 Okay, how's that? Can everyone hear me alright? Okay, thank you. So you'll find that our SEGS is very different from the one you just heard about with David. And in our SEGS, it's called the Michael's Scale Life Sciences Center. We started at University of Washington in 2001. You saw on his chart that we were one of the first SEGS in the program. I moved the headquarters of it to ASU, Arizona State in 2007 when I went there to be Dean of Engineering. And our goal here is to develop Michael's Scale modules so that we can analyze live single cells and perform multi-parameter measurements on them and correlate those events with genomic information so we gain an understanding of the Z's. Since we're analyzing live cells in chip formats, we call it life on a chip. And how is our Michael's Scale Life Sciences Center, I say, first of all, and you'll hear some of the same things you heard from David, we could not have done the things that we've done in our center with R01s. We've been pioneering new technologies for live single cell analysis to understand cell life and death pathways and correlate those with disease, whoops, and focused on coupling these metabolic measurements with genomic and transcriptome analysis. It's a highly multidisciplinary collaborative environment. In fact, we have every discipline you can imagine, all the engineering, physics, chemistry, genomics, material science, computer science, you name it. And a multi-investigator team and also multi-institutional, that's not a requirement, but that's how our center is organized. We have what we think is a unique training platform for students and new investigators. We are flooded with applications on a daily basis from people wanting to be students in our lab and people that work in the lab and in the center talk about how they love the environment with all the different disciplines and the combination of the experimental work and theoretical work. We have worked on and increased the pool of genomic scientists and engineers and the number of underrepresented minority professionals in genomics in STEM fields through the diversity action plan. And when I was at University of Washington, Mary Liz Sherman and I created a new course on biology and genomics for engineers that's still there as a regular part of the engineering curriculum. It's a multi-investigator, multi-institutional. We have investigators at Fred Hutchinson Cancer Research Center, Arizona State University, University of Washington, and the Fred Hutchinson, I mean, Brandeis University. And at these different institutions, in particular, University of Washington and Arizona State. At UDUB, we have very tight linkage with the engineering, along with the medical school and genome sciences. And at Arizona State, we're housed in the Biodesign Institute, and that's also a very multidisciplinary environment with engineering, life sciences, and so on. We've been building this interface between the biomedical applications and the technology. You can see here how the investigators kind of map out here in terms of the biology and the medical or the diseases that we're going after, and then a series of technology developments. And we're constantly driving each other to advance in the technology and to be able to answer new questions in terms of the biology. And also the biologists keep the engineers on track so that we're developing something that's hopefully useful and answering biologically relevant questions. We could develop all kinds of cool technologies that would have no use for the biology. Our goal is to understand, predict, and diagnose cell life and death pathways. The challenge here is inherent heterogeneity in the cellular populations. And this heterogeneity is believed to underline transitions to different disease states, in particular in cancer and inflammatory-based diseases. So the approach is to develop these dynamic real-time multi-parameter analysis of single cells and apply this to fundamental problems in biology and health. We have two model systems that we focused on, a premalignant progression of barisosophagus. That's with Brian Reed at the Fred Hutchinson Cancer Research Center. He's been studying that for decades and has patients that he has also followed for decades. And then pro-inflammatory cell death or pyrotosis with Brad Cookson at University of Washington. And the model system there is seminella infection of the mouse macrophage. This is the vision that we laid out in about 2000, 2001, where we would start with a single cell and learn as much as we could about a single cell, then go into cell-cell interaction studies, tissue, and in vivo. And this is really kind of a 20-year vision. And we're really at the cell-cell interaction stage at the beginning of that and some of our new things are getting into tissue. But it's been very challenging in terms of the technology development at the single-cell level. So what we're trying to answer is why does a cell decide to live or die and why does it go down different pathways of cell life and cell death. There's apoptosis, which is non-inflammatory cell death, and we've focused on pyrotosis, pro-inflammatory cell death, as well as neoplastic progression or cell life, cell proliferation. This in one slide shows really the heart of everything in terms of what we can do with the technology. And that's to perform stimulus-response experiments on single cells. The stimuli could be anything like biochemical, infecting agent, or temporal cues to help us understand metabolic, infection, cancer, and aging. Same kind of system could be used for drug dosing experiments. You name it. It's very versatile technology. And what we can do is start out down in the bottom left corner, look at the state of a cell pre-stimulus, and then track it over time with our sensors, and then do post-stimulus analyses. So why do we care about individual cells? First of all, there's the intrinsic cell-to-cell variability in tissues in vivo and the cellular heterogeneity. And that's thought to be one of the players in onset progression and also therapeutic response of complex diseases including cancer. Most of the research has been done with bulk cell populations. And when you do that, you're taking an average of millions of cells. And actually, when we look at single cells, there's a lot of information that's being lost there, and you'll see in some of the results that I show you why this is important. It's technologically challenging. There's extremely low amounts of analytes in the cell and requires very challenging technical capability, high detection sensitivity and specificity, high content and high throughput due to the variability, and then time resolved on invasive methods. So when we were working on proposing our SEGs in around 1999-2000, there was skepticism about the biological relevance of the events at the single cell level. The technologies that were available were few. There's fluorescence-activated cell sorting, imaging cytometry and so on, and really not a lot of capable for analyzing live cells, live single cells. Now, if you fast forward to the last few years, there's the functional role of cellular heterogeneities recognized and widely accepted. And there are a lot more technologies. In fact, now one of the common fun projects at NIH is on single cell and I participated in Francis's Big Think in May of what 2010 or something like that. And that was one of the projects that emerged from that. And if you look at the publications in 1999, those that had the word single cell on them, there were about 661 and there's triple that number now. And Mary Listrom and I had one of the first publications in 2003 talking about cellular heterogeneity and the need for single cell analysis. So in our technology, we can do live dynamic measurements on a single cell. One of the important measurements is oxygen. That gives you the state of the cell and its health. If you have a rapid upshift or downshift in the oxygen, you typically indicate some kind of a stress. But if it's a slowly decreasing or some kind of degeneration or slowly increasing some kind of proliferation. And then there are other things that we're interested in measuring in terms of physiological parameters. And you can see those there but membrane potential membrane integrity, various ion gradients and so on. Then after we take dynamic measurements of the cells over time in a very controlled environment, we can fix the cells and take snapshots of the DNA RNA proteins. So this is a diagram kind of showing the core technology. This is just one chamber that's been in a microarray, a micro fabricated system. And this is to show you that we have put a single cell in a well that then has a lid. And in the lid, we can put in these different sensors that are spatially and spatially separated. We also have the ability to seal off this chamber with a hermetic seal so that we can do rate consumption measurements such as oxygen consumption rate. And then those all get fabricated into a microarray. And we fabricate the sensors as well. Over the years, we've developed a suite of extracellular and intracellular sensors has been very challenging. You can't just go buy these off the shelf. We have some really great sensor development wizards that can make almost anything for us. And these are all optical based sensors, but you can see the suite of them here. The ones that we've chosen have been driven by the biological questions that we're working on. And we have more recently had some dual and trio sensors so that we can more easily multiplex those in the micro fabrication process. And then these intracellular sensors here. And we continue to build this suite of sensor capability. This shows the micro patterning of the sensors, we can place them really however we like, down to about three microns in size. Those are oxygen and pH sensors. And this shows one of our devices with pH and oxygen sensors. And in this particular case, we had clusters of cells. And here we show what it looks like when you have live single cells in each of the wells here. Once we've done the dynamic measurements, then we can fix the cells. So those are the live cells in a well, then we can fix them and transfer them to some other module for some kind of post fixation measurements. For example, we can measure the mitochondrial copy number or the gene transcripts. And the process is shown here. It's pre amplification free. And we can use this for doing transcriptome analysis as well as the mitochondrial DNA copy number. So I'm going to show you some of the results on each of the model systems. More extensively on the model system one, baris esophagus esophageal cancer. Baris esophagus is a precursor to esophageal adenocarcinoma. It's caused by the insult of acid reflux in the esophagus causes a chronic inflammation environment causes damage to the cells in the genes and selects for genetic variants in the population. And then what it does is it causes this change in the epithelium of the esophagus to have this columnar epithelium similar to what you would have an intestine. So there's these crypts. And the incidence of this has been increasing dramatically. In fact, there's been an increase in incidence of over 600% over the past 30 years. And you can see those that have baris and progress to adenocarcinoma do not have a very good survival rate. This is a model of clonal evolution that's been around for decades showing the selection of variants and variant populations. And the question is, why do these cells survive? So the Brian Reed and his team at the Fred Hutch have developed a set of cell lines, they have the primary cells and have biopsies that they've taken on periodic basis of many different patients, but also develop cell lines where hypoxia is one of the things that will help give you a condition that's common in inflammation. It's a selective pressure for inflammation. So what we can do is create cell lines that then give the same conditions as you would see in vivo and the various patients. So by doing selective rounds of hypoxia, we can create these cell lines CPA, BC, and D that have metaplasic and various forms of dysplastic conditions and these different mutations. So we use those in our studies. And our goal here is to understand clonal evolution and baris esophagus, looking at the dynamics of clonal evolution by correlating the phenotypic and genotypic alterations. And we have experiments in both cell populations are looking at the metabolic profiles, growth rates and so on, and then a series of single cell experiments looking at the metabolic profiles, mRNA expression levels and so on. So some of the data here, this is to show you what the raw data on the oxygen consumption rate measurements look like. These are the CPA metaplasic cells and the CPC or the dysplastic cells. Each one of these curves is the oxygen consumption rate on single cells. So this is the rate here on the y axis and then time along the x axis. So you can see here extensive heterogeneity. And also just in this data shown here that we have a little bit less variation in the oxygen consumption rates for the dysplastic cells. If we look at more of these cells here, we see now this is just a different way of looking at the data. The CPC cells are in the white bars, but then the CPA cells we seem to see as some population here statistically it is like a double Gaussian here that's emerging. And if you were to look at the averages of the oxygen consumption rates for these cells, you wouldn't see this difference. In this case, we're taking cells that are metaplasic CPA cells. And then we have a set of hypoxia adapted cells. These are cells that were selected, sent through six rounds of hypoxia conditions 20 hours each time. Then we grew them in ramaxic conditions for a month. And then we looked at the oxygen consumption rates. And what we find here are that the hypoxia adapted cells don't vary quite as much and have lower consumption rate. And this is all consistent with the theory of the in clonal evolution. We've also compliment our studies with other kinds of things. This is a fax DNA aneuploid analysis, where we're again look in this case looking at metaplasic and dysplastic cells, and subjecting them to different rounds of hypoxia. And what we see is that there's an enrichment of a different population in these dysplastic cells, but not in the metaplasic cells. So again, that's something else that you would see because we're adapting to the hypoxic condition. And then this is some SNP array data again showing the same kind of thing. This is alterations in the copy number. Again, as we go through different selected rounds of hypoxia, we see the enrichment of these different clonal populations. And then we can do as I've mentioned, the multi parameter measurements. This is some data on the CPD cells where we've combined oxygen, pH and mitochondrial membrane potential into each well for multi parameter analysis on the same cells. And these cells, this is a cell cluster. So about 45 cells per well. And you can see here that even with the 45 cells in a well, we have a lot of heterogeneity here in variation in these different parameters. And also kind of a delayed response in the mitochondrial membrane potential as the as you have the lack of oxygen as because we've sealed the well and done the hypoxic experiment. This is looking at multi parameter data on the live single cells. These are CPA cells. And again, loss of heterogeneity and variability among the measurements. And again, the mitochondrial membrane potential is collapsing here at different oxygen levels. So what kinds you can see how is very difficult to start drawing conclusions on this, we have to do hundreds of cells for each one. And we've done these statistical analysis to see how many you have to do, depending on what kind of experiment you're doing. In this particular case, those are all the dynamic those are dynamic cell measurements in complemented with some of those other measurements that I showed you. And then with the technology where we can do the transcriptome analysis, we can also look at the mitochondrial DNA copy numbers, the same approach. And this is showing the measurement of the mitochondrial DNA copy numbers in the CPA and CPC cells. And we see that first of all, there's a lot of variation, and that they're different. And there's a publication showing that if you looked at the bulk cell populations, you see no difference in the mitochondrial DNA copy number. With that same technology, we're looking at gene transcription changes in response to hypoxia. These are mitochondrial encoded genes. We selected some that are in the hypoxia response pathway. And what we've done here is a short term hypoxic experiment. So the controls of CPA and CPC, CPA and CPC are the control. And then we subjected them to hypoxic condition for 30 minutes and looked at the transcripts before and after the hypoxic experiment and see that we get up regulation of some of the genes here, including 16S, which is highly conserved and is often used for as a reference in bulk cell populations. But in that case, we see a regulation of that one as well as COX-1. And these were in the CPA. We don't see as much change in the CPC. So maybe there's some adaptation or something, some compensation for the reduced oxygen and less ATP production. We've also more recently been getting primary samples from the Mayo Clinic and through the experiments right when we get the samples. These are a couple of transcription factors just to give you an example of what we're doing here. These are some of the bulk cell results here looking at the CDX-2 and the VEGF. But then when you look at the single cell data, you can see tremendous heterogeneity. And in particular in the VEGF, we see a couple of rare variants here. You would never see any of this when you look at the transcripts on the bulk cell populations. And now if we combine these, what I'm showing here is data performed on the same cell. We took a dynamic measurements, in this case oxygen consumption rate, and then looked at the transcript, the VGF, VEGF transcript analysis here on this very same cell and then overlaid those here. So what we're seeing here is that in those cells that have a much lower oxygen consumption rate over here, over here, ah, wait, a higher oxygen consumption rate have a very low transcript level for the VEGF and vice versa. So there's very strong negative correlation here. And so what we're seeing is perhaps that in this condition with the low oxygen consumption rate in the high VEGF, that they may be more resistant to the hypoxia conditions. And they're just an idea of how would you use this data? Of course, there has to be a lot more done on this, but perhaps the fraction of those total population, the fraction in the total population of cells could perhaps be used as some kind of biosignature for risk stratification of progression to salvageal adenocarcinoma. So these are kind of the things that we're trying to get to in terms of what are those biosignatures and what are these different mechanisms occurring in pre-malignant progression of barisosophagus. So some of the conclusions on the short term hypoxia experiments that the dysplastic cells exhibit features commonly found in transform cancer cells indicating early manifestations of cancer related phenotypes. The dysplastic cells are much less sensitive to the short term oxygen deprivation in terms of the transcriptional activity. And based on the differences in the single cell distribution parameters, the different gene transcription regulation mechanisms may be in place in the dysplastic versus the metaplasic cells. And then in the long term hypoxia experiments, we've demonstrated selection acting at the genome and for the first time the metamolic level using single cell analysis. And that this hypoxia insult selects for subpopulations of cells with inherently low respiration rates, which then results in reduced heterogeneity of the metabolic rates, and also that the hypoxia insult selects for clones with specific genotypes, again corroborating this notion of clonal evolution in cancer. We also have been doing some cell-cell interaction studies. This is just an example showing what happens if we put one, two or three cells in a well and measure the oxygen consumption rate. And what you see here is a nonlinear increase in the oxygen consumption rate when you go from one to two to three. And then another thing we're looking at is the interactions on the metabolism and the gene expression levels when you have different kinds of cells or the same kinds of cells together. So here we transduced the CPD cells, the dysplastic cells with a green fluorescent protein, did a similar thing with EPC-2. Those are the normal cells with a red fluorescent protein. Then we grew up monocultures and then because they're fluorescently coated here, we can very carefully design our experiment and grow up a cold culture. And then we took those out and selected 30 cells at each and then did next-gen sequencing on those. And these are RNA-seq results showing the pathways most affected by these interactions of the monoculture versus the cold culture. And what we can see here are the pathways that were most affected by these interactions are the ones that are key in cell survival and proliferation. So these are the ones that came out on top. So as you might expect, the interaction between the different cell types significantly affects the function in live-death pathways. I've mentioned the mitochondria and coated genes are interested in the mitochondria. That's a powerhouse of the cell and very important. And with Larry Wang at Brandeis, he's been developing a variety of different technologies that are all PCR-based. And we've been doing studies on the mitochondria and in the baris cells. And this is even more challenging than I guess you would say than the single-cell studies because every cell contains thousands of mitochondria. Every mitochondria contains up to 10 genomes and then each genome contains 16,000 plus nucleotides. So Larry Wang and his PCR-based technologies late PCR, linear after the exponential, primer safe to minimize mispriming and lights on, lights off methods don't have really time to go into those. But this is an example showing the point mutations in three gene targets, looking at bulk cells. And then when we look at the single cells, these are CPB cells. So early dysplastic cells in the baris cell lines. And we see more variation here in the HV2 and the COX2 than we do in ND1. And this is when it's conserved in the mitochondrial DNA. So there are a variety of conclusions on this mitochondrial DNA heteroplasme. But in particular the technology has been developed to make it truly possible to study the mitochondrial DNA heteroplasme at the single cell level. And we're working on experiments that looking at this in single cell but also in a single molecule mitochondrial DNA. Now in model system 2, my way over time. Oh good, you don't know. Okay, I will try to hurry. This one will be very quick. In model system 2, this is piratosis pro-inflammatory cell death. This is with Brad Cookson at University of Washington. He's the expert in this. And this is an important process to understand because his inflammation is known to be involved in many diseases. Cancer, cardiovascular disease, stroke, you name it. And so try to understand the mechanism of and how this whole inflammatory process works. So our hypothesis is the heterogeneity in the cellular physiological states influence the life and death decisions and phenotypically manifest as resistance, susceptibility, and intermediate states in response to environmental stressors. What happens in in piratosis is some activation of caspase 1, DNA damage, poor formation, secretion of inflammatory cytokines, and then swelling and ultimately release of inflammatory intercellular contents. And what we're interested in is trying to understand the temporal events in the piratosis pathway. In particular, define the roles of the cytosolic and mitochondrial potassium during piratosis and the signature VTP concentration changes of the cells undergoing piratosis, which result in different physiologies and then that and combined with whatever kind of stimulus. What is the cell death response in the biological output in particular inflammation? How does this occur in different diseases? So with BRAD, there have been a variety of conclusions. There have been a lot of things that have been worked to get everything ready for the single cell analysis and taking advantage of a variety of different genomic technologies. And some of the work there has resulted in heterogeneity at the level of stimulus can generate distinct physiological outcomes. That's done with a fax analysis. A set of genes involved in the distribution of active caspase 1 has been identified. How the active caspase 1 is diffuses and goes through the cytoplasm has been figured out. There's a bunch of other conclusions here. Temporally map the key biochemical vents of piratosis in single cells and on going utilizing the ATP sensors, the potassium sensors, and also the ability to do the mitochondrial membrane potential measurements. We're continuing to do a variety of experiments here to gain a better understanding of the piratosis pathway. And you can imagine there I've talked about inflammation and barisosophagus and there's connections there and many more experiments that could be run there. So that's kind of a quick run-through of the different technology development and biological problems that we've been addressing with the technology. And then the diversity action plan has been an important part of this. The director of this since 2002 when we first started was Lisa Peterson and she's been marvelous. Betty and others can attest to that but she's really done a super job with this. This is really data from University of Washington and that part of all of this. But we've had 28 graduate students all in STEM. We currently have 116 undergraduates in the program. And then when we look at what where undergraduate alumni, what they've gone on to do, a large percentage of them are working in STEM and science. And some of them gone on for PhDs, enrolled in MD-PhD programs, completed MDs, and so on. So really I would say very successful program and the environment of the SEGS and also the other programs at the universities have made all of this possible. And some of the results we have incoming freshmen in the program called ALVA and they've had a very high success rate of first of all coming into college and also going into STEM related fields. And we've had five Native Americans in Hawaiians earn PhDs and four former graduate students now in faculty and all the different students that participated in the diversity action plan combined have given 810 publications and presentations which is really quite astounding. And you heard Jeff and David mention about dissemination and how do we gauge success or utility or use of what's developed in the SEGS. And ours is very technology heavy or centric. So we have been over the years working on our patent portfolio. This is our since our intellectual property or tree or invention tree where we continue to work on our disclosures and build that patent portfolio. We have 93 peer reviewed journal publications in press or published and done and participated in a variety of conferences and workshops. So now what about beyond our SEGS. You don't want to just end. There's so much more to do. You saw we had like a 20 year vision. Well some of the things that we've done is we have now been funded by LEAKS. And I'll talk a little bit more about that. That's a common fund program. We more recently received a grant from the common fund single cell program where we're doing laser license of single cells. There's a two photon laser license system to go directly from tissue keeping the spatial information and going to a QPCR. Our system is our racetrack thermal cycler that we've developed. And this is a really exciting project because our end goal there is going to take one of those crypts from Barrett's and do a full analysis of QPCR on these cells from the Barrett's crypt. And then another thing is combining the technologies that we've developed in the SEGS with other technologies. So for example we have one of the only we have the only research instrument from the company called VisionGate called CellCT. It's cell-computed tomography. It does create true 3D images of cells. Those are fixed cells. For looking at cell morphometry can quantitate over 800 different parameters. And then with funding from the Keck Foundation we're developing a next-generation instrument that will be doing 3D images and computed tomography on live single cells. So my dream is that we'll be able to take those cells and do the physiological measurements from our SEGS, the morphometry measurements, genomic, transgram, all the different omics, and really interrogate a cell in all the ways possible. So just quickly on the links, what we're doing here is this is directly building on our Microscale Life Sciences Center, the SEGS, to develop the next-generation system that's much higher throughput. The goal is to have, be able to process 10,000 cells in what we could now call a Celerium. And this becomes part of a system and in links, this common fund project, this is to build a database on with cell data. And what we're adding to links is the dimensions of single cell and tom. And in the future, this is my grand vision for other things I work on, which is bioscene chairs, all these things I've been talking about play into this, but the goal is to really transform health care or the concept of bioscene chairs for pre-symptomatic diagnosis and actually prevention of diseases through bioscene chair discovery, figuring out what are those omic and imaging and behavior elements are important and predictive of someone's future health status and then standardization, qualification, clinical malination of these for personalized health and improved quality of life. And this is the team that's worked on our sex over the years. I'm extremely grateful to NHGRI and the sex program for this opportunity to provide a really unique environment and opportunity to develop technologies and work on science that we really couldn't do otherwise. Thank you. Want to answer my question? I mean it is interesting to hear how these things evolve from initial conception. They absolutely do evolve. So when we first started in around 2000 and in our first proposal, we had so many applications that we're going to apply our single cell technology to but we hadn't even developed a single cell technology yet and we were working, we had an investigator on HIV AIDS yeast to study aging, you name it, we had all these different applications and with our, we had a scientific advisory board that was constantly advising us and we seek their advice and they kept saying focus, focus, focus and you saw on Jeff's slide about focus, well that really helped us a lot because we really focused on getting technology that would be work, that would work and then as you saw we have those two model systems and we've stayed focused on those. I have people ask me all the time well can you apply to this, can you apply to that, can you apply to this which is great and we want to do that but we've really remained focused so that we could get the technology development to the stage where you saw now with where we are and with the LEANS program that we can produce data that's useful and we are really at a stage that we can take on more collaborative projects and in the LEANS program we're just the beginning of a collaborative project with Columbia on looking at oxidative phosphorylation for example. So that's the evolve and thankfully the program allows this kind of flexibility but you also get a three-year review and then a competitive renewal and in our competitive renewal by that time we had certainly gone down to the two systems and had a much more focused thanks. So I just want to add to that so this is I mean this is really hard right these are your collaborators and they're part of your grant but then you have to make decisions and so in addition to the biological applications that they had to decide which ones they were going to take forward they had some fantastic technology development investigators. Oh yeah we had a million other technologies that we've been developing but we had to make decisions were the ones that would yeah that's true. We're going to come back to an issue about that when I come back up. I know what that one is I think. I wanted to ask so you made a comment at one point I think earlier on that you thought of this investigation as a 20-year process and you were about halfway through. So just speaking to if you could speak to the way the program is structured that it's five years and five years and potentially ten years and you know granting that all of us would rather have more funding than less. Can you talk about whether that five-year five-year structure was a good structure or some other kind of arrangement might be better and how did you maintain how are you going to maintain funding after this now. Okay so I think five years five years is good. I think it's healthy to have a competitive review at that kind of a time period because the fields are moving so rapidly and it's important to I think revisit what it is you're doing. It makes you think carefully about what you're going to do and you have to write it down in your proposal and say I'm planning to do this. So if you had like a 10-year I think that could be dangerous because you could just go all over the map and maybe in the end not produce something so useful. I think the amount is a good amount because well maybe a million more. Two to three million two I'm talking about direct cost but I think it depends on what kinds of problems you're tackling and what the scope is. There I can see times where if you're trying to do something that carved off some neat piece of something that maybe it wouldn't require a certain amount and you could scale it that way so maybe even some flexibility in what people can come in with. As you see all along I've been getting other funding and I think that's also this this provides like a core and it allows us to have a team that's you know very multidisciplinary and focus on certain things but we complement this with so many things like that cell CT technology for example we use for studies in physical sciences and oncology center with National Cancer Institute and there's different ways to leverage our drive new capability like now this single cell laser license thing is going to take advantage of a lot of the things that we've developed here but take it to the next stage along this continuum here. So you showed this patent pathway and I'm wondering if you could talk a little bit about what if anything you're doing about commercialization and also about the issue of exclusive versus non-exclusive licensing by your institution. So we're we have a whole bunch of disclosures and things we're putting in right now to complement what you saw there and we work closely with our it's called Arizona Technology Enterprises. They're pretty flexible in terms of how to deploy things and what and do it on a case-by-case basis whether it's exclusive or not. For example we've had commercial entities of course interested in our sensors and they want to just take those and there's other avenues and they're parts of our technology that could easily be commercialized for that one thing but what the other thing that I've been doing with with the ACTE is to keep them informed about the bigger picture of our systems so we don't so as decisions are being made by them that we're not losing something in the grand scheme of things. So they're been pretty flexible about that but we they have a whole process on advertising what the technology is trying to gain commercial interest and that way for commercialization and so we're in the process of that with some of our things right now. That answer your question. I was just wondering if you had plans or if you have looked at the single cell evolution of resistance to chemo therapeutic agents by looking by exposing. We have not yet but all but we have a collaborator at Mount Sinai and we actually had that as a project in a sport that we tried that we didn't get. I guess those are hard to get we tried just one time that was in a lung cancer sport. But it is something that we're interested in. Yeah. Jeff, you're going to.