Complexity.
Nothing quite embodies the word
like the human brain.
So for centuries we've studied
the complexity of the human brain
using the tools and technology of the day.
If that's pen and paper
from the age of da Vinci
through advents in microscopy
to be able to look more deeply
into the brain
to a lot of the new technologies
that you've heard about today
through imaging,
magnetic resonance imaging,
able to look a the details
of the brain.
Now one of the first things
you notice when you look
at a fresh human brain
is the amount of vasculatur
that's completely covering this.
The brain is this metabolically
voracious organ.
Approximately a quarter of the oxygen
in your blood,
approximately a fifth of the glucose
in your blood
is being used by this organ.
It's so metabolically active
there's a waste stream which comes out
into your cervical spinal fluid.
You generate 0.5 liter of CSF every day.
So, as you know, researchers
have taken advantage
of this massive amount of blood flow
and metabolic activity
to begin to map regions of the brain,
to functionally annotate the brain
in very meaningful ways.
You'll hear a lot more
about those kinds of studies,
but basically taking advantage of the fact
that there's active metabolism
with certain tasks going on.
You can put a living human in a machine
and you can see various areas
that are lighting up.
For example, going around right now
is the temporal cortex auditory
processing going on there,
you're listening to my words,
you're processing what I'm saying.
Moving to the front of this brain
is your prefontal cortex,
your executive decision-making,
your higher-thinking areas
of the brain.
And so the thing that
we're very much interested in
from the perspective
of the Allen Institute
is to go deeper,
to get down to the cellular level.
So when you look at this slice, it doesn't
really look like gray matter, does it?
It's more tan matter, or beige matter.
And scientists about, I guess,
around the late 1800's
discovered that they could stain tissue
in various ways,
and this sort of came along
with various microscopy techniques.
And so this is a stain, it's called Nissl,
and it stains cell bodies,
it stains the cell bodies purple.
And so you can see
a lot more structure and texture
when you look at something like this.
You can see the outer layers of the brain
and the neocortex,
there's a six-layer structure,
arguably what makes us most uniquely human.
As you've heard before about
there's on average in a human
there's about 86 billion neurons
and those 86 billion neurons
you can see are not evenly distributed,
they're very focused and specific structures.
And each of them
has their own specific function
both on an anatomic level
and at a cellular level.
So if we zoom in on these cells,
what you can see is large cells
and small support cells
that are glias and astrocytes
and these cells are as we know
connected in a variety of different ways.
And we like to think about
although there's 86 billion cells,
each cell might be considered a snowflake,
they're actually able to be binned
into a large number
of cell types or classes.
What flavor of activity
that particular cell class has
is driven by the underlying genes
that are turned on in that cell,
those drive protein expression
which guide the function of those cells,
who they're connected to,
what their morphology is
and we're very much interested
in understanding these cell classes.
So how do we do that?
Well, we look inside the cell
at the nucleus,
and it will get to the nucleus,
and so inside we've got
23 pairs of chromosomes,
we've got a pair from mom,
a pair from dad,
on those chromosomes about 25000 genes
and we're very much again interested in
understanding which of these 25000 genes
are turned on and
at what levels they're turned on.
Those are going of course to drive
the underlying biochemistry of the cells
they're turned on in and again every cell
in our bodies more or less has these
and we want to understand better
what the driving biochemistry
driven by our genome is.
So how do we do that?
We're going to deconstruct a brain
in several easy steps.
So we start at a medical examiner's office.
This is a place
where the dead are brought in
and obviously it's useful,
for the kind of work we do
is not non-invasive,
we actually need
to obtain fresh brain tissue
and we need to obtain it within 24 hours
because the tissues start to degrade.
We also wanted for our projects304306.1
to have normal tissue
as much normal as we could possibly get.
So over the course of a two-
or three-year collection time window
we collected 6 very high-quality brains,
5 of them were male, one was female,
That's only because males
tend to die untimely deaths
more frequently than females
and then to add to that females
are much more likely to give consent
for us to take the brain than vice versa.
We have to figure that one out.
We've heard people say,
“He wasn't using it anyway!”
So, once the brain comes in
we have to move very, very quickly.
So first we capture
a magnetic resonance image.
This, of course,
will look very familiar to you,
but this is going to be the structure
in which we hang all of this information,
it's also a common coordinate framework
by which the many, many researchers
who do imaging studies can map
into our ultimate database,
an Atlas framework.
We also collect diffusion tensor images
so we get some of the wiring
from these brains
and then the brain is removed
from the skull. It's slabbed and frozen,
frozen solid,
and then it's shipped to Seattle
where we have
the Allen Institute for Brain Science.
We have great technicians
who've worked out
a lot of great techniques
for further processing.
So first, we take a very thin section,
this is 25µm thin section,
which is about a baby's hair width.
That's transferred to a microscope slide
and then that is stained
with one of those histological stains
that I talked about before.
And this is going to give us more contrast
as our team of anatomists
start to make assignments of anatomy.
So we digitize these images,
everything goes from being wet lab
to being dry lab.
And then combined with anatomy that we get
from the MR we further fragment the brain.
This is to get it into a smaller framework
for which we can do this.
So here's a technician
who's doing additional cutting.
This is again a 25µm thin section.
You'll see da Vinci's tools, the paintbrush,
being use here to smooth this out.
This is fresh frozen brain tissue.
And it can be very carefully
melted to a microscope slide.
You'll note
that there's a barcode on the slide.
We process 1000's and 1000's of samples,
we track all of it
in a backend information management system.
Those are stained.
And then we get
more detailed anatomic information.
That information is, playing here,
this is a laser capture microscope,
the lab technician is actually describing
an area on that slide.
And a laser,
you see the blue light cutting around there,
very James Bond like.
Cutting out part of that,
and underneath there,
you can see the blue light again,
from the microscope in real-time.
It's collecting in a microscope tube that tissue.
We extract RNA,
RNA is the product of the genes
that are being turned on,
and we label it,
we put a fluorescent tag on it.
Now what you are looking at here
is a constellation of the entire human genome
spread out over a glass slide.
Those little bits are representing
the 25000 genes.
There's about 60000 of these spots
and that fluorescently labeled RNA
is put onto this microscope slide
and then we read out quantitatively
what genes are turned on at what levels.
So we do this over and over and over again
for brains that we've collected.
As I mentioned we've collected
6 brains in total.
We collect samples
from about 1000 structures in every brain
that we've looked at,
so it's a massive amount of data.
And we pull all of this together,
back into a common framework,
that is a free and open resource
for scientists around the world to use.
So at the Allen Institute for Brain Science,
we've been generating
these kinds of data resources
for almost a decade.
They're free to use for anybody,
they're online tools,
just for example today a given workday,
there'll be about 1000 unique visitors
that come in from labs around the world
to come use our resources and data.
They get access to tools like this,
which allows them
to see all that anatomy
and the structure that we created before
and to start mapping in then the things
that they're particularly interested in.
So in this case you're looking
at the structure
and they're going to look
at these color balls
are representing a particular gene
they're interested in that's
either being turned up or down
in those various areas depending
on the heat color that's specified there.
So what are people doing when they come in
and using these resources?
Well, one of the things
that you might hear lots about
is human genetic studies.
Obviously if you're very interested
in understanding disease
there's a genetic underpinning
to many of them.
So you'd like more information,
you do a large-scale study
and you get out of those studies
collections of genes
and one of the first things
you're going to want to know
is more information.
Is there something I can learn
about the location of these genes
that gives me additional clues
as to their function,
ways in which I might intervene
in the disease process.
They're also very interested
in understanding human genetic diversity.
Now we've already looked at 6 brains
but as we know, every human is very unique.
We celebrate our differences;
this is a snapshot of the great workforce
at the Allen Institute for Brain Science
who does all the great work
that I'm talking about today.
But remarkably when we look at this level
at the underlying data
and this is a lot of data from 2 completely
unrelated individuals
there's a very high degree
of correlation, correspondence.
So this is looking at 1000's
of different measurements of gene expressions
across many, many different
areas of the brain.
And there's
a very high degree of correspondence.
This was very reassuring to us.
First because when you generate data
on this scale
you want to make sure that it's high quality,
so reproducibility is obviously important,
but it was also important
because we feel that it's given us
a great snapshot into the human brain.
And the people using the data,
even with our low n have confidence
that what they're seeing has some relevance.
Now not everything is correlated here,
you can see some outliers,
and of course those outliers
are going to be interesting
related to human differences.
We did study a couple of years ago
in which we tried to understand
a little better about those differences
and looked at multiple individuals
and different gene products
and what we find is that a tendency
and as a rule is that those differences
tend to be in very specific
cell populations or cell types,
cell classes as I mentioned before.
So, this is an example
of 2 different genes that are turned on
in a very specific layers
of the neocortex only in one individual
nd not found in another.
Now we have no idea
if that's due to environmental changes,
environmental influences
or if it's just genetics.
But we did do a study in which we looked
at the mouse several years ago
and we were looking at genes
that encode for, in this case a DRD2,
the gene listed on the top
is a dopamine receptor.
Tyrosine hydroxylase (TH)
is a gene involved in dopamine biosynthesis
and those 2 gene products
are very different in the cell types
in these individual mouse brains.
So, over on the left is “C57 black 6”
which is a commonly used mouse strain,
and then spread at the other end
is a wild type strain.
And so the further you go
the more genetically unrelated you are.
And when we looked in total across,
sort of evolution if you will,
across genetic relatedness,
the further you were genetically unrelated,
the more of these very specific cell types,
specific changes, you could see.
So at the Allen Institute
for the next decade
we're embarking
on a pretty ambitious program
to start to understand the cell types,
understand the cell differences
and how they ultimately relate
to the functional properties of the brain.
This is, I think, critical information
for the entire field,
to start linking up all
of these fundamental parts which are cells,
to how they're connected,
the underlying molecules
that drive those connections,
the underlying molecules
that drive the physiological properties,
the electric chemical properties
and then ultimately
the functional properties of those cells.
So we're doing this
in 3 different areas of research.
First we're focusing on the mouse,
the mouse visual system,
to look at, in real-time,
in the living animal
the functions of a variety
of different cells.
We're linking these in this concept
in the middle of cell types,
trying really understand
the underlying molecules
in all the properties
as they relate to those functions
and then we're looking at the human.
In the human we're doing this both
in the middle and cell types
using the tissue driven work
that I talked about before
but also we're doing it in vitro
using stem cell technology.
We're learning
how to make very specific cell types
within the dish
and then being able to test
those functional properties
and go back and forth
between what we learn in the mouse
to the human.
So, with that I will finish
and just say that it's an exciting time
to be in biology
and an exciting time
to be in neuroscience.
I think the technology of the day
has come well beyond the pen and paper
and it's really time for a renaissance in
our understanding of this complex organ.
Thanks.