#62 — Lucy Collinson (The Francis Crick Institute) and Kirk Czymmek (The Donald Danforth Plant Science Center) join Peter O’Toole to discuss volume EM. Together they highlight what volume EM is, how it has been a quiet revolution, and share resources to help those wanting to get started using this powerful new technology.
They discuss how amazingly friendly and engaged the volume EM community is, how you can get involved, and the dangers of saying web addresses too quickly.
Watch or listen to all episodes of The Microscopists: themicroscopists.bitesizebio.com
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(00:01):
Welcome to the Microscopists,
a bite-sized bio podcast hosted by Peter Oto,
sponsored by Zeiss Microscopy. Today on the Microscopists.
Today on this special edition of the Microscopists,
I'm joined by Lucy Collington at the, from the Francis Creek Institute,
and Kirk Sek from the Donald Danforth Plant Science Center.
(00:25):
And we discussed the waves that Vol Eem is starting to make.
For the last few years, we've called what's happening, a quiet revolution, um,
because it's been a bit under the radar with the Nobel Prizes in super
resolution night microscopy and in prior electro microscopy.
But I think we can say it's arrived now,
How enthusiastic and helpful the volume end community is.
(00:49):
You know, the reality is, is that it really depends on the biological question,
on which tool's appropriate.
And to someone just walking into this tomorrow may not be obvious.
So definitely reach out. I'm meaning it could be myself or Lucy, or even, um,
the Volume M community.
You can send off a little note and you'll get lots of feedback from people who
care.
And what publications should you go and look at if you're just getting started.
But
I think, uh, Lucy has, uh, put out a primer last year.
(01:12):
Yeah. So that was in nature, uh, reviews, methods, primers.
We were really lucky.
We got some key experts from across the Volume M pipeline
to write on it. So,
Oh, in this episode of The Microscopist,
(01:35):
hi, welcome to this special version of the Microscopist. So,
I'm Peter Oton from University of York,
and today I'm joined by Kirk Zeek from Danforth Center and Lucy
Collington from Crick. And we're gonna be talking all about volume e m,
and if you know a lot about Volume M you may learn a bit more.
If you know nothing about volume e m hopefully you'll learn how it may be
(01:57):
relevant to you. Kirk, Lucy, how are you both?
Very good, thanks. How are you?
I've got, I'm good, Kirk. How are you?
I'm great, Peter. Thanks. Thanks for inviting me.
Well, I, I'm, let's kick this off. Actually, I,
I'm gonna kick this off because this is a big thing.
If you've never heard about volume, em, this was actually, I think,
(02:19):
one of nature's seven technologies to watch,
which kind of means in the next seven years,
a Nobel Prize will probably be given for volume. Em, so,
so I've gotta ask first, Lucy Ka, who, who, who's gonna get that Nobel Prize?
Oh, You know,
we can't name names like that because we'd probably get it wrong and somebody
(02:41):
would be offended, but it was very exciting that it was in nature,
and it's, it's not just one of the,
named as one of the seven technologies to watch in 2023,
but it was also along things like the James Webb Space Telescope and
High resolution Radiocarbon dating and crispr.
And so we all like to call for the last few years we've called
(03:06):
what's Happening, a Quiet Revolution, um,
because it's been a bit under the radar with the Nobel Prizes in super
resolution light microscopy and in cryo-electron microscopy.
But I think we can say it's arrived now.
Yeah, that's a cool answer. And I,
I do think we will be ending up in that Nobel direction at some point.
(03:27):
If you think about those tech, you've compared them to the supervisor,
the cryo eem, Nobel Prizes have been awarded there.
You've talked about all the other things that are in those seven technologies.
They're making huge waves. And this is, this is for the biologist. This is it,
you know, this, this is a big thing,
is what we're gonna be seeing a lot more of, but it's not easy to get into.
(03:49):
So I'm, I'm gonna start first maybe asking Kirk, why,
what, what is vol, you mean?
Well, I guess to put it simply is, is that, um,
we wanna see structure inside of cells that are in a nanoscale.
Most things inside of a cell are smaller than the human eye can see by far.
(04:11):
And so we would use a technique called electron microscopy that can see these
small features. Um, the challenge is, is that of course,
cells formed tissues and tissues form organisms.
So looking at very small structures generally can only look at a small area at a
time. But volume M allows us to extend that to larger volumes,
greater than a micron, um, actually into, um, tens of microns,
(04:35):
hundreds of microns, and even millimeters. And of course,
a millimeter is 1000th of a meter for those who don't use that part of the ruler
very often.
I, I, I've thrown a picture up. So actually, if you,
I know most of you'll be listening to this podcast,
but there is a YouTube that will actually have some movies add some images that
will actually demonstrate, uh, what we'll talk about today.
(04:57):
So I do encourage you to go and just have a quick,
even if you skimm through it really fast, don't forget,
you can play it at one and a half times speed on YouTube,
so you don't have to lift and listen to me, which along so much.
But there's several different techniques, isn't there, Kirk, on this?
Yeah. Um, so just to extend what I was saying a few moments ago,
what we have to do with our samples is,
(05:19):
is that we have to fix them or immobilize them and, um, stain them with,
uh, chemistry that allow us to see them by the electron microscopes. Uh,
one example for scanning electron microscopy is where you will fix the
materials. You'll put these metal stains in them, you embed them in a plastic,
which you can section. And one of the most common techniques,
(05:40):
and actually the lowest cost of ENT entry is called array tomography,
where you can actually collect ribbons of sections and then reconstruct those
slices into a three D volume. Um, yes, exactly.
So you can do that both on the transmission electron microscope or the scanning
electron microscope. Exactly.
And so these techniques now allow you to use, um,
(06:04):
ribbons in the T e m or the s e m.
And what's nice about this particular approach, again, a rate tomography is,
is that the sections are, it's non-destructive,
meaning you can probe or interrogate those sections.
You can image them in different types of microscopes, even light microscopes,
and get chemical information using fluorescence and combine them back together.
Then when you're done, you can create these large volumes, which again,
(06:26):
is the characteristic hallmark of volume m uh, much greater than a micron,
oftentimes tissues and whole organisms.
Now, I, I'm sure we're gonna come back to this movie later,
but I'm gonna drop it in now because I think it's relevant. So if you,
if you watch it,
if you're listening to give you an idea of what this can do today,
uh, you, you, you forwarded a movie, go watch, but Kirk, so, so what,
(06:50):
come on. Describe, besides, look at my head blown up.
Yeah. So this is actually a single algae cell,
and what you're seeing is all the little planes that are,
that are acquired in these serial sections. And then of course,
we can segment out the structures inside that are biologically irrelevant in the
case on the left, the purple, um, for those who can't see as a chloroplast,
(07:10):
which is responsible for photosynthesis.
And the cyan colored structure is the nucleus.
So you can see all the structures, the texture,
and inside also the yellow is the GOGI body,
which involved the secretion in the cell.
So all of these are put together and captured in a full volume.
We have all of the context, the spatial information,
which you can look at other important cell structures like mitochondria, ex uh,
(07:32):
endoplasmic reticulum, whatever we can contrast,
we can go in and segment and then find their spatial relationships.
And just as importantly, pathologies.
'cause a lot of times we're looking at disease and we wanna know the difference
between a disease cell and a healthy cell.
That's cool. And, and I, I love your descriptions of the colors.
It reminded me of watching snooker listening to snooker on the radio when they
(07:54):
said they're screwing back from the red to go for the black ball. Next.
Just for me, it works Okay. Having listened to snooker for many,
for many, many years. So, okay, so that's what it can do.
Where did it start, Lucy?
Well, for me, it started back with, um, the worm.
(08:16):
So very small organism called C gans. Um,
don't try and spell it. So back when I was back at the L M C B in London,
um, I was collaborating with a group who wanted to do electron microscopy on the
worm. And they, you need volume.
Normally back then,
you were talking about imaging two D planes in a transmission, em,
(08:39):
but to go through something like the worm, you need to start moving into volume.
And, um, back in the 1980s, uh,
Sydney Brenner's team published the structure of the nervous system of the
nematodes, the elegance, otherwise known as the mind of the worm. And, um,
I was lucky enough to go to New York, to David Hall's lab at Albert Einstein,
(09:00):
and, um, have a look at the original data that they collected.
So they went through the entire nervous system of,
of the worm by collecting serial sections, about 10,000 of them.
Um, and then imaging them one by one in the transmission electron microscope.
And if you lose even just a few sections during that effort,
(09:23):
then you'll lose your connections through to the neurons. So in the end,
it was an effort that took around 10 years,
and I spent a week in a small room with all of the original printed
micrographs.
So David's lab was in the process of scanning them and putting them up online so
other would analyze them. Um,
(09:44):
How were the images captured? Were they photographs then? So
That was a transmission electron microscope, but it was onto film.
And then the film film would've been printed,
which was how we did it when I started as well,
which makes you very selective about what you take images of,
not like a digital camera where you snap everything.
'cause you have to develop everything and print it out. So these were the,
the real printed micrographs,
(10:05):
which have all of the hand labeled annotation on them to say which neuron is
which, and then following them from section to section.
So the work that was done then in 10 years would probably take
us less than two weeks now on the more automated systems that Kurt was talking
about. And actually, we are now in the era of the,
(10:28):
the mind of the fly.
So there are really big groups across the world in the US at, for example,
Janina and in the UK at the L M B and many other places,
gradually, um, increasing the,
the volumes that can be imaged and even more difficult,
reconstructed and analyzed.
And what we're seeing now is that the techniques are not just used in
(10:50):
neuroscience, but they're the increases in speed and, um,
resolution and analysis are pushing out through cell biology,
through plants and marine life. You've got some, um,
infectious organisms on the bottom left there and right out into the CL clinic.
So there's some beautiful work on, uh, breast cancer, uh,
(11:11):
samples from patients on the right.
Uh, go. Just, just, just, just just for clarity.
So obviously you were using photographic film when you started all those very
many years ago. Lucy, uh, no comments.
I think you've got I yeah, I, I, yeah, I can't talk. I mean, uh,
(11:32):
but, uh, obviously now you're using digital cameras.
Yeah. How much do the digital cameras cost? Well,
Digital cameras or detectors, depending on which,
Well, yeah. Okay. So if you're going tmm you're using a digital camera,
which is costing Kirk, uh,
(11:53):
Okay. Uh, d they can be 180,000, a hundred thousand,
a couple hundred thousand depending on the sensitivity of these systems,
I think. Yeah. Uh, they can be expensive, um,
or you can get one that's good enough, uh, for a bit less than that,
but it normally tens of thousands of range.
And so I'm just thinking, my camera's not gonna take a picture of this,
is it my phone? And, and if you think about the chips size, this,
(12:17):
the chip is tiny. The chip that you are looking at is almost speaker. The,
it's huge, isn't it? That chip, that's
Part, yeah, that's part of the driver of the cost. Um,
the more expensive chips to be able to collect the signals from electron or the,
you know, from the electron beam, um, you know,
smaller chips are less expensive. And of course,
on the phone the quality's a little different too.
And the sensitivity is, is remarkably for
(12:39):
Electrons. Yes.
Different on that. So, so Lucy, you've finished on talking about neuroscience.
Uh, what, what sort of samples are you generally looking at yourself?
Oh, could be anything.
So we use volume em for most of our projects now because as,
as we say, life happens in three D. Um,
(13:01):
the image on the bottom left there is actually from, from the team, from a,
a collaboration with Eva Frick Freckles lab and Serge Moav,
I think I mentioned it before. Um, that's on Zebra Fish.
And looking at toxoplasma in the Zebra fish brain and trying to localize it as,
as the, um,
fish mounts an immune response to see if you can use a zebra fish as a model for
(13:25):
disease states, because they're much easier to image. Um,
you can get a lot more of them than you could with a mouse model.
And what would the typical disease states be that this would then be used for to
study, which would bring it back to human relevance as well.
Um, we've used Volume M to look at h i v infected cells, uh,
(13:45):
mycobacterium tuberculosis in cells, uh,
that one's toxoplasma. We look at plasmodium, which causes malaria. Um,
we are looking at tumor tissues at the moment and how the metabolism of
tumor tissue, uh, changes.
Its kind of very heterogeneous, even within a single tumor.
(14:09):
Um,
Yeah, no, no, it's, it's a huge variety. And that's just run on. Yeah,
We run around 60 projects a year, and each of them will be Yes.
And most of them will be Volume M and each of them will be on something,
something different. And Kirk, again, will be working on, uh, different
On get Yeah, on getting to, you've got 60 projects. 60 projects,
(14:33):
Sorry. A hundred projects a year is 60 teams.
Oh, the data, yeah. We'll come to data later on, but this,
that any,
the time on the microscope to do this is huge because it's not the fastest
experiment in the world. And then the day, how many instruments do you have?
We've got six electron, six big electron microscopes, so different flavors.
(14:56):
So we can pick and choose them according to what's needed for each project.
And then we have X-ray microscopes and light microscopes,
and a few benchtop microscope, uh, electron microscopes,
which are small enough that you can put them in the boot of your car and drive
them around if you wish.
So you've got a bassis, all sorts of electron microscopes. I, I love the idea.
And Kirk, I I, I noticed on this we've got plants as well,
(15:19):
which is kind of where you major, isn't it?
Yeah. Uh, um, I, I consider myself, uh, um,
biological and soft material. So I would,
I would go beyond that just in my career.
But now I'm focused on plants which have special challenges, uh, and,
uh, um, to get volume m to work. Uh,
one of the challenges with volume M is, is that you're, you have samples in a,
(15:41):
in a non-conductive resin often. And so part of the,
the efforts of the, of the, the, of the,
of the community is to find protocols that help improve that.
And one of the efforts I have is, is, uh,
been making sure that plants get enough signal out of them and enough
connectivity that we can get high quality images.
(16:01):
Okay. I, I kind of get it what Lutie said, you know,
looking at how the parasites,
how infections actually affecting the infrastructure of the cells and where it's
been found in the cells, and how it's changing. What's the use in plants,
why are we interested?
Ah, okay. So we'll go a little bit further. Um, you know, part of the, the,
the thing about the plants that I was talking to is it made a little bit harder
to interrogate them, but there's a lot of things. So plants, just like, um,
(16:25):
other organisms have development. So we, we do look at a lot of development,
how the root grows, how the chute grows.
The other thing we look at is how pathogens are invading.
So we can go in and inside of the plants and inspect how pathogens are causing
disease, how the plant responds to it. Those are just, um, some simple examples.
The other thing is, is that, of course,
there's phenotype variations with crops that are resistant to drought,
(16:50):
for example, or, you know, climate change.
So we're trying to understand what cellular, um, improvements that a plant can,
uh,
have and subcellular using volume M we can actually learn from that and
help develop improved plants.
So, so I, I, I have a question for both of you actually.
(17:11):
Why isn't everyone doing it?
Who's gonna answer that one first, but
I'll definitely make a comment on that too.
Go Lucy.
Uh, because the instruments are big, uh,
like a few tons each, they're expensive.
They need very special rooms to house them. So at the crick,
(17:32):
we're actually in a,
in an extremely challenging environment to put those microscopes. So they're,
they're all on anti-vibration bases to remove, uh,
physical and acoustic vibration.
And then they all have six-sided shielded boxes to remove the electromagnetic
fields coming from the train stations and the tubes passing us. Um,
(17:52):
I don't think it's that much more difficult to train somebody to operate some of
these instruments than to operate a high-end light microscope nowadays.
I think people sometimes think that's the, the issue.
But the, the hardest thing, I think is the sample preparation.
'cause we use lots of toxic me metals. Um, we have to slice the samples,
(18:14):
which is very difficult. Um,
saying that's the thing that stops most people from implementing it in their own
labs.
Okay. Cook.
Yeah, I would just add to that, I agree with everything that Lucy said. Um,
you know, the,
the reality is a lot of it is about awareness is probably one of the reasons why
we both are here today. Um, you know, it's out there,
(18:36):
there's a few groups that have been working,
and the good news is that they've actually solved a lot of the problems that
made this not as easy to adapt.
I think things have changed and, um, now I think our efforts are to, um,
help educate, um, other laboratories that could quickly, uh,
plug in and start using this, this type of technology.
That's, I, I'm gonna jump, because if you ask, you know,
(18:58):
if you'd have gone back five, even just five, six years ago and said, Pete,
user comes, Pete, I want to get into this, I'd have gone great.
But you're gonna have to commit a huge amount of resource to it. And it's,
it's staff resource because Lucy,
you touched on all those projects.
How much data is coming off these microscopes?
(19:22):
Um, per microscope on our standard volume electron microscopes,
somewhere between 50 and 250 gigabytes per day.
But the new systems that are coming through will go into terabytes per hour.
So at the, at for a small sample,
then we can do some manual low throughput
(19:45):
image analysis,
which then allows us to interpret what we're seeing in a normal sample versus
a drug treated, something like that.
But you very quickly get into the regime where you just
can't analyze all of the data that you have
manually. So that's, that's where you need to move to automated techniques.
(20:09):
Okay. So, so that, that, so, so obviously you can share data.
Yeah.
Okay. So, so I dunno who wants to talk about, I say this is, sorry,
coming at a random order with this,
but I think it's important because it's not just a sharing your data, it's data.
How long it would it have taken you to analyze, to segment the data,
(20:29):
uh, six, seven years ago?
So we use the nucleus of one cell as an example,
and we still have, we,
we have some algorithms to pick out the nucleus,
but we still do a lot of segmentation manually because the algorithms don't work
on every single cell and tissue type that you, uh, challenge them with.
(20:54):
So for a single nucleus in a single cell,
it takes one of our experts about a week to segment it.
So it is just not sustainable because quite often we're imaging pieces of
tissue, and if you're interested in the nuclei,
there might be 50 or a hundred nuclei, and then you are into, you know,
eight years' work, doing nothing else. Um,
(21:16):
data sharing is extremely important because we just can't,
even if we're using automated algorithms to analyze the data,
they're normally only looking at one structure.
And you can see in the images that Kirk and I have up that each image,
so this is three D cut through a cell with the nucleus in the middle,
we've got mitochondria and endoplasmic reticulum and other organelles.
(21:38):
So if your research question is about mitochondria,
which power the cells,
and you make three D models of all of the mitochondria in the cells,
somebody else might be able to use the same data, especially if it's a,
a control cell and segment out the nucleus 'cause that's what they're interested
in. Or the endoplasmic reticulum, which is making proteins.
(21:58):
So the data's so rich,
one team won't usually use the whole lot, even if they're generating it.
And the empire is the electron microscopy public image archive,
which is held up at the European Molecular Biology Laboratory,
European Bioinformatics Institute, or E M B L E B I, as it's much easier to say.
(22:20):
Um, so that was set up,
first of all to hold em images from cryo transmission
electron microscopy, um,
but now takes depositions of images from all different types
of volume, em and x-ray imaging. And that means that the,
(22:40):
the data gets out to other scientists, not just, um,
biological researchers who might wanna reuse it and reanalyze it,
but also computational scientists who might want to develop new algorithms,
analyze that data. And empire is growing very quickly. You can see this from,
I dunno, which way I'm pointing, uh, behind Pete's it says two petabytes.
(23:02):
So they reach two petabytes worth of image data in 2022,
and they're almost up to three petabytes now.
And I'd imagine that's not all the data that's been generated,
that's just what's been uploaded.
Yes. And normally,
normally data sets that are associated with a publication or have been
produced as example, uh,
(23:24):
data sets for a specific use like image analysis. Um,
so there's a big push in the community to try and increase the number of
depositions, particularly associated with,
with papers so that other people can really dig into the raw data.
So, so this is cool. So, so it's gone from,
from what my biggest hold up with this was was the speed of,
(23:48):
uh, analysis. It wasn't the acquisition, it was the analysis side. That was the,
that was the rate limiting step, which is getting faster. Uh,
you mentioned the sharing of data, uh,
we've talked about the different techniques or some of the techniques we haven't
got there fully yet. I'm gonna challenge you on that in a minute,
and that'll probably be cut, come and get with both of you at that point.
(24:09):
But I think a big thing when you're sharing data,
when you're learning new techniques as a community,
and you mentioned the community sharing data,
so how have the community got together?
So there have been different groups of the community that got together at
different times, but the,
the current community effort with the nice logos that you can see behind us
(24:32):
for V E M that really started in 2019, and again,
actually Empire and the E E M B L E B I were really important in that.
So I was up in Cambridge at their scientific advisory board meeting
and over dinner, which is where all the good ideas happen. Um,
I was chatting to AAN Peran and Herod clx,
(24:56):
uh, as Herod at the back in the green T-shirt and Luigi Martino
from the Welcome Trust. Perfect. Um,
so a lot of the scientific advisory board and a lot of what, um, um,
the molecular cell structure team were focused on was a huge field of protein
structure and structural biology. And I was there for cells and tissues.
(25:21):
So you're just starting to,
to see the deposition of images of cells and tissues from em, the
Outlier, I presume at this point?
Yes.
There's a picture actually,
Lucy sent in and she's right on the end just showing just how much of an outlier
she was to rest of the structural biologists to use
I to squeeze into the public archives. Um,
so over dinner we were talking about how quickly this technology
(25:45):
is having an impact in, in biology, and actually how often it's,
the data is included in publications, um,
but you don't necessarily know it's there because EM is has been around for a
very, very long time. Unless you dig into it, you don't realize it's volume ym.
And from that came the idea of the first community meeting,
(26:06):
which Luigi organized and hosted at the Welcome Trust in 2019.
And we had around 60 experts.
So that was end users biologists for whom it was really important. Um,
we had Ollie Flory talking from, um,
Cambridge on how it's being used in cancer research. Um,
and then we had the microscopists, the imaging scientists,
(26:30):
people from companies involved in Volume M data scientists.
And then in 2020, obviously everything had to move online.
We had a second town hall meeting,
and from that we had the formation of six working groups.
So behind you,
you've got pictures of all the co-leads for the volume e uh,
(26:51):
working groups. So we have six working groups, community outreach, sample prep,
data training, and infrastructure. Um,
and I should mention actually, that infrastructure was chaired,
first of all, um, by Charlie Wood and Chang Chang,
and Paul and I have recently taken over, but it's,
(27:12):
it's these people who have volunteered their own time.
It's not part of their day jobs to organize the working groups.
And that at that point there were about 70 working group members,
uh, distributed across the groups. It's now more than 80,
and they, in two years,
(27:32):
they've delivered huge amounts in terms of
connecting the community resources for the community,
um, telling people what volume EMM is and how you can use it.
So trying to increase its impact throughout the biosciences
and the clinical sciences, um, and really putting it on the map, which helps,
(27:56):
helps science because you are, you are, um,
letting everybody know that the technique is there and that it can reveal
new insights into important biological questions. Um,
but also pushing the development of the technique forward as it gets better
known and attracts funding. And behind you,
(28:18):
there's a map of the distribution of the current, um, group members.
It's truly global. It it is in every con,
every every practical continent.
Yeah.
But there's a huge concentration of people in the UK because it started in the
uk. So after that,
so that first meeting was also organized with Paul Ard from Bristol and
(28:42):
Herard, aan, Paul and I, but the, the working group members
really nucleated in the uk and then the last year or so has been really pushing
out to try and be more and more international because you can see there's
volume, electron, microscopists everywhere, and it's all about networking.
And that's where Kirk comes in.
(29:04):
I was about to say, so this, this, this is, so, uh,
Chan Zuckerberg comes into so many of the different podcasts for many different
reasons. Uh, so Kirk, the community side, i, it,
it's moving on and it's moving fast and it's still open to everyone.
Yeah. So, um, I actually, I was in, um,
(29:24):
I was actually in an industry, uh, once I came back into academia, um,
once I learned about the volume group, and of course we work with all of this,
these, uh, folks, um, that are in these pictures, uh, all the time. Um,
uh, I joined the group and, um, we were always interested in, you know,
there's only so much free time we can dedicate towards it as a group.
(29:46):
And I think it was really, really, really beneficial.
But we recognized if we had some funds,
some of our initiatives could really accelerate this forward. And so, um,
as a group, we decided to submit an application, uh,
for the Chan Zuckerberg initiative and, uh,
specifically to build resources and expand our global community and to emphasize
(30:06):
outreach and dissemination. And now we have funds to do that. Um,
actually the funding just started a, a few months ago or, and we've have a team,
a program manager, a community officer, and resources to help, um,
develop some software. Uh, and um,
this is very exciting for us because now we can really accelerate it,
have higher quality material, um,
(30:26):
to teach people the nuances of the sample prep,
which Lucy Rudley said is one of the bigger challenges and so forth.
So what's very, very exciting. I mean, I, there's many examples of,
of what we're trying to do here,
but right now we're just getting the funds to kick that off.
And, and this isn't a closed shop, is it? You, you've got these groups,
these working groups, you're up to 70 people,
(30:48):
but I presume it's still open for anyone to join.
Definitely it is being as open as we can possibly make it the whole way through.
So all of our documentation is, um, available to everybody.
Everything's shared on, on Google drives. Um,
the community's really friendly and I think it's because
(31:10):
ensemble preparation is so difficult and so long that as electron microscopists,
we don't want everybody to go through the same pain as us. So ever,
ever since I've been an electron microscopist,
everybody always supports each other and helps each other with protocols and
tips and tricks.
And I think the same thing rolls over into the community as well.
So if anybody is interested and wants to join,
(31:32):
we would love to have you as part of this. And there's,
you can get in touch through, there's a Volume EM website, um,
we have a Twitter page.
I've, I, I've gotta keep up with this. So you've got the,
We have a, a list server,
which all of the details of the list server on the Volume EM website as well.
(31:52):
So you can join that and get news from us. And,
And there's CHANBURG links as well. Kirk,
I, I'm sorry, uh, that comment, but, uh, what was your question again? Uh, Pete,
Uh, so it's not anyone can join.
Oh yeah, sure. And actually I wanted to actually add that. And, uh, a couple,
uh, really important things. Um, one of them and is,
(32:13):
it's not just the labs that are doing the work or running the,
the core facilities. Um,
it is also we're bringing commercial entities to listen in and what our pain
points are. 'cause in the end,
we're not building electron microscopes with the vast majority of laboratory.
So we depend on them giving us the tools we need to,
and they've been listening quite well, but as a community,
I think we can improve that.
(32:33):
So I wanted to actually add that as one of the groups. Um,
the second part of it is, and I actually wanna emphasize this,
for those who are listeners on here, um,
if you go to volume m, do org two E,
so V O F V o uh, l u m e
e m.org, um, you'll get the, all the information you need.
(32:56):
Sorry, I have to be able to read that. Uh, Pete, that's your fault.
Your head was in the way of the words. Sorry. Um, but uh, yeah,
so you'll get our, our Twitter account links and, and uh,
Facebook and everything else that we're on, um, in there.
Do you wanna just repeat the link?
Yeah,
www.volumeem.org
(33:19):
g uh, and if you go there, you'll actually get a great start. And not only that,
as the Chan Zuckerberg Initiative, uh,
really starts taking off when we have our new videos and our resources all
consolidated, um, you'll be able to keep up with everything we're doing. Okay.
One comment, that map was beautiful. The reality is,
my guess is we're only reaching about a third of the existing labs already.
(33:41):
And of course we'd love that to double. Um, I just, you know,
this is no actual data to support that, but I know there's a lot of other,
my colleagues and friends that aren't there,
and we're gonna work to try to bring them into our, our group.
I, I, I've gotta say, so for those listening, you may not appreciate this,
but there,
there was an image here of the CHA who was awarded for the CHANBURG initiative,
and, and there's a big call out to lots of people who I'm familiar with,
(34:03):
but I noticed that Kirk is twice as big as anyone else on this picture.
I, I thought that that's vanity, isn't it?
I, I,
I gotta tell you that that was actually following the format of the Chan Zucker
Park edition. Apologize, uh,
everybody who was l p I got the big head, sorry.
(34:24):
I definitely would've been happy to be, uh, just like everyone else thing
Is
Well deserved for the work you have to do, putting, you know,
the paperwork together for these grant grant applications.
That it, yeah. Okay. So this is sounding about a great community,
everything else, but
what's the biggest challenges that you're facing now?
(34:46):
I dunno, who wants to pick up that first? I,
I'll just make, um, and I think you've already touched upon this,
but you know, we've gotten really good at collecting a lot of data.
It's amazing that every minute I can get a slice, you know,
in a thousand minutes I can get a thousand slices that are, you know,
oftentimes pretty close to registered. But it is,
(35:09):
as loosely alluded to earlier, it's a bit like drinking out of a fire hose.
So I think one of the things is, is that, you know, this, you know, we,
we need a pain point to drive the innovation here. So in my view,
one of the things will be, um, faster segmentation volume.
Being able to manage all of that data is one of the key challenges.
And that's in my perspective. There are many others,
(35:31):
but that's one that I think about every day is I have a vision of so many experi
experiments. Um, I can do lots of smaller things now,
but I'm moving towards the bigger,
and I can't wait for the tools to become more efficient, um,
and make it even faster so that, um, maybe smaller labs can, uh,
even get bigger benefit.
Okay, Lucy?
(35:52):
Yeah, I agree. Uh, the data side has to change and I think,
yeah,
so your background now is showing a figure from a paper called The Mind
of a Mouse.
And I love this figure 'cause it makes it so easy to communicate the,
the next steps in volume, electron microscopy or something else.
But we assume at the moment it'll be volume M. So, um, I said earlier that we,
(36:16):
that the mind of the worm has been mapped at least,
uh, from some worms. And now the connectomics community,
which focuses on generating these, um,
maps of the connections of neurons. Uh,
the connectomics community is working on the,
the fly brainin and has started delivering fly brains,
(36:39):
but the next challenge is gonna be the mind of the mouse.
And if the challenge of mapping the mind of the worm was the width of one
airline seat,
then the challenge of mapping the mind of the fly is
the, uh, size of six and a half Boeing, 7, 4, 7 airliners.
And by the time you get to the mouse,
(37:00):
then you're talking about the distance from Boston to Lisbon. So the,
the scale increase in challenge across the entire
pipeline in sample preparation in imaging speed and imaging volumes,
and then in data analysis is almost unimaginable in unimaginable at
the moment. Um,
(37:21):
and the effort to get the mouse brain will deliver us
the next generation of imaging technology.
And, and so I, Jeff Liman, uh, I dunno if you're, listen, Lucy,
I know actually thank you for being an avid microscopy listener, uh,
on your tube or on the bus on the way home.
(37:41):
I get messages from Lucy every now and then saying,
I can't believe you just said that. That's really funny. Or whatever else.
I can't believe someone else just said that. That's shocking. Uh,
so you must have heard Jeff Liman and talking about similar type of work as
well. Uh, you also sent these. Yes.
So the, I think this is another big challenge. So the, the delivery of the,
(38:04):
the mind of the mouse is one, and the,
the first effort will be delivering the, the structure of the,
the connections between the neurons.
But then you have interesting areas here in
correlative and mul multimodal imaging.
So that's where you connect different types of imaging together,
(38:27):
like light microscopy, x-ray microscopy, iron microscopy,
and electron microscopy.
And you start to layer different types of information on top of each other.
So that might be showing you where the molecules are in the brain or
where chemicals are in the brain,
um, um, it's, uh, like epilepsy or in brain tumors.
(38:52):
And when you add those layers of information,
then you start to really get to the, the differences in health and disease.
But we,
we can't at the moment image across all of the scales from atoms
or even macromolecules whole proteins through to organisms
on a single sample. But you can see in these two images,
(39:14):
so on the left you have a nature methods piece highlighting in situ
structural biology, and that's a fairly new field where people are imaging
native protein structures inside cells using beautiful
cryo samples. Um,
so all of these molecules encased in ice.
(39:35):
And then on the right hand side you have volume correlative vitamin M.
So that's where we link, say, light microscopy with volume M.
And you have a really nice example there of spatial transcriptomics
where you can see which genes are being expressed in which cells and the,
the cells you can see in Volume M.
(39:57):
And these experiments are extremely difficult,
where you are really identifying molecules in the context of,
of the structure of cells.
So linking those two communities is really a grand
challenge and being able to move seamlessly across the scales, um,
that's a tough one.
(40:20):
Okay. And moving on,
what other initiatives are there in the community? I,
we've heard about Chan Zuckerberg, we've heard about yourself.
Are there other initiatives? Are there other groupings that are closely related?
You can speak about the, the, the Gordon Conference that's coming up. So it's,
(40:42):
okay. So, so this, so how go,
what conference would you go to to learn more?
Yes, so Kirk already gave that one away. Actually there are quite need
More details.
We,
we have to give a plug for the first volume electron microscopy Gordon Research
conference, which is gonna be chaired by Paul Riccardo and myself. Um,
(41:06):
and then that will happen this year in July in California.
And then in two years time when it will be chaired,
I think by Yanick Schwab and Kada or Ryan,
who are both like key members of the volume EM and volume Clem
communities as well. So that's gonna be a lot of fun.
The registration's still open and you just search for volume M G R C and that
(41:29):
should come up. But there are other key conferences that have been going on for,
for a while, including the Zeiss three D L M EM meeting,
which was first organized by Chris Gerin, um,
at the I b and Gent.
And now moves between there and with, uh,
Saskia Lipins and Anna Kramer,
(41:50):
and then goes to E M B L Heidelberg with Yannick and then will come to Crick,
um, with us.
And there are meetings spring up everywhere and there are dedicated
set volume EM sessions and a lot of the big micro view conferences. Now,
It's interesting, if you think about the,
the one that's started with Chris and then moved to Yannick over to, uh,
(42:13):
e em on the note coming over to us actually swap between themselves and then now
into London as well with yourself, Lucy, which is sponsored by Zeis.
Uh, but you know, they're not the only players on the scene, but I've gotta say,
I think all the companies have played quite a critical role in the development
of W U E M. Uh, you know, this is not just an academic endeavor, is it,
(42:36):
you know, how important are the companies for this? And actually I,
Kirk you come from a, a commercial background, you,
you flitted to commercial and then back into academia.
What's your thoughts on the role of companies in this?
Um, okay. I would say that it's key.
And because of my hybrid commercial academic background and working in a company
(42:59):
that was actually developing these techniques, um,
it's huge in, in fact, I would make a comment,
even things like the mind of the mouse, these key thought leaders,
these lighthouse customers that are working on these technologies actually give
us a problem to solve. And then once we, uh,
when I was in that capacity, I would say at the time, um,
(43:22):
once we try to solve their challenge,
it'll cascade down for the rest of the group. So it is,
is instrumental of course. Um, uh,
all of these companies are excited. I think about this, um, you know,
recent attention to volume e m because they've been putting energy and
developing tech detectors and, you know, automation of the software.
So this becomes automated. By the way, I will make a comment.
(43:45):
They have done a great job on the automation.
There's still more room for improvement,
but the fact that I don't have to have an operator to sit there for three days
in a row catching slices is huge.
That is because of what the manufacturers have done.
I would say I want manufacturers to do more.
I want them to think about the downstream side of things and how do we take all
of that data and if they can engage on that, then we have like a full workflow.
(44:06):
That's my, my comment is it's critical. That's why we want,
and the volume community to keep them engaged. They are doing that. Um,
but I think, um, it's exciting.
Lucy, any other comments or thoughts on that side?
Oh, just to agree that the, yeah,
the manufacturers are absolutely critical in this because the,
(44:28):
these kind of instruments, you can't build them in your own lab. You,
you can set the challenge and you can have some ideas and you can make some
modifications, but, um, it really takes the,
the big
companies and that the experts they have in them to design the systems that are
gonna answer the science.
(44:49):
So I'm gonna give a shout out to Zeiss at this point who actually kindly
sponsored this podcast, but not because of that,
that first meeting in v I B was,
was put together from Zeiss.
They brought together not just the leading electron microscopies,
but they brought together some of the biggest light microscopies across the
(45:11):
world and put this all in the same room, you know, and, and,
and then into groups that brainstormed and then came out with ideas. I,
and there were some big names in there, uh,
that wouldn't, that would not naturally come together and talk. Uh, but I,
I would say that's where the community, well, for me,
(45:32):
that's where the community really started because that put people together.
It made that those connections. And, and Lucy, you were there at that meeting?
I wasn't at that one actually. I, I was,
I was at that one. Oh my God, how come I wasn't at that one?
You weren't at that one. That's
The thing. You have the community seeding in different places.
So C D A R as well in the US had, uh,
(45:52):
held what he called a micro lab and brought 50 experts together to discuss what
needed to be done to, to push the field forward and put to answer science.
So you get these different nucleation points all happening around the same time.
And now I think, you know, everybody's come together, which is
amazing.
Can I make one other comment? Um, uh, and this is really key. Um,
(46:17):
one of the drivers that will ensure that we can continue to move forward is not
only the technology development,
but it's also the realization from the funding agencies how critical, um,
having this, these types of platforms and, and um,
and enough institutions that we can continue to,
to grow what's possible and and improve the science because we have all of this
(46:37):
additional insights that just doesn't come across in the two dimensional.
I agree. And there aren't that many facilities that have,
there are quite a few facilities that might have one volume EM modality,
but there aren't many that have lots.
And what we're seeing now is the bigger your sample,
the more you need to tie together the different volume em imaging modalities to
(47:00):
work from bigger scales to isolate smaller pieces,
to image at higher resolution to really get down to the subcellular level.
So there's only a few places around the world that have
most of the volume yen modalities in one place to make them really flexible and
answer, able to answer any biological question.
(47:23):
And the other really good thing about the community,
I would say it's a gender balance within it is pretty good. You know,
if I go back to those early meetings and think about Judith Erman, you know,
leading on one of the tables, you've got yourself Lucy, you've got Jemima,
you've got Raffi, you've got this whole community, uh, uh,
which I think is actually also fairly unique, you know,
(47:46):
to have such a fairly gender equal community.
I think that's quite something the community should be proud of and carry on
embracing as well.
Yes,
and I remember that did tend to be the case even back when I first
got into electron microscopy,
which is really unusual and I'm not sure how that came about. But then, um,
(48:09):
we need to work on our other types of diversity and make sure that everybody
has the opportunity to get into this kind of high technology so that we have the
best minds from everywhere working on it.
So I have two more bits before we wrap up. And the first one is actually,
if you want to learn more about this, there's quite a lot of, uh,
(48:30):
information out there. Uh, so there's different online resources. I think Lucy,
you involved in some of these?
Uh, this is actually the training group. Um, so this is Erin and Raffa and,
um, everybody in that working group who've built some, um,
slides that show how you build toolkits.
So if you're just getting into a rate tomography or focused I N B S E M or
(48:54):
serial block Face s eem for the first time,
there are some slides that show you things that you should get together.
So for a rate tomography there, you've got an eyelash duck to a cocktail stick,
which you need to move your sections around your boat of water when you cut
them on your diamond knife.
Do you use students to make sure you've got enough eyelashes because you're not
from eye eyelashes?
(49:14):
So it's always voluntary. We never make anybody give us their eyelashes. Um,
but different people have different, uh,
eyelashes and some are best than others. So Martin Jones,
who's actually in charge of, um, microscopy prototyping in our team,
which is much more computational and engineering based,
apparently has extremely good eyelashes.
And I think Anne's used hairs from her dogs in the past
(49:39):
as well. Apparently d particularly good.
I can't wait to see Martin. Next time I see Martin,
he's gonna wonder why I'm like this close to him. He
Has still got eyelash
Intensely, but not quite making eye contact. Just look at his eyelash.
He's gonna, he's, he's gonna,
Are you gonna try to grab an eyelash or you just gonna look at him? Peter,
No treat warning.
(49:59):
So you don't pull them out, you wait for them to fall out and then collect them.
Health and safety first.
Mine don't fall out, get away and mine are useless. So keep off. Uh,
and there's also a load of movies that aren't there, so there's,
there's clips and movies that you can go, where can you find this information?
So this is all on the Volume M website and there's a page for training movies.
(50:21):
So again,
this is the training working group who've been making some fantastic videos.
But Kirk, you're gonna be carrying this on, aren't you?
Yes. Yep. So we'll be adding to the, uh, the, the great starting content.
There's now and, uh, yeah, again, volume em org.
Yeah, O r g. You say that so quick, it does sound a bit like orgy.
(50:46):
That's, that's really on you to be honest with you.
It really isn't anyone listening. All they're gonna hear is, or G O R G. Just,
just, anyway, I'll
Speak more slowly.
No
One's gonna forget the address. Now
I fine. Final question. Uh, I'm gonna start with you Kirk on this.
(51:08):
What's the best public,
if you are coming into em for the volume Em for the first time,
what would be the best publication to read?
Oh, um, actually I'm there, there are so many out there. Okay.
I'm just gonna make a comment to you.
I think you made the comment in the beginning.
There are many different techniques and it really depends on what you might have
access to in your laboratory, but I think, uh, Lucy has, uh,
(51:31):
put out a primer last year. Um, and maybe Lucy,
you can describe a little bit about where they can find that. Um,
which would be my recommendation as a first source because it kind of
consolidates these together and gives you the, the source literature, uh,
that some of it's based on.
Yeah, so that was in nature, uh, reviews, methods, primers,
um, at the moment it's not on the open source, um,
(51:55):
but Chris is getting it up onto you at P M C,
but you can drop me an email if you'd like a copy.
And that was bringing together, we were really lucky,
we got some key experts from across the Vol m Pipeline
to write on it. So it's, I would go there.
It's written for people who are just coming into volume electron microscopy to
give you an overview of the techniques and the applications and it mentions the
(52:19):
community. We just have a piece out with Nature Methods a comment as well, um,
which was harod his idea, um,
but then written with Paul and,
and with all of the, um,
working group co-chairs that describes what we've been talking about today,
how the community came together and how you can get involved in all of the
(52:41):
resources that are available.
Is there any other resources you go to publication wise for people to read,
to get a grasp of it?
One of my favorites, I have to say is, um,
Han Shoe's paper with Harold Hess and Song, working on the,
the sample preparation where they,
(53:02):
they describe the next generation of focused I N B ems,
which they call Enhanced Fibs. And, and Shannon, that the team,
uh, spent 10 years working on these systems to make them more stable
and to run for longer time periods. And then Han lined up,
uh, with Harold, 10 of them in a row to run, uh,
(53:25):
pieces of fly brainin in parallel to get to bigger volumes.
And it's just the most astonishing piece of work.
It kind of references what happens in the semiconductor industry where
you really do like line up those iron beams to look at, um,
computer chips and things like that, but porting that over to biology.
(53:47):
And I think it's an indication of, um,
across the entire pipeline how we might, um,
move forward into more quantitative imaging at Banana Scale.
Yep.
And I, I'm just gonna round off, I,
there are so many different techniques,
strategies for doing volume, em,
(54:07):
that I think the resources that you've spoken about are the,
are the starting point for anyone wanting to do it or to find someone
in the community and just talk to them. Yep.
Sometimes often the best way is just email, phone, zoom,
whatever it is. Yeah. And just, you know, pick an expert. I,
it's a really communicative community that is always happy to help and
(54:32):
welcomes the most dumb questions.
So thank you for tolerating my dumb questions today. Uh, and I
Was gonna say there's no such thing as a stupid question, but
Yeah. But then you've met me, so thank you Lucy. I, I,
Kirk knows me all too well about
Yeah, you're pretty tamed today.
(54:52):
Yeah. Professional mode. Yeah. Uh, so Kirk, Lucy,
uh, is there anything else you'd just like to add before we finish?
I should give you one last word.
Is there anything I've missed that you'd like to say?
Pete two, Pete. Two things I actually wanna double click. Um, you know,
the reality is, is that it really depends on the biological question,
on which tool's appropriate. And to someone just walking into this tomorrow,
it may not be obvious. So definitely reach out. I mean,
(55:15):
it could be myself or Lucy or even, um, the Volume M community.
You can send off a little note and you'll get lots of feedback from people who
care. Second thing is, is that nice little climate demos sample?
I wanted to make sure that it's clear that that actually is on Empire.
So anybody can look at the raw underlying data for that. And, um,
Jurgen Blitz go from the Max Plank Institute for allowing us to, to, uh,
(55:36):
to use it.
Lucy,
Maybe just that there's no way that we could name all of the people involved in
the community,
and there's no way that we could name check everybody who's developed these
techniques. We really are standing on the shoulders of giants.
So if you wanna know how any of these techniques were first developed,
(55:57):
then go read the stories in the literature.
Yeah. And guys, I I, I'm gonna say it's not just those early developments,
it's the, it's the application being put to them.
It's making it widely available. And so actually,
I always say you're both pioneers. So you may be looking on the of giants,
(56:18):
but actually you,
you standing with the Giants to make this accessible to everyone,
to put the applications to, to make it relevant.
And it's great having those initial things,
but to actually make it work and do the science is amazing. So on that note,
while I've flattered you both, uh, you can buy,
I can't remember what conference we're gonna meet at next,
(56:38):
but you now both owe me a drink.
I'd just like to say thank all of the listeners and I would encourage you to
watch a YouTube because there are some super cool movies in there if you want
to. Uh, thanks for listening to the microscopy today. Thank you to Kirk,
thank you to Lucy. And I just point out,
I think you've heard Judith Mann's name, you've heard Jeff Liman,
you've heard Howard Hess, uh, mark Elliman, y Schwab, Lucy, herself,
(56:59):
all guests on the mic so you can hear more about them in person.
And I'm bound to have missed someone off that list in the volume end. Uh,
but just the names that came through quickly, everyone, thank you.
Don't forget to subscribe, but do you know what? Thank you so much, Lucy.
Thank you so much, Kirk. You've been stars. Keep up. Thanks, Pete.
Thank you for listening to the Microscopists,
(57:22):
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.
Welcome to the Microscopists,
a bite-sized bio podcast hosted by Peter Oto,
sponsored by Zeiss Microscopy. Today on the Microscopists.
Today on this special edition of the Microscopists,
I'm joined by Lucy Collington at the, from the Francis Creek Institute,
and Kirk Sek from the Donald Danforth Plant Science Center.
(00:25):
And we discussed the waves that Vol Eem is starting to make.
For the last few years, we've called what's happening, a quiet revolution, um,
because it's been a bit under the radar with the Nobel Prizes in super
resolution night microscopy and in prior electro microscopy.
But I think we can say it's arrived now,
How enthusiastic and helpful the volume end community is.
(00:49):
You know, the reality is, is that it really depends on the biological question,
on which tool's appropriate.
And to someone just walking into this tomorrow may not be obvious.
So definitely reach out. I'm meaning it could be myself or Lucy, or even, um,
the Volume M community.
You can send off a little note and you'll get lots of feedback from people who
care.
And what publications should you go and look at if you're just getting started.
But
I think, uh, Lucy has, uh, put out a primer last year.
(01:12):
Yeah. So that was in nature, uh, reviews, methods, primers.
We were really lucky.
We got some key experts from across the Volume M pipeline
to write on it. So,
Oh, in this episode of The Microscopist,
(01:35):
hi, welcome to this special version of the Microscopist. So,
I'm Peter Oton from University of York,
and today I'm joined by Kirk Zeek from Danforth Center and Lucy
Collington from Crick. And we're gonna be talking all about volume e m,
and if you know a lot about Volume M you may learn a bit more.
If you know nothing about volume e m hopefully you'll learn how it may be
(01:57):
relevant to you. Kirk, Lucy, how are you both?
Very good, thanks. How are you?
I've got, I'm good, Kirk. How are you?
I'm great, Peter. Thanks. Thanks for inviting me.
Well, I, I'm, let's kick this off. Actually, I,
I'm gonna kick this off because this is a big thing.
If you've never heard about volume, em, this was actually, I think,
(02:19):
one of nature's seven technologies to watch,
which kind of means in the next seven years,
a Nobel Prize will probably be given for volume. Em, so,
so I've gotta ask first, Lucy Ka, who, who, who's gonna get that Nobel Prize?
Oh, You know,
we can't name names like that because we'd probably get it wrong and somebody
(02:41):
would be offended, but it was very exciting that it was in nature,
and it's, it's not just one of the,
named as one of the seven technologies to watch in 2023,
but it was also along things like the James Webb Space Telescope and
High resolution Radiocarbon dating and crispr.
And so we all like to call for the last few years we've called
(03:06):
what's Happening, a Quiet Revolution, um,
because it's been a bit under the radar with the Nobel Prizes in super
resolution light microscopy and in cryo-electron microscopy.
But I think we can say it's arrived now.
Yeah, that's a cool answer. And I,
I do think we will be ending up in that Nobel direction at some point.
(03:27):
If you think about those tech, you've compared them to the supervisor,
the cryo eem, Nobel Prizes have been awarded there.
You've talked about all the other things that are in those seven technologies.
They're making huge waves. And this is, this is for the biologist. This is it,
you know, this, this is a big thing,
is what we're gonna be seeing a lot more of, but it's not easy to get into.
(03:49):
So I'm, I'm gonna start first maybe asking Kirk, why,
what, what is vol, you mean?
Well, I guess to put it simply is, is that, um,
we wanna see structure inside of cells that are in a nanoscale.
Most things inside of a cell are smaller than the human eye can see by far.
(04:11):
And so we would use a technique called electron microscopy that can see these
small features. Um, the challenge is, is that of course,
cells formed tissues and tissues form organisms.
So looking at very small structures generally can only look at a small area at a
time. But volume M allows us to extend that to larger volumes,
greater than a micron, um, actually into, um, tens of microns,
(04:35):
hundreds of microns, and even millimeters. And of course,
a millimeter is 1000th of a meter for those who don't use that part of the ruler
very often.
I, I, I've thrown a picture up. So actually, if you,
I know most of you'll be listening to this podcast,
but there is a YouTube that will actually have some movies add some images that
will actually demonstrate, uh, what we'll talk about today.
(04:57):
So I do encourage you to go and just have a quick,
even if you skimm through it really fast, don't forget,
you can play it at one and a half times speed on YouTube,
so you don't have to lift and listen to me, which along so much.
But there's several different techniques, isn't there, Kirk, on this?
Yeah. Um, so just to extend what I was saying a few moments ago,
what we have to do with our samples is,
(05:19):
is that we have to fix them or immobilize them and, um, stain them with,
uh, chemistry that allow us to see them by the electron microscopes. Uh,
one example for scanning electron microscopy is where you will fix the
materials. You'll put these metal stains in them, you embed them in a plastic,
which you can section. And one of the most common techniques,
(05:40):
and actually the lowest cost of ENT entry is called array tomography,
where you can actually collect ribbons of sections and then reconstruct those
slices into a three D volume. Um, yes, exactly.
So you can do that both on the transmission electron microscope or the scanning
electron microscope. Exactly.
And so these techniques now allow you to use, um,
(06:04):
ribbons in the T e m or the s e m.
And what's nice about this particular approach, again, a rate tomography is,
is that the sections are, it's non-destructive,
meaning you can probe or interrogate those sections.
You can image them in different types of microscopes, even light microscopes,
and get chemical information using fluorescence and combine them back together.
Then when you're done, you can create these large volumes, which again,
(06:26):
is the characteristic hallmark of volume m uh, much greater than a micron,
oftentimes tissues and whole organisms.
Now, I, I'm sure we're gonna come back to this movie later,
but I'm gonna drop it in now because I think it's relevant. So if you,
if you watch it,
if you're listening to give you an idea of what this can do today,
uh, you, you, you forwarded a movie, go watch, but Kirk, so, so what,
(06:50):
come on. Describe, besides, look at my head blown up.
Yeah. So this is actually a single algae cell,
and what you're seeing is all the little planes that are,
that are acquired in these serial sections. And then of course,
we can segment out the structures inside that are biologically irrelevant in the
case on the left, the purple, um, for those who can't see as a chloroplast,
(07:10):
which is responsible for photosynthesis.
And the cyan colored structure is the nucleus.
So you can see all the structures, the texture,
and inside also the yellow is the GOGI body,
which involved the secretion in the cell.
So all of these are put together and captured in a full volume.
We have all of the context, the spatial information,
which you can look at other important cell structures like mitochondria, ex uh,
(07:32):
endoplasmic reticulum, whatever we can contrast,
we can go in and segment and then find their spatial relationships.
And just as importantly, pathologies.
'cause a lot of times we're looking at disease and we wanna know the difference
between a disease cell and a healthy cell.
That's cool. And, and I, I love your descriptions of the colors.
It reminded me of watching snooker listening to snooker on the radio when they
(07:54):
said they're screwing back from the red to go for the black ball. Next.
Just for me, it works Okay. Having listened to snooker for many,
for many, many years. So, okay, so that's what it can do.
Where did it start, Lucy?
Well, for me, it started back with, um, the worm.
(08:16):
So very small organism called C gans. Um,
don't try and spell it. So back when I was back at the L M C B in London,
um, I was collaborating with a group who wanted to do electron microscopy on the
worm. And they, you need volume.
Normally back then,
you were talking about imaging two D planes in a transmission, em,
(08:39):
but to go through something like the worm, you need to start moving into volume.
And, um, back in the 1980s, uh,
Sydney Brenner's team published the structure of the nervous system of the
nematodes, the elegance, otherwise known as the mind of the worm. And, um,
I was lucky enough to go to New York, to David Hall's lab at Albert Einstein,
(09:00):
and, um, have a look at the original data that they collected.
So they went through the entire nervous system of,
of the worm by collecting serial sections, about 10,000 of them.
Um, and then imaging them one by one in the transmission electron microscope.
And if you lose even just a few sections during that effort,
(09:23):
then you'll lose your connections through to the neurons. So in the end,
it was an effort that took around 10 years,
and I spent a week in a small room with all of the original printed
micrographs.
So David's lab was in the process of scanning them and putting them up online so
other would analyze them. Um,
(09:44):
How were the images captured? Were they photographs then? So
That was a transmission electron microscope, but it was onto film.
And then the film film would've been printed,
which was how we did it when I started as well,
which makes you very selective about what you take images of,
not like a digital camera where you snap everything.
'cause you have to develop everything and print it out. So these were the,
the real printed micrographs,
(10:05):
which have all of the hand labeled annotation on them to say which neuron is
which, and then following them from section to section.
So the work that was done then in 10 years would probably take
us less than two weeks now on the more automated systems that Kurt was talking
about. And actually, we are now in the era of the,
(10:28):
the mind of the fly.
So there are really big groups across the world in the US at, for example,
Janina and in the UK at the L M B and many other places,
gradually, um, increasing the,
the volumes that can be imaged and even more difficult,
reconstructed and analyzed.
And what we're seeing now is that the techniques are not just used in
(10:50):
neuroscience, but they're the increases in speed and, um,
resolution and analysis are pushing out through cell biology,
through plants and marine life. You've got some, um,
infectious organisms on the bottom left there and right out into the CL clinic.
So there's some beautiful work on, uh, breast cancer, uh,
(11:11):
samples from patients on the right.
Uh, go. Just, just, just, just just for clarity.
So obviously you were using photographic film when you started all those very
many years ago. Lucy, uh, no comments.
I think you've got I yeah, I, I, yeah, I can't talk. I mean, uh,
(11:32):
but, uh, obviously now you're using digital cameras.
Yeah. How much do the digital cameras cost? Well,
Digital cameras or detectors, depending on which,
Well, yeah. Okay. So if you're going tmm you're using a digital camera,
which is costing Kirk, uh,
(11:53):
Okay. Uh, d they can be 180,000, a hundred thousand,
a couple hundred thousand depending on the sensitivity of these systems,
I think. Yeah. Uh, they can be expensive, um,
or you can get one that's good enough, uh, for a bit less than that,
but it normally tens of thousands of range.
And so I'm just thinking, my camera's not gonna take a picture of this,
is it my phone? And, and if you think about the chips size, this,
(12:17):
the chip is tiny. The chip that you are looking at is almost speaker. The,
it's huge, isn't it? That chip, that's
Part, yeah, that's part of the driver of the cost. Um,
the more expensive chips to be able to collect the signals from electron or the,
you know, from the electron beam, um, you know,
smaller chips are less expensive. And of course,
on the phone the quality's a little different too.
And the sensitivity is, is remarkably for
(12:39):
Electrons. Yes.
Different on that. So, so Lucy, you've finished on talking about neuroscience.
Uh, what, what sort of samples are you generally looking at yourself?
Oh, could be anything.
So we use volume em for most of our projects now because as,
as we say, life happens in three D. Um,
(13:01):
the image on the bottom left there is actually from, from the team, from a,
a collaboration with Eva Frick Freckles lab and Serge Moav,
I think I mentioned it before. Um, that's on Zebra Fish.
And looking at toxoplasma in the Zebra fish brain and trying to localize it as,
as the, um,
fish mounts an immune response to see if you can use a zebra fish as a model for
(13:25):
disease states, because they're much easier to image. Um,
you can get a lot more of them than you could with a mouse model.
And what would the typical disease states be that this would then be used for to
study, which would bring it back to human relevance as well.
Um, we've used Volume M to look at h i v infected cells, uh,
(13:45):
mycobacterium tuberculosis in cells, uh,
that one's toxoplasma. We look at plasmodium, which causes malaria. Um,
we are looking at tumor tissues at the moment and how the metabolism of
tumor tissue, uh, changes.
Its kind of very heterogeneous, even within a single tumor.
(14:09):
Um,
Yeah, no, no, it's, it's a huge variety. And that's just run on. Yeah,
We run around 60 projects a year, and each of them will be Yes.
And most of them will be Volume M and each of them will be on something,
something different. And Kirk, again, will be working on, uh, different
On get Yeah, on getting to, you've got 60 projects. 60 projects,
(14:33):
Sorry. A hundred projects a year is 60 teams.
Oh, the data, yeah. We'll come to data later on, but this,
that any,
the time on the microscope to do this is huge because it's not the fastest
experiment in the world. And then the day, how many instruments do you have?
We've got six electron, six big electron microscopes, so different flavors.
(14:56):
So we can pick and choose them according to what's needed for each project.
And then we have X-ray microscopes and light microscopes,
and a few benchtop microscope, uh, electron microscopes,
which are small enough that you can put them in the boot of your car and drive
them around if you wish.
So you've got a bassis, all sorts of electron microscopes. I, I love the idea.
And Kirk, I I, I noticed on this we've got plants as well,
(15:19):
which is kind of where you major, isn't it?
Yeah. Uh, um, I, I consider myself, uh, um,
biological and soft material. So I would,
I would go beyond that just in my career.
But now I'm focused on plants which have special challenges, uh, and,
uh, um, to get volume m to work. Uh,
one of the challenges with volume M is, is that you're, you have samples in a,
(15:41):
in a non-conductive resin often. And so part of the,
the efforts of the, of the, the, of the,
of the community is to find protocols that help improve that.
And one of the efforts I have is, is, uh,
been making sure that plants get enough signal out of them and enough
connectivity that we can get high quality images.
(16:01):
Okay. I, I kind of get it what Lutie said, you know,
looking at how the parasites,
how infections actually affecting the infrastructure of the cells and where it's
been found in the cells, and how it's changing. What's the use in plants,
why are we interested?
Ah, okay. So we'll go a little bit further. Um, you know, part of the, the,
the thing about the plants that I was talking to is it made a little bit harder
to interrogate them, but there's a lot of things. So plants, just like, um,
(16:25):
other organisms have development. So we, we do look at a lot of development,
how the root grows, how the chute grows.
The other thing we look at is how pathogens are invading.
So we can go in and inside of the plants and inspect how pathogens are causing
disease, how the plant responds to it. Those are just, um, some simple examples.
The other thing is, is that, of course,
there's phenotype variations with crops that are resistant to drought,
(16:50):
for example, or, you know, climate change.
So we're trying to understand what cellular, um, improvements that a plant can,
uh,
have and subcellular using volume M we can actually learn from that and
help develop improved plants.
So, so I, I, I have a question for both of you actually.
(17:11):
Why isn't everyone doing it?
Who's gonna answer that one first, but
I'll definitely make a comment on that too.
Go Lucy.
Uh, because the instruments are big, uh,
like a few tons each, they're expensive.
They need very special rooms to house them. So at the crick,
(17:32):
we're actually in a,
in an extremely challenging environment to put those microscopes. So they're,
they're all on anti-vibration bases to remove, uh,
physical and acoustic vibration.
And then they all have six-sided shielded boxes to remove the electromagnetic
fields coming from the train stations and the tubes passing us. Um,
(17:52):
I don't think it's that much more difficult to train somebody to operate some of
these instruments than to operate a high-end light microscope nowadays.
I think people sometimes think that's the, the issue.
But the, the hardest thing, I think is the sample preparation.
'cause we use lots of toxic me metals. Um, we have to slice the samples,
(18:14):
which is very difficult. Um,
saying that's the thing that stops most people from implementing it in their own
labs.
Okay. Cook.
Yeah, I would just add to that, I agree with everything that Lucy said. Um,
you know, the,
the reality is a lot of it is about awareness is probably one of the reasons why
we both are here today. Um, you know, it's out there,
(18:36):
there's a few groups that have been working,
and the good news is that they've actually solved a lot of the problems that
made this not as easy to adapt.
I think things have changed and, um, now I think our efforts are to, um,
help educate, um, other laboratories that could quickly, uh,
plug in and start using this, this type of technology.
That's, I, I'm gonna jump, because if you ask, you know,
(18:58):
if you'd have gone back five, even just five, six years ago and said, Pete,
user comes, Pete, I want to get into this, I'd have gone great.
But you're gonna have to commit a huge amount of resource to it. And it's,
it's staff resource because Lucy,
you touched on all those projects.
How much data is coming off these microscopes?
(19:22):
Um, per microscope on our standard volume electron microscopes,
somewhere between 50 and 250 gigabytes per day.
But the new systems that are coming through will go into terabytes per hour.
So at the, at for a small sample,
then we can do some manual low throughput
(19:45):
image analysis,
which then allows us to interpret what we're seeing in a normal sample versus
a drug treated, something like that.
But you very quickly get into the regime where you just
can't analyze all of the data that you have
manually. So that's, that's where you need to move to automated techniques.
(20:09):
Okay. So, so that, that, so, so obviously you can share data.
Yeah.
Okay. So, so I dunno who wants to talk about, I say this is, sorry,
coming at a random order with this,
but I think it's important because it's not just a sharing your data, it's data.
How long it would it have taken you to analyze, to segment the data,
(20:29):
uh, six, seven years ago?
So we use the nucleus of one cell as an example,
and we still have, we,
we have some algorithms to pick out the nucleus,
but we still do a lot of segmentation manually because the algorithms don't work
on every single cell and tissue type that you, uh, challenge them with.
(20:54):
So for a single nucleus in a single cell,
it takes one of our experts about a week to segment it.
So it is just not sustainable because quite often we're imaging pieces of
tissue, and if you're interested in the nuclei,
there might be 50 or a hundred nuclei, and then you are into, you know,
eight years' work, doing nothing else. Um,
(21:16):
data sharing is extremely important because we just can't,
even if we're using automated algorithms to analyze the data,
they're normally only looking at one structure.
And you can see in the images that Kirk and I have up that each image,
so this is three D cut through a cell with the nucleus in the middle,
we've got mitochondria and endoplasmic reticulum and other organelles.
(21:38):
So if your research question is about mitochondria,
which power the cells,
and you make three D models of all of the mitochondria in the cells,
somebody else might be able to use the same data, especially if it's a,
a control cell and segment out the nucleus 'cause that's what they're interested
in. Or the endoplasmic reticulum, which is making proteins.
(21:58):
So the data's so rich,
one team won't usually use the whole lot, even if they're generating it.
And the empire is the electron microscopy public image archive,
which is held up at the European Molecular Biology Laboratory,
European Bioinformatics Institute, or E M B L E B I, as it's much easier to say.
(22:20):
Um, so that was set up,
first of all to hold em images from cryo transmission
electron microscopy, um,
but now takes depositions of images from all different types
of volume, em and x-ray imaging. And that means that the,
(22:40):
the data gets out to other scientists, not just, um,
biological researchers who might wanna reuse it and reanalyze it,
but also computational scientists who might want to develop new algorithms,
analyze that data. And empire is growing very quickly. You can see this from,
I dunno, which way I'm pointing, uh, behind Pete's it says two petabytes.
(23:02):
So they reach two petabytes worth of image data in 2022,
and they're almost up to three petabytes now.
And I'd imagine that's not all the data that's been generated,
that's just what's been uploaded.
Yes. And normally,
normally data sets that are associated with a publication or have been
produced as example, uh,
(23:24):
data sets for a specific use like image analysis. Um,
so there's a big push in the community to try and increase the number of
depositions, particularly associated with,
with papers so that other people can really dig into the raw data.
So, so this is cool. So, so it's gone from,
from what my biggest hold up with this was was the speed of,
(23:48):
uh, analysis. It wasn't the acquisition, it was the analysis side. That was the,
that was the rate limiting step, which is getting faster. Uh,
you mentioned the sharing of data, uh,
we've talked about the different techniques or some of the techniques we haven't
got there fully yet. I'm gonna challenge you on that in a minute,
and that'll probably be cut, come and get with both of you at that point.
(24:09):
But I think a big thing when you're sharing data,
when you're learning new techniques as a community,
and you mentioned the community sharing data,
so how have the community got together?
So there have been different groups of the community that got together at
different times, but the,
the current community effort with the nice logos that you can see behind us
(24:32):
for V E M that really started in 2019, and again,
actually Empire and the E E M B L E B I were really important in that.
So I was up in Cambridge at their scientific advisory board meeting
and over dinner, which is where all the good ideas happen. Um,
I was chatting to AAN Peran and Herod clx,
(24:56):
uh, as Herod at the back in the green T-shirt and Luigi Martino
from the Welcome Trust. Perfect. Um,
so a lot of the scientific advisory board and a lot of what, um, um,
the molecular cell structure team were focused on was a huge field of protein
structure and structural biology. And I was there for cells and tissues.
(25:21):
So you're just starting to,
to see the deposition of images of cells and tissues from em, the
Outlier, I presume at this point?
Yes.
There's a picture actually,
Lucy sent in and she's right on the end just showing just how much of an outlier
she was to rest of the structural biologists to use
I to squeeze into the public archives. Um,
so over dinner we were talking about how quickly this technology
(25:45):
is having an impact in, in biology, and actually how often it's,
the data is included in publications, um,
but you don't necessarily know it's there because EM is has been around for a
very, very long time. Unless you dig into it, you don't realize it's volume ym.
And from that came the idea of the first community meeting,
(26:06):
which Luigi organized and hosted at the Welcome Trust in 2019.
And we had around 60 experts.
So that was end users biologists for whom it was really important. Um,
we had Ollie Flory talking from, um,
Cambridge on how it's being used in cancer research. Um,
and then we had the microscopists, the imaging scientists,
(26:30):
people from companies involved in Volume M data scientists.
And then in 2020, obviously everything had to move online.
We had a second town hall meeting,
and from that we had the formation of six working groups.
So behind you,
you've got pictures of all the co-leads for the volume e uh,
(26:51):
working groups. So we have six working groups, community outreach, sample prep,
data training, and infrastructure. Um,
and I should mention actually, that infrastructure was chaired,
first of all, um, by Charlie Wood and Chang Chang,
and Paul and I have recently taken over, but it's,
(27:12):
it's these people who have volunteered their own time.
It's not part of their day jobs to organize the working groups.
And that at that point there were about 70 working group members,
uh, distributed across the groups. It's now more than 80,
and they, in two years,
(27:32):
they've delivered huge amounts in terms of
connecting the community resources for the community,
um, telling people what volume EMM is and how you can use it.
So trying to increase its impact throughout the biosciences
and the clinical sciences, um, and really putting it on the map, which helps,
(27:56):
helps science because you are, you are, um,
letting everybody know that the technique is there and that it can reveal
new insights into important biological questions. Um,
but also pushing the development of the technique forward as it gets better
known and attracts funding. And behind you,
(28:18):
there's a map of the distribution of the current, um, group members.
It's truly global. It it is in every con,
every every practical continent.
Yeah.
But there's a huge concentration of people in the UK because it started in the
uk. So after that,
so that first meeting was also organized with Paul Ard from Bristol and
(28:42):
Herard, aan, Paul and I, but the, the working group members
really nucleated in the uk and then the last year or so has been really pushing
out to try and be more and more international because you can see there's
volume, electron, microscopists everywhere, and it's all about networking.
And that's where Kirk comes in.
(29:04):
I was about to say, so this, this, this is, so, uh,
Chan Zuckerberg comes into so many of the different podcasts for many different
reasons. Uh, so Kirk, the community side, i, it,
it's moving on and it's moving fast and it's still open to everyone.
Yeah. So, um, I actually, I was in, um,
(29:24):
I was actually in an industry, uh, once I came back into academia, um,
once I learned about the volume group, and of course we work with all of this,
these, uh, folks, um, that are in these pictures, uh, all the time. Um,
uh, I joined the group and, um, we were always interested in, you know,
there's only so much free time we can dedicate towards it as a group.
(29:46):
And I think it was really, really, really beneficial.
But we recognized if we had some funds,
some of our initiatives could really accelerate this forward. And so, um,
as a group, we decided to submit an application, uh,
for the Chan Zuckerberg initiative and, uh,
specifically to build resources and expand our global community and to emphasize
(30:06):
outreach and dissemination. And now we have funds to do that. Um,
actually the funding just started a, a few months ago or, and we've have a team,
a program manager, a community officer, and resources to help, um,
develop some software. Uh, and um,
this is very exciting for us because now we can really accelerate it,
have higher quality material, um,
(30:26):
to teach people the nuances of the sample prep,
which Lucy Rudley said is one of the bigger challenges and so forth.
So what's very, very exciting. I mean, I, there's many examples of,
of what we're trying to do here,
but right now we're just getting the funds to kick that off.
And, and this isn't a closed shop, is it? You, you've got these groups,
these working groups, you're up to 70 people,
(30:48):
but I presume it's still open for anyone to join.
Definitely it is being as open as we can possibly make it the whole way through.
So all of our documentation is, um, available to everybody.
Everything's shared on, on Google drives. Um,
the community's really friendly and I think it's because
(31:10):
ensemble preparation is so difficult and so long that as electron microscopists,
we don't want everybody to go through the same pain as us. So ever,
ever since I've been an electron microscopist,
everybody always supports each other and helps each other with protocols and
tips and tricks.
And I think the same thing rolls over into the community as well.
So if anybody is interested and wants to join,
(31:32):
we would love to have you as part of this. And there's,
you can get in touch through, there's a Volume EM website, um,
we have a Twitter page.
I've, I, I've gotta keep up with this. So you've got the,
We have a, a list server,
which all of the details of the list server on the Volume EM website as well.
(31:52):
So you can join that and get news from us. And,
And there's CHANBURG links as well. Kirk,
I, I'm sorry, uh, that comment, but, uh, what was your question again? Uh, Pete,
Uh, so it's not anyone can join.
Oh yeah, sure. And actually I wanted to actually add that. And, uh, a couple,
uh, really important things. Um, one of them and is,
(32:13):
it's not just the labs that are doing the work or running the,
the core facilities. Um,
it is also we're bringing commercial entities to listen in and what our pain
points are. 'cause in the end,
we're not building electron microscopes with the vast majority of laboratory.
So we depend on them giving us the tools we need to,
and they've been listening quite well, but as a community,
I think we can improve that.
(32:33):
So I wanted to actually add that as one of the groups. Um,
the second part of it is, and I actually wanna emphasize this,
for those who are listeners on here, um,
if you go to volume m, do org two E,
so V O F V o uh, l u m e
e m.org, um, you'll get the, all the information you need.
(32:56):
Sorry, I have to be able to read that. Uh, Pete, that's your fault.
Your head was in the way of the words. Sorry. Um, but uh, yeah,
so you'll get our, our Twitter account links and, and uh,
Facebook and everything else that we're on, um, in there.
Do you wanna just repeat the link?
Yeah,
www.volumeem.org
(33:19):
g uh, and if you go there, you'll actually get a great start. And not only that,
as the Chan Zuckerberg Initiative, uh,
really starts taking off when we have our new videos and our resources all
consolidated, um, you'll be able to keep up with everything we're doing. Okay.
One comment, that map was beautiful. The reality is,
my guess is we're only reaching about a third of the existing labs already.
(33:41):
And of course we'd love that to double. Um, I just, you know,
this is no actual data to support that, but I know there's a lot of other,
my colleagues and friends that aren't there,
and we're gonna work to try to bring them into our, our group.
I, I, I've gotta say, so for those listening, you may not appreciate this,
but there,
there was an image here of the CHA who was awarded for the CHANBURG initiative,
and, and there's a big call out to lots of people who I'm familiar with,
(34:03):
but I noticed that Kirk is twice as big as anyone else on this picture.
I, I thought that that's vanity, isn't it?
I, I,
I gotta tell you that that was actually following the format of the Chan Zucker
Park edition. Apologize, uh,
everybody who was l p I got the big head, sorry.
(34:24):
I definitely would've been happy to be, uh, just like everyone else thing
Is
Well deserved for the work you have to do, putting, you know,
the paperwork together for these grant grant applications.
That it, yeah. Okay. So this is sounding about a great community,
everything else, but
what's the biggest challenges that you're facing now?
(34:46):
I dunno, who wants to pick up that first? I,
I'll just make, um, and I think you've already touched upon this,
but you know, we've gotten really good at collecting a lot of data.
It's amazing that every minute I can get a slice, you know,
in a thousand minutes I can get a thousand slices that are, you know,
oftentimes pretty close to registered. But it is,
(35:09):
as loosely alluded to earlier, it's a bit like drinking out of a fire hose.
So I think one of the things is, is that, you know, this, you know, we,
we need a pain point to drive the innovation here. So in my view,
one of the things will be, um, faster segmentation volume.
Being able to manage all of that data is one of the key challenges.
And that's in my perspective. There are many others,
(35:31):
but that's one that I think about every day is I have a vision of so many experi
experiments. Um, I can do lots of smaller things now,
but I'm moving towards the bigger,
and I can't wait for the tools to become more efficient, um,
and make it even faster so that, um, maybe smaller labs can, uh,
even get bigger benefit.
Okay, Lucy?
(35:52):
Yeah, I agree. Uh, the data side has to change and I think,
yeah,
so your background now is showing a figure from a paper called The Mind
of a Mouse.
And I love this figure 'cause it makes it so easy to communicate the,
the next steps in volume, electron microscopy or something else.
But we assume at the moment it'll be volume M. So, um, I said earlier that we,
(36:16):
that the mind of the worm has been mapped at least,
uh, from some worms. And now the connectomics community,
which focuses on generating these, um,
maps of the connections of neurons. Uh,
the connectomics community is working on the,
the fly brainin and has started delivering fly brains,
(36:39):
but the next challenge is gonna be the mind of the mouse.
And if the challenge of mapping the mind of the worm was the width of one
airline seat,
then the challenge of mapping the mind of the fly is
the, uh, size of six and a half Boeing, 7, 4, 7 airliners.
And by the time you get to the mouse,
(37:00):
then you're talking about the distance from Boston to Lisbon. So the,
the scale increase in challenge across the entire
pipeline in sample preparation in imaging speed and imaging volumes,
and then in data analysis is almost unimaginable in unimaginable at
the moment. Um,
(37:21):
and the effort to get the mouse brain will deliver us
the next generation of imaging technology.
And, and so I, Jeff Liman, uh, I dunno if you're, listen, Lucy,
I know actually thank you for being an avid microscopy listener, uh,
on your tube or on the bus on the way home.
(37:41):
I get messages from Lucy every now and then saying,
I can't believe you just said that. That's really funny. Or whatever else.
I can't believe someone else just said that. That's shocking. Uh,
so you must have heard Jeff Liman and talking about similar type of work as
well. Uh, you also sent these. Yes.
So the, I think this is another big challenge. So the, the delivery of the,
(38:04):
the mind of the mouse is one, and the,
the first effort will be delivering the, the structure of the,
the connections between the neurons.
But then you have interesting areas here in
correlative and mul multimodal imaging.
So that's where you connect different types of imaging together,
(38:27):
like light microscopy, x-ray microscopy, iron microscopy,
and electron microscopy.
And you start to layer different types of information on top of each other.
So that might be showing you where the molecules are in the brain or
where chemicals are in the brain,
um, um, it's, uh, like epilepsy or in brain tumors.
(38:52):
And when you add those layers of information,
then you start to really get to the, the differences in health and disease.
But we,
we can't at the moment image across all of the scales from atoms
or even macromolecules whole proteins through to organisms
on a single sample. But you can see in these two images,
(39:14):
so on the left you have a nature methods piece highlighting in situ
structural biology, and that's a fairly new field where people are imaging
native protein structures inside cells using beautiful
cryo samples. Um,
so all of these molecules encased in ice.
(39:35):
And then on the right hand side you have volume correlative vitamin M.
So that's where we link, say, light microscopy with volume M.
And you have a really nice example there of spatial transcriptomics
where you can see which genes are being expressed in which cells and the,
the cells you can see in Volume M.
(39:57):
And these experiments are extremely difficult,
where you are really identifying molecules in the context of,
of the structure of cells.
So linking those two communities is really a grand
challenge and being able to move seamlessly across the scales, um,
that's a tough one.
(40:20):
Okay. And moving on,
what other initiatives are there in the community? I,
we've heard about Chan Zuckerberg, we've heard about yourself.
Are there other initiatives? Are there other groupings that are closely related?
You can speak about the, the, the Gordon Conference that's coming up. So it's,
(40:42):
okay. So, so this, so how go,
what conference would you go to to learn more?
Yes, so Kirk already gave that one away. Actually there are quite need
More details.
We,
we have to give a plug for the first volume electron microscopy Gordon Research
conference, which is gonna be chaired by Paul Riccardo and myself. Um,
(41:06):
and then that will happen this year in July in California.
And then in two years time when it will be chaired,
I think by Yanick Schwab and Kada or Ryan,
who are both like key members of the volume EM and volume Clem
communities as well. So that's gonna be a lot of fun.
The registration's still open and you just search for volume M G R C and that
(41:29):
should come up. But there are other key conferences that have been going on for,
for a while, including the Zeiss three D L M EM meeting,
which was first organized by Chris Gerin, um,
at the I b and Gent.
And now moves between there and with, uh,
Saskia Lipins and Anna Kramer,
(41:50):
and then goes to E M B L Heidelberg with Yannick and then will come to Crick,
um, with us.
And there are meetings spring up everywhere and there are dedicated
set volume EM sessions and a lot of the big micro view conferences. Now,
It's interesting, if you think about the,
the one that's started with Chris and then moved to Yannick over to, uh,
(42:13):
e em on the note coming over to us actually swap between themselves and then now
into London as well with yourself, Lucy, which is sponsored by Zeis.
Uh, but you know, they're not the only players on the scene, but I've gotta say,
I think all the companies have played quite a critical role in the development
of W U E M. Uh, you know, this is not just an academic endeavor, is it,
(42:36):
you know, how important are the companies for this? And actually I,
Kirk you come from a, a commercial background, you,
you flitted to commercial and then back into academia.
What's your thoughts on the role of companies in this?
Um, okay. I would say that it's key.
And because of my hybrid commercial academic background and working in a company
(42:59):
that was actually developing these techniques, um,
it's huge in, in fact, I would make a comment,
even things like the mind of the mouse, these key thought leaders,
these lighthouse customers that are working on these technologies actually give
us a problem to solve. And then once we, uh,
when I was in that capacity, I would say at the time, um,
(43:22):
once we try to solve their challenge,
it'll cascade down for the rest of the group. So it is,
is instrumental of course. Um, uh,
all of these companies are excited. I think about this, um, you know,
recent attention to volume e m because they've been putting energy and
developing tech detectors and, you know, automation of the software.
So this becomes automated. By the way, I will make a comment.
(43:45):
They have done a great job on the automation.
There's still more room for improvement,
but the fact that I don't have to have an operator to sit there for three days
in a row catching slices is huge.
That is because of what the manufacturers have done.
I would say I want manufacturers to do more.
I want them to think about the downstream side of things and how do we take all
of that data and if they can engage on that, then we have like a full workflow.
(44:06):
That's my, my comment is it's critical. That's why we want,
and the volume community to keep them engaged. They are doing that. Um,
but I think, um, it's exciting.
Lucy, any other comments or thoughts on that side?
Oh, just to agree that the, yeah,
the manufacturers are absolutely critical in this because the,
(44:28):
these kind of instruments, you can't build them in your own lab. You,
you can set the challenge and you can have some ideas and you can make some
modifications, but, um, it really takes the,
the big
companies and that the experts they have in them to design the systems that are
gonna answer the science.
(44:49):
So I'm gonna give a shout out to Zeiss at this point who actually kindly
sponsored this podcast, but not because of that,
that first meeting in v I B was,
was put together from Zeiss.
They brought together not just the leading electron microscopies,
but they brought together some of the biggest light microscopies across the
(45:11):
world and put this all in the same room, you know, and, and,
and then into groups that brainstormed and then came out with ideas. I,
and there were some big names in there, uh,
that wouldn't, that would not naturally come together and talk. Uh, but I,
I would say that's where the community, well, for me,
(45:32):
that's where the community really started because that put people together.
It made that those connections. And, and Lucy, you were there at that meeting?
I wasn't at that one actually. I, I was,
I was at that one. Oh my God, how come I wasn't at that one?
You weren't at that one. That's
The thing. You have the community seeding in different places.
So C D A R as well in the US had, uh,
(45:52):
held what he called a micro lab and brought 50 experts together to discuss what
needed to be done to, to push the field forward and put to answer science.
So you get these different nucleation points all happening around the same time.
And now I think, you know, everybody's come together, which is
amazing.
Can I make one other comment? Um, uh, and this is really key. Um,
(46:17):
one of the drivers that will ensure that we can continue to move forward is not
only the technology development,
but it's also the realization from the funding agencies how critical, um,
having this, these types of platforms and, and um,
and enough institutions that we can continue to,
to grow what's possible and and improve the science because we have all of this
(46:37):
additional insights that just doesn't come across in the two dimensional.
I agree. And there aren't that many facilities that have,
there are quite a few facilities that might have one volume EM modality,
but there aren't many that have lots.
And what we're seeing now is the bigger your sample,
the more you need to tie together the different volume em imaging modalities to
(47:00):
work from bigger scales to isolate smaller pieces,
to image at higher resolution to really get down to the subcellular level.
So there's only a few places around the world that have
most of the volume yen modalities in one place to make them really flexible and
answer, able to answer any biological question.
(47:23):
And the other really good thing about the community,
I would say it's a gender balance within it is pretty good. You know,
if I go back to those early meetings and think about Judith Erman, you know,
leading on one of the tables, you've got yourself Lucy, you've got Jemima,
you've got Raffi, you've got this whole community, uh, uh,
which I think is actually also fairly unique, you know,
(47:46):
to have such a fairly gender equal community.
I think that's quite something the community should be proud of and carry on
embracing as well.
Yes,
and I remember that did tend to be the case even back when I first
got into electron microscopy,
which is really unusual and I'm not sure how that came about. But then, um,
(48:09):
we need to work on our other types of diversity and make sure that everybody
has the opportunity to get into this kind of high technology so that we have the
best minds from everywhere working on it.
So I have two more bits before we wrap up. And the first one is actually,
if you want to learn more about this, there's quite a lot of, uh,
(48:30):
information out there. Uh, so there's different online resources. I think Lucy,
you involved in some of these?
Uh, this is actually the training group. Um, so this is Erin and Raffa and,
um, everybody in that working group who've built some, um,
slides that show how you build toolkits.
So if you're just getting into a rate tomography or focused I N B S E M or
(48:54):
serial block Face s eem for the first time,
there are some slides that show you things that you should get together.
So for a rate tomography there, you've got an eyelash duck to a cocktail stick,
which you need to move your sections around your boat of water when you cut
them on your diamond knife.
Do you use students to make sure you've got enough eyelashes because you're not
from eye eyelashes?
(49:14):
So it's always voluntary. We never make anybody give us their eyelashes. Um,
but different people have different, uh,
eyelashes and some are best than others. So Martin Jones,
who's actually in charge of, um, microscopy prototyping in our team,
which is much more computational and engineering based,
apparently has extremely good eyelashes.
And I think Anne's used hairs from her dogs in the past
(49:39):
as well. Apparently d particularly good.
I can't wait to see Martin. Next time I see Martin,
he's gonna wonder why I'm like this close to him. He
Has still got eyelash
Intensely, but not quite making eye contact. Just look at his eyelash.
He's gonna, he's, he's gonna,
Are you gonna try to grab an eyelash or you just gonna look at him? Peter,
No treat warning.
(49:59):
So you don't pull them out, you wait for them to fall out and then collect them.
Health and safety first.
Mine don't fall out, get away and mine are useless. So keep off. Uh,
and there's also a load of movies that aren't there, so there's,
there's clips and movies that you can go, where can you find this information?
So this is all on the Volume M website and there's a page for training movies.
(50:21):
So again,
this is the training working group who've been making some fantastic videos.
But Kirk, you're gonna be carrying this on, aren't you?
Yes. Yep. So we'll be adding to the, uh, the, the great starting content.
There's now and, uh, yeah, again, volume em org.
Yeah, O r g. You say that so quick, it does sound a bit like orgy.
(50:46):
That's, that's really on you to be honest with you.
It really isn't anyone listening. All they're gonna hear is, or G O R G. Just,
just, anyway, I'll
Speak more slowly.
No
One's gonna forget the address. Now
I fine. Final question. Uh, I'm gonna start with you Kirk on this.
(51:08):
What's the best public,
if you are coming into em for the volume Em for the first time,
what would be the best publication to read?
Oh, um, actually I'm there, there are so many out there. Okay.
I'm just gonna make a comment to you.
I think you made the comment in the beginning.
There are many different techniques and it really depends on what you might have
access to in your laboratory, but I think, uh, Lucy has, uh,
(51:31):
put out a primer last year. Um, and maybe Lucy,
you can describe a little bit about where they can find that. Um,
which would be my recommendation as a first source because it kind of
consolidates these together and gives you the, the source literature, uh,
that some of it's based on.
Yeah, so that was in nature, uh, reviews, methods, primers,
um, at the moment it's not on the open source, um,
(51:55):
but Chris is getting it up onto you at P M C,
but you can drop me an email if you'd like a copy.
And that was bringing together, we were really lucky,
we got some key experts from across the Vol m Pipeline
to write on it. So it's, I would go there.
It's written for people who are just coming into volume electron microscopy to
give you an overview of the techniques and the applications and it mentions the
(52:19):
community. We just have a piece out with Nature Methods a comment as well, um,
which was harod his idea, um,
but then written with Paul and,
and with all of the, um,
working group co-chairs that describes what we've been talking about today,
how the community came together and how you can get involved in all of the
(52:41):
resources that are available.
Is there any other resources you go to publication wise for people to read,
to get a grasp of it?
One of my favorites, I have to say is, um,
Han Shoe's paper with Harold Hess and Song, working on the,
the sample preparation where they,
(53:02):
they describe the next generation of focused I N B ems,
which they call Enhanced Fibs. And, and Shannon, that the team,
uh, spent 10 years working on these systems to make them more stable
and to run for longer time periods. And then Han lined up,
uh, with Harold, 10 of them in a row to run, uh,
(53:25):
pieces of fly brainin in parallel to get to bigger volumes.
And it's just the most astonishing piece of work.
It kind of references what happens in the semiconductor industry where
you really do like line up those iron beams to look at, um,
computer chips and things like that, but porting that over to biology.
(53:47):
And I think it's an indication of, um,
across the entire pipeline how we might, um,
move forward into more quantitative imaging at Banana Scale.
Yep.
And I, I'm just gonna round off, I,
there are so many different techniques,
strategies for doing volume, em,
(54:07):
that I think the resources that you've spoken about are the,
are the starting point for anyone wanting to do it or to find someone
in the community and just talk to them. Yep.
Sometimes often the best way is just email, phone, zoom,
whatever it is. Yeah. And just, you know, pick an expert. I,
it's a really communicative community that is always happy to help and
(54:32):
welcomes the most dumb questions.
So thank you for tolerating my dumb questions today. Uh, and I
Was gonna say there's no such thing as a stupid question, but
Yeah. But then you've met me, so thank you Lucy. I, I,
Kirk knows me all too well about
Yeah, you're pretty tamed today.
(54:52):
Yeah. Professional mode. Yeah. Uh, so Kirk, Lucy,
uh, is there anything else you'd just like to add before we finish?
I should give you one last word.
Is there anything I've missed that you'd like to say?
Pete two, Pete. Two things I actually wanna double click. Um, you know,
the reality is, is that it really depends on the biological question,
on which tool's appropriate. And to someone just walking into this tomorrow,
it may not be obvious. So definitely reach out. I mean,
(55:15):
it could be myself or Lucy or even, um, the Volume M community.
You can send off a little note and you'll get lots of feedback from people who
care. Second thing is, is that nice little climate demos sample?
I wanted to make sure that it's clear that that actually is on Empire.
So anybody can look at the raw underlying data for that. And, um,
Jurgen Blitz go from the Max Plank Institute for allowing us to, to, uh,
(55:36):
to use it.
Lucy,
Maybe just that there's no way that we could name all of the people involved in
the community,
and there's no way that we could name check everybody who's developed these
techniques. We really are standing on the shoulders of giants.
So if you wanna know how any of these techniques were first developed,
(55:57):
then go read the stories in the literature.
Yeah. And guys, I I, I'm gonna say it's not just those early developments,
it's the, it's the application being put to them.
It's making it widely available. And so actually,
I always say you're both pioneers. So you may be looking on the of giants,
(56:18):
but actually you,
you standing with the Giants to make this accessible to everyone,
to put the applications to, to make it relevant.
And it's great having those initial things,
but to actually make it work and do the science is amazing. So on that note,
while I've flattered you both, uh, you can buy,
I can't remember what conference we're gonna meet at next,
(56:38):
but you now both owe me a drink.
I'd just like to say thank all of the listeners and I would encourage you to
watch a YouTube because there are some super cool movies in there if you want
to. Uh, thanks for listening to the microscopy today. Thank you to Kirk,
thank you to Lucy. And I just point out,
I think you've heard Judith Mann's name, you've heard Jeff Liman,
you've heard Howard Hess, uh, mark Elliman, y Schwab, Lucy, herself,
(56:59):
all guests on the mic so you can hear more about them in person.
And I'm bound to have missed someone off that list in the volume end. Uh,
but just the names that came through quickly, everyone, thank you.
Don't forget to subscribe, but do you know what? Thank you so much, Lucy.
Thank you so much, Kirk. You've been stars. Keep up. Thanks, Pete.
Thank you for listening to the Microscopists,
(57:22):
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.
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