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Welcome to the Microscopists,

a bite-sized bio podcast hosted by Peter Oto,

sponsored by Zeiss Microscopy. Today on the Microscopists.

Today on the Microscopist, I'm joined by Janni Vez from Institute pastor,

and we discussed why you find supporting other researchers at an imaging core

facility. So rewarding.

Sometimes, you know, he would call me and say, Jo, the microscope doesn't work.

And that would make me incredibly happy. I would, you know,

run on the roller, skate to the elevator,

go down to the micro facility and arrive on Uber Little electric.

Show me what doesn't work. And I knew I was at the time, I said, okay,

if I have to sample a job for one day,

I realize it must be something related to techniques, support or technology.

Right?

I cannot have a carry if I'm only happy when the microscope doesn't work.

His love of comic books,

Yeah. In France, it's very good.

There are a lot of authors that are excellent nowadays, and, uh,

the stories they produce are incredible novel and everything. It's,

if you went into comics right now, there's really a good need,

good literature to read. And so it's, it's fascinating,

but it's also very expensive and it takes really a lot of space.

And why he loves using Twitter to disseminate his research, or should we say,

using X to disseminate his research.

The tone that you get to use in Twitter is

make it amenable to, to speak about your, your personal story,

your view on the paper, like taking risk,

like this is not something you put in review, for instance, or on the paper. Oh,

just say, okay, this is what this work means for me.

Oh, in this episode of The Microscopist,

Hi, welcome to this episode of The Microscopist.

I'm Peter Oto from the University of York,

and today I'm joined by Johnnies t from Pastor John Eves. How are you today?

I am

Fantastic. Thank you so much, Pete. Hello everyone.

I've gotta say, I'm, I, I spoke too fast of that intro.

I'm actually really excited to be recording with you today. Uh, I met you,

oh gosh, many years ago, maybe 2001,

2002. Imp pastor. No,

It's unlikely. I started working in pastor in 2009.

I think we met in 2012 during one of the event with Spencer.

I was traveling with him in his pocket.

No, it must have been, I thought I met, he did a car, he did a meeting,

and I'm sure you were at the meeting in pastor Okay. Was in Paris, was it?

But,

but Spencer's so old now that I can't believe it was that recent.

Come on, Spencer's gonna listen to this, isn't he? Right,

right,

Right. You're speaking by boss respect.

So Colin, why don't we start with Spencer? I I, in our careers,

there's always inspirational people,

people who lead the field and drive them forward and their influence. I I I,

I'd be presuming that Spencer has been a very big influence on your career.

Actually, before Spencer,

there was Ivan Bains in the Max Mark Institute in resin.

Just before joining Spencer in Paris,

I was working there as a core facility engineer. And you know,

Ivan Baes is a person that recruited me.

So when I had to leave like resin to go to Paris,

actually I was looking for the Ivan Veins of the, of Paris. And, uh,

and for me that was, you know, professor Spencer Shortz.

And what was Spencer like to work for?

Awesome. Like Really inspiring. And he's, you know,

he has this ability to encourage you when you have weird ideas or things like

this. He said, let's go forward. He also like an incredible charisma,

you know, the working in a car facility can be like stressful.

There's a lot of job that never finishes, but he always have, like,

every time he speaks or voice something has this voice that says, you are good.

Everything's gonna be okay. We will manage together. And he can, you know,

imbue on a team, like a sense of trust, which is amazing.

And that's like, it's to work with him, right. Also,

what's surprising,

but you probably noticed because you interviewed him like a couple of, uh,

months ago, I think is very cultured. And, you know,

you can have like a good and deep discussions about philosophy, for instance,

with him. And so I was amazed that, you know, the, the, the,

the breadth of his knowledge, like ranges really also far from science. That's,

that, that's, that's, that's kind of

I and his impacts so far.

Obviously he had one of the first proper big core facilities and then made

a multi-core facility by wrapping more into it as, as we've done at York.

But Spencer was just a year ahead of us. Uh,

so it's quite nice following through that,

but also with Stratco and Ppp m Sly,

and a lot of people won't know that Spencer was behind that one,

one of the key protagonists to, to, to to,

to develop that one of the directors of the company.

So you spent time in Spencer's call. So, so you, I should,

I should actually say actually, for those who dunno your background, I,

and I was gonna say you're not a microscopist,

which you'd be deeply insulted by,

but you are now more known for your image analysis

side. So how did you get into the image analysis side?

Well, you said, well, you're right to say that I'm not a microscopist.

Right? I'm a physicist. And then I, by trade,

I was even supposed to be a teacher.

Then I just started to do biology because it was super interesting, right?

And there was a microscopy image analysis, or single processing, you know,

that was mainly the tools I needed to master just for my research and so on.

And so the, I think this is, uh,

common to many people that work in car facility or research engineers like,

you know, all our domain and so on. It may not be what we come from,

like for instance, as a someone who do research on image processing,

this is the techniques and the technology we had to master to do research and so

on. And simply, you know, that's, that's how I came to it.

I simply had to do it a lot because of my research on biological objects

and so on.

I could return you the question where you trained initially as a microscopist,

Uh, because I had to use a mic. I,

I hated microscopy when I started using the microscope. Actually, I loathed it,

it was, I'd only played with or used really bad microscopes.

And it wasn't until I looked down a really good microscope and realized I could

use it well, but then I sort of fell in love with it. But I,

and I used to have to do loads of image analysis. Uh,

Ian Morrison was a postdoc in the lab who made his own point spread fitting

software to do single molecule imaging and point spread fitting down to 20

nanometers.

And I spent weeks literally clicking on the spots.

It's some automated analysis, but this was back in the early nineties

date me that on mid nineties. So it was quite early on, on that side.

But image analysis is not something I,

I was happy to move away from the image analysis sideways. You've embraced it,

you certainly found your vocation, I presume, in image analysis.

'cause it's incredibly fun. Like, and fulfilling actually the, ah,

how can I say that, right? The, the, if you start from the microscope, right,

the microscope is a fantastic tool of research and the, the,

the insight you get on the biology is unique, right? You get the single object,

you get the dynamic at a incredible scale. But if you are,

let's say you're into machines if you like,

like really the businesss how things work, how people built interesting things,

the microscope is that tool, right? It's a fantastic object of study in itself.

And uh, when you get a good microscope, you know, from any companies,

they're actually the product of, in my humble opinion, like what we produce,

the people we produce,

the best people that are excellent engineers that are extremely clever.

They manage to do that.

And so I was interested in the interested in you into what you can get with the

microscope, but also what you, what it's itself,

the connection in image analysis was the same, right? The, the, let's say it's,

uh, I do it to serve, to support.

Sometimes in your previous interview, I think you discuss good supporting,

I remember that I do that to support my users biology and so on. But it's,

it's a field of research like image processing. But for me, like, you know,

it's a,

it's a pass into a computer or programming languages or

like image analysis algorithms that are also incredibly clever. Like the, the,

the, the level of intelligence you can discover in that,

just looking how people create or program things, it's incredibly free.

And once, you know,

you put your little finger in that you get vacuum

inside it, it's very, very hard to go back. It's incredibly fulfilling.

And like the fact that you can,

you don't have to depend on the presence of a microscope to let's say,

have fun with a computer. It's hard to go back.

I, I I would say almost the ultimate collaborator.

'cause lots of people need that help. Uh, there's, there's something to be said.

I think that so often you look down a microscope and it's grunt work,

you, you are not taking the prettiest picture.

'cause you have to make it a scientifically,

scientifically good quality picture, which is not necessarily the prettiest.

Yes. And actually, you're, you're right to a degree,

sometimes you see something in the image for the first time that no one's ever

seen and you discover. So that moment is magical.

But I bet you nine tenths of the magic is when the data analysis shows you

that the information is there and that the result is there.

And immediately you do it again because you don't trust it.

Well, the heterogeneity in biology is one that we,

that is nice to embrace.

'cause I think that's what makes it interesting and and challenging. So we,

when you are a school, at school, at school, yeah. What was the first,

your earliest memory,

what was the first job that you could remember wanting to be,

Ah, teacher Clearly,

Right from a young, young age you wanted to be a teacher. What type of teacher?

Well, I was supposed to be the teacher of the village, right. Uh,

where in my region and so on. But, you know, uh,

one thing leading to another that that's, that's kind of the result, right?

I think that the, the, the,

the school after the baccalaureate or where I went actually was supposed to

train teachers and which researchers and you,

I did everything. I do everything I could, sorry, to become a teacher in France,

there's this, uh, everything works with competitive exams of concur.

Maybe you've heard about it. Yeah. And there was this concur to become,

you know, a teacher. But, um,

and then I passed it and I was lucky it worked.

But when I went to receive the results and decide where I would teach in

September, the, the gentleman from the minister and the education told me like,

okay, so you're like 21. You're supposed to teach people that are 19,

20 something like this. You see the problem. Then I say, yes,

I see very much the problem. Then ask him if I could, you know,

continue into doctoral studies. You know, just actually to, to to get,

to get granted, to get experience. Although I had a taste for that, like,

you know, physics. And actually I started biophysics already at the time.

That was like super interesting. I didn't want it to stop. Like the idea of,

you know, being relatively young and starting a career that would be a

straight line to retirement was a kind daunting thing.

Ly and then,

like the p g was like completely unknown territory for me. So I asked, you know,

for a delay and says,

I would like to do doctoral and postdoctoral studies for some time. Is it okay?

And then it was okay. And I was mission by the minister to do that.

That's the initial goal when I was a young person, was to teach.

What was your first, what was your PhD in?

My PhD is in biophysics actually on a field that would be close to neuroscience.

I was working in the institute career in the lab of Pascal Martin.

We were working on the Hena hair cells, you know, the,

the cells that mediate our hearing zo our special

orientation and detection of, of vibration from the ground. And two,

this was hardcore physics. Like, uh, this,

this is what was so exciting. That's another story, right?

What was the biological samples you were using?

Frogs? That was my first

artis. I think that's not the one on the,

on which I did most of my experiment.

The one that worked really well for us was Han Cab.

That's folks that are relatively large, typically coming from South America,

but I think they're kind of parasitic in, uh, south of France nowadays.

Now with Pascal, we actually try to catch some of our, not to catch, but to, um,

let's say to have collaboration with people to were, uh, supposed to catch them.

And, but, uh, that was interesting. Let's say that,

so this was my first biological sample, right? The,

like before that I was only doing like physics where you did, you know,

with photon electrons, materials, everything's fine.

Then the big switch was like, okay, you have a deal with it. Ah,

Now you sent me some pictures and it's just,

just dropped on me that this must have been your PhD. 'cause this,

the one picture of this looks like a, an artificial ear.

This is, I was much, much younger. Okay, so this was, uh,

I was an undergrad intern, so I was still studying physics.

And at the time actually on the field I wanted to work on was acoustics.

I was fascinated by, you know, sound speech and, uh, music actually.

And I wanted to work on that. And I wanted to say, okay,

if I have to do physics, I want to do it as far as I can go into acoustics.

So for one of the internship, I went to Australia in the lab of Joe Wolf,

that's, this is the lab where you see me.

And this is a PhD student in the lab. Then we are working on your, uh,

what do you call that, implement adapter for actually some measurements.

And after that, another part of the internship was actually to measure the,

let's say the spectrum sensitivity on the angle of perception, actually,

just to try to assess how you can understand where the noise

is coming from, even if you just have one here or something like this.

I was very young on that. I still had hair.

I, I, I, I concur. You look very young on that.

I

Think that was your thousands, something like this, right? 23 years ago.

Uh, your,

your impedance model that you have actually could be a building in today's

world.

Well, other people have made other kind of joke on this,

but the total length of sing, I think was like 28 meters or something like this.

And the shape that you see at the bottom of the table, right,

to feel exact a certain shape so that, you know, the,

the waves will go there without any kind of reflection and so on.

And so you had to do everything by end. That was nice.

So you moved from the, the, from the sound wave to the light wave.

So do you remember what your first microscope was that you used

Perfectly? Uh, like expert from, apart from cities?

You mean the pg It was, uh, on piece,

but heavily tuned to actually play with the hair cells of the,

the inner here.

And so that the microscopic part was actually the cool one, which means that,

you know, you could move, uh, from the frog to the,

let's say the cochlear of the frog. And uh,

t step was kind of messy and difficult for me, but you know,

when you see it on the microscope with the sample, everything is under control.

And so here what we are doing is that, you know, hair cells, it's,

let's say it reassembles, I would say global cells,

except that there's you hairs acting and micro hair protruding from

that about maybe like five to 10 microns.

And with the glass fibers under the microscope, I was, you know,

deflecting this hair cells just to like generate force,

measure force and so on.

And so the microscope was heavily modified to project the shadow of the fiber

onto a detector that would've a high speed higher than the cameras,

for instance.

And with a bunch of fantastic electronic just to integrate that and a feedback

loop and a p I DSS to stabilize everything plus inject at the same time.

You know, like calcium cator or calcium indicators,

all of them control in the end by computer. And I was, I was feeling very geeky,

like in a big qu room controlling like the biology for it. That was magical,

right? This is the process of life.

But everything built around that with optical electronics and everything,

I was really cool, really tough as well.

Which is amazing actually to hear. So, you know,

I know you for all your image analysis side of work,

but this is hardcore, it, I wouldn't even call it hardcore microscopy.

It's hardcore biophysics and bespoke instrumentation,

optimizing for sample types and that, that's a different skill again,

but perfect or PhD post-doctoral type material.

I feel like you're trying to, to nail a specialty, something very narrow.

But you know, I'm a research engineer ultimately, you know, the pastor,

what we strive, even if we are organiz, you know,

in small core facilities and so on,

is to publish good paper and make fantastic science.

And so I'm just a tool or a means of that. It doesn't matter what my specialty,

as long as I advanced biology and, you know, signal processing,

microscope electronics at the time and our image analysis, these are tools.

These are, you know, just the,

the skills I have and I nurture to support the researcher with which I

collaborate, all of them for me, you know, it's just one big thing.

And so that's, you know, a set of skills with which we could work together.

So I, I've gotta ask, and it is a daunting thing.

So you're in a core facility and someone comes along with a, with a problem,

And then I'm happy.

It's like, well, and think,

I dunno how to solve that. You haven't got a straight answer. You know,

you kind of, you want to give straight answers.

You want to have solutions really quickly.

And sometimes people come with problems that look very tangible,

but the answer isn't immediate. How, how, how do you cope with those moments?

I don't, the, the, the, the one thing is that people expect from us, you know,

best, uh, not probably best results. You know, TC science,

we don't know the answer in advance, but let's say best effort.

The one thing like, I don't know, Pete,

if you are used to work with core facilities, but this is great, you know,

I would strongly advise is that, you know, you,

you go to this persons research engineer with problems,

you know, and it's their duty,

their work and their specialty to help you with them and to say, I don't know,

let's try something. Or I know, I know what you,

what we can do together and so on. I think like the, the,

I mean this is, you know,

my job is really core facilities like microscope image analysis and so on.

It's all good. But, um,

the may say the main specialty I would say I have is that right? I am.

And you know, all the people that work with me under this walls and so on,

we have the same, I would say pattern.

We are super happy when, you know,

our collaborators have difficulties and problems. We marry the problems.

And this is what we strive. Like we, we really are motivated by helping others,

not so much by, you know, the science or the stories we could write ourself.

The main, let's say happiness in work,

we find out is that when someone has innovative problems, we can work in, in,

and that would invite on that.

I, I, Janice, I, I was talking to someone who was it today actually,

I'm talking about this type of role. And I, and I said, it must bere, they said,

it must be really hard, really challenging. I said, actually,

I think compared to an academic career, I'm not saying our job is easy.

You make it as hard as you want to, you, you put the challenges there.

But we are the ultimate collaborators. Yes,

we're the ultimate cos co-authors, you know, and yes, oh, come on.

We know your publications, track mates, everything else.

But ultimately we are a, a,

a pi to hire to collaborate with a post, uh,

the teams of postdocs to hire, to collaborate with.

'cause they have specialties that they love to apply to different challenges.

And you know, how hard you find it, recruiting people.

This, it's, I would say rather difficult,

but I've been very lucky so far the okay, because you wanna find,

you wanna search in people two things, right? Of course, skills. The, the,

the one thing is that you want to, let's say improve, accelerate,

but also really improve the research of your collaborators.

So it's a good idea if you recruit people that have skills that do not exist in

a normal biology lab. So for instance, me,

I'm looking people who are good with computers, good with mathematics,

good with programming, good of course with image analysis, right?

That's not good enough because the, the, the job can be tough. Like,

no, all these ities are user requests

and ongoing projects in the facility. And there's a lot.

And so you need to find, you know,

let's say a subset of people that are really motivated by the

problems of others that are really into support.

And we exist by it. And, uh, I'm one of them. But

I, I'm gonna point out for those listening, uh,

that Johnny is just pan to his window and there must be, I don't know,

30 post-its there, just as a quick guess. There's a lot of post-its there.

And the irony that you develop software packages for tracking cells,

for doing image analysis, for ordering data,

and you resort to post-it notes on your window,

Do not worry. I also have, you know,

something tidy in my P P M S instance and I also have a code repository,

but sometimes, you know, you need things to be analog and, uh,

need like something tangible in particular, when you do only computer,

you need to compensate that this was the worst professional ID

I had because now we are a little bit better. But you know,

while ago we were really, really severely understaffed. And uh,

at some point I say, okay, we have a lot of requests.

I want to keep an eye on them. And so I just say, okay,

we put a post-it per project request, arrange them differently on the window,

depending on what's the state and who's responsible.

And so there was really a lot, and my mistake was that, you know,

my window just in front of the entrance of Pastora.

And so the first thing I would see in the morning is the big,

Oh my goodness,

Waiting for me in the office, right? And it's been like this early and you know,

coming back from the canteen says all,

every time I see the number of s and they never,

we never managed to bring them below acceptable as a

reasonable limit. And so

I, you'd never want it to be zero because then there's no job.

I also noticed looking around your office, you have, and I, I've spoken to you,

this has been in the past small Dutch houses on your shell,

You know where they come from.

Let go, tell, go on.

They come from a famous and actually excellent company that provide the

scientific software.

Go on.

Can I tell these are the s v i, the makers of Oregons,

a very good scientist, very good company. And I'm lucky to receive the windows.

And so that's the one I received as a ih. But before that,

when I was with Spencer's facility, I had them and so on.

Ah, so you need to tell them. There you go. Ss b i organs,

if you are listening to this, Yuni is missing a house.

He needs really one of these little Dutch houses. But I,

I've gotta say I'm a huge fan of them. Uh, whenever I go to the Netherlands,

I will, I will bring one home. And I had a Dutch pen pal, uh,

and he would always bring one over as a gift when he came over. Uh,

uh, but since then, when I go there, I'll still pick one up. Uh, when I,

he, he passed away last year, but I was over there recently. Uh,

and actually again, so I met his wife, uh,

his mother and I bought a house as well and bought that back. And,

and my mantle piece at home, uh, above the fire has a, has has a,

a village worth. I think it's getting bigger and bigger.

I think too, for us, I was the, a tangible measure of our careers,

right?

There's five houses means five years as a client for them

since like the facility creation and so on.

If I would put them next to the houses I went when I was working for Spencer,

that would belong. And I would say, so that's the length of my current.

It's long and, you know, it's something tangible. Again,

Janna, I'm gonna move this on slightly now and talk about Trackmate.

Okay,

Sure. Very popular for, and I, I,

I'm gonna say just very quickly. So we published Reach, uh,

recently with selfie. Uh, and we came to you for help.

'cause we had some really tough reviewer comments. Uh,

one of the reasons this series actually Besim, uh,

actually declared herself as one of the reviewers for that.

And she actually said this was beautiful. You know,

she gave us a hard time to make sure the users of the app had an easy time.

And it was such a beautiful message to put on it,

but it was tough. And I'm not an image analyst and I'm not a software writer,

nor is Will the PhD student. Uh, Laura,

he's a PhD student, so this is her first publication. It was a big publication.

So he came to you for advice, and your advice was absolutely gong dust.

And it really significantly helped us address what we needed to address and how

to address the reviewers. So thank you for that. So thank you off me.

But have you ever had problems publishing Trackmate?

Not so much, actually. The, actually, oh,

yes, I had a lot of problems actually.

I started working on Trackmate in 2000, uh,

11, I think 2010, relatively early when I was working in Spencer.

It was mainly the goal was to make a tool that I could use for my research.

Again, like working for Spencer, it took just because I'm,

to be frank, I'm a terrible writer. And, uh, I, I feel like, you know,

the drive to write is not very strong with me.

The drive to program is stronger. And so every time, you know,

I kept adding stuff to Trackmate.

I was using it in the facility for other project and so on. It started growing,

growing, growing, growing. But I never could publish it at all.

And just when we started collaborating with Kevin, yeah, too, Kevin,

he's actually an excellent writer. He said, well, you know,

why don't you spend some time with us in Madison and then we will work on the

Trackmate publication, for instance, trying to showcase how we could work.

So I started working with Curtis Rodin at the time on this and that this is how

it happens. But the, the review were, were actually,

I would say I was impressed, like pretty, pretty good. People realized that,

you know, it was not a novel tracking algorithms,

mainly let's say a tracking platform and so on.

And the comments we had on the biological applications were, were very good.

It just took me like eight years also to have the first publication.

And that's a terrible mistake. I'm doing this mistake again. By the way,

just as we speak now, like,

I've been working a lot with TOAs and other fantastic people on Masteron,

which is still not published, but still used, you know,

by others in publication that cannot cite Masteron at all.

So really don't do what I do. Write early,

Don't

Make the same mistake. It's a big mistake.

I was gonna say also, it's not just the fact when you pub, when you publish,

you also tweet out almost the paper. And that is super useful.

I that, that's such a good way to disseminate.

It's so people know it's available to use.

It's been like far more impartial to that.

I think it's a really great platform for a scientist. Uh,

the tone that you get to use in Twitter is

make it amenable to, to speak about, you know, your personal story,

your view on the paper. Like taking risk.

Like this is not something you would put in a review, for instance,

or on a paper. All you just say, okay, this is what this work means for me.

This is why I think it's important or completely subjective and so on.

And again, you know, we are very lucky because apparently Twitter,

with all due respect, right? It's starting to get down. I also, initially,

that was my, my personal account, I was using it to follow, like,

people like that write comic books. 'cause I like comic books a lot.

And uh, they tell me that, you know,

what they see in Twitter and the followers and so on, highly toxic.

It's a really nasty social networks. But us for scientists,

the scientific part of Twitter is really fantastic. It's really respectful.

The values that people pro their actual equality, uh, uh,

diversity and so on. Inclusion, inclusiveness, very good.

There's very like voices that are conservative and so on. It's very refreshing.

So before, before, before we started the interview and we,

we spoke about like venting procedures for scientists,

like when you need to vent, when you know the, the pressure. Mm-hmm. Like,

and so Twitter has this kind of, of, of views, right?

You use it to kind of normalize and say, oh, that's nice.

I'm not the only one having difficulties. Oh, people are also annoyed with you.

Just administrative procedures and so on. I really like that. It's,

it's a pity that it's,

I feel like kind of getting done work downwards nowadays. And it's like, no,

you to speak about. I wish, okay, to be frank,

I wish sometimes I would do it about papers by others. I can say,

this paper is fantastic and this is what it means for my career and so on.

I never dare because you know, it's not my paper, it's not my work and so on.

But, uh, maybe in the future I, this would be

I, but I've seen people tweet about Trackmate, do you publish there?

Other people come on the back of it. Once you publish it,

then people come on the back of it and say how great it's for them and show in

the applications, which is great. I, I think I,

I I live a very sheltered Twitter life because I only really look

at scientific content. And so I,

every now and then you'll see I,

I guess Twitter bait where it's something that's a bit more toxic,

I just ignore them. I don't touch it.

I try and scroll off it quickly so it doesn't even pause on it and it just

filters out. And over time it disappears and it does make it.

So just make sure you can keep clicking on what you'd like to see when it comes

to science. Uh, keep yourself rounded by all means, but keep looking at it.

I I think it's a still a fantastic resource and it helped me learn more about

Trackmate, uh, and going through it. So I No, it's good.

So, so you didn't have too many problems.

It was just actually writing that was the problem itself. A

Story like, you know, making a story about a tool. This is tough, right?

Because the, the, The typical way we build tool is, is different,

right? I start like from Mastodon, Mamu,

everything we worked on the tool are the same.

Like we have a biological problem and so on.

We need to do some image analysis and what, what, you know,

what's available as tools does not fully make the job.

So we have to build the tool and then there's come another project and the, the,

the tools kind of get further and further, uh,

better and better or let's say, not necessarily better,

but let's say more amenable to a wide range of projects.

But how do you write a story with that? It's, it's kind of difficult.

I don't have a,

I don't have very seldom good ideas how to write a story about a tool that

is super useful, hopefully. But, you know,

this is publishing like scientific papers still about that, right?

What's the biological story behind that?

Yeah, no, you are right. Here's a tool. Use it.

But

It's, isn't it, it's like, but you, what's your intro to it? Well,

there's a problem, but then the discussion actually, well it's here, use it.

So you have to, I guess use different, well, look,

you publish successfully nature methods of trackmate and stuff. You get there,

but it isn't

'cause there's a there because, you know, there's a category for that.

Yeah. And which is where actually nature methods has been fantastic, hasn't it?

And uh, I think Rita at Nature Methods has really helped the imaging community

May uh, I was about to shout in French, but yes, absolutely. The,

the impact not only data, not only nature methods, but on field,

particularly like quantitative imaging, let's say that, you know,

everything at the corner of microscope to imagine as this computing

computational biology, everything the, the, let's say the, the, the,

the or of editors and journals about, that's been fantastic.

It really pushed, you know, the first, it pushed the domain forward,

but also made it super friendly. I'm super happy to say, okay,

you are a young PhD, you young postdoc, you wanna start a career and bio come.

It's super friendly, really respectful.

You will have a great time with us. And by the way,

everybody knows everyone.

Uh, that is true. Uh, actually I've, I've got, uh,

there was a picture somewhere. Can I find, look it, it, he's a,

this is not obviously the whole community, but this is some of the community.

So you've got Cota, you've got Sebastian there,

you've got teaching to a course with all the PTs, they're teaching the courses,

networking people together. I,

and it's a small community. I,

I like the picture at the bottom so I can replace you and look like I'm the

clever one we go to and seven.

But this is your Paul fabric, Cordier myself with her at the time.

And Julian Colo on the top, top, uh, sorry, bottom left hand,

bottom Per and Julian,

that's one of the first new BS conference. And at the time, I,

I think Junior is trying to explain to me what new new bias is going to be

and whether or not I feel I fit in and so on.

So what is go, go on for those who do know what new bias is? What is it?

Very quickly,

No, I can't

Ah, come on

Accurate. Okay. New. Okay. Uc started, you know,

with the gentleman you see on the top right the time working in the M B A,

he organized, you know, several courses around image analysis and with a,

let's say, unique point of view. We say, you know,

image analysis for biologists,

you image image analysis as a technique to advance biology,

not image processing.

And he was creating T C U B S European bio image analysis,

right? And he was very successful. Kota, you know, is really a visionary.

He has a vision. He will just, he will not just teach,

he will make sure that you know, what teaching means and uh,

are different ways and so on. And uh,

he was able to put, you know, to inspire many people, uh,

in recording the click and know,

which is partly represented at the bottom and say,

let's make a network of us, like as an emerging,

let's say set of technology that would be network of European bio image

analysts. And we were funding by you, uh, the sea and, uh,

cost actions, which which we could organize, you know,

schools that were kind of famous and very animated,

but also conferences. And this really,

let's say kicked off bio analysis for me. And I think, you know, for,

for the community in Europe.

And

Honestly that was fantastic.

And now getting, because it sort of had that, uh, the funding dropped.

It was hard to keep together,

keep that's back keep and new bias is going global

slowly. Yes. It's not just Europeans anymore. Welcome. It's new bias. It's a,

a bigger entity, which is terrific. Who is it who's funding that

Right now? It's a CDI Berg initiative and there was a grant for like, uh,

uh, focus on communities in imaging.

And actually the project is led by, uh, Robert,

ER and Kota. Again, that's gonna be the Soviet. And we will build,

when I say we, I mean we will build a big,

let's say community or let's say association for bio

image and so on. And this time, because of the funding,

we don't have to limit ourself to Europe.

But actually we never limited ourself to Europe, like Best Chimney. She was,

you know, a early heavy contributor, Tobias.

We also had a lot of guests from Australia. And uh, it,

it always worked beautifully. But now, you know, with this new finding now,

we really must not limit ourself to Europe, which is even more exciting,

I think.

And anyone who listens to these podcasts will have heard Chan Zuckerberg many

times over.

And I think my all science is international.

You know, we, we shouldn't be competing against each other but working together.

But most of our funders and national funders that support national,

and there are some efforts to go broader, but for obvious reasons,

taxpayers money need to stay fairly local.

The like to Chan Zuckerberg have been transformative, I would say,

for the imaging community or start starting to transform the

microscopy and scientific community in what they're supporting. And it's just,

I just thought it's worth a good shout out. I,

Stephanie Otter has been a get in guest in the past for C z

I'm a big plan like the, we've been very lucky also because, you know, they,

they, they chose, they chose to put pressure on imaging,

which is really fantastic. Imaging communities, image analysis.

The, the really, I think it's like science very really benefit from that. Right.

I'm gonna ask you some quick questions 'cause we will run out of time if I'm not

careful. How many different countries have you worked in?

You've worked in Australia, you've worked in Germany,

obviously you work in France. Any others? Just,

okay, so, uh, France or Germany, what's better,

Sorry,

France or Germany. Where is the best country to work in?

That's a tough question because I would say Germany.

Oh, I didn't expect that. Germany or Australia?

Germany.

So at least you keep a European passport,

but you just lost your French passport.

I'm good. You work

At pastor, you just said Germany's better.

No, no, the for honestly, like if you ask any of my colleagues,

I'm really happy with pastor. Like the, the,

the pastor people they use me to publicize Pastor. It's a great place to work,

honestly. The, the, I have a beautiful campus.

The people I work with is, are awesome.

Like we organized like three weeks ago,

a biomedical analysis course with the people from the high performance

computing. Fantastic people. The resources we have are awesome.

We could teach in a room that was beautiful in a settings.

That's incredible. Pasta is really fantastic.

And I also know that they enabled you to start up an imaging

analysis core.

Yes. Which,

Which was then, then almost unheard of. So I know,

I know where your heart is, don't worry. I I am teasing

The, the, the, like I come from a small village like, you know,

everything like 16 inhabitants and so on. My sister and I were the,

the smallest one. The, and so, you know, Paris, it's really a big city.

And now, now I'm okay with it, you know,

it's a 46 city now because you can bicycle everywhere and it's very relaxed.

But in Dre you had, that's such an incredibly beautiful city.

That's the main reason I accepted to do a poster.

'cause the city is incredibly nice and in I was a foreigners and

everybody were kind to us. The Max Splunk Institute, it's a fantastic building.

This is where I had my first facility,

it was the image processing facility at the time.

And you Yout season institute gave me my chance. Like I was, you know, just a,

a random postdoc, not perfectly skilled,

but they gave me my chance and they put inspiration for us,

like supporting science, supporting others. They could put this together.

And so I really have a firm souvenir of resident,

but I was not there for a long,

I always try to keep collaborating with people there so that I could visit.

But it's such a fantastic city. But you know,

I say Germany because I'm in France. Like if I would be working in Germany,

probably I would say the otherwise because I'm French and I like to complain

about the situation.

And, and just to a call out to your, this is your current team,

isn't it open you

Partly. Yes. So here you see from left to right, your story. Stefan Rigo,

uh, Laura Ziner, who's courageous and brave PhD student.

Marvin Albert Gay letter. Gay actually she's an image analyst,

but not working in my team. She's working for department,

but we do almost everything together. This is son and this is Sharif, uh,

intern and so on. It's nice. Now again, I'm really incredibly lucky.

You have no idea how lucky I'm

I. I I think we're all good.

Okay,

So next quick, final question.

I was super stressed by your invitation and then when you ask photographs,

I had to dig to my photographs and that was really a weird feeling.

But then when I see them now, I'm like, Jesus Christ, Jesus, cool,

cool place

And, and your team now get caught doing

A fantastic job with fantastic people. We're incredibly lucky.

I'm the

Next. Great.

I have the next quick fire question. Sorry. Image J on Nari.

I'm the old boss. So I would go for Image J because I am people going from Napa.

It's covered by the young ones and you know,

I'm going to be obsolete someday and then Napa will approve.

Do you think you'll ever make the jump to Nari from Image J?

Yeah, but it's not my work. It's some someone else's work. You

Are not that old yet.

You are very kind. You're very kind Pete. But you're also a little bit blind.

I've got a high, high definition monitor. I know exactly. This

Is something we could discuss, right? I see the,

the dichotomy between Python and Java,

it has like a funny impact on our work in the facility.

But that's a long and boring discussion. My,

my position is that the people next to me, they work beautifully.

All of them are very good in Python. So then I said, okay, that's good,

that's covered. So you know,

I can still do Java so that we covered approximately everything.

Do you think that the likes of Chatt p t will help you convert your JavaScript

to Python and so you can actually just have your image jss script and just

convert it into a power dispute?

I'm actually using a lot of Python, right? And I say,

I would say now one quarter of the user project I do, I do them with Python.

It's fantastic. But also I do a lot of administrative duties with Python.

So you know, I'm not that separated and so on. But to answer your question,

I use strategy of course such a fantastic tool, fantastic innovation.

But I'm really happy that to say every time I needed some help with an algorithm

or an implementation for instance, like, okay, help me with the,

or help me with the SEC cross section of a triangular mesh and so on.

Every time I asked Chad, he was completely wrong.

It's so, yeah,

It's really kind of nice to think that, you know,

even with my model skills,

I'm on a career stage where Chad g pity cannot help me.

So your real intelligence is doing better than the artificial intelligence. Uh,

I I will actually, I, while we're talking actually,

I would suggest some of the future courses you act,

I I don't maybe you do that you put in a chat G p t content of how to use it

as a tool to help accelerate programming. Not to use it completely,

but 'cause it's coming. Uh, uh, people are already

Really, have you seen what did

Assistant into nari and that's nice.

And now there's also transformative things like people have made some chat like

interface where you can actually say is make me this program,

the AI ask for clarification and generates the code.

And then when it's for like everything related to, you know,

web or internet and everything, it's fine. The one thing is that,

what I see is that for science, scientific things like, you know,

specialized algorithms and so on, maybe they are not that good yet,

but clearly it this will come. Yes, I have no doubt I will be made obsolete.

Kind of swish

It. It's well no, because you still have to pose the right questions.

There's an art to pose the right questions and then you've got to edit it to

make it actually work. Correct. 'cause it will never, it's not perfect.

And then the heterogeneity in biology will keep it on.

Its to keep us on our toes and keep us in jobs. Okay.

So next quick fire question. Jolies, are you an early bird or NightOwl?

Early bird?

Uh, PC or Mac?

Pc.

Pc McDonald's or Burger King?

Ah, McDonald's.

McDonald's. What's your favorite? What do you, what's your go to at McDonald's?

Do you have a go-to?

Ice cream.

Ice cream? Okay. That's, that's a good answer. Coffee or tea?

Coffee.

Chocolate or cheese? Uh, chocolate or cheese?

Cheese.

Ooh, beer or wine?

Beer. Yeah. I'm a bit disappointed as a French person, I realize.

So you got to ice cream to beer, to cheese to beer?

Not, well, not, okay, that's good. What, so what is your favorite food?

Lati Flat.

Oh, okay. And do you cook it yourself?

I do cook a lot actually. I have, you know, cookies and I'm cooking for them.

And it's like the, the,

the one hobby that you want to do if you want to relax is really cooking,

batch cooking in great quantities. Although I'm,

I'm really lucky 'cause I have fun sometimes, you know,

you don't get positive feedback from your users. Mm-hmm. But for the food,

I always get positive feedback. So far

I, I, my my middle son is always the one who always goes, oh,

it's the best ever dad.

Isn't it fantastic?

No, I've only just thought,

does that mean that all the other ones have been that bad?

That that was the best ever? Maybe I've been looking at this at the wrong way.

It's the ability to be grateful. It's fantastic to be near people like this.

It's good. What is your, uh, least favorite food?

Oh, I think everything from, I'm not a great fan of meat,

particularly like raw meat or uncooked or unprocessed meat and so on.

I would say, uh, cheap, clearly.

Yeah.

Or rabbit.

Okay. Ah, sorry you caught me on the hop with that answer.

I gotta think of the next question, but that's a bad joke.

With rabbits and Hopping Doesn't matter. TV or book,

Book 10,000 times. Book.

Book. What, what, what type of books do you read? Oh,

Anything typical? Only, almost only fiction.

What sorts of fiction?

Right now I'm into science fiction. Before that I was into American literature.

It's the, the, this is really the, when I open a book I'm like, okay,

I don't want to work. It must absolutely not be about work and so on.

So anything non-scientific?

And you said, okay, book or comic book,

because you said earlier you're a big fan of comics.

Yeah, in France it's, ah, it's very good.

There are a lot of authors that are excellent nowadays and the,

the stories they produce are incredible. Nova Nova and everything. It's,

if you went into comics right now, there's really a good, good,

good literature to read and so on. It's, it's fascinating,

but it's also very expensive and it takes really a lot of space.

Imagine a small Ian apartment when it comes to books. You know,

you can have everything on your small

Screen.

I don't wanna say the brand, but

Next question. What's your favorite film? Favorite movie,

sorry,

Lia.

Okay.

It's the one by the, one of the Pauls Anderson.

It is one of the early movie and song about you meteorology and that kind of

thing. I really liked it, but,

and I think I'm one of the only one to have liked it.

Have you seen the latest spin Spiderman?

No. It's, I'm really not a big action.

I'm not a really big superhero besides Batman.

Forget about it.

Forget about all your prejudice against your superhero and so on.

It's a piece of art. You will have a great time, latest

Body,

Whether you like Spiderman or not.

I, I have, yeah, I, I, I can watch that with my,

my son finished his exams today, so there you go.

I can watch that with him and do something with him on that.

What's your favorite Christmas film? You've got children, come on.

You must have a good Christmas film that you like to watch every year.

Yeah, actually Potters one

Harry Potter. Oh, goodness sake. That's awful. No, God, come on.

Have he Potter is a Christmas film.

Okay. No. Okay. Okay. But because the obvious answer would've been, you know,

the movie with uh, uh,

I think that the movie in English is diehard.

Ah, yeah. Yes. Good. Classic, proper. Uh, okay.

That's controversial. But yes, I would say it's a Christmas film too.

And we like diehard every Christmas.

Okay, okay. Okay.

Uh, star Wars or Star Trek?

No, none.

None. Like either.

Yeah, yeah, yeah. The, the Star Trek, I never got it to do into it. Right. Uh,

I think it's really, uh, nish things, but when I talk to, uh,

talk about it to my, to my colleagues, when there's someone from Great Britain,

it's, it rings, uh,

a bell and the Star Wars is that I'm just not happy with what they did with the

story.

I, I, look, I was a next generation fan.

I'm not first and Picard really liked that series. The rest, nah, not,

not massive. What's your favorite color?

I say green.

Uh, do you know what I'm looking at your chair Green.

I'm looking at the inside of your bookshelves green.

Some of your post-its notes going green from being in your window to longer used

to be yellow. It's, uh,

Not a choice.

Your plant green, everything's green. It, it's kind of cool.

I should have guessed that answer,

but I What sort of hobbies do you do when you go back?

Obviously you like reading. What other things do you do at home? I, I,

you sent me a picture of one thing. Do you still do acting?

Uh, never did acting ha. This is when I was a light technician actually.

Ah,

So you light technician at the theater? Yeah.

This is how I meet my, or met my, my future postdoc boss.

She was Lady Mac bus and I was the, I was the life technician on this,

on this production. But the,

the kind of thing I have is that, you know, every time I,

I have a hobby or something like this,

I'm always more comfortable being on the technical side.

And so I've been a single light technician for four years.

What you see on the top left is in Paris doing the PhD.

And what you see on the bottom right in is in Dresden,

I think like there's Mac base, uh, le false Confidence, the Mar and Thisis,

the Merchant of Venice.

Right. So, so you were into light back then as well, so it wasn't just sound,

it was the lighting as well. You see,

It was the lighting at some point. The, the, in the rein,

I was also what is called the star.

I don't know if it's a true word, but I was mainly in charge of the, the scene.

Now the stage. It's fantastic.

It's a incredibly satisfactory hobby,

unfortunately incompatible with another job.

Yes.

And I'd imagine quite nerve wracking because the timing and the importance it

is, you know, the act is very dependent on you, your timing,

And there's a lot of preparation.

And typically if you do the lights you need the night.

And so in theory that would be compatible. You know,

you have a day job and then at night you do the lights and so on. Unfortunately,

one of my favorite hobbies actually sleeping,

Even though you're an early person,

I you think I can make it?

Uh, we are nearly up to the hour. I did say it would go really fast,

but I have to ask you, you want to be a teacher,

you become an exceptionally successful scientist.

Be

Debated. If you could try out any job for a day, what job or a week,

what sort of job would you like to sample for a day, a week,

whatever it would be to get a feel for what that job would like to be.

I'm pretty happy with the job I have. I'm gonna be frank again. You know,

I was thinking about what you said. So, and it kind of reflection.

Before I became a research engineer,

I was working in the max as a postdoc in the team of Eva Palor.

And there was this, you know, Jacob Kuba. Ky is very successful.

Both of them are incredibly successful. He was a PhD at the time.

And so at night, you know, I was, you know, counting cells or whatever.

And then Kuba was at the microscope sometimes, you know,

he would call me and say, if the microscope doesn't work,

and that would make me incredibly happy. I would, you know,

run or even roller skate to the elevator,

go down to the micro facility and arrive, you know, Cooper literal like this,

show me what doesn't work. And then you, I was at the time, I say, okay,

if I have to sample a job for one day,

I realize it must be something related to techniques support or technology,

right? I cannot have a career.

If I'm only happy when the microscope doesn't work,

this would be a mistake. And so honestly,

I'm pretty happy with the job I have now another job, I,

you know, I'm pretty into supporting others,

so I would be maybe a measured them.

It's interesting. And, and just reflecting it,

the reward is less about the results, but actually making someone else happy.

Giving them what the results, the, that the, that they, that they,

not that they need necessarily,

but giving them the results and the analysis and even better if it's actually

worked. You know, it's that. So you are an ultimate supporter.

Supporter in the, the terms earlier. And to be able to provide that role,

that critical coi supporting role gives you most pleasure more than being your

own researcher.

We could talk about it for hours to be honest, but yes, absolutely. Right.

I have this pattern. The people I work with have this pattern as well.

We are uninterested by our own problems. We actually like the problem of others.

And this is, it's a job beyond yourself, incredibly rewarding.

But you,

And that is a problem in itself. And, and so you say, because there'll be,

people go, well, that's not very aspirational, not to have, you know,

you can see as some academics thinking, well, you should have your own problem.

You shouldn't be, actually, no. From the technology's perspective,

it's the application of our skills to solve their problems is

the skill that is the, the,

the expertise that is different solving One question we'd, gosh,

how boring would it be to answer the same question year in, year out,

think of the diversity we have,

but it's not simple to be that multi-dimensional.

I, I don't know, but it's pretty rewarding. The, the Okay,

so you also touch on something else, right? Um,

how do you make people with this pattern, this activity,

and this job fit into a career in academia? Micro,

because typically, you know,

when you are a pi you are not evaluated by how many microscope you can fix,

how those people you can help, right? You have to write own story and so on.

This is another reason why it's so, I'm sorry I'm making too much publicity. No,

No, no.

It's great to work in pasta because people like me,

like we have dedicated current path, we are evaluated for our use,

who we are to the scientific community.

We are evaluated for the novelty and the usefulness of the tools we produce.

You see this, this is kind of the perfect balance.

It, it is. I, I I I I will say, you know, as a young postdoc,

there was two places that would attempt to me,

one would've been pastor through Spencer's influence and what you set up.

The other would've been E M B L. Yes. At the time.

Now there's Janelia and Janelia course we bumped into each other in Janelia.

That was most bizarre.

I was there for another meeting and you were there doing image analysis bumped

into each other in Janelia over in the us One of those odd coincidences

that those three places are inspiring places and there's many others.

They're the three that I was aware of. And then York came along and York is a,

wow. York's better. I'm sorry. It's better than pastor. Definitely.

I'm, I'm, I think I'm really a lucky man, right?

I, I, I think you are. I think you've got a good team,

not enough staff, but you've got a team that thrive probably on that intensity.

Uh, intensity makes you more efficient, would you say? Gee,

have to, anyway. We are up over the hour. And I have to say, Jonni,

thank you so much for today. I,

I hope it's open anyone who's listening or watching to understand the importance

and the potential career path in image analysis.

It's not going away. It's getting bigger. And how,

how long have you had your core for Yas?

I think it's, uh, fifth year now.

So five years. And these things are popping up and growing bigger. Get your,

if you are interested in this, get yourself into new bias, you know,

come and join their community. It's really important on that. Uh,

you've heard Stephanie Sain, you've heard Rita Str, you've heard Beth Sim, uh,

all people involved, uh, with supporting this, uh, in many different ways.

But Janice, yourself, you are,

I don't think you appreciate how big an influence you are on that whole field,

and it's been a pleasure to meet you,

and I hope everyone's had a pleasure listening to you, Janice. Thank you,

you so much,

Pete.

Thank you for listening to the Microscopists,

a bite-sized bio podcast sponsored by Zeiss Microscopy.

To view all audio and video recordings from this series,

please visit bite-size bio.com/the-microscopists.