For this episode we host another of Thermo Fisher Scientific’s own. Dave Bauer is a PhD-educated Application Scientist specializing in qPCR and digital PCR. His knack for using analogies to explain difficult concepts helps illuminate the benefits of digital PCR and the statistical aspects of this analytical method. This is a great overview episode that also touches on specific applications such as SNP detection. We also learn about Dave’s career path, hear some valuable advice, and get a sense of how poor our intuitions can be at evaluating probabilities.
Polymerase chain reaction (PCR) was discovered in 1983 by Kary Mullis and Michael Smith, who were jointly awarded the Nobel Prize in Chemistry in 1993. Since then, PCR has been a cornerstone method that has been a pillar of discovery and applied science. The various types of PCR are sometimes confusing, and the relative pros and cons of each method are not always clear, which is why it’s so great to have this episode's guest explain them all in a simple and clear-cut way.
Dave Bauer, PhD, is an Application Scientist at Thermo Fisher Scientific that specializes in real time PCR (qPCR) and digital PCR (dPCR). He has an educational background in physics, mathematics, and biology, but what’s more important is that Dave loves to help others learn and to break down a topic’s complexities to make it more understandable and approachable. In this episode we hear Dave explain the difference between qPCR and dPCR, the importance of Poisson statistics to dPCR, dead volume, reaction chamber volume consistency, and more. We learn how qPCR and dPCR complement each other and how they relate to sequencing methods for applications like single nucleotide polymorphism (SNP) detection.
As you’ve come to expect from Absolute Gene-ius, you also get a good sense of who Dave is and how he got to his current role. We learn about how he knew right away that academia wasn’t for him, how he ended up unexpectedly working in forensics after his PhD, and how he eventually landed in his current Application Scientist role. Dave shares some great insights and advice, including how students should care less about their degree’s name and more about what techniques they’re learning and using in their studies.
Visit the Absolute Gene-ius page to learn more about the guest, the hosts, and the Applied Biosystems QuantStudio Absolute Q Digital PCR System.
This episode includes the following sound effects from freesound.org, licensed under CC BY-ND 4.0:
Jordan Ruggieri 00:00
All right. That’s cool.
Cassie McCreary 00:02
Back to school. It's all smooth sailing now. Nobody panic. You'll be fine.
Jordan Ruggieri 00:07
Every time, every time I hear back to school I think of Billy Madison. The Back to School song he sings to prove to Dad “I'm not a fool.”
Cassie McCreary 00:28
Welcome to Absolute Gene-ius, a new podcast series from Thermo Fisher Scientific. I'm Cassie McCreary.
Jordan Ruggieri 00:34
And I'm Jordan Ruggeri. After a few weeks away, we are back. I am so glad to be back with you and thank you for all your kind notes for the birth of our first child. We're so excited to bring you the last few episodes of Absolute Gene-ius season one and today the wonderful genius joining us is Thermo Fisher Scientific's own, Dave Bauer.
Cassie McCreary 00:54
Dave earned his PhD at Carnegie Mellon University in 2015 and joined the Thermo Fisher team in 2020 as an Application Scientist. He loves all things dPCR and is the king of helpful explanatory analogies. Get ready for a bit of math, a lot of fascinating discussion about dPCR! and a great conversation.
Jordan Ruggieri 01:13
Cassie, I wrote a few haikus about digital PCR. Are you a haiku fan?
Cassie McCreary 01:19
I am now.
Jordan Ruggieri 01:23
Codes delicate hum. Amplified whispers of genes. Binary lifes song.
Cassie McCreary 01:29
Oooooh. Ooohhhh, spicy!
Jordan Ruggieri 01:38
Dave, and thank you so much for joining us today on Absolute Gene-ius. I know I'm thrilled to speak to you and have our own digital PCR expert on the line. Really excited to dive into some of the technical aspects and also get some of your insights in your career journey and how you got here. Just to hop in real, you know, real fast here, face first, in your own words, what is digital PCR? How can it be used? And why should our listeners care about it?
Dave Bauer, PhD 02:06
I always got to compare digital PCR to real time PCR. If you're used to real time PCR, you know you have your reaction mixture, it's got your sample of your DNA that you want to target in there. And there's usually more than one copy, maybe there's 5,000 copies. But with real time PCR, you take all 5,000 copies, you just put them in their reaction volume, and then you get a value out of that. With digital PCR, though, we take that reaction volume containing those thousands of DNA targets. And we're spreading them out into many, many, mini reactions, or for our system we call micro chambers. But those mini reactions, they're so small, that you actually expect some of them to not have any of your 5,000 targets in that example. And the whole idea of digital PCR is that some of those mini reactions that do have a target, they give you signal. The mini reactions that don't have any target don't give you signal. And then you're dividing it up in the analysis to say which chambers had target and which didn't. And that's really where the “digital” comes from. It's you're classifying it as a yes, no kind of a binary one, zero. That's how we classify each of those mini reactions.
Jordan Ruggieri 03:17
Talking about those micro reactions and those micro chambers. What does the result look like for digital PCR?
Dave Bauer, PhD 03:25
So with the analysis of the data, really, is that yes, no thousands and thousands of times. But that's not really what we want. In the end, we want something informative to our experiment. And usually that's going to be a concentration, how many copies per microliter. So there's some extra math under the hood in the software for any digital PCR system that lets us convert that string of yeses and no’s into a concentration a copies of your DNA target per microliter.
Jordan Ruggieri 03:55
Now when you are when you are actually looking at this and you're splitting your sample into those thousands of micro reactions, is it safe to say that every micro reaction will get one target? And will you know truly have that one to one yes answer and one to one no answer. Or, you know, is there something else maybe under the hood that happens there?
Dave Bauer, PhD 04:19
Generally, if you're doing digital PCR, it is not a good assumption to assume that every chamber that has signal, counted as a yes, means that you have one and only one of your DNA targets there. It's definitely possible that you're going to have one, two, three- a couple. And the thing is you can't distinguish that so all you know at the end is that chamber was bright or not. And so personally, I always cringe a little when I hear people say digital PCR is “counting molecules,” because we really aren't doing that, we're not actually capable of it. And I think a little example, we can go down to illustrate that concept and that need is thinking about birthdays. Let's say, Cassie and I were walking past a room and there's 10 people in that room. And I say, "Cassie, bet me that someone in that room of 10 people has the same birthday as you." You think that's likely? Would you take the bet?
Cassie McCreary 05:17
Well, I might have a small gambling problem. No. I'm just kidding. I'm just messing around. I would say probably not. I feel like my birthday is weird.
Dave Bauer, PhD 05:29
Hopefully it's not February 29. But,
Cassie McCreary 05:31
It's not. It's June 29.
Dave Bauer, PhD 05:33
Oh, close. So, the idea though, it's a pretty unlikely event, not typically going to happen. If the room had enough people in it, though, you would probably take that bet. You know, if there was hypothetically more than half the number of days in the calendar year, let's say there was 200 people in that room. You then would you take the bet?
Cassie McCreary 05:52
I'd feel better about it.
Dave Bauer, PhD 05:54
Yeah, you'd win more often than you lose on that bet. Makes sense. But now, Jordan, and I are walking past a room. And I'm going to ask him a slightly different question. I'm going to say, "Hey, Jordan, there's some number of people in that room. Bet me that any two people share a birthday in that room?" And I can ask you guys on the fly, you know, do you have a guess. Jordan? How many people do you think would need to be in that room for you to be comfortable taking that bet?
Jordan Ruggieri 06:21
Maybe 100 people. Maybe 25%, a third actually, of the calendar year, somewhere around there?
Dave Bauer, PhD 06:29
Cassie, do you want to be on the spot too, or..
Cassie McCreary 06:31
I'm like sweating right now. Matt?
Dave Bauer, PhD 06:34
You thought you'd be interviewing me? I'm interviewing you, right?
Cassie McCreary 06:36
Oh my gosh. What is this? How are these tables turned like this? I'm out of here now.
Jordan Ruggieri 06:40
You can copy off my test, Cassie.
Cassie McCreary 06:42
I'm not copying off your test, Jordy. I can do this myself. I'm going to go safe. And I'm going to send you like 300 people.
Dave Bauer, PhD 06:50
I guess you won't lose the bet in that case. And yeah, numbers in like in the hundreds or so. And Jordan, you said like a third or 25%. And that's a good, like, guess for your intuition. The problem is our intuition is so bad at comprehending the real problem under the hood. And the actual answer, 23. That's it. Very small number compared to 365. And the reason I like to use that illustration, because it opens that door to your mind of “Alright, I don't have a good intuitive grasp of this.” And this is going to play into digital PCR shortly. So it doesn't take a lot. And the reason for it is because it doesn't matter that there's 23 out of 365. It's that with those 23 there's so many possible combinations amongst them. There is on the order of 180 or something combinations that you can make. So all of that to say the same problem now exists or same thing exists in digital PCR. Despite having 20,000 micro chambers, like our MAP16 plates do, it only takes a really small number of DNA molecules to be present before we have DNA molecules sharing chambers, or like people sharing birthdays. The number of DNA molecules, I don't have the exact calculation, but it's on the order of just 1% of that 20,000. So not a lot. And for that reason, that's that whole thing about, we can't assume that each chamber has just one molecule, because there's a tiny range of concentration where that might be safe to assume, but it quickly goes away because of how many combinations and ways those molecules can be sit in the chambers.
Jordan Ruggieri 08:34
Interesting, sounds like you're describing Poisson statistics. Is that the word for it?
Dave Bauer, PhD 08:40
Poisson statistics is what we need for digital PCR. Because all we can measure is how many are bright how many aren't? And then Poisson distribution, Poisson statistics, that lets us convert that measurement, that observation, into our concentration.
Jordan Ruggieri 08:54
How is that applied, then? Is it just a straight, you know, percentage factor that gets added on to that yes/no number to give you a little bit more accurate concentration?
Dave Bauer, PhD 09:06
There's a formula. So, if on average, you had, on average, one DNA molecule per chamber. So with 20,000 chambers, let's say I have enough exactly 20,000 molecules. There's then a distribution expected of this fraction of the chambers will be empty, this fraction will have one I'll have two, three, and so on. But like we said before, we can't tell the difference between one two or three molecules. So really, all we care about is saying what fraction are empty relative to the total. And that's number that gets plugged into the formula and then spit out a concentration we care about.
Jordan Ruggieri 09:44
How about volume of the of the reactions? Are there are there any considerations in terms of what happens if those many reactions are different volumes? Does that affect the modeling as well?
Dave Bauer, PhD 09:56
Yeah. So there's a couple of assumptions built into using Poisson statistics and one of those has to do with the individual mini reactions all being the same volume. Because if there was a couple of them that were bigger than the others, well, they're going to be a little more likely to get extra DNA molecules than the small ones. So you'd have to account for that in your calculation if that was the case. A lot of the systems assume that uniform size, throughout the calculations and I'm sure most vendors do, you know, testing to make sure that's valid.
Jordan Ruggieri 10:25
In terms of the actual micro reactions, and micro chambers, is there a number that is, you know, statistically important to use to get you the most precise data?
Dave Bauer, PhD 10:39
So there's no magic number, where if you say you have above this value of chambers, that you are “safe.” It truly is a just continuous, more is better. And the big change, when you have more chambers, you get a bigger dynamic range. Meaning you can measure concentrations across a wider range of values. And that's really where real time PCR kind of is a champion of saying you could have something at a given high concentration, get a Ct value, measure it. Or you could do a billion-fold dilution of that and still get a Ct value and still get some kind of measure. But with digital PCR, that dynamic range window is usually a bit smaller, we don't have as much range, we have a kind of a couple orders of magnitude that we can reliably work inside of.
Jordan Ruggieri 11:31
That makes sense. I mean, if you're talking, you know, 20,000 reactions, these micro reactions, or so and you have 40,000 targets, you're not going to get any zeros. So your range that you can measure would be, you know, skewed. Is that a correct assumption or correct statement?
Dave Bauer, PhD 11:49
Yeah, the idea that you need some of the chambers to be negative is critical. So with digital PCR, if I had such a high concentration that all 20,000 chambers were showing brightness, because they all had target in them, I don't really know anything about that concentration. I might be able to say that it's at least you know, 100,000 copies per microliter or something. But I have no idea if it's 100,000 or 100 million, you have no information.
Jordan Ruggieri 12:14
So going back to the micro chambers and micro reactions. You know, as you, what is the benefit you get as you get higher numbers of these micro reactions?
Dave Bauer, PhD 12:27
Yeah, there's definitely benefit in having more micro reactions. I mentioned before that you get a little bit more dynamic range as you have more. But there's also the concept of the precision, how much certainty you have in that concentration. If I had a really small number of micro chambers, I might say that I have 100 copies per microliter. But my range of uncertainty might be 10. So I'm saying something like 100 plus or minus 10. But if I had more and more micro chambers, I can make that concentration get better, or like confidence and concentration gets better and better. So instead of 100 plus or minus 10. Now we might be saying 100 plus or minus 2. You can really there's no limit on how small you can make that uncertainty as the number of chambers grows.
Jordan Ruggieri 13:12
Well, that's awesome. Another question for you, you know, we say we say it's, it's a measure of yes and no. And you know, you're looking at that endpoint data. The fluorescent intensity, does that ever play a factor, that that threshold level, does that ever play a factor in the precision or the accuracy of the data?
Dave Bauer, PhD 13:33
It can. So in a perfect assay, you have a cloud of positives that are clearly separate from the cloud of negative. In those cases, the threshold doesn't really matter where you put it. But there are times where you have some chambers that are weakly bright. You know, maybe there's some inhibitors present, maybe your assay isn't operating perfectly in the PCR reaction. And then some of the chambers have signal that's close to the middle and a little bit harder to say exactly what it is. Or you might have cases where there's something about the sample or the technology that's giving a background fluorescence signal. And if you have some background fluorescence signal, you don't know if that signal is because of just some background noise or is it truly weak amplification. The Absolute Q is a little bit unique in that it does create background images for every single run to then subtract out that noise. So, it's not truly a simple endpoint detection. It's a little bit more sophisticated with that background subtraction, which gives you more confidence in the answer that you have at the end. And that's extremely important if you're working with lower concentrations of targets. Because if you're only expecting maybe a handful of molecules of your target present a handful of yes positive chambers, you really want to know that all of them are really because of DNA versus because of something in the background.
Jordan Ruggieri 14:53
And it's, so it sounds like reaction efficiency, while still playing somewhat of a role in digital PCR, is not necessarily as critical as, say, in qPCR, where if you have inhibitors, you may actually see a large shift in Ct value that can affect your concentration calculations. Is that a true assumption?
Dave Bauer, PhD 15:17
Definitely true. Yes. Digital PCR, and there's a handful of other bullet points I like to think of as where it's valuable. And that's one of the key ones is if your samples have inhibitors, if they're dirty, or samples that might compromise the PCR enzymes, for real time PCR you're dependent on the efficiency of that reaction from start all the way until the signal crosses your Ct threshold. Any changes to that efficiency down that pathway is going to change your answer, change the concentration. However, with digital PCR, because it's that binary do we have signal or not, I don't care if you know if you're seeing an amp curve and your individual mini reaction. You might see it super sluggishly growing really poor efficiency. That's fine. As long as by the end of cycling, it gets above the cloud of the background noise that's still counted as a positive either way. And so that gives you a lot of robustness against inhibitors. And the other value it adds is multiplexing. Because a lot of times with PCR with real time PCR, if you're multiplexing you might compromise the efficiency of the different primer and probe assays in there as you mix more and more together. So, it's hard to make a fourplex quantitative assay with real time PCR. But with digital PCR, it's so much easier to have multiplex assays. Multiple targets with different colored dyes all in the same reaction because we don't care about that efficiency.
Jordan Ruggieri 16:38
This brings up a really interesting point, thinking about how digital PCR works. What problems could digital PCR allow you to solve maybe even better than qPCR, real time PCR?
Dave Bauer, PhD 16:52
Yeah, I think if I had to kind of give like an elevator pitch for digital PCR, there'd be a couple bullet points. One, of course, is quantification, absolute quantification. If you want to know the concentration of a target, digital PCR is the gold standard. Real time PCR can do it, we can talk about some of the nuances there, but absolute quantification is a critical value for digital PCR. Another one is precision. We talked about before. You get better precision with digital PCR generally than you would with real time PCR. Also, if you have a really low concentration, if you want to quantify at the lower end of concentrations, digital PCR usually is going to outperform real time PCR. And finally, another point I like to make on it is looking for rare targets that are kind of in a sea of wild type or a sea of sequences that are really similar. In that case, digital PCR can often get better resolution than real time PCR would.
Jordan Ruggieri 17:50
Can you give some examples of that rare target detection and when you have kind of that sea of wild type? Where would you see something like that?
Dave Bauer, PhD 18:00
One application I've seen a lot of is related to cancer marker detection. So, if you have a single base pair change or a SNP in your genome, that single base change, sometimes it's hard to have an assay for PCR that is going to be perfectly finding that single base pair change and not getting some background signal from the wild type, from the normal healthy sequence. So with real time PCR if you take, for example, a liquid biopsy from a subject, you have the majority of the sequence, hopefully is going to be the healthy normal sequence. But if you're trying to find that rare single base pair change that's an indicator for some kind of cancer marker, real time PCR you might be limited in how low you can go. Can you detect 5%? Yeah, probably. 10%? Sure. 1%? Maybe not because of that sea of background noise that you're getting. But with digital PCR, you can certainly get down to 1%, even less, a tenth of a percent.
Jordan Ruggieri 19:03
Very Interesting. So I know talking about liquid biopsies and some of these cancer biomarkers. NGS, next generation sequencing, is used frequently in this research and the studies. Is there a way that digital PCR complements or can be used in conjunction with next generation sequencing techniques.
Dave Bauer, PhD 19:25
Yeah, one way, you know, sticking with the cancer example, if you had a sample from a subject where you don't know what the single base pair change might be related to their cancer, you could use NGS to determine what is the single base pair change. And now knowing that you could design a qPCR or digital PCR assay for that single base pair change. And then over time, you could keep checking samples from that subject to see are the levels, are the amounts of that mutated sequence changing over time. In theory, you could do that with next generation sequencing as well, but it's a much more time consuming, expensive process than a quick digital PCR assay.
Jordan Ruggieri 20:08
It's really interesting. Are there any other considerations when looking at rare targets?
Dave Bauer, PhD 20:14
Yeah, there's a couple considerations, for digital PCR, if you're looking for a rare target, you really want to cram in as much of the sample into the experiment as possible. But people often think that just because you're loading the sample into the instrument, it's actually being used. And there's a concept known as dead volume. Where I might load 20 microliters of reaction mix into my instrument, but only a fraction of that, say half of it, is actually analyzed, it's actually making its way into one of those mini reactions, and getting called. So, our systems, the dead volume is very, very low, you know, on the order of less than 5%. We're using or utilizing pretty much all of the reaction volume that gets inserted into the instrument to make our answer.
Cassie McCreary 21:04
Hey, Jordan, have you heard about Connect to Science?
Jordan Ruggieri 21:06
I have, but maybe our listeners haven't.
Cassie McCreary 21:10
Connect to Science is your portal to innovation, education, sustainability, and diversity in life science. Be in the know with the latest tools and resources to help you achieve success at the bench and beyond? Check out thermofisher.com/connecttoscience.
Jordan Ruggieri 21:25
Sounds like an awesome resource. Again to check it out, visit thermofisher.com/connecttoscience. Now back to the conversation.
Cassie McCreary 21:36
Dave. Jordy. Welcome to Cassie sees sees. Career rear rear. Corner ner ner ner. Jazzy music here. Intro. I'm campaigning for my own little intro music. And you know, I just have to do it myself for now. And so that's as good as it gets. Confetti, cheering, rounds of applause. We're here. How exciting! Step into in my office if you will. So, this is the part where I grill you about your career. Not really, it's a fun, funky, fresh conversation. So, you have educational background in biology, physics, some mathematics, and then your PhD, or “phid”, as I like to call it, in molecular biophysics and structural biology. I suppose my question is, what? What are what are things? What does that mean?
Dave Bauer, PhD 22:29
Honestly, so most of the time, when people ask me what I went to grad school for, I just say biochemistry. Because when they hear molecular biophysics, they're just like, "Don't care. Bored." So oh, biochemistry, it's more relatable, and it honestly is very similar. There's a lot of overlap. The main idea for biophysics is that the technology, the tools we use were things that maybe in the past had been more thought of for physical or physical chemistry, ideas, experiments. But now with evolving technologies, we can utilize them for biological molecules, which tend to be a little bit harder to work with than most chemicals. And so the application of more, you know, structural biology uses something called NMR, nuclear magnetic resonance. So that's where the structural biology often comes from X ray crystallography, things that have been around for a long time, but developments in the technology made it easier to use larger biomolecules with them. And I personally tried to stay away from the structural biology as much as possible. I care more about the biophysics. So, I was in the thermodynamics world for most of my “phid”, PhD, looking at viruses, and looking at the thermodynamics of their genome being packaged and released into their actual virus body itself.
Cassie McCreary 23:49
I think a lot of people while they're, and this is me totally speaking as not having gone through PhD studies, but I think a lot of people tried to decide, right, “Do I want to follow kind of an academic route, or do I want to move more into industry and that type of thing?” Did you ever come to that crossroads? Or did you know the whole time that maybe you didn't want to go into the academic side of things? Or what did that look like for you?
Dave Bauer, PhD 24:09
I think it took me about, in grad school, about three months to realize I want to get out of here as soon as possible and stay away from academia. I wanted to be out of the lab, or not the lab, but out of academia. I wanted to get into the industry for my own value. And that's actually part of the reason I ended up in forensics is because when I went to grad school, I thought about what degree I wanted and thought that would be a key factor in the future. But it turns out, at least for the jobs that I was looking for, they don't care as much about the degree as about what did you study? What technology did you use in the lab? Because that's what they want you to be capable of in their lab. And I used a technology that was really rare, differential scanning calorimetry. Not something a lot of people are doing in industry and I had a hard time then finding a place to apply myself and ended up in forensics as somewhere I never imagined. Just thought "This would be fun." And part of it was because I had a background in software, like computer science, and was able to leverage that for the forensics role as well. So, I really did enjoy that time learned a lot about the legal system, things I never considered.
Cassie McCreary 25:21
Pretty consistent theme across guests I would say, and Jordy feel free to say if you feel otherwise, but it's been that people have kind of started to build their careers around the things of not necessarily what they would have expected as their next steps, but kind of things that came across their path in one way or another. Or they followed something just because it sounded interesting and not necessarily would be kind of the cookie cutter next step. So it sounds like for you, that could kind of be the case a little bit, where you didn't even expect to go into kind of the forensic side of things, and then you did for a hot second. Now you're here at Thermo Fisher and you're an Application Scientist. So what is, what is your role look like now?
Dave Bauer, PhD 25:59
So now, real time PCR, digital PCR, that's the core technology that our team as Application Scientists focuses on. I was so excited when the Absolute Q came along because I personally love digital PCR. But didn't get a lot of opportunities to deal with it in the role, it was focused mainly on the real time PCR. But now digital PCR, as soon as I heard about the next version of the instrument, I wanted as much to do with it as possible.
Cassie McCreary 26:27
Here at Absolute Gene-ius we like to lightly cyberstalk our guests before speaking to them. So, you share a quote on LinkedIn about, like statistics. So what is it about statistics and just I guess, math in general that you're so passionate about?
Dave Bauer, PhD 26:42
I love probability, as opposed to just statistics. They're kind of related, but kind of different paths. And I've always been fascinated with probability, mainly because of the fact that as we saw earlier, we're not, our brains aren't intuitive about it. We have a hard time with really getting probabilities right. They're not always intuitive.
Cassie McCreary 27:02
So have you always been very, very good at those games where like, they're like, "Hey, guess the number of marbles in this jar, or like guess the number of Hershey Kisses?"
Dave Bauer, PhD 27:11
No, I am terrible at those. And when I'm in a room with people and someone's like "Hey, how many people were in the conference?" I'm like more than ten less than a million. That's all.
Jordan Ruggieri 27:24
Dave, I have a question. How do you go from studying pressure inside viruses to helping people bring up real time and digital PCR experiments and instruments?
Dave Bauer, PhD 27:40
My career path between those points, you mentioned the forensic science, I really did enjoy parts of that. But I honestly missed the lab and missed the kind of fundamental biology aspect of it. Forensics, it's very kind of cookie cutter, and you can see what we're doing and how we interpret it. I missed the kind of bigger, broader questions of science position. So, I moved into a lab doing method development, developing assays, including real time PCR for cell therapies. And then someone reached out about an Application Scientist position, and I was really intrigued by the idea of being a expert on some small slice of technology, as opposed to being more of a jack of all trades.
Cassie McCreary 28:21
Let's mentally go back to like your undergraduate studies, or your PhD studies, or even just the very beginning of your career. Insert, like shrieking scared noises here as we think about PhD studies and everything else. But what's some advice that you would give to yourself back then knowing what you know now?
Dave Bauer, PhD 28:41
I think the advice I would give, I may have touched on this before about don't overemphasize the degree itself. Really, it's what you're doing in the lab or in that environment is really important. Because I just assume that biophysics, it sounds cool and people want biophysicists. I've seen posting for them. But really, it comes down to what technologies did you learn? What did you, in that big range of whatever your degree is in, where did you narrow in, like focus in on? And that's important, probably worth I wish someone had told me, you know, check job applications, see what people care about what's in what's in right now. And that way, when you graduate, you can use that buzzword or that key concept that people care about. And keep in mind that it evolves over time, so maybe don't talk to someone who's retired about it, because they're going to have a very skewed opinion. Talk to someone who's fresh in the field.
Cassie McCreary 29:33
So a lot about focusing on like, skills you could pick up and that type of thing and I think that's pretty good advice, you know, across a whole like wide array of careers. Like it's, it's, you know, if you go after maybe a particular job title, or in your case, like we were talking about a particular degree or something like that. It's more about what you pick up along the way that you can apply in the future, rather than any kind of label on something. So, I think that's really solid advice. Out of your career so far, what would you say is the most rewarding aspect that you've had?
Dave Bauer, PhD 30:08
I love to design things. And so I did have a lot of satisfaction as an analytical method development scientist. In the prior role where you're designing an assay you say, "We need to test for this." And now you have to figure out how do we have to do it? But not only how do we do it, but how do we do it in a way that's easy enough to then tell someone else who's going to tell someone else how to do it? So, you have to really think about the problem from not only the science perspective, but also from a user perspective. And I do really appreciate, or enjoy, putting myself in the shoes of others to try and, you know, see it from their worldview, how can I simplify it for them. And that also then plays into the roles of Application Scientists. A lot of the job is teaching and training people and I do enjoy saying something and just watching their eyes gloss over because they're not interested didn't get it, and then thinking, "Alright, how do I get to rephrase this to make them care, or at least pretend to care, for ten minutes?"
Cassie McCreary 31:03
Probably very rewarding when you get to kind of have that, I guess, breakthrough moment, if you will, when you're speaking with somebody, and you're like, "Oh, there's the lights are on, but no one's home." And then all of a sudden it clicks. Right. That's very cool. So I like to ask this of everybody on our show, what is your biggest lab oops, or funniest lab moment that you can share with us, please?
Dave Bauer, PhD31:27
You know, I must have had a boring time in the lab, because I can't think of a like novel funny thing. Like I remember just failing miserably early on, like, just for the stupid mistake, where just being. I put a little mini beaker inside of a water bath, and you know, anyone who's been in a lab for more than a day would tell you "Don't just let that float in there. It might fall." But I let it float in there and it fell. And it ruined, you know, a good couple of days or hours of time, I don't remember at this point. But it was just a real, like, “Alright, don't be stupid again.” You know, just assume the worst and then go from there.
Cassie McCreary 32:03
That's okay. That's a good one. We've had all kinds of answers that range from something like that, all the way to
Jordan Ruggieri 32:10
Things blowing up.
Cassie McCreary 32:11
Things blowing up. Things melting. Things, we've had a lot. What about your, what about your best lab moment, over time? Or proudest? Or, you know.
Dave Bauer, PhD 32:21
Yeah, I think one of the most vivid memories from grad school pretty early on. So I mentioned that technique of scanning colorimetry. The idea was, we had to, we wanted to measure something as a function of temperature. And there had been previous experiments published in literature saying it's not going to work. And I just felt convinced that it could work. And for all the people in my lab at that time, we would spend a week or two growing our virus and then we'd harvest that, and for most people that week, or two, a virus that they collected would last them months of experiments. But for me, for this particular thing, I needed a ton of it. And I didn't want to partially do it, I was going all or nothing. So, I took the entire amount that it took me a week or two to get and put all of that into one experiment. And just remember watching the signal on the instrument go and just waiting for it to do something. And I was so excited when finally it started to drop right at the temperature I was hoping it too. And it definitely ended up being, you know, a pretty big part of my remaining three or four years in PhD of saying that one moment, that one decision of an experiment really paved the way for two of my publications in grad school.
Cassie McCreary 33:34
That's awesome. So taking a little bit of a risk there paid off. What had you so convinced that I was going to be the case when everybody else was like, hmm?
Dave Bauer, PhD 33:43
I honestly think it being my background in physics as well. Because knowing the physics, the thermodynamics of that situation and having an understanding of the biology, someone even cared it asked that question, you know, in a physical sense. Having those two aspects, those two perspectives, convinced me that it could work. Though it turns out the thing that I ended up measuring that day wasn't exactly what I thought it was. And so it was partially wrong, but we ended up figuring out how to make it work. So it worked out in the end.
Cassie McCreary 34:10
That's all, yeah, well, it ended up being like a big I guess tipping point for you on that. And that's great. Looking at your career now, what would you say you're hoping for like your next career steps? Or do you subscribe to the whole five-year plan thing? Or do you kind of take more of a as interesting opportunities at your way? Like, where do you see yourself next?
Dave Bauer, PhD 34:32
Yeah, I don't really think that much about the five-year plan and that's a terrible way to answer a like job interview, because that's always like you've got to make something up. But honestly, I feel like so much of my career and the biggest moments, of not just career but life, none of it was planned. For me at least I never imagined. I didn't even know what biophysics was, until the very end of my undergrad. And when I heard of it, I said, “That has to fit me being a biology and physics student.” So, I just ended up in there. Forensics never would have saw myself in that either. I feel like a lot of times, maybe when you ask someone 50 years later, their career path, it's almost like retroactively, they stitched together this like perfect pathway that they intentionally laid out. When in reality, it's like you're in the right place, you got lucky at the right time, and that really ends up driving so many things that happen. And you got to be open to those moments. So I think my advice is, “Always to be open to something.” Like this podcast, you know, at first, I didn't know if I want to do it, but hey, “Let's see where it leads me.” Someone says you want to go do this one-off thing. “Sure. Let's see what happens from it.” Give yourself those lucky moments.
Jordan Ruggieri 35:41
That was Dr. Dave Bauer, Application Scientist at Thermo Fisher Scientific. Thank you so much for joining us for today's episode of Absolute Gene-ius. Stay curious, and we'll see you next time.
I'm going to take one line from each. That's what I'm going to do. That's
Cassie McCreary 35:55
Oooh a super haiku. Okay.
Jordan Ruggieri 35:57
And that's going to be I'm going it's going on LinkedIn. I'm posting it.
Cassie McCreary 36:01
There it is.
Jordan Ruggieri 36:02
I'm going… Molecular dance. Amplified whispers of genes. Genes quantified.
Cassie McCreary 36:07
Yeah, yeah. Ooh, I like that. Okay, well, once again.