In this episode of
Expert Insights for the Research Training Community, Dr. Bridget
Carragher, co-director of the Simons Electron Microscopy Center, discusses
the current and future applications of cryo-electron microscopy, or
cryo-EM, and training opportunities for researchers involving cryo-EM.
The original recording of this episode took place as a webinar on May 14,
2020, with NIGMS host and director Dr. Jon Lorsch. A Q&A session with
webinar attendees followed Carragher’s talk.
Recorded on May 14, 2020
Download Recording [MP3]
Welcome to Expert Insights for the Research Training Community—A podcast
from the National Institute of General Medical Sciences. Adapted from
our webinar series, this is where the biomedical research community can
connect with fellow scientists to gain valuable insights.
Dr. Jon Lorsch:
Hi everybody, looks like it’s right about time to start. I’m Jon Lorsch,
director of NIGMS.
I want to thank all of you for attending this, which I think is our
fifth webinar series for trainees. We started this series in order to
support trainees—both those supported by NIGMS and also others
throughout the country and throughout the world—during these difficult
times where many of you are working from home, aren’t able to go into a
lab and do what you would like to be doing, and we’re trying to find
ways to make this time productive and interesting as well for you, even
though you can’t actually be in a lab conducting science.
We also are going to have a question-and-answer session after Dr.
Carragher gives a fairly brief presentation, where we want to get
questions from you and have Bridget answer those. So thank you very
much, Bridget, for coming.
Let me just introduce her. Bridget Carragher was born in South Africa.
She had her education at the University of the Witwatersrand in South
Africa in the field of physics. In 1982, she graduated from Northwestern
University in Illinois with a master’s in physics and started her PhD at
the University of Chicago, where she defended in 1987.
She has had a variety of private-sector and academic positions,
including as the chief operating officer of NanoImaging Services, a
company that she cofounded, and as a professor at the Scripps Research
Institute. Together with Clint Potter, she is the director of the
National Resource for Automated Molecular Microscopy or NRAMM. She’s the
director of the Simons Electron Microscopy Center at the New York
Structural Biology Center, in New York City, and the principal
investigator of the National Center for CryoEM Access and Training,
which is an NIH-funded cryo-EM service center that she will tell you
more about. She is also an adjunct professor at Columbia University. She
has numerous publications and numerous patents and numerous awards.
And so with that, Bridget, thank you so much for doing this and I’ll
turn it over to you.
Dr. Bridget Carragher:
Thank you so much, Jon, for that kind introduction. So a quick,
15-minute introduction to cryo-EM.
It’s come a long way since 1985, when it sort of started, and now it’s
definitely structural method du jour. And in the future, I think it’s
got even further and much more exciting places to go.
So why should you care about cryo-EM?
One reason is because it’s become very much a structural technique to
pay attention to. Over the first many years of cryo-EM lifetime—you can
see we started in 1985. Not until about the year 2000 did we even have a
structure in the PDB, and now it is growing exponentially and it’s on
track to catch up with X-ray in the next five years or so. But it’s on a
very steep exponential growth.
Another reason you might want to care about cryo-EM. These should look
familiar. These are, of course, the infamous SARS-CoV-2 spike proteins.
These were two cryo-EM papers that came out three weeks after the gene
was delivered to these people. Three weeks from gene to structure is
very, very rapid.
I think it’s fair to say without cryo-EM we wouldn’t have this
knowledge, and I’m going to talk a little bit more about that at the
end, because we are certainly paying a lot of attention to COVID-19
projects at the moment.
So where it all started was about 1985, at least for many of us. This
was the very first 3DEM Gordon Research Conference, where cryo-EM was
brand new and people weren’t quite sure even what it was, but there were
three very important people at that meeting. That was Jacques Dubochet,
Joachim Frank, and Richard Henderson, and they got the Nobel Prize in
chemistry in 2017 for the development of this technique.
So what is cryo-EM? I think it’s actually two things at the moment. It’s
single-particle cryo-EM, and that’s the thing that’s responsible for
these exquisitely high-resolution structures of these macromolecules and
macromolecular complexes. It’s also cryo-electron tomography, sometimes
called in situ cryo-EM, and these are responsible for looking inside the
cell and examining what these molecules are doing inside the cell.
And I want to very quickly, in the next few minutes, just explain what
those are and what the difference is. So single-particle cryo-EM. These
slides were made by Gabe Lander very many years ago, but I think they
still do a good job of explaining what it is that we do.
So we start with a purified solution of either macromolecules we care
about in solution, and they are tumbling about in random orientations.
We put a tiny drop of that on an electron microscope grid, which is a
few millimeters across, remove most of it to make it into a thin film,
and we drop it very fast into a liquid cryogen which snaps that thin
film into a solid state, trapping the molecules in this vitrified
sample. That means it’s not crystallized; it’s just like solid water. We
can then take that layer of vitrified ice, and we put it inside a
transmission electron microscope—that means the electrons are
transmitted all the way through the sample to form a projection image,
and that’s what we’re imaging.
And so this is more or less what those images look like. What you see is
a bunch of shadowy images of your particles, and they look different one
from the other. And that’s simply because they happened to be snapped
into the solid state while they were tumbling around and then they are
presenting different views of themselves. And so now the electrons go
all the way through these different orientations of these agents and
form these projection images below, and that makes these
different-shaped projection images.
You’ll also notice it’s not a great image, it’s very grainy, and that’s
because we can use very few electrons in order to take this image
because our biological samples in this vitrified condition are
exquisitely sensitive to electron radiation. So can’t use much radiation
to take pictures of them; it’s as if you opened the shutter on your
camera for a very brief instant to get a grainy image, not a very
And if we opened the shutter for longer on the electron microscope we’d
simply cook our protein and damage it. But we can get around that by
using averaging. While there’s lots of these projection images in the
same orientation, what we do is we chunk them out of the image.
Now I’ve got these individual projection images of particles, and we
rearrange them until they are all lined up with each other and add them
together and that improves the signal to noise. And that’s because when
we add noisy images, the signal is always adding—the noise is sometimes
positive, sometimes negative, so over the course of the averaging, the
noise goes away, and the signal improves.
So we can do it for that one projection view. We can do it for all the
other projection views. And we know if we have a lot of projection views
of a three-dimensional object we can combine that using tomography
methods to form that 3D object—and that’s just what happens in a CAT
scan or an MRI scan. You take lots of projection views of a 3D object,
and then you can put them together without physically slicing up the
So that’s single-particle cryo-EM. What about cryo-electron tomography?
Sometimes called that, sometimes called in situ cryo-EM, sometimes
called cellular cryo-electron tomography. And that comes about when you
say, I do want to know about these molecules, but they’re inside a cell.
And a cell’s a very large object. It’s not a bunch of molecules tumbling
We can’t reduce it to a thin film by blotting away most of the liquid.
So in order to vitrify cells that are large, we have to use a fancy
device called a high-pressure freezer, and that’s exactly what it says.
It uses high pressure to very rapidly freeze something so it’s still
vitrified, it’s still perfectly well preserved, but we can do it on much
larger objects then just dumping it into the liquid cryogen.
So now this is what it looks like when it comes out of the high-pressure
freezer. It’s a big clump of cells. But how are we going to find what we
need to find inside that big clump of cells? And what we do then is we
use a cryo light microscope.
So we take this clump of cells that has been vitrified in the
high-pressure freezer and we put it in a cryo light microscope, and if
it’s been labeled in the appropriate way, we can find these things we’re
interested in. But that thing is very, very large. It is not suitable
for transmission electron microscope where electrons have to go all the
way through, so it’s way too thick.
So how do we thin it down in order to make it suitable for transmission
electron microscope? We put it in another fancy instrument called a
focused ion beam scanning electron microscope. And what that does is use
the focused ion beam as a knife. It cuts away most of the sample,
chisels it away, leaving only a thin window of your sample left. And
this is a nice little animation made courtesy of Thermo Fisher.
What it looks like in reality is something like this. This is a bunch of
yeast cells that were high-pressure frozen and then taken into this
focused ion beam, and we chiseled away everything except this thin layer
of yeast cells. Now that’s thin enough, ready to go into the TEM. We can
stick it in the TEM, but we need three-dimensional views of this, so
what we do is we tilt the sample. We tilt it to an angle, take a
picture, tilt it to the next angle, take a picture, tilt it to the next
angle, take a picture, which is very similar to what happens when you
get a CAT scan.
And then this tilt series is shown below, and we can take that because
we have projection images now for many different views, we can turn that
into a three-dimensional volume. And these are very complex-looking
volumes. Here’s a typical one. There’s a lot of grainy, noisy stuff
there, but you can see things. There’s a nuclear core complex, the
gateway between the cytoplasm and nucleus. Here’s some microtubules cut
end on, sliced through. Here’s a ribosome in the cytoplasm. And here’s a
ribosome in the mitochondria.
But you can imagine it’s quite a hard job to find these, hunt them down,
and see them, and we can’t see much because, again, we had to use very
low doses of electrons—otherwise we would have cooked the sample. Or
what you can do is pick many, many, many, many of them and average them
together, just like we do in single-particle cryo-EM and then you can
get a better view, not this grainy view but an actual domain view of
what those molecules are like inside the cell. And that’s, of course,
So I told you cryo-EM developed about 1985. Where have we been for the
last 30 years?
Well, prior to about 2012, 2014, we did a lot of cryo-EM, but it was
described as blob-ology. It was very low resolution for the most part.
And that’s because we didn’t have very good cameras. Then around 2012,
2014 came along this next generation of cameras.
And they were good for two reasons: They directly detect electrons, not
photons, so they are much better for us. And they also allow us to
acquire movies so we can take out any blur in our images as we are
acquiring them. Just like your iPhone does. It goes snap, snap, snap,
and it deblurs things for you and you get these nice, crisp images. And
the cameras did the same for us.
And as a result of that, cryo-EM entered the near-atomic structural
realm. So now on our very best specimens we get sub-2 angstrom
resolution. That means you can see every side chain, you can see
well-ordered water molecules, and I think somebody has even got this
perfect sample. This is the lysozyme of electron microscopy, but this is
now at something like 1.2 angstroms, so it’s a very high-resolution
technique for a well-behaved sample.
So the resolution revolution, as we’ve called it because it was so
revolutionary for us, a lot of credit goes to this new generation of
detectors, and they well deserve that credit. But so, too, do the
fabulous microscopes that we use now.
These are exceptionally stable, robust, well-behaved instruments. We can
put samples into them and collect data for days and days and days. They
really are well behaved. They produce beautiful data on every day of the
We also have a lot to thank for computers, and that’s nothing to do with
our field, that’s hundreds of millions of gamers all over the world
reducing the cost of computing and especially GPUs, and really the cost
of computing these structures that we have to do all that alignment and
reconstruction, that reduced by thousands of times over the last few
So overall in the last few years the whole field became better, faster,
and cheaper. You hear this better, faster, cheaper thing in industry all
the time, and certainly that’s exactly what happened to our field. The
microscopes got better, the cameras got much faster, and the computers
got much cheaper. And along with that came a ton of really excellent
software that allowed us to control these instruments and do the
And the software is great because it allows us to collect enormous, huge
data sets very automatically. It then allows us to sort out millions of
images and pull out the few percent that are really the good ones and
that go into the map. It also allows us to sort out from one data set
many, many different structures.
One of the huge advantages of cryo-EM is that we can find many
structures in the same sample. We don’t have to turn them all into
identical copies of themselves into a crystal. We can have a look at the
conformational landscape, if you like. OK, so that’s what cryo-EM is.
I’m going to stop in a minute, but I’m sure one of the questions you’re
going to ask is where you can get cryo-EM access and training. And as
Jon mentioned, thanks to the foresight of NIH and the Common Fund
initiative, we now have three major cryo-EM access and training centers
in the country. One of them is in New York—that’s the one I help direct.
There’s another one in the Pacific Northwest and one at Stanford. And
aiding those are four other centers that are developing training
materials. So let me tell you a little bit about our center. It’s called
the National Center for Cryo-EM Access and Training. And what we provide
is four KRIOSs.
These are very expensive, very high-end instruments, so they work very
well in a major center. We have screening microscopes so we can train
our users as they come in to get training, and then we have a bunch of
fancy vitrification devices, etc. And we do a lot of onsite training. we
are doing virtual training at the moment, and then we do a lot of
providing access to these high-end instruments for people who have
samples ready for structure.
Right now, as you might imagine, the center is on pause because of New
York, but we are allowed to do essential work, and essential work is
anything to do with COVID-19. So we have 10 projects on the go at the
moment. Many of those have to do with examining the trimer complex, the
infectious machinery of the virus, and seeing what antibodies are
attached to it.
Some of the work has to do with examining what drugs will bind to the
polymerase that’s in the virus, and there’s a few others as well. So
we’ve 10 of these on the go. We’ve done 28 data collection sessions,
many, many maps have been produced, and there’s information starting to
come out about how these antibodies are interacting with the trimers.
Also just wanted to mention we are doing a lot of remote cross-training
at the moment, and if any of you are interested, these sessions are
provided all the time.
There’s anything from one-on-one office hours if you have an ongoing
project and need some help. Or just general roundtables to discuss
things. And you can see of the 34 people who have joined in so far, many
of them are beginners and they are from all stages of the work. So we
welcome you to go to this website and sign up for any of these training
things. And of course once we get back online, that will be onsite as
So that’s all I had to say. This is the amazing group I work with in New
York. Lots of exceptionally good research scientists and technicians
that keep all of those machines running absolutely perfectly. A lot of
people developing new technology to automate things and develop new ways
of going about this. And then lots of IT staff. And I’ll stop there and
be very happy to answer questions.
Thank you, Bridget, for that fantastic talk. That was really a tour de
Just to emphasize to everyone out there that training is a key aspect of
the centers that Bridget mentioned, so I hope that you all think about
availing yourself of that, and we’ll probably talk a little bit more
about that in a minute.
Bridget, why don’t we get started by a question about you and how did
you get involved in cryo-EM research? You’ve been in it since 1985,
early days. How’d you get there?
Even before that, as it turns out. I was a physicist, and then I
switched to biophysics, and the first thing I came across was electron
microscopy. It was the very early days.
This was even before that first Gordon conference. I got fascinated by
it. It’s very visceral. You get images and you get to process them and
then you get something from it. You get a structure from it. So I
started at the very earliest time you probably could start this field. I
was at that very first Gordon conference. I went to every other year, we
only had them every two years in those days, went every other year for
years and years and years. So I’ve been in it forever. And I love it.
That’s great. What advice do you have for a trainee or postdoc who wants
to get into cryo-EM? They know their project would advance, but they
have no idea how to do it.
Well, I can’t say enough how valuable these new centers are that the
Common Fund has funded. It was a massive problem before that. People
were always wanting to get into this, and individual research labs could
do it on an occasional basis as a favor or as a collaboration, but now
these centers are simply available to you.
You want to get trained in cryo-EM; you send in an application. The
applications are reviewed because we want to make them very transparent
and open. So the applications get reviewed and then you get time
allocated. And we have various forms of training. Some of it’s workshop,
but some of it is embedded training.
That means you come and live with us for a few months, get completely
embedded into how it is to do cryo-EM in a lab—how to make samples, how
to find out whether they are any good, how to take images—and all of
that, after about three months you could go home and use it.
We have several examples of embedded trainees who’ve come, done
beautiful work, now they’re back at their own universities teaching
everybody else. That’s really what we want. We call it cross-training,
actually, because we want those people to go back and take that
information back to their centers.
So I would encourage you to talk to the centers. Talk to us. We’re happy
to have a meeting one on one telling you where we think you’re at with
your project and where it might be ready to go to next.
This is something someone could start even now working from home. They
could contact you.
Exactly. We’ve had a lot of very interesting one on ones where people
are showing us their data, and some of it is great data. They are
agonizing, is it ready? Yes, it’s ready. People don’t really know what
they’re looking at, but we’re very happy to have those consultations and
tell you where we think you are and how you should do it.
We also are going to start virtually training prior to the whole country
opening up. The embedded training is really you working side by side
with a scientist who’s been doing it for 20 years. We’re going to see if
we can do that with a GoPro and a cell phone—a GoPro at our site showing
you what we’re doing, cell phone at your site showing us what you’re
doing, and then we can pass advice back and forth.
So that’s our plan to get that started even prior to the whole country
opening up. So contact us and let us know what you’d like.
That is terrific. We’ve already got a lot of great questions. Some of
them are technical, some of them are more general. Here’s one. What’s
the difference between cellular cryo-EM and traditional tomography EM
using high-pressure freezer?
There isn’t a difference. We’re terrible at naming things in our field.
We call this method single-particle EM that takes millions of particles
to be averaged together and we haven’t got a single particle, so I think
we’re bad at naming it. But people have sort of started talking
cryo-electron tomography as if it’s the whole cellular pipeline. But you
can do cryo-ET, you can do electron tomography even on single particles
if you like. You can do cryo-ET on anything. The cellular side, though,
if it’s a big, fat cell, you have to slim it down if you want high
resolution from the EM.
And that’s where you have to start cutting these windows into the cell
and needing a cryo-LM maybe to investigate that. So cryo-ET is sort of
the end of that cellular in situ pipeline. You cryo pressure freeze, you
cut thin windows, and then you get it into the electron microscope, and
you do cryo-ET.
I’ll just add to that, that a follow-on to the cryo-EM centers of the
Common Fund is going to be cryo-ET centers and resources, and that will
be coming soon for those of you interested in doing cryo-ET using a very
similar model. If I want to visualize my protein of interest in cryo-EM,
this is an interesting question, does the sample provided need to be a
purified protein or can it be a cell lysate sample?
It can be cell lysate. In fact, we did a very interesting project
recently at our center where somebody just lysed cells that were making
viruses and we could pull out enough of them to actually get to a
The only difficulty with that is if there’s enough of them. A big
virus-like particle, that’s great; you can find it easily. Ribosomes you
can find very easily, which is why even in situ there’s a ribosome
structure now at 3.7 angstroms, which is amazing.
If it’s very, very, very tiny and there isn’t much of it, you saw what
those noisy images look like. How are you going to find them? How are
you going to find enough of them? But having said that, I wouldn’t be
astonished if we can do that sometime in the next few years, because the
methods are proceeding so rapidly, and things like machine learning are
coming online that are making a huge impact in enabling us to find very
small, noisy things in these cluttered environments.
Great segue into the next couple of questions, actually. Very good. What
do you think will be the next revolution in cryo-EM and cryo-ET? Where
are we headed next?
I think exactly that, Jon, into the cell. I mean, I think sooner or
later we will have solved all high-resolution structures in solution,
one hopes, between X-ray, NMR, and electron microscopy, we should be
able to solve them all. Then we want to know what is happening inside
the cell. What happens if you change conditions, if you mutate
something? Where are these regions of interest in the cell? How are
things going in and out of compartments?
All of that is going to be really fascinating. And that’s super hard at
the moment. There are only a few places in the world that do it well.
And as Jon just mentioned, again, NIH in their wisdom has decided to
fund centers to help the rest of the country get going. There will be
something like five or six specimen prep centers—that’s really the hard
thing—and then one big center to get cryo-electron tomography done. So
that’s going to be extremely fun and interesting.
A little bit selfishly, I would say I think time-resolved cryo-EM is
also very exciting. We’ve just started projects to do that. And being
able to mix two things together and watch what happens in the first
hundred milliseconds of interactions, I think that’s quite interesting.
And just making everything better, faster, and cheaper so that everybody
can do it. And it’s just a method available to you rather than a postdoc
lifetime to get something done.
I keep imagining, with the current crisis, when we get to the point
where we can actually see maybe in time-resolved fashion, as you said,
intermediates of viral infection on a cell and then viral budding out of
a cell. We’re starting to see things with phage, but imagine if we could
see SARS-CoV-2 doing that.
Going back to what you said before, we have a question on machine
learning. What’s the role of machine learning in cryo-EM image
processing, and what areas are in urgent need—for instance, particle
picking, class selection, what else? This is someone, I think, who wants
to get involved maybe in that.
All of the above.
And in fact we had a fabulous, fun time in the lab the last two weeks
because we had these COVID-19 projects coming in, we had these trimers
come in with antibodies on them, and it was a very, very messy sample.
But some people in the lab—Alex Noble, working with Bonnie Berger’s
group, Kristen Becht at MIT, have had this big machine-learning project.
So they had a nice particle picker, but this new COVID-19 project drove
that to excellence because we were forced to make it better because they
had this very messy sample and we were desperate to get the
high-resolution structures out of it, so that machine-learning program
got worked and worked and worked and improved and improved until we
managed to solve these structures very rapidly.
We can do them now within hours or at most a day of it going into the
microscope. All of that nice improvement now went back into that
program, which is in CryoSPARC, which is one of the well-used packages,
and now that’s available to everyone. So particle picking certainly was
one of them.
The other one, as you saw, I showed this picture of the cell, a section
of the cell. How are you going to find ribosomes and microtubules and
nuclear pores in that? You can sit there and pick them out one at a
time, but it’s super tedious, super-high burnout, and also you won’t do
it 100 times in a row because you can’t. So machine learning is going to
have a huge role to play in that and being able to be good at that will
And don’t forget, a lot of this stuff has been done by hand, so there is
a lot of annotated data sets out there to learn from. So I’d encourage
anybody doing machine learning, go into imaging. That’s where we need
most of it. And we’re an imaging method.
That’s great. Let’s see. Talk a little bit about the big data aspect of
cryo-EM. How are we dealing with that? Because the data sets are getting
bigger and bigger, that’s going to become an issue soon. How are you
thinking about that?
I think it is an issue, but it’s not as bad as we thought. We always
manage to outrun it, or computers manage to outrun it. At the beginning
we thought, “Oh, this will be terrible.” And then of course discs just
got bigger and bigger and cheaper and cheaper and cheaper, and you can
get an amazing amount of disc for not that much money anymore.
But having said that, we do not keep every movie anymore. The movies are
huge. The movies can be 100 frames and then they get compressed down to
a single frame by realigning everything. And long term, we store those
aligned ones, and that’s easy enough to do because disc is cheap enough.
If we had to store 100 times more data than that, that would be a
problem and that would become a major expense and we’d have to do
something. What we are doing, though, is talking to the cloud people. If
we distribute that cost, we as a center, if we had to pay for every
single byte that we put into the cloud, that would be expensive, but if
a user just wants one data set and they’re only going to have that and
work on that for a while, that’s not so expensive to keep in the cloud
for a while, so that’s what we’re…and also to process in the cloud, by
So we’re working partly with NIH and other people to try and put the
processing in the cloud and the storage in the cloud so people who do
this occasionally and don’t want to set up a whole EM IT center to do it
can just go do it and then shut it down and then come back to it
whenever they want. So that’s working fairly well.
Here’s a question that’s perfect for you, as are these others. What
about automation and increasing throughput in cryo-EM? What are the
Great prospects and great question. Thank you. I love that question,
because our whole lives have been spent on trying to automate
everything, partly because it’s burdensome on the operator and partly
because you can get more control that way.
So one of the things we’ve worked very hard on the last few years is to
automate the specimen preparation. And that’s still done by pushing
blotting paper against the grid and it’s a bit hit or miss. So we want
to automate that whole aspect. The same with the in situ, the cutting
those thin sections. That’s starting to become automated, and that’s
important because again it’s very time consuming and very tedious.
The data collection, we sped up data collection through automation by a
factor of five over the last few years. And in case you don’t think
that’s a big deal, those microscopes cost $7 million. So if you can
speed up data collection by five, that means it’s like you’ve just
bought five $7 million microscopes and housed them and fed them. So
that’s a huge deal. And that will keep going, I think.
And then the software processing is already highly automated, but it
needs to become more so in these very complicated environments—lots of
heterogeneity, lots of complexity, lots of different states of these
proteins—and I think automating that and understanding the sort of
landscape of conformations that these proteins will go through could
stand for a lot more automation too.
Great. Here’s a very specific question. Compared to other software, has
the use of CryoSPARC Live made data collection faster or requiring less
Yes, it has. We have always evaluated our data on the fly. So for those
who don’t know, this is a sophisticated question. Somebody is using a
lot of nice software.
CryoSPARC Live is a processing engine that gobbles up your data and does
it while you’re collecting, but in our lab, we’ve always done that.
We’ve always evaluated the data as it comes in and been able to make a
decision. But it certainly is a good idea to make a decision while the
grid is in the scope, so you should evaluate your data, say, “Is this
good data? Do I need more data? Can I stop right here?”
Because our scopes are extremely expensive and extremely in demand, so
you do not want to waste lots and lots of hours if the sample isn’t
looking any good. So CryoSPARC Live is great. We love it. We use it.
There are several other packages as well, though, and many, many
on-the-fly processing packages available, and I think they’re absolutely
essential to maximize the efficiency of your very expensive instruments.
That’s great. Here’s another technical question. Why is the resolution
lower at the surface of a protein while it’s higher in the core, in
It’s not always, actually, but if you’ve noticed that it’s probably
because the bits on the outside are wiggling around, so the stuff in the
core may be locked into a very rigid conformation and the bits on the
outside are able to move and able to do things, but that’s not always
Something like a proteasome or something that has an inner space,
sometimes you get a lot of variability inside, so it’s just a matter of
where things can move and where they’re not locked into rigid
Here’s an interesting question. What’s it like working and running a
national research resource?
The most fun you can ever have. It really is tremendous fun. This field
has been around since 1985, and I was always happy in it. But I’ve got
to say the last five years has been just a complete crazy trip.
The field burgeoned out, hundreds and hundreds of people have poured
into it. It’s had these astonishing successes. We never imagined a
ribosome at 2.5 angstroms with every water molecule, structures of 60
kilodaltons being solved, fantastic membrane proteins, CFTR and all
these other high targets for disease, for drug discovery. So that’s been
And running a center means you have a huge amount of expertise and
people all being synergistic. As I said, the other day we were trying to
solve this protein. We have the people doing the machine learning, we
have people doing the processing, we have the experts collecting the
data, and then we have the biologists who desperately want that answer,
all working together. And that sort of driving force is super fun. I can
Apply for a job there. That sounds good.
Here’s an interesting question too. Someone’s working with a
conformationally flexible macromolecule and complex. What advice to you
have for this person on how to make that amenable for cryo-EM work?
First, just go take a look at it. The best thing about microscopy is you
can just go and take a look. First in stain, maybe. Negative stain is a
method we use, quick and dirty. And then in ice.
You may be surprised how much you can do even with the nonflexible bits.
And that’s another big area of software development. People are trying
to sort of examine their flexibility, capture it, unravel it so to
speak. If that all fails and it’s still completely flexible, you may try
to bind it to something—bind it to a fab or bind it to something it
complexes with, and that might stabilize it a bit. But there’s nothing
like just go take a look. It’s not that hard to do. It’s quick and you
might find out something anyway.
Here’s someone who’s interested in maybe getting involved in technology
development and wants to know what are the major barriers in cryo-EM
that someone could get in and try to solve and make better?
So on the single-particle cryo-EM specimen prep is still a bear, and
it’s a problem because of something we ignored for the first 20 years,
and that’s because we have our samples in very thin layers of ice, so
that means they almost certainly are seeing the air/water interface. So
they bump into the air/water interface and proteins don’t like to be at
the very hydrophobic interface, so we need to solve that problem.
So if you’re a hardware person or if you’re somebody who understands
surfaces, if you do physics or you’re a materials scientist, we would
love to talk to you. Because we think there are ways to approach this,
but we really need to understand the physics, the materials science of
what those surfaces look like, what is happening at those surfaces much
better in order to sort of be rational about how we approach how to
So on that hardware side, that’s certainly a lot of work to be done. And
then on the processing side, the machine learning is going to be
critical. Speeding everything up, being able to figure out these
conformational landscapes is going to be important, so it depends on
where your interests lie. There’s room for everything to be done, but it
depends on what you like to do.
That’s great. Here’s a question again about how do you get started. Say
you’ve got a sample; you think this might be amenable for cryo-EM.
You’re not sure, you have no experience. What’s the person going to do
Come talk to us. Call us up and talk to us. We would suggest you apply
to be trained, because then you can come to us, or now we can do it
virtually. So one way or the other. But we will help you through that
process. We will get you through the staining.
You have to have a microscope available, so if you can’t come to us then
you need to find a local microscope, and we’ll try and help you find
that too. We have various contacts and we know where the core centers
are. So we’ll help you have a first look at it, evaluate whether it’s
ready for cryo; if it’s ready for cryo, we’ll help you get it frozen.
These days with COVID-19 maybe that means you ship it to us, and we give
it a shot and tell you what’s going on. Otherwise, if you have access to
equipment in your own hometown, we will help you get that through. So
it’s a step-by-step process. Just come talk, start a conversation. Let’s
think about where you’re at. What does your gel look like? What does
your size exclusion look like? And we’ll tell you whether we think it’s
ready for prime time.
That’s great. Related question to that was, what kind of approaches do
you need to look at your sample before you start? You mentioned gels,
size exclusion, anything else? And are there any no-no’s, like buffers
you shouldn’t use? Glycerol, etc.
We don’t like a lot of glycerol, and that’s because it’s a contrast
killer. We are looking at our sample in solution, and the only reason we
can see it is because its density is slightly different to water. But if
you start bringing up the density of the water by adding glycerol or
sugars and things like that and you get very little contrast, that just
makes your life harder. So 15 percent glycerol? No. Or very, very high
But as we always say to people, your protein has to be happy. If you
make your protein unhappy by taking all these things out, then you’re
nowhere. So you get your protein into the conditions we like, as happy
as possible, and then we’ll work with you.
Sometimes what we do if it will not live without glycerol, we’ll dilute
it just before we put it on the grid or tricks like that. So we can work
with just about anything.
What was the first question?
What other techniques besides gels?
The usual biochemistry techniques. And then the first thing we’ll do is
put it on a grid and stain. And that takes literally 10 minutes. It’s
not a big deal, but you might as well have it cleaned up and as well
known as possible before you do that.
It sounds as if talking to you early is a good idea. Before you invest
all your time, talk to you guys.
Right, come talk to us and see where you’re at. We’ve seen hundreds and
hundreds and hundreds of projects, so that helps.
A lot of experience. Is the virtual training at NCCAT for anybody or is
it only for people who have a grant with NCCAT?
No grant with NCCAT. Sorry, I’m getting mixed up myself. It’s an NIH
Common Fund-funded center, and there are no rules as to who can and
cannot apply. Anybody, literally anybody can apply.
We tend to favor people from the United States, obviously, but we have
served a few elsewhere. And all you have to do is write the application
and we will review it. It gets reviewed, so that means we want to be
open and transparent. We’re not just choosing our friends and relatives
to give time to. So the review committee reviews it and then we evaluate
those reviews and then allocate time.
Great. And just a reminder that there are two other centers too, so
depending on where you are in the country or if the center is busy,
there are three places you can go. Can you comment on the role of
elastic and inelastic light scattering in cryo-EM?
Electron scatter, but if a sample is very thick, you’ll have a lot of
inelastic scattering, and then the kind of formulas we use to do the
reconstructions start to break down. So that’s why we want very thin
samples that the electrons go through elastically rather than
So there are things called energy filters where we can sweep away some
of the inelastic scattering, and that helps if your sample’s a little
bit thick. That’s probably quite a long technical question, but anybody
is very free to call me up later or email me later, and I can refer you
to some good literature if you’re wanting to know more about these
Terrific. Can you give us some perspective on phase plates and how they
might influence cryo-EM structure determination?
That’s a good question. Phase plates were very, very much in vogue, and
we have a bunch of them at lab, but I am sad to say we don’t use them
anymore. At first we thought they would be the game-changing thing that
would allow us to see everything that we couldn’t see anyway. It turned
out the cameras were so darn good we didn’t really need the phase
That’s not to say I don’t think a phase plate would help if it was a
perfect phase plate. And Bob Glaeser’s working in Berkeley on this laser
light beam phase plate, and that would be better than the ones we have
now. The ones we have now are problematical enough I think you
don’t use them unless you’re desperate. We use them occasionally, but
most of the time we can do it without.
But if we had a perfect one like Bob might invent, then we would use it
all the time. It’s too much trouble at the moment, Jon.
We need technology development there, it sounds like.
Very much so. And what Bob’s doing is super cool but jolly hard. Very
You mentioned this before. Why do you do negative stains before you make
the cryo-EM grid? What’s that for?
Because it’s very easy to do and it’s very quick to tell what’s going
on. Whereas, if you make a cryo-EM grid, if you’re not an expert and you
look at the grid and you don’t see anything and you think, “Is it
because my ice is too thin or is it too thick? Do I not have any protein
there? Did the protein fall apart? Is the protein stuck to the carbon?
What is going on here? Is my dilution wrong?”
Whereas, a negative stain, which you can make in 10 minutes, you can
make 10 different dilutions, quick look at them and say, “Ah, I really
do have particles there, so I should expect to see them.” But if you go
straight to cryo, it takes longer to do that. It takes longer to get the
grid into the scope.
You have to be better at it, you have to have more training, so it takes
a bit of time. That’s not to say the experts, the people who do it every
day of the week, wouldn’t just bang out a quick cryo grid and have a
look at it, but if you can’t figure out what’s going on, then back to
stain you go.
Interesting. Somebody’s thinking about cryo-ET and wants to know if… you
mentioned the problem of how do you know which of the things that you
see there are your complexes. Could they use gold-conjugated antibodies
to detect which are their complexes in cryo-ET?
Sort of. One would like to say that’s true. The problem with
gold-conjugated antibodies, the antibody is probably bigger than your
molecule and the gold might be bigger than your molecule, so it’s kind
of messing and cluttering up the view. Also, is it 100 percent effective
When you’re labeling things in the lab microscope if 1/1,000 is labeled
and you’re looking at a million of them in your field of view, you still
see them. We have to hunt them down one by one. If 1/1,000 is labeled,
well, we only have one in this field of view. We’d have to take hundreds
of thousands of fields of view. So complications like that.
But we are desperate for a GFP for EM, and there have been some that
have come along. I wouldn’t say there’s a perfect solution yet. Somebody
else may jump in and contradict me because I’m not an expert on this
labeling area, but I think there isn’t something that’s perfect yet. The
thing that comes closest is fabs or nanobodies. You can stick those on,
and they act like a fiducial at least, but they are still hard to see.
That’s great. Someone wants to know whether you can actually see—you
talked about looking at drug-polymerase complexes in COVID-19—can you
actually see that resolution with the drug and the specific amino acids
it interacts with?
Absolutely. You need high resolution. Above 3 angstroms maybe you can
see a blob of the drug; below 3 you can see the whole drug, and you can
tell what conformation it’s in. Even in this amazing cryo-ET project,
this was an in-situ ribosome, that thing had a drug on it.
I think it was a bacterial ribosome, and there was a drug on that
ribosome; you could see the drug. So of course more resolution is always
better if you want to exactly know where every atom of the drug is, but
absolutely we do that all the time. And Jon mentioned that we run a
company in San Diego. That’s what our clients want there. They want to
look at targets with drugs on them, and we get that routinely for them.
That’s fantastic. Here’s another great question. We’re having really
great questions. Can you talk a little bit about cryo-electron
MicroED. Great question. New kid on the block, MicroED. And I think it
has huge potential, particularly for the small molecules. We’ve solved a
lot of not-so-small molecules, actually, things up to 1,000 daltons, and
it’s very good at doing that. You can get 1-angstrom resolution data off
a pretty middle-of-the-road instrument. You don’t need a KRIOS for this.
And if you get 1-angstrom data, you can solve the de novo using the
brilliant methods that the X-ray crystallographers have been developing
for years and years.
Protein crystallography, a little tougher because the proteins are
harder to handle, harder to get on the grid, but that’s another area of
good development. We need to develop better techniques to do that. And
then if you’re only getting 3-angstrom data or so out of that protein
crystal, solving it de novo, of course, is a little difficult.
But that may not be all that people want to do. They may want to solve
something or have a homolog and then do it with many, many different
drugs, and they may only be able to get small crystals and, therefore,
MicroED is the perfect method for it. I think MicroED will be very
synergistic with X-ray crystallography and the two will feed back and
forth from each other, but it’s a very exciting new method and it works
very well, actually.
That’s terrific. Exciting to watch that happening. OK, here’s another
really great question. I was wondering if you could comment on possible
issues that could arise during data processing. It’s a complicated
process, so are there opportunities for data manipulation or
misinterpretation that people should be aware of, so they don’t do it?
Yes, for sure there are. It used to be much worse.
People are probably remembering some 10 years ago there was quite a bit
of scandal in our field because there was back and forth as to whether
something was right or not. It used to be much easier to get this wrong
at low resolution. So if you get a blob, you can interpret that blob
just about any way you like. If you’re at atomic resolution where you
can see every atom and you can see alpha helices and you can see beta
sheets, it’s easier to evaluate and judge whether that thing is right or
So we do have all kinds of checks and balances. We don’t have the fancy
R factor that the X-ray crystallographers have. Sadly, there isn’t
something just ideal like that. But there are many different evaluations
of it, and ultimately if you look at the thing and if it’s a high-enough
resolution it’s pretty easy to tell whether something is going wrong.
Which is not to say that people couldn’t cheat, but we hope they don’t,
and you shouldn’t be able to get it wrong just by doing the wrong thing.
You should be able to get to the right results if you’re honest and you
use the methods in the right way.
I suppose more and more computational approaches, machine learning, are
taking the human out of the alignment in things like that. Is that
Yes. And there’s a lot of checks and balances inside these packages.
It’s a while since I’ve seen anything that went wrong in a way that
wasn’t so obvious, like, “Oh, well that didn’t work. Let’s try again.
Let’s do something else. I wonder what went wrong in the software.”
It’s not that you get to something and say, “Oh, that looks right,” and
then a month later realize it’s wrong. That doesn’t happen anymore. It’s
either horribly wrong, looks like some ghastly thing, or it’s kind of
right. And then you can always make it better.
So here’s a really interesting question. Coming from a low-income
country with a background in basic medical sciences in Rwanda, how can
one get this kind of experience and training in cryo-EM so that they can
advance this sort of research there?
Well that is an excellent question. We’d have to talk to NIH about how
they feel about having trainees from other countries. Maybe there’s
workshops that could be run. We’d have to think about that. It’s not
obvious to me what resources we have. But we do work with people from
We have a very lovely project on Zika vaccine going at the moment with
people in Mexico, because they do not have access to cryo-EM, so we said
let’s start that as a collaboration. That’s really done with the NRAMM
side of our lab, because it’s a driver of some interesting technology
we’re developing. So we do collaborations with other countries.
I would encourage you to contact us and talk about it and we’ll talk to
NIH and see what can and cannot be done.
And I would say the one place at NIH you could contact is the Fogarty
International Center, which has a number of programs for fellowships for
people from low-and middle-income countries to come to the NIH, to come
to labs in the U.S. and study and learn these kinds of techniques.
That would be ideal.
Here’s someone who wants to know more about dynamic cryo-EM. What’s the
cutting edge there of doing time-resolved cryo-EM and seeing things
change and move over time?
Well that’s a nice question for us because we just sent a paper out—it’s
under review, actually, at Nature Methods on time-resolved cryo-EM. And
we’ve developed a fancy instrument that uses piezo dispensing onto
self-wicking grids in order to try to make the specimen prep better. And
once we’d done all that—that’s been commercialized now—we decided we
could split two streams of piezo droplets at the grid and mix them on
And we just did some really fun experiments with that, mixing DNA
promoter with RNA polymerase and watching that first hundred
milliseconds of how that DNA melts into the promoter. Throwing ATPase at
a dynamin tube and watching this unravel and things like that. So that’s
Joachim Frank has done this for quite a while with ribosomes. So they
mix two different ribosomes together or mix components of the ribosomes
together and watch that. So they’ve been quite doing a lot of
time-resolved on ribosomes up to now.
But this new time-resolved method that we have with Spotiton, with our
specimen-making device, allows you to just mix very tiny quantities
together, and that’s helpful. So we’re very excited about doing more of
Really cool. A couple of career questions. I think you’ve convinced at
least one person that they should come work at the National Center. If
you’re interested in working at the National Center in the future, what
type of training would you recommend?
Well we take people in at all levels. We’re always looking for great
cryo-EM scientists, but a lot of our cryo-EM scientists who are at the
National Center right now started as techs. They came out of
undergraduate; they started out as technicians; they trained up and now
they’re running KRIOSs and doing amazing work. So we regard our training
mission as doing that too.
We don’t expect people to come in perfectly ready to work. We do a lot
of training. So if you can learn some cryo-EM, great. If you are
comfortable around computers, very good because we do a lot with
computers. And if you love instrumentation, excellent. But other than
that, you can be trained to do these things.
That’s great. Here’s a slightly more sociological question. What advice
do you have for an outsider trying to break into a community when so
much in science is built on pedigrees and on your CV on paper?
Again, I’d say start somewhere. Start as a tech in a lab or doing
something useful in a lab and get to know some people and get a little
bit of experience. Is it built around pedigrees? I mean, it’s certainly
built around knowledge and competence. You can’t just walk in and say,
“I’m going to now solve all these problems.”
You need to bring something to that table and maybe what you bring is a
willingness to learn. We take in so many postdocs and students who come
in and say, “I’d love to learn cryo-EM.” It’s very hard to say “no” in
that case because you also want them to learn it and we’d love them to
learn it too. So it’s very powerful to be willing to go in and learn and
just tell someone, “I’d like to enter into this. Can you help me out?”
Don’t be afraid to say that.
I think that’s good advice, just ask.
Yeah, just ask.
It’s good advice. People like to be asked, right?
Yes, and you’ll find PIs find it very hard to say no.
I think that was Tip O’Neill who said people like to be thanked and they
like to be asked.
Yeah, that’s right.
What about smaller proteins? NMR, for instance, has a size limit—can’t
go above it. Cryo-EM can solve huge things, but is it going to be good
for really small proteins or other things?
For sure. We just solved with Filippo Mancia’s lab at Columbia
University the PFCRT, which is a transporter in malaria, a parasite,
that is responsible for inhibiting drugs. And that thing is 50
kilodaltons. It’s an integral membrane protein, and it’s 50 kilodaltons.
That definitely would have been said to be impossible a few years ago.
We cheated a bit because we put a fab on it. [unintelligible] lab made a
nice little fab and we added a fab and that made it a lot easier. But
somebody has done one without a fab at about 50 kilodaltons. So 50
kilodaltons is definitely possible now, and I think NMR can do anything
below 50 kilodaltons maybe. And I think that’s a pretty sweet spot
anyway, 50 kilodaltons. Many, many things are that size and above, so
we’re definitely making progress on that.
Do you think cryo-EM is going to replace crystallography?
No. Not at all. I think they’ll all be synergistic. I think NMR,
crystallography—I think NMR is going to have a little comeback too by
the way. I think all three of them are these three legs we build
high-resolution structural biology on. All of them will feed off each
other and go back and forth.
If you want an example, so cryo-EM is being hugely successful for
COVID-19, but the fragment-screening people in Diamond did 1,500 samples
in a few days I think—they at least did something like 600 in 72
hours—and in the end they did 1,500. It was a small piece of the protein
and it was screened with fragments, but wow, 1,500 in a few days? We’re
not there. We can’t do that. So the two of them will work well together.
Cryo-EM might do the entire trimer or the polymerase and say this is the
working part. Crystallographers can pull that out, they can make a
smaller protein out of it, and then screen it against thousands of
fragments. That’s cool synergy between the two. And then NMR has its own
role to play in dynamics and all kinds of things like that.
I’m being reminded by one of our great program directors, Paula Flicker,
that I should mention that each of the centers does training in a
different way and they have different workshops, so you may actually
want to look at what all of them have to offer, because they can offer
very complementary and synergistic things.
Let’s see. We still have lots of questions, but we only have two minutes
left. And there are lots of compliments here too, I’ll tell you,
Bridget, to your talk. Here’s a great question. What do you think the
200-kilovolt microscope is in the future—and this is versus 300, so
maybe you could just give a little background on that, but then I’d love
to hear your opinion too on this idea of lower-voltage microscopes that
might be cheaper.
So Richard Henderson and Chris Russo had a very nice paper recently that
was written about quite a lot. I was interviewed, toward democratizing
EM, saying $7 million, an enormous room, and half a million dollars a
year to look after it; that’s too expensive. We need lower end.
What they really want is 120K of a cheap instrument with a fig that’s
capable of 3 to 4-ish-angstrom resolution. And the idea is that
instrument would be less than $1 million, nice camera, nice gun, nice
instrument. All small research centers, small universities, could afford
to have it, could afford to house it, could afford to look after it, do
most of their work at home, and then if they really need that sub-3
angstrom embedded, they’ll send those samples to the centers, and the
centers should be able to cope with that.
So I think there is a future for that. We have to get the instrument
companies to agree they would like to do that and build such an
instrument, and I don’t think any of them is at the moment. So Richard
and Chris are building it themselves. There are the 200 kV instruments,
and they’re very good, but they are still quite up there in terms of
That’s half a KRIOS, it’s not less than $1 million or anything like
that, but they are cheaper than the KRIOSs and they do very well. I
would still say the KRIOS is the creme de la creme instrument, and if
you really want the very best data, that’s where you all want to end up.
Great. Well, we are at the end of the hour. There are, unfortunately,
still questions I could have asked you. Sorry I can’t ask everyone’s
questions, but boy did you guys have great questions. Bridget, this was
an absolutely terrific session and talk. I’m so grateful for you and
grateful to all the trainees who tuned in and everyone who’s working so
hard right now, either at home or on the front lines of this epidemic.
Please, everyone, stay safe and keep doing your part because you’re
doing great things.
Thank you so much, Jon. It was really fun, and those were terrific
questions. And feel free to contact us so we can answer more questions
Good. Thanks, everybody. Tune in to the next one.
Thank you. Bye.
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