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Anne Carpenter (Broad Institute)

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About this episode

#18 — You may know Anne Carpenter (Broad Institute) best as the brains behind the CellProfiler™ image analysis software, but today we learn more about what makes Anne tick, how she dealt with imposter syndrome, the challenges of setting up a lab and starting a family simultaneously, and her love of baking.

We’ll learn more about Anne’s move from life sciences to computer science, how her Valentine’s Day roses ended up being dissected by her children, and her love for home renovation shows. Anne also discusses the potential clinical impact of machine learning in the future and her next big career challenge. 

Follow Peter O’Toole and Anne Carpenter on Twitter.

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Please note that this is a Machine Transcription and may not be 100% accurate.

Intro/Outro (00:00:02):
Welcome to The Microscopists, a Bitesize Bio podcast hosted by Peter O’Toole, sponsored by Zeiss Microscopy. Today on The Microscopists.

Peter O’Toole (00:00:20):
Hi, This week on The Microscopists I meet Anne Carpenter and the Broad Institute of MIT and Harvard, as we discuss surviving impostor syndrome.

Anne Carpenter (00:00:29):
Does it matter if I’m the best, the best scientist in the room or the smartest person, or what, like, does any of that really fundamentally matter or are there other aspects of my life that gives me meaning and value

Peter O’Toole (00:00:38):
Overworking microscopes?

Anne Carpenter (00:00:42):
They sent another one and then within a week it was burnt out again. I called them back and they’re like, what are you doing with that shutter?

Peter O’Toole (00:00:48):
The simple joy of chocolate chip cookies.

Anne Carpenter (00:00:53):
It’s gotta be in the thousands of batches of cookies at this point in time

Peter O’Toole (00:00:58):
And the best way to get round nose, to tail eating when growing up on a farm.

Anne Carpenter (00:01:04):
I remember, I think we always gave all the weird bits to my grandmother. I don’t recall ever having any of the sort of funkier parts that a person might want to eat.

Peter O’Toole (00:01:14):
All in this episode of The Microscopists

Peter O’Toole (00:01:29):
Welcome to today’s episode of The Microscopists. And today I’m joined by Anne Carpenter from the Broad Institute over in the US, Anne good morning, good evening. Good afternoon. Whichever time of day it is. How are you?

Anne Carpenter (00:01:41):
I’m doing well. Thanks.

Peter O’Toole (00:01:43):
And firstly, thank you very much. I guess one of the things you are most famous for is Cellprofiler. And as a Microscopist running a core lab, lots of use, and we pointed lots of users in the direction of Cellprofiler as the deep platform to go to for large cell analysis. I even my PhD student was actually recently on a course for which was co run by yourself EMBL yeah. So yeah, it’s been a huge asset to us, but I guess to many listeners, they may not even of used. They might not even understand the importance of image analysis, which seems really easy. Doesn’t it? You just look at a cell and you analyze it, but it’s not. So, Anne I do I give me two minutes on this because actually I think so many people do not understand the difficulties in image analysis and we will come to lots of other things later, but it’d be great to hear why this is so important.

Anne Carpenter (00:02:43):
Well, I think it’s something that the younger generations of scientists are kind of born in a world where you quantify images. Some of us grew up in a time where images from micro microscopes were just either observed by eye or just one snapshot is what makes it into the paper. But I think it’s increasingly becoming the norm that if you see something by eye, that’s not enough, you really, you really want to quantify any behaviors that you’re, that you’re seeing in samples. And so it’s important of course for reproducibility and for being sure that you’re not fooling yourself of what you’re seeing by eye, but then the, the domain of science that my laboratory tends to focus on is this high throughput microscopy world where it’s just an absolute necessity that it’s when I was first starting my career, people really did look through piles and piles of images. Often hundreds of thousands in order to do a screen. People certainly did that by dissecting fly eyes and looking at fly wings as well. But these days it’s, it’s really becoming quite standard to quantify. And, and especially in the high throughput world, there’s, there’s really no, no, no serious alternative these days to quantifying images,

Peter O’Toole (00:03:49):
Which is obviously really important for research, but also pharmaceutical companies for drug trials and just seen global analysis. We think of when you add a drug or compound to something, we need to see the changes. And actually quite often we can see that by our naked eye or some changes within that population, but quantifying it or surely if I can see it, it’s really easy. No

Anne Carpenter (00:04:14):
That is true. If you can see it the quantification should be pretty straightforward. And I think we really can say that now, I think 10 years ago it was still the case that a lot of phenotypes were challenging. A lot of organisms were challenging. A lot of cell types are challenging. And now the out of the pie of all the things you might want to measure, I would say it’s really the, the proportion that is difficult is really pretty, it’s becoming a small sliver at this point. So if somebody’s working with kind of a standard mammalian cell type most phenotypes, if you can see them by eye, they can be not just, not just can they be quantified like by collaborating with a computer scientist for six months, but they can be quantified using off the shelf tools that are really convenient and easy, easy for any, any biologists to be able to learn how to use.

Peter O’Toole (00:05:03):
So I’ll go, I’ve got it. I’ll come back to you after this meeting, because I’ve got loads of label-free quantitative bays that we are still struggling to really get.

Anne Carpenter (00:05:11):
So that’s, so that’s part of the sliver. That’s still a little challenging. So I will say you know, certain cell types like neurons can, can, you know, the signal to noise and it’s quite quite a challenge. And and Brightfield phase imaging can, can also be a bit of a challenge, but it’s, it’s, we’re right on the cusp, especially with deep learning methods with Brightfield and phase images, we’re just right on the cusp of these becoming more and more tractable.

Peter O’Toole (00:05:38):
Yeah. I think the quantitative phase images pseudo fluorescents in a way, but anyway, we will talk about that later, but that comes to an interesting point. You’ve, you’ve introduced yourself and you’re talking about computer scientists but that’s not where you started out life. So, and I think that’s really valuable us as biologists, that you are one of us, you’re a life scientist. So I think you had your biological sciences degree at Purdue and then a PhD. So I did some background research over Illinois Champaign. Is that correct as well?

Anne Carpenter (00:06:13):
You got my credentials. Yeah.

Peter O’Toole (00:06:15):
Okay. So that’s fine. That’s into molecular biology. How did you get into computer science?

Anne Carpenter (00:06:22):
I think out of necessity is the, is the short answer to that question. So for my own project that I needed to accomplish during my PhD, it became clear that I was going to need to it’s just too tedious. It was literally a holding up or a ruler to the screen of the computer to measure things. And and I, I set out, gosh, there’s gotta be a better way because I don’t, I don’t want to fool myself to thinking I’m seeing things that I’m not and so on. And I really wish this could be a bit more objective and automated. And so I started dorking around a little bit with with image day at the time at NIH image at the time. And and just really fell in love with the, the it just feels like magic, honestly, when you take all these messy images and each one is a little unique and has some, you know, some funny things going on with it, but then when you take dozens of them and you extract all their measure metrics and you end up with a nice little scatter plot data, it just, to me, it’s really a beautiful thing to be able to turn the kind of messiness of biology into something a little more more measurable

Peter O’Toole (00:07:26):
That it’s safe to. So from my side, I used to measure things we rulers, and I’d still be tented to measure things by rulers, but I guess that’s maybe old school now for some kids. How’d you make that transition, like, because that’s not an easy switch because then that became, you built a tool or tries to develop a tool to solve that, but then that became your career. So you kind of moved out of the life science and into this huge asset and the computer science side, how’d you skill yourself?

Anne Carpenter (00:07:55):
Yeah. So I think that the reason I was successful in that transition is because I didn’t realize I was doing it at the time. If I had known that was ahead of me. I’m not sure I would have been so brave as to, as to make the leap, but I had that exposure during my P end of my PhD for my project. And then during my postdoc at the Whitehead Institute with David Sabatini, he was creating these life cell micro arrays that were producing data at an image data at a scale that was previous, previously not to common. And so it was clear in that project. I was going to need some automated analysis. So I really just started by serving the existing tools and found nothing was going to do what I needed. And so I found a couple of papers that I could scarcely understand, but were from the computer science literature that looked like they had algorithms that would help me.

Anne Carpenter (00:08:42):
But as you might be aware of computer science papers almost never have codes that go with them, even if they did have code. It’s unclear whether I would have been able to execute such code by on my own at that time. So I just reached out literally a mass email to the MIT computer science department grad student list, and just said, I need help on my project. I have some fellowship money. I can pay you hourly for your time. Can anybody help me implement this algorithm for my project and connected with Thouis R Jones? And he implemented the algorithm in a weekend. It was very straight forward for him to be able to do it. And that really started it seeing the effect of that on my projects really got me hooked. And so we ended up collaborating. He ended up switching his thesis project to work entirely on this. And over time I ended up focusing more and more on the software and less and less on any particular biological problem. I started helping everybody in the departments around us working on their projects and the software tool, which I never intended to become a major thing. It was really just for me and my friends, so to speak. Eventually it became clear that this was filling a niche for, for automated high-throughput analysis that wasn’t being served by the existing software.

Peter O’Toole (00:09:57):
So I, a that software I presume is Cellprofiler at this point, you said you built that to you, for you to use. And then obviously your friends started using it all the benefits of it. Now it’s used by almost everywhere. Most Universities across the world will be using this. It, the impact is huge. It’s free to use. It costs nothing. Do you regret not commercializing it,

Anne Carpenter (00:10:24):
Not one bit not one bit. And I mean, for, for two reasons, one noble and one practical the, the practical one is that it’s not easy making a living trying to, trying to wrench dollars out of biologists, right? I mean, given a choice, most biologists are like, Oh, you know, cost 15 bucks for this thing. Or I could just, you know, sort of finagle it myself in some other way. So I think partly it’s really just the practical that it’s, it’s hard to sell software to, to scientists in general, we’re sort of used to everything being kind of free or coming with a microscope or it’s, it’s, it’s, it’s not easy. And I think if you, if you buy commercial software hearing people complain about the cost of commercial software. I mean, it’s just the reality. That’s how much it costs to have the marketing department and the, and the and the actual engineering that goes into a tool.

Anne Carpenter (00:11:07):
And so on. It’s just, it’s an expensive enterprise. We’re a small community, so things are gonna cost quite a lot. So I didn’t think it was very practical at the time. But then of course, there’s also the noble reason, which is it just seemed to me at the time, you know, the world doesn’t need another commercial package. The world needs for this field, which at the time was just about to take off this whole high content imaging, really high throughput imaging field. It just seems like this, this, this is what the world needs right now is, is something open that can be readily adopted. And I think if I make it open, it will be very well taking care of. So I think the things that have made me slightly regret the choices when I try, when I try to obtain funding for the software.

Anne Carpenter (00:11:46):
So, you know, every, every day a couple of papers come out that site using the software, it’s, it’s very useful across the community and it’s not a lot of money, usually just one software engineer to maintain a software project of this, of this scope. It’s like an incredible bargain from the funding agencies perspective. But at times when it, when grants have come, come through rejected, I’ve been like, ah, you know, if, if only if only we could get some amount of income, you know, just a little bit of income to, to make this thing support itself, that would be maybe, maybe that would be a way to go, or maybe we’ll splash ads on the, on the software from time to time. It’s, it’s been a little bit of a struggle to try to figure out how to keep it funded. But I do have to say we’ve been extraordinarily we’re just really grateful for the support of mostly two agencies over the time over time. One is the National Institutes of Health, which is the major US funding agency. And then also the um surprisingly came along, the Chan-Zuckerberg Institute has has taken, has adopted this bioimaging ecosystem as one that they want to invest in. And it really couldn’t, it couldn’t have come at a better time because it’s a real struggle otherwise to make software engineering you know, financially solvent in the, in the academic world.

Peter O’Toole (00:12:55):
So I think that’s interesting to hear because you’re hugely successful, you know, you’re the Broad Institute or you’re Harvard, MIT behind you. And, but it’s still not a given. You still have to fight to get even the basics kept running and yeah, the impact, I think actually if you’d be commercialized it, you would not have had the impacts across science that you’ve managed by having it freeway. I think that actually that route that I think there’s a world for both commercial and freeway, I don’t, I neither is wrong and I, I don’t see the commercial world is evil far from it. There can be lots of support as you say, it’s keeping it running and supporting. That’s why there’s a cost to it. But I think your impact across science, and it’s not just biology or microbiology, it’s a broad impact that you’ve been able to have because you went to freeway in this instance which is terrific, but it’s great that you’ve got funding now. Cause I was going to say, it’s amazing that some of the big pharmaceuticals don’t club together and just, you know, just holistically put some money in cause they, they use it that’d be effective.

Anne Carpenter (00:14:10):
Yeah, that’s right. And well, our, our software project is, is quite secure at the moment. There are other software projects that aren’t, and it’s it’s amazing to me that we haven’t figured out a decent mechanism to make that work right. Because again, you know, a software, a software engineer to work on a project is typically all, all you really need, you don’t need, you know, 20 or 30 of them working together on most of these projects that are so useful in the scientific community. So even just funding one of them, you know, it’s, it’s 10 or 15 K from a dozen companies that make use of the software would, would make most of these software projects will be very, very happy. But we just haven’t quite figured out how to, how to make that work in a, in more of a social, cultural kind of way pharma company, you know, send me an invoice, I’ll pay it, but I don’t really know how to sort of make a donation to, to something that I, that I value.

Peter O’Toole (00:14:59):
So it sounds from that. So we’ve got to be careful here that it’s, it’s you and one other person and it’s far from that. And I haven’t, I, I, I can’t find that picture of the background for your team to have big is your team.

Anne Carpenter (00:15:16):
Yeah. So so typically my team is around 10 people, but only less than half of them in the past few years or so, less than half have been focused on image analysis pur se. My group is moving a bit more towards the data science and machine learning of, of image-based data. And so that I would call is very separate. So as far as the actual software engineers for the cellprofiler project, it’s almost always been around on average. It’s been one, one software engineer for the past 14 or 15 years. I was the, the first quasi software engineer. And then, and Ray Jones as well. But it’s, that’s not, that’s not a huge undertaking, but you’re absolutely right. It’s not a, it’s not a one-person effort in, in the real sense because we also have in my group alone, we have three or four people who use the software all the time.

Anne Carpenter (00:16:07):
They’re engineering savvy enough to identify problems. And in some cases fix, fix individual bugs that they encounter in their own work. And so there’s the saying in the software world that we eat our own dog food that we we, we have to deal with the, with the with the software as it is. And of course that makes us want to make it constantly better. And so, yeah, there’s about half the group that’s focused on image analysis. Most of them are focused on applications and not necessarily the engineering of the software itself. But there’s, it’s a very blurry line because a lot of folks in the group work across both sides.

Peter O’Toole (00:16:42):
So just thinking about your team, I’ve met Beth a few times, how is Beth

Anne Carpenter (00:16:49):
She’s doing very well. Thank you. Yeah. So that’s doctor th this is Dr. Beth Cimini, she’s been with me ever since around 2016 or so. And she’s now leading the, the, the side of the group that I just described, the whole image analysis. She, she runs, she launched and runs a bio image analysis training program. That is our, our mission while I’m sure there’s an official mission statement somewhere, but the way I see it is it’s, it’s a, it’s a method of turning biologists and Microscopists into, into employable image analysts. So, so moving people more towards the computational direction where where they can really have a, a meaningful career helping people do I do image analysis.

Peter O’Toole (00:17:32):
So to say it’s a big team. And so they they’re your children, but actually I think this picture here, so there we go. This photo please.

Anne Carpenter (00:17:44):
Yeah. So those are my, my five kids. I have the three older are my step-kids and the two little ones are my biological kids. And yeah, I, I was just thinking about it this morning, but I, I acquired them over a five-year period. So it went from zero to five in a pretty short period of time, kind of, kind of high throughput there. And it’s definitely been a just an incredible transition in my life, I guess it’s the way to say it.

Peter O’Toole (00:18:09):
So you haven’t segmented them yet and started tracking each individual if they did still in that five-year high throughput. So how old are they all,

Anne Carpenter (00:18:20):
The youngest is is just about to turn seven. Yeah, so, and then the, the oldest is is an employee to teacher just, just finished college out in Oklahoma.

Peter O’Toole (00:18:32):
Yeah, that’s cool. And how so you had that five-year spurt. So we took in youngest is seven five years. Oh gosh. It was 12 years ago. So you’re going to be quite early in your career still.

Anne Carpenter (00:18:47):
That’s right. So yeah, all, all of my, my children were acquired, let’s say just after I started my position at the Broad. And it is a funny question. People ask like, when is a good time to have children. And I think yeah, there’s, I think you should probably not think about your career when deciding this question because there’s, there’s just pros and cons for every career stage. I think the advantage of having kids a little on the later side, when I had my, my independent PI position the advantage there is I had enough money that I could hire housekeepers and and, you know, could not worry so much about every little expense and, and you know, because a lot of times you, you just spend a lot of energy and time when, when you could replace that just with a little bit of extra money.

Anne Carpenter (00:19:33):
And so for me, it was, it was wonderful to have kids during that period of time. On the other hand, you could argue, well, the first you know, first couple of years establishing my lab is just incredibly intensive and it’s sort of it’s a pretty anxiety provoking time. I mean, of course you’re anxious during the PhD. Am I going to get a good in it? Am I going to get a good position afterwards? And during your postdoc, am I going to go get a good position afterwards there’s anxiety throughout the spectrum. But I think when you’re starting a lab, like this could sink or swim, you really don’t know if it’s going to work out well. And you don’t know to what extent is you know, is, is your vision for a lab really going to play out? Are the grants going to get funded?

Anne Carpenter (00:20:11):
Is this really gonna work out? So that was not the ideal time to be, you know, up all night with, with babies and so on. But but it, it worked out so likewise, there’s, there’s all kinds of advantages to having kids in the, in a PhD or postdoc period because your, your schedule is just, you’re not responsible if your, if your project pauses for six months, no one in the universe is going to have a problem with that. Right. It’s it, you know, it, it affects your productivity overall, for sure. But, you know, you’ve got a three or four or five year time span, depending on what stage you’re in and you know it, but on the other hand, you have no money. So it’s, it’s it can be a challenge that way as well.

Peter O’Toole (00:20:49):
I was going to say, you know, I think there’s a lot of PhD students an early post-docs that would love to have children. But, but financially can’t take that risk at that moment in time. And then it’s quite a nomadic life for quite, you know, people move around cities, countries, continents.

Anne Carpenter (00:21:12):
Yep. So not only do you not necessarily have your, you know, your nuclear family of origin then support or extended family around, but you may not even have friends around because you’ve just moved ’em to one location or another. So that’s, that’s a real challenge of the typical scientific life, right? I mean, of course you can make sacrifices and decide to stay in a, in a certain geographic local area, but it’s, it’s, it’s, it’s a sacrifice. It makes it, it has some trade-offs in some other directions as well.

Peter O’Toole (00:21:37):
How did you, how did you balance having the children? How, how, how, how did you even segment your, your thought process to get to work? Is we are very focused people. How did you partition that? Or how did you merge them? Yeah.

Anne Carpenter (00:21:52):
Yeah. So for me, it was actually it was not a terribly difficult time. And I think this advice is very applicable to just about anyone. So if well, before I had children back to the very first days of my PhD, I had decided that I was going to work during work hours, and I was going to not work during non-work hours. And, you know, those work hours were typically 45 ish, you know, sometimes 50 or 55, but never 80 or never, you know, never well, okay. Occasionally they would just spill outside, but by, by kind of enforcing that really early on in my career, when it didn’t matter, I didn’t have anything to do outside of work hours, but but you know, volunteering and entertaining myself. But I think because I took that kind of structure really early on then when I had children, you know, suddenly I had no leisure time outside of work, you know, it went from all work to, and all leisure to all work and all kids.

Anne Carpenter (00:22:48):
But at least it wasn’t trying to shove a 70 hour work week into, into 40 hours or something like that. So for me, I would say the transition was, was not as difficult as it would have been if I, if I had let work, spill over in the first place. And so, you know, I’m not going to say that it’s trivial, there’s just a lot of logistics to a family with that many children and, and, and, you know, some of them have some special needs that are, that are a little bit challenging at times as well. And so it’s definitely not, not trivial to you know, I am pretty much doing something for somebody at every waking moment of my life, but a little less. So now that my youngest is seven, I actually, I actually do have leisure time again in my life, which is pretty exciting. So it’s not easy, but I think it’s a lot more manageable if you have kind of a, a bit of a S Le put limits on your work life before you have children, I think it’s a better transition.

Peter O’Toole (00:23:44):
So you mentioned measure what do you do in that leisure time?

Anne Carpenter (00:23:47):
Yeah, so you know, I’m, I’m not that interesting. I, there was a a TV show, the public television station wanted to do a TV show about women in science like more than a decade ago now. And there they are, they sort of asked me this question and I went through my list of hobbies. They’re like, you got anything else? You got anything else? Like, nothing, nothing interesting to televise, I guess, is the answer to your question. So my hobbies are reading, reading baking, sitting around, chatting with my friends you know, you know dinner parties like this kind of thing. So yeah, nothing, nothing too, nothing too dramatic or interesting. I mean, I guess I would say now my, my leisure time is spending time with my kids and doing, doing fun things going on hikes and stuff like that. There’s there’s the baking. Yep.

Peter O’Toole (00:24:31):
Yeah. So tell us what your youngest is it to actually say you are now doing baking and looking after your children at the same time.

Anne Carpenter (00:24:42):
Multitasking is the name of the game. That’s right.

Peter O’Toole (00:24:45):
I think we all have a photo of our children baking at some point, just to remind them that when they were young, cause they don’t anymore actually once come back from university and decided that maybe his cooking is better than ours. So maybe when they leave, they decide to actually our home cooking is no longer the best that’s out there. And this, this interesting.

Anne Carpenter (00:25:09):
So this is a, this is a Valentine’s day aftermath. One of the things that’s really fun with my kids lately is they’ve gotten re I mean, I, I don’t, I don’t know how to convey this. I don’t, I don’t want to make it sound like, Oh, my, my, my children are scientific geniuses and that everybody, every scientist kids should also be because this is actually a fairly new development, but it’s, it was hilarious. We got some kind of science-related handbook and a little lab notebook for, for Christmas. And so when we got some flowers for Valentine’s day, the first thing that girls wanted to do is chop them up into bits and name, name, all the different parts. So this is the, the aftermath of Valentine’s day at our house. Just a few weeks back.

Peter O’Toole (00:25:48):
Cool. ditto, have you put slices of these or elements under the microscope so they can see it in more detail?

Anne Carpenter (00:25:54):
Oh, that’s a great idea, but no, we haven’t. We do have a little teeny toy microscope laying around.

Peter O’Toole (00:26:00):
Yeah. You just need to find an old one that works throwing away and bring it home. I actually, I haven’t, yeah, I haven’t done that. I hated microscopy as a child, so I’m not, they will find it in their own time.

Anne Carpenter (00:26:13):
Yeah. And I’m I’m definitely hesitant. I hate the idea of kids feeling like like they’re pushed in a certain direction. And so we’ve tried to stay pretty pretty open-minded. I mean, I, I think I’m, I’m adamant about my kids being curious and, and like, you know, kind of questioning things about the world and pursuing things that interest them for sure. But I, I, I think I actually mostly hesitate away from science. So when they’re, when they’re expressing their interest, just so that they don’t feel overly shoved in that direction. I think that’s

Peter O’Toole (00:26:44):
A wise decision. And actually when they get smarter than you, it’s quite difficult that that’s a learning. So yeah. So yeah, really smart cookies. And it’s just like, well,

Anne Carpenter (00:26:56):
I’m, I’m bracing myself already. That’s why I’m also teaching them to be sort of humble and kind to those that are maybe not as as bright as they are. Cause that’s probably going to be me someday.

Peter O’Toole (00:27:08):
Yeah. See, my eldest is actually a computer scientist. That’s what he’s studying at the moment. And well maths with computer science and that’s why I’ve got so much admiration for yourself because I just couldn’t do it. I just completely lost on it. And it’s yeah, he does it. So actually when I’ve got problems at work, I can go to him. Excellent. And he’s actually quite, quite keen, but he hasn’t seen the light when it comes to putting his skills towards biology yet. Yeah, yeah. Yeah. So he hasn’t quite seen cause everything seems really trivial. He’s going to watch this, listen to this. It’s so important. Well, that be easy. And I know it’s not that I want to be done, but anyway,

Anne Carpenter (00:27:51):
It is a real challenge for, for getting computer scientists into this field is that it’s very application oriented and the, just the reward system built into computer science departments does not reward applications. It rewards interesting theory and sort of clever, clever mathematics. And so that is a real challenge is, is getting folks to be interested in working on problems that are you know, sort of too easy for them. And it’s, it’s a real problem.

Peter O’Toole (00:28:14):
Yeah. But they’re not that easy if they just conceptually easy until you want to do so it’s like, Oh

Anne Carpenter (00:28:25):
Yeah, yeah. In practice, it’s, it’s easy enough to get something to work on one image or a handful of images, but then when you’ve got thousands and I think actually that’s why, why Cellprofiler has been really popular and really successful is first of all, it was designed by a Biol biologist who needed it. So it wasn’t, you know, designed by somebody who had no clue what our typical workflow is like. And secondly, it was designed for the worst case scenario, which is you’ve got to run on a hundred thousand images and not tweak each one to, to make it work. Right. It was really designed to be robust to a large image that, which then I think also translates to being robust across different laboratories, experiments and and so on.

Peter O’Toole (00:29:04):
So I will come back to that in a minute. But you said a minute ago that one of your pastimes, one of your, for leisure, you enjoy reading. So what sort of, what sort of reading, what are you into

Anne Carpenter (00:29:16):
It’s embarrassing because since, since I’ve had had little ones, I would say almost all of my reading is on my phone and, and it’s either Twitter or articles linked from Twitter is, is, has to be the vast majority of my reading material these days. Just because it’s just bits and pieces of time scattered throughout the day. Before that I don’t know really the spectrum, I, I, I like historical fiction, historical non-fiction and novels as really quite a broad just theology, like all kinds of things is what I used to read in the old days when I had, you know, the mental bandwidth to focus on something for a few hours on a Saturday.

Peter O’Toole (00:29:59):
It was interesting. I have notice you are a prolific retweet on Twitter itself, which is a, you support a lot of initiative. So you actually, you do promote a lot of positive messages on there as well. So, so yes, I’m a follower. So

Anne Carpenter (00:30:18):
My bio in, in, in 2016 or maybe 2015, I put, I will stop tweeting politics soon. Because I, it felt so sheepish of, of that aspect, but you know, over time with the politics in America being what they are I, I finally just took that out because it was probably not likely I’ll stop doing politics, but I’m glad you’re focusing on the other stuff, which is, which is the kind of you know, trying to, trying to, I think it’s really heartening to me to see a lot of Pi’s. I’m not going to say my age. I just mean kind of a new cohort of PI’s, whether they’re older than, than me or younger, really making an effort to make science. I’m not going to say a nurturing place, but at least a place that is less toxic. Like, can we at least start there and, and, and hopefully more so making it a place where people can do their best work.

Anne Carpenter (00:31:06):
I think that there’s a lot of I don’t think science selects for weirdos, but it certainly doesn’t weed them out along the way in the same way that a lot of other careers do. And and I think there’s a lot of behaviors that just wouldn’t be tolerated in other environments. And so I, I really, I, I, I see this happening in, in the PI’s that I talked to, that I, that are interacts most with this real enthusiasm for creating, you know, a better place, like the kind of place we would have liked to train with that. So that’s a more drama free than, than what a lot of people have been subjected to in their careers.

Peter O’Toole (00:31:42):
So, yeah, I think Twitter for those who don’t follow Twitter actually in the science in the academic world, certainly the life scientists, it’s a really good way to keep on top of your subject. There’s loads of good stuff out there that comes out in snap bites, easy to digest and can delve deeper because the links are there to delve deeper. I think different platforms are good for different purposes. I also use LinkedIn, but actually that tends to grab a different community compared to

Anne Carpenter (00:32:10):
More of the industry focused as well. Yeah. I don’t see much on LinkedIn, but but whenever I do poke in there, I’m impressed by the degree to which it captures that community. Right.

Peter O’Toole (00:32:21):
Yeah. Precisely that’s, it depends on what your messaging and what you wanting to read. And I think there’s still sort of a misunderstanding Twitter. Certainly celebrity wise is all about they’re conscious of thought. And I think in the science world, it’s not about that. Mostly it’s about messages, information. Yeah. You can always see home publications printed with the chains on it. It’s like, Oh my goodness. I can’t, Henriquez my Poti 20, 71 or something once it’s like, this is brilliant. I can see all the figures, all the information. Yeah.

Anne Carpenter (00:32:56):
Yeah. So it’s, it’s just scientific content. And then it’s also just sort of critiques and movements to, to shove, shove the community and the culture in a certain direction. So for example, just peer review and open, open, open science, open data, open publications. There’s just a lot of peer pressure on Twitter for for making science a more open place, which I think is a really great direction to be going. So it is it’s definitely a segment of the society. It doesn’t reflect really all of science. But it, for the most part reflects a direction of science that I’m excited about. And I hope, hope in general that we move more towards. Okay.

Peter O’Toole (00:33:34):
Yeah. So actually I think for the people listening, watching get on there, you know, I think there’s a lot of resistance because they see it through different eyes, you know, follow the right people, putting the right tags and you will learn loads. It will broaden your horizons. You’ll literally, it will do your literature reviews for you, almost your searches for you too many respects. So thinking about publications and each two reviews, what is, what’s been your favorite publication that you’ve authored or co-authored?

Anne Carpenter (00:34:05):
I have to say probably the, the Cellprofiler paper, which was the product of my postdoc was was one that I was the most proud of because, you know, just every little fig being so intimately involved in every little figure and every little chunk of data that ends up, you know, each of it has a bit of a story behind it. And what was really nice about that paper is that we I thought it was important to demonstrate it, that it was useful across a wide variety of science. So that meant I, as I said, all my friends who had different projects in different areas kind of came together and were in that one paper for a more recent one. It’s hard to pick a paper, but just the whole cell painting, the whole cell painting series of, of papers, I think would be the one that I’m most proud of as well, more recently. And and there it’s just because we’re now th the original assay was developed, I guess that must be about a decade ago now. But now we’re just starting to see the fruits of it being adopted in the pharma industry. And we’re starting to see drugs going into clinical trials that were discovered using this approach. And it’s really, it’s really been tremendously satisfying for me.

Peter O’Toole (00:35:12):
So I think, again, we sort of touched on this at the start, and I think it’s quite something that your impact now is on drugs and what could end up helping society in general. And that’s never where you started this out, and this is the importance of blue sky research to an extent and encouraging more niche research. I’m not saying what you do is niche, but, you know, but the impact the benefit, we’d never been busy this impact then, and the impact that he’s had, these huge, I think that’s people, I don’t think realize the importance of blue sky research.

Anne Carpenter (00:35:56):
Yeah, for sure. You know, when one phenomenon I love to see is no matter what random crisis is going on in the world, whether it’s, whether it’s whether it’s COVID, whether it’s you know, killer wasps, whether it’s ice shifting off a glacier. What’s fascinating to me is, you know, within, within hours journalists have found some, some scientists who’s devoted the past 40 years of their life to studying that exact thing, no matter how obscure it, it ends up being, and, you know, nobody, nobody cares about this period of this type of, you know, nobody cares about Corona viruses until we’ve got a coronavirus outbreak. Nobody cares about killer wasps until, you know, their suddenly rampaging across the Southwest. And so I just, I love that aspect of supporting basic research that we’re creating this stable of folks.

Anne Carpenter (00:36:49):
I mean, I don’t mean to imply that they’re only useful because eventually there’ll be useful someday. I mean, there’s, there’s this inherent, like, let’s understand the world better. It, it will probably be useful in some fashion in the future. It may not be immediately useful, but it’s, it’s cool to figure out how things are working. So, yeah, I’m definitely very, even though I would say my lab is pretty fiercely focused on, on applications and on translation on drug discovery these days, like really trying to be very practical about what’s, what’s going to make a difference for, for humans in the next 10 years or something where we have a pretty short term focus, I would say in that sense, but I really appreciate the fact that there’s basic researchers across the board studying all kinds of things. I think it’s really important for society as a whole.

Peter O’Toole (00:37:31):
So what do you find the most fun aspect of your job then?

Anne Carpenter (00:37:37):
Most fun aspect. I mean, I guess I have to be, I have to be a nerd in the sense that just seeing, seeing beautiful data and, you know, the social experience of sharing a view on some beautiful data for the first time is probably one of the highlights. There is some lovely data. It it’s usually in the form of scatter plots and P values these days, but do that as well. Okay. Yeah, there we go. There we go. So this shows the cluster of genes of genes that you know, you overexpress these genes and the cells look a certain way. And if you group cells based on the quantitative way that they look, this is what you get done. The gram that I mean, this, this paper was so exciting to me because this is the Rohban elife paper that that was about overexpressing genes with, with an using cell painting to, to cluster them.

Anne Carpenter (00:38:29):
Basically these clusters that came out of this experiment were recapitulating something, a tree of all these different pathways that have taken us biologists decades to figure out, you know, who binds to whom who’s nearby, whom in the pathway, who’s upstream and downstream, which, which ones are negative and positive regulators of pathway. And just looking at looking quantitatively at how the cells look when there’s a bit too much of that gene present protein product. They, they naturally will fall into these clusters. I just thought that was really a beautiful, that was definitely a beautiful moment when, when we saw this kind of data coming out,

Peter O’Toole (00:39:05):
I think it’s one of the best virtual backgrounds I’ve had to date because it kind of just makes it look like I’ve got a sort of crown or,

Peter O’Toole (00:39:11):
Yeah, I feel like a piece science is just read coming out in the back.

Peter O’Toole (00:39:22):
This is certainly different. I, and that was interesting though, because again, you’re, you’re Microscopist or image analysis, computer scientist, I’m looking at genes and how technologies are merging together. And that’s a big challenge for you now is to integrate, you know, that spatial transcriptomics and the imaging I’m putting those the Omix together with the imaging.

Anne Carpenter (00:39:48):
Yeah. So interdisciplinarity is the name of the game. It’s, it’s hard to be an interdisciplinary science scientist. I think, you know, in general, if somebody is asking for career advice, the easier path is to pick a thing and be very good at it. When you’re interdisciplinary, it means you just don’t have time to be the absolute expert at, you know, the six different things or two different things that you’re trying to combine. And that can be a real challenge. And I think imposter syndrome has already, already rampant, but then you add on top of it, the fact that you’re trying to be an expert in two, in two fields at the same time can, can be even more so but I think the payoff, the payoff is, is concomitantly better when you’re, when you’re trying to merge a couple of different worlds, because that’s, it’s a space that others are tend not to be as much.

Peter O’Toole (00:40:36):
Yeah. And I can’t believe you used that phrase because we have just before we started, I said, I was going to ask you a question, which I haven’t asked anyone else. I know a few bought it, and that was imposter syndrome. And it just brought it up and say, how confident are you? How often do you get imposter syndrome? Where are you most nervous?

Anne Carpenter (00:40:53):
So I I’m glad you asked this because it’s, it’s such a fascinating thing to me that I, I feel, I, I don’t experience imposter syndrome in very many contexts anymore. I don’t know if it’s being like in the Harvard MIT environment, it’s just, everyone is just so full of themselves. And and sort of, you just sort of absorb it by osmosis. I don’t know, but I really want to be clear that this was not the case at the beginning of my career. So I think I’m, I’m a good trajectory to follow if you’re, if you’re sort of down here at the moment in, in confidence level in your, in your career maybe this will help this story will help some of the listeners. So I, yeah, at the, at the beginning, I think like almost every other kind of high achieving person in science you have this one, well, like I look good on paper, but you know, am I, am I really, am I really all that?

Anne Carpenter (00:41:43):
Do I have what it takes to survive in this career? Am I smarter than everybody around me at all? All those kinds of anxieties that I, that I understand are really common for everyone. So w what to say about, I mean, I could, I could, I could give a, you know, an hour long lecture on my, my progression through this, this trajectory to where I am now, which is not worrying about it. And I think a few things helped. Number one, I had a folder of all of my different accomplishments and certificates and awards and things like this earlier on my, in my career stage when that was more of a thing. And I’m just flipping through that every once in a while, remind me of something my, my best friend in grad school said, which is don’t compare other people’s outsides to your insides.

Anne Carpenter (00:42:25):
Right? Like you also look awesome on paper. You know, you also have collected these awards. You also look fantastically amazing from the outside. And the only reason you, you think you aren’t just cause you kind of know how you feel on the inside, but, you know, that’s, that’s a comparison you don’t need to make. So that’s, that’s one thing that was helpful. I think as well for me over the course of my career it’s been really valuable to me to have other sources of, of of identity and value in my life. And so perhaps the most obvious one you could imagine is becoming a mom was, was one such dramatic experience that made me realize that you just kind of cemented, you know, does it matter if I’m the best, the, the best scientist in the room or the smartest person, or what, like, does any of that really fundamentally matter or are there other aspects of my life that gives me meaning and value and so on?

Anne Carpenter (00:43:18):
And I think for me as well since the beginning, I, I became a Christian in high school and underlying, I think my whole career has been this general sense that I don’t have to, I don’t have to perceive my value as other people see me or how I’m assessed or what my H index is or any of these external, like it’s part of the culture and the faith to, to not pay attention to these external sources of value. But instead to, to understand that each person is intrinsically valued and has, has purpose in, you know, the world, whether it’s recognized by anyone else outside or not. Me living my purpose in life is really is really just, you know, between myself and my God.

Peter O’Toole (00:44:02):
This is cool. And so you, you, you felt that imposter syndrome you’ve had a very successful career, but what have you, what has been the most challenging time in the career that’s challenging career stage? It’s also challenging.

Anne Carpenter (00:44:28):
I guess I, you know, for me, it probably was just by my personal circumstances at the time, but I think starting up a lab was, was, was I think the hardest period because I was also doing it while I was raising children. And so it was just like everything needed, you know, it’s, you’re building in both cases, you’re sort of building a solid foundation, like things are going to get easier in the future, if you can just, you know, if you could just get your first grant and if you can just get your first awesome postdocs joining the lab, if you can just do everything yesterday, then things are going to be easier going forward. And the same thing with children, if I can just get them asleep through the night, then, you know, it’ll be as I just need to put in this energy now, and then things will get easier in the future. And so I do feel like now I’m, I’m living in that future where things have gotten easier, but that was, that was a pretty, a crunch time for wishing and trying to get, trying to build this nice foundation across a lot of friends in my life at one time.

Peter O’Toole (00:45:17):
How did you cope with that stress? How’d you petitioner? How did, how did you actually address the stress and move forward? Yeah,

Anne Carpenter (00:45:27):
May maybe the same answer as the, as the imposter syndrome. I think I think having dear friends who understand my, my situation and then having this like source of value, knowing that you know, I’m going to give it a try. If, if it doesn’t succeed, if I don’t get any grants, then I will find another job. And, you know, I, I want to be a scientist and I love it’s, it’s a pretty deep part of my identity. But I can do, I can be a scientist in other environments. I can not be a scientist at all. I can go into business and, you know, feed my family if that’s what I need to do. And so kind of trying to detach it’s, it’s hard. Like, I don’t mean to make it sound like who cares. I’ll just find something else to do, but telling myself that at least kind of lowered the stress levels. If I don’t get this grant, it doesn’t mean, you know, I’m a terrible person that doesn’t mean I’m a terrible scientist. It doesn’t mean I’m unemployable. But, but it’s just sort of coaching myself internally, I think it’s been helpful.

Peter O’Toole (00:46:22):
So for difficulty, from fun to difficulty, what’s the next big challenge? Where are you heading? What’s got to be addressed. What’s the big problem to solve? What do you really want to do?

Anne Carpenter (00:46:36):
So, yeah, I think, I think I do have a, I mean, it’s a pretty broad and vague answer, but it’s a pretty, it’s a pretty clear mission from, from this point forward. And that is to, to really push things more translationally and into the clinics. So as I said, roughly half the group now is working on the data science of, of images and the informatics of images. And as we’ve become increasingly focused on machine learning, it’s clear to me that there are a lot of low-hanging fruit where the methods of machine learning applied to images. I mean, as my cross campus and cell biologists, you know, we love to look at images. We understand there’s a lot of information there, but I think the, the information content is, is just so much more powerful than we, than we really can absorb with our, with our little brains.

Anne Carpenter (00:47:26):
And so I’m, I’m so excited about, but I think really just being able to extract practice information and set machine learning algorithms loose, to be able to predict so that we don’t even cert certain experiments. We don’t even have to do anymore. We can just say, Hey, Hey, machine learning algorithm, look at all these images that I’ve treated with a hundred thousand different drugs and, you know, 50,000 different genetic perturbations knocking genes up knocking genes down, take a look at all of that and tell me all the pathways that, that existed in the cell and how they interact with each other in this cell type and that cell type and so on. There’s just a lot to be had there. And I, I know there’s I know there’s skepticism about machine learning in, in the biology world. And there’s certainly some things that it can’t do. Some things that can do very well. Some things it can’t do, but I know there’s a lot of low hanging fruit and it seems like we’re just coming to the, to the orchard and starting to reach up. So I think the next 10 years are really going to be about pushing things more in that direction of, of discovering medicines that are useful for particular disorders.

Peter O’Toole (00:48:35):
I would totally agree actually. I actually think we can’t do it without machine learning. I think it’s the only way forward, but the question is how long until it becomes commonplace to be using machine learning to understand more about images.

Anne Carpenter (00:48:53):
Yeah. That’s yeah, that’s a great, that’s a great question. It’s, you know, it’s something you need, you need data at a certain scale to be able to, to you know, if you want to know if, if a particular gene is unusual, you have to test a lot of genes, or if you want to know if a particular compound is, is remarkable in a certain way, it helps to have the baseline of what everything else looks like. And so it’s not to say that it’ll be everywhere in the sense that every lab will, you know, all biologists need to be machine learning experts, but I mean, we’ve already seen quite a transition as to how computationally savvy most bile, most young biologists are these days. They certainly have, they certainly can enter a gene in blast and, and, you know, find a match.

Anne Carpenter (00:49:35):
And so on. Like there’s certain tools that are becoming more and more commonplace. It’s hard to find a biologist who hasn’t used image J or Cellprofiler to quantify their, their images if they, if they take microscopy images. So I think it it’s you know, certain tools are going to become really commonplace and really user-friendly, we won’t really even think about them as, as machine learning tools so much. And others are going to be a little bit more restricted to those who have access to huge datasets and, and really the latest AI techniques. But but yeah, that definitely a wave is, is coming.

Peter O’Toole (00:50:08):
I’d be interesting to see how that is adopted and we’ve missed. I think the other important thing thing to mention is this so much we miss in our images. And when we ask a question or we are asking questions about data and we only look for what we want to see, and actually I think machine learning will start pop, popping out populations trends that we are just blind to.

Anne Carpenter (00:50:34):
Well, like I said, it’s, it’s low hanging fruit. It’s right there. It’s right there for us to grab. So I’m just looking at images in this way. I think there’ll be a lot that we can capture from it even without really fancy techniques.

Peter O’Toole (00:50:46):
So what was the first microscope you’ve ever used? Can you Remember the first time you looked down a, a good microscope proper microscope?

Anne Carpenter (00:50:54):
I, yeah. I, you know, I don’t, I, I CA I couldn’t even tell you the brand of it. I guess it was probably Zeiss or an Olympus I don’t know, but it was, you know, a kind of I can tell you what mattered about it, which is, it was in a big dark room on a big table and had all kinds of contraptions hanging off of it and different pieces. And my job was to was to basically automate it take up my microscope that was not intended to be automated in a real way. And I set it up to automatically capture images. And within the first week, the filter completely was like burnt out and, you know, like starting to smoke. And so you know, I thought that, well, that’s kind of unfortunate and odd that I just set up my microscope and the filters burnt out, or the the sorry the shutter had, had died on it and asked the, I asked the the manufacturer, like, can you send us another one? Cause this one broke. They sent another one and then within a week it was burnt out again. I called them back and they’re like, what are you doing with that shutter? And the answer was, I’m running it literally 24 hours a day, seven days a week. And they’re like, yeah, it’s not really designed for, for that kind of intensity. So yeah, that’s what I remember about the, the first, you know, big scope that I, that I worked on pushing, pushing it to the limits, let’s say,

Peter O’Toole (00:52:12):
Yeah. I seem to remember mine, how much pour liquid nitrogen in its chill to chill on the CCD camera. But that was yeah. Some years ago now. Yeah. Two quick things thinking about your first microscope. Great. What about your favorite items in life? So I’m going to say, what is your favorite drink?

Anne Carpenter (00:52:32):
It’s going to be water

Peter O’Toole (00:52:37):
That turns a wine or is it just water and that’s it

Anne Carpenter (00:52:40):
Mostly water? I’m not, not I, I really don’t like wine. I somehow have managed to get through like college and grad school and postdoc and children and have never gotten addicted to coffee. So I’ll drink decaf, but just because it’s warm and nice. But yeah, I can’t say that I have like a, a serious favorite.

Peter O’Toole (00:53:00):
Aye, aye, aye. Aye. By the way, say so. Yeah, it is decaf coffee that I’m drinking. Okay.

Anne Carpenter (00:53:08):
Decaf coffee that you’ve got there. Yep. Yep.

Peter O’Toole (00:53:10):
Yeah, I, I do take caffeine, but only a couple of times a week, because then you really get the kick. So yeah. It’s actually worth it. Yeah. At that point, but otherwise decaf cause you’re right. I like the taste. Just, just, I don’t need the caffeine. I’d be, I’d be nuts on caffeine. I’ll be wired all the time. What about your favorite food? What’s your favorite food?

Anne Carpenter (00:53:35):
Probably pat Thai. So I grew up in an, a, on a farm in Indiana in the most culinarily, like drab part of the country in I’m sorry to say yeah, upbringing was, had no exposure to anything interesting. And so when I went off to grad school and then of course coming to Boston as well, it’s just been so exciting to be exposed to all kinds of different cuisines. So but I know Pat Thai is not particularly the most exotic thing out there, but for me it was quite a revelation.

Peter O’Toole (00:54:09):
And what about to cook? Do you cook or do you prefer

Anne Carpenter (00:54:13):
Mostly baking? I would say mostly baking. I’ve made, it’s gotta be in the thousands of batches of cookies at this point in time across the course of my life, because I started when I was, you know, seven or so, so yeah. Baking is definitely the, the big yeah.

Peter O’Toole (00:54:29):
So what’s your signa? What’s your signature dish? What’s your signature cake or bake or what is it more

Anne Carpenter (00:54:35):
So it’s chocolate chip cookies. Yeah, it’s pretty, pretty wholesome and American, but yeah, chocolate chip cookies for sure.

Peter O’Toole (00:54:41):
I have to come and visit if that’s the case. Sure. I’m on board with that through talks about your books. What about TV? Do you ever get to watch any TV?

Anne Carpenter (00:54:51):
Yeah. Yeah. So, I mean, there were definitely many, I mean, there were many years in my, of my life where I didn’t have a TV and I was very, very proud of that fact. Now that I’m an exhausted parent, I do enjoy watching a bit of TV from time to time. But nothing like nothing worth even commenting on what do I, what do I watch? Like nothing it’s it’s mostly just brainless brainless kind of trash TV.

Peter O’Toole (00:55:13):
Yeah. But that, that, that sounds more interesting. Go and watch. What’s your, what’s your vice, what’s your dirty secret with watching TV? What is it that you’re most embarrassed about watching?

Anne Carpenter (00:55:23):
Oh, everyone saw it get sucked into effect into one of these like relationship shows where it’s, you know, like however many, not the bachelor, but those kinds of shows. But you know, I, I will say like as a middle-aged person, it’s much more fascinating these days watching people choose houses and then renovate houses. So I just remember my parents watching this old house when I was a youngster. And here, here I am in that stage of life where I find it absolutely fascinating to see what happens when they knocked down a wall and then they, they see what horrible electrical or plumbing is to happen underneath.

Peter O’Toole (00:55:57):
You’ve just called yourself middle-aged do you realize

Anne Carpenter (00:56:01):
It’s true? It is absolutely true. I’m I’m 44. I, I don’t, I don’t know that I can expect to live a much past 88. So yeah,

Peter O’Toole (00:56:11):
You’re not thinking of yourself as one of the young generation in science, still early career scientists.

Anne Carpenter (00:56:18):
Science is such a funny thing because you’re kind of raised on this. Like you have, you have potential, you have potential and like at some point, you know, you’re actually middle aged or, you know, at some point, presumably you become senior and and yet you still always have this mindset of like I’m up and coming, you know, I’m going to be something someday. So it’s, it is a funny career in that sense, but I, I will say you, you do feel middle-aged when you notice that all of the the undergraduate students on campus every year, they get younger and younger and younger and younger, and you, you know, you stay the same age, but somehow they are getting younger. And at some point you, the cognitive dissonance is too strong and you realize, yeah, I’m actually a middle-aged person,

Peter O’Toole (00:56:59):
But I think you also realize that I no longer wear fashionable clothes compared to what are wearing at that point. You’d be like,

Speaker 2 (00:57:07):
But not because I don’t know, it’s because they don’t care. And that’s a really big distinction that I didn’t have when I was young. I thought the old people just like didn’t realize it, or didn’t, you know, couldn’t, couldn’t figure out how to be cool, but I hadn’t realized that they actually have things, other, other things in their lives, they care about more. So that’s, that’s kind of comforting.

Peter O’Toole (00:57:23):
I, I know, I know the clothes I am comfortable wearing almost like a brand if you like, but, so, so what is your favorite item of clothing?

Anne Carpenter (00:57:31):
Well, so now, now that we’ve been in pandemic for a year, it’s clearly yoga pants. So that’s that’s for sure. Top front runner for me.

Peter O’Toole (00:57:40):
And what about music,

Anne Carpenter (00:57:43):
Music? All my music is kind of stuck from like, you know, 15 to 20 years ago when I used to actually sort of pay attention and to go to concerts and that sort of thing. So yeah, I would say it’s a, it’s a, just a collection of, of music from back then.

Peter O’Toole (00:58:00):
Okay. Do you know, I’ve asked you what your favorites are, what’s your least favorite food?

Anne Carpenter (00:58:07):
Liver was not great. I was told that it was a good thing to eat when I was pregnant and I was, I gave it a shot, but if, if my children are missing IQ points, it’s because I couldn’t, I couldn’t stomach it. I

Peter O’Toole (00:58:19):
Have to tell his kidney.

Anne Carpenter (00:58:22):
I have not. Cause can’t, can’t say I’ve tried

Speaker 3 (00:58:24):
Surely that is worse than liver, liver, liver, but kidneys, that was just as a child. That was the worst thing that I could. That was definitely not

Anne Carpenter (00:58:35):
Well growing up on a farm, we had, we had our own cattle. So yeah, we, we were exposed to the whole, the whole gamut, but I can’t remember. I think we always gave all the weird bits to my grandmother. So I don’t recall ever having any of the, the sort of funkier parts that a person might want to eat.

Peter O’Toole (00:58:53):
So you just gave your grandmother all the offal food, awful food, offal food, I guess it’s the same thing, isn’t it the same thing? I actually, I guess if you were, I’ve go be careful what I say now, because I’ll offend some of the Scots here and there haggis with all the offal that goes into that. I know we’ve got Andy recording this for risk, that we’ll be chuckling away and waiting to get his revenge on me after that comment about haggis just being offal. I am joking Andy. I actually really quite like how, I guess we’ve covered quite, we’re actually coming up to the hour, which I cannot believe it’s been far too fast through, through that. Any as a last thought for where would you like to see your career and how would you like to see, do you see yourself becoming a Meritus and just keep going or do you see, we’d like to go out on a Swan song and then go back and just sit back.

Anne Carpenter (00:59:48):
Interesting, interesting. It’s hard. It’s definitely, it’s hard for me to, I mean, I, I don’t, I, I like to, I like to think that I’m not a workaholic in the sense that I, as I said, I try to constrain my work hours, but I think I certainly have the elements of like, I would kind of like to keep doing this forever because it’s, it’s, you know, really an entertaining way to spend one’s time. And so it’s hard to say it would, it certainly would be nice to stay like deeply connected to science in some way, like often to in, into the, to the end years. But, you know, I, I like a lot of people. It’s, it’s kind of fun to imagine just sort of winning the lottery so that you can self-fund your own lab and just sort of just, just focus on that for the, for, for the rest of the time. So for me, I wonder if I did retirement might be a little bit like that. Just latch onto somebody’s lab, where they can, they can fuss about keeping everyone employed. Then I can just maybe participate in some kind of a loose way.

Peter O’Toole (01:00:41):
That’s cool. And well, I will see you well before then. Cause you’re not that middle-aged

Anne Carpenter (01:00:48):
Not, not quite yet. I’m not dead yet.

Peter O’Toole (01:00:52):
Anne Thank you so much for joining us today. That’s been absolutely brilliant. Thank you very much.

Anne Carpenter (01:00:56):
Really nice to talk to you, Peter, take care.

Peter O’Toole (01:01:00):
Oh, just keep, keep rolling for a minute so they can just get, so this is, yeah. So this is where you were brought up then.

Anne Carpenter (01:01:09):
Yeah, so my parents had around a hundred acres of farm land in Indiana and, you know, probably six or eight different barns and outbuildings and so on. So that’s one of them, them there and it’s just beautiful country. It’s really a special thing to be able to grow up in the, in the wild, so to speak. And that’s another view of one of the, one of the pastures that’s actually mostly wetlands. So that’s cat tails, you see in the foreground, but eventually it turns into a bit of pasture. Do you miss it? I do. I mean, it’s I enjoy not living on a farm on account of all the chores and work that need to be done, but it is delightful to visit for sure. And, and my kids absolutely love being, being there and seeing all the newborn kittens and and interesting animals that are there. So it’s, it’s a wonderful to have it be part of my life without having to actually handle it day to day. But that’s where my work ethic comes from. There’s no secret that that to succeed in sciences to some degree a function of just plowing through things that you don’t necessarily want to do just to, to kind of get things done. So that’s, that’s where it’s come from for me.

Peter O’Toole (01:02:17):
So genuinely pharma to farmer in that respect, if you kind of go from agricultural farmer to pharmaceutical farmer. Oh, that’s right. That’s right. Thank you again, sure thing.

Intro/Outro (01:02:32):
Thank you for listening to The Microscopists, a Bitesizebio podcast sponsored by Zeiss microscopy to view all audio and video recordings from this series, please visit bitesizebio.com/themicroscopists.

 

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