Fiona Connolly, formerly with bit.bio, joins Absolute Gene-ius to explore how deterministic programming of induced pluripotent stem cells (iPSCs) is transforming disease modeling and drug discovery. She explains how genetic “cheat codes,” automation, and digital PCR are driving consistent, scalable production of highly defined human cell types.
In this episode, Fiona Connolly, former Platform Innovation and Automation Scientist at bit.bio, explains how deterministic programming replaces the traditional “pinball machine” model of stem cell differentiation with precise genetic instructions. By identifying the exact transcription factor combinations, the “cheat codes” that define specific cell fates, Fiona’s team engineers induced pluripotent stem cells (iPSCs) to reliably become neurons, oligodendrocyte-like cells, and other specialized types. She discusses how digital PCR enables accurate transgene copy number validation and multiplexed screening of hundreds of clones, while RNA-seq and qPCR confirm expression fingerprints. The result? Consistent, scalable, high-quality human cell models that accelerate disease research, particularly in areas like neurodegeneration and multiple sclerosis while reducing reliance on animal models.
In Career Corner, Fiona shares her journey from curious “why” kid to molecular biologist obsessed with CRISPR, robotics, and automation. Her advice: don’t over-optimize your career path. Stay pluripotent, explore interdisciplinary skills, embrace new tech, and let curiosity guide the way.
Jordan Ruggieri 00:00
I don't know what it is about calls with you guys, but every time so much there's spiders. I had to kill another spider. It's like the second or third time.
Jordan Ruggieri 00:09
Only on these calls.
Lisa Crawford 00:09
What?
Lisa Crawford 00:10
Just like have spiders in your house,
Jordan Ruggieri 00:13
There was one, it was very early on. It might have even been season one, where I had a big old spider like crawl across the camera while we were talking, and I jumped out frantically, out of my chair.
Lisa Crawford 00:22
Do you live in a haunted forest by chance?
Jordan Ruggieri 00:25
I guess so. Yeah.
Lisa Crawford 00:39
Welcome to Absolute Gene-ius, a podcast series from Thermo Fisher Scientific. I'm Lisa Crawford.
Jordan Ruggieri 00:44
And I'm Jordan Ruggieri, and we're delighted to welcome Fiona Connolly as today's guest Gene-ius.
Lisa Crawford 00:50
Fiona is a platform innovation and automation scientist at bit.bio, a biotechnology company that specializes in coding cells for novel therapeutic applications. We loved learning from Fiona about IPSC and, of course, how she uses different PCR tools in her work.
Jordan Ruggieri 01:07
Lisa, did you know that pluripotent cells are the overachievers of biology? They can be anything they want.
Lisa Crawford 01:15
Jordan, oh my gosh. Anyway, let's get on to our conversation with today's Gene-ius.
Jordan Ruggieri 01:23
Fiona, thank you so much for joining us on today's episode of Absolute Gene-ius. We are thrilled to have you here. Could you tell us a little bit about yourself, what you're doing, and maybe a little bit about bit.bio as well?
Fiona Connolly 01:37
Yeah, so I am a scientist here at bit.bio. I started out as a molecular biologist, and then I went into cell engineering. I sort of hopped back across because I did my Master's in France. So I hopped back across the pond to Cambridge, and we ended up doing some cell engineering at Horizon. And so that was really fun, because basically what we would get is the weird and wonderful cell lines and cell edits that people weren't able to do themselves. And so if it was like, “Okay, you don't have the time, we've got the time, this is our whole job.” So we would take those cells that didn't want to be edited, put the like crazy edits inside, you know, put five copies of something in somewhere. So from that, I sort of saw what you can do with genetic engineering, what you can turn it into. I always interested in new tech, and it's a lot of work to engineer these cell lines. So I kind of also got really into the automation side of things, and then I was asked to join the technology development group here at bit.bio, when it was just like a little baby startup. I think there was about 60 of us, and everything was new. The technology was new. And so just to explain a little bit of the technology, it's based on the opti-ox platform, and it's quite a sophisticated transgene system. And so what it does is it allows you to put in the factors that you need, in the way that you want, and then switch them on to turn an iPSC into a cell type that we have predefined. So it's called deterministic programming. And so you predetermine what that cell is going to be, you give it cell fate, you tell it where to go. So I think typically with iPSC differentiation, it is a stochastic process. You have the Waddington's Model, which I don't know if you're aware, and it's like the hills and valleys. So you have a cell, and it could roll through those hills and valleys a bit like a pinball machine, and it could end up over here in the neuronal side. It could end up towards the cardio heart cells. It's never 100% sure where you're going to do, what's going to happen. So what we try to do is we find the cheat code. So the factors that tell it, “I'm going to be a neuronal cell, I'm going to be a myocyte,” we find those sort of like little coordinates, and we say, "Okay, this is what you need. This is how you get to myocyte." So we'll put these in, we'll give you the cheat codes, and then we switch them on, and it gets straight to neuronal, straight to myocyte.
Jordan Ruggieri 04:01
I see. So it's like, just like that pinball model, if you, if you launch it back the same way, it might hit something slightly different, or it gets, kind of goes different pathways, but you're kind of putting in walls or putting a ruler in and helping it go down the exact path that you want the stem cell to go is that, is that a kind of like that?
Fiona Connolly 04:20
Yeah, I really think of it like giving it sat nav coordinates to be like, "Okay, you need to get exactly here. Don't worry about this hill or this valley. Don't get stuck." And it just takes it directly there. So your typical directed differentiation models that people do, iPSC, they, they can take months. Like, for instance, we make OLCs, so oligodendrocyte-like cells. And they can take is months, and you have to take them from 2D so stuck on the plate, and then you have to bring them out of the plate into 3D and then you have to put them back down into 2D and you can get a completely different version of the OLCs each time with your classic differentiation.
Fiona Connolly 04:21
So is the. Idea that customers come to you and say, "Hey, I need a cell type, this specific one." And you say, "Yep, here you go", like, plug and play. Or because, in that case, how would you know which ones to create? Are there certain ones that are more common or desirable that you know of? Or how does that work?
Fiona Connolly 05:15
So I would love to be able to do that. Someone says, “Hey, I need a cardiomyocyte”. It would be like, “Okay, give us a couple of weeks.” But when we first started, we our aim was to create every cell type in the body, and there's about 2000 factors that can combine in any random way to make every cell type. And obviously it's a complicated process. So what we have done is we have an entire platform built on finding out what those cheat codes is, because that's the crucial part. It's like, “What will tell that cell? How does it know that it's going to become microglia? How does it know that's going to become a glutamatergic neuron?” There's a huge portion of our scientists who are just dedicated to figuring out those cheat codes. And what we do is we look on, we look at what researchers biggest struggles are. So the ones where you really don't have an appropriate animal model, or you can't get the primary tissue. So majority of the cell lines we've made initially are neuronal, because they're very hard to find primary tissue for because obviously it's brain tissue. We sort of focused in on there. So things that you really couldn't make yet, and we decided, okay, we're going to make them.
Jordan Ruggieri 06:23
So taking a step back, couple kind of maybe more foundational questions. Can you elaborate on what are iPSCs? You mentioned that that abbreviation a couple times. What are they?
Fiona Connolly 06:36
So they are the induced pluripotent stem cells. And what it means is you've taken an adult cell, so for instance, like it will be a cell that might have been donated, but you take the known factors, and it's a fundamental process. It's a very famous fundamental process where you can revert that cell back into from an adult cell, back into a pluripotent stem cell. So you take it from its predestined perhaps it's a skin cell now, and you essentially send it back in time so that it can become anything. And that's kind of the thinking behind the pluripotent. It has the power to become any cell type that it can essentially. It's almost like an embryonic stage.
Jordan Ruggieri 07:16
Makes sense. So it's not like an embryonic stem cell from utero, or it's not an adult stem cell. Maybe that's, you know, you find in, like bone marrow or places like that. But it's a, it's a, it's a cell that you've sent back to its stem cell stage, that it would have been when you were in development, right, or when you were in utero, right? It's, and hasn't been differentiated into a cell type?
Fiona Connolly 07:40
Yeah, I think that makes complete sense. It's basically, it's like a cell that's not made its life choices yet. It's gone back to zero. Yeah.
Lisa Crawford 07:46
It's a little high school student cell.
Fiona Connolly 07:49
Yeah,
Lisa Crawford 07:49
Figuring out what it wants to be. Curious, though, like, because if I were to just scrape my skin and give you a bunch of cells, not all of these can be magically reverted back, can they? Like it's certain cells in the body.
Fiona Connolly 08:03
Majority of cells can go through the induced pluripotency system. I think the level of success is always iffy. So you could maybe do it with 100 cells and come out with 20 that have a nice pluripotent line. But in reality, what's going to happen is probably going to take a lot of perseverance.
Jordan Ruggieri 08:22
So even, even taking another, another step back here talking about stem cells and these different cell types, why is having a certain cell type important for science? Why do they want neurons? Why do they want brain cells? Why does that actually matter, going from an iPSC into these cell types that maybe are less common?
Fiona Connolly 08:49
It's really a lot about looking at diseases. So there's one, there's a fundamental to understand, so to understand the pathology, so the mechanism of disease, and also the treatment of the disease. So there's two aspects to it, but it's all about disease and human health at the end of the day. So we want to understand ourselves, our bodies, better, and also understand the disease versions of those. So for instance, with our oligodendrocytes, our OLCs, which is oligodendrocyte-like cells, they're the myelinating cells in the brain. And so they're the myelinating cells of all those nerve cells that you have. And so for instance, with multiple sclerosis, which is can be a very debilitating disease. And my aunt has multiple sclerosis that I've really seen how it can affect your day-to-day life. And so when you destroy that myelinating sheath, or you wear it away, as in the case of multiple sclerosis, those nerve cells wear away, those protections wear away. And then they get damaged. They get attacked. And then your ability to pick things up, to hold them as you want, to even walk as you want, starts to also get degraded. You start to see how that it's all reliant on it. And so if someone can study those OLCs and study this process of myelination and the demyelination process as well. So the disease and the pathology, then you can start to understand it better, and we can start to test drugs on this. So you can just take your plates in a dish, and what we hope to do is that they can take the OLCs, put them in, you know, a bunch of 384-well plates, and run thousands of experiments. A screener gets thousandss of drugs all at once and get straight to the most effective treatments, rather than having to test it all individually, wait for that primary tissue, we can just really speed up that process from disease mechanism and study, to drug discovery, to effective treatments to test.
Jordan Ruggieri 10:41
Makes sense, especially, and I can see why neurons are and brain tissue is so, so important. I mean, somebody with this disease, you're not necessarily able to get, you know, not only just healthy, but diseased cells. And in situations where you can do some of these larger scale studies, right to see if there are particular drug targets or research areas or even model out what that disease progression looks like. What does a typical workflow look like for this? And can you dive into that a little bit more? I mean, are you, how are you going from iPSC to a differentiated cell type and what does that involve in terms of validation, or taking those kind of critical developmental milestones, making sure you're getting it at an efficiency and effectiveness that works and makes sense?
Fiona Connolly 11:42
So we have a whole team that is dedicated to the discovery of those factors that will help us decide or help that cell decide its fate and help it determine which cell type we want it to be. So to discover, “Okay, what does the fingerprint of a multi neuron look like?” And we have to take that all the way back to the expression level. So then we have to look at, “Okay, what is the expression markers? What does it look like?” So when we're testing them, testing how we can deterministically program them, does the program always go right? And what we're really focused on, and what is the key underpinning value of this is that it's consistent. Each time you do this process, you get the exact same cell type out, and it behaves the exact same way. We have to keep that consistency, whether we did it a year ago or five years ago, it should behave exactly the same. So we do a lot of expression-based assays. We've used a lot of qPCR, traditionally, and then we brought in digital PCR. And actually, for the digital PCR, we do two aspects with it. So we did a lot with when we're putting the factors in, it's very important that they go in the right place. Nothing that we didn't want goes anywhere, we don't disrupt any other genes, or any of the mysterious like noncoding RNA that you've got going on. So we just want to make sure it's exactly where we put it, how we want to put and in as many copies as we need it to be. Because all of these things, it's a very finely controlled system to build our opti-ox platform exactly as we need it. And so we use digital PCR in order to do that all-in-one pot. So we can use multiplexing, so that we can answer four questions in one reaction, we can say, "Okay, is it where I need it to be? Do I have everything I need? Is it in the right way around? And do I have as many as I need?" That is really great, because then we can screen hundreds of clones and then just pick our like favorite clones to really do our functional assay testing on, and then that one lucky clone ends up becoming the final cell type that everyone can use. So it's been really great for that, because you can quantify with it, and that's what we really like. We can quantify our transgene copy number. We can really look at it, and we can look at it from batch to batch, from clone to clone. We can really compare and quantify, it's not relative. And then beyond that, we have the expression fingerprint. So we're also looking at utilizing digital PCR for that. So traditionally, we've used qPCR, and we also use RNA-seq to get that whole transcriptome view. So you really see what's going on across all the genes that are expressed in that cell, you get the full fingerprint. And obviously, the sides, we want to see that full fingerprint. We want to know that's everything that's going on. But before that, we know what we're looking for. You know what makes, what does, what's the main markers that makes a multi neuron. And so we can go fishing for that. And so traditionally, for that, we use qPCR, and we should see the expression levels and how they might compare. But if we want to look at that consistency, what we need is an assay that can help us with that consistency and that quantifiability. So that's where digital PCR has been really nice to see, where we can sort of compare different processes. We can compare different columns and we can quantify and essentially pitch them against each other and pick the best.
Jordan Ruggieri 15:05
I am not, not an expert in iPSCs. I mean, if you’re, you actually inserting genes to help them differentiate, or to go from a adult, you know, differentiated cell back to a stem cell, pluripotent cell. Or is it like, just, is it chemicals? Is it, is it like, how does that actually work?
Fiona Connolly 15:29
We take iPSCs, so we leave it to other experts to give us nice, beautiful iPSCs. And then once we figured out what the code is, so the code being a combination of genetic factors. And then we put that those factors in, and then we make sure that we've we have to also put them in exactly where they need to be in order for the cell to respond. Because it's also a lot to do with where they are in the genome, how accessible that they are, how many copies of them, how powerful is their signal. And also, we put in our full opti-ox platform, which allows us to control switching those factors on and off, more so switching those factors on than off.
Jordan Ruggieri 16:14
What is the most exciting aspect for you about iPSCs, or about the research, and maybe even what's coming in the future? Is there, is there something that's just like, “Hey, this is really exciting.” And you just, you know, love it.
Fiona Connolly 16:33
I'm going to be really cheesy, and to be honest, I really love what bit.bio is trying to do. They're trying to create cell types that didn't exist. Like previously, we have only cancerous versions of these cell types, or you have to go and get find some primary tissue or an animal model. And all of these are sort of proxies. So for instance, cancer cell types, they, we all know they behave kind of wild, inherently, and animal proxy models, sometimes you've had to force this kind of disease model on the animal because it doesn't naturally occur. So these kinds of cell types allow that access to what a researcher wants to study. They want to study the brain, we can give them all the elements that build the brain. So I do find that really exciting, especially with the recent directives, where the regulatory bodies have also recognized this, that animal models is not necessarily the way forward. Obviously, to me, it doesn't feel very ethical. I feel I'm very lucky because I've not had to do any research in animal models, but it can be very distressing for, I've known scientists and to know that this is the only way to do your research, it's really tough. So we can take that element out of it. So to be able to move away from those things, animal models and the regulator bodies have sort of started to recognize this as well, so they start looking at new approach methodologies. So that would be, for instance, like our deterministic program cells, or even in silico models. I think that's also really exciting, being able to do in vitro experiments that are accurate. So then, when you take it to the lab, it works exactly as predicted. And for that, you need lots of data, though, so we need to be take collecting that data on the accurate models first. So I find the potential to for each one to build on itself to be really exciting. So we can create those accurate models. We can generate all that data, and we're really focused on that actually. So we look at that whole transcriptome data for every cell type that we have, for every batch. We make sure it's matched, and then we sort of store it away, because we know that this data is gold. We know that if we want to learn from it, perhaps even build models around it, it needs to be collected well, accurately, and you need to trust your model. So with that, hopefully we can start to build in silico cells. And you can do in silico tests, and you can say, “Okay, my protein is this shape, and I'm going to use this cell. And this cell, how would it react to this protein in this drug?” and not have to do anything at the bench, not have to toil away and keep your cells alive, feed them every day. You just type it in, you run the model, and then you can go and test it. So I think that idea that we could just be more efficient with much less resources based it at the bench would be super nice.
Lisa Crawford 19:37
Hey, Jordan, have you heard of Taq Academy?
Jordan Ruggieri 19:40
No, but tell me more.
Lisa Crawford 19:41
Well, it's a free on-demand learning hub, all about qPCR and dPCR. You can access technology how-to guides, white papers and expert led webinars, led by scientists who use these technologies every day in their research.
Jordan Ruggieri 19:56
So it's by scientists, for scientists? That sounds awesome.
Lisa Crawford 20:00
Yep, it's a great place for both newcomers and technology experts to pick up new tips and stay current. And it's all available 24/7, 365.
Jordan Ruggieri 20:09
Nice. I'll check it out. How do I find it?
Lisa Crawford 20:13
Just visit thermofisher.com/taqacademy. That’s T-A-Q academy. And now back to our guest.
Lisa Crawford 20:23
So we'd like to shift a little bit and talk more about you and your career and your education and just how you ended up here, you know, for anyone listening who may be interested in this field. So I'd love to hear you talk a little bit more about how you got into science. What was it when you were growing up that steered you into science? Was there anything in particular? What do you remember about, you know, thinking, "Oh, this is something that I want to go into?"
Fiona Connolly 20:49
Yeah, I think, I mean, I've always been that annoying kid who will ask, why about everything. You know, "Why is this sky blue? Why is it like this?" And then also, a total cliche of the little girl who wanted to save all the animals in the world. I was like, "Okay, I'm going to be a vet. That's it." And that was like my 10-year plan since I was about five years. So, yeah. And then they tell you, “You need sciences,” right. So then I was very focused on the sciences. And then you it's, it's always so interesting as well. I like the sort of the process, the rigorous scientific process, you know, this is how you find out why. You test your theory, you get the data back and okay, if you're wrong, that's actually a good thing. It's an opportunity to experiment again and try again. So I've always really enjoyed that sort of investigative process of it as well. And so then I took that to biology degree. And as I went more into the genetic side of things, I saw what you can do in the molecular biology world, in that sort of tiny little world, so much is going on in the transcribing DNA, turning into a protein, turning into a function, how everything interacts. It was just wild when all of this was opened up to me. And like and it was, I was finishing my degree at the exact same time as CRISPR was coming alive. So all that excitement just sort of came to a head with my first research project. And I was tasked to design a plasmid circuit board. So to try and engineer the biology. And obviously it was, it was tough work, you know, I was trying to check each little piece that I put in. I have to check on the PCR and pray, pray that I'm going to see my band there. So it was, but was really satisfying when we put it into the cell and we could see it. And, I mean, it was a very basic student project, so I turned, I made the cell turn green, right. But I was just so happy with myself. So I got super into the genetic engineering side of things, and the things we can do if we understand what the genes do and we can apply them to functions that would be useful for our applications and for therapies. Not just even therapies, like for agriculture as well. So one of the internships I did was actually a computational study into the effects of nitrogen fixation, and there's nitrogen fixation in plants. And if you can really optimize this process and take bits and pieces and gene expression profiles from different plants, what you could do is essentially engineer a super crop that would grow amazingly in whatever soil you put it in. So even if it's very dry, very arid and tough climate, you're going to get your crops. So the things we could do is, like, the potential is huge for engineering biology. So I got all excited about all of that stuff. And then I went to do my master’s in interdisciplinary sciences, where I saw that intersection of different technical fields and what you can do when you bring different disciplines together, you bring different perspectives together, and particularly when I did one which was focused on robotic experiment. So it's an experiment entirely run by a robot, all I had to do was sort of set the robot up, make sure he had everything he needed, keep an eye on him, and I was able to generate like 10,000 data points. So I had an entire genome library of E. coli, and I was able to do the entire genome screen each night. I started to see what can happen when you can bring all these different expertise together. And so I went into more of the genetic engineering, but I brought that automation sort of fever, if you want, with me. So then when I went to join Horizon for the cell engineering team, I was all about using every robot I could get my hands on to do more of the work for me, make it bigger, screen more clones. And it was really nice to see that sort of marriage of the engineering and the cell biology come together. So you're just, you're genetic engineering, but you're also engineering the process of how you get there. And that's how I ended up joining the, I think, the technology development team, because I just love new tech, anything new, yes, give it to me, I'm going to use it. And that's how we ended up bringing digital PCR in as well, because that is the new technology. Everything was very qPCR based. It is standard. It is your baseline for expression assays and checking for molecular signals, but digital PCR really just brings that extra sensitivity that you can get. It's just, it's really great new technology. Now I do a lot more robotics as well. Now we're looking to scale up the cells that we make at bit.bio but we still want to make them exactly the same every time. And that is one thing that robots really excel at, doing exactly what you tell them to do. So we're building very large-scale systems for the production of these cells so we know that it's consistent in the handling. It's consistent in the programming, so it's consistent all the way through.
Lisa Crawford 25:41
No, I just, I like how you talk about bringing in, you've been kind of everywhere with Ag, and then robotics, and then genetic, then I just love that you're able to pull it all together. There's a theme I've noticed when you're talking about, especially with, like, your master's work was this, I mean, you just like to go in and just see what happens, right? Like, we're just going to do this thing and, you know, kind of follow where it leads. Not saying you don't go in with specific ideas, but there was kind of that overarching theme of like, let's just see?
Fiona Connolly 26:10
I've been very lucky that also in this, at bit.bio, I was given a role where I could just say, “What if we did this? Would this go? Would this be okay?” They'd be like, “Try it, try it and see.” So it's been really cool. Because then sometimes the answer is yes, it works, and it's it just like really opens new doors for everyone.
Lisa Crawford 26:30
Is there advice that you think is really important to give someone who may be just starting to get into you know, the STEM fields wants to become a research scientist in any sense? Are there qualities that you think are really important, or just something that maybe you wish you would have known going into this field that might help someone else?
Fiona Connolly 26:48
I think in the STEM fields, I mean, what I would say is like, don't be too focused. And obviously I'm biased, right. But I from what I don't have any regrets. So don't be too focused on optimizing your career path. There is a very traditional career path for STEM scientists where you go right through academia and you get your undergrad, maybe you get your masters, and you get your doctorate, and then you do some postdoc, and you really focus in on something. And I think that is it's really great to research something and break through new areas of knowledge. But if you want to apply all the knowledge that already exists and really build on it in applied ways, don't be afraid to not follow that path. I have really enjoyed my weird and wonderful masters, where I just went into new places. I had no idea what's going on. And then I just absorbed all of that knowledge and expertise that I was exposed to and I was able to apply it. And every day here I work, basically, we have teams of sort of pure engineers and biologists, and it's just great to have those conversations from different perspectives and be able to say, “Okay, how could we bring those engineering principles into what we do in the cell biology?” So I think, don't be afraid to deviate. Don't be afraid to absorb a different field. Don't like be afraid to break out of your mold a little bit. The more that you talk with different fields, the more that you can learn. It's also been really fun to explain to sort of hardcore robotics engineers why we care so much about the cells. You know, when we're trying to build the process, I try to tell them, “Okay, it can only spend 30 seconds at this station. It can only spend two minutes at this station. Otherwise it's not going to be happy.” And they just look at me like, I'm crazy. But I'm just like, yeah, that's what sells me. They need a lot of TLC.
Lisa Crawford 28:38
Respect the cells.
Fiona Connolly 28:39
Yeah.
Jordan Ruggieri 28:40
It's a great, a great pun that I have for this, which is, keep calm and proliferate on.
Fiona Connolly 28:47
Oh, very nice.
Lisa Crawford 28:48
It's not a pun, sir.
Fiona Connolly 28:50
It's a t shirt slogan. I think
Lisa Crawford 28:52
I'm going to be a pedant and say that's not a pun
Jordan Ruggieri 28:54
Be open to all the different pathways.
Fiona Connolly 28:58
Yeah, be pluripotent, right?
Lisa Crawford 29:00
Oh, there's a sticker.
Lisa Crawford 29:01
Are there any memories you have you know, at any point in your career that you know is a proudest moment, and then I'm also going to go on the opposite side and you have, like, a fun, funny, embarrassing moment that's ever happened to you in the lab?
Fiona Connolly 29:15
My proudest moment calls back to the lazy bench scientist in me. And essentially, what we had done at Horizon is take the cell engineering principle and really scale it up and automate it and build some hands-free systems. So I, with a team of other scientists, I was really lucky to be able to build a system that would screen hundreds of clones for me from different projects. So it would be running dozens of different genetic engineering projects and screen hundreds of clones within that. And I set up the system so that it could manage the plates, it can screen the plates, it can put them back, and I'm able to run it from home in my pajamas. So my proudest moment is when I wake up in the morning and I say, "Okay, go robot." And then, you know, I could live my life get ready at my own pace. And he's been working for hours by the time I get in the office, so. And funniest, I guess it's part, you know, when we were developing these things, you never know what's going to happen. So we always had to try weird things, and we didn't know how it's going to go. And I remember one where we were, we were trying to design standardized process for everything. So we wanted to standardize how the cells were mixed before that you seed them. So when you seed them, they're all in a nice, even homogenous suspension. You get the right number in every well. And we had been testing different ways to do this with instruments, so and we had found an air pump-based instrument that had been able to mix it well. And so me and my colleague, we were really proud of ourselves. We're like, "Okay, this is great. We're going to take a video and show our boss and he's going to be so amazed.” And so we get ourselves all set up in the hood, and I'm there with the camera, and my friend is there with the, my colleague is there with her tube of cells, so precious cells, and the tube, and we're all ready to go explaining what we're doing. And she puts the tube in for the air mixing. And it turns out that someone has put the pump up to like 100 and it just blows air into the tube. And there's this immediate foam explosion all over the hood. And I have the whole thing. I'm just like, "Maybe we shouldn't show our boss this."
Jordan Ruggieri 31:16
Oh, that's awesome. We've had so many fun, embarrassing stories over the, over the past seasons. It's, everybody makes mistakes and does something that just fantastically fails and it's wonderful.
Fiona Connolly 31:28
Yeah. I mean, if you don't try, you never know, right. So you have to, I think, one of my favorites from another, it's not, it wasn't from our lab, but we used to send sequencing to a lab. So we'd send like, you know, plates and plates of sequences and one time, and usually we get our answers back, like, two days later, you know, you get your sequence back, great. And there was one time it was silent for like, a week, silent for two weeks. And so we had to send them a message like, "Hey guys, what's up?" And eventually they sent a message back, being like, "Oh, we're so sorry, but the technician carrying your plates, just tripped and dropped them all." So then they just spilled, like, hundreds of customer samples. And the thing is, it's so human that we were like, "Okay, makes sense. Happens to everyone."
Lisa Crawford 32:13
Oh my gosh,
Fiona Connolly 32:14
Yeah, that's been my favorite.
Lisa Crawford 32:16
If we have a bad day, we just know that that puts it in perspective.
Fiona Connolly 32:19
I just think about, yeah, that technician, imagine the moment you're just like, "Oh no."
Jordan Ruggieri 32:24
Well, Fiona, thank you so much for joining us on today's episode. We were absolutely thrilled and really excited by the conversation. I learned a ton, which is always great from our conversations. So I really, really appreciate your time.
Fiona Connolly 32:37
Thank you so much for having me.
Jordan Ruggieri 32:41
That was Fiona Connolly, speaking as a platform innovation and automation scientist at bit.bio in Cambridge, England and since recording she has recently moved to sunny California. We have more great conversations around the corner in upcoming episodes, so 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.