continuing to go down through 2024, but then tick up in 2025. You can see free cash flow also come back from, you know, the trough that we hit in 2021 and continue to become positive in 2025. As you heard earlier, that is our true North Star and, you know, we believe that we have an execution strategy to get Berkeley Lights there. This final slide that I have is just our capital allocation framework. Today, we have approximately $135 million in cash and cash equivalents, and we believe that provides ample runway to execute on our strategy. We have additional liquidity if we need it. And then currently, we have about $20 million in debt with a very manageable, you know, amortization profile. But within that framework, you know, we're prioritizing investments to drive sustainable, profitable growth. The chart on the right is illustrative and shows that, you know, in rounded numbers, our cash flow in 2022 is roughly negative $60 million, and how do we get to positive cash flow? Through strategy execution, expense management, and then CapEx and working capital initiatives. Again, this is our base case. When you look at the slide this way, it is very achievable, and I think the management team has a plan in place to really execute on this strategy. Again, it is our base case. I would highlight one last thing, that you know, the actions that we've taken in 2020 are all, you know, around the reduction in force and focus on certain high priorities is already paying dividends and supporting this strategy. With that, I will, I guess, turn it back to Siddhartha and the management team for Q&A. Thank you, Mehul. I'll be happy to take questions, and hand them over to the people if I don't have the answer for them. You have to turn on your mic. Sorry. Your mic. Turn it off. All right. You guys can put the chairs the way it was before. Thank you. Go ahead, Dan, please. Yeah. Yeah. Thanks. I just wanted to ask. Great presentation, by the way. Thank you. Just wanted to maybe ask a couple questions on. Yeah. Okay. Consumables utilization and just the way that you're thinking about that, which is obviously an important part of what you're doing. When you talk about the addressable markets, it looked like for academia, the multiple for the consumables portion was a lot higher than it was for the antibody discovery portion. I think it was like six times the instrument pull-through, and it was two or three times for antibody discovery. I would actually think it would be the opposite, just given what feels like it would be more consistent usage amongst your commercial partners. Do I have that right? If so, why would the pull-through opportunity be so much higher for the academic market? I think in a two-year case or a five-year case, I don't know. You're right, that we should have more consumption opportunity for people who already bought the equipment versus those who are gonna buy it. In many cases, we are also contemplating sort of rental reagent models with academia, where we place the machine in exchange for a certain amount of commitment for reagent consumables. These are small numbers, and perhaps, you know, the numbers might not be actually, you know, telling the full story. Okay. Yeah. Okay. Yeah. On the new boxes themselves, can you talk about what the pull through per system might be? If you don't wanna give a number, because I'm sure that's probably not set yet, can you just talk magnitude relative to the Beacon? Because obviously, what we're gonna have to do is just sort of look at the replacement of one for another per application. Yeah. The way we thought about it was if a cell line development Beacon is X percent discounted, we wanna recover that discounts in the two or three years of consumption. We actually gonna raise the prices for those consumables significantly to make that. I'm not gonna give a specific numbers because it's something we are working towards and I wanna be sensitive to customers, individual customers. Last one for me, if I could then. Yeah. Yeah. Can you maybe just talk about profitability per application? There, I'm not trying to be overly pedantic either, but, you know, I'm trying to understand what the optimal mix of workflow is for you guys. If you had your way, what would you like to see the mix of customer usage be like? Between antibody discovery, cell line development, and others? Yeah. Yeah. I think I don't have a financial sort of astute answer on that, but I can tell you that our current pricing for cell line development is not appropriate for certain geographies for entry of the capital, but perhaps too low in terms of cost structure for consumables. We'd like to see more traction with our cell line development in certain geographies. On antibody discovery, it is our workhorse, and the consumption is actually the highest right now for those customers. Okay. We'd like to see it shift more for cell line development. More cell line development, and that would not be overly negative for the margin profile of the business. No, because the consumables prices will be higher. Yeah. Yeah. Hey, Siddhartha. Thank you know, for the presentation today. Very helpful. Maybe just starting with one aspect of the strategy you mentioned, you know, taking those one or two attributes and deploying it on other sort of benchtop devices, the out licensing model, if you will. Can you just help us think about, you know, maybe a couple of concrete examples to help sort of crystallize that in our minds? Also, you know, over what timeframe do you expect sort of to see some progress on that? Yeah, I mean, I don't wanna talk about any active discussions we have right now, but a concrete example would be a benchtop device that does, you know, simple cell sorting. One other example could be a microscope that is focused on live cell imaging. Another example could be a single cell-based proteomics system, all of which could use our ability to, you know, be gentle with cells and export them to treat them. It could be either a workflow where we design our workflow to work with somebody's tool before or after, or it could be a combination tool if we're able to put some part of our technology in there. Got it. You know, perhaps just as a natural follow-up to that, how will you decide between that sort of contract structure versus just, you know, your roll-up strategy that you've alluded to a few times here in terms of M&A? I think it's all driven by economics, whatever makes us, you know, our shareholders the most value. I mean, the one thing that this management team brings into the mix here in the industry is our ability to actually, one, assess what I call the science, that is science project with science versus the money. That's a money project. Okay? Ability to actually see through and make sure that we actually have a business case here. And number two, evaluate those business case with the expertise that we have in-house, both technically as well as commercially, and then take it case by case. I mean, frankly, look, you know, when you're dealing with other parties, you need multiple parties to dance together, okay? I'm not gonna presuppose what's gonna happen, but I can tell you three years from now, we'll have done a lot of what we're talking about right now because industry is right for that. The time is right for that. The large consolidators in the marketplace, for them, these small cap companies are actually a lot of work to bring in their portfolio. There's a lot of negative EBITDA associated with them and somebody has to do the hard work, which is what we are here to do. Got it. On the gross margin trajectory bit, I mean, obviously a lot of it will come from just the recurring revenue mix shift. You know, Mehul, can you talk about, I mean, are there any other low-hanging fruit sort of beyond the Ginkgo contract where you feel like, you know, that's gonna give you a bit of a step function uplift to just rebase where your gross margins are and then the mix shift will do the rest? Yeah. You know, there's obviously mix shift within the instrument business between, you know, and it'll get even higher as we launch these new products. There's also a geographical mix, right? That has slightly different pricing. You know, that can vary margins plus or minus. I'll leave it at that. I think there is some low-hanging fruit in from our operations perspective and some of the things I highlighted around, you know, supply chain, ISO certification, inventory management. You know, I think the ISO certification will really help us in terms of where we can drive incremental efficiencies in the production aspect of our platform, but also in the quality of our platform that won't require as much support, right? Once, it's at a customer's, you know, location and being used. You know, there are a few other contracts that we're looking at from a development point of view. You know, it's a fine line, right? If the development contract is going to be paid R&D, we may do it, right? Even if it's, you know, a slightly lower margin than our target. You know, if it's not, then we're not gonna, you know, continue down that path. Tejas, I think to kind of make a broad comment on, you know, company, I don't think you'll be able to find another $85 million of revenue company with 70% gross margin. We are not sitting with a challenge that our gross margin is challenged. We are in a situation to actually be able to and willing to give a little bit of a couple of hundred basis points of margin in exchange for getting our technology in the hands of more people and then get more consumable consumption done as a result of that. You know, I think 2025 will have 70% margin, sort of the journey to that, I think is gonna be sort of dependent on all these initiatives that we are ongoing. I wouldn't hold us to kind of a 70% margin for each quarter going forward. Fair enough. Final one for me. Siddhartha, how do you think about sort of the emphasizing the service provider model here in the near term, you know, given all the macro backdrop? I mean, you know, Cellares does something similar, and they've taken a you know that route and, you know, had some success with it. Is that sort of a strategic priority for you know, while you sort of roll out these lower cost instruments? Yeah, I think you can anticipate a journey here where, let's say there are 5,000 therapeutic, you know, companies in the world, okay? In 2025 or beyond. You know, a few, some portion of them will buy our technology. There'll still be a large number of them. Oftentimes, it's virtual companies that get popped up, don't have any assets themselves, they just have an idea, they won't be able to or wouldn't wanna buy our technology. I think the service model, of course, has a potential, especially in a capital-constrained world, that, you know, it shifts. We're not gonna sort of say it's not for us. As you see, we actually focused on the high margin portions of that right now and get good at doing it. Running a life sciences tools company is very different than running a services business, okay? I've done both in my life, and I know that both are equally challenging but different. We have to just make sure that we don't get ahead of ourselves, which is what you know we had in the previous version of the company. Expenses were actually going a lot in all different directions. We're just trying to simplify, focus, do it well, and choose things that we think there's money there. Not writing off the market, but you know we have a lot to go after in the platform part, trying to capture more market share. We have a lot to go if we stay focused on services in high value areas. You know, would we become like one of the companies you mentioned? Maybe. We'll see how they do. Thank you. Yes, please. Maybe just a question on the platform expansion as you add the 80 over the next three years. Could you maybe elaborate when you build up the pipeline, is there a certain number of customers, potential customers that you've engaged with in the past that gave you price as a reason not to engage? If you could just sort of talk about new customers versus placing additional Beacons. At the existing customers. In the existing base. Look, existing customers are still continuing to buy additional units as their capacity becomes full. There's a timeline involved, though. I mean, you know, the customers like Brian, who, you know, went from one to three very quickly in a two or three years timeframe. In other people's cases, like a Catalent, which is a very large company, we also have a customer from there, they only have one, and it will take them some time to get to a higher throughput needs. It depends on which applications they focus on, sort of how much they wanna put on the Beacon versus using the existing, you know, technologies that they have. One thing I have noticed is that in life sciences tools broadly, it takes a while for people to switch from what they were doing before to doing things in a different way. It takes a period of time. This is not a, you know, it's a sticky business over a longer period of time, lots of, you know, inertia to change in the early years. You know, we are seeing that in our customer base. Some people completely switch everything, some people take a time to migrate over. Thanks. Can I take another shot? Please, yeah. Thanks. Maybe just wrapped within this debate on price or discussion on price sensitivity and what your customers are looking for, as you guys have gone about your due diligence work in order to understand where these sensitivities lie, have you come to a conclusion on just what percentage of the placements might be unlocked by economics versus the science and the applications? I think that's a great question. Frankly, it's one of those theoretical questions that you cannot answer. Yeah, it's hard. Unless you experiment with it. We will find out more about it. We believe that some of it is purely application-focused, but. Yeah. You know, the ticket price is not insignificant, and we cannot discount the fact that in a capital-constrained world, $2 million is a strong ticket price. Now, our current transaction price is actually $1.6 million or $1.7 million already for the last few years, right? It's still extremely profitable at a gross margin level for us. We just wanna be more flexible in our ability to actually place more devices. Maybe just one on AAV work, which is obviously a focal point for you. Can you just maybe update us on where you are with Thermo Fisher? I think on the call you mentioned that they were in an evaluation stage. Prior to that, I think you had mentioned that there was a tech transfer that was expected to happen maybe at the end of the year. Do you think that's something that we should look out for? Yeah. Well, maybe you can answer the question. Yeah. Our current schedule is that the product is ready to launch in mid-December. Tech transfer will happen probably early Q1. We're in discussions with the clients in this case around what that looks like. They are in the process of scaling it up. Their scale-up results are likely due in that early part of the quarter as well. Thank you. Yes. All right. We have a couple questions that have come in from online. First, how can we think about the technical feasibility of putting Berkeley Lights technology onto another life science company's box? Your technology is incredibly advanced. Do you have a good line of sight that it's technically feasible, and have you had any conversations on that front? Yeah. We've been in active discussions with several different participants. Of course, it needs to be a co-creation in the cases that the companies are not actually owned by us. We are in discussions with companies. I think there's a significant possibility of something happening here in the next one or two years timeframe for our ability to do that. It is not a short-term project, so I, you know, ask for the patience on that. Integrating two technologies is never an easy task and, you know, we wanna go about doing this in a way that is most productive, also most efficient in the marketplace. Thanks. One for Mehul. The bar chart out to 2025 on the top line, can you comment if that chart was drawn to scale? It doesn't appear that 2023 will have more moderated growth, likely below 20% and then higher inflection point for 2024 and 2025. Is that thinking about it in the right way? It was drawn to scale. You know, we do expect you know, growth somewhat consistent throughout the next few years. You know, it'll be moderated by right, our launches, our mix and all of that. It's based on an assumption set that we've created. You know, we're very confident in the 2025 number. You know, whether we get there in a straight line, you know, is yet to be seen and you know, all based on how our platform business and our install base grows, how our partnerships and services business grows. You know, we're very confident in the outcome in 2025. We'll provide the full guidance for that when we announce the 2022 results. On 2023 numbers. Yeah. This discussion wasn't meant to be giving a year-by-year forecast, but, you know, giving you the kind of broad plan that we have. Thanks. Just to follow up, so for license and partnerships, can you confirm that you would need that to inflect to hit that 2025 revenue target? Yes. Yeah. All right, thank you. Mehul, to that point, is a significant portion of that tied to commercialization of drugs? You know, the royalties portion of that, does that require. Uh. Market? No, no. No. Significant portion of that is just more people adopting our platform for AAV. Yeah. Yeah. Cause of the, you know, life cycle of drug development is very Yes, just launch. Yeah. In fact, in this case, you know, more people trying to develop the therapy is better for us, 'cause each time they have their new gene of interest, they have to use the workflow. We make the money in the development time, and when the drug becomes commercial, we have the ongoing services and manufacturing. This is a typical bioprocessing kind of a workflow. Tejas, you had a question. Since we were playing eyeball the chart while, you know, the installed base number looks like about, I don't know, 210 units or so. Can you share some color on what the mix would be between the Beacon versus the Select versus Beacon One? You know, I think we're probably not gonna share that at this point, Tejas. You know, we do have an assumption set there, but I think we could talk more to it around 2023 when we provide guidance potentially. We don't typically share, you know, the types of placements. We'll share the total placements, but not the type. Got it. Thank you. You must have really good eyes. All right, just two last ones from online. Elasticity of demand is a common topic in life sciences. How can you ensure that raising your consumable pricing for existing customers will not put pressure on the demand volumes for consumables? Yeah, it's a very good question. We will not be raising the consumables prices in the same magnitude if they bought our capital at an expensive price. It would be a normal yearly raise of prices that the life sciences tools industry is used to. We would be raising prices for the people who are getting access to the capital at a lower price. The chips will be only usable on each of those separate units in a separate way. If you bought a lower expense capital, your chips will be more expensive. Only those chips will work on your machine, and vice versa. Great. Thank you. Last one. You indicated that you're pursuing other business models to capture downstream economics. It sounds like the logical path would be to seek milestones and/or other royalties based on the success of a cell and gene therapy, for example. Are we thinking about this correctly? Could there be some other potential sources of downstream structures look like? That's correct. Rolando, do you wanna take that? Yeah. Yeah. You know, that's absolutely the case. That's how the industry is used to operate. There's precedent of licensing fees and milestones as drugs get through the clinical development and they get commercialized. That's the model we will follow and that's the model the industry is used to. Yeah, there are lumpy revenues. My CFO will probably suffer a bit from that, but it is the nature of it. It's also high margin revenue. Can I ask one more question? Yeah. Maybe an obvious one just given the nature of the customer base that we're talking about. Is it fair to assume that your focus on academia will lead to some publications that sort of raise the profile of the technology? Because obviously, one of the issues that you guys and others have is pharma doesn't really like to talk about anything, so. Yeah. Getting the word out there on capabilities can sometimes be a challenge. Absolutely. Look, I think, you know, the academic market does three things for us. One, it allows us to have tool in the hands of the users who are real thinkers. Academia loves to do that. They wanna be the first to publish. There was a lot of resistance in the company in early years to actually allow academic users to even have access to our technology. There was a fear of sort of competition from academia, if you will. I think it was ill-founded. The industry has actually proven over and over again that when you put the tool in the hands of multiple people, they innovate around it and they help you out. So that's number one. The second benefit is exactly what you say, which is the spreading of the word. That's the best form of marketing in this industry, is word of mouth. I used it. I could do this amazing thing. 50 other people who are following that key opinion leader now want to do the same thing, and they have to have access to the tool. The second benefit. The third benefit, which is kind of a tangential benefit for us, is to have an ability to not have to replicate every experiment in our four walls. You know? We have an ability to now, you know, amplify application development in an unprecedented way, when we put this tool in the hands of multiple people. All of these things kind of point to, you know. This is something ideally we should have done five years ago, but I am not the person who thinks about the past. I'm moving forward, and we're gonna try to put this in the hands of the people as fast as possible. Yeah. Any more questions? Well, it sounds like we are good to go. Thank you so much for being here. Happy to spend time here for people who are here. Mingle for a few minutes. Otherwise, have a great weekend, and thanks for being here on a weekend day. Thank you.