- So I'd like to start by sharing a personal story.
So all scientists have I think,
a memory, a story that they can go back
to when they think about they became interested
in science and I wanted to share mine.
This is mine here, these are baby leatherback
turtles that are hatching from a nest
not far away from where I was born
and raised in Puerto Rico and when I was a child there,
I always found fascinating that these animals
are coming out of the nest and they
haven't been to turtle school
and they know exactly where to go.
They are bee-lining it towards the ocean.
There's something really profound happening here also,
very important for the turtle, but invisible to our eyes.
Which is that these turtles are forming a memory.
They are forming a memory of this beach.
These are leatherback turtles, they grow up to be
about the size of a Volkswagen, about nine feet long.
And they will travel the world's oceans
and decades later when they have to make
what is perhaps the most important decision
in a turtles life, which is where
its gonna lay it's eggs,
it will remember this beach,
because that's where it was born
and it'll come back to it and lay its eggs at that place.
So with that I'd like to course grain behaviors
and essentially group them into innate or hardwired
behaviors like these animals capacity to know
to go towards the ocean when they're born,
and experiential memories like their capacity
to remember this specific beach and come back to it.
Now innate behaviors are facilitated
by the developmental program that leads to the
formation of the neural circuit architectures.
And experiential memories are facilitated
by a pre-complex interplay between that architecture
that develops, and the environment
that the animal is experiencing,
what the animal is seeing.
But I wanna emphasize that both of them
are ultimately facilitated by the architecture
of the nervous system.
So what do I mean by the architecture
of the nervous system, and this is essentially what I mean.
If you look at a mouse hippocampus for example,
you'll see these incredibly exquisite organization
of the neurons.
This is actually an image here using a technique
called Brainbow by Jeff Lichtman at Harvard.
And this organization raises all sorts
of interesting questions, one of them
which my lab is interested on,
is how is it that this organization is actually established?
And in thinking on how this organization is established,
if you take for an example the case of the human brain,
you have about 80 billion neurons.
That's about, that's more neurons
in a healthy human brain that you have,
starts in the milky way and about a hundred
trillion synapses and during development
you have hundreds of millions of neurons
almost simultaneously during development,
specifying fate, growing out axions,
connecting to each other, finding each other
in a very specific way to layout
this amazing architecture.
So how does that happen?
What are the organizing principles
that rules this organization that ultimately
underpin human behaviors?
Now we look at this question and we don't do so
in turtles or humans or mice actually,
we do so in a worm.
It's a tiny Nematode called C.elegans, it's over here.
One of the characteristics that C.elegans has
that we really like is that it's transparent,
so it allows us to look at the organization
of the nervous system which is very finely organized
as you can see here.
Now I wanna acknowledge that C.elegans
is far simpler than a healthy human brain,
so instead of 100 billion neurons,
C.elegans has exactly 302 neurons
and instead of 100 trillion synapses,
C.elegans has approximately 7,000 synapses.
So some of you might be wondering,
how is it that we can extract any useful information
out of such a simple system that will tell us
anything useful about a complex organ like the human brain?
And to address this, I'd like to use a quote
by Diderot That said, "A worm is only a worm.
"But that only means that the marvelous complexity
"of its organization is hidden from us
"by extreme smallness."
Now, I'm not as eloquent as Diderot
so I'll say it in a wordier way.
When nature finds a solution to a problem,
it recycles that solution over and over again.
So the fundamental principles that allows
for the proper functioning of the nervous system
in this tiny worm, are not unlike the fundamental principles
that facilitate the functioning of our own brains.
And to exemplify that, I'll just give a few examples.
Studies done on this worm by other groups,
ranging from the development of neurons
like sulfate specification or axion guidon,
to fundamental physiology having to do
with the biogenesis of neurotransmitters
or neurotransmitter release, how neurons
talk to each other, to even systems levels
examination of sensory perception and behavior
all done in this Nematode have actually shed light
on important and fundamental aspects about how
our own brains and brains in higher meta neurons work.
So it's actually, it's really true that evolution
kind of conserves these principles.
So using that as a foundation,
we wanted to essentially examine this question
of how this nervous system comes together.
And both are studies and the studies I just mentioned,
benefited from the fact that this is also
the only animal for which we have a wiring diagram.
Now we're hoping this is gonna change soon
and you're gonna hear other talks today
that are working very hard to make this
a thing of the past and we're very happy
that that's the case, but today this the only animal
for which we have that wiring diagram.
So what is the wiring diagram?
Is the ability to know where each neuron is,
essentially the morphology of the neuron
and what that neuron is connecting to.
And we use this wiring diagram
to examine two fundamental questions.
One of them is, how is it that the brain
of this animal is organized?
And the second one is, how does this organization
comes out during development?
Now we've had the wiring diagram
for about 30 years.
The first wiring diagram was done in 1986
and we still have these two questions
that we need to address.
And I'll explain what we know
and what we don't know in the next slide.
So the way that the wiring diagram was originally done,
was that somebody took, a group of scientists
led by John White took this animal
and essentially fixed it and sliced it like a salami.
So imagine that you're slicing all over the animal here
and then you get this cross sections.
And then they did electro microscopy on the cross sections
and by segmenting each of the neurons
which we are putting here in different colors,
by hand, this is 1986, they segmented them by hand.
They were able to recreate the connectivity map
for every single cell.
Now this was incredibly powerful
for the field and it has benefited our studies,
but I just wanna emphasize that all of this was,
it wasn't digitalized.
It was all done by hand.
So you could go neuron by neuron
and you can kind of tell the shape
and who it connected to, but we didn't have
a systems level understanding
about how this happened until recently,
where Scott Emmons at Albert Einstein University
and his student Steve Cook actually went in
and did the same segmentation that John White did
30 years ago, but with computers.
What that allows us to do is to be able to know
who's neighboring who in a quantitative way.
In a way that we can actually analyze.
And we can analyze using tools not unlike the tools
that are being used by companies
like Twitter and Facebook to understand
interactions between human beings.
So our collaboration included computational
scientists Smitha and Alex and what they did
is that they took that data
and we worked together with them
in using clustering algorithms.
Again similar to clustering network algorithms
that are used to understand personal interactions.
And cluster neurons with similar contact profiles,
so essentially neurons that are contacting
each other as they're traveling together
in the nervous system in fascicles.
And iterative clustering to understand
how that structure of the brain is organized.
And you end up with essentially a flow diagram
like this where in one corner you have
all the neurons and this clustering algorithms
iteratively bring them together.
So this different clustering of the different
sub-groups of neurons that eventually
cluster into these larger families.
What it represents, what underlies
that is real biology of how these neurons
are interacting with each other.
Real biology that we, that was inaccessible before
we were able to digitize these programs.
And we're capable of overlaying
that on top of the original EM micrographs.
Each of these pseudo colors here
that we have placed represent a community
of neurons that are interacting together
more than the other neurons.
I'm gonna just walk you through the brain
of the animal and this is gonna be playing
and moving so that you can see how the different
communities are actually snaking
and how they relate to each other
and how they come together.
As this movie plays, I'll explain that what I'm showing
you here is actually reproducible across the animals
for which we have connectomes.
So this has been a really fun project
because we were able to work
with the computational biologist
informthe algorithms with the biology
and use the algorithms to inform our own biology.
We were able to find four main bundles,
that account for 83% of the whole connectomes.
So now we have a pretty good idea
about how these different brain regions
are organized and this neighborhoods,
I just mentioned that they reveal
important biological insights.
So there's a reason why they're organized this way
that have to do with development and function and I'm,
I could give a one hour talk about this.
So I'm gonna use one example to present this,
and I'll talk about how these neighborhoods
led to insights about how this organization
is actually established during development.
And this gets to a fundamental question
that is a blind spot in the field.
Which is how do you go from a group of cells
in an embryo to this organized connectome in the worm?
And this, this is essentially
a question that we wanted to understand.
And the reason that this has been a blind spot
is that for many organisms this part
of the development happens inside,
like for example in mammals,
it happens inside the uterus,
so the organisms are essentially inaccessible.
In C.elegans, they lay eggs so the organism is accessible,
but there were a number of challenges
that have prevented in the past,
like four or five decades to image
embryonic neuro development.
So this is a pretty large blind spot.
We have this information but we didn't know
how this was happening.
The reason that imaging neuro development in C.elegans
was challenging, were multi-fault.
One of them is that, embryos when they're inside the egg,
they're moving very quickly.
So if you're looking for example,
the development of a single neuron
using even the fastest microscopy that we had available
at the time which spinning disc microscopy
for the aficionados in the audience.
This is a single neuron and you get a motion blur
where it looks like four neurons.
Because the animal is moving and you're taking
different pictures so it looks like you're taking
four neurons there.
The other problem that it's hard to capture
in an image is the animals just die.
So when you're imaging for prolonged periods of time,
which is what you want to do,
to be able to capture these different
neurodevelopmental events, many of the embryos
just die so you're end up taking
snapshots at different point of development
that you have to stich together later.
And neurons are actually pretty thin.
These are, they're near the fraction limit of light.
So and they're traveling in ways
that are not convenient for imaging.
They're not traveling in planes,
they don't care about our imaging needs.
They're actually snaking in through
the whole nervous system so we needed methods
that allow us to have good resolution,
not just in two dimensions but in all three dimensions.
So to address this, we established a collaboration
with a fabulous Microscopist, Hari Shraff
and his team of scientists.
And Hari essentially, we had these discussions
together and he came up with the design of this microscope.
I won't go into the nuts and bolts
about how this microscope work,
but what I will tell you is the aspects
of this microscope that makes it usable for our studies.
This microscope is called a light sheet microscope
which essentially means that instead of imaging
a single point of light like many other microscopes do,
it creates a whole sheet of light
so it's much faster than regular microscopes
because you're, it's like in Star Trek when they scan you,
they're just scanning you point by point.
So it's faster, because it's faster,
it exposes the animal to less light.
So less photo toxicity also the way the light
is generated to be able to image
this animal exposes the animals to less light.
And the other aspect of this is that,
for the microscopist in the audience,
or people that have done microscopy,
they will recognize that when you take
a three dimensional image of an object
in a microscope, the resolution in X Y
is always really good, but when you turn it
the resolution you see is always bad.
And that has to do with just the physics
of light which I'm not going to go into,
but I will say that X, Y, and Z
is relative to how you're standing.
So you guys right now are in my Z,
but if I was standing this way,
that will be my Z, so if I had two images,
where the Z varied, then I could combine them
to essentially have an isotropic image.
So if I could, so if I look at this object like this,
this is my X, Y, and then I look at it this way,
and this is my X, Y, and I combine those two images,
it's the same resolution in all three axis.
And this is essentially what we're doing
with this microscope because we have two cameras.
This one illuminates, this one image
and then they take turns, you end up with two images
that you can then fuse and the Z of this axis
is different from this one.
So again, I'm discussing this superficially
because I know Phil Keller will discuss this in more detail.
So we have this microscope working in my lab
and this is a slide that I put here
just to acknowledge two institutions
the Marine Biological Laboratories
and the University of Puerto Rico,
which were meeting places where technologies
like Microscopy, like people from Hari's group,
and biologists from my group were able
to come together and exchange ideas.
And I think that these places are gonna be increasingly
more important for interdisciplinary collaborations
necessary to address these problems.
So what can we do with this?
So this is actually the brain
of the animal here in an embryo, the tail,
the head, and that's the brain,
that ring that you're seeing there.
And I'm gonna play a movie where we can actually see
the development of this animal in real time,
as a biologist, this is spectacular that we can see this.
You're gonna see the animal starts to move,
he starts to twitch as the nervous system comes online,
but even when it's twitching, you can get very crisp images.
Now if you look at subsets of neurons,
which we're going to do here,
you see sub-cellular markers,
or markers that are more restricted,
you can still see, you can see neurides,
specific neurides growing into the brain of the animal.
This is all taken again in live animals
and essentially you can stop it at anytime
and you can rotate it, so I'm doing two
dimensional projections because I'm using a screen,
but these are three dimensional data,
so as you can see the resolution
very good in all angles that you look at it.
And you can continue the movie.
So this gives us unprecedented access
to events that are leading to the formation
of this nerve ring.
And then we had another collaboration
that was really enabling which is that
in C.elegans we know the linage of every single cell.
We've known that since the animal
was developed as a model organism.
That's how the original studies
on programmed cell death were done.
And this person, Zhrong Bao, he's a scientist
at Sloan Kettering who's a computational biologist
and a developmental biologist.
And what I'm gonna be playing here
is an embryo that is in a four cell stage.
Now this is like playing a movie
that you know what's gonna happen,
because we know the lineage.
So we know for each one of this cells,
we know who their progeny is gonna be
because those studies have been done before,
but what he did is that he trained a computer
to be able to recognize this in real time.
So as the animal is developing,
the computer can keep track of all of this nuclei
and he knows what each cell is gonna become.
So why is this important to us?
It's important for two reasons:
one of them is because when we're marking neurons,
as we're gonna be doing here,
we can tell their identity because if we have nuclei
that we're labeling in the background in red,
then we can keep the identity of every single one
of these neurons that are emerging.
But the other aspect is that we have
an internal coordinate system,
as the neurons are growing out,
and crossing the different nuclei,
we know where each neuron is,
so if we take different embryos,
and we're imaging different embryos,
we can overlay them because we essentially
have a multi-point internal coordinate system.
Does that makes sense?
So what can we do with that?
Here I'm gonna summarize the work
of a post doc in my lab, Mark Moyle,
this is five years of work summarized in one slide.
So essentially, what Mark did is he was interested
in identifying the pioneering neurons
that lead to the formation, that trail blaze
the formation of the brain of the animal.
So he looked in embryos he labeled O membranes
and he found the first membranes that are formed
that are part of the brain, he traced them
back to the cells here, we're pseudo coloring
here so you can see them, then if he looked
at the nuclei he can actually identify
by name and last name those specific cells,
then he can image as I mentioned before
the development of the brain, so he can take
the cells and kill them and the hypothesis is,
if you kill them because they're the first cells,
if the first cells are important,
then you shouldn't be able to develop a brain.
And we get these brainless animals,
in which we cannot see, essentially the brain
is really abrogated so these neurons
are actually very important, and it turns out
when we look back in the maps that we have created,
with the computational biologist,
that this pioneer cells are actually the cells
that are that are here in purple,
and they're like SIM cells, they're cells
that are holding together the whole nerve ring.
And with that, I'd like to essentially bring you
to the attention of how we can both look
at how the brain is organized and how the organization
is established during development and I'll finish
by saying that what we have been able to do with this
and our aspiration is to create what will be
the first map of meta neuro development for any animal.
So we're systematically tracking all of the ascensions
of this neurons and creating these virtual embryo
that allows examination of all of these decisions
in the contest of developing embryo
which we think will be enabling for neuroscience.
And with that, I'll finish by thanking
the people that did the work.
This motley crew and you for listening to me today.
Thank you.
(audience applauding)
(relaxing music)