In this episode of Absolute Gene-ius, Dr. C. Dustin Rubinstein, Director of the Advanced Genome Editing Laboratory at the University of Wisconsin-Madison, shares how CRISPR and digital PCR are revolutionizing genome editing and disease research. From developing pig models for cancer studies to refining gene-editing confirmation with dPCR, he highlights the power of precise quantification and the collaborative nature of cutting-edge molecular biology.
In this episode of Absolute Gene-ius, Dr. C. Dustin Rubinstein takes us inside the world of advanced genome editing, where cutting-edge tools like CRISPR and digital PCR are helping shape the future of biomedical research.
As the Director of the Advanced Genome Editing Laboratory at the University of Wisconsin-Madison, Dr. Rubinstein shares how his lab develops genetically engineered pig models to study diseases like neurofibromatosis and cancer, providing researchers with more clinically relevant models than traditional small animals. He explains how digital PCR plays a crucial role in confirming genome edits with absolute precision, eliminating the ambiguity that often comes with qPCR and sequencing alone. The discussion dives into the advantages of dPCR for copy number variation analysis and gene editing confirmation, emphasizing the importance of multiple complementary technologies in modern molecular biology.
Beyond the science, Dr. Rubinstein reflects on his career path, the value of mentors, and the unpredictable nature of scientific discovery. He also joins in on some lighthearted lab humor and shares his most embarrassing (and proudest) moments in research. Tune in for an insightful and entertaining look at the intersection of gene editing, career evolution, and the future of molecular biology.
Visit the Absolute Gene-ius pageto learn more about the guests, the hosts, and the Applied Biosystems QuantStudio Absolute Q Digital PCR System.
Jordan Ruggieri00:00
What's a dPCR machine's favorite song?
Christina Bouwens 00:03
What?
Jordan Ruggieri00:04
Another one counts the dust. Makes no sense, but that's okay. Some of these jokes, man. Chat GPT needs a joke, a joke plug-in. What did the scientists say when dPCR perfectly quantified the target? That's what I call confidence.
Christina Bouwens 00:30
Welcome to Absolute Gene-ius, a Thermo Fisher Scientific Podcast Series. I'm Christina Bouwens.
Jordan Ruggieri00:35
And I'm Jordan Ruggeri. And when they said that good things come in threes, they were totally right. We are back for season three of our show, and it is going to be absolutely incredible. If you're a long-time listener, welcome back, and if you're just finding our show, thanks so much for joining us. We've got a fabulous lineup of guests for you to learn from over the coming months, and we're kicking things off today with none other than Dr Dustin Rubinstein.
Christina Bouwens 01:02
Dustin is currently the Director of the Advanced Genome Editing Laboratory at the University of Wisconsin Madison. He's an expert on all things CRISPR, a researcher for new disease therapies, and the lover of dPCR which of course, we're excited to discuss today. We hope you enjoy our conversation with one of our earliest Absolute Q’ers.
Jordan Ruggieri01:20
When dPCR and CRISPR team up, it's a cut above the rest.
Christina Bouwens 01:24
I like that one.
Jordan Ruggieri01:30
Well, Dustin, thank you so much for joining us today on our episode of Absolute Gene-ius. We're thrilled to have you here. Are you able to give a little bit of background, who you are, where you're at, what you're doing?
Dustin Rubinstein, PhD 01:41
Sure. So Dustin Rubinstein, I'm here at the University of Wisconsin. I'm at the Biotechnology Center. I lead a CRISPR-focused lab here at the biotech center. So we really try and provide access to all kinds of genome editing tools to whoever might need it. Collaborate with a bunch of different labs and work on projects that kind of span mostly biomedical realm, basic science, biomedical realm. But yeah, so that's, that's, that's me, and that's what I'm doing these days.
Christina Bouwens 02:11
Awesome. So I have, I have a couple first questions, and I know one of the first things we want to talk about is like, what does a day look like in the life of your lab? And you know your life looks like in the in the day of the core lab?
Dustin Rubinstein, PhD 02:23
I think the thing that I love about my job is I get to really interact with different scientists in different groups, right. So, like, I'm kind of, apparently have the attention level suitable for being in this job, which is to have about 30 different projects rolling at once, right. So I'll, you know, be talking to someone you know, about their diabetes project and then switch over to oncology and then switch over to neurodegeneration or things like that. So I really like interacting with people and different groups and kind of different disciplines. I think that’s really fun, I think that one of the like, you know, what I spend a lot of time doing. There's a lot of administrative stuff too, right. We've got to make sure, essentially, to keep the lights on, so to speak, in the science lab. But, you know, make sure that we're, that the things that we think are funding us are funding us, and make sure that projects are moving along. And, you know, kind of helping the ship stay steady.
Jordan Ruggieri03:15
You don't, you don't need lights. Just as long as there's electricity for qPCR and dPCR machines, you're good, right?
Dustin Rubinstein, PhD 03:21
Yeah. Exactly, yeah, exactly. Like, put another log in the fire and, like, get it up to ninety-five degrees.
Jordan Ruggieri03:31
As director of the Advanced Genome Editing Laboratory, what are some of the most exciting projects or breakthroughs that you and your team might have been working on using CRISPR?
Dustin Rubinstein, PhD 03:41
I think one of the fun things, probably one of the bigger projects that we've had throughout my time here, is developing models for neurofibromatosis type one, which is a rare monogenetic disorder. So there's a single gene that causes it. It's rare in that it's like one and 3,000 people. But if you think of 3,000 people, that's not that many, and one of them probably has it. In nutshell, we built some genome edited pig models to recapitulate the mutations that we see in patient kids[MF1]. And then, you know, used all kinds of genomic tools and imaging tools to try and better understand the disease. That's been a really exciting project. We've got a number of other projects too. We've just developed, I guess I'm just naming pigs here, but we do more than pig stuff, but we've created a pig that we call onco-pig, that we can actually induce genes that induce cancer. So if you want to, if you have an, if you think you're, if you're a drug company, you think you might have a new drug for kidney cancer, you know, we could give them like, you know, kidney cancer, and if you think it might work well for, you know, liver cancer, we can give the pig liver cancer and induce cancers that way.
Jordan Ruggieri04:51
Can you talk a little bit about, why are pig models beneficial for things like cancer research? Why not other models like zebra fish or mouse or, you know, is there, is there a particular reason that pig models are useful?
Dustin Rubinstein, PhD 05:05
That's so, that's a great question, and it's funny, because we're just coming hot off the heels of the International Biomedical Swine Conference that just wrapped up. So I think it kind of gets back to the whole idea of tools complementing each other, right. No one's ever using digital PCR, you know, for all aspects and no one's ever doing seq, all DNA sequencing. You know, every tool has got its right place. We've learned a lot about basic biology and some translational biology from invertebrate models like, you know, drosophila and yeast for and, you know, from danio and zebrafish, and we're, you know, learning a lot from mice and rats, but they don't do everything well. And probably some good evidence for that is how extraordinarily expensive it is to bring a drug into the clinic, and then how overwhelmingly likely it is to fail. So, you know, you spend what seems like a whole lot of money developing the idea for a drug and designing and making the drug and checking that the drug works in small animal models. But that's nothing compared to actually bringing it to the clinic and giving it to humans and then after that is when it usually fails. So why does it fail? I think that's probably a relevant question that we're all kind of asking each other. And some of that is because, well, maybe the models that we were using weren't really the best models that we had on hand. Like I said, we've learned a lot from all kinds of models. Like I don't, there is no one model. We need all kinds of different models to ask different questions in the right context. But, you know, if we're thinking about cancer, we can study mice in, study like cancer in mice, but generally, when mice get cancer, you know, they're, you know, experiments with mice are usually measured on, you know, months to weeks. Whereas cancer, for humans, we often, you know, one of the buzzwords you hear when you talk about cancer in humans is “management,” right. It's not just about, like, cure the cancer, right? Like, "Oh, I got cancer. I'll take a pill and now I'm cured. My headache is gone, and my cancer is cured," right. I mean, it's about management, right. It's a long-term process about, maybe, like a, you know, first step, how do we manage, what's the first thing we need to do to try and knock back the cancer? And then, how do we track it and see how well that worked? And then, how about, you know, circle back and do, you know, another dose or something. The problem is that mice generally get cancer and die, whereas, you know, with humans, we're talking about, you know, managing this problem that is cancer. With mice, it's they get cancer, and then they die. So that is not really a great model for the way cancer actually works in humans. Pigs have a much similar developmental trajectory to humans. They're much more similar in size, right. So I'm like from like from the suburbs. So I always think when I heard pigs, I always thought like Charlotte's Web, like cute little pigs, but I didn't realize that pigs are basically like horses with short legs, like they're enormous. So if you want a pig that's actually going to match a human size, you have to use what are called mini-pigs. So when I'm talking about biomedical pigs, it's often these mini-pig varieties that, again, are not that mini. They're not like the cute ones you carry in your purse, right. That, you know, they're super adorable. You know, they're 150 pounds-ish, or a little higher. The idea is that they match humans.
Christina Bouwens 08:34
That's awesome. So Dustin, you and I have known each other now for quite, quite a number of years, at this point. You were one of the early adopters of the Absolute Q itself. But I know that you've been using dPCR for quite a long time. Can you describe, you know, why your lab decided to, you know, integrate dPCR technology in and kind of the reason why you find it to be such an important technology to have in your lab as a tool for the genome editing?
Dustin Rubinstein, PhD 08:57
Yeah, I think, I think what really hooked us was just how easy and precise it was to get an answer for things that like, you know, maybe qPCR, which is great, like, in in some respects, but, you know, sort of the reliability and the precision that you get from digital PCR was really helped us hurry things along. Like, one of the big applications that we had were copy number variant assays. Not really, maybe in the way that most people, for all you real qPCR folks out there, maybe a little different in the way that you might usually think about, but more in terms to confirm the edits that we made. And that's where digital PCR became really, a really critical component for us to sort of kind of a QC quality assurance step.
Christina Bouwens 09:42
Can you talk a little bit more about, like, why that works in your favor for digital PCR? Like, because I know that's a topic that comes up a lot, and I know our listeners might like to have a little bit of education about why digital PCR is advantageous for kind of understanding, gene editing confirmation?
Dustin Rubinstein, PhD 09:58
In a nutshell, I think it basically comes down to this. So when you're doing genome editing, you make the change, and then, obviously, it's really important to know what the change you just made is, right. That becomes the next big problem. What's the change you just made? Almost everyone uses some sort of PCR-based method to read that out. That you know, PCR-based methods can sometimes, like, kind of add, like, a little fog can help you, kind of maybe see what you want to see, rather than what's actually there. So you might amplify, you might be trying to amplify a certain part of the genome, and you get your band, you get a PCR band, and you think, okay, great, this fragment exists in the genome, just as I think it did. But one example might be that, well, maybe that fragment exists in the genome, but only because maybe there are many, many copies of that insert and that fragment exists, as well as tons of other fragments that you can't see with PCR, right. And you know the thing is, sometimes you PCR something, and we might forget that we actually have, like, two chromosomes, and you're not just PCRing and getting a band from one, you'd want to make sure that you're interrogating both chromosomes. But when you do an endpoint PCR, you have no idea, like, which chromosome gave you that band, right. So using digital PCR kind of allows you to tease out some of those problems and address them. So we're finding that those are, these are kind of really key things to do. And when you actually, it turns out, when you actually, like, peel back, like the layers, sometimes it's not as pretty as you think you did, as you as you think it is.
Christina Bouwens 11:26
Yeah, so it gives you a little bit more precision as to, as opposed to some of the other methods that have been around for a little bit more time, and, you know, maybe don't have quite the resolution?
Dustin Rubinstein, PhD 11:36
Yeah, exactly, right, exactly. And it gives, it gives us, it gives it like allows us to test those, and then it does it in a matter that's like so much more precise and really, really fast and easy. So what's not to like there?
Jordan Ruggieri11:48
Yeah, Dustin, I actually want to take even one, one step back and talk a little bit about genome editing and, you know, CRISPR technology is that something you can comment on? I know it's, you know, it's a newer technology. I think it's definitely getting to be more well known and more well utilized in labs. But what does CRISPR actually allow you to, to do when it comes to, you know, gene editing techniques in your lab?
Dustin Rubinstein, PhD 12:12
Yeah. Boy, yeah, that's hard. It's really exciting. So I think from a fundamental perspective, we've always thought of genomes. It would be described in terms of, like, hard copies, right. It would be like, if the human genome was printed, it would be like volumes that would go from the floor to the ceiling or, you know, because we always talked about reading the genome and what's the genome sequence, but, you know, for the first time, we actually had the ability to not just, you know, read it in a printed form, but to actually, like, open it up in Word and change it. Rather than kind of making really passive inferences about, like, well, these patients have this mutation and these patients don't[MF2]. So therefore, it, you know, correlates pretty well with like, you know, a disease. You know, of course, you know, correlation is not causation. What you need to do in any science experiment is actually make the manipulation, and that's what CRISPR really finally allowed us to do. So that's kind of the really exciting thing, is finally get a causality of genomics, which is kind of huge. But, I mean, things are moving so fast, that's sort of like CRISPR version one, right. Now, one of the concerns with CRISPR was that, well, you create this double stranded break, which means, you know, you create a break in the DNA. When you create a break in the DNA, the cell freaks out, because if it can't fix that, you know, it's mitotically dead, right. I mean, it can't, you can't just have, like, you know, chromosomal arm floating away. So there are, there's, there are many, many DNA repair pathways that are always monitoring the safety and the health of, you know, the chromosomal arms. And sometimes, you know, it might, those mechanisms might try and stitch the ends back together in ways that, like, you don't really want, right. Or maybe something really funky could be happening that might, like, cause more problems than you wished. So there are actually versions of CRISPR now where you don't even have to cut the DNA right, and whether it's base editing or prime editing, some of these other really exciting things where, I mean, you're just using CRISPR as a homing device into like, a certain spot in the genome, and then once it's there, you can stick whatever kind of funky tools you want on it. There’re all kinds of really creative ideas, and some of them are working, which is great.
Christina Bouwens 14:08
So I have kind of a question to think about how, how your lab assesses new techniques and technologies to bring on board, to keep you know, keep up to date and keep up the standard. So how does that go about? How do you make sure that your lab is keeping up to date? How do you decide what to bring on when it's the right time? And, you know, how do, how do people who are collaborating with your groups get to, you know, get to leverage these technologies? Is it just through the products that you're developing, you mentioned, you're developing these models that the groups can kind of work with, or is it more in the service space? Can you talk a little bit about how that works?
Dustin Rubinstein, PhD 14:42
Yeah, yeah, no. I think that's a great question. Because trying to, you know, stay in the know with CRISPR is sort of like sipping from a fire hose. Because, like, every time you, like, check, there's like a new acronym with like, CRISPR tagged onto it, and it's like a tiny variation of the thing before, right. So at what point is it something that you really need to pay attention to? For us, I think it's really like, keep it simple. What is it that you need, right? Are your needs being addressed with this platform as it is, and if not, are your needs like so much more advanced that you're really willing to put in the cost to try this other thing? I think it kind of comes down to something like that. We really get by in the fact that we're experts in kind of one set of tools. People generally like, I mean, they come to us because we sort of know these tools. We know what's reasonable to try and what it’s like just not a good fit. We work with them on a kind of a kind of a case-by-case basis, and see, you know, try and find the toolkit that works with them.
Jordan Ruggieri15:45
Do you ever come across cases where you may actually have to utilize multiple different technologies? Maybe it's digital PCR and sequencing, or digital PCR and qPCR in order to solve a problem?
Dustin Rubinstein, PhD 15:58
Yeah, I mean, you're exactly right. I mean, digital PCR is important. But just like no one person can do everything on their own. No one tool is going to fix everything, right. So just to give you, like, a broad sense, like, we create these models, right. And we want to make sure that at the end of the day, we've got a model that we know exactly what it is, right. We can, like, sequence confirm and say that's exactly right. So we can start with, like, the PCR, and we'll know which ones we can throw away, right. And we'll throw out a whole bunch of ones that aren't so good. The next step we want to do is use, like, you know, digital PCR, and ask, “Okay, like of those, you know, can we get rid of the ones that look good by PCR, but maybe weren't quite, right?” And then we, you know, throw out a whole bunch of other ones. And then, you know, our new standard now is to go to long-read, whole genome sequencing and confirm that way, right? So then we'll pick, you know, basically, like, one allele, and sequence that and be like, “Okay, is this exactly what we think it is?” These tools don't exist in a vacuum, and they're all really complimentary. You know, they're short shortcomings of one can complement the strengths of another.
Jordan Ruggieri16:58
How are you designing your assays, uh, you know, for digital PCR? Is there any particular assay or types of assays that you utilize frequently?
Dustin Rubinstein, PhD 17:10
Yeah, so, like I said, we probably do more CNV analysis than anything else, right. So what does that mean? So we're generally targeting somewhere in the place we're trying to edit, right, like a representative, tiny little chunk in maybe whatever we in a, let's say we humanize a gene, and we're going to target part of that humanized little gene. So that'll be one essay, and then we'll run it in duplex with some other random spot in the genome, right, something that we haven't edited, most likely, it would be really bad, like if we did, but something that should still have two copies, right. Or maybe it's a cell line that has three of those chromosomes or something, but you know, so now we know exactly how many copies of the gene, because we measured that other random spot, and we know how many, you know, down to like the molecule, which is kind of amazing thing about digital PCR, right, is it gives you an absolute number, so we know exactly how many copies well, that other random spot is there, so that's where that means we know how many copies of the genome are in our sample. So then we can say, “Well, if we have this many copies of the genome in the sample, how many copies of our humanized gene do we have?” And that's kind of the strategy of, sort of what we what we often do.
Christina Bouwens 18:18
Can you talk, because I know a lot of this you'd be designing kind of custom targets for these specific edits that you're making, can you talk a little bit about what you do for optimizing assays? Do you have, do you find yourself having to optimize a lot of assays? Do you find yourself using different platforms? I know a lot of users find themselves kind of optimizing on qPCR and then moving to dPCR. Do you have any strategies that your lab uses to do that?
Dustin Rubinstein, PhD 18:44
That is like a evergreen question in our lab. Sometimes we have issues with optimizing, and we're kind of, like, it's always that question, like, “Is it worth, like, spending time optimizing it, or should we just kind of move ahead with what we have?” So we've been lucky. I mean, you know, Thermo Fisher actually has been pretty kind to us about helping us and guiding us with some of those assays, but yeah, generally, we take it right out of the box and try and let it rip. And if that doesn't work, then we'll go back and see what we need to do.
Christina Bouwens 19:15
Are there any types of features of like the digital PCR systems that were really important for you when looking for systems that are, that were, you know, important when you're integrating a system into your lab?
Dustin Rubinstein, PhD 19:25
Yes, yes. Number one, we wanted it to be something easy. I mean, there are all kinds of different ways to make PCR digital, and just that, the simplest, the simplest and quickest way we could get those preps done, the better. So that was probably one of our first, our first interests. I think the other thing, the few other things that were really important, of course, they had to be reliable and had to be precise, obviously, else we're not going to use it. But for us, like, we don't always necessarily have the same type of like sample input, right? It's not like we always just have like DNA that’s as pure as the driven snow. Sometimes we take lazy shortcuts. They're not lazy which we don't have that much input. So they're kind of, you know, like cruder representation. So we also needed a system that worked on all kinds of different levels of, they need to be tolerant to all kinds of other potential contaminants in the sample, and for us, once we screened a few different platforms, it was pretty clear which one was most robust for us.
Jordan Ruggieri19:29
Talking a little bit more about CRISPR, in your opinion, what are the most promising applications of CRISPR technology in the understanding mechanisms behind disease, genetics and research behind the development of novel therapies?
Dustin Rubinstein, PhD 20:47
I think one of the things that's really exciting about thinking about using CRISPR to cure diseases is that you might think, “Well, using CRISPR to cure disease, you just change the mutation, right, that is causing the disease.” I mean, that'd be great if it was that easy, but sometimes it's not so. A lot of times what it does is it forces you to go back to the basic biology, right. So to understand what are the genetic networks. So there might be a particular mutation that's causing a disease, but like, what is the genetic network in which that mutation is causing the disease? Because now, all of a sudden, now you're not tethered to just that mutation. Now you can find all these other potential genes in the network. So if you look at a kind of eye-poppingly success story of sickle cell, right. I mean, there is a single mutation that segregates in the population, right? I mean, there's, there's, there's one nucleotide that changes that is responsible for sickle cell, and using CRISPR to cure sickle cell wasn't the goal wasn't to fix that mutation. It was, well, what are the other genes that we could maybe hit to actually find another way to cure it? And so and so, really, that's what they did. It was basically, basically, an understanding of how that genetic network happens throughout the course of, you know, human lifetime. So, you know, it really kind of shows that there's always going to be place for basic biology, right. It's never going to be as simple as just changing the mutation that was discovered. But also, you know, some crazy things that look like science fiction now too, right. So I don't know how much you know, you've heard about xenotransplantation, right. Before I started applying CRISPR tools to the idea of organ transplants. It was shocking to me to learn that you're more likely to die waiting for an organ transplant than you are to receive it. It made me realize how really acute this issue is. And there had been a long-standing interest in maybe looking to the pig, you know, historically, pre-CRISPR, and thinking maybe about, you know, looking to the pig and asking, well, okay, so this, there's an animal that's, you know, kind of human sized, it's a mammal, it's, it's got similar developmental, you know, timelines. Maybe we could kind of like, it kind of would fit about in, like, in that spot, maybe when the thought was always, well, how can we change the human immune system to accommodate a pig for many, many years, that was kind of thought like, “How can we, how could, how can we make humans accommodate pigs?” And I'm sure you'll see where this is going. But now the idea is, “How can we change the genome of pigs so that they'll actually fit in nicely to a human, right?” So now we can ask, “How can we make a pig accommodate the human immune system?” Which is one of the kind of really exciting things that's happening, and there's been some tremendous steps forward in progress in that we have a xeno program here that we're really excited and proud of. Some of these benefits seem to almost be like science fiction, right. But here they are, I guess.
Christina Bouwens 23:44
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Jordan Ruggieri24:04
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Christina Bouwens 24:28
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Jordan Ruggieri24:37
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Christina Bouwens 24:54
And now back to our conversation.
Jordan Ruggieri24:59
So Dustin, welcome to the career corner element of the podcast. In this corner, we like to talk a little bit about how you got to where you are and kind of your career pathway. Could you give us a little bit of background on your educational background, you know? What kind of training did you receive? And maybe even a little bit of how you got to where you are today?
Dustin Rubinstein, PhD 25:23
Yeah, all right. So I went to undergrad at the University of Illinois in Champaign Urbana, moved to Ithaca New York, got my PhD in neurobiology and behavior at Cornell University. Maybe that kind of like started to see, because that department is a little unique in that in the same department, we had people who were going out to like, you know, islands off the coast of Colombia and studying bird song, and like, you know, zip-lining off canopies and trying to understand the function of bird song all the way down to people who were understanding how individual genes and individual neurons are affecting the way, you know, nervous systems grow up and develop, and trying to cover that ground in between. And maybe that's why I can't really sit down and focus on one topic. And I love all kinds of fields of biology. That was sort of my home department as a grad student. I was always interested in sort of the genetic basis of behavior, right. So how can genes influence the nervous system to be set up so that we engage in these, you know, crazy complex behavioral patterns? So I studied drosophila and drosophila courtship song. So males actually sing to females, and the song has to be just right, and but somehow, that's not learned, right. It's programmed into their nervous system, and for the males to make it and for the females to prefer a certain type. I mean, that's pretty cool. So I was kind of a behavioral genetics person and understanding how the nervous system changes. So I was in a lab studying neural development and understanding the genes that you know affects synapses, and synapse growth and synapse changes, synaptic changes, and that's the time when those like, you know, CRISPR papers finally, like, hit the airways, right. When it totally changed the way geneticists, you know, did their work. So we worked with some amazing folks in a group with several different labs on campus, and we got CRISPR up and running in drosophila around that time, you know, if you knew what CRISPR was, let alone, you know, actually got it working, it'd be more, you know, bothering you all the time to figure out how you can help them. And, you know, realize that I could, kind of like, you know, take this background that I have and maybe help others. And that's when I was asked to found this CRISPR core here. So I was basically formally asked to do that, right. Actually get paid to help other people with their CRISPR projects. And that's kind of what landed me in this seat here. And here I am talking to you.
Jordan Ruggieri27:50
What a journey! You mentioned, even the bioinformatics with the website. How has, you know, maybe taking part in different opportunities shaped the skill set that you have now? Have you noticed that being part of multiple different things has kind of helped with your core lab and helping run your lab now?
Dustin Rubinstein, PhD 28:13
Yeah, I think it kind of goes back to that. It goes back to that idea that we're all like, we have to surround ourselves by experts in tons of different fields. I wasn't necessarily like a pure bioinformatics person, but I was lucky enough to be able to work with them in close proximity here at the biotech center. I wasn't really, I'm still not really, officially like a cancer biologist, but I'm lucky to have the UW Comprehensive Cancer Center here. You know, layering in different forms of expertise, which is also kind of layering different people in different backgrounds, has really enhanced the way, I don't know, I think about problems and try and find solutions for them.
Jordan Ruggieri28:52
I think this segues perfectly into my next question, is mentors. A common theme that we've seen on a lot of the, with a lot of podcast guests is that they had mentors that kind of helped guide them and helped shape their knowledge and skill sets. Is that also true on your end? Did you have any mentors that really helped build your skill set and knowledge to get you to where you are today?
Dustin Rubinstein, PhD 29:18
Absolutely. I wouldn't be where I was today if it wasn't for, you know, a bunch of amazing mentors that I've had. You know, I'd have to say, you know, I wouldn't be where I was now, if it wasn't for sort of the bravery and audacity of my advisor, Kate O'Connor Giles, who I mentioned before. Just a brilliant person who was sort of fearless and just knew, if we just knew, if we just kind of keep doing the work we need to do, we'll get this, you know, project done and wrapped up, and then, you know, kind of can get back to our regularly scheduled synaptic development experiments. Having her teaching me to see, you know, to have that, you know, vision, to follow through, but finding a place for me in it as well. Yeah, I wouldn't be where I was now if I wasn't for, if it wasn't for her.
Jordan Ruggieri30:05
Absolutely amazing. How about anything, you know, one or two things, whatever comes to mind that you have learned in your career that really sticks out, that you think would benefit others that are maybe early in their career, or even later in their career? You know any, any tidbits or things that that you've learned in your pathway?
Dustin Rubinstein, PhD 30:32
I think it's primarily that you can never really plan your future, right. I mean, you want to have a goal. You know where you want to go, and you take the steps to get there. But there will be other opportunities. There'll be other doors that will open, and you need to be ready for those things that may, it may not be you know exactly the same destination you're going, but you want to at least be open minded enough to think, well, this is a pretty cool opportunity that I just hadn't really considered. So I think don't necessarily get wrapped up so much in this ideal endpoint that you think you need to be in. But look around you, and there are probably opportunities. You know, if you just kind of take a moment and realize what your role is in this situation right now, there are probably opportunities that are different than what you thought, but something that really plays into things you love and things you're good at. Kind of just have an open mind and be willing to take where, to take what life hands you, I guess.
Jordan Ruggieri30:33
I have one more. It's a two-parter, one more question that we ask everybody, what is your most embarrassing lab moment? And then, on the flip side, what are you the proudest of?
Dustin Rubinstein, PhD 31:46
I'm trying to think through all the explosions and the egregious pipetting errors.
Jordan Ruggieri31:53
It can be a general thing too, Dustin. It doesn't have, like, one story.
Dustin Rubinstein, PhD 31:58
I'm trying to think of a time I, like, tripped and fell. I know I got to give you something. I spilled coffee on myself. I know, I don't know, I’m so bad at these questions. My proudest, I don't know I'm kind of, I'm kind of proud of this group that we have now. Like I think we all, we all bring some really, you know, exciting, unique talents, and I think we're doing some really cool stuff, and I'm excited, excited for what's in store. I don't know. I mean, it sounds like a cop out.
Jordan Ruggieri32:29
That's a good answer from a director, right?
Dustin Rubinstein, PhD 32:35
Better than the alternative, well
Jordan Ruggieri32:37
I've got eight ball and chains hanging on my leg…
Dustin Rubinstein, PhD 32:39
Really proud of things long gone, the good old days. You have no idea.
Jordan Ruggieri32:45
Christina, any questions on your end? I am career questioned out here.
Christina Bouwens 32:51
No, I think that's it. It's been. It's been so nice having you on the podcast and really nice talking to you again. It's been, it's been absolutely great.
Dustin Rubinstein, PhD 32:58
Christina, it's always great. It's good to see you again, Jordan, it was super fun chatting with you.
Christina Bouwens 33:03
That was Dr Dustin Rubinstein, Director of the Advanced Genome Editing Laboratory at the University of Wisconsin Madison. With more fascinating conversations around the corner in upcoming season three episodes. Subscribe now and get notified when new episodes drop. Stay curious, and we'll see you next time. This episode of Absolute Gene-ius was produced by Sarah Briganti, Matt Ferris and Matthew Stock.
Christina Bouwens 33:25
Jordan, why don't skeletons fight each other?
Jordan Ruggieri33:28
Oh, why don't skeletons fight each other?
Christina Bouwens 33:30
They don't have the guts. That would be a good Halloween one.
Jordan Ruggieri33:35
That would be.
[MF1]This seems OK as they’re saying they build models in pigs and don’t imply they’re doing diagnostics or medical use. This is clearly different from their research.
[MF2]I think this is still OK to leave as it’s not talking to the work they do but the literature-based knowledge about general genomics in medicine.