Wisconsin Public Television University Place Wednesday Nite at the Lab  Kevin Eliceiri Imagining and Imaging Molecules  September 7, 2011 UP619   cc >> Melissa Bruenig: Good evening, and welcome to Wednesday Nite at the Lab. Tonight, I'm pleased to present our presenter, Kevin Eliceiri. He received his undergraduate and graduate training in microbiology and biotechnology at the University of Wisconsin-Madison. He worked in Professor John White's laboratory, developing imaging approaches for the model nematode C. elegans. He received further post-graduate training at the National Integrated Microscopy Resource in Madison, Wisconsin, in the area of computer science microscopy. Since 2000, he has been at the Laboratory of Optical and Computational Instrumentation, or LOCI, at the University of Wisconsin-Madison. He is currently the director of the LOCI, and a principle investigator in the Laboratory of Molecular Biology at the University of Wisconsin-Madison graduate school. His current research focuses on the development of novel optical imaging methods for investigating cell signaling and cancer progression, and the development of software for multidimensional image analysis. Everyone, let's welcome Kevin. [APPLAUSE] >> Kevin Eliceiri: I'm delighted to be here. 

Can everybody hear me okay? I naturally speak loud, so I can always speak louder. I also warn you, I also speak very fast, but I will try tonight to speak slower. If I speak too fast, if you do this, I will get the hint. This is one of the best parts of my job, is you hopefully will see I love what I do. I love imaging. Basically, everything she just said, is that I love looking at things. So my passion is trying to understand the good and bad of biology. I want to understand why we are here, how we get from a one-cell embryo to an intact human being. I also want to understand the bad part of biology, the cancer, how cancer happens and what it does. One of the real exciting things is in the area of imaging. 

I'm biased of course, because I love imaging, but we really do feel in the field that we know so much about biology. We know the parts list. It's like knowing all the parts to your car, but we don't know what and how they work. And imaging can do that. So, my whole talk is on imaging. It's a very informal talk. You can ask questions at the end. You can ask questions during. I have some things to pass around. On purpose, I'm going to stop a few times and show movies. 

So, most of my talk is going to be as many pretty images as possible. I have tried, most of my talk is actually going to be a modification on a research talk. I did that on purpose, because I don't like dumbing down talks. I like you to see the real data. I'm going to skip over some details, but if you see something I skipped over you like, you can stop me. Really, this is designed for you to see what my lab does. When I say "my lab," LOCI represents 40 faculty on campus. It's not just me. It's many of us on campus. We're all real excited about what we do. 

Before I get into the PowerPoint, I put up this image. It's one of my favorite things. It's an homage to my graduate training. You can see the movie, actually perfect timing, it just restarted. This is a movie of this little worm called C. elegans. It's a little worm that's a nematode. It lives in the soil. It's this little worm that normally you wouldn't care much about, except that it has a really exciting story. This little worm actually has great genetic similarity to us. So a lot of the genes that are in us are in this worm. 

It's a little worm that we can study. You're watching it go from a one cell embryo, before your eyes in a microscope, you can watch it through its life cycle. You can watch the cells become more specialized, and watch in the process of division, how this little guy becomes more and more complex. You see more cell types, the round circles, the nuclei. It's dividing and dividing. And before your eyes, the entire cycle over the course of a couple nights, you can actually watch this become an early worm on the staged microscope, squirming. I love this image, this movie, because one, it's exciting, the biology. It's really amazing that this little worm has so much relevance. It's so different than you and I, it's appearance, and it has so much genetic similarity. People study aging in this worm. 

They study cancer. They study basic embryology. But it's also exciting that imaging can do this. The power is being able to watch it and be able to see what happens. This is one of the most common images that we acquire at the UW, but we never tire of it, because of the power of it. This is what imaging can do for you. Imaging really is about observing and watching. And over the last 20 years, what we've been doing with imaging is pretty amazing. You're going to hear about techniques that allow us to look, not only with high resolution, so very high magnification, great detail, but also allow us to really look, not only at the pretty picture, but look at the quantitation behind it. I'm going to talk a bit about our ability not only to be qualitative, pretty images, beautiful colors, but also be able to add some math and rigor behind it as well. 

That's one of many movies I'll show. So, who am I, and who is our group? We really are a group that is trying to develop techniques to image, and collaborative work-- Let's pull up the PowerPoint here. The goal of our group, LOCI, has really a very long acronym, which largely just makes it easier for writing grants that it's called LOCI. The purpose of our lab, really, is to collaborate. The point of that name, when you hear Optical and Computational, is just to get across that our goal is we're trying to build instruments that are a combination of software and optics. Our passion is that we want to look at things that are cell based. So a phenomena in your body that involves cells, which is everything. But we want to look at the cell part. We're not interested in just the whole view. A lot of imaging just looks like the entire body scan. 

We want to look deeper. We want to see, not just my arm. We want to see the cells in my arm. When we look at breast cancer, we don't just want to see the topography of the breast, we want to see the cells that make up the breast. It's because cells are where it's at. Cells drive everything. We want to understand what happens in the good again, cell division, and why I'm standing here, how we become more specialized as I develop, and also what happens in bad things like cancer. So we're developing these methods. It's all part of a really neat class of work we call biophotonics. All that means is using light to understand biology. 

That's what drives the whole talk tonight. I will say that I'm only going to cover one small subset of imaging. There are many other types of imaging on campus that involves other techniques. You maybe have been to the doctor for a PET scan, or an MRI, or seen whole body scans of your head. There are many different methods. I will focus on methods that are high resolution, that are deep in, but there are other methods that give you a more holistic view. So I will explain that in a bit. We are NIH funded, NSF funded, so basically we're funded by you. You guys pay all our bills. We're all taxpayer funded. 

All our work is what we call soft money. So, there's not actually any University of Wisconsin money in our lab, directly from the state. It's all competitively based on grants. Everything I do is grant based. We like that because it really keeps us on target. Every few years, we have to write a grant explaining what we're doing, and make sure we're being competitive and doing it right. It's all taxpayer money that funds NIH and NSF. Our mission is to collaborate. So again, we're made up of many, many labs that collaborate together. It's all driven by the work of great students. 

Again, our mission is software and hardware. So, the word is biophotonics. I stole this from Wikipedia. Basically, all it is is the intersection of biology and light. So the point of that whole slide there is that if you look at the film strip, that film strip on the right is that same movie I just showed you, I just put little clips so you can see how it starts from the one cell all the way down. That's what we want to be able to do. We want to be able to use lights to understand an image. Then you may ask, well, why don't I just call it light microscopy. It's a microcopy method. We use a microscope with light. 

It's because biophotonics can be more than that. One of the real powers is we can use light, not only to image, but one of our dreams is we can use light to manipulate the sample. We can actually use light to explore what's going on. I'll give you some examples. I can use light not only to image, but to manipulate your micro-environment. For example, be able to knock cells out, maybe move a cell around, so we can peer deep into an intact environment. Like you could peer into your arm, and not only get an image, but I could tell some cells to be quiet. I could turn them off. I could actually move things around. So you can use light like a tractor beam, like a way of controlling what's going on in the intact environment. 

There are already many examples of this. Biophotonics has already been used a lot for clinical imaging. It's being used more in the fields I'm in, which is tissue engineering. You may have heard of stem cells. I work on stem cells, as well, trying to use stem cells for therapy, and understanding how they can be used for people with different diseases. Then, cell biology is the main thrust of our group. Biophotonics has been used for many years for techniques you may recognize, Lasik surgery, that's classic biophotonics. Corrective eye surgery is using lasers to correct the membrane of your eye. That is using light to manipulate the micro-environment, the environment of your eye. It's also used for studies of macular regeneration, photo dynamic therapy. 

Folks with a tattoo? That's biophotonics. You can actually remove the tattoo via lasers. That's using light to manipulate the micro-environment. In this case, in a cosmetic way. Hair removal, kidney stone ablation. These are tried and true techniques that are biophotonics. But there are many new methods that are being used that you'll hear a bit about tonight. One in particular being breast cancer. You don't have to understand these terms, but these are all clinically proven techniques that are being used right now in the clinic using light. As I go through the talk, I hope you appreciate the reason we like light is light is all around us. 

Light is something that's very compatible with our bodies. Light is something that can be very non-damaging. So the power of light is that you can peer through tissue, but also hopefully not damage it. So, I left this slide in. It's a fairly technical slide. But the only part to concentrate on is the part that says size scales. I always leave this in, because when people hear any imaging guy talk about his work, he always gives an impression that his method is best, right? So you may come away from my talk, and you may think, well, you know, what he works on is really good, and it's good stuff, and this is the best. But it's not. Every method is the best for what it's supposed to do. The 20 or 30 imaging methods out there each have advantages and disadvantages depending on what you want to get. 

And the only word to really get from this slide is that term resolution. Resolution is what drives everything that we do in imaging. Resolution, there's two types of resolution. There's your spatial resolution, so when you look at your monitor, when you mess around with your resolution on your monitor, you can have high res and low res. The images look pixilated or higher definition. For those of you who have played around with your TVs, high definition TVs are crisper, more 3-D. That's important in imaging as well. We want to actually capture images that are more faithful, better representing what goes on. So spatial is important, but also dynamic resolution, tempo resolution. By that, I mean is the event, how fast is it happening, is it happening really quickly. 

When cells talk to each other, they're very, very quick. But other events are slower, and some events are just completely fixed, they don't change much over time at all. So your spatial and your temporal dictate what type of imaging method you can use. So that's the big theme. You can use the microscope for things that are very fast, that are very high resolution, but are very small in scale. But you need to use like an MRI and CT if you're trying to do whole body scans. That will make more sense, hopefully, as I go through it. So here we are. I left this up because we're many, many people. I represent a group, and I'm proud to do so, of many investigators. 

I was looking at this. I've actually probably left about 20 people off. This is just representative for fields. But basically, the neat thing about light microscopy or biophotonics is we can use it for everything from breast cancer research, and there's going to be quite a bit. I thought I'd talk tonight about what we're doing with that work. People are surprised that microscopy folks can be involved in breast cancer research, but we are. We're using it for skin tumors and burn victims, as well, with L. Hoffman, looking at cell division and signaling, and the worm, and other models. Neuroscience, trying to find out how neurons talk to each other. We're involved in a project too, where we're trying to understand brain injury, and trying to actually re-stimulate parts of the brain that have been damaged, like in car accidents and disease. Stem cell biology, trying to understand what happens in stem cells. 

How do they differentiate, trying to understand basic models. And then, we work with many, many engineers, people doing everything from, H. Jiang is trying to understand how you can model the eye and use that to make better optics for a microscope, so basically eye-inspired optics. Computer scientists are helping us demonstrate our data, and creating really beautiful visualizations. On the right there, that same data set I showed you, we have now reconstructed the worm that we saw earlier, now you can actually see the worm turned. We've actually used special colors that are specifically for different proteins where you can actually see the different membranes of the worm. I'll talk more about that. Or, on the bottom, we can actually look at the chemical environment of breast cancer. That T-47D is a particularly nasty breast cancer cell. We can actually look at how that cell is invading the breast cancer. So again, using imaging methods and unique ways. 

As well, we're very into software. I have this slide up just to really show you that we are trying to develop software methods. A project I'll talk a little bit about, a national or international effort to catalog images to share with the public, develop tools for that called the open microscopy environment. I'll talk a bit about that. If you're interested, I'll be happy to talk more afterwards about the software side of things. I'll try to show as many images as possible, but I will talk a bit about the software as well. We can do everything from spectral imaging, this is actually a uterus sample. We're actually looking at the different spectra that happen naturally in the uterus, and trying to understand with uterine cancer, the differences of how light hits the cancer, you actually get naturally different colored spectra. You can have a marker for cancer just based how light interacts with a sample. So again, totally non-invasive. 

On the right there, is actually looking at something called fluorescence, which I'll talk briefly about, but I often call it genetic paint ball. Basically, it's trying to look at the genes, and trying to tag them with color. A large part is based on the fact that we saw the jellyfish glow green in the tanks, in an aquarium. They found a gene that allows that color to happen. We can now put that gene in many different organisms and they naturally glow green. We also figured out how to manipulate that gene so that it does different colors, so it is kind of a genetic paint ball. But it's better than paint ball, because it's naturally occurring once you put it into that organism. Not only does it glow green for the gene you told it to, so the tag you put it on, but all its children do, all its progeny. That's really exciting. You can have a whole line of different colored organisms. 

We'll talk more about that as well. Also, we can look at naturally occurring properties like collagen. You may have heard of collagen. I'll be talking a lot about that tonight. Collagen is one of the major structural proteins in your body. Without it, I'd be a pile of goo here. Collagen actually means glue. I think it's a Greek word for glue. Basically, it's what keeps us together. Collagen not only is good, in fact, models sometimes use it in their lips so they have fuller lips and cosmetic things. 

But we are interested in it because collagen, unfortunately can be hijacked by cancer. It can be the highway of cancer. There's actually a lot of data showing that cancer can use collagen to spread in your body. So there's good and bad aspects to all biology. Then one of the real big things is that we, of course, love imaging. One of our goals is still, we like pretty pictures. A big part of what we do is we like collecting pretty images. That hasn't changed. This is one of my favorite images showing cell division. I don't know if you can quite see it back there with the light in this room. 

But basically, we're actually looking at all the different colors. You're actually seeing the mitochondria, the spindles, the cells they pull apart, all the components. We're very interested in that. Historically, that is the good and bad rap about this, is that we're about pretty pictures, which is good, because they can be very useful. But the concern is, how quantitative can we get? Are we just looking at the event or are we measuring it? The good news is we can measure it. I'll be talking a bit about new methods that not only allow us to look at that pretty picture, collect those colors, but allow us to tell you for example, how far apart those green spindles are, what kind of scale are they operating on, what is the color arrangement, what is the chemical environment, how is the pH, the hydrophobic, these lipids, are interacting. What's happening in the background? We can now do that. Go ahead. >> I'm wondering if we can turn down the lights a bit. I can see that's a gorgeous picture, but I can't quite see it. 

>> What's that? No lower? Okay, sorry. All right. Unfortunately, he's filming. But afterwards, come up and I can show you these. We normally do these in dark rooms, but it's not good for filming. But that's good, people don't have to see me that way. We'll leave the lights on. So, we can quantitative these kind of images, and it is a very pretty image, and that's a big part of what we do. Of course, with all this excitement, there's problems. I'm talking about the good stuff, but we keep plenty busy, because I've told the story about the power of imaging. 

But we have so much work to do. We're actually overwhelmed at what we're acquiring. So I also will be going through, but only briefly, of all the challenges we have. If we collect all this data, you know, if we look at breast cancer and collect all this data, or look at the dividing cell, the C. elegans data, your brain is not very good at dealing with three-dimensional data. So as you collect all that data, we need ways, practical ways, one is we need lots of hard drives, right? We've got to figure out how to store all that data. One of the worst days in my life was a grad student leaves my lab. One, I miss them. They're great people and all that. But I miss them because figure out their data. So what do we do without them, right? It's a big problem. 

The same way, trying to figure out, you know, how all the files in your house are stored, we have the same problem. We have lots of file cabinets to keep track of all this data, digital file cabinets, organizing and tracking it. Then, we need tools for acquiring this data. When I started out ten years ago, 30% of what we did was software related. Now it's 85%-90%. Everything is driven by computers these days. We cannot collect any data without software. Everything is digital. When I first started working in this field, it was all analog. In fact, I remember working for a very famous scientist Hans Rees, and I spent months learning how to develop a print. 

I finally got good enough where I could, in a darkroom, be able to get a print that he would use in a publication. Then we got a digital camera and I never used the skill again. That's what happens. Technology has changed, and everything is digital. That creates a lot of challenges because we have archiving issues. It's much easier to archive prints than digital. There's a real concern about the longterm archival, but it's also difficult to analyze this data because it's three-dimensional. There's a lot of challenges. But the good news of all that is there's great opportunities, is that we can use a lot of things together. This, again, is a fairly technical slide. 

Again, only concentrate on the words that are underlined. The modality, which is the imaging type, the detection method, the way you manipulate your sample, and how you process it. All I'm trying to say is it used to be we had to have compromises. If I wanted to look at a sample, I had to decide what I wanted to do, and use that method by itself. But now, I can combine methods together, so there's less compromise. You can now build systems, and this is a biased view of our work, but it's true of other fields, MRI, CT, and so on, that you can pick and choose and use technologies together for the first time. So you can actually do real time processing of data. For example, let's say that my microscope can see deep in, but it's not deep enough, we're using techniques like adaptive optics that was used by NASA to go deep into space. We can use the same techniques used on telescopes, we can use them on microscopes now. We can actually real time process data. 

So we can pick up where the optics is not good enough and use computer techniques to go even deeper. So this is an example of where you can really pair technologies. Again, that's the good part. But the challenge is, at the end of the day, this is all a human driven process, so you have to make sure the humans can control all this, and be able to managed data flow. I won't really have time to talk about this, but I can talk about it later on. But one of the most fascinating things happening in all of biology is this concept of fluorescence. You saw a couple images so far, when you see color in my images, that's from fluorescence. This idea that you can have things in biology that glow. You can excite it with a light source, special room lights or lasers, and you basically have this excitation of energy where you actually, showing here, you're basically exciting photons and to bring them to a higher state, they become visible, and you get this beautiful glowing, green and red colors. It's really exciting, because these fluorophores can be produced naturally in biology. 

One of the most exciting things, as I mentioned a few minutes ago, is that we found in the case of C. elegans, and other organisms, that we can actually make genetic constructs where we can make these organisms glow different colors and attach it to the gene, so their genes, the same way our genes naturally produce proteins, their genes produce proteins, but these proteins would be labeled, so this genetic paint ball idea. That's really been something that's completely changed the field of cell biology. Fluorescence imaging, the idea of being able to tag with color has really changed what we do. But what surprised us is the benefit for clinical. People thought clinically, this would never really be a great benefit, fluorescence, because no human, including myself, wants a jellyfish protein put in them to make them glow green, right? But the neat news is we have our own proteins, we have our own natural fluorescence. In my body, and all your bodies, we have lots of proteins that you may recognize that glow, that if you put them under a special light, your cells will naturally glow. Collagen glows. NEH, which is a metabolite, tryptophan, elastin, all these really important things, that are really key to nature, nature was kind enough to put in a built-in little marker, so a little transmitter that says hey, I'm here. That's exciting to us, because unlike all the other methods, we have a specific label that tells us who it is, or where it's going, and we haven't done anything. So for the first time, this is why we're getting into clinical. 

In a few slides, I'll show you a clinical study we're doing. We actually can look at clinical sides, just using the natural glowing in our body, this property of fluorescence. In the limited time I have, I can't go into great detail, but I'd be happy to talk about it, because it's really a pretty amazing idea, that you can look at a cell and with a certain wavelength of light, you can actually watch it glow these pretty colors, and you can watch it interact and go around the body in dynamic ways. So, we used a class of imaging that I will not talk about at all called non-linear imaging. All that means is we use special types of lasers that are very expensive, with a very, very long wavelength that allow us to penetrate deep into skin and biology, and excite this fluorescence. This just shows, you probably can't see it with how light the room is, but we actually can use these technologies to, in this case in dye, that normally when you use a laser, you get this really, really big band of light. We're using lasers as a tiny little dot, but that tiny little dot allows us to see deep into tissue, so it gives us very, very good high resolution, and really good signal to background. That means you can see your signal and very little background behind as interference. It's like you're looking at me. Because I'm against this white wall, it's easy to pick me up, maybe not with the white shirt, but it's easy to pick me up. 

That's what we're looking for. We're using techniques that are deep in tissue. In particular, we're interested in trying to collect images like this, where we can use this light to look at collagen. This is collagen in our body, for example, breast cancer. Breast tissue normally is made up of a lot of collagen, and our body has a lot of collagen in it. If you use this laser, collagen has this beautiful structure, you can see kind of a squiggly nature to it. We can actually see all that naturally. In a few seconds, I'll show you some slides showing how we can now exploit that clinically. We also can look at the micro environment of cells. This might be a confusing concept. 

I don't know if I can explain it well. Right now, I'm standing with you. You guys are in my environment. I'm interacting with you. I often talk about the idea of a fingerprint. My fingerprint will tell you who I am, but it won't tell you that I'm standing here or here, versus back there. My fingerprint is only useful for identifying who I am, but not where I am and who I'm interacting with. Well, the real exciting thing with fluorescence based methods of biology, is they have a fingerprint, but it's better than my fingerprint. It's a fingerprint that not only tells you who that protein is, but where he or she is, and how they interacting, what they're doing. So, it's a dynamic fingerprint. 

That's what we're after. One of the real hot pursuits of biology is we want to kind of peer into the world of the cell, watch everything it's doing, who all its players are, on all the scales it's doing it, without it knowing we're there. So we don't want to perturb that environment. And this is what you're seeing here. That image on the left is showing cancer cells and cancer, breast cancer, and showing the domains. You're watching the cell spread through this collagen environment. Think of it like invading through Jell-O. So basically, in breast cancer, for example, you have this matrix of Jell-O, almost, made of collagen in this case. The cells crawl along through it. We want to image that, but we just don't want pretty images. 

We want that quantitative aspect I mentioned. The image on the right shows that we can use special detectors that pick up that fingerprint. All of a sudden, you see all these colors appear. Those colors represent all the changes going on when the cancer is coming through. It tells you what's going on with metabolism, how energy is being produced. It tells you who's interacting with whom, so it's a way of getting a quantitative readout. We build microscopes to do it. Our microscopes, and you're welcome to come to my lab and look at them, we build basically big, fun toys that are just lots of big, 4x8 optical tables, with lots of stuff on them. It's to actually peer into these cells. We also can ablate cells. 

As I mentioned, Lasik surgery. I don't know if you can see this, but we actually can use lasers to poke holes in a worm and knock out gene types. Or in this case, actually nick a little hole in its membrane, so dye could come in. So we can do all sorts of things. We can design new kinds of microscopes. These are the kind of systems that acquire the kind of data I showed you. They actually build detectors and arrays and stuff. So that's the optical part we do. I don't know if you guys have ever heard of adaptive optics, but you've probably heard of the Hubble telescope. Or you may know that the university has its own telescope in Africa that we own with a bunch of universities called the SALT telescope. 

They're using special mirrors that flex. So instead of fixed glass, they can actually go through the atmosphere and peer deep in. We're using that kind of technology, this idea of adapting for going through murky soups of materials, to go through tissue. So, that is a very rough introduction. But now, I thought I'd give you kind of a focused, mini talk within a talk, of how we can use it for breast cancer, because so far, I've shown you lots of images. I'll show you more movies before I'm done of cell biology  examples, watching cells crawl around, watching worms, watching things divide, watching cancer cells. But you may wonder, how is that relevant to breast cancer. Because to be relevant to breast cancer clinically, it's got to be a tool that can be used clinically. It can't just be something that's brought to my microscope. It's got to be something they can interject in the clinical process and be meaningful. 

You know, the challenge of breast cancer is it's a 3-D event, it's three dimensional. It happens in the body, and it happens on a very large scale, not a micro scale that you can just capture it once. You need to really look at the whole environment. But we're very interested in this idea of what we call the breast cancer highways. What you're seeing, obviously, on the left is a beautiful LA and its highways. On the right, you're seeing the highways in breast cancer. So, what you're seeing is a very high resolution image, showing all the cells. And all around these cells is this matrix of fiber. That is actually in a human breast sample. You are actually seeing all those kind of fibers that draped around. 

It's almost like barnacles with rope around them. That fascinates us, because we want to understand, why does that happen. Why do you see this aggregation of cells and this unusual interaction of the natural collagen in your body interacting with this invader. Why does that happen? We have evidence, and when I say "we," I mean the whole field, not just our lab, that what's happening is that the cancer cells are interacting with the collagen, and using it as a netting or a highway to spread around and do its bad stuff. So, as you may know-- Excuse me, I didn't really realize until about five or six years ago, one of the biggest risk factors to women is how dense the breast is. Breast density is a major, major risk factor. It's known to be genetically based. You can see here in this image, the mammogram, that we actually can see a real increase between the two in a higher density of breast. But the challenge is, we actually don't know why. We know it happens, but until recently, we didn't know exactly what was causing the increase in density. 

But one of the real leading hypothesis that we have evidence for is that collagen is what's causing it. We see a real increase in collagen density that corresponds to the overall density of the breast. So our goal is really to understand the good and bad of the collagen. So, collagen is by far the most natural protein in our body, as I mentioned. It's actually the Greek word for a glue producer, so it's meant to be the glue of our body. It's the glue or highway that connects your body. You need it. It's not a bad thing. It's a good thing. But it can be hijacked in bad ways. 

The wavy property of collagen that you're seeing here is what gives it its strength and ability to stretch. It's a matrix. It's ability to really be flexible and be simultaneously tough and durable, but also flexible. As those of you have ever had, I had a mole a few years ago that was concern for cancer, they did a little biopsy, as you may know, the gold standard is one of our great doctors in the hospital will take a little biopsy needle, take a little tissue from you, slice it up, stain it with a dye, and then he or she will look through a microscope and make a diagnosis based on these classic histology slides that are hundreds of years old. This really is the standard in the field, and a trained histologist can look at that image and go, that's normal breast tissue, everything's fine there. The image on the right, which by the way, these are real human slides, is invasive cancer. So they know the pathology based just on their eye, and the stain. But our goal is, can we improve our prediction, because as many of you many know, cancer is a horrible, horrible disease. And our ability to treat it has really gone way up if you catch it early. If we catch it early, we have great success rates. 

But the longer you wait, the bigger the tumor gets, the harder time we have, because in most cases, what really is killing you in these cancers is the spreading of the cancer and the metastasis. Go ahead. >> Just out of curiosity, what is the cancer in the slide on the right? >> What you're seeing is basically the phenotype of the cells. The cells changed in morphology, so it's the shape of the cell types. You see the red guys there. It's the way the dye is picking up the phenotype of the cells, and the way that they're spreading. It's all based on morphology. I have some better images I can show you, but the entire-- When they use visual techniques for cancer, they're completely basing it on this morphology of the cells and their aggregation to a tumor, to a mass. But that's the big problem, because if you wait that long, if you're looking for that type of phenotype change, it's often too late. We want to look for things that are not just visual, but are earlier cues that are micro environment based, so early readouts before you see that big ugly mass. 

Because if you're waiting so long so a pathologist can say I recognized that big, ugly mass of a tumor, it's often way too late. When tumors get that big, they start breaking up and dividing, and the cells start spreading around to the rest of the body. So what we want, is what we often, it's recently been coined by one of my collaborators that she watches a breast cancer surgeon, and she wants the "Google Street View" of cancer. By that, she always says that she can go to Google, she can type her address, and she can see all her neighbors and her house. She can look and zoom out. She wants that for cancer. She's very frustrated, that she wants to be able to get that big view. She wants to see the whole body, the whole scan, but she wants to be able to go individually and see an unusual player, the same way I can see the entire Earth View of Google, and I can keep zooming and zooming in. If you've ever done it, it's kind of fun. You can zoom all the way in to your house address, through countries, and states, and cities, and so on. 

Well, when you look at this kind of image, we want that, and we want the ability. Again, it's hard to see in this room, but I want to be able to put up all the players. So what you're seeing there is a street view, a time lapse view, of cars on a street. You're seeing stoplights and the different lights on the cars. Well, we have that same issue. We want to collect this kind of image, a dynamic image, something happening in real time, but we want to watch it in cancer. The traffic lights are the regulators of normal cell growth, and also what happens in bad things, like cancer progression. The streets are the collagen, and then the cars are the cells, including the bad cancer cells. So I often give this analogy, is that early microscopes were really this equivalent. We were trying to look into a big mammoth with a technique that only allowed us to look this much into it. 

We want to use techniques that allow us that same powerful resolution, but see deeper in. That's the challenge, the elephant in the room, pun intended. So we want to be able to have techniques that are clinically compatible. We want to be able to use methods that allow us to actually look at real samples, and not use dyes or stains, that are high resolution, so these are sub-micron, nanometer resolution, so incredibly high resolution so we can make out individual fibers. It has to look deep in tissue. It's not good for us if we have to be restricted to that gold standard. As I mentioned, the doctor takes my biopsy out, and he sliced it up into five micron slices. Well, we miss so much of cancer. Cancer is 3-D. If you just put 2-D little individual things, imagine if you're trying to understand how this room was set up and all I did was just take a bunch of pictures all the way across and had individual sheets. 

If I messed up the order, you'd have a very hard time assembling the room. Well, we want to see the room intact, what it does, seeing you guys sitting in the chairs, and how it's used. We also need techniques to be able to tell cancer cells apart from normal cells. I won't have time to talk about this, but we have ways of understanding what happens in cancer cells and labeling them with different colors and different kinds of techniques, so we can tell them apart. So we can tell that's cancer, that's normal, and then, also being able to see the highway of the collagen. Such imaging requires a multidisciplinary effort, so I always like to point out that at the end, pretty much my collaborators, you know, often in any talk, including myself, you know, we often at the end, but I want to point out all the great people that make this kind of work happen. It's clinical people, everything from doctors and surgeons to radiologists. It's computer vis people, electrical engineers, computer scientists, and image processing. We even have librarians that work on our projects to actually classify data, so it involves every field. Biologists, everything from breast cancer biologists, to cell biologists, to pathologists. 

And then engineers, people that develop new kinds of techniques, micro worlds for looking at cells, ways of creating realistic environments for people that are physicists, and so on. So, we always joke that catching cancer requires the right tools and people. Again, I've shown this slide before, but we're actually trying to use new kinds of light techniques that allow us, the image on the right, to see those individual fibers. This is what we're after, so for those of you that want to know, and I think to answer your question, you want to know what differences are we seeing in cancer. Well, this is an example on cancer. We are seeing changes in those highways. When you drive your car, you always think about your car on the road. But your car actually does interact with the road. You know, it affects the road, oil spills, that kind of thing. But for the most part, you're not changing the shape of the road, except you may wear down the asphalt. 

For the most part, the shape of the road is more dictating where your car goes than the other way around. Not so in cancer. Cancer, imagine if you were driving in to work, and you saw that it was going to be that you were short on time, imagine if you could move the road so it would bring you right to work instead of going around the bends. That's what cancer can do. Cancer can actually redirect where the collagen goes, and where it is directed. So what you're seeing here are pictures of different morphologies of cancer. We don't understand this. It's the whole chicken and the egg problem. We don't know if the highway, the collagen, is first changing and causing the cells then to respond, or if the cells are causing that. We think it's the interplay. 

We think both happen. But this is something we're really trying to figure out. We have movies, and unfortunately, maybe you can't see it with the lights, but where actually a movie is showing the cells invading along the collagen. So, we're watching the car drive along the highway to its destination. This is a cartoon that hopefully better illustrates what I just showed you. I showed you real data there. This is just a cartoon showing that, where you can see the normal kind of phenotypes up top, but then you can start seeing how the collagen starts breaking up. You can see it's radially around, and then you can see down there on the bottom, the collagen starts sending out a little highway so these cells can start invading your body. This has all been shown. This is really scary, right, that collagen actually becomes this highway, because what kills you is the cancer spreading. 

So we want to understand that for a bunch of reasons. We want to be able to find this early, before it's a tumor, so we can stop it. But what's really exciting, is if we can understand how it happens, we can develop drugs that will stop that process, drugs that wouldn't allow that remodeling, that highway change to happen. This is very practical, because there's already a few problems with drugs on the horizon. So we call this, I have a few buzz words here. You don't have to know this stuff, but we have something we call a Tumor Associated Collagen Signature, so it's a signature for cancer. It's a biomarker. We're wondering if this is useful for breast cancer. Again, we have images that correspond, and this might be hard to see back there, but if you look at human breast cancer and see the tumor, which is black down here and up there is surrounded by the pink there, we can actually see how our stuff matches up. We can take the classic pathology view, which is 2-D, but stained, and we can actually do direct views to show our collagen view, not only correlates with that, but it's better because we can see it 3-D and we can see it earlier. 

We don't require stains. And potentially, we could do this before you even take the tissue out of your body. So one of our goals is, can we inform the surgeons so they get all the cancer. That'd be another goal. This is actually very exciting to us. Actually, a first for my career. I spent my career studying organisms, not humans. So I've studied models. I'd look at worms, and zebrafish, and other things. But we did our first human trial where we looked at 200 patients and samples, and we published in the Journal of Pathology showing this is true in humans, which is really amazing to us, because we always say that animal models are great for humans, as far as models, but to show that it's the same in humans is very exciting. 

So it might be hard to see here, but I can't resist but show some toys. We're trying to actually figure out how we can make this something that will impact you more directly. The image on the left, I don't know if you guys can see it back there, is one of our big microscopes. We'd never be allowed to put that into a clinic, and the clinician wouldn't want to use it. It's too big, unwieldy. It's not clinician friendly. We've built a new version with a local company here, Prairie Technologies, which is a great Wisconsin success story. They've built versions of this instrument that are more turnkey. By that I mean anybody can walk up to it, and we could train you guys in an hour how to use it. It's very friendly. 

Clinicians who are very short on time could drive it. But it's still not small and compact enough. It's still requires heating and cooling. What we're trying to make is that little guy on the right. We're trying to make it just a little scanner. We have a prototype of this. The same guys who are behind Tomotherapy are working with us, trying to develop versions of this that are very compact. So again, our system is nice. We like it because it's big and robust, and we can put things on it. We can change things. 

I can put my hands, it's like working on a big old Chevy car. You can put your hands inside the engine and actually do things. But this other guy is like, you know, the new cars that have higher mileage. You can't work on them as much, they're compact engines, but they get better mileage. They last a long time and are durable. Then the last guy is portable, clinically practical. The one thing I haven't talked much about, those of you who have maybe had any kind of colon imaging or anything done, is that the endoscope. One of the real dreams is get rid of the microscope, just use a fiber, so a bundle of fibers, and actually use a camera on that and stick it where it needs to go, depending on the type of cancer. So, one of the last things to kind of finish that clinical emphasis, is to show the benefits of 3-D. I don't know if you can see that, but this actually is a human breast sample. 

We're actually watching a 3-D view of the collagen highways and that remodeling around that black tumor in the middle there. That black shape is the tumor. Again, it might be hard to see in this room, but the collagen doesn't just happen on one plane, one view like this. It's happening like this. That's why we can't just use those slides. We have to have a method that watches this cancer go. Because highways aren't just like this. They're like that. You have to watch what they do. That's why we need these imaging methods. 

If you're interested, you can come up later. It's much easier to see on the screen. So, the other thing I'm interested in doing is, let's get through some of this, but is looking at metabolism. You've probably heard about metabolism. It's a very important thing in our body. There's things like NADH, it's the energy of our body. It drives everything we do. It's very important, metabolism. But what's really interesting is it's important to cancer, too. Cancer has to have energy to produce. 

It hijacks your body. So, again, I've told you about this highway idea. But the really neat thing in our bodies is also this idea of metabolism, how it's hijacked and used. I won't have time to talk about it, but you're welcome to come up afterwards, is that we can actually understand how metabolism is used, and the difference between normal and cancer cells. The only thing I will say that is quite interesting, but also very scary, is on your body, when you look at cells at they get more and more crowded, of course the metabolism changes. The cells have to adapt, because they have less metabolism, less energy. But in the case of cancer, it's much more resistant. The more crowded it gets, it has ways of preserving its energy and how it operates, that it can actually maintain a much more normal phenotype for it in a crowded environment and a less energy-rich environment than normal, which is why cancer is so effective at being a bad disease and spreading. All these pictures are showing is our ability to image these metabolisms in different ways. We have the ability, I won't have time to explain it, but using special kinds of microscopes, we can not only get pretty pictures, but we can collect that fingerprint I mentioned. 

We can look at its natural environment and figure out what's going on. We can look at things that happen in the metabolism. We can look at changes that normally you think would only be biochemistry, but we have ways of measuring what happens with binding, who's play with who, and what happens. Then we also can combine the two. This is a case of again, where we looked at the metabolism in the samples, but now we can go back and look at the collagen. This shows you a view of the same kind of slide, where you can see a duct there. Then you can see all the beautiful collagen in this breast tumor. Again, this is high resolution images. I threw these in just to show you the high resolution of those fibers. That's what, the kind of corn on the cob look there, that's collagen. 

We're trying to understand how that collagen, this collagen is much more straight than you saw earlier. What happens in our body is not like that, it's all curvy, and particularly in cancer, it changes shape. We're trying to understand why that happens. We can actually use these methods together, where we can actually look at the collagen. But as I mentioned earlier, look at that image there on the far right, the green and the red there, we actually can look at the red as the cancer cells and label the cells invading the collagen. As I mentioned, we can do genetic paintball. We can have two different colors, one representing the natural background, the highway. So we can get to that example, to go back to that picture of the Google Street View, we can label the streets, the collagen, the highway, and we can label the cells. So you can have very specific. This shows more examples of this, where we're now labeling. 

The green there is all the collagen, and the red and the black, you can see the red cells invading. Again, this is another picture I showed you earlier, just showing how we can classify. Ignore all the numbers and stuff, just look at the pictures. You can see these really weird changes in the collagen, you know, the radial outbranching, the kind of squiggly ones. These are all different identified phenotypes that we found that we can stage cancer. We can tell cancers apart. We can look at the chemical properties and look at different properties that happen with metabolism and that together. So for example, you can see here where we're actually looking at the chemistry, which is labeled "B," the stain on the left here. We can watch what happens in the environment and how does it respond. You can see all these different colors. 

These colors are like a heat map that represents everything going on. Then you can see the traditional pretty image above, without the heat map over it, just the seeing the straight image from your eye. I'm going to finish up in the last probably 15 minutes, I'm going to quickly gloss over some computer stuff. I left a lot of my computer slides in, because I just wanted you to appreciate the software, but I'm not really going to talk about the packages and software and how it works, but you can ask me afterwards if you want. I thought I'd like to spend the last time after that, I'm just going to give you some more movies, and what we can do with the imaging. But I wanted to leave this in so you can of course see my bias. I think it's the golden age of optical microscopy. I think most people would agree. But why do I think that? It's because we have, you may have heard in the last 15-20 years, we've made an incredibly investment in the human race, in the Genome Project. We know the genome of our body. 

Not only that, but we're now actually sequencing individual humans. We can sequence people with diseases. We know a lot of the genes for different diseases. We know, not only humans, but we know a lot of the model organisms. We sequenced C. elegans. We sequenced the zebrafish, all these organisms. But the challenge is, it's like knowing all the parts to your car, but you still don't know how your car works. So, you can say I have this list, but how does it work. Well, unfortunately, the way that people often have approached it, is they take the car apart. We want to drive the car. 

So, we want to actually see what it is and how it operates, and still be able to look inside the car and see the individual guys do their job. So we want to be able to label all the parts of the engine and see how they interact. Well, that's what live cell imaging can do. We can see the components, what they do. We can label components, but we can watch them in the attacked entity. One of the challenges, and again, just pay attention to the stuff in yellow is the most important thing, is that we can look at things in 3-D. We have to be three dimensional. Microscopy, going all the way back to the 17th century, was really, the very early days, in the 18th century, it was all about techniques that were 2-D. It was basically a big magnifying glass. So, light microscopy was often thought of as just magnification only. 

We want to do better than that. We want to do more than just magnify. We want to look in 3-D, and we want to label the components and see what they do. That's what we can do now. We can look at things in 3-D. We can look at, not only the structure, but the function. What are they doing. We also can look at growing libraries of fluorophores, again, it's the idea of genetic paintball. We have designer colors. We can actually put them on different genes. 

It's easier with the live cells. We can keep them alive and happy now. We now have the ability that we can mimic environments. We can trick cells into thinking they're in our body, and we can get them to operate the same way that they do outside the body, as they would in the body. And fewer trade-offs. Not only has technology gotten cheaper and cheaper, which is important, but it's more accessible. It's high resolution, higher throughput. It used to be I'd have to decide if I wanted to keep them live, I had to be lower resolution. Now I can be high resolution and live, so things have really changed. Again, one of the last few technical slides I have is that, ignore the stuff on the right, but on the left, this is what my group is about. 

We want to use optical microscopy to go after those four things in yellow. We want dynamics. We want to look at the time lapse. We want to see want to see what happens in real time. Even waving my hand is time lapse. We want to see what happens. We look at the chemistry. In our body, we can look at the chemistry of cells, what happens when the differences in pH, when they're near a membrane, are cells different near our membrane than they are near other cells. We can look at the physics, how light interacts with these things, the structure, the force of cells, and X, Y and Z. That's what we can do with these techniques. 

And what's the opportunity? Well, the opportunity is one, is that we want to use as few animals as possible, whether it's a simple worm or a mouse model, the less animals the better. For all the obvious reasons, plus we do better science if you can understand the biology in one animal all the way through, rather than many, many animals. So we can have better insight. It's because we can get more, gain it from a single animal, and we can look at it live. And every time point can be in one animal, rather than in many organisms making up each time point. We can have a faster path to system level. So a big part of the Wisconsin Institutes of Discovery, which I'm part of as well, and if you're interested we can talk more about it after the talk. In that building across the street here, we're trying to understand systems level. We want to go from the cells to systems, and understand how things interact. We can do this with this 3-D live aspect. 

The other goal is that we want to do things automated discovery for drug discovery. We want to be able to screen large populations of cells and what they do very quickly.  So, I'll probably skip this slide, but basically the holy grail, as we often call it, is we want to see all these measurements, structural, functional. But the biggest thing is we want to know why they occur, where they come from. So the evolution of associations is what we're really after. And of course, I think I'll leave the computational aspect. My lab, we're developing tons of software to address these problems. The complexity of the data, so it's visual and numeric, and your brain's not good at three dimensional data, trying to deal with this. The sheer volume of it, trying to deal with terabytes and petabytes of data. And then, what types of measurements make sense? That's really the challenge. 

So in our lab, we've developed systems to deal with this challenge, and how you can collect this raw data, visualize it, quantitate it, and get your results. So I'm going to end the technical part of the talk there. I'm just going to show you a few movies in the last five minutes or so. It's a good time now to ask me any questions you might have. So, partially I want to show images, because I just like showing images, but also to give you some idea of the survey of what you can do. So I've shown you really neat images, I think, of C. elegans development. But we also can do the same thing in zebrafish. So you're watching before your eyes, this fish develop. You're watching it start becoming the embryo there. I'll just kind of be quiet and let you watch it. 

It's kind of fun.  So, many people on campus work on this, Anna Huttenlocher, Mary --, and this is an amazing model, this little fish. You walk into these labs and they have huge fish tanks that they can image these guys. You may wonder, one of the reasons we love C. elegans and zebrafish, is that not only do they have great genetic homology to us, but they also are clear. So we can really use great imaging methods, because there's a built-in glass bottom boat, so you can see the mystery. Then, it's always fun to think about what worms look like. These are some worms here. I call it crawling critters, but basically this is actually watching worms move on a dish, but we've labeled them green. So, you can see the tips are actually green, so this is a case where you can actually see the fluorescence. I've shown you so far, fluorescence at the cell level, but this is a case where you actually can see the fluorescence from an outside view. 

You're watching these worms move around. The reason they're moving around is they're hungry. They're actually going after bacteria on the plate. They're on a plate where they can move around. So that's an idea of macro imaging. One of my favorite images is this big guy. This is an image that's different than any I've shown you so far. Hopefully-- Unfortunately it's hard on the left there, but what you're seeing is so far I've talked about the fact that all our microscopy data, think of it like we have real high resolution postage stamps, so we can see things in much higher resolution than any other imaging technique, but we're postage size stamp compared to the size of a breast tumor, or overall breast. When we look at that, we're looking at one part of it. We're trying to figure out how to deal with this. 

What we realized, the answer's pretty simple. We can actually just automatically capture all the postage stamps across the image and stitch them together. It's the same way that those of you who might have been to the Grand Canyon who use a camera and do a panoramic view. We can do the same thing. So this image on the right, this is where we took 81 different pictures and we stitched them together so we see the entire view. It's the best of all worlds. It's closer to what my collaborator Lee Wilke wanted. We're getting closer to that Google Street View, because we're doing what Google does. We have the big view, the macro view, and we have as you zoom in, you can see the micro. So in this image on the right, which I can show you up here much better, you can see that huge view in high resolution, but better than any other imaging technique, you could, with your magnifying glass, you could zoom in. 

And if you zoom in, it won't be pixilated, because the data was collected at high resolution. You're actually looking at the native resolution. That is so exciting to imaging and biologists, and cancer folks, clinical folks, the ability to do that. Again, this is all new by good software, because back in the day, I know with this kind of stuff, the automated stage was my hand. I would move the thing across and wait, and you know, grad student labor. That's what grad school was about, you do that. But now we have automated systems. We have automated ways of collecting all that data with a motorized stage, and very smart software that can take those 81 things and stitch them together and make that montage. That's where software can really come into play.  I think I showed you this movie already, but this is maybe a better view. 

This is actually watching the worm again. But in this case, we've used a different dye, in this case for a membrane. You actually can see the worm. I don't know if you can see that view, but the worm is like this, and it's curled up, and you can see it moving around. It really allows you, the cool thing about-- Now, we're just having fun. Computer guys like to show off, so we're just zooming in and out. But it's kind of fun that you can actually see the different views, what goes on. This shows you the power of fluorescence. Because fluorescence is a genetic paintball, because we can label certain things, it means we can isolate those components. We can only show the things we want. 

So we can declutter, we can minimize the view and look at things of interest. So here, we're only looking at part of what goes on, but that's what we wanted.  Of course, as a teaser to other talks is that there are a lot of reasons you want to do other kinds of imaging. I did bring images from my collaborators, you know, showing the ability to actually scan a head with MRI and CT techniques. We can't do that. We can show you the cells within that guy, but we can't give you that kind of whole view, because we're only going to go a millimeter into your head, not the whole head. So there is still very much a need for those techniques. And hopefully, there will be future talks and seminars about that. The other kinds of things that we can't do, and I apologize if this is too close to dinner time for me to show this, but we can't, for example, we have colleagues that want to zoom through the colon. So a little groty, but really cool. 

We can't do that. But we can in imaging, so we have the ability to use techniques, colonoscopies, where now, for those of you who have it done, are going to be very happy to hear we now can do it virtually. We can use methods that don't require the endoscope anymore. We actually can use methods that actually can scan, obviously through, and reconstruct that view, instead of having to have a physical device be inserted. So I want you to understand that, again, every method, even though we love what we do, every method has its pros and cons. So I think I will end there, because I don't have time. But let me end here by just thanking a few people here.  So, of course, many, many people make this work happen. There we go. All right. 

So, I ran out of room on the slide, so I wasn't able to thank everybody, but I think the core people of the work I showed. It's mostly thanks to people that directly contributed slides. This is by no means thanking everybody that's really made what we do work. But very importantly, I always like to start first that we absolutely rely on funding, NIH, NSF, DOD, all these entities make it all happen. They're all partners in this effort. They're are many, many web pages. We're going to hear more about this. Probably the easiest thing to do is just type LOCI in Google, and we're second down. Feel free to email me with any questions. I love talking about this stuff. 

Again, what drives our work, our scientists, particularly the students. Students make everything happen at this university, the grad students, the undergrads, they do it all. These are many, many talented students that make this all work. Then the many, many professors and investigators that we collaborate with. With that, I will end. Thank you very much for everyone's time. [APPLAUSE]