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Complexity.

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Nothing quite embodies this word
like the human brain.

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So for centuries we've studied
the complexity of the human brain

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using the tools
and technology of the day.

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If that's pen and paper
from the age of da Vinci

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through advents in microscopy

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to be able to look more deeply
into the brain

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to a lot of the new technologies
that you've heard about today

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through imaging,
magnetic resonance imaging,

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able to look at the details
of the brain.

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Now one of the first things
you notice when you look

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at a fresh human brain
is the amount of vasculatur

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that's completely covering this.

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The brain is this metabolically
voracious organ.

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Approximately a quarter of the oxygen
in your blood,

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approximately a fifth of the glucose
in your blood

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is being used by this organ.

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It's so metabolically active
there's a waste stream

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which comes out
into your cervical spinal fluid.

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You generate 0.5 liter
of CSF every day.

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So, as you know, researchers
have taken advantage

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of this massive amount of blood flow
and metabolic activity

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to begin to map regions of the brain,
to functionally annotate the brain

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in very meaningful ways.

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You'll hear a lot more
about those kinds of studies,

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but basically taking advantage of the fact
that there's active metabolism

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with certain tasks going on.

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You can put a living human
in a machine

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and you can see various areas
that are lighting up.

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For example, going around right now
is the temporal cortex

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auditory processing going on there,
you're listening to my words,

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you're processing what I'm saying.

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Moving to the front of this brain
is your prefontal cortex,

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your executive decision-making,

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your higher-thinking areas
of the brain.

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And so the thing that
we're very much interested in

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from the perspective
of the Allen Institute

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is to go deeper,
to get down to the cellular level.

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So when you look at this slice, it doesn't
really look like gray matter, does it?

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It's more tan matter, or beige matter.

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And scientists about, I guess
around the late 1800's,

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discovered that they could stain tissue
in various ways,

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and this sort of came along
with various microscopy techniques.

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And so this is a stain, it's called Nissl,
and it stains cell bodies,

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it stains the cell bodies purple.

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And so you can see
a lot more structure and texture

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when you look at something like this.

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You can see the outer layers of the brain
and the neocortex,

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there's a six-layer structure, arguably
what makes us most uniquely human.

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As you've heard before about
there's on average in a human,

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there's about 86 billion neurons,
and those 86 billion neurons

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you can see are not evenly distributed,

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they're very focused
and specific structures.

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And each of them
has their own sort of function

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both on an anatomic level
and at a cellular level.

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So if we zoom in on these cells,
what you can see is large cells

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and small support cells
that are glias and astrocytes

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and these cells are as we know
connected in a variety of different ways.

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And we like to think about,
although there's 86 billion cells,

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each cell might be considered a snowflake,
they're actually able to be binned

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into a large number
of cell types or classes.

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What flavor of activity
that particular cell class has

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is driven by the underlying genes
that are turned on in that cell,

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those drive protein expression
which guide the function of those cells,

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who they're connected to,
what their morphology is,

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and we're very much interested
in understanding these cell classes.

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So how do we do that?

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Well, we look inside the cell
at the nucleus,

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(and it will get to the nucleus)

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and so inside we've got
23 pairs of chromosomes,

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we've got a pair from mom,
a pair from dad,

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on those chromosomes about 25000 genes
and we're very much again interested in

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understanding which of these 25000 genes
are turned on

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at what levels they're turned on.

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Those are going to, of course, drive
the underlying biochemistry of the cells

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they're turned on in and again every cell
in our bodies more or less has these

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and we want to understand better

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what the driving biochemistry
driven by our genome is.

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So how do we do that?

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We're going to deconstruct a brain
in several easy steps.

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So we start
at a medical examiner's office.

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This is a place
where the dead are brought in

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and obviously it's useful

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for the kind of work we do,
[it] is not non-invasive,

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we actually need
to obtain fresh brain tissue

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and we need to obtain it within 24 hours
because the tissues start to degrade.

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We also wanted for our projects
to have normal tissue

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as much normal as we could possibly get.

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So over the course of a two-
or three-year collection time window

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we collected 6 very high-quality brains,
5 of them were male, one was female.

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That's only because males
tend to die untimely deaths

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more frequently than females,
and then to add to that,

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females are much more likely
to give consent

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for us to take the brain
than vice versa.

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We have to figure that one out.

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We've heard people say,
“He wasn't using it anyway!”

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(Laughter)

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So, once the brain comes in
we have to move very, very quickly.

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So first we capture
a magnetic resonance image.

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This, of course,
will look very familiar to you,

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but this is going to be the structure
in which we hang all of this information,

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it's also a common coordinate framework
by which the many, many researchers

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who do imaging studies can map

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into our ultimate database,
an Atlas framework.

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We also collect diffusion tensor images

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so we get some of the wiring
from these brains.

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And then the brain is removed
from the skull. It's slabbed and frozen,

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frozen solid,
and then it's shipped to Seattle

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where we have
the Allen Institute for Brain Science.

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We have great technicians
who've worked out

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a lot of great techniques
for further processing.

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So first, we take a very thin section,
this is 25µm thin section,

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which is about a baby's hair width.

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That's transferred to a microscope slide
and then that is stained

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with one of those histological stains
that I talked about before.

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And this is going to give us more contrast
as our team of anatomists

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start to make assignments of anatomy.

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So we digitize these images,

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everything goes from being wet lab
to being dry lab.

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And then combined with anatomy
that we get from the MR,

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we further fragment the brain.

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This is to get it into a smaller framework
for which we can do this.

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So here's a technician
who's doing additional cutting.

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This is again a 25µm thin section.

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You'll see da Vinci's tools,
the paintbrush,

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being use here to smooth this out.

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This is fresh frozen brain tissue.

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And it can be very carefully
melted to a microscope slide.

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You'll note
that there's a barcode on the slide.

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We process 1000's and 1000's of samples,

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we track all of it in a backend
information management system.

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Those are stained.

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And then we get
more detailed anatomic information.

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That information is ... (playing here)
... this is a laser capture microscope,

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the lab technician is actually describing
an area on that slide.

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And a laser, you see the blue light
cutting around there,

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very James Bond like.

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Cutting out part of that,
and underneath there,

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you can see the blue light again,
from the microscope in real-time,

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it's collecting in a microscope tube
that tissue.

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We extract RNA,

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RNA is the product of the genes
that are being turned on,

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and we label it,
we put a fluorescent tag on it.

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Now what you are looking at here

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is a constellation
of the entire human genome

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spread out over a glass slide.

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Those little bits are representing
the 25000 genes.

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There's about 60000 of these spots
and that fluorescently labeled RNA

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is put onto this microscope slide
and then we read out quantitatively

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what genes are turned on at what levels.

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So we do this over and over and over again
for brains that we've collected;

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as I mentioned we've collected
6 brains in total.

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We collect samples
from about 1000 structures in every brain

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that we've looked at,
so it's a massive amount of data.

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And we pull all of this together,
back into a common framework,

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that is a free and open resource
for scientists around the world to use.

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So at the Allen Institute
for Brain Science,

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we've been generating these kinds
of data resources for almost a decade.

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They're free to use for anybody,
they're online tools,

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just for example today a given workday,
there'll be about 1000 unique visitors

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that come in from labs around the world
to come use our resources and data.

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They get access to tools like this,
which allows them to see

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all of that anatomy and the structure
that we created before

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and to start mapping in then the things
that they're particularly interested in.

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So in this case you're looking
at the structure

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and they're going to look
at these color balls

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are representing a particular gene
they're interested in

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that's either being turned up or down

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in those various areas depending upon
the heat color that's specified there.

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So what are people doing when they come in
and using these resources?

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Well, one of the things
that you might hear lots about

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is human genetic studies.

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Obviously if you're very interested
in understanding disease

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there's a genetic underpinning
to many of them.

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So you'd like more information,
you do a large-scale study

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and you get out of those studies
collections of genes

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and one of the first things you're going
to want to know is more information.

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Is there something I can learn
about the location of these genes

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that gives me additional clues
as to their function,

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ways in which I might intervene
in the disease process.

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They're also very interested
in understanding human genetic diversity.

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Now we've already looked at 6 brains but
as we know, every human is very unique.

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We celebrate our differences;

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this is a snapshot of the great workforce
at the Allen Institute for Brain Science

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who does all the great work
that I'm talking about today.

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But remarkably when we look at this level
at the underlying data,

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and this is a lot of data from
2 completely unrelated individuals,

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there's a very high degree
of correlation, correspondence.

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So this is looking at 1000's
of different measurements

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of gene expression across
many, many different areas of the brain;

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and there's
a very high degree of correspondence.

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This was very reassuring to us.

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First because when you generate data
on this scale

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you want to make sure
that it's high quality,

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so reproducibility is obviously important,

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but it was also important
because we feel that it's given us

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a great snapshot into the human brain.

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And the people using the data,
even with our low n, have confidence

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that what they're seeing
has some relevance.

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Now, not everything is correlated here,
you can see some outliers,

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and, of course, those outliers
are going to be interesting

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related to human differences.

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We did study a couple of years ago

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in which we tried to understand
a little better about those differences

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and looked at multiple individuals
and different gene products

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and what we find as a tendency
and as a rule

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is that those differences tend to be
in very specific cell populations

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or cell types, cell classes,
as I mentioned before.

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So, this is an example
of 2 different genes that are turned on

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in very specific layers
of the neocortex

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only in one individual
and not found in another.

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Now we have no idea
if that's due to environmental changes,

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environmental influences
or if it's just genetics,

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but we did do a study in which we looked
at the mouse several years ago

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and we were looking at genes
that encode for, in this case a DRD2,

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the gene listed on the top
is a dopamine receptor.

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Tyrosine hydroxylase (TH) is
a gene involved in dopamine biosynthesis

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and those 2 gene products
are very different in the cell types

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in these individual mouse brains.

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So, over on the left is “C57 black 6”
which is a commonly used mouse strain,

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and then spread at the other end
is a wild type strain.

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And so the further you go
the more genetically unrelated you are.

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And when we looked in total across,
sort of evolution if you will,

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across genetic relatedness,

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the further you were
genetically unrelated,

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the more of
these very specific cell types,

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specific changes, you could see.

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So at the Allen Institute
for the next decade

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we're embarking
on a pretty ambitious program

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to start to understand the cell types,
understand the cell differences

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and how they ultimately relate
to the functional properties of the brain.

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This is, I think, critical information
for the entire field,

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to start linking up all
of these fundamental parts

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which are the cells,
to how they're connected,

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the underlying molecules
that drive those connections,

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the underlying molecules
that drive the physiological properties,

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the electric chemical properties

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and then ultimately
the functional properties of those cells.

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So we're doing this
in 3 different areas of research.

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First, we're focusing on the mouse,
the mouse visual system,

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to look at, in real-time,
in the living animal,

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the functions of a variety
of different cells.

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We're linking these in this concept
in the middle of cell types,

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trying to really understand
the underlying molecules

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in all the properties
as they relate to those functions

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and then we're looking
at the human.

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In the human we're doing this both
in the middle and cell types

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using the tissue driven work
that I talked about before

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but also we're doing it in vitro
using stem cell technology.

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We're learning how to make
very specific cell types within the dish

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and then being able to test
those functional properties

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and go back and forth between
what we learn in the mouse to the human.

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So, with that I will finish
and just say that it's an exciting time

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to be in biology and an exciting time
to be in neuroscience.

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I think the technology of the day
has come well beyond the pen and paper

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and it's really time for a renaissance in
our understanding of this complex organ.

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Thanks.

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(Applause)