(mellow electronic music)
- Alright so the question that we are
studying in my lab is, how does something
as complex as the brain form in the course
of embryonic development?
How do the first circuits develop?
How do they become functionally active?
How do they perform the first computations
and instruct the first behaviors?
And so because all of these aspects
are highly dynamic aspects,
we spend quite a large fraction of our time
actually also developing technology
that allows us to visualize and also measure,
at the cellular level and at even smaller scales,
what's happening during the development and the emergence
of function in the nervous system.
And so, I'd just like to start with two
examples of microscopes and imaging,
as we've developed for this purpose.
What you can see here is a life-imaging
experiment with a nuclear labeled, Drosophila embryo,
going through embryogenesis.
And we can basically follow all of
the different cells, as they move and divide,
and specifically, reconstruct the formation
of the early nervous system.
So that the structure is the equivalent
of the vertebrate spinal cord that's being formed here.
So we have this len-esh-wee construction and the
morphodynamic building plan of the early nervous system.
Now, complementing this developmental perspective,
we also like to capture what's happening
in the angio neurons once the
nervous system is really up and running.
So we need tools that are even faster.
Now we're imaging at 100 times higher speed,
and even larger volume.
This is the brain of the larval zebrafish,
with about 100,000 neurons.
And using calcium indicators, we can follow the activity
during different times of behaviors.
And so from these whole-brain function imaging recordings,
we can then figure out what kind of circuits
are involved in certain kinds of behaviors.
And what kind of networks across the brain
are communicating with each other,
and are jointly involved
in certain types of computations.
I chose these two examples, also,
because in both of these cases,
these imaging experiments mark the first time
that an analysis like this was possible.
This was the first time that we could actually
track cells systematically, across a high-end vertebrate.
And the first time that we could image
neural activity at the cellular level,
across a vertebrate brain.
And what's made it possible to do these types of...
To record these types of data sets,
to do these types of analysis,
is the concept of light-sheet microscopy.
So I'd just like to remind you, very briefly,
what the key concept is behind this technique.
It's actually a very old technique,
it's been around for more than a hundred years,
which makes it twice as old as confocal microscopy.
And to the present day, the fundamental concept
has basically remained the same.
What we are doing, is we are illuminating
a sample with a thin sheet of light,
of laser light, that is entering the
sample chamber from the side.
So we are selectively illuminating
a thin volume section in the sample.
And then the throo-ess-ance that is emitted
by this thin section is imaged with a commercial
light field detection subsystem, oriented at
right angle to this incidence light sheet.
So we have this textual objective
and then a camera further up in that arm,
that takes a snapshot of that plane,
of that thin section.
And so, that makes this technique a very fast imaging
technique because, with one snapshot,
we can capture millions of volume elements
across this plane.
And we can do rapid 3-D imaging
by just moving the light-sheet through
the sample, step-by-step.
Just recording the image sequence.
And so then,
we're basically just limited by the
speed of the detector, the camera,
can get gigavoxels per second with
modern eh-see-muss cameras.
But, it's also a very gentle technique.
And so it's gentle because we are
only sending light exactly to the
part of the sample that we're taking
the image off at any point in time.
We're not sending light to all the focus regions.
We are not breaching or damaging parts out of focus.
And so this combination is
really important for our work.
And so now, I'd like to be a little
more quantitative about the imaging
experiments I just showed you on the
first two slides.
And as a reference,
just basically embed these experiences
in a parameter space that we would care about a lot.
In most of these live-imaging experiments,
we are looking at the size of the sample image,
the duration for which we can image it,
and the spacial temporal resolution we can achieve.
And as a reference point,
just connecting to Dana's talk, again,
I think Dana made this nice point that,
if you use a state-of-the-art spinning disc
confocal microscope, a very powerful,
fast imaging device, you can still kind of
do this experiment of tracking cells
in a se-li-ance embryo,
but only up to a certain point of time.
If you go too far into development,
the embryo moves too quickly that you can't follow
the cells anymore.
But, this is kind of like at the limit
of the performance of the system
and so that puts us somewhere here
in this parameter space.
And I'm sure you've heard of the work
of my colleague, Eric Betzig at Janelia,
who is also using light-sheet microscopy
to push spacial resolution in these experiments.
And so he's able to get these really
dramatic improvements in spacial resolution of more than
a factor of 10, same-model system.
And so using these really, really thin
light sheets, either using bessel beams,
or lattice light-sheet techniques.
So for us the challenge has been a different one.
We wanted to stay at the single-cell level,
but scale this up to a much,
much larger model system.
So for the Drosophila we were imaging
at 1,000 times larger volume.
We still want to follow cells at the same speed,
at the same volume rate.
And then, if you push it into the functional realm,
we need to image, not only at 1,000 times larger volume,
but also, image that volume at 100 fold faster
volume rate.
So, this is where light-sheet microscopy
has been really helpful to us.
But if you look at these different
data points in this plot,
there's one thing that's really unsatisfying about it.
And that's...
The implication here is that
there's seemingly this trade-off.
You know, you can either image a fairly small
sample at very high resolution,
or a fairly large volume,
at the cellular level, very quickly.
But you can't do both at the same time.
And so I think what we really want is a technique
that allows us to image a fairly large volume,
like the brain of a larva zebrafish,
over long periods of time,
and over the mental time scales,
during behaviors, both at high spatial and
high temporal resolution.
And so, I'd like to show you
first how we can get to this part in the
parameter space.
And so the goal here is really to find a way
to add this fourth parameter.
Three out of the four parameters,
we're kind of happy with.
We have the large volume, we have the high speed.
But what's missing is the spatial resolution.
So how can we add spatial resolution to this
mix without trading-off, sacrificing
any other parameter?
So, let's look at the challenge first.
What's the problem we need to solve?
The reason why spatial resolution
is not as high as you'd want it to be
in these experiments, is that we suffer,
in many light practice groups, in fact,
from anisotropic spatial resolution.
So the problem here is that,
we are not really collecting all
of the light that is provided by our object.
So we have a limited numerical aperture,
only collecting light on a certain,
small angular range.
And so this anisotrophy and sampling information,
which starts in the anisotrophy,
in the way in which we form the image,
and represent the samples,
we have typically much lower axel resolution.
That lateral resolution as Dana pointed out earlier.
So, ideally what you'd want to have
is axle resolution be as good as lateral resolution,
so that would be a big step forward.
I mean, light-sheet microscopy often has
a factor of 10 that you're talking about.
And then, on top of that,
it would be biologically much more meaningful
because we are treating all of the dimensions
the same way, which makes much more sense.
So, what we need to find, now,
is a way to solve this problem without
compromising any of the other aspects of our performance.
Because for our experiments,
it's really important that we
can maintain the high temporal resolutions,
we can still stay in the realm of calcium imaging,
maybe even push this forward towards watt-age imaging
in the future.
We really don't want to touch speed
and still get imaging at the whole-brain level.
So the way we can do this,
is we combine light-sheet imaging
with another fairly old concept.
And that's the concept of multiview imaging.
So what we do in this approach is that,
counter-intuitively, the first step is that we don't
solve this problem of anisotropic resolution.
We simply accept the fact that,
let's say you have a point-like object,
the image that we get is ellipse,
so to store it at res-in-tay-shun.
So we have low-resolution along the axle component,
high resolution laterally.
But then in the next step what we do,
is we rotate the sample by 90 degrees to the microscope.
And so we caught a second view
of the same scene.
And so now, we've permuted
the dimensions along which resolutions
high and low, respectively, and
taken together, across these two images,
we have actually almost all of the frequency
content that we're interested in of our sample.
And we basically just have to register
these two views, combine them, for example,
for a process called multiview deconvolution,
and we have this neo-isotropic spatial resolution.
So, that would be a powerful way to improve
spatial resolution because we can just keep
our microscope and the way in which
we build our light sheets, the way it is.
Basically, the main challenge that
we have to solve now is to find a way
to record these multiple views at the same time.
Then we haven't sacrificed temporal resolution.
And so, Raghav Chhetetri post-doc in my lab,
addressed this problem, and built a microscope
that can do that.
And so yes, the idea.
The arrangement is a little bit more
complex than this basic idea.
As it describes,
we don't only have two views of the sample.
We actually have four opposing objectives,
so that gives us four following views,
which simply means that we can scale
this idea up to larger specimens that
are not entirely transparent.
So for for Drosophila and zebrafish,
it's actually very helpful.
Also for mouse embryos that we are studying.
But now, the problem is, if we
sent in four light sheets at the same time,
from all these four objectives,
and then take four images,
light-filled images of these objectives,
we've violated a fundamental concept
of the optical sectioning concept in
light-sheet microscopy.
So we've actually sent in light
in a way that now illuminates out-of-focus
structures with respect to the respective
other light sheets.
So we get out-of-focus light,
we basically degrade the image quality
by doing this at the same time.
So the way we can solve this is
that we don't actually use light sheets
in our light-sheet microscopes.
What we do is, we send in a very,
very thin pencil beam.
And so then, if you take this pencil beam
and you scan it up and down very quickly,
across your plane of interest,
and you just keep the camera shot open,
you get the equivalent of a light sheet.
And so we have a scanned light sheet.
And so, if you use this concept now,
and implement it in a way that...
The beams that come in from these different views
are slightly offset in space.
So we keep them maybe a couple of tenths
of microns apart, and then we scan them
across their respective planes at a constant pace,
so they maintain that same constant distance,
so they never cross their paths in real space.
Then all we need to do is match this up
with a set of confocal line detectors,
that really only detect the light
that comes from the beam and blocks
out the light that comes from the
ee-phon-or-al beam that is slightly offset in space.
It's just hitting the detector at the wrong spot.
Not where the rolling shut is at that point in time.
We have the same place offset
in our confocal line detectors.
Everything is matched up and synchronized,
and now we can scan four views at the same time,
without any signal crosstalk.
So that's the key idea in what we
call the isotropic multiview light-sheet microscope.
The ay-si-you microscope.
And so here's the way that we can
get with this approach.
Again, the low anasotropic resolution in the
contributing views, but if you
combine these views into one single image,
this is a multiview deconvolved image,
we now have isotropic resolution.
We can turn this in any way.
And you don't have any perceived difference in
spatial resolution.
This is a sheet cAMP expressing drosophila larva,
chee-cAMP throughout the entire nervous system.
And so let me just zoom into this cluster
of neurons so you can actually see the impact
on spatial resolution.
So now the contributing views,
we have one dimension along which,
it's very hard to distinguish these individual neurons.
They're just blurring into each other.
In the other set of views,
there's a different dimension, but the same problem.
But then combining all this information
into the IsoView construction, we now have high resolution,
from any view along the X, Y, Z in the axis.
And so we basically restored cellular resolution, even in
these deeper regions of the nervous system.
And so overall we find we have about
four nanometers spatial resolution.
And so, because we haven't sacrificed
temporal resolution, we can now do these
types of functional imaging experiments.
For example, imaging at the whole animal level,
the entire drosophila larva expressing
sheet cAMP, during motor behaviors.
You can see forward/backward crawling.
And then, sort of concurrent calcium activity that is
sort of orchestrating this behavior
in the ventral nerve course,
you see these waves in the vee-and-see
and sort of a similar context in the zebrafish.
So we have a larva zebrafish here,
that's executing different types of swimming behaviors.
And you can see this activity in the hind brain
and the spinal cord during these behaviors.
And so this is imaged at two hertz and this is at one hertz.
And it's just rotating the fish here.
So you can see, there's no change in resolution
as we rotate the volume.
So I think live imaging is where this
really unfolds its full potential.
But you can, of course, also use it for structural imaging.
Experiments with fixed samples.
And so the main gain is that you get isotropic resolution
and you can image it very quickly.
So this is now an expanded drosophila brain,
combined with the expansion microscopy.
So that makes it about two millimeters long,
along each side.
And so within about 15 minutes,
we get then 100 nanometer isotropic spatial resolution
across the brain.
And there's a sparse pattern of fruitless
neurons that are labeled here.
And so if you zoom in,
you can see the fine structural details,
which can aid connectomics-types analysis,
in relatively high throughput,
with these imaging experiments.
So let me just summarize this.
We have basically an improvement of
about 10-fold in resolution,
without sacrificing speed and field of view.
And in more absolute quantitative terms,
that means 400 nanometers spatial resolution,
system resolution, at sub-second time scales
for the volume of about 800 microm cubed.
And so I just showed you very short
sequences from actually longer imaging experiments.
We've imaged these samples for hours to days
in each of these cases,
so you can use it under physiological
conditions in this parameter space.
But one thing I'd like to point out is that,
intentionally, I set the system resolution here.
Which means, this is the performance we expect
on ideal imaging conditions,
if you have a fairly transparent sample.
And unfortunately, most of these we're looking at
are far from transparent,
and we end up in this realm of imaging
relatively large specimens for live-imaging,
drosophila larva, zebrafish brain.
They actually perturb the light that we
need to use to rely on, to do the measurement
in the first place.
And so this is a problem we need to solve.
Because the way it impacts our light shield microscope,
is that because of the heterogeneity,
the optical heterogeneity of the sample,
so this toy model of this cross section of, let's say,
this drosophila larva.
As the light sheets enter the sample,
they are deviated from the light path
to change their course.
And so this can change further inside the sample,
depending on the optical properties,
can change over time, you know,
as optical properties change, as the sample develops.
And the same thing happens in the detection process.
We have increasing curvature of...
The focal planes are not really clean planes anymore.
They get bent as a function of this spatial
location and a function of time.
And so bottom line is that we lose this
nice coplanarity that we rely on in the
light-shield microscope.
The light shield should be coplane with the focal plane
to get an in-focus image.
That's not really the case anymore,
and so we basically image out-of-focus,
and we get low-resolution and low image contrast.
And so, Loiic Royer, another post-doc in the lab,
addressed this problem in the following way.
Basically, developed, first of all...
So there are two components to this solution.
First of all, we need a microscope
that has access to the degrees of freedom
that we need to bend things back into shape.
We need to be able to rotate
and translate the light sheets and the focal
planes so we can actually fix the problem,
if you know what the problem is geometrically.
And so the second part is that we need to measure,
take the right measurements.
We have algorithms deployed on the microscope
that allow us to estimate image quality
as a function of changes in these degrees of freedom.
And basically, build a model of this sampling in a
background of the live-imaging experiment,
to constantly improve in space and time,
the spatial relationship between focal planes
and light sheets.
And so with this combined,
sort of adaptive imaging approach,
we can now hand the experiment over
to this real-time control layer,
and basically have it optimized in image quality.
For example, in the zebrafish embryo
that has the falling effect,
if you look at different regions here,
we're trying to track the cells as they're from
the nervous system.
We can't really, with all the adaptive imaging,
follow cells in many parts of the sample anymore, over time.
Just, image quality degrades,
and with the adaptive imaging approach,
we can recover that high resolution.
We can also use it in a functional imaging context.
So this is now a zebrafish larval brain.
Again, I'm showing you the quality
with the adaptive imaging engaged
and with all this framework.
And after some time of imaging,
you can see how quality really
degrades because as the brain is growing,
the fish is actually growing.
And we are changing the optical context
and losing the ability to really accurately
measure activity at the single-neuron level.
And with that, I'm at the end.
I'd just like to give you a quick outlook.
We are now integrating these imaging tools
with optical manipulation tools.
We don't only want to watch what's happening,
we want to control what's happening, manipulate it.
And so particularly, with a layer that allows us
to classify behavior in real time,
so that we can interact with behavior at different stages,
at different phases.
And for example, control the state that the network is in.
Control, for example, motor behavior
from forward-walking to backward-walking and so on.
And so that's the next stage now
for this technology, for us.
And so with that I'm at the end of the talk,
and just would like to acknowledge, very briefly,
Raghav Chhetri who built the IsoView microscope,
Leo Guignard who developed the adaptive imaging framework,
built a lot of the imaging,
helped a lot with these imaging experiments.
And then we have a lot of fantastic collaborators at Janelia
that contributed a lot to the work that I showed you here.
And Misha Ahrens and Kristin Branson, in particular,
to whom I am very grateful.
Alright.
Thank you.
(audience claps)
(mellow electronic music)