Come get your assay on with us. If you’ve never considered how PCR assays are designed and developed, this episode will help you appreciate the skill and artistry involved.
Designing a successful PCR assay is all about selecting the right primers to deliver the sensitivity and selectivity for which PCR is known for. But anyone that’s designed an assay themselves will know that doing so successfully is a lot harder it sounds.
We’re joined by two PCR assay design pros for this episode. Kimi Soohoo Ong, and Dr. Rounak Feigelman, both from Thermo Fisher Scientific, shine a light on the many factors that must be considered to design a winning PCR assay. From the level of fragmentation of nucleic acids in the sample, to what other species’ genomes that may be present in the sample, to what the sample matrix may contain, to the PCR master mix being used, if multiplexing is required, to what assay controls will be, and more! These two practiced bioinformaticians cover these challenges and then tell us how their team overcomes challenges to develop winning assays for both qPCR and dPCR applications. Our conversation uncovers the level of skill and artistry that goes into this craft.
As always, you get to learn a bit more about our guests’ backgrounds and career paths in the Cassie’s Career Corner portion of the interview. They share how they both chose a bioinformatics path over wet lab work, while also acknowledging how important the wet lab work is to what they do. They also share some great advice and resources for anyone looking to explore a career in bioinformatics.
Visit the Absolute Gene-ius page to learn more about the guests, the hosts, and the Applied Biosystems QuantStudio Absolute Q Digital PCR System.
Cassie McCreary 00:00
I want us to record the whole thing like that guy that always does the movie trailers. "In a world", like.
Jordan Ruggieri 00:10
Welcome to Absolute Gene-ius.
Cassie McCreary 00:13
That's it.
Cassie McCreary 00:25
Welcome to Absolute Gene-ius, a podcast series from Thermo Fisher Scientific. I'm Cassie McCreary.
Jordan Ruggieri 00:31
And I'm Jordan Ruggeri. And today, we are stoked to get our assay on with two of Thermo Fisher's own resident Gene-iuses, Dr. Rounak Feigelman and Kimi Soohoo Ong.
Cassie McCreary 00:42
Rounak is a staff bioinformatics scientist at Thermo Fisher with a diverse background in next generation sequencing, computational biology and bioinformatics. Kimi works with Rounak, in qPCR and dPCR digital assay design. She has experience in pharmacological chemistry and holds a graduate certificate in data mining and analytics. They're both extremely knowledgeable about digital PCR and assay design. And we had a great time learning about the mix of analytics and creativity that they bring to their work. We hope you enjoy our conversation.
Jordan Ruggieri 01:17
Well, Kimi and Rounak, thank you so much for joining us today on Absolute Gene-ius, we are very excited to have you. I'm really excited to dive into the world of digital PCR assays. I think it's a really cool conversation and we absolutely love digital PCR here at Absolute Gene-ius. So, you know, one question I want to kind of pose to both of you is, you know, when you're looking at digital PCR and developing an assay for digital PCR, what are some of the key factors that researchers should consider?
Rounak Feigelman, PhD 01:50
Some of the things that we try to learn beforehand when we start designing an assay is, what's the kind of sample that we are dealing with. So, what's the level of fragmentation in the templates that we should consider. Oftentimes, if you try to cross-apply, like an assay that was initially designed for a different application on something else, then the issue that people can run into is that your amplicons are oftentimes longer than the template in the sample. So, for example, if you're dealing with like degraded samples and FFPE samples, your assay should be designed with that in mind, such that your amplicon length is short enough to be able to amplify like a degraded target. So, I think that's an important logistical consideration that you should have. Others I would think is that the setup, for example, like, you know, do you in future want to multiplex your assay with other targets? And in that case, like, you know, if you know this beforehand, that down the line, that's something I would like to do, it's best to be upfront about it. Anything else you can think of Kimi?
Kimi Soohoo Ong 03:07
I mean, I just thought of like, the general things that I would think most people are looking for like specificity. And then like, making sure you don't have any primer dimers, so that your assay itself works and it's binding to only the target that you want.
Rounak Feigelman, PhD 03:22
So, with that respect, like, yeah, I wanted to also add one more thing that like, generally it's a good idea to also think about what else will there be in your sample. So, what's the background in your sample. Sometimes, if your sample is going to be just from human then like, you know, your assay, specificity just needs to be with regards to human genome. If there'll be a sample, which will have a mixed community, like if it's a microbial sample, if it's going to be like, you know, something for surveillance, then you have to design the assay with what you can find in the background in the sample as well.
Jordan Ruggieri 03:59
So, it sounds like you know, a good summary, as you know, it's not just thinking about digital PCR itself, but about everything that's going in and your inputs and your outputs, right? Where the sample is coming from, how intact that the nucleic acid is going to be in that particular sample, and what the exact data you're looking to get. Is it multiplex? Is it simplex? Are you, you know, trying to avoid primer dimers? And is that, is that a good kind of summary?
Rounak Feigelman, PhD 04:32
Yes, yeah, I like to take a step back and I like to think of the application. And I like to go from the application to the assay design and not just like let's just design for this target.
Jordan Ruggieri 04:44
Makes sense. Awesome. So, you get a request in for a new assay and, you know, from somebody looking to run an experiment with digital PCR. You know, what, what steps do you take to get a good understanding of what's needed and kind of how to take that from, you know, concept to an actual assay?
Kimi Soohoo Ong 05:08
The easiest type of requests might come with a target sequence already included. But sometimes that's not the case, sometimes they'll just give us like, "Hey, I want this mutation." So, we might have to do a little research and figure out where this mutation is in the genome, get the target sequence, and then get a FASTA file for that target and then we'll design for it. And sometimes you find, I guess, little surprises along the way. Sometimes they request to target in a region, and it's like, “Wow, they're like pseudo genes in this region.” So, you kind of have to figure out how to design it so that you're not like amplifying the pseudo gene, or maybe it's like in a high GC region, and you have to play with parameters to make it work so that you can design an assay in this region.
Jordan Ruggieri 06:00
Can you explain what are, what are pseudo genes?
Kimi Soohoo Ong 06:04
Yeah. So, a pseudo gene is like, a similar or like another gene in a different part of the genome, but it has a very similar sequence to the gene that you're interested in.
Jordan Ruggieri 06:16
Makes sense. So, if you're targeting one particular gene, but other genes are very similar, you're going to get amplification of those off target genes that you don't necessarily want to analyze. Let's talk a little bit about you know, some of the assay optimizations that happen. So, know let's say you are now at the stage of you have primers and probes developed. Are there considerations after that? How do you look to see that those primers and probes are behaving as expected?
Rounak Feigelman, PhD 06:46
So, first of all, like when we are actually designing the primers and probes we like to see like, you know, if we are avoiding any natural variation, population wide variation, while we are like, you know, designing our primers and probes and after that, basically, we try to make sure that our primers and probes do not like you know, form primers and dimers amongst themselves and within themselves also. After that, we also like to check for specificity in the genome in terms of targets. Sometimes, like, you know, if your targets are close to one another, then we need to be more creative in our designs, because we do not want like competition between our probes. There's like, you know, some more advanced methodologies come into play.
Kimi Soohoo Ong 07:46
I was going to add after, I guess, we have an assay designed and it's tested and then we need to do some troubleshooting. Like, some different things we'll play with is like, probe length, or like, if it's like a SNP, so there's two probes. We'll play with like, the way like either the pros are like right on top of each other, or we'll like stagger the start and stops. And then sometimes we'll also play with the quencher, so we have MGB or QSY quenchers. And then occasionally, we'll also switch the strand of the probe. So, from like the positive strand to the negative strand, just to try different ways of troubleshooting.
Rounak Feigelman, PhD 08:26
So, I want to add a little bit to the quenchers. So, we have two quenchers. One is the QSY quencher, and the other one is a nonfluorescent quencher - NFQ. And the probe length differ amongst the two quenchers. That's something that also helps optimize, helps in optimization,
Jordan Ruggieri 08:47
Can you describe the role of a quencher when it comes to dPCR reactions?
Kimi Soohoo Ong 08:52
Basically, the quencher makes it so that the fluorescent dye will not fluoresce unless the probe is bound on to the target sequence and then when the Taq comes by it cuts off the quencher and the dye, I think. And then that's what makes the dye fluoresce.
Cassie McCreary 09:11
I'm sitting over here thinking about how assay design is an art. You guys are artists. You're like, you're like the daVincis of assay design.
Kimi Soohoo Ong 09:21
One of our managers was like "You're like a fashion designer of assays."
Cassie McCreary 09:26
Sure. We can go with that. I need like a female artist who's, you're like the Georgia O'Keeffe of assay design. That's what it is. I'm in awe. Snaps to you guys.
Jordan Ruggieri 09:37
Another question for you, you know, when you're troubleshooting, how do you know, what is the kind of tip off if a reaction is not working properly and needs some additional troubleshooting?
Rounak Feigelman, PhD 09:51
Generally, when we look at like the 2D plots, and we see like, you know, how we would expect the signal to look like. There is, I would say a template of how we would like our signal to look like, you know, we would like it sort of 90 degrees for both dyes. When they start sort of coming closer and start making a wedge kind of a shape and, you know, it makes it hard for us to cluster and distinguish one population from another one. That's when we start sort of troubleshooting. Yeah, and…
Kimi Soohoo Ong 10:27
Yeah, and then also, specifically with snip or like mutation, sometimes you can get the probes like, I had a case recently where my wild type probe was binding to both the mutant type template and the wild type template. So, then that's another case that was very obvious that we needed some troubleshooting.
Jordan Ruggieri 10:47
It sounds like considerations for digital PCR assays are very similar to some of the considerations for qPCR assays. Is that the case? And are there any differences between designing assays for these two technologies?
Rounak Feigelman, PhD 11:02
Even if you design an assay for qPCR you can try running it on digital PCR and see whether it performs well. A lot of times it will perform well, but there are some considerations for digital PCR. Digital PCR, the readout is at the endpoint, right? And in qPCR, you have like a real time readout. And so sometimes when an assay does not perform so well in qPCR, you might see that it still performs well in digital PCR, because the readout is at the endpoint. Design considerations differ slightly. But overall, I do see that like the transference of an assay from qPCR to digital PCR is quite high.
Jordan Ruggieri 11:51
So, for digital PCR, does reaction efficiency play as large of a role in like say it does in qPCR, in real time PCR?
Rounak Feigelman, PhD 12:02
It does, right. Because you have very few templates and every chamber. So, with that respect, like, you know, reaction efficiency would be like an a key factor in making sure that like, you know, we do get the signal that we're expecting towards the end. And since we do take pride on the fact that like, you know, we can detect like low copy number targets. That also makes it important that the reaction efficiency is good. Yeah. What?
Jordan Ruggieri 12:33
Yeah. What databases do you use? Are they are they public databases? And can you talk about, a little bit about how these evolve, and gene assignments are, sequences are updated regularly?
Kimi Soohoo Ong 12:45
The databases I like to use are NCBI, the UCSC Genome Browser, Ensembl, and then also, we do use the COSMIC database, which is, I think cancer mutation related.
Rounak Feigelman, PhD 13:02
So, NCBI is basically a large repository of sequences. So, anybody who's like an aspiring bioinformatician would benefit from like acquainting themselves with like, NCBI, because they have several databases as part of it. They have actually done a wonderful job of really cataloging all the genome sequences, but also, like, you know, cataloging like known single point mutations, or multiple point mutations, and, or, I should say, multiple nucleotide mutations. And so that's a good reference point. We encourage generally, our customers to give us the target information with reference to, like, you know, one of the standards like formats from these databases and the ones we like, are like, you know, dbSNP from NCBI, where they have like, really catalogued the mutations. And they also list like, you know, the, like, you know, pathogenicity, like you know, link to a mutation or not, and it also actually, one of the important or crucial, like, you know, metadata information that they store is the frequency of the mutations that are observed in different populations. So, that really also helps us understand like, you know, what's the prevalence of this point mutation across different, like, you know, populations. So, yeah, NCBI generally, for mutations and for target information, and like, you know, genome templates. UCSC, we use, or I would encourage people, like an inspiring bioinformatician to look at it from like, you know, just genome visualization. And looking at like you know, if you have any, like, you know, tandem repeats in the genome, because they have a very nice, like browser, where you can actually like, you know, see what are the other like, you know, structural variations or other components that are associated with the sequence, and they sort of overlay it very nicely. So UCSC is good, like, you know, visually, they have good visualization tool, among other things.
Jordan Ruggieri 15:29
How does bioinformatics play in? What kind of bioinformatics are you running as you design assays?
Rounak Feigelman, PhD 15:37
Yeah. So, the design for the assay does sometimes warrant a change in our experimental setup. That's why it's very important that when we are actually developing an assay, you know, there are two parts to the assay development. One is the wet lab, like the R&D, a molecular biologist with whom you really work closely, the bioinformatician. And so, a lot of times, like, you know, you might have to play with the extension temperatures, you might have to play with the, like, extension time, different parts of the PCR or thermal cycling conditions. Because on bioinformatics end the assay design, like, you know, warrants, like, you know, longer extension time, and or lower extension temperature or something. Yeah, because we do predict, like, sometimes that we would like a specific probe to set before another probe. And so for playing around with, like, all those factors, or we do work closely with our molecular biologists.
Jordan Ruggieri 16:48
And I can imagine, you know, it's, it can be difficult enough, sometimes designing primers and probes for one target on one species, but if you throw in, you know, two, three or four targets and multiple species, you know, if you're looking at an environmental sample, per se, and now you just, you know, exponentially increased potential pseudo genes and potential nonspecific binding and I can just imagine, sometimes that the difficulties that can happen, even when you go on a computer, it looks like you have some good primers, probes, and targets identified. When you put that in an actual sample it can cause some complicated results and interactions?
Rounak Feigelman, PhD 17:30
Yes. Yes, we actually launched an assay actually, for wastewater surveillance for SARS-CoV-2, and I was on the design team for that assay. And it was interesting to see like, you know, wastewater does not have a standard definition, right?the So, wastewater is really just a descriptive term. And so, you really need to account for, like, very, like, large background community. And so, when we were doing the testing, you cannot like you could not really take the results from one test and just like, you know, predict the results on the next one as well. So, that needed a fair bit of optimization. And I'm glad that it works as well as it does now, but a lot of hard work went into it from in silico side and from testing side.
Jordan Ruggieri 18:28
Yeah, makes sense. Different amounts of, for lack of a better term "gunk" in the in the sample. My last question, and we'll pass you over to Cassie, for Cassie's Career Corner. Anything else or any advice, from both of you for, you know, a scientist looking to create their own digital PCR assay in the lab? What advice would you give them?
Kimi Soohoo Ong 18:54
I don't know if I just want to reiterate what Rounak has said, but definitely think about your sample type so you know, your amplicon limitations. I guess if the assay doesn't do well, this is like an important thing Rounak brought up to me is like, yes, don't throw away the assay design, just from one test not going well. I've had this happen where I was testing an assay, and I did not realize that my template was RNA, and I was using DNA master mix and nothing amplified. It was not the assay’s fault.
Jordan Ruggieri 19:35
Don't worry Kimi, I've done the same thing.
Rounak Feigelman, PhD 19:37
Yeah, I can reiterate some points that I mentioned earlier. But I would like to add a topic that we didn't really touch on is the controls.
Jordan Ruggieri 19:45
Oh, yes. Controls. Yes.
Rounak Feigelman, PhD 19:47
When do we talk about?
Jordan Ruggieri 19:49
You need the controls.
Rounak Feigelman, PhD 19:49
Yes, exactly. When?
Jordan Ruggieri 19:50
Yes.
Rounak Feigelman, PhD 19:51
When do we talk about the controls?
Jordan Ruggieri 19:55
That is a great point. Let's talk controls for sure.
Rounak Feigelman, PhD 19:57
Yeah, no. So, when you're designing your assay, always first think about, what's your control? So, digital PCR, one of the main strengths for it is like real target quantification, right. So, a lot of times, like when customers send us SNP requests, we would assume that, oh, it might be like, you know, a somatic mutation target, or so. And so, we started designing, we did a design, and we tested it in the lab, and we looked at the results, and it seemed like both like, you know, the wild type and the mutant were lighting up. And everyone, like, you know, my molecular biologist counterpart, like she came to me, and she said, "That assay's not specific." And I said, "Well, the assay does, like, the results looked like, there might be mutant and wild type, both in the sample," like, you know, so then we took a step back, and we actually relooked like, you know, reanalyzed the targets. And we realize that actually, these are not somatic mutations, these are mutations that are just general population wide variation targets. And the test sample, the control in which we were testing it, those controls had the population wide variation. So, what we were seeing was actually the ground truth. And so, it's important that like, you know, we actually select our controls according to this target in mind. Like so if you have like a somatic mutation, then people do end up using synthetic strains in the sample as a control, like you know, if, or you might use like a CEPH DNA, also. And when it comes to actual, like, you know, population wide variation, do make sure that the sample that you're using, the control that you're using, actually does not have the underlying mutations.
Cassie McCreary 22:14
Taking a quick break from our conversation to tell you about Applied Biosystems™ QuantStudio™ Absolute Q™ dPCR system. This instrument enables quantification of your targets without the need for standard curves in only 90 minutes. Digital PCR can be as simple as preparing your samples, loading onto the plate, and running the instrument
Jordan Ruggieri 22:23
Unlike other digital PCR systems, the Absolute Q dPCR instrument does not use emulsion or other droplet-based methods to compartmentalize reactions. In fact, the microfluidic array plate (MAP) technology enables consistent delivery of more than 20,000 micro-chambers. It's a great solution for anyone looking to quantify gene.
Cassie McCreary 22:55
And Thermo Fisher Scientific has a suite of dPCR assays for applications like AAV viral titer quantification, liquid biopsy analysis, and wastewater surveillance. You can learn more at www.thermofisher.com/absoluteq or visit the Absolute Gene-ius webpage. Again, that's www.thermofisher.com/absoluteq or visit the Absolute Gene-ius webpage.
Jordan Ruggieri 23:22
The Applied Biosystems™ QuantStudio™ Absolute Q™ dPCR system is for Research Use Only. Not for use in diagnostic procedures. Let's get back to our conversation.
Cassie McCreary 23:66
Ladies, welcome to Cassie's Career Corner. This is the part of the podcast where you don't have to think very hard. I'm going to ask you fun things about yourselves and it's a very warm and fuzzy section. So, we're here to have a good time or no time at all. You understand what I'm saying? So, we've heard a little bit about your kind of your academic journey, a little bit about your careers. How is it that each of you decided to focus on the areas that you have? Like Kimi, for example, you said you studied pharmacological chemistry and economics. How have you decided you want to move towards bioinformatics? And Rounak, how did you decide from the get go, pretty much, that you kind of wanted to be in the bioinformatics space?
Kimi Soohoo Ong 24:22
So, I chose basically chemistry and economics because they were both subjects that I enjoy learning about. And then through learning more about economics, I realized that it's basically a lot of statistics. And statistics is really important in science. Like regular, like chemistry, biology type sciences as well. So they did kind of go hand in hand even though they don't sound like they would. And then that kind of led to me getting my graduate certificate in data mining and analysis. And because I wanted to take on a more, or I guess, a less wet lab based role at work towards bioinformatics and I kind of wanted to use my interest in data analysis and statistics as well. We don't use too much of it in assay design, but I'm hoping like within bioinformatics, I can use that more.
Cassie McCreary 25:16
And behold, here we are. How about you, Rounak?
Rounak Feigelman, PhD 25:19
Yeah, let me give you a bit of a background. I grew up in a community where everyone's a doctor. Okay, everyone on this, everyone on the street was a doctor. So, it was pretty clear that I don't want to be a doctor.
Cassie McCreary 25:34
Everybody's a doctor, okay, cool.
Rounak Feigelman, PhD 25:37
But that did help, like, you know, develop my interest in medicine or infectious diseases. And I wanted to take a more research role. I did try my hands in the wet lab in the very beginning because I was exploring, like, you know, if I would like to go towards the biotechnology side, or bioinformatics side, and I pretty much realized very early on that I was an anti-talent in the wet lab.
Cassie McCreary 26:09
That answered my next question, but okay, go ahead.
Rounak Feigelman, PhD 26:14
So, I did want to build upon my strengths. And I took that as a learning, and I moved towards bioinformatics. Like, in my undergraduate degree, like, you know, it was very broad learning, we didn't really specialize in like genomics or proteomics, or like, you know, any one specific field, but as in how I started picking up my summer projects, my bachelor thesis, and I started realizing my interests really, were in the infectious diseases part. I really wanted to understand, like, you know, or rather use, like, you know, the computational tools to understand like, you know, how a pathogen evolves, or like, you know, really applied in like, you know, improving our understanding. And I always enjoyed working on very applied projects. So, that's how I ended up going the route of becoming like a bioinformatician, in context of like, you know, genomics.
Cassie McCreary 27:19
Okay, interesting. So, Rounak, you've had a, you've had a very international, like, background. I mean, you studied in India, then you studied in Switzerland, and you're working the. Like, what are all those jumps like? I mean, that's, that's like, those are abrupt changes.
Rounak Feigelman, PhD 27:37
It definitely like was a big change. I think what helped me through these different changes was like, you know, first of all my love for the subject, like, I really enjoyed studying. So, I, when I landed in Switzerland, that was my first international flight.
Cassie McCreary 27:58
Wow!
Rounak Feigelman, PhD 27:59
I had never been outside India before that. But what really helped during my master’s and my PhD was, you know, your community, your friends, your people with whom you study, like, you know. We had a very nice, tight knit student organization. Free coffee helped.
Cassie McCreary 28:23
Yes.
Rounak Feigelman, PhD 28:25
We could even swipe student ID and get a free beer.
Cassie McCreary 28:30
Oh, even better.
Rounak Feigelman, PhD 28:32
Yeah, exactly
Jordan Ruggieri 28:34
Wow. I went to the wrong school.
Cassie McCreary 28:36
Switzerland is where it’s at. Yeah.
Rounak Feigelman, PhD 28:39
This was only the computer science department.
Cassie McCreary 28:42
Okay, only the computer science department is where it's at. My bad.
Rounak Feigelman, PhD 28:45
And so like, you know, you had like a very nice, like, student organization where people would hang out and like, you know, feel like we were all in this together. You need to find like, you know, other ways of like, you know, sort of becoming part of like, the social community there, like the society.
Cassie McCreary 29:05
Right. That's great advice. I have one more question for each of you. And then I know Jordan has a fun one. My question is, and Kimi, you may have already answered this earlier. But you could give us another story. But it's, I would like for each of you, if you could briefly share with us your most embarrassing lab moment. And also, your proudest lab moment, or like your most rewarding?
Kimi Soohoo Ong 29:31
I did already give,
Cassie McCreary 29:33
You gave a prime example.
Kimi Soohoo Ong 29:36
An embarrassing lab. Yeah, um, I guess maybe another one is that on my second day of my very first job out of college, I was going into a test tube with a syringe, and I missed the tube and stabbed right into my finger.
Cassie McCreary 29:56
Oh, no,
Rounak Feigelman, PhD 29:57
Oh, no.
Cassie McCreary 29:59
Do you still have your finger?
Kimi Soohoo Ong 30:00
Yes, I do still have my finger and might have some tiny little remnants of the chemical that was in that syringe, but I'm still alive.
Cassie McCreary 30:07
I'm glad you’re still here. Excellent. How about your proudest moment?
Kimi Soohoo Ong 30:09
Um, my proudest moment is probably a multiplex that I'm currently working on. And it came from being given the target and then I did all the research, found the target sequence, designed the assays, and I just on Friday tested the multiplex, and it's working!
Cassie McCreary 30:30
Yes, Kimi! Whoo-hoo. Celebrate!
Kimi Soohoo Ong 30:35
And this my first assay that I have, like, supported fully from design through to testing.
Cassie McCreary 30:42
Crushed it. Well done. We're proud of you, Kimi. That's awesome. All right Rounak, how about you? What's your most embarrassing? Because Kimi shared, you have to share. That's how it works.
Rounak Feigelman, PhD 30:51
I'll talk again about like, when I was telling you that I'm an anti-talent in the lab. So, I was fresh out of high school, and I was doing like internship in a lab. And I was supposed to like, you know, pipette some samples, and I think run a centrifuge or something, something very straightforward. Like you would like, you know, high school level stuff. I ended up contaminating the entire lab.
Cassie McCreary 31:27
Oh!
Rounak Feigelman, PhD 31:27
And they had to shut it down and fumigate for like, two days or three days. I don't know why.
Cassie McCreary 31:36
That's juicy. Wow, we're getting all the good stuff this episode.
Rounak Feigelman, PhD 31:43
So yeah, I. that pretty much like you know, crossed out different options.
Cassie McCreary 31:51
Process of elimination.
Rounak Feigelman, PhD 31:56
I was like computer it is for me.
Cassie McCreary 31:59
All right. And how about your, how about your proudest moment?
Rounak Feigelman, PhD 32:03
I think working on this SARS-CoV-2 wastewater surveillance assay was a very proud moment. For me, it was technically very rewarding when we sort of solved the assay design. And seeing it just applied in a like apply in like public health context was also extremely, like, you know, it felt really like I felt really proud to be a part of a team that contributed to something that was so relevant in current times.
Cassie McCreary 32:38
That's awesome. That's a good one. All right, Jordi, we only have a few minutes. Did I leave you enough time?
Jordan Ruggieri 32:43
I think so. Yeah. This is just a straight up fun one. What is your favorite gene?
Cassie McCreary 32:49
Oh, yes.
Rounak Feigelman, PhD 32:50
I find CYP2D6 very interesting because I'm able to use that gene in a lot of different contexts. Like I do give like, you know, some seminars now and then. And whenever I need to look for a gene, like, you know, sequence for any application, or any example I'm giving I use CYP2D6.
Kimi Soohoo Ong 33:14
I've actually going to also say CYP2D6. Mine's a slightly more sentimental reason, though. The very first assay I ever designed that Ronak was teaching me how to design assays was for CYP2D6.
Rounak Feigelman, PhD 33:29
This goes to the point where I say that whenever I have to use an example sequence, I give CYP2D6.
Kimi Soohoo Ong 33:40
But it was a nice challenge because it had a pseudo gene. So, it, it's like it sticks out in my mind.
Jordan Ruggieri 33:47
Awesome. Well, thank you both very much for joining us on the podcast. We love to have you diving into the world of assay development and your background. It was an honor to have you here.
Rounak Feigelman, PhD 33:59
Yeah, I enjoyed the conversation. Thank you.
Kimi Soohoo Ong 34:01
Thank you.
Cassie McCreary 34:09
That was Dr. Rounak Feigelman and Kimi Soohoo Ong from right here at Thermo Fisher Scientific. Thank you so much for joining us for today's episode of Absolute Gene-ius. It was produced by Sarah Briganti, Matt Ferris and Matthew Stock. With more great science around the corner in future episodes, stay curious and we'll see you next time.
Jordan Ruggieri 34:22
Have I told you about my motto for marketing when it comes to any creative. If the response is "That's not terrible" you're golden. You never get anybody that says, "That was fantastic". It's always "That's not too bad. It's not terrible."
Cassie McCreary 34:38
We love a low bar.