Kedar Narayan (Frederick National Laboratory and National Cancer Institute)

This is a machine transcription and therefore it may contain inaccuracies, errors, or mispronunciations. Notice an error you think needs changing? Please contact the Bitesize Bio team using this form:

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 Cdar NA Ryan from the Center of Molecular Microscopy,

the Frederick National Laboratory and the National Cancer Institute.

And he discusses why he never took high school biology

In high school. I was pretty sure I wanted to be, um, uh,

a designer and architect. And, and so it was just math, physics, uh,

uh, computers, uh, chemistry.

And I decided biology was icky. Didn't wanna do it, just, just never did.

High school biology,

Why developing techniques is really,



But with technologies,

what was impossible yesterday is normal

today, and is outdated tomorrow. And so there's a,

an inherent pressure on technology, heavy sciences,

or people who develop technologies.

There's this inherent need to keep the foot on the gas to some


And how AI makes it harder to spot bad sciences.

So we are already in this place where it is so dependent on trust that everyone

else is, is doing a good job. I think with ai,

it's going to be that whole thing on steroids,

All in this episode of the Microscopists.

Hi, I'm Peter Oto from the University of York, and today on the Microscopies,

I'm joined by Caar from the N I h, uh,

Frederick National Laboratory at the National Cancer Institute. Good grief.

That's a lot to say, but essentially from the N I h Caar. How are you today?

I'm doing well, Pete. Good to be with you.

Uh, thank you for joining me today. Uh, I, I,

a lot of your work now evolves around volume. Em. Mm-hmm.

How did you get into volume? Em? Where, let's start.

What was your first microscope you ever used?

Oh, the first microscope I've ever used. Um, well, I think it was one of those,

um, little dinky things, uh, in high school. Uh,

one of those nondescript things with, you know, really lousy, focused knob,

one of those desktop things. And I looked at, um, uh, the usual, I think,

onion cells and something from the inside of his cheek. Um, but yeah, the,

the way I came to microscopy was actually quite circuitous. It's,

uh, quite a, uh, quite a long path. So, um, I started off,

okay, here, here's a, a, a little secret, if you promise not to share, is, um,

I never did high school biology,



You mean,

Um, well, in, in high school, I was pretty sure I wanted to be, um,

uh, a designer and architect. And, and so it was just math, physics, uh,

uh, computers, uh, chemistry.

And I decided biology was icky. Didn't wanna do it, just,

just never did high school biology. So, um, I then did, uh,

I had a very good chemistry teacher,

so I did my first undergrad in chemistry with a side of math and physics.

And I still remember, uh, my, my project was on binding,

you know, kinetics and thermodynamics, and I chose to study antibody binding.

And then I was like, well, these antibody things are curious.

And then that led to, well, these B-cell things are even more curious.

And then you get this realization that biology might not be that bad after all,

except I didn't know anything about it. So I had to go back, uh,

do another undergrad degree. Um, so I, I, I went to Cambridge.

I did a natural sciences tripod, uh,

in pathology this time with the side of biochemistry and molecular biology.

And then got my interest in really, uh, uh, you know, with immunology.

And so my PhD, um, at that time, uh,

I decided I still need to study a little more of my foundations,

I felt was still a little shaky. So I then came to America, uh,

to do a PhD in immunology. And my final, uh,

year, there was a paper that we had in, uh, in, in review that looked at,

uh, binding of, uh, you know, uh, super antigens on M X C major,

just compatibility complex molecules on, on the cell surface. And the,

and the re reviewer said, well, uh,

can you actually show this with immuno gold that you're seen clustering on the,

uh, on the surface of these cells?

'cause we'd shown it with fret and what have you. Um, and my,

my PhD advisor at that time, uh,

mentioned that she knew somebody at the N I H who did this funny kind of

electron microscopy that looked at things in three dimensions. Uh,

and so I came to the N I H and I kid you not, I, I came for, uh,

a, a week and pretty much never went back.

So finished my, uh, graduation sort of, uh, in, in,

uh, mostly in Accenture, but then I went back and forth a bit,

and then it was only in my postdoc that I, um,

really got introduced to electron microscopy.

And so this was at the N I H and I, the lab that I went to, uh,

through Armstrong Learning Learnings Lab at, at N I H. This was,

it was primarily a cryo EM lab,

but there was a small component where they just started this

technology of looking at cells, not just protein, protein complexes,

but cells in three D using this strange contraption I'll call a

focus on beam scanning electron microscopy. And so I said, well, I,

I didn't really feel like I wanted to, uh, to pursue structural biology,

even though that was when, you know, cryo em was going through its inflection,

right? The, the start of the resolution revolution or whatever you call, uh,

the thing. Um, and fib, em spoke to me,

and that's how I got into looking at cells in three dimensions.

How long ago was that?

That was, um, about 10 years ago or so. Um, and so,

you know, at that time this was still, you know, volume M wasn't a thing.

In fact, my pre handle is, uh, make volume m a thing. I mean,

I think we take volume electron microscopy it term a little bit for granted now,

but it really wasn't a thing until very recently.

And so it's been interesting as some of the, um,

technologies have sort of rallied into this,

this group that fits under this rubric that I think just sort of,

we all sort of got together and decide, well,

this is volume electron microscopy, and that's a thing right now.

That's what I do.

So you've gone from wanting to be an architect to now looking at the

architecture of the cells.

Isn't that crazy? I know, it's, it's, yeah. It's, it's weird. I,

I was thinking about that, uh, a while ago, and I thought, yeah,

that's a little cute. But, but that's exactly how it works.

So little bits of chemistry for the sample prep and, uh,

obviously physics with, with the electron optics and, uh, uh, yeah,

biology. Finally, I got around to learning a little bit of biology,

and here we are.

Uh, but yeah, but the architecture of the cell is all physics.

If you think about it, the physical courses, it is biochemistry, biophysics.

It is,


It's just a biological architecture, isn't it?

Yeah. It, it's what goes where and, uh,

how do things get there and how do they interact with one another? And,

and it's true that you can break this down and study it in many different ways.

Um, it's true. But there's, to me,

there's something just intrinsically fascinating about looking

at what things go where and how they look.

I mean, I've always been sort of interested in that kind of thing, right?

And I think volume mean this, this really what, what we are doing in, in,

in some respect.

So you mentioned how you've moved from being the designer architect,

then starting to like biology, coming back from biology into immunology,

moving your immunology, which got you into the,

the iron milling of the fib stem side of things. Yep.

Why did you decide to pursue a career more with a technology than the question?

Or, or am I wrong in that presumption? I see,

You're wrong. No, you're not wrong at all. No, you're,

you're absolutely right about that. Um, it's, I think,

I think part of it is that, um,

I have this annoying habit of getting bored rather quickly,

and it seemed very clear to me that the way science

has sort of, uh, or certainly most of scientific research in,

in academia, at the very least, appears to be, well,

here's my disease model, or Here's my protein complex of interest,

and I'm gonna study it for the next three decades,

and I just couldn't see myself do it. So, um, part of it was just, uh,

the interest in being able to push something.

I could tell that, uh, while cryo em, yes, it was, it was, um,

very interesting at this point when I really started getting into this,

this area, I felt that volume E was the next thing,

and there would be a lot of, um, technological, um,

work that needs to be done in order to get it to where clearly cry had gone.

So part of it was that interest, you know,

to be at that place when things are really nascent.

And I would argue that volume is still relatively speaking in its nascent,

right? Um, so there's that, uh,

interest in making things work, uh, and also the opportunity to really,

uh, not just develop these technologies,

but deploy them in a variety of challenging questions that could

be really anything. So, so that was sort of how I did it. But is is that,

is that how you No,

No. Look, I'm, I'm, I'm a technologist as well, so I, I see the side,

but it was interesting you say, Hey, you know, you,

you didn't want to look at one question and so on,

but look at it for the next 30 years. And I guess from that perspective,

they look forward to the new technology that helps 'em to progress their

question forward. From your side, you are interested in the technology,

and as I think you always deployment to lots of different samples as it

develops. So I,

I would say you've just chosen 30 years of being an electron microscopy,

You know? But the funny thing about that though, Pete, is that, you know,

no disrespect at all to people, uh, the folks who do this kind of thing, right?

They drill down on this one question for years and years and years.

But in some ways, the job of a technologist is really demanding,

because you can, well, you can get, you know,

great papers on a disease model, let's say malaria. Malaria,

Bert Law says it ain't going nowhere, brother. You know, it's,

it's gonna be around for a long time. But with technologies,

what was impossible yesterday is normal

today, and is outdated tomorrow. And so there's a,

an inherent pressure on technology, heavy sciences,

or people who develop technologies.

There's this inherent need to keep the foot on the gas to some

extent, to keep sort of moving forward,

which I think is sometimes underappreciated, right?

It's a little bit of this Alice Wonderland kind of thing, right? With the,

with the red queen. You gotta keep running twice as fast to get anywhere. Um,

so I think that's, that's something that's,

is a little challenging. Um, I mean, it's fruitful and enjoyable,

but it is, it is something that, that I think as technologists, I think you,

you, you know what I'm talking about, right? There's this,

this thing of always wanting to

No, a absolutely,

I guess it's like a classic academic who's studying their one question that if

their question, if they get to the end of the road,

they have to completely move.

So have you ever thought that electron microscopy may one day become surpassed

by another type of technology that completely makes electron microscopy

redundant? Expansion microscopy isn't going to do that,

that that's solving different things. But, you know,

another technology that comes to the fore could remove that. You,

have you ever thought about that?

And get slightly scared by the fact that suddenly all your expertise is suddenly

Oh, within five years, just God.

Yes. Uh, and, and this is something that is an inherent,

uh, danger with, uh, technologists, right? Just ask the people who,

uh, developed, I don't know,

DVDs or any number of technologies that have just completely been

superseded the, I'm,

I'm less worried about light microscopy completely taking the place

of electron microscopy, simply because at the end of the day,

even if the resolution of whatever fancy, uh,

approach we have just sur equal or even surpasses, uh, electron microscopy, I,

I doubt, but let's say it's, it approaches it. At the end of the day,

with light microscopy, you are, you are looking,

you are looking for something, and you see it, right? You,

you only see what you're looking for. Whereas with electron microscopy,

you capture everything. You capture the structures, pretty much everything,

all the membranes or, uh, uh, significant chunk of, of, of protein complex,

et cetera, et cetera. So I think there's some utility in that,

in the ability to see things as they are the structure of everything.

But I would not be surprised if at some point,

some related approach starts really, uh, encroaching.

But is electron microscopy going to completely banish? Not entirely sure.

I I, I, I don't foresee a technology at the moment that would do that.

It's the same challenge to electro to light microscopists. You know,

I don't see a technology coming to completely supplant what they're doing on

that side either. So,

But, well, I think, I think the one thing though, Pete, is that

I think we're in,

even though we're not realizing this right now,

I think that with I speak for volume electron microscopy,

I think we're gonna see a change in the kinds

of questions that we're gonna be asking.

And I think we are already seeing a little bit of that perhaps in connectomics,

and now a little bit in cell biology as well, is perhaps, um, a,

a tweaking back of the dial back to just discovery. I think back in the day,

there was a lot of, you know, science was a lot about discovery, right?

You didn't know anything, or you, you,

you took a stab at it with the best approach that you had. And it's like, okay,

this is this journey of discovery that we're embarking on. And then we,

in the recent past, I think there's been a lot of hypothesis driven signs,

which is fair. There's nothing wrong with it. But I think with, with,

with the newest groups of technologies that have been developed,

certainly I can say for volume electron microscopy,

now we have a chance of getting back to just discovering

things again, simply because you couldn't access them previously. Uh,

and I think there's going to be, uh, another wave of, of, uh,

just discovery papers.


is the lack of completely blue sky discovery science

really led by the funding side of it. And, and, you know, we, as scientists,

we have to sell our research to the public.

The public are while paying the taxes to the government,

it's the government that's funding the research. And even for politicians,

they need to be on to say, yes, we are looking at this.

We are going to solve this.

So is it the funding model that, that detracts from the,

from that discovery science and, and will that ever change?

Would money becoming ever more difficult? You know, the funding stream


I wonder if that would,

Is, is the funding situation dire in,

in the UK as it is here?

I, I, I would, uh, this is where I, I gotta be very careful. I would say not, I,

I would say even since the, the financial meltdown back in the,

the late nineties, yes, in real terms, the funding may have gone down,

but compared to other areas that are having to cost money mm-hmm.

I think might have done pretty well outta it. And I think,

I think over covid science stepped up to the plate Yeah. In a big way.

And not just helping to solve and look at research into covid,

but even in the response of setting up the covid testing labs into the

instrumentation, the lab expertise, all sorts of repurposed,

and it was in place because of the funding. Now look, it's not perfect, right?

And I don't think, you know, anywhere is ever going to be our utopia for that.

Right? Uh, but I, but no, no, not, I don't think it's dive at all.

But I think financial times with governments the way that the globe is at the

moment, there's financial pressure is everywhere. Yeah. Some point.

Um, Yeah. I, I think, uh, well, I,

I have a slightly less rosy picture of,

of how things are, to be completely honest. I, um,

I think that the, um, funding systems, uh,

incentives are not at all and sick. I,

I don't think things are where they should be. I mean, I think, uh, the way,

one, there are many different ways to, to look at it, but, uh,

one sort of anecdotal piece of evidence is you see the people, uh,

you getting into graduate school as, uh,

the economy goes up and down and there's an inverse correlation when the,

when the economy is doing well, you know,

you have fewer people applying to grad school, and when there's a recession,

then you have a, a glut of applicants. And that in and of itself, to me,

I think is problematic.

'cause it tells me that a lot of people are getting into, uh,

PhD level, uh, uh, studies or, or courses, uh,

to essentially write their way out of a bad economic time.

There's no jobs available. So I'll get, go, go to grad school,

and I'm not entirely sure that that's a

viable path. I don't wanna be,

I don't wanna be too precious about this or too much of a purist, but because,

you know, people who are scientists, yes, we have to make money.

But I think that really scientific pursuit is

probably best when there's a little bit of daylight between, um,

sort of money requirements and the job at hand. 'cause otherwise,

I think that's part of the reason is we've got into a bit of a death spiral

with, well, I'm going to do, uh, what it takes to get my next round of funding.

I think there's a pretty common refrain, right? I'm sure you've heard this.

And I think partly that's because there's a set amount of money,

there's a huge amount of people coming through the pipeline. Um,

and well, we've gotta make things work. We've gotta pay people, and therefore,

this is what I'm gonna study,

not because it interests me or because it's important,

but I know that this will give me my greatest chance of,

of keeping my lab afloat. So it's, it's not,

it's not cynical or selfish or anything,

but it's simply we're what you have to do. Yeah. No, absolutely. You,

you have to. Otherwise, your lab sinks. Yeah. Your labs. And I,

and I suspect that, that, um, small institutions

are going to bear the brunt of this. They probably are already,

but I think the situation is probably gonna get worse before hopefully,

uh, it gets better. Uh, and this is something that, that worries me. I mean,

you know, I'm, I'm deeply involved in the volume community, uh,

and folks who know me, sort of here, several reframes that, that,

that I keep bringing up.

And one of them is this increasing gap between what I call the one percenter

labs and the rest, right?

There are a few of us who are lucky who have stable and well-funded labs,

and we are the one percenters, and then there are the rest. And so here,

here we are talking about these fancy microscopes and all of these advanced

concepts and how best to, uh, you know,

incorporate AI and how metadata standards should be all of these things that we

love and we know that are important. And meanwhile,

you have people who are working with one Ian data

set that they got from a demo lab for a year. Um,

and it, it really came, this,

this gap came home to me when I was giving a talk sometime ago.

And I was talking about, you know, some of our deep learning work.

And I realized that, um, folks had

absolutely, um,

no access to what we had assumed was basic computational requirements

to be able to do something or the kinds of access to, uh,

a reasonable sort of corpus of data to be able to, for example,

train a deep learning model and so on. So I think that gap is, again,

something which has been exacerbated by our current funding models.

And I don't know how we'll get out of it,

but somebody better pay attention to it.

Yeah. And,

and maybe more community driven access gives people to sharing that

access to the, to the 1% and the 1% opening, opening themselves out, uh,

central nodes is called facilities and so forth, uh,

will help that and think, thinking of that side of it, uh, I,

I must apologize. I think I've, uh,

made you scared for your job because electron microscopy disappear. Uh,

scared for your job because the funding's gonna disappear. I thought, ah,

you're gonna go away terribly sad after this. I'm gonna to round

It. It is never too early to have a drink in despair,

The, the volume EM community. You sent me some pictures and, uh,

this is one of those pictures, uh, with a few familiar faces, certainly for me.


So tell me how the importance of the Volume EM community and what they're doing

at the moment. Well,

The, the volume community is, is a small community,

but growing and it is a wonderful place to be, frankly. Uh,

so you're seeing over here at, this was at, uh, microscopy and Microanalysis,

which is sort of the flagship Yeah. Microscopy, um, uh,

conference here in the States. Uh, and you see, uh, Alice, she's at N Y U,

uh, this Paul who obviously, you know, um, there's Kirk Sinek,

who I believe you spoke to recently.

Oh, I know Kirk. I meet Kirk every Friday.

Do you really?

Yeah. Yeah. How so? I don't ever, actually,

I dunno if I've ever met him in person. It's really strange. Uh,

he and Jessica b I meet once a week, uh, writing a paper together.

Is that right? And I met Jessica back the other week. It was like,

oh my goodness, you are taller than I appreciate.

Okay. Right. That's the other thing with Zoom meetings though, isn't it? I mean,

like, I have no idea. You could be seven feet tall

Just under,

Just under That's right. That, that's exactly right. I'm, I'm, I'm,

and I'm six foot seven, Pete. That That's right. Yeah. Um, but yeah, the, these,

you know, there's, there's Paul La Carta there, there's, uh, uh, Alex,

Ellie from, from Zeiss, Phil Ians from Zeiss as well. Um, so I think, um, yeah,

the community is in a good spot right now. Um,

we really, there are a few of us who've been deeply involved in this thing,

really sort of neck deep and trying to get this thing together.

'cause it is a very disparate group of technologies, unlike, for example, cryo,

right. Uh, vol is really a collection of approaches.

And not every lab, in fact,

most labs don't have more than one or two of these, uh,

techniques at hand. You know, either a ethnography and Fib,

Sam or Serial Block Face and xrm or what have you, except for Lucy,

who I believe has everything. Uh, but, um, but, but, but, you know,

so it it's important to have the, there she is. Yeah. This is when I,

I just happened to drop in with very little announcement, uh, when I, uh,

went to, to go visit my parents for a little bit. Um, I just went,

uh, across down and, and, um, met Lucy at Creek and,

and had a really, really nice time. So,

Yeah, Lucy's a actually, she's, she's up in a couple of weekends time,

which will be,

Is that right? Nice. Yeah, I mean, uh, she, she's absolutely great. And I think,

I think, uh, the, the community has really come together.

I think we have these working groups, you know, whether it's for data,

for sample prep and so on. And, um, fortunately,

fingers crossed up until now, at least there hasn't been a jerk.

'cause you, you know, you only need one of them to really,

to really mess things up. And fortunately, uh,

perhaps because we've been a little bit, uh, in,

in the shadow of the cryo EM guys, I don't know. Uh, but it,

it has been so pleasurable and so fun.

And I'm really looking forward to the first Gordon Research conference.

The first G R c, uh, in the summer of 2023, I think is,

is when all of us, or most of us, uh, will get a chance to get together and,

and just do something like this, except in real life.

Uh, it is a, it is a very buoyant community.

And actually that the previous slide, you mentioned Zeis, and they were in,

in my opinion,

they were actually very instrumental in bringing that community together at a

very, very early stage, uh, through the meeting that was in Gantt,

In T, yes. E

Bl. So with Chris, Gary, and then to E M B L and back to Gent. Right.

And it was, it wasn't about Zeiss,

it was about bringing the community together and seeing how they could progress

forward. And I think that was, that was quite smart.

That, that was, I mean, this is something I've, I've, you know, I've, I've been,

um, fond of saying is, is it Zeiss is,

is is a science company that happens to do business. And,

and that's, that's, uh,

significantly different from folks who are in the business and

happen to do science. Uh, it's a philosophical difference. And it,

and it actually does, um, make a difference.

And it manifests itself in, in exactly something like this, right? Um,

they came in and they've been very, uh, supportive,

although they could be more supportive, uh, to our lab, wink, wink.

Um, but, but it's been, it's been a pleasure working with,

with industry partners. Not just ice, but, uh, but also the other, uh,

the other, uh, other vendors as well. Uh,

because it really does take everybody to sort of come together and,

and make sure that the science and the technology,

all of that is in sync to the extent possible,

By the way. And it needs competition.

'cause competition speeds up development and research, which is, and,

and keeps the prices competitive as well, which is good.

And you mentioned you were back in the UK to your parents,

so I presume your parents are London based, not American based. They,

They actually, uh, live in Scotland. So, um, I, I,

I went to London for my niece's graduation, but, but yeah,

my mom and dad live in, in Scotland. So you, if you ever go to, uh, this little,

uh, town just, uh, west of Glasgow,

you see two very jolly South Indian people. It's probably my mom and dad.

Um, so it's, they, they, uh, uh,

bless their arts. They're in, they're in very good shape.

They spend their summers in, uh, Scotland, but then the winter's back,

uh, in South India where it's, it's still, you know,

they get the best of both worlds. Yep.

So you see, you might not know this, but Richard Henderson,

so not thinking cryo and, and fem from, from your side,

but cryo from structural side,

a lot of his childhood was up in the Scottish Highlands in Cober Dallas.

That's right. Yeah, that's right. Yeah. Yeah, yeah,

Yeah. No, that's quite neat. So you went to university in Cambridge,

is that correct? Yeah,

That's right. Yeah.

You sent some pictures. And this is, are you rowing

This? This is me. I'm, uh, I'm in Bow right there to the, to the,

to your right. Uh, with absolutely horrendous form. Um,

so the, um, I, I took up rowing. I, I went to Christ College,

and um, uh, that's where I really started growing.

And then when I came over stateside, I, uh, continued.

'cause there's an alumni association. And so we have an annual regatta,

you know, sort of just like the, uh, the race on, on the Thames. We have, uh,

down at, uh, on the Potomac in Washington, DC between, uh,

Cambridge alumni and, uh, Oxford, uh, alumni. So, uh,

and we get plastered every year. Every year. 'cause they've got such,

Oxford's got such a good team, you know, ex Olympians. And then here we are.

I mean, people like me, if if people, if someone like me makes it to the boat,

you can tell it's not going to be a particularly strong boat. Um,

but yeah. So, um, I, I had been rowing, unfortunately,

that got to the point where your brain thinks you can still roll really well and

your lower back decides to give out on you. So, uh, unfortunately more recently,

it's, it's been mostly, uh, yoga and hiking and that kind of thing. And, um,

I'm hoping to get back in the boat at some point in time,

but we're not there yet.

So you say hiking, uh, you also sent some pictures around hiking.

So as hope is to relax, I presume hiking's one of your things, I think this was,

well, you'll know where this picture's from,

better than I know this picture from.

This is the,

At those listening, what are we looking at?

So this is, uh, this is logs, uh, this is small, uh, down, uh,

in, in Scotland, in the, uh, on the, on the West Coast. And it's just, uh,

a really, you know, your typical sort of Scottish beach, glorious. And I just,

uh, I sent this photo to you 'cause I just, I love the, uh,

the lighting was just perfect.

And I was just thinking that was a photo that could have been taken years and

years and years ago. Uh, and it, you know, it, it just captures the whole thing,

just, just beautifully. Um, and so yeah, this was,

this was on one of my trips just to go visit my mom and dad. And, uh, you know,

Is this also

Southern India? Did your mom and

Dad go back to India? Southern India.

This was when they were back in India. That's right. So this is,

this actually is in South India. So there are hills in South India too.

And, uh, this is one of the tier states, uh, in, in South India. And so,

uh, this was when I visited them and, and, you know, just, uh,

a whole bunch of walking around. Uh, and, and this is, um,

uh, in, in a, in a place called Muna, uh, in Kerala, actually. It's,

it's a beautiful place. Uh, and so every time, so my parents travel quite a bit,

and so we, that's, that's our excuse to go and, and meet them, uh,

which we have in, in many, many different places. What I didn't show you,

I probably shouldn't, is, uh, photographs from, uh,

New Zealand or Australia from the Middle East, where, um, certainly not in,

in the Middle East, you've got mostly sand use, but, but, uh, from New Zealand,

you've got just these beautiful, beautiful hills and mountains and, uh, fjords,

uh, that, that, that also looks spectacular, but I couldn't find them.

I thought you were gonna say that.

You should've sent me pictures other than Scotland and Southern India to show

that you walk somewhere other than where your parents are.

Yes, that's right.

So you did send me this one, but this is in West Virginia. So this guy, well,

this is an American house, so, yep.

I, I felt, I've gotta put in, you know, one, one for, for America. Um,

I think I, I don't know to what extent West Virginia has a,

a reputation across, uh, the Atlantic. I'm not so sure. But I,

I think over here, there, there's sometimes, you know,

Appalachia has a little bit of a, uh, a kind of, uh,

I wouldn't call it stigma, but, but there are certain, um,

easy stereotypes to, to assign to that, that part of the country.

But it's actually beautiful, uh, really, really beautiful.

And so when we get a chance, which we did during, uh, the pandemic a little bit,

you know, one of the few things you could do is, is go outdoors. Uh,

and so it's just nice to be out, uh, and about. But again,

that's one of those things that I should do more of.

And I don't, but I haven't,

So, so you

Mentioned, sometimes I do, I will happily share those images.

So you mentioned Covid, uh, and so, sorry, I,

we don't wanna linger too much on Covid, but you sent this picture, which is,

which, which is titled is from Em Family Covid.

That's right.

What does that mean? So,

Well, none of us, um, um, I wouldn't say none of us got covid,

but nobody had covid at that time. Uh, and you can tell there's,

there's one or two people that are flirting a little bit with danger there.

'cause we did have, uh, uh, not an entire lockdown,

but uh, activity went down significantly at the National Lab.

Um, and we sort of limped back to normalcy now.

But for a couple of years it was, it was, um, you know,

masks and distancing and all of that. Uh, and, and this,

I I should clarify something important that this isn't the size of our entire

group. This is the entire EM family. So within the National lab and, and,

and National Cancer Institute here at Frederick, there's a, a Cryo EM group.

Uh, there's a traditional electron microscopy lab. Uh, and then there's us,

the, the Volume M lab. So, so our group is about, uh, five people,

or say a half dozen, uh, strong. And so everybody got together. Uh,

this is specifically for when kno uh, Naoshima, who's the, uh,

elder gentleman right in, in the middle, holding a laptop.

This was when he retired. And, and, uh, after,

I believe it was 47 years of service, uh, here at, at the N t I,

even before the National Lab started, you know,

we are the baby of all the national labs. Uh,

most national labs in in America are funded by the Department of Defense or

Energy. We are funded by the National Cancer Institute.

And we're specifically the only national lab that's specifically geared towards

biomedical research. And, and Kunio was just,

is way older than the, the National Lab. Uh, and,

and after half a century of works,

just a remarkable career in Omicron microscopy. Um,

he retired a couple of years ago. Is he

Working for 47 years?

I believe so. I think 47 is the right number. Yeah, it's, um,

and he is, uh, just a remarkable person. Um,

one of those guys that has, luckily for us, really,

um, has this memory, uh, which is so important in for old school microscopy,

certainly, right? Where you ask, you know,

and if you've done electron microscopy, you often ask, well,

I don't know what this is. Have you seen this before? Again,

this is one of those differences between light and electron microscopy or

fluorescence and electron microscopy. 'cause there is a lot of, huh,

what's that? Uh, that you still get in, uh, electron microscopy and,

and our go-to person used to be, and more often than not,

most often, he had already seen something and he was ready to, uh,

sort of help you out. So,

truly a a remarkable and an extremely patient

person. So, yeah. When,

When you retire, will you ever want to look down electron microscope again?

Or when you retire, do you retire and move on? What do you think will happen?

You know, I have no idea about that. It, it's, um,

it's far into the future,

but I suspect that in 20 years time,


our processes and structures around this kind of work

will be so dramatically different that I'm not sure that that question will be

validated. First of all, I'm not entirely sure that there will be,

um, you know, how there's this, um, uh,

there's this pithy sort of, uh, thing, uh, term, you know,

seeing is believing. Yeah,

I'm not sure we're gonna have to see to believe anymore.

I think we are definitely going to move away from having

to visually confirm an observation.

I think we are getting to the point, certainly with volume electron microscopy,

where the

field is certainly moving towards greater th

throughput vaster amounts of data.

I'm not so sure that the

insights that we will get will be in, uh, be,

will be captured in a two dimensional image or even a three dimensional

reconstruction. I think our insights are going to be in some nth dimension,

and I am not so sure that

our current approaches of traditional observation,

hypothesis conclusions are going to be valid.

So I, I was gonna ask you, I'm gonna follow on this track for a minute.

What is going to have to be the biggest innovation to make that happen?

Oh, we're in the middle of it right now. I think the genius out of the bottle,

as far as artificial intelligence is concerned,

and I think it's going to vastly change things.

I'm not gonna say improve things, but I will say it, it,

it will change things. Um, and at the end of the day, I'm not so sure that,


science will or should be constrained by

what can be printed on a postage stamp sized, uh,

figure in a journal. I mean, I think that's just, we've gone,

we've already are going past it. And very soon we'll be well past it.

The inordinate amount of time that is spent right now to

condense these complex, uh,

multi-dimensional observations into something that can be projected on a

two-dimensional plane and can be fit into a figure panel,

it's already infuriating. And I suspect in a decade from now,

it will be just so archaic, uh,

that we'll have to come up with other ways of doing this.

Well, I, I, I'll, I'll go back. Seeing his believing, uh, I think Wendy,

the AI is coming out with the solution. The answer, the, the, the, the,

I think that's a hypothesis still.

And I think we'll still have to go back and then now we'll be using the

microscopy to prove the AI's correct in its hypothesis and to check it,

check it out. So I think it may go,

Yes, that is, you, you are right about that. Yes, you, you are right about that.

But I, uh,

I think the use of artificial intelligence or deep learning models

to analyze that data that comes out of microscope, I think will,

um, will, will, will create insights that are difficult to then,

in fact, they may be even difficult to, for a human to accurately,

um, and precisely a certain or confirm.

And that's a little worrisome to me, Pete. I,

I don't know what your thoughts are about this, but I, I really do worry that,

uh, with the, with the advent of ai, even before this, right, with multi,

uh, expertise or, or, or multidisciplinary science,

it already was difficult to tell good science from bad, bad science.

It already is very difficult because no one reviewer is,

or even many of the authors,

to be completely honest on any of these mul multidisciplinary papers, uh,

can really tell what's good science and what, what's crap, right?

We just don't have, uh, the expertise of the bandwidth to do it.

So we are already in this place where it is so dependent on trust that everyone

else is, is doing a good job. I think with ai,

it's going to be that whole thing on steroids.

'cause I just don't think it's gonna be, uh,

humanly possible to

accurately confirm one way or the other. What, it's gonna be very difficult.

I I, I'm not gonna say it's going to be impossible,

but it's gonna be increasingly difficult.

I'm curious as to what you think about this.

Yeah, no. So I, I don't disagree.

I look at alpha fold Yeah. As a biochemist, as a crystal crystal, yeah.

If you were gonna go to crystal structures,

alpha falls quite disruptive potentially.

But now they're having to confirm that the alpha form is correct,

but it accelerates and they can put more hypothesis to it quickly.

I think overall AI will be good. It will get there eventually,

but we still have to confirm it.

And the best thing about biology is it is so



That actually to model just one disease from one patient to the next,

it's utterly different. And so actually that, that,

that whole mechanistic biology from one person to another, the biases,

the percents, the balances, the protein, protein interactions, the co-factors,

that such subtle, subtle difference between patient to patients.

I would like to think that AI will help us significantly 'cause that will help

us with the treatments, the drug designs and everything else.

But I think the complexity is still, and, and the other side,

I think the cost of the instruments to back up the throughput to prove

it is going to be prohibitive at least for the next 10 years. You,

you talk about the top 1%,

probably only the top 0.1% have the very latest high throughputs, you know,

multi-beam sems for your rate homography type approaches.

So the throughput's really slow to back it up and without it being backed,

the pub, the, the, the,

the biomed companies won't pay that much attention to it. So

I think that's a worrying thing though, because I, I, I worry that the,

given the incentive structures that we have in place, not just in academia,

but in society in general, the

push to have a profitable, um,

model that will answer question X is, uh,

so strong that I worry that we will get poor and incomplete

models that will push out predictions or data that will be ever more difficult

to truly say whether, you know, this is valid or not.

Until of course it crashes and burns and then, uh, well,

we're gonna have to do something about it. Yeah.

But okay, to challenge back,

that happens even with good human science led research. Uh,

and Ron Germaine, oh, you know, Ron Germaine, we spoke earlier, you know,

with suppressor T cells and, and

how things change, you know, at the time it is the right science,

it is the correct science, but then you learn more technology, as we mentioned,

drives that question forward and you start to learn more about these and you can

carry on to do, you know,

so almost correct yourself as time goes by.

So even absolutely, right. Human research is deeply, it's flawed,

but it's the best we've got at the moment.

You are, you, you are right, Pete, but

I fear that the speed at which things are moving

makes it more and more difficult for that course correction and reversal, right?

So it's, it's, uh, that, that's, that's the concern that, that you have these,


these advances that are sort of out there somewhere. And then,

given that those, a a, a simple example would be, uh,

to look at some of these large learning models,

given how they've been trained and, you know,

they're doing the best they can and whatnot, I think we, it's safe to say that,

that that results are mixed. Uh, and I'm not so sure what the corrective,

well, there is no real correction to it, right there.

There's no real correction to whatever you get from

say, Chad g p t in answer to a question. Um, it,

it just gives you what it thinks is right.

And it's very difficult to really tell, um,

whether my right is different from it's right.


and if that's gonna be in turn different from someone else's concept of what's

right. So I think we're, we're all getting into difficult territory here.

I guess that's where the publications and and publications will change how we

publish will change.

But I think that's where peer review and good diligence will still,

I, you know, I wanna ask you this question. Um, this is something again that,

that worries me. It's bad enough. It,

it's difficult enough right now to catch fraud, um, with,

with, you know, deep fakes and so on.

I think it's gonna be virtually impossible. I mean,

we already have a whole bunch of, uh, I think someone famously, I,

I don't know if there's scientific data to back this up.

I think there might be that 80% of science is just crap, right? And just 1% of,

uh, clinical studies were reproducible. I, I'm not entirely sure of the numbers,

but it's, it's shockingly low.

And I'm just wondering whether with with unscrupulous

players, you could do anything, and you are right. Maybe,

maybe the system will eventually catch up to it,

but it'll become more and more difficult to do. So

It, it's a good point. I,

I dunno how well familiar you are with Elizabeth's bits work, uh,

picking out fraudulent data and, and makes a,

a career outta finding fraudulent data because it's very important.

But a lot of that fraudulent data is very, okay.

I was gonna say very badly done, very


Uh, but actually then she posts on her Twitter site pictures and you look at 'em

thinking, I'm struggling to find the difference here.

And she's just got an eye for it. Yeah. Actually,

AI can probably tease those out, so it can probably help on that side.

But you're right, with deep fake, the data could be completely,

it could you yourself, you, you know, if you look at some of

That's right,

Images, so your, your, your metadata Yep.

You can start to make artwork.


That artwork can very quickly become,

become a visual looking

Yeah. Instead of

Being true, honest data to start with.

I'm really hoping that with, you know, this is the metadata and, and, and to me,

something that I do spend some time thinking about, um,

and the White House has recently released, you know, uh,

they called it an AI bill of Rights, which is a very good stab,

first stab at, at trying to get some sort of a structure around,

well, how are we gonna deal with this tsunami? Right? Um,

and I don't think we have enough of that yet in, in this scientific,

uh, community in a cogent manner. I think we've all sort of,

we're all sort of making our own rules to the best we can, um,

the way we can. But I think, um, it, it's, it's,

it's something that's gonna be interesting because I don't think there are clear

directives of what is kosher and what's not when it comes to using

artificial intelligence and data. But, but this particular one, uh, I just,

just a little digression on the art. Yeah. So I, I, um, I,

I did study art, um, uh, formally for year,

but I like this kind of thing. Um, it's just because I,

I felt like it really showed that without metadata, what's really, uh,

blocky and clunky and gray, really,

once you have once bathed in a really, uh,

descriptive set of, uh, information,

which is what your metadata is really brings out all there is in the data.

So that was what I was trying to, to, to sort of show in that,

that piece of art. Yeah. Although I, I will say I only did the, the,

the schematic, the, the wonderful, uh, work was done by professionals.

So I just did the initial sketches and so on. Uh, but, but this was done with,

uh, an artist collaborator. So if

We are looking art-wise, you also have this, which was, uh,

for nature methods.

Yes. Um, and so this is, this is a commentary on Volume M that, um,

a few of us had written that came out recently. Uh,

and this is another one of those things where, uh, the volume e data,

which is the gray scale stack that you see in the back,

what you are looking at is, uh, the first moment of, of,

um, sort of fertilization.

And this is where, you know, in a sea elegance embryo, where you have, uh,

the parental, uh, genomes mixing for the first time. You know, you, you,

you have your, uh, paternal, uh, pre nucleus and,

and pronucleus and your maternal, and they have to come together.

And at some point the nuclear envelopes have to sort of

melt right. In some way for the genomes to actually mix.

And that's what we captured that first moment where the paternal and the

maternal d n a sort of just touch each other before you get complete genomic

mixing. And then of course, you have your, uh, deployed, first deployed cell.

Now I'm, I'm gonna move things on 'cause we're fast go ti time is running fast.

I'm gonna ask some quick fire questions.


Early Bird or NightOwl,

Increasingly early Bird,

PC or Mac?


McDonald's, or Burger King?


Us or uk?

Oh, um, uk.


Good choice. But, but I, I, I love the us Please don't cut my funding.

It's okay. I'm just making sure you lost your US passport and, uh,


UK or India?

Um, uh, uk.

Oh, well, I think all I'm, I'm, here's what I'm gonna do.

I'm gonna say I'm gonna get

A UK passport. You're gonna be homeless soon. Yeah,

Be homeless. Absolutely. I, I think New Zealand

And the Fjord, it going

Right. I think, I think the, the thing is, every time the,

the dial changes a little bit, something happens, you know,

it's either an election here in the States or a certain, uh,

set of decisions in the UK, or, or it's usually,

I think it's more politically driven than it probably should be, but this is,

there's the world we live in, right?

So I think at different times during the year,

my answers might change a little bit.

Well, again, the heterogeneity of life and yeah.

Politics is short term all short to me, so it's always fleeting to and fro.

So next one, coffee or tea?


No, I guess that short espresso or longer Americano.

Um, I, I cheat. So, um, espresso,

so they call it an Americano here. So, uh, so an espresso shot,

but a little with a little bit of hot water so that you can actually savor it

for a little longer.

Okay. Uh,

With a little bit of oat milk on occasion. Yeah.

Beer or wine?


Chocolate or cheese?


Okay. What is your favorite? A good

Cheese, but like a good cheese, not, not the, you know,

gooey stuff that they slap over a, a burger or something. Yeah.

What is your favorite food? If you were to be taken out to the conference and,

and you weren't to be given a choice, they just puts it down.

What would be your food Heaven favorite food? Oof.

Um, well, I do love, um,

the Mediterranean ballad. I, I really, I really like it, although,

um, I'm, as I'm rediscovering more of my, uh, you know,

the traditional South Indian food,

I'm really trying to cook more of that at home. Um, so when I'm feeling,

you know, sick or, or not feeling that great, there is absolutely nothing like,

um, yogurt and rice.

That's pretty gentle. Okay. What is your worst food?

What is one food that you, if you served up, you'd be think,

I I really don't wanna eat that.

No, there's really nothing that I, oh, well, maybe,

uh, octopus.


Yeah. I mean, I think, um, yeah, octopus or eel.

And what about, uh, what's your favorite film?

Well, fiction.

Oh, good choice. What's your favorite Christmas film?

Uh, I, well, diehard it has to be

Die Hard. Well, it'll be up there for me as well. Star Trek or Star Wars.



Yep. I'm not a, I'm not much of a trek. I, I gotta be honest,

but I will say with a huge caveat, uh, when I say Star Wars,

I'm specifically assuming that one through three never existed.

They just didn't happen. So we're just gonna leave that first trilogy out.

Okay. And TV or book

A book.

And what are you reading?

Graphic novels specifically, but, um, I say

Novels. Did you say

Graphic novels? So, yeah. Um, I, I, I read everything, um,

with tv. Um, I, I do like movies. I, I, you know,

just for Memorial Day, we just did a, uh,

war movie marathon just this past week. Um,

but I've no patience for some of these Netflix things that just go on Netflix or

whatever you have, uh, Hulu or whatever that just go on and on and on.

Season after season, remember how 40 towers one season,

comedic gold boom, we're done.

And now you just go on for eight seasons and I just have no patience for that.

No. Gimme a good graphic novel or a book I'm happy, or a one hour,

two hour movie done.

That's fair enough. And what's your favorite color?

An odd bluish gray.

Okay. And favorite conference?

Favorite conference? Oh,

it's gonna be the next G R C for volume M the first volume M G R C.

That was typical. Oh my goodness. It looks like I'm waving.


So you sent me some more pictures of you chilling out. So again, on the,

so not just rowing, you sail and you,

Uh, I love the water. Uh, absolutely love the water.

So whenever we get the chance, um,

whether it's kayaking or, or sailing, um,

I just love the water, so it's a great way to just, uh, decompress a little bit.

Okay. And

Pottery. So I do, I do like working with my hands. Uh,

and so, uh, I, I started pottery, uh, a little bit ago,

and this is just a collection I just thought was a nice photograph of some of

my, uh, somewhat clumsily made pots.

You can tell by the thickness of the rims that, uh,

these are some of my beginner pots. I'm still not very good at all. Uh,

but perhaps just a wee bit better than what you see behind you.

Are you doing pot for at home or are you going somewhere to do your

Pots? No, there's studio close by.

So you haven't got your own kiln?

Nope. Not there yet. Not there yet. Not yet. Yeah. Not, not there yet. So I,

I really want to, uh, in the, I'll, I'll,

I'll get this done in the next few years. So I,

I've done potter and woodworking, so play and wood, uh, I do want,

there's a smithy close by so that we iron glass blowing, I'll be glass,

and then perhaps sculpting stone.

I really wanna do all of these five things,

just have a good time working with my hands.

So we've got pottery, sailing, kayaking,

rowing, uh, chess is another hobby of yours.

So actually, this is weird. I, I almost turned pro, um, when I was, uh,

I, I was a very good chess player. Um, and I didn't

Realize the computer can beat you.

So this was, fortunately, this was before deep blue.

This was fortunately before deep blue and all of that. So our,

just around when Deep was, was around, but, uh, well before stock,

Phish and, and, and, uh, alpha, um, all of those new, uh,

approaches, uh, I used to play quite well when I was a kid,

and I was competitively did quite well.

And finally I made a call in high school that I couldn't make a living,

uh, if I pursued chess. And, and, um, yeah,

that's how things happen. So I, and, but, but that was woodworking that I,

'cause I made the, um, I made the chess board. So that was my, uh, that was, uh,

a board that, that actually came out reasonably well. Yeah. I,

I made that board.

And that,

Not the pieces, though.

It's an impressive chess board.

Oh, thank you. Well, if you look really close by, really close, you'll see some,

the lines are a little wonky,

Still impressive. And, uh, I, I, this picture is the wrong orientation,

but Mahjong as well. And

This is Ong. Yeah, this is, this is, uh, entirely.

So this is how people make fun of me because I, I, uh, somehow that,

uh, the competitiveness from chess translates to Mahjong. Except Mahjong has,

you know, there's, there's a slice of luck. Depends on what dials you get.

So it's an extraordinarily frustrating game for me. I'm terrible at it.

And I keep promising myself that I'll play because I enjoy it,

but I just suck at it so bad.

But you were persevering, which is impressive now. So you wanted to be,

uh, a chess player, an architect designer. So when you were young,

these are what you are in, this is what you wanted to do. Yes.

We know what you want to do now.

'cause you are very passionate about the volume Em community.

If you could do any job for a day, a week to get a feel for a different job,

what, what job would you like to do or try out?

Um, farming.



Farming. Yeah. What sorts of farming?

Um, really, uh,

it's pacts on, on the one hand, I really like just,

just working, you know, on, on, uh,

really reading more than anything else because I don't even wanna do something

that comes with it, the pressure of expectations. Oh, I hope this,

these tomato grow well, or I hope those peppers look great.

None of that stuff just put me on a patch of, uh, earth to just clear it up.

And I think I will be very happy. Um, uh, but on the other hand,

I do think that agriculture, because of climate change in the coming decades,

uh, is one of those things that we're gonna have to seriously rethink.

And I think that that's gonna be a very exciting place actually.

I, I just love to think that farming, as long as I'm weeding,

you do know that they use just weeded killers these days. I

Know. They just,

And just chemical.

Why don't you napal the entire field and then have it float in our bloodstreams?

Um, no. Um, I'm, I'm hoping that there will be,

I think there will be a,

a resurgence of new agricultural technologies soon,

or at least I hope so. I think one way or the other,

I believe it's gonna have to happen.

We are really,

really close to the hour and I have loads of questions I haven't asked you,

so I'm another quick fire question. Sure. Or array tomography

Em now. Array tomography in the future.

Oh, that's a cop out.

That'll cop out.

Why array tomography in the future?

Uh, because I think that there will be, uh,

AI based approaches that will be able to fill in the resolution

limitations of ery tomography,

and you will then get the advantages of the larger volumes that ery tomography

will do. Which fib sem is inherently limited


What's that?

Today? It's inherently today.

Today, today, right. Today, uh, right. With, with,

with plasma approaches, et cetera, et cetera. It that, uh,

that answer is going to be completely outdated. Very sure. I'm, I'm,

I'm very sure about that. Yeah.

And who have been your inspirations,

quick call out to those who have inspired you throughout your career?

I'm very careful about that because I, I am, I,

I'm careful about put putting people on pedestals because, uh,

we're all fallible and I think some of us

sometimes do some things that are special,

but I think as society,

we are a little too keen on calling those people special,

just in general. Whereas the really are they? So, uh,

there, there are certain people at certain times who have inspired me. Uh,

but, but that's about it. No, no, no. Uh, shining heroes.

Uh, I, I, if your mom listens to this,


He's gonna say, Kada, I wanna speak to you.

No, I think, I think, I think I've, I've,

I've gotten this healthy skepticism of, of people around me, from my parents,

actually. They're just very even keeled.

And I think they'll be happy to know that,

that I don't look up to them anymore than they would want me to.

So now you've just said they're not the group.

You just got your whole I have, I have insulted, I have insulted my family,

my colleagues, all my mentors, everybody. No, I'm very thankful for all,

actually, all jokes aside though, I, I really have had, um, many, many,

many people, I, and you know this, I mean, it, they,

sure it takes hard work and perseverance, but there's a massive chunk of luck,

not just in terms of circumstances, but also in terms of the people you meet.

Um, and it, it would be, uh,

unfair to single out one or two people simply because of the richness

of the, uh, of, of, of the richness of,

of the, um, uh,

kinds of relationships that you have with everybody.

Com some people who may be completely unknowns, right? Or, or who,

who from the outside may have very, very modest, uh, achievements, quote,

but they're equally, equally, uh, special in their own little way.

Ed, on that note, without asking some of the questions I wanted to ask,

we are up to the hour and I have to say thank you. So Kayla,

thank you very much for today. Thank you. Everyone who's watched, listened. Uh,

please don't forget to subscribe to the channels you've heard about Elizabeth

Bick. We talked briefly about Ron Germaine, we talked about the Volume Em,

and there's, there's a podcast about volume, EM community.

So please do tune and listen to that and, uh,

get yourself to the Gordon Conference. Kado.

That's right. Join the Volume EM community.

Thank you very much, Kado.

Thank you, Pete. It was been fun.

Thank you for listening to the Mic Microscopists,

a bite-sized bio podcast sponsored by Zeiss Microscopy.

To view all audio and video recordings from this series,

please visit bite-size

Creators and Guests

Dr Peter O'Toole
Dr Peter O'Toole
Head of Imaging and Cytometry, York
Kedar Narayan
Kedar Narayan
Frederick National Laboratory and National Cancer Institute
Kedar Narayan (Frederick National Laboratory and National Cancer Institute)