Transcriber: Robert Tucker Reviewer: Ariana Bleau Lugo
Complexity.
Nothing quite embodies this 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 at 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 vasculature
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 cerebral 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 prefrontal 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 in specific structures.
And each of them has their own sort of 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 glials 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,
got a pair from mom, a pair from dad,
on those chromosomes about 25,000 genes and we're very much again interested in
understanding which of these 25,000 genes are turned on
at what levels they're turned on.
Those are going to, of course, 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 as you saw before
for the kind of work we do, [it] 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 projects 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, 1 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!"
(Laughter)
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 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 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 a 25 micron 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 25 micron thin section.
You'll see da Vinci's tools, the paintbrush,
being used 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...
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 25,000 genes.
There's about 60,000 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 of 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 upon the heat color that's specified there.
So what are people doing when they start 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.
We've only 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 thousands of different measurements
of gene expression 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 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 a 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, as 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 very specific layers of the neocortex
only in one individual and 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 the cells, to how they're connected,
the underlying molecules that drive those connections,
the underlying molecules driving the electrophysiological properties,
the electrochemical 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 to 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.
(Applause)