Hello and welcome to today's webinar titled Using Halo Tech Technology and Genelia Floor Dyes for Live Cell Single Molecule Imaging. My name is Simon Moe and I'll be your moderator today. Before we begin, I would like to cover a few housekeeping items. And so on your screen, there are multiple windows, all of which are movable and resizable. So feel free to move them around to get the most out of your desktop space. In this webinar, we also have many ways that it can be interacted with. You can submit a question at any time during the webinar. We will be answering these during a live Q&A session at the end of the webinar. There's also a resource library that has a list of helpful materials, which also includes a copy of today's presentation. So if you want to open that up and follow along, feel free to do that. You can also download any of those resources or bookmark any of the links if you find them useful. After the presentations, there will be a survey. Please take a moment to answer these few questions. We're really going to appreciate your feedback on that. And then we're also welcoming you to share the webinar with any of your colleagues if they couldn't make it today, if they're interested in learning more about that. There are a couple polling questions I'm going to start with before I introduce our speaker. And so the first polling question is, do you currently use a specific labeling system for live cell imaging? And you can select all that apply. We have option a Halo tag, B snap tag or clip tag, C fluorescent proteins, and then D is none yet. And so I'm going to give you guys a little bit of time to to answer these. All right, So let's see how the attendees have responded to that. So we got a good, good mixture of people that are familiar with Halo Tag, some that are maybe using alternatives and even some that are that are not yet using any of these. So that's really exciting to see. We have one more polling question. And so this one is, do you currently use Halo Tag or Genelia floor dyes in your research? And so this is a single answer. We have a yes, both B only Halo tag, C only Genelia floor dies, and D, which is neither. So again, I'll give you guys a little bit of time to to respond to this question before we move on. All right. And again, it looks like we have a good mixture of people that are using Halo Tag and Genelia Floor and some that aren't at all. And so hopefully we get some really good insight gained from today's webinar for everyone in attendance. All right, now it's my pleasure to introduce the speaker of today's webinar, Doctor Young Schmidt. He's an associate professor at the Institute of Quantitative Health Science and Engineering at Michigan State University. He received his PhD from the Massachusetts Institute of Technology, where his dissertation research with Dr. Ian Cheeseman focused on understanding the mechanism by which the kinetic core powers chromosome movements during cell division. First post doctoral fellowship, he joined the lab of Doctor Tim Tom Catch at the University of Colorado where he developed single molecule imaging methods to analyze telomerase recruitment to telomeres. At Michigan State University, Dr. Schmidt's laboratory uses single molecule imaging and other quantitative microscopy approaches to define the molecular mechanisms underlying telomerase function, DNA damage repair, and autophagy in human cancer cells. And so with that, I'm going to hand it over. All right, thank you very much for the introduction. So what, what I really want to accomplish today with, with this webinar is try to provide some insight into our experience with using the Halo tag and the Genelia floor dyes for, for single molecule imaging. And so we, we have quite a bit of experience with this, using it for, for a variety of different biological mechanisms like what just mentioned. So we started out with telomeres and telomerase and that's what you can see here in these videos. These are five different telomere proteins that we tagged with a Halo tag and labeled with Genelia fluidized. And you can see that they have a sort of kind of different properties here in these movies where each single spot that you can see each signal is a single molecule. But before, before I get into all the, the, the nitty gritty details, I just want to thank all the people in my lab that have contributed to the, to the slides that I'm going to show today, to the data that I'm going to show. So we have Maria here in the front, Tom in the back and, and Josh in the back. And let me just make sure my pointer thing is workable. So here, here you've got Maria right here, I'm over here and Josh all the way in the back. And they, they all contribute to, to different aspects of the, the data that I'm going to show. So, so let me start off by talking about why would one want to do single molecule imaging and what kind of information can provide? So in this, in this slide here, what I'm showing is 2 nuclei of cells where we have Halo tagged adna repair factor. And they're just two different alleles of the same exact protein. And so by by just looking at this image, you can see how it's a nuclear protein, right? And so in this case, we just added a bunch of ligand, made sure all the molecules are labeled so we can see sort of the overall distribution of this protein. So what we can conclude from this is that it's a nuclear protein and we're not much beyond that. But if you look at it at the single molecule level, what you can appreciate here is that they have very different dynamic properties. So on the left, most of the molecules are static and don't move around very much. And on the right, almost every single molecule is moving around quite a bit, right? So that's something that you can appreciate when you're looking at the single molecule level, but you cannot see that when you're labeling all of the molecules, which gives you sort of an overall picture of the distribution of these proteins. So for example, if you have working on a project where things localized in specific spots, you might be able to appreciate that here, but you can still get so much additional information from looking at single molecules. SO11 key thing that we have to be able to do to, to use this technology or to, to study the biological processes that you're interested in is you have to have basically something important happened that changes the movement pattern of the protein, right? So in our case, a lot of the proteins that we're studying get recruited to chromatin when DNA damage is induced, right? So what we you can, you can appreciate that when the molecules is kind of zipping around in the nucleus and then when it recognizes the site of DNA damage, it stops when it binds to it because the underlying, the underlying chromatin doesn't move around very much, right? And so that's just a simple example of that. So you're going from something that's just diffusing through the nucleus by Brownian motion and then it interacts with adna break and it stops, right? But there's also other aspects of biochemical changes that we could address. For example, you can of course look at localization. So if something moves from the nucleus to the cytoplasm or vice versa, or for example, when a macromolecule forms a complex with something else, that it might move more slowly through the nucleus. And this is typically something where the changes have to be very large, right? So like timerization of a protein is not something that would typically be easily accessible. But for example, if you have a small molecule that binds to a very large complex, you might be able to see a reduction in the step sizes that the molecule takes in the nucleus. And then there's also some other things like this. These methods have been extensively used to look at the search mechanism, for example, of transcription factors that might bind to chromatin loosely and kind of scan around on the chromatin before finding their cognitive binding sign. So, but basically what you have to have is you have to have a biological process where the the interesting biology that happens leads to a change in the movement pattern of the protein. And, and for those kind of questions, this method is tremendously powerful. OK, So what do we have to do technically to to be able to do this, right? So I showed in the previous slide what it looks like when you label all of the molecules versus labeling only a subset of the molecules. So what you need is you need a tool where you can only label a subset of the molecules, right. So historically there has been ways of doing this with photo activatable proteins. For example, in the initial Palm iterations, one would use a photo activatable or photo convertible fluorescent protein. But now with the Halo ligand, this gets much, much easier and much more. You can much better control the amount of labeling. So that the way the Halo tag works, and I just have the chemistry line out here on the top is basically you have the Halo tag protein, which you can fuse to your protein of interest. And the Halo tag basically has a, an carboxyl group in its active site that will react with this Halo ligand here. And this Halo ligand is basically just a more, more or less a PEG molecule and has an active group at the end that can react with this carboxyl. And that makes a covalent bond, right? So then when, when you're when you're labeling the Halotech, the covalent bond is made. And at the other end of this Halo ligand, you can append whatever you want. And obviously in our case, what we're doing is we're using the Generea fluor dyes, which are very bright and photo stable small molecule fluorophores. And I'll get into the details of them in a couple of slides. And and obviously the key thing for us here is that these are actually cell permeable and so that they can get into the nucleus and inside of the cell to label the proteins that we're interested in studying. OK. So then the other thing that we need to be able to do is we need to be able to, you know, in terms of the microscope that we're using, we typically use turf microscopes and we use them in this high, low mode where we're basically instead of reflecting the laser beam off the off the interface of the glass with the with the aqueous media layer, we just basically shoot the laser through at A at a shallow angle, which gives us high laser power that can reach all the way into the nucleus at the cell. And what it does on top of that is it basically creates kind of a sort of a pseudo light sheet where not all the molecules in the nucleus are illuminated. So for example, these two molecules here would not be hit by the laser beam, but the ones here are hit by the laser beam. So this combination of only labeling a subset of the molecules and this high-powered, high, low light sheet basically gives us the ability to detect single molecules and follow them over time. All right, so So what, what, what else do you need for, for accomplishing this? Right? So we have our our chemistry set up we have our microscope set up There's a couple of additional things that you have to take into consideration before starting experience like this. So what you need is on your microscope. You need a turf microscope. I already mentioned that you have to make sure that the laser powers on that turf microscope are sufficiently high. So typically anything above 100 to 200 milliwatts of laser power are required coming out of the laser box. And most microscope systems have that. There might be some that you know, might be older systems where you don't have enough power coming out of the laser. The other thing is that you need a high end, either EMCCD or SC mas camera that has a very low noise level and can read out very fast, right? So you might have been able to appreciate in, in that movie that I showed earlier, typically we're imaging at around 100 frames per second, which means that the exposure times are only 10 milliseconds. So you need a camera that can basically at least read out that fast, right? So there's a couple of different a couple of different options. We typically use SC MOS cameras at this point, which are very fast and sensitive at this point. OK, so let's get into the the Genelia fluid dyes and the different flavors and varieties that we have and what sort of their their benefits and benefits and potential downsides are. So first of all, I want to give a big shout out to to Luke Levis and Jonathan Graham, who are the chemists that Genelia farm that are sort of the the inventors of all of these ligands. There's also other people involved, but these are the two people that have been driving this over the past ten years or so to get to get these fluorophores in, in our hands. So there's basically two different spectral flavors of this. On the left we have JF549 and JFX 549. So, so the difference between JF and JFX is whether there's hydrogen atoms out on these rings or deuterium, right? So these are all deuterated versions of the same exact chemical compound. And those typically help with photo stability. So the the JF549 and the JFX 554 are sort of your, your orange dyes, right? So they would be similar to maybe a side three or a, you know, and cherries a little longer ship red shaped. But these would be something that you would illuminate with the 561 or 532 laser line. And on the right is your far red dyes. And and the key difference if you look at the chemical structures here is, is the difference between this oxygen and this silicon right here. And again, same same thing with with the JF and JFX, there's the deuterated versions and the non deuterated versions. So these are for, I would say for 9 out of 10 of our experiments, we will probably use the far red versions of this. And there's a couple of different reasons for that. One, it's that the cells in general will absorb and have sort of auto fluorescent and scattering much more strongly at shorter wavelengths. So going to a higher wavelength like the guys on the right here have you end up with less background. And so that that's one of the key reasons. The other is that basically you have there's a couple other things to take into consideration based on the cameras that you're using. Typically the cameras are more sensitive in this wavelength range. So that might be a small advantage, but but the key differences in our hands that we have found is that these the ones are the the Jet X, sorry, the 554 dies over here on the left are they label a lot faster than the the far red ship that dies. So when you if you we've tried this out, if you add the same amount of two of these ligands to the same cells, you'll see mostly the one on the left and hardly the one on the right, because this one just gets in the cell and reacts with the Halo tag a lot faster. On the flip side, though, all the dyes on the right, and I'll get back to the JF657 in a second, they are, they're bright. So brightness in terms of how much signal you get out of them, all of these are very good. But the dyes on the right, they are much more photo stable than the ones on the left. And with each iteration, this has gotten better. So JF when when I first started doing these experiments, we used almost exclusively JF646, which is pretty bright and photo stable. JFX 650 improve this a lot. So we got more photo stability, which is absolutely critical for these experiments because if if you look at the movies, you have very high time resolution, you use very high laser power, which means that most of the molecule will molecules will bleach in about 10 ish seconds, right. So you don't have a very large time frame that you can observe. So now just get back real quick at this JF657. This was only introduced about a year or two ago. And this is a chemically very different. This is based on an an ADO dye. And these are in terms of photo stability are extremely, extremely good. I mean an order of magnitude better than the 657. They do have 1 little downside as if you use them at a higher concentration, you get some mitochondrial background signal. And so if you have a protein that is pretty highly expressed and you can just use a little bit of this ligand to make sure this background doesn't pop up, this is the best choice for single logical imaging. OK, so basically what we need right for, for, for making these observations is we need a tagged protein that we that we fuse to a Halo tag and then we have to label it with a genelia flour dye. So in our hands, what we always do for these experiments is we insert the Halo tag into the endogenous locus of the gene using CRISPR CAS 9 genome editing. And there's a couple of different strategies for that. We, we have a pretty nice review that that summarizes all these options. But basically the, the, the reason we want to introduce the Halo tag at the endogenous locus is that we want to maintain the endogenous expression levels, right? Because if you use, for example, a transient transfection or viral integration to express your Halo tag protein, you usually you will be overexpressing it quite a bit, which can dramatically change the, the single molecule. So the, the properties of the protein when you're using single molecule experiments. So we always integrate everything at the endogenous locus. And then when it comes to labeling, and this is sort of one of the things where you just got to try it out because the, every protein is a little bit different, right? So you can imagine that the expression levels of the protein have a huge impact on how you label, right? And for for the the movies that I showed, what you don't want to have happen is that the particles are too close together, right? So I don't have a lot of time in this in December and this webinar to get into the data analysis aspects of of our work. But you can imagine if you want to track the molecules from frame to frame, if they're really close together, it's very hard, very easy to make wrong connections, right? So you want that labeling density to be pretty sparse so that you can unambiguously track them. So you have to have sort of the sort of the sweet spot with the number of molecules in each frame. So here, here's a couple of examples that that we from, from our recent work where we have DNA repair factors, which I'll explain in a little bit more detail in a couple of slides that have very, very different expression levels. For example, this Crew 70 protein, we have about 1.3 molecules in each cell. And then in contrast, all the way at the bottom here, XRCC 4, there's only about 20,000 molecules. And so you can appreciate when we're doing these, this labeling and each one of these, there's no magical formula. You just got to try out and and optimize your labeling the way you end up with the right density. For the Crew 70, we only use 100 pico molar of the JFX 650 Halo. They can for 30 seconds, right? So 30 seconds and then you wash it away and then you end up with a very nice labeling density. In contrast, the XRCC 4, we use 500 times more of the ligand and we label for a minute, right? So there's a pretty wide range of, of labeling conditions that are optimal for the particular protein that you're working with. Sort of a general ballpark that I can provide, and I should have put that in writing in here, is if you want to label all the molecules in a cell, if you use 100 nanomolar objects at 6:50, it takes about 10 minutes. And that that for that you are labeling. So we've done like by, and you can actually look this up in this paper that was recently published in Nature Communications. We did like a titration and we make sure at what level do we get optimal labeling or I'm sorry, complete labeling. OK, so that's something that you just have to empirically figure out for your protein. And that's also why having the hemo tag inserted into the genomic locus locus is so important because then every cell will be very consistent, right? If you're doing a transient transfection or a viral expression, there might be big differences from cell to cell, which might might makes it much, much harder of finding a labeling condition that works for the majority of cells. Or in some cases, if you do a transaction, you won't be able to image some cells but not others because they're over or under labeled. OK, so let's get have a couple of examples in in the presentation of what you can actually learn using these types of experiments. And so I had already alluded to the, the idea that what, what you need to have to, to study some biological processes, you need to have a change in, in protein motion as a result of some interesting biological process, right? And, and so here in this slide, what we have is, is an example of some control experiments that we did where we just expressed the Halo tag all by itself. So it's not fused to anything else. It just has a nuclear localization signal on it. And then on the left on the left here is a click that under there on the left here is a Halo tag fused to the histone H2B. So what you can immediately appreciate is that if you put the Halo tag on the histone and so you incorporate it into chromatin, it doesn't move very much. On the other hand, if you have the Halo tag by itself, it steps around pretty quickly. And so another thing to appreciate here is if you can see this the time scale, right? So this is slowed down about tenfold or so, right? So this is very, very, very fast imaging. OK, so now how with these kinds of movies, what do we need to do to get the information about the biology out of them? So typically we form a bunch of cells, right? And so each one of these movies is a, is a set of images that have the, the different signals on them. And so that what we do is we use single particle tracking. And, and this is in this case, I don't have the reference in here. This is a tool called spot on that is used. Basically, there's two steps. First, you localize all the molecules and then you can connect them into trajectories, right? So you can have different types of trajectories. You have molecules that are freely moving, you have molecules that are static. And then what we can do is we can plot the step size distributions and analyze their properties, right? And so the, the, the key pieces of information that we can get out of this is we can get a diffusion coefficient for the moving and the static particles and we can tell what fraction of the molecules is not moving and what fraction of the molecules is moving. So that's sort of the key property that we're going to use moving forward. And the story that I'm going to tell is we're going to use this fraction of immobile particles as a proxy for the fraction of molecules that are bound to chromatin. OK. So the biological process that I want to talk about is non homologous and joining. And this is a process by which double stranded brakes are repaired themselves. And basically it's a very, on a surface, very simple mechanism where you take the DNA ends here in green on both sides. And then you lighted them back together to, to make sure that the DNA strands is connected again. And so here on the right, what I'm showing is, so this is all done by Maria Mikova in the lab and I needed to change this. That was just just published last week. She tagged all of these proteins here on the left. So DNAPKCS, which is this big magenta kinase here at the top, Ku 70, which I already talked about a little bit, binds directly to the DNA end. And then you have these other factors, XRCC 4 and XLF, which connect the two DNA strands together by bridging the brake in the middle. And XRCC 4 also binds to ligase 4 and Gray, which is the enzyme that actually puts the DNA back together. OK, so, so that's a biological process that we want to look at. And one of the really important things that we always do when we generate these Halo tag fusion proteins if we want to make sure that they are actually functional, right? Because in the many cases, what what can happen is when you generate a fusion protein is that the the protein might not work. I would say 9 out of 10 times the proteins work. In some rare cases they don't. So, so we, we just did some test experiments when we challenge the cells with the DNA damaging agent and all of the cell lines can repair DNA just fine. So that means that the Halo tag doesn't change the biology of the process. So when you're, when you're looking at using microscopy to study DNA repair, what people typically do is they, they take their cells that you know that you can either in living cells or in fixed cells, analyze the localization of your protein. You damage it with a drug like CSM. And here's an example on the right where you can see that once you do damage, the protein makes these nice bright foci where the repair factors accumulate around sites of DNA damage With the NHJ factors, that does not happen, right? Because first of all, they're very highly expressed and second of all, when you induce a break like you saw in the structure that I showed you, there's only two to four molecules of each of these that are localizing to each break. So that's not enough to give you a beautiful bright signal like you see here on the right. So this is probably each one of these spots, it probably has hundreds of the repair factor rather than only two to four, right? So, so by by regular microscopy, we can't see, we can't analyze these proteins localizing to DNA breaks. So of course I wouldn't be telling you this if you couldn't use single molecule imaging to do this. So in in this movie that's going to pop up here in a second, what I'm showing is on the left a control cell and on the right, a cell that's been treated with the drug Zeus and we're looking at the Halo Q70 protein. So what, what you can appreciate on the left is that the that the the molecules are very mobile. They don't move around very much because they're not bound to DNA breaks. Once you generate DNA breaks, you start seeing a lot of these molecules that are not moving around, which is a consequence of the association with the DNA breaks. So what we can do with this now is we can carry out the single particle tracking and determine the the fraction of molecules that are bound to chromatin. And you can appreciate here at the bottom for all of the repair factors. When once we induce DNA damage, the fraction of chromatin bound molecules jumps from from the baseline here for DNAPK usually about 20% all the way up to to 30 to 40%. So that's now as a consequence or a direct readout of watching these molecules bind to DNA breaks. And again, this would not be visible by any other methodology other than single molecule imaging. OK, so now what what Maria did next with this is we basically want to make biochemical measurements in cells, right? So we want to watch this repair process as it occurs over time after we induce DNA damage. So we had to play a little bit of a trick in a in a perfect world where photo bleaching wasn't an issue, we could just film 1 cell for 20 minutes, watch the molecules become static, and then watch them fall back off. Unfortunately, photo bleaching is an issue. There's some other tricks that we can play to stretch the time frame out, but for this experiment we kind of got around that by just filming individual cells each for 10 seconds over an extended period of time. So what we do in this experiment is we we start filming cells at 10.0. So here's a bunch of control cells. Each one is filmed one per minute or so. Then we have a short window of time in the middle where we induce DNA damage with a drug called calcium icin. It's similar to ZSN. It induces double stranded breaks. It's just a little bit more potent. And then we keep filming cells 1 cell per minute and we watch the DNA repair factors get recruited to chromatin and then we determine the time point where they fall back off, assuming that that's when DNA repair is completed. So here's an example of that for, for the crew 70 protein, right? So we, we have our control timeline from here to here where we saw themselves before damage. And it's very consistent. The fraction bound is low and then we induce DNA damage and the fraction bound jumps up. And then after 30 minutes or so, it was that done. So basically what we think is happening here is that we induce the damage, the KU 70 gets recruited to chromatin it, all the brakes get repaired over time and then it goes back down. What's important to know about this is that this is not, we're not looking at one repair event. We're concerned of looking at the sum of all repair events in the cell, right. So that's it's sort of using Sigma molecule imaging to do a bulk biochemical experiment. So what we can do then is, you know, what do what do we do as a biochemist, if you want to, you want to alter things, we change, we change the concentration of breaks, right? So what we can do is we can just use different concentrations of the drug. So here we have for a range of four different concentrations. And what you can appreciate is that the higher the concentration of the drug that we use as the higher the fraction bound becomes, because now there's more brakes, there's more sites that this Crew 70 protein can bind to. And on the right here I have, I have a cartoon of the way we think the repair process works. And I'll explain that a little bit more detail in a second. So what's really cool here is that you can see basically that the Crew 70 bind to chromatin and then over time it falls off. And that's sort of, you can imagine this being your 1st order process of DNA repair. It's obviously a little bit more complicated than that. But so here you can get an idea of the, the time that it takes to repair the brakes. So what's interesting though, is when you look at the other proteins, so now we're looking at in the middle here, DNAPK and in the bottom XRCC 4 is they don't quite have the same response. Instead, they with increasing numbers of brakes, they they plateau at a certain level. And then with the increasing the number of fakes, it's not necessarily the height of the signal that increases, but it's only the duration for which it's found. So basically what we think is going on here, and I have to just remind you that this coup protein here is super duper abundant. There's about 1.6 molecule, 1.6 million molecules of this per cell, which means that once adna break is formed just by mass action, each one of these breaks is really quickly going to bind to one of these coup molecules or coup header dimers because there's just so many of them, OK. The other factors on the other hand are much less abundant. So DNA PK is about 100,000 molecules and XRCC 4 is only about 40,000 molecules. So what we think is happening here is that when we induce damage, we are introducing more DNA breaks than we have DNA PK and XRCC 4 molecules available. So when a when when we introduce the brakes, coup binds to all of the brakes, but there's not enough DNAPK and XLF or an XRCC 4 to go around to go to all of the brakes. And So what happens then when a brake gets repaired is that the, the fraction of pound of coup goes down because it gets displaced from this brake that got repaired. But now all the other molecules go to the next break, which is basically analogous to a protein or sort of if you're doing an enzymatic assay and you saturate the you saturate the substrate concentration and your protein is operating at maximal speed, right? So they will plateau just like we observed. And we're just going to go back a couple slides here to bring that back up. They plateau because they're operating at maximal speed. This is the highest you're ever going to see it. And then they fall off when they're done. And so we can, we can then use this information to actually calculate how brake repair, how fast brake repair occurs in the South. So we can use the height of the coup peak, assuming that every brake associates with coup to estimate the number of breaks and the repair time we can estimate by the time window during which extra CC four and by by association the ligase associates with the break. So when we do that, we have to do some some other, we have to take care of some other assumptions because we only label cell that we don't label all of the molecules and we don't observe the entire nuclear volume, right? So that's actually one of the things that I didn't point out earlier is when you're doing these experiments, in most cases you're only observing a single focal plane, right? So you're not observing a 3D volume. You're limited to one focal plane, which has a couple of downsides. For example, molecules can diffuse out of your focal plane and disappear because they moved away rather than for for other reasons like photo bleaching. If you work with bacteria, for example, that's not so much of A concern because they're thin enough that it doesn't matter. OK. So basically what we get for that is we can we can look at the different, different concentrations that we use and for all of these concentrations what we end up with is a repair rate of about 1000 breaks per minute. So basically what we what we can conclude from this experiment is that the non homologous and joining pathway has an absolute maximum capacity of repairing about 1000 breaks per minute, right. And so all of these measurements in living cells would not have been possible without being able to detect similar molecules. OK, so that's, that's super cool, right? Because we can now basically make precise measurements of biochemical activities in living cells rather than in test tubes. And what's really fun about this is that it actually made a lot of sense in terms of what we saw in in cryogram structures or other in vitreous experiments. So, So what else can we run? So what what I showed you before in this little cartoon on the right is basically there's this idea that first two binds and then the critical thing that you have to do to repair DNA breaks is you have to make sure that the two ends are held together. If the two ends are not held together, bad things can happen. You can have translocations or you can have other end processing that happens that leads to mutations. So, but what we know from these experiments is that from a Detroit experiments is that supposedly you get this tethering in one of these two confirmations and then this DNAPK molecule falls off before you get to the state here, which is we call the short range complex where the DNA ends can actually be ligated together. So the question that we wanted to address is can we actually see this in living cells? So now instead of looking at the sort of the rate at which these signals go away, what we wanted to look at is the timing of the recruitment. So here again is that 270 graph that I showed before. Now let's look at the the magenta protein DNIPKCS, which comes on super fast, just like the coup molecule. But then it falls off after about 18 minutes. And there's a whole big window of time where coup is there, but DNIPK is not, which is consistent with a coup free, sorry, ADNIPKCS free complex form. So this wouldn't be the complete story without looking at the other two proteins. And voila, the other two proteins stay just as long as coup does, right? So basically, if this complex here forms, all the three proteins are there. And the other fun thing is that we can see is that there's actually you can appreciate these two coup and DNAPKCS go up immediately after we induce damage. On the other hand, XLF and XRCC 4, there's a delay, right? So basically what we can say from these in vitro from these lifestyle experiments is probably this confirmation forms first holding the ends together before these other factors get recruited to then go through the the repair. And so that that's, that was a super nice observation. But what's always important for for these types of experiments is while we can see differences in in the recruitment patterns based on the mobility of the molecules, we can't say just without doing additional experiments if that is truly binding to a break or if it's just some random occurrence that has nothing to do with break induction. So what we can do then is we can manipulate the system. And so one of the things that was really powerful in this case is that what we can do is we can block the transition to this short range complex because it requires the kinase activity of DNAPKCS. So when we do this experiment in the presence of an inhibitor that blocks the kinase activity of DNAPKCS, what we can now see is DNAPKCS is not removed. That's the compared to this, right. If you have activity, it gets removed really quickly. And if you block the kinase activity, it's retained at the break and just stays on, right? So that makes us very confident that what we're observing in these experiments is actually these complexes forming because once we manipulate them, they behave exactly like we would expect when biochemical activities are modulated. And just one other example here, we can also do. So this was basically a treatment where we added a drug, a small molecule inhibitor. Here is an experiment where we did a knockout experiment. So we knocked out the ligase, which if you knocked out the ligase, you can't form this complex here. So what we see in those cases is XRCC 4, which is down here, this protein is never recruited and COUP and DNA PK are recruited but fall off really quickly. And this is something that we're not really interested in studying because it likely means that when this process can't move forward, potentially other repair pathways take over to fix these brakes because they're really dangerous. So basically what we can conclude from all of these experiments is, and I'm not going to bore you with all the details here, is that we can really dissect how these this repair pathway progresses biochemically by doing these single molecule lifestyle experiments. And I just want to conclude by bringing up another short project that I don't want to go into much of the, of the biological details, but I what I really want to do is tell you a little bit about some cool tricks that you can play with a Halo tag and Genelia floor system. So in this project we're looking at, and this is actually the movies that I showed up, the very first slide, we tagged all these proteins that are telomeric proteins that are important in protecting chromosome ends. So we did the same thing. We introduced Halo tags for all of these proteins at the endogenous low side and then did single molecule imaging. But the point that I want to make is that you can actually basically do two kinds of imaging in one experiment by using two different Genelia fluoridyes. So what I'm going to show here, right? These are the single molecule imaging movies that I, I showed in the first slide. This is when you're just adding the one genelia fluor ligand at a low concentration to make sure you only label a subset of the molecules, right? And then what we can do is we can first label like this and then we can add in a spectrally distinct genelia fluoridye like JF. And I should have written this here like JF55, JFX 554 or JF549 to label all the remaining Halo tags, Halo fusion proteins and that. But what you end up with then is this what you can see in the next slide is you can see in magenta here the single molecules and in green you can see the the rest of the molecules that are labeled. And So what what's really fun about this is that now you can see, OK, well in this movie in the previous slide, you can see all these static molecules. Do we know those molecules are at telomeres from this experiment? We don't. And so if you do the dual labeling of the same exact protein with two different concentrations of spectrally distinct size, what you end up with is in green, you mark all the telomeres. And now you can say, OK, look, here is a static molecule, here's a static molecule, here's a static molecule. And all of those are super close proximity to a telomere signal where you can see all of the molecules, right? And, and what would be learned from this experiment is that basically this TRF 2 protein is much, much more dynamic than TRF one or pop one, right? And so you can, you can make these kinds of conclusions, not just about the binding times and the immobility, but you can then actually use the protein as a itself as a marker for the structure that you want to visualize because you don't sacrifice the ability to label all of the molecules by doing the single molecule. OK, with that, I'm going to conclude. I just want to bring up this slide one more time to thank the people in my lab that did the work. So Maria did all the work on the lab molecules and joining with some help from Josh and then Tom did make all the telomere protein solids. And with that, we can move to the Q&A. All right. So thank you so much. That was a great talk. That was super cool. So we have a couple questions that we can start with. Again, I encourage anyone who is attending to submit any questions that you might have. While we have Yen's here. And so someone asked earlier in the talk, is there any advantage to using Halo tag or Genelia floor versus quantum dots? While so, so I, I don't have a lot of experience with quantum bats, I am not sure how easy it is to get quantum dots into cells. So I think for there's definitely applications of quantum bats that are familiar with where people have used them in a Detroit experiments, for example, to mark diamine or actually also DNA repair proteins. I think that the big advantage of the Halo tag is that it's genetically encoded, right? So you can introduce it into the the genome and then you can use the the endogenously expressed protein to do your experiments, right. So I think that that is probably the biggest advantage. And, and of course the other big advantage is that you have this ability to titrate the amount of protein that is labeled because the you can, you can dial down the number of genial fluid molecules that you're putting into the cell. Great. I, I also, I, I saw at the end you, you had a, a note about the 1000 DNA breaks per minute that you can kind of identify, you know, do you thinking about like the therapeutic applications of this taking a step back, you know, like the classic grant funding question, Like how does this maybe apply to, to general, you know, to, to people that you know, outside of this, like what are the larger implications of your, your work? Yeah. I think that that's a that's a great question. So I think the, when, when we think about cancer therapy, there's a lot of cancer therapeutics where you're basically inducing DNA breaks as a therapeutic approach, right? And, and obviously you want to make sure that that's in the right window, right? So you don't want to do too much damage because then you might also harm other cells. You don't, and you want to make sufficient damage to actually harm the cancer cells, right? And so building this this catalog of repair rates for the different repair pathways, we hope eventually we'll be informative to how we use DNA damaging agents in cancer therapy. All right. And then we have another question here. Have you done these experiments on cell lines that have different levels of resistance to DNA damaging cancer drugs or that are more resistant to DNA breaks? Yeah. So not yet. It's a short answer. So right now basically the DNA, our DNA repair work is limited to U2 S cancer cells. So that's a human cancer cell. And it's worth noting that the Halo tag technology and the single molecule imaging experiments have been done in a wide variety of model systems. So people have done it in embryonic stem cells, they have done it in Drosophila embryos. So there's a wide range of model systems that this can be applied to with, with other model systems. So microscopy approach might have to be adjusted. But I think it's a great question to say, OK, let's look at a different cell line, maybe not a cancer cell line and, and look at how, how they respond and whether they are especially if they have different sensitivities to these powers that, that that's definitely our future plans. Great. And then we have one that says if one would be more interested in shutting in shuttling of a protein from the cytoplasm into the nucleus, would this also be possible to use at a confocal setup? Absolutely. So I think that there's a a couple of things that I actually didn't mention here because we don't use this very much. There's in a confocal setup, one could use photo activatable versions of these genius. So when when we're doing these experiments, we're going to label all of the molecules just because we can't distinguish where the Halo ligands go in the confocal setup. What you could do is you could use a photo activatable version of the Genelia fluor ligand, label all of the molecules and then photo activate only molecules with the confocal microscope that are either in the nucleus or in the cytoplasm and then watch them transition. So what we have done, I didn't show this today. We have looked quite a bit at proteins moving in and out of the nucleolus with a nuclei, right. So that's a just a, a structure, the phase separated structure where ritosomal RNAs are made and we can watch molecules move in and out of those. But those same approaches could be used to to look at single molecules transitioning between the nucleus and the cytoplasm. And this is actually there, there's papers from Robert Singer's lab where they have looked at the export of individual mRNA molecules so they can watch them bind to the nuclear pore complex and then being released into the cytoplasm. I think it depends just on how how many molecules there are and how often they would move in and out of the nucleus. But you could certainly do that with a confocal setup using the activatable versions of the dyes. Great. And then I have a couple other questions here. So one that I had written down before this was when you're looking at really highly dynamic environments, how do you kind of distinguish between, you know, bound and unbrowned bound protein states? And so one thing I noticed when I was listening to your talk is, you know, you're you're distinguishing going from highly dynamic, highly moving to stable kind of standing in one place signal. Is that something that, you know, people are typically doing when they're using this approach where they're going from a highly dynamic, highly movement to stationary? Is it easier than doing the flip where you're studying transition from stationary to highly dynamic and highly mobile? I know you said that a lot of it is kind of a a very dramatic change in protein motion is kind of what you're looking for. Yeah, Short answer is that it's just easier to do that because the analysis is much more straightforward. That being said is you can absolutely do the, the, the flip side. And in this, in this presentation, I only talked about data generated, generated using one analysis tool. There's also other analysis tools that have much more that provide much more information, not just the fractions of the, because this is sort of the simplest way of looking at it is what fraction of molecules are bound versus static. We can also get information about transition rates. So what is the rate constant of a transition from a balance to a Free State, which would be sort of your dissociation rate constant in the biochemical experiment? There's ways to get all those all that kind of information out. You just have to use different analysis pipelines. One additional thing to note is, is that it is very important that for the single molecule experiments to be successful that a large fraction of your molecules do something interesting, right? Because if, if you had a case where you, you do a perturbation and only 1% of the molecules do something different than in the control case, you can't find that it's, it's like a needle in the haystack. So that's why in these experiments we actually have to introduce a lot of DNA breaks. So we're introducing on the order of 10 to 50,000 DNA breaks so that we can actually see this, this recruitment have happened. And then sort of one final thing to say, if you're looking at cytoplasmic processes, everything is a lot faster. So the, the, the Brownian motion of the Hemotag and the cytoplasm is much, much faster than in the nucleus. So that means that in terms of just the the logistics of your experiment, you have to image a lot faster or you have to have maybe some processes with dramatic changes to be able to to see them. Got you All right. With that, I might move us to the close. So again, huge thank you Jens for for going through your work again, really fascinating. I want to encourage anyone if you have additional questions to contact him. His e-mail is up there or you can contact per mega support. Our tech serve would be happy to help you if you have any Halo Tiger Genelia floor questions. And so with that, thank you for all in attendance. Really appreciate your time and we hope you have a great rest of your day. Thank you very much. _1733338788559