0:00:09.000,0:00:11.000
Complexity.

0:00:11.000,0:00:15.000
Nothing quite embodies this word[br]like the human brain.

0:00:15.000,0:00:19.000
So for centuries we've studied[br]the complexity of the human brain

0:00:19.000,0:00:22.000
using the tools[br]and technology of the day.

0:00:22.000,0:00:26.000
If that's pen and paper[br]from the age of da Vinci

0:00:26.000,0:00:29.000
through advents in microscopy

0:00:29.000,0:00:32.000
to be able to look more deeply[br]into the brain

0:00:32.000,0:00:36.000
to a lot of the new technologies[br]that you've heard about today

0:00:36.000,0:00:38.000
through imaging,[br]magnetic resonance imaging,

0:00:38.000,0:00:41.000
able to look at the details[br]of the brain.

0:00:41.000,0:00:44.000
Now one of the first things[br]you notice when you look

0:00:44.000,0:00:48.000
at a fresh human brain[br]is the amount of vasculatur

0:00:48.000,0:00:51.000
that's completely covering this.

0:00:51.000,0:00:56.000
The brain is this metabolically[br]voracious organ.

0:00:56.000,0:01:01.000
Approximately a quarter of the oxygen[br]in your blood,

0:01:01.000,0:01:04.000
approximately a fifth of the glucose[br]in your blood

0:01:04.000,0:01:06.000
is being used by this organ.

0:01:06.000,0:01:09.000
It's so metabolically active[br]there's a waste stream

0:01:09.000,0:01:13.000
which comes out[br]into your cervical spinal fluid.

0:01:13.000,0:01:17.000
You generate 0.5 liter[br]of CSF every day.

0:01:17.000,0:01:21.000
So, as you know, researchers[br]have taken advantage

0:01:21.000,0:01:25.000
of this massive amount of blood flow[br]and metabolic activity

0:01:25.000,0:01:29.000
to begin to map regions of the brain,[br]to functionally annotate the brain

0:01:29.000,0:01:31.000
in very meaningful ways.

0:01:31.000,0:01:33.000
You'll hear a lot more[br]about those kinds of studies,

0:01:33.000,0:01:38.000
but basically taking advantage of the fact[br]that there's active metabolism

0:01:38.000,0:01:40.000
with certain tasks going on.

0:01:40.000,0:01:42.000
You can put a living human[br]in a machine

0:01:42.000,0:01:44.000
and you can see various areas[br]that are lighting up.

0:01:44.000,0:01:48.000
For example, going around right now[br]is the temporal cortex

0:01:48.000,0:01:51.000
auditory processing going on there,[br]you're listening to my words,

0:01:51.000,0:01:53.000
you're processing what I'm saying.

0:01:53.000,0:01:56.000
Moving to the front of this brain[br]is your prefontal cortex,

0:01:56.000,0:01:58.000
your executive decision-making,

0:01:58.000,0:02:02.000
your higher-thinking areas[br]of the brain.

0:02:02.000,0:02:08.000
And so the thing that[br]we're very much interested in

0:02:08.000,0:02:10.000
from the perspective[br]of the Allen Institute

0:02:10.000,0:02:13.000
is to go deeper,[br]to get down to the cellular level.

0:02:13.000,0:02:17.000
So when you look at this slice, it doesn't[br]really look like gray matter, does it?

0:02:17.000,0:02:20.000
It's more tan matter, or beige matter.

0:02:20.000,0:02:25.000
And scientists about, I guess[br]around the late 1800's,

0:02:25.000,0:02:28.000
discovered that they could stain tissue[br]in various ways,

0:02:28.000,0:02:33.000
and this sort of came along[br]with various microscopy techniques.

0:02:33.000,0:02:37.000
And so this is a stain, it's called Nissl,[br]and it stains cell bodies,

0:02:37.000,0:02:40.000
it stains the cell bodies purple.

0:02:40.000,0:02:43.000
And so you can see[br]a lot more structure and texture

0:02:43.000,0:02:45.000
when you look at something like this.

0:02:45.000,0:02:49.000
You can see the outer layers of the brain[br]and the neocortex,

0:02:49.000,0:02:54.000
there's a six-layer structure, arguably[br]what makes us most uniquely human.

0:02:54.000,0:02:58.000
As you've heard before about[br]there's on average in a human,

0:02:58.000,0:03:03.000
there's about 86 billion neurons,[br]and those 86 billion neurons

0:03:03.000,0:03:05.000
you can see are not evenly distributed,

0:03:05.000,0:03:09.000
they're very focused[br]and specific structures.

0:03:09.000,0:03:11.000
And each of them[br]has their own sort of function

0:03:11.000,0:03:15.000
both on an anatomic level[br]and at a cellular level.

0:03:15.000,0:03:19.000
So if we zoom in on these cells,[br]what you can see is large cells

0:03:19.000,0:03:23.000
and small support cells[br]that are glias and astrocytes

0:03:23.000,0:03:27.000
and these cells are as we know[br]connected in a variety of different ways.

0:03:27.000,0:03:31.000
And we like to think about,[br]although there's 86 billion cells,

0:03:31.000,0:03:36.000
each cell might be considered a snowflake,[br]they're actually able to be binned

0:03:36.000,0:03:39.000
into a large number[br]of cell types or classes.

0:03:39.000,0:03:43.000
What flavor of activity[br]that particular cell class has

0:03:43.000,0:03:49.000
is driven by the underlying genes[br]that are turned on in that cell,

0:03:49.000,0:03:53.000
those drive protein expression[br]which guide the function of those cells,

0:03:53.000,0:03:56.000
who they're connected to,[br]what their morphology is,

0:03:56.000,0:04:00.000
and we're very much interested[br]in understanding these cell classes.

0:04:00.000,0:04:02.000
So how do we do that?

0:04:02.000,0:04:05.000
Well, we look inside the cell[br]at the nucleus,

0:04:05.000,0:04:07.000
(and it will get to the nucleus)

0:04:07.000,0:04:10.000
and so inside we've got[br]23 pairs of chromosomes,

0:04:10.000,0:04:12.000
we've got a pair from mom,[br]a pair from dad,

0:04:12.000,0:04:17.000
on those chromosomes about 25000 genes[br]and we're very much again interested in

0:04:17.000,0:04:22.000
understanding which of these 25000 genes[br]are turned on

0:04:22.000,0:04:24.000
at what levels they're turned on.

0:04:24.000,0:04:28.000
Those are going to, of course, drive[br]the underlying biochemistry of the cells

0:04:28.000,0:04:33.000
they're turned on in and again every cell[br]in our bodies more or less has these

0:04:33.000,0:04:35.000
and we want to understand better

0:04:35.000,0:04:41.000
what the driving biochemistry[br]driven by our genome is.

0:04:41.000,0:04:44.000
So how do we do that?

0:04:47.000,0:04:51.000
We're going to deconstruct a brain[br]in several easy steps.

0:04:51.000,0:04:54.000
So we start[br]at a medical examiner's office.

0:04:54.000,0:04:56.000
This is a place[br]where the dead are brought in

0:04:56.000,0:04:58.000
and obviously it's useful

0:04:58.000,0:05:03.000
for the kind of work we do,[br][it] is not non-invasive,

0:05:03.000,0:05:08.000
we actually need[br]to obtain fresh brain tissue

0:05:08.000,0:05:13.000
and we need to obtain it within 24 hours[br]because the tissues start to degrade.

0:05:13.000,0:05:15.000
We also wanted for our projects[br]to have normal tissue

0:05:15.000,0:05:18.000
as much normal as we could possibly get.

0:05:18.000,0:05:24.000
So over the course of a two-[br]or three-year collection time window

0:05:24.000,0:05:30.000
we collected 6 very high-quality brains,[br]5 of them were male, one was female.

0:05:30.000,0:05:35.000
That's only because males[br]tend to die untimely deaths

0:05:35.000,0:05:39.000
more frequently than females,[br]and then to add to that,

0:05:39.000,0:05:42.000
females are much more likely[br]to give consent

0:05:42.000,0:05:45.000
for us to take the brain[br]than vice versa.

0:05:45.000,0:05:49.000
We have to figure that one out.

0:05:49.000,0:05:52.000
We've heard people say,[br]“He wasn't using it anyway!”

0:05:52.000,0:05:55.000
(Laughter)

0:05:56.000,0:06:00.000
So, once the brain comes in[br]we have to move very, very quickly.

0:06:00.000,0:06:06.000
So first we capture[br]a magnetic resonance image.

0:06:06.000,0:06:08.000
This, of course,[br]will look very familiar to you,

0:06:08.000,0:06:11.000
but this is going to be the structure[br]in which we hang all of this information,

0:06:11.000,0:06:15.000
it's also a common coordinate framework[br]by which the many, many researchers

0:06:15.000,0:06:17.000
who do imaging studies can map

0:06:17.000,0:06:20.000
into our ultimate database,[br]an Atlas framework.

0:06:20.000,0:06:22.000
We also collect diffusion tensor images

0:06:22.000,0:06:25.000
so we get some of the wiring[br]from these brains.

0:06:25.000,0:06:29.000
And then the brain is removed[br]from the skull. It's slabbed and frozen,

0:06:29.000,0:06:32.000
frozen solid,[br]and then it's shipped to Seattle

0:06:32.000,0:06:34.000
where we have[br]the Allen Institute for Brain Science.

0:06:34.000,0:06:36.000
We have great technicians[br]who've worked out

0:06:36.000,0:06:39.000
a lot of great techniques[br]for further processing.

0:06:39.000,0:06:45.000
So first, we take a very thin section,[br]this is 25µm thin section,

0:06:45.000,0:06:48.000
which is about a baby's hair width.

0:06:48.000,0:06:52.000
That's transferred to a microscope slide[br]and then that is stained

0:06:52.000,0:06:55.000
with one of those histological stains[br]that I talked about before.

0:06:55.000,0:06:58.000
And this is going to give us more contrast[br]as our team of anatomists

0:06:58.000,0:07:02.000
start to make assignments of anatomy.

0:07:05.000,0:07:06.000
So we digitize these images,

0:07:06.000,0:07:11.000
everything goes from being wet lab[br]to being dry lab.

0:07:11.000,0:07:15.000
And then combined with anatomy[br]that we get from the MR,

0:07:15.000,0:07:17.000
we further fragment the brain.

0:07:17.000,0:07:24.000
This is to get it into a smaller framework[br]for which we can do this.

0:07:24.000,0:07:27.000
So here's a technician[br]who's doing additional cutting.

0:07:27.000,0:07:30.000
This is again a 25µm thin section.

0:07:30.000,0:07:32.000
You'll see da Vinci's tools,[br]the paintbrush,

0:07:32.000,0:07:34.000
being use here to smooth this out.

0:07:34.000,0:07:37.000
This is fresh frozen brain tissue.

0:07:39.000,0:07:42.000
And it can be very carefully[br]melted to a microscope slide.

0:07:42.000,0:07:44.000
You'll note[br]that there's a barcode on the slide.

0:07:44.000,0:07:46.000
We process 1000's and 1000's of samples,

0:07:46.000,0:07:50.000
we track all of it in a backend[br]information management system.

0:07:50.000,0:07:53.000
Those are stained.

0:07:53.000,0:07:57.000
And then we get[br]more detailed anatomic information.

0:07:57.000,0:08:08.000
That information is ... (playing here)[br]... this is a laser capture microscope,

0:08:08.000,0:08:12.000
the lab technician is actually describing[br]an area on that slide.

0:08:12.000,0:08:15.000
And a laser, you see the blue light[br]cutting around there,

0:08:15.000,0:08:17.000
very James Bond like.

0:08:17.000,0:08:20.000
Cutting out part of that,[br]and underneath there,

0:08:20.000,0:08:23.000
you can see the blue light again,[br]from the microscope in real-time,

0:08:23.000,0:08:28.000
it's collecting in a microscope tube[br]that tissue.

0:08:28.000,0:08:30.000
We extract RNA,

0:08:30.000,0:08:35.000
RNA is the product of the genes[br]that are being turned on,

0:08:35.000,0:08:38.000
and we label it,[br]we put a fluorescent tag on it.

0:08:38.000,0:08:39.000
Now what you are looking at here

0:08:39.000,0:08:43.000
is a constellation[br]of the entire human genome

0:08:43.000,0:08:45.000
spread out over a glass slide.

0:08:45.000,0:08:48.000
Those little bits are representing[br]the 25000 genes.

0:08:48.000,0:08:52.000
There's about 60000 of these spots[br]and that fluorescently labeled RNA

0:08:52.000,0:08:56.000
is put onto this microscope slide[br]and then we read out quantitatively

0:08:56.000,0:09:00.000
what genes are turned on at what levels.

0:09:00.000,0:09:04.000
So we do this over and over and over again[br]for brains that we've collected;

0:09:04.000,0:09:07.000
as I mentioned we've collected[br]6 brains in total.

0:09:07.000,0:09:10.000
We collect samples[br]from about 1000 structures in every brain

0:09:10.000,0:09:14.000
that we've looked at,[br]so it's a massive amount of data.

0:09:14.000,0:09:18.000
And we pull all of this together,[br]back into a common framework,

0:09:18.000,0:09:22.000
that is a free and open resource[br]for scientists around the world to use.

0:09:22.000,0:09:24.000
So at the Allen Institute[br]for Brain Science,

0:09:24.000,0:09:28.000
we've been generating these kinds[br]of data resources for almost a decade.

0:09:28.000,0:09:32.000
They're free to use for anybody,[br]they're online tools,

0:09:32.000,0:09:38.000
just for example today a given workday,[br]there'll be about 1000 unique visitors

0:09:38.000,0:09:43.000
that come in from labs around the world[br]to come use our resources and data.

0:09:43.000,0:09:47.000
They get access to tools like this,[br]which allows them to see

0:09:47.000,0:09:50.000
all of that anatomy and the structure[br]that we created before

0:09:50.000,0:09:55.000
and to start mapping in then the things[br]that they're particularly interested in.

0:09:55.000,0:09:57.000
So in this case you're looking[br]at the structure

0:09:57.000,0:09:59.000
and they're going to look[br]at these color balls

0:09:59.000,0:10:02.000
are representing a particular gene[br]they're interested in

0:10:02.000,0:10:05.000
that's either being turned up or down

0:10:05.000,0:10:11.000
in those various areas depending upon[br]the heat color that's specified there.

0:10:11.000,0:10:14.000
So what are people doing when they come in[br]and using these resources?

0:10:14.000,0:10:17.000
Well, one of the things[br]that you might hear lots about

0:10:17.000,0:10:19.000
is human genetic studies.

0:10:19.000,0:10:23.000
Obviously if you're very interested[br]in understanding disease

0:10:23.000,0:10:25.000
there's a genetic underpinning[br]to many of them.

0:10:25.000,0:10:28.000
So you'd like more information,[br]you do a large-scale study

0:10:28.000,0:10:31.000
and you get out of those studies[br]collections of genes

0:10:31.000,0:10:34.000
and one of the first things you're going[br]to want to know is more information.

0:10:34.000,0:10:41.000
Is there something I can learn[br]about the location of these genes

0:10:41.000,0:10:44.000
that gives me additional clues[br]as to their function,

0:10:44.000,0:10:49.000
ways in which I might intervene[br]in the disease process.

0:10:49.000,0:10:52.000
They're also very interested[br]in understanding human genetic diversity.

0:10:52.000,0:10:59.000
Now we've already looked at 6 brains but[br]as we know, every human is very unique.

0:10:59.000,0:11:01.000
We celebrate our differences;

0:11:01.000,0:11:05.000
this is a snapshot of the great workforce[br]at the Allen Institute for Brain Science

0:11:05.000,0:11:09.000
who does all the great work[br]that I'm talking about today.

0:11:09.000,0:11:15.000
But remarkably when we look at this level[br]at the underlying data,

0:11:15.000,0:11:20.000
and this is a lot of data from[br]2 completely unrelated individuals,

0:11:20.000,0:11:24.000
there's a very high degree[br]of correlation, correspondence.

0:11:24.000,0:11:26.000
So this is looking at 1000's[br]of different measurements

0:11:26.000,0:11:30.000
of gene expression across[br]many, many different areas of the brain;

0:11:30.000,0:11:32.000
and there's[br]a very high degree of correspondence.

0:11:32.000,0:11:33.000
This was very reassuring to us.

0:11:33.000,0:11:36.000
First because when you generate data[br]on this scale

0:11:36.000,0:11:38.000
you want to make sure[br]that it's high quality,

0:11:38.000,0:11:41.000
so reproducibility is obviously important,

0:11:41.000,0:11:43.000
but it was also important[br]because we feel that it's given us

0:11:43.000,0:11:46.000
a great snapshot into the human brain.

0:11:46.000,0:11:50.000
And the people using the data,[br]even with our low n, have confidence

0:11:50.000,0:11:53.000
that what they're seeing[br]has some relevance.

0:11:53.000,0:11:58.000
Now, not everything is correlated here,[br]you can see some outliers,

0:11:58.000,0:12:00.000
and, of course, those outliers[br]are going to be interesting

0:12:00.000,0:12:03.000
related to human differences.

0:12:03.000,0:12:04.000
We did study a couple of years ago

0:12:04.000,0:12:09.000
in which we tried to understand[br]a little better about those differences

0:12:09.000,0:12:12.000
and looked at multiple individuals[br]and different gene products

0:12:12.000,0:12:15.000
and what we find as a tendency[br]and as a rule

0:12:15.000,0:12:19.000
is that those differences tend to be[br]in very specific cell populations

0:12:19.000,0:12:23.000
or cell types, cell classes,[br]as I mentioned before.

0:12:23.000,0:12:27.000
So, this is an example[br]of 2 different genes that are turned on

0:12:27.000,0:12:29.000
in very specific layers[br]of the neocortex

0:12:29.000,0:12:32.000
only in one individual[br]and not found in another.

0:12:32.000,0:12:36.000
Now we have no idea[br]if that's due to environmental changes,

0:12:36.000,0:12:39.000
environmental influences[br]or if it's just genetics,

0:12:39.000,0:12:43.000
but we did do a study in which we looked[br]at the mouse several years ago

0:12:43.000,0:12:48.000
and we were looking at genes[br]that encode for, in this case a DRD2,

0:12:48.000,0:12:52.000
the gene listed on the top[br]is a dopamine receptor.

0:12:52.000,0:12:58.000
Tyrosine hydroxylase (TH) is[br]a gene involved in dopamine biosynthesis

0:12:58.000,0:13:03.000
and those 2 gene products[br]are very different in the cell types

0:13:03.000,0:13:06.000
in these individual mouse brains.

0:13:06.000,0:13:11.000
So, over on the left is “C57 black 6”[br]which is a commonly used mouse strain,

0:13:11.000,0:13:15.000
and then spread at the other end[br]is a wild type strain.

0:13:15.000,0:13:19.000
And so the further you go[br]the more genetically unrelated you are.

0:13:19.000,0:13:23.000
And when we looked in total across,[br]sort of evolution if you will,

0:13:23.000,0:13:25.000
across genetic relatedness,

0:13:25.000,0:13:28.000
the further you were[br]genetically unrelated,

0:13:28.000,0:13:30.000
the more of[br]these very specific cell types,

0:13:30.000,0:13:34.000
specific changes, you could see.

0:13:34.000,0:13:36.000
So at the Allen Institute[br]for the next decade

0:13:36.000,0:13:38.000
we're embarking[br]on a pretty ambitious program

0:13:38.000,0:13:43.000
to start to understand the cell types,[br]understand the cell differences

0:13:43.000,0:13:46.000
and how they ultimately relate[br]to the functional properties of the brain.

0:13:46.000,0:13:50.000
This is, I think, critical information[br]for the entire field,

0:13:50.000,0:13:54.000
to start linking up all[br]of these fundamental parts

0:13:54.000,0:13:57.000
which are the cells,[br]to how they're connected,

0:13:57.000,0:14:00.000
the underlying molecules[br]that drive those connections,

0:14:00.000,0:14:04.000
the underlying molecules[br]that drive the physiological properties,

0:14:04.000,0:14:06.000
the electric chemical properties

0:14:06.000,0:14:09.000
and then ultimately[br]the functional properties of those cells.

0:14:09.000,0:14:14.000
So we're doing this[br]in 3 different areas of research.

0:14:14.000,0:14:17.000
First, we're focusing on the mouse,[br]the mouse visual system,

0:14:17.000,0:14:21.000
to look at, in real-time,[br]in the living animal,

0:14:21.000,0:14:25.000
the functions of a variety[br]of different cells.

0:14:25.000,0:14:28.000
We're linking these in this concept[br]in the middle of cell types,

0:14:28.000,0:14:33.000
trying to really understand[br]the underlying molecules

0:14:33.000,0:14:37.000
in all the properties[br]as they relate to those functions

0:14:37.000,0:14:40.000
and then we're looking[br]at the human.

0:14:40.000,0:14:44.000
In the human we're doing this both[br]in the middle and cell types

0:14:44.000,0:14:46.000
using the tissue driven work[br]that I talked about before

0:14:46.000,0:14:51.000
but also we're doing it in vitro[br]using stem cell technology.

0:14:51.000,0:14:55.000
We're learning how to make[br]very specific cell types within the dish

0:14:55.000,0:14:58.000
and then being able to test[br]those functional properties

0:14:58.000,0:15:04.000
and go back and forth between[br]what we learn in the mouse to the human.

0:15:04.000,0:15:08.000
So, with that I will finish[br]and just say that it's an exciting time

0:15:08.000,0:15:11.000
to be in biology and an exciting time[br]to be in neuroscience.

0:15:11.000,0:15:15.000
I think the technology of the day[br]has come well beyond the pen and paper

0:15:15.000,0:15:20.000
and it's really time for a renaissance in[br]our understanding of this complex organ.

0:15:20.000,0:15:21.000
Thanks.

0:15:21.000,0:15:27.000
(Applause)