Amy Herr

Spectrum

Amy Herr's research focuses on bioinstrumentation innovation to improve quantitative measurements in life sciences and translating that work to provide better clinical diagnostics. Amy is Professor of Bioengineering at UC Berkeley.

Transcript

Speaker 1: Spectrum's next.

Speaker 2: Mm MM.

Speaker 3: Yeah.

Speaker 1: Welcome to spectrum the science and technology show on k a l x [00:00:30] Berkeley, a biweekly 30 minute program bringing you interviews featuring bay area scientists and technologists as well as a calendar of local events and news.

Speaker 4: Good afternoon. My name is Renee Rao and I'll be hosting today's show. Our guest this week is Amy, her associate professor of bioengineering at UC Berkeley. Amy is a teacher and a researcher. Her research focuses on bioinstrumentation innovation to improve quantitative measurements in life sciences [00:01:00] and how to translate that work to provide better clinical diagnoses. She is a pioneer in the new field of proteomics. Brad swift and I interview Amy, her.

Speaker 5: Amy, her. Thanks very much for coming on spectrum and welcome. Thank you. I'm very happy to be here. How did you become interested in bioengineering? So I am actually a trained mechanical engineer and I think what really peaked my interest in bioengineering was during graduate study in mechanical engineering. I realized that a lot of [00:01:30] the measurement and instrument challenges that exist that face engineering today really are in the life sciences. So this messy area where things are not necessarily tractable or well-described protein measurement is an area that I've been interested in for some time and I've been working on. And it's especially challenging from the perspective of designing instrument technology, measurement technology. What are protein biomarkers and what makes them elusive? Yeah. So protein biomarkers really is just sort of a catch [00:02:00] all phrase for indicators of disease state, um, indicators of living, organisms, response to treatment, just sort of indicators of what's going on in the organism at a particular time.

Speaker 5: So there's many different types of biomarkers. You may have heard quite a bit about this genomics revolution and our use and understanding of information that's coming from nucleic acids. And what we're really looking for in Dow is building on what we've learned from our understanding of nucleic acids. How can we try [00:02:30] to understand proteins, which are the effectors of function, if you will, in living organisms and really try to use that information from proteins to understand all of these questions surrounding disease. So who has a disease, who might respond to specific treatments, who might not respond to specific treatments? How you are responding to specific treatments and in our mind it's released the next phase of what genomics has laid the groundwork for an area that we call proteomics. Can you give us a quick run through [00:03:00] of how molecular diagnosis works now and what new things you are trying to detect and what new information we can get from those?

Speaker 5: I guess it has been striking to me as an instrument designer, innovator developer. If you take a look at our understanding of the role of proteins in disease right now, there's a treasure trove I would say, of information that's come out of basic discovery. So trying to understand what proteins are upregulated or downregulated or modified in [00:03:30] response to disease or treatment of disease. Right. So I would say there's definitely more effort that needs to be done in discovery, but we've done a lot of great work in discovery. A huge challenge and unmet need to use the engineering design terminology that exists right now is we have these potential indicators of disease or response to disease or prognosis, but very, very few of them have made it into a clinical setting into a diagnostic. Right now there are less than a hundred [00:04:00] different biomarkers that are being used for diagnostics.

Speaker 5: That includes nucleic acids of DNA, RNA and proteins as well, just metabolites as well, right? So very, very few of the known existing bio molecules are being used in any way as a diagnostic measurement. And so there's really a huge gap right now between all of these promising markers that have been identified and those that are currently being used to make a diagnosis. So one of the things that we're [00:04:30] trying to do is to just build a basic framework for measurements that will allow people to make many, many, many measurements of a particular biomarker of potential interest so that you can look at many, many different patients' samples, many, many different disease states. We won't be really data limited. So the technologies that we use right now for a lot of these protein biomarkers to see whether or not the promising ones actually answer a clinical question, they're really rate limiting.

Speaker 5: [00:05:00] They're really slow or they require a lot of material and in some cases this biospecimens these materials from patients are precious, hugely limited, right there, sparingly available. So we're just trying to think about ways that we can use these microfluidic architectures that require just tiny amounts of sample to run one measurement. How we can use those to scale up to make thousands of measurements. We're right now tens of measurements can be difficult and to make those measurements on, you know, a [00:05:30] microliter of sample from a patient as opposed to tens to hundreds of microliters. So that for us, this so-called biomarker validation question getting from yet this might work too. Okay, here are the clinical questions this marker can or cannot answer as the gap that we're trying to fill. Are you building these instruments? A major focus of my research group is looking at innovating new instrumentation, new technologies.

Speaker 5: So by understanding the underlying physical principles [00:06:00] of the types of transport that we use. So electrophoresis and diffusion and by understanding unmet clinical or life sciences needs. So questions or challenges that currently exist out in life sciences laboratories or in clinical laboratories. We're basically trying to bring those two aspects together to develop new tools. All of the new tools that we develop are developed really to meet an unmet need either in the clinical setting or the life sciences setting and they're built with an understanding these underlying principles, but they all [00:06:30] have to be validated. So when we make a measurement with a new tool, we have to have some confidence in how well our measurement reflects our current understanding of the systems. And we typically do that by using conventional gold standard measurement technologies where appropriate. I think recently we've just come into this really interesting and exciting gray zone where we can make measurements that there really are no existing tools to be able to validate whether our measurement makes sense or not. And so we've had to put some effort and careful thought [00:07:00] into how do we validate our measurements using maybe indirect approaches so that we can say with some confidence the limits and the benefits of the tools that we're introducing.

Speaker 4: You said earlier that a lot of your research comes from trying to meet the unmet needs of both the life sciences and the technological aspects. How do you go about picking which needs to meet? Do you find ones that you think, okay, well this is doable, or do you find ones that you think, maybe no one else can do this? I'm going to work on it?

Speaker 5: Right. [00:07:30] That's a great question. So as an engineer, as an engineering designer, one of the first things that we do is really try to understand the world around us and try to understand how people approach existing problems, how they define those problems, why they approach them in a particular way. But I think this is one of the most exciting aspects of the work that we do. It's certainly true that if you get this first stage, this identification and understanding of unmet needs wrong, you're going to go down the wrong path, but if you get it right, you can make a huge difference in terms [00:08:00] of how people are approaching either science or medicine and our work is really translational in that way. So we're engineers and we're passionate about making excellent measurements and as you say, measurements that are currently not possible are the measurements that we're really looking to impact.

Speaker 5: Measurements that are currently possible but needs significant improvement. We do focus on those as well, but when you can find a measurement that when you're talking to a biologist and explaining kind of what you can do and they look at you and say, oh my gosh, there's no [00:08:30] way I could do that right now, then you know you've hit upon something that's really important to at least consider further to fill a gap and unmet need that's out there at the present time. In many ways, I think it reminds many of us of why we chose to be engineers in the first place. I mean, certainly I can speak for myself and say I'm really excited about being able to make measurements that no one else can make. And understanding how those measurements, how good they are, how much more improvement they need, and maybe trying to understand the physics and think about [00:09:00] is something possible that we've discounted to date. But I think in many ways connecting with the end user also adds another layer of excitement and passion and motivation because you can really see how your work in the lab can make a difference in the world around us.

Speaker 6: Aw. [inaudible] you're listening to spectrum k A. L. Alex Berkeley. Our guest today is Amy her in the next segment, [00:09:30] Amy talks about her lab at UC Berkeley. [inaudible]

Speaker 5: how long has your lab been up and running? So my lab has been a, at Berkeley six years before I came to UC Berkeley. So I did my doctoral research at Stanford in mechanical engineering and then I loved a research and I wanted to continue doing research and so I worked for five years at a national lab and then coming to UC Berkeley was a big change in many ways, but I think [00:10:00] I'm working with an excellent team of, in many cases, junior colleagues here now, training them, postdocs and students just being invigorated every year with the fresh approaches that students, the frust questions that students ask about why are we doing some things in the way that we're doing them, or why is our understanding limited in this way as a faculty member? Just a huge source of inspiration and motivation over the six years. Has Your approach within the lab changed much?

Speaker 5: Our lab has certainly changed. Yeah, [00:10:30] and I think as an individual, you as a researcher over the course of six years, certainly I will have also changed. You learn as you go and you learn on a technical level for sure. Absolutely. I would also say I've learned a lot from my groom to the students and the postdocs and the way that they approach problems. It's been just a fantastic honor to be able to work in bioengineering here at UC Berkeley with an amazing group of people who all come with different perspectives. And I've really pushed the research directions [00:11:00] in my group in ways that I couldn't have imagined six years ago. And they also come from very different disciplines as well, don't they? And has that mix changed for you over the six years? Yeah, that is absolutely true. So bioengineering, when I was in graduate school, which I'd like to think was in a long time ago, but it was, I finished almost a decade ago now.

Speaker 5: It didn't even really exist. Right. It was just kind of starting and the graduate level widely at universities around the u s and globally as well. So most of the faculty, if you look at bio engineering, our formal training is [00:11:30] not in bio engineering. We're too old for that, I guess. And so the students who currently come to do doctoral study at UC Berkeley and with our partner institution, University of California, San Francisco, they all come with different backgrounds. More and more of them are coming with a biomedical or bio engineering undergraduate degree. But we certainly, you know, in my group alone have had students who have come from uh, aeronautics, chemical engineering, electrical engineering, chemistry, just a wide range of backgrounds. As someone who essentially [00:12:00] witnessed the genesis of an entire field of engineering and especially one that is so connected to the world. Can you tell us what that was like and how that's affected you?

Speaker 5: Yeah. Seeing bioengineering starts really and become just the huge discipline in the really impactful area of research and study that it is today has been really inspiring. It's also does raise a lot of questions, questions about what is the appropriate curriculum for undergraduates who are studying. Bioengineering is something that [00:12:30] the faculty in my department, we talk about all the time. We try to refine our approach to this really, really important basic study that students undertake in their undergraduate years. Right? So there's that aspect of it wanting to make sure that we help them prepare themselves to be the best engineers possible when they leave UC Berkeley on the other hand, just seeing the huge advances that engineering is making in medicine and the way that it's changing the lives of people and has been for some time [00:13:00] for the better is really inspiring. I will say I often notice that students that I come into contact with here, they're really driven to make a positive impact in the world around them.

Speaker 5: And I think that is really at the core of what engineers want to do. We want to understand, but we also want to make something, we want to make a positive impact with what we're doing and maybe I think in a very practical sense that's what an engineer is. I wanted to ask you a little [00:13:30] bit about what you've referred to the engineering mindset and I think it's a really interesting perspectives to want to maybe put us in that mind frame. Yeah, I think the engineering approaches to really just question question what you're observing, question what people are telling you. And so the engineering mindset I think is to be skeptical and to be observant, to not listen to necessarily what people tell you, but to use your own eyes and to discuss with peers or mentors [00:14:00] to try to understand and make sense of all of the different perspectives you're going to get when you're trying to understand the problem.

Speaker 5: And so as engineers, we're always challenged with getting into kind of one way of thinking and that can push you down a path that could be productive. But if you really step outside and try to integrate a really holistic view of the world or the problem you're trying to understand, you might happen upon new approaches that users would never have dreamed of. Right? So there's that aspect. I think the engineering mindset is also to be objective. And in [00:14:30] our case in bio engineering, trying to be as quantitative as possible and to understand the limits and the advantages of being quantitative. And then certainly in bioengineering, there is a huge aspect of our mindset, which is to translate our solutions out into the world around us so that we can have a positive impact on society and the world more broadly. Spectrum is a public affairs show on k l x Berkeley. [00:15:00] Our guest today is [inaudible]

Speaker 6: Amy her in the next segment, Amy offers advice to students interested in bio engineering.

Speaker 5: Can you explain how you're using mathematics to reveal biological systems and create new medical applications? Yeah. One of the big things that we've seen lacking in instruments to make protein level measurements is any sort of quantitation, so a lot of the technologies [00:15:30] are just qualitative. You can see the presence of a particular protein of interest or okay, maybe it's higher presence in one sample versus another, but inherently in the way a lot of the conventional approaches, the conventional assays are run, there's very little confidence in being able to pull out exactly how many micrograms and material are present in a sample two it's hard to do comparisons between different samples except in a very qualitative way. What we're working on are technologies that are quantitative. [00:16:00] So that can allow you to pull out absolute mass level or concentration level information about how much protein is present in a particular sample.

Speaker 5: And the hope there is that by doing that we can allow ourselves to create large databases of quantitative information about how much protein or particular form of protein is present under specific conditions. So you can imagine if you were doing a study, for example, on a particular biomarker [00:16:30] of interest, so prostate specific antigen, let's take, right. So if you knew that a particular isoform of this protein was present in certain cases, you could actually quantify how much is there. Enter that information in a database and a researcher say in Norway, who's also making similar measurements, but maybe on a different patient cohort could also upload their information. You can compare head to head. So these data sets could get bigger and bigger and bigger. And then potentially looking at questions of cell signaling [00:17:00] and in proteins that carry that signaling information. Perhaps integrating those quantitative levels of these particular proteins back into bioinformatics models that have been developed would lend insight into the exact response of a protein signaling pathway to a particular stimulation and give those bioinformatics models some actual numbers to work with as opposed to just relationships between specific proteins and are you building some of those models?

Speaker 5: So a lot of what we do is collaboration with [00:17:30] specialists in protein signaling pathway models. So my lab is into bioinformatics lab, so we don't do a lot of that ourselves. But through our collaborations with the bioinformatics community, we know that quantitative levels of proteins at particular times is really important to these dynamic models. And so that's a major focus of our work as well.

Speaker 4: It's interesting that you bring their PSA test up because I think that's been getting a lot of attention lately. I'd say look at more data. They're realizing it's not quite the silver bullet that people thought it was. [00:18:00] Are there any other examples like that that waste have completely overturned people's ideas of what we were seeing once we look at this large scale data? Yeah,

Speaker 5: in particular a very striking example that you bring up the test for free versus total prostate specific antigen in blood. Right. And that's been used for many years as an indicator of prostate cancer. I think there are just three beautiful studies that have come out in the last year, one from UCF that have really pointed to the fact [00:18:30] that actually some of these PSA tests are really good at finding prostate cancer. They're just really bad at telling us if it's an aggressive or a slow moving prostate cancer. Right? So the prognostic information, how the patient is going to fare in the long run is just not there. So we're finding the prostate cancer, but we're not able to determine whether we should just watch full weights and see what happens or if we should actually embark upon some treatment. That's been a big interest of our group is looking [00:19:00] at specific diagnostic questions.

Speaker 5: Who in the case of prostate cancer, can we improve prognostic information and trying to look at specific forms of the protein. So in this case, working with the researcher at Stanford Medical Center looking at different glyco forms of prostate specific antigen that may be more indicative of longterm outcomes for the patient. That in particular is a really interesting one for me because we started working with this researcher maybe six years ago before these big studies came out that showed the prognostic usefulness of the PSA [00:19:30] test was not so good and I definitely remember us submitting several proposals to funding agencies and basically getting the comments back that will we have an indicator for prostate cancer right now, we don't need another one. And so just even over the short time that we've been working on it, seeing that just turned on its head because of this ability to integrate all of this patient level information across countries and across different sites to try to understand how good is this test really have led us to realize it's not, as you [00:20:00] said, the silver bullet that we once hoped or thought that it was. I think that's a really good example. I think in some of those same studies, mammograms have also come out yet, right, is not necessarily answering the diagnostic questions that they hoped that that diagnostic would answer. What advice would you give to a young person thinking about bioengineering, about preparing for work in a multi disciplinary lab?

Speaker 5: I think major advice that I would give to a young [00:20:30] person who's thinking about working in an interdisciplinary lab like those that you'll find in bioengineering, but also across the campus for sure. I know this interdisciplinary focus is something that permeates engineering right now and I think rightly so on many levels. Many of the problems that we're trying to solver so big are complex. That having these different inputs is just critical. I honestly think that is part of our community. We've not done a great job of communicating to either new engineers or people who are thinking about going into engineering and just this idea that [00:21:00] I can work on these really big challenges with teams of amazing people trying to have a positive impact through my work. I can get paid to do that. I can travel the world to do that. I can work on many different types of problems over the lifetime of my career.

Speaker 5: Just an amazing career path really for anyone to consider. It's certainly very exciting and it certainly challenges you and it allows you to operate in these spheres that you would never imagine you could. So either with [00:21:30] different teams of people or just on problems that you maybe never even imagined you would come across. I think some of the advice I would give a undergraduate here at UC Berkeley, I would definitely urge them to seek out opportunities, clearly urge them to seek out mentors, so people who are maybe several years older than them, so people who are role models, who they might want to be like when they quote grow up. Right? We all have those people that we look for no matter how old we are. Look also for people around you who are maybe just a couple years [00:22:00] older than you, who have gone through a programmer or embarked upon research in a particular field and pick their brain about what worked for them and what didn't.

Speaker 5: If they went back in time, what would they do differently or what are they so glad that they did? I think just finding these resources and making use of them and then paying it forward when your time comes and you have the experience to share insight with other people and advice is advice. You don't have to take it. But I do think it's certainly in my own career really helped me to listen to it and then weigh it for myself. [00:22:30] I think in an interdisciplinary field like bioengineering focusing on getting the rigorous fundamental understanding of engineering and the particular area that you're interested in is really key. Certainly advisees, I urge them to consider either a minor or some sort of emphasis material science, mechanical or electrical engineering cause it might help them out a little bit. But just making use of the resources that are around you and finding those resources is something I would urge students to do. I'd love to [00:23:00] know your favorite protein. Oh my favorite protein. I think actually right now it would be prostate specific antigen. Yes. Because there is so much controversy around it for sure. Yeah. So it was a good question. Sure. Amy, her. Thanks very much for coming on spectrum. Great. It was a pleasure. Thank you so much.

Speaker 6: [inaudible] [inaudible] [inaudible] [00:23:30] [inaudible]

Speaker 5: on the webcast of spectrum, we've chosen to include a new section of Amy's interview suitable. The more technologically inclined among us, she would discuss her exciting work inventing novel means of biological measurement. One other term I wanted to have you weigh in on is the term scale dependent physics and chemistry, and how is that important to your work? So [00:24:00] we are a bioengineering lab. We're an instrument innovation and development lab. So what we look at, or are there new ways to make protein level measurements that can inform our understanding or our approaches to disease? Right. And that's through this portal of proteins is indicators of disease. It's really interesting as you look at some of the basic fluid and material transport phenomenos. So things like diffusion or things like, in our case, we're interested in electro migration, so charged analytes. If you apply an electric field, [00:24:30] they're going to migrate, right?

Speaker 5: They're going to go towards the cathode or the anode depending on their charge. These sorts of physical transport phenomena can really benefit from shrinking link scales. So in our case, we're interested in using tiny channels, so channels that hold fluids, liquids in particular channels that have a dimension about the size of a human hair. So they're very small. As you scale down channels to that size, you start to get some really beneficial properties that come out about the fluids. And then in particular, the use of [00:25:00] the electric fields benefits from those tiny channels because the channels have a very high surface area to volume ratio. So as you shrink a channel down, you get more and more surface area for a tiny volume. And that essentially means that if we apply a field, an electric field along a fluid that's in that channel, we can apply a very, very high fields and those high fields are going to make the fluid start heating something that's called jewel heating.

Speaker 5: So in the electric circuits you have in your computer, for example, if you apply a field, you're moving electrons, not liquids, [00:25:30] but you're still getting this jewel heating because of the motion of those particles. As we have these really high surface areas, we can dissipate heat really effectively. So we can apply high, higher and higher fields than you could even say a millimeter diameter channel. Now we have channels that are microns in diameter, so orders of magnitude smaller and they cool very effectively. So that allows us to access a transport spaces that aren't accessible kind of in the macro scale.

Speaker 5: So my [00:26:00] lab is really focused on taking fabrication approaches that have been developed for the semiconductor industry. So moving electrons around in tiny channels, if you will, and applying that with those sorts of approaches to now, not moving electrons but moving fluids, right liquids around. Um, and the reason we do that is because as we scaled the channels down, the channels that hold the liquids, we get beneficial properties. So heat dissipation is one of those beneficial phenomena. It really starts to [00:26:30] become more and more efficient as we scaled it. The dimensions, the cross section of the channel down. So in our case we like to use these tiny structures, these tiny fluid channels. Again, diameter of about if human here in cross section because it allows us to operate under really, really harsh conditions if you will. So at very, very high field strengths. In addition to that, another beneficial aspect of scaling down is much of the transport that happens inside these tiny channels really ends up relying on diffusion [00:27:00] as being the major mechanism of transport and diffusion a is very efficient over short distances, over long distances.

Speaker 5: The scaling is not necessarily favorable and it might take you a long time for a molecule to diffuse a long distance, but as we use these techniques, these fabrication techniques to develop micro and Nano fluidic channels, those distances in those channels are tiny. So microns or nanometers and that means diffusion all the sudden becomes a very effective transport mechanism. [00:27:30] So we use these effective transport scalings these beneficial scalings to allow us to do things like mixing. So we can bring two analytes or two reagents in contact with each other and just rely on diffusion to get them to mix. Whereas in the macro scale we would want to stir or agitate the fluid in some way so we can use passive approaches and rely on diffusion to get effective mixing. Whereas on the macro scale we would have to have some sort of active stirring in order to get those, those species [00:28:00] to come together and react.

Speaker 5: So are these techniques being applied to both your understanding of biological systems and in your applications that you're trying to build? It's a great question. So I think primarily a lot of the physical phenomena that we're using are really trying to drive towards efficient assays, efficient measurement technologies for specific applications. So for example, we might be looking at a particular protein mediated signaling pathway [00:28:30] and we might be really interested in different isoforms or different versions of proteins, the same protein, but maybe it has some sort of phosphorylation modification on it. Um, and by using these really efficient separation mechanisms like electrophoresis on the micro scale, it's electro migration properties, we can actually start to resolve species or separate them when if we were to use a less efficient architecture, we might not be able to separate them basically and tell them apart. So it allows us to in some ways [00:29:00] access information that sometimes is not accessible using conventional methodologies, conventional assays.

Speaker 5: Um, but it also lets us get at looking at reactions for example, on timescales that you just can't do using macro scale techniques. So being able to look at very fine time points because we have really precise control of fluids using these micro architectures, these microfluidic channels. So there's kind of two answers. One, we want to look at specific proteins as related to clinical [00:29:30] questions. So those applications and in many cases we can do that more efficiently. But on the flip side, the fundamental understanding of biology, we might want to look at timescales that we can't measure box systems as well. Have you discovered anything really new and exciting with this novel level of precision? We have started to move into an area that's a little bit unknown and recently some of the work that's being generated in my lab and we're excited to be preparing now for communication to the broader technical [00:30:00] community is being able to look at protein signaling pathways on a single cell level.

Speaker 5: So flow cytometry is one example of technology that exists that allows you to look at literally millions of individual cells and you've basically stained those cells with antibodies to a particular protein. So the cell is going to glow a particular color because the antibody has a floor for conjugated to it. The cell is going to glow as particular color of the antibody binds to an analyte of interest of a protein of interest in that cell. But the [00:30:30] problem is with flow cytometry, if you're looking for proteins that we don't have antibodies that are specific to them. So some of these isoforms for example, there's not an antibody that's just specific to a particular isoform. It's very difficult to to make a flow cytometry measurement or there's other cases, for example, with stem cell research or circulating tumor cells. We have so few starting cells that if you use flow cytometry, you're basically going to lose all of the material before you can make the measurement.

Speaker 5: [00:31:00] So using these microfluidic architectures, we can um, do separations of single cells and be able to look at isoforms of particular proteins even if we don't have antibodies specific to one of the isoforms. If we have an antibody that's specific to all of the isoforms but we can resolve them from each other before we use an antibody to probe for them. Or if we have such a tiny starting population of cells like circulating tumor cells, we're going to be able to make measurements of the protein signaling pathways on those, you know, 10 or a hundred cells [00:31:30] that are of interest that we just can't do using conventional technologies. I should say. One of the major methods that my group has been working on over the last couple of years is this idea of western blotting. And this is a really powerhouse work horse analytical technique that's used in clinical and research labs all over the world.

Speaker 5: Basically it's an assay that allows you to separate the protein contents of a particular sample, so to resolve species proteins by differences in molecular weight, for example, and [00:32:00] then it allows you to come in with an antibody that's specific to a target of interest and see at a particular molecular weight. Does this antibody recognize that protein? If so, most likely that is the protein that I'm looking for, that's my target or the candidate that I'm looking for. And so we've pushed in several different lines of inquiry, new ways to make this specific measurement. It's two measurements, molecular weight and this binding to an antibody or an immune regent of interest. We've really benefited from materials design, so developing [00:32:30] materials that we can change basically from molecular sieving matrices that are useful for the separation stage. Two materials that actually immobilize the of interest upon exposure to light and after we immobilize the proteins, we can come in with the antibody and probe to see if that particular band at that specific molecular weight is the target of interest. This is, I think, been really informative from the perspective of allowing us to design these systems to operate, say, at the single cell level [00:33:00] or to operate on clinical samples that are difficult to analyze using conventional technologies.

Speaker 2: Mm MM.

Speaker 3: Okay.

Speaker 1: The music heard during this show was written in, produced by Alex Simon. Thank you for listening to spectrum. If you have comments about the show, please send them to [00:33:30] us via email. Our email address is spectrum dot k a l x hit yahoo.com join us into

Speaker 7: [inaudible]

Speaker 3: [00:34:00] probably.

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