0:00:09.429,0:00:11.030
Complexity.

0:00:11.030,0:00:15.379
Nothing quite embodies this word[br]like the human brain.

0:00:15.379,0:00:19.682
So for centuries we've studied[br]the complexity of the human brain

0:00:19.682,0:00:22.508
using the tools and technology of the day,

0:00:22.508,0:00:26.537
if that's pen and paper[br]from the age of da Vinci,

0:00:26.537,0:00:29.641
through advents in microscopy

0:00:29.641,0:00:32.563
to be able to look more deeply[br]into the brain,

0:00:32.563,0:00:36.223
to a lot of the new technologies[br]that you've heard about today

0:00:36.223,0:00:38.530
through imaging,[br]magnetic resonance imaging,

0:00:38.530,0:00:41.895
able to look at the details of the brain.

0:00:41.895,0:00:44.372
Now one of the first things[br]you notice when you look

0:00:44.372,0:00:48.906
at a fresh human brain[br]is the amount of vasculature

0:00:48.906,0:00:51.310
that's completely covering this.

0:00:51.310,0:00:56.156
The brain is this metabolically[br]voracious organ.

0:00:56.179,0:01:01.235
Approximately a quarter[br]of the oxygen in your blood,

0:01:01.235,0:01:04.783
approximately a fifth[br]of the glucose in your blood

0:01:04.783,0:01:06.865
is being used by this organ.

0:01:06.865,0:01:09.771
It's so metabolically active,[br]there's a waste stream

0:01:09.771,0:01:13.463
which comes out[br]into your cerebral spinal fluid.

0:01:13.463,0:01:17.671
You generate 0.5 liter of CSF every day.

0:01:17.671,0:01:21.920
So, as you know, researchers[br]have taken advantage

0:01:21.920,0:01:25.215
of this massive amount of blood flow[br]and metabolic activity

0:01:25.215,0:01:29.584
to begin to map regions of the brain,[br]to functionally annotate the brain

0:01:29.584,0:01:31.085
in very meaningful ways.

0:01:31.085,0:01:33.906
You'll hear a lot more[br]about those kinds of studies,

0:01:33.906,0:01:38.078
but basically taking advantage of the fact[br]that there's active metabolism

0:01:38.078,0:01:40.028
with certain tasks going on.

0:01:40.028,0:01:42.086
You can put a living human in a machine

0:01:42.086,0:01:44.411
and you can see various areas[br]that are lighting up.

0:01:44.411,0:01:48.378
For example, going around right now[br]is the temporal cortex,

0:01:48.378,0:01:51.475
auditory processing going on there,[br]you're listening to my words,

0:01:51.475,0:01:53.153
you're processing what I'm saying.

0:01:53.153,0:01:56.787
Moving to the front of this brain[br]is your prefrontal cortex,

0:01:56.787,0:01:58.887
your executive decision-making,

0:01:58.887,0:02:02.153
your higher-thinking areas of the brain.

0:02:02.153,0:02:08.151
And so the thing that[br]we're very much interested in

0:02:08.151,0:02:10.764
from the perspective[br]of the Allen Institute

0:02:10.764,0:02:13.935
is to go deeper,[br]to get down to the cellular level.

0:02:13.935,0:02:17.674
So when you look at this slice, it doesn't[br]really look like gray matter, does it?

0:02:17.674,0:02:20.694
It's more tan matter, or beige matter.

0:02:20.694,0:02:25.851
And scientists about, I guess[br]around the late 1800's,

0:02:25.851,0:02:28.953
discovered that they could[br]stain tissue in various ways,

0:02:28.953,0:02:33.369
and this sort of came along[br]with various microscopy techniques.

0:02:33.369,0:02:37.225
And so this is a stain, it's called Nissl,[br]and it stains cell bodies,

0:02:37.225,0:02:40.795
it stains the cell bodies purple.

0:02:40.795,0:02:43.795
And so you can see[br]a lot more structure and texture

0:02:43.795,0:02:45.579
when you look at something like this.

0:02:45.579,0:02:49.879
You can see the outer layers of the brain[br]and the neocortex,

0:02:49.879,0:02:54.604
there's a six-layer structure, arguably[br]what makes us most uniquely human.

0:02:54.604,0:02:58.533
As you've heard before about[br]there's on average in a human,

0:02:58.533,0:03:03.555
there's about 86 billion neurons,[br]and those 86 billion neurons

0:03:03.555,0:03:05.749
you can see are not evenly distributed,

0:03:05.749,0:03:09.354
they're very focused[br]in specific structures.

0:03:09.354,0:03:11.619
And each of them[br]has their own sort of function,

0:03:11.619,0:03:15.233
both on an anatomic level[br]and at a cellular level.

0:03:15.233,0:03:19.848
So if we zoom in on these cells,[br]what you can see is large cells

0:03:19.848,0:03:23.085
and small support cells[br]that are glials and astrocytes

0:03:23.085,0:03:27.910
and these cells are as we know[br]connected in a variety of different ways.

0:03:27.910,0:03:31.720
And we like to think about,[br]although there's 86 billion cells,

0:03:31.720,0:03:36.422
each cell might be considered a snowflake,[br]they're actually able to be binned

0:03:36.422,0:03:39.597
into a large number[br]of cell types or classes.

0:03:39.597,0:03:43.920
What flavor of activity[br]that particular cell class has

0:03:43.920,0:03:49.320
is driven by the underlying genes[br]that are turned on in that cell,

0:03:49.320,0:03:53.281
those drive protein expression[br]which guide the function of those cells,

0:03:53.281,0:03:56.131
who they're connected to,[br]what their morphology is,

0:03:56.131,0:04:00.485
and we're very much interested[br]in understanding these cell classes.

0:04:00.485,0:04:02.160
So how do we do that?

0:04:02.160,0:04:05.768
Well, we look inside the cell[br]at the nucleus,

0:04:05.768,0:04:07.858
-- and it will get to the nucleus --

0:04:07.858,0:04:10.858
and so inside we've got[br]23 pairs of chromosomes,

0:04:10.858,0:04:12.749
got a pair from mom, a pair from dad,

0:04:12.749,0:04:17.866
on those chromosomes about 25,000 genes[br]and we're very much again interested in

0:04:17.866,0:04:22.225
understanding which[br]of these 25,000 genes are turned on

0:04:22.225,0:04:24.481
at what levels they're turned on.

0:04:24.481,0:04:28.080
Those are going to, of course, drive[br]the underlying biochemistry of the cells

0:04:28.080,0:04:33.087
they're turned on in and again every cell[br]in our bodies more or less has these

0:04:33.087,0:04:35.416
and we want to understand better

0:04:35.416,0:04:41.548
what the driving biochemistry[br]driven by our genome is.

0:04:41.548,0:04:44.974
So how do we do that?

0:04:47.610,0:04:51.320
We're going to deconstruct a brain[br]in several easy steps.

0:04:51.330,0:04:54.206
So we start[br]at a medical examiner's office.

0:04:54.206,0:04:56.510
This is a place[br]where the dead are brought in

0:04:56.510,0:04:58.668
and obviously as you saw before

0:04:58.668,0:05:03.149
for the kind of work we do,[br][it] is not non-invasive,

0:05:03.149,0:05:08.750
we actually need[br]to obtain fresh brain tissue

0:05:08.750,0:05:13.129
and we need to obtain it within 24 hours[br]because the tissues start to degrade.

0:05:13.129,0:05:15.989
We also wanted for our projects[br]to have normal tissue,

0:05:15.989,0:05:18.904
as much normal as we could possibly get.

0:05:18.904,0:05:24.853
So over the course of a two-[br]or three-year collection time window

0:05:24.853,0:05:30.545
we collected 6 very high-quality brains,[br]5 of them were male, 1 was female.

0:05:30.545,0:05:35.610
That's only because males[br]tend to die untimely deaths

0:05:35.610,0:05:39.782
more frequently than females,[br]and then to add to that,

0:05:39.782,0:05:42.816
females are much more likely[br]to give consent

0:05:42.816,0:05:45.657
for us to take the brain than vice versa.

0:05:45.657,0:05:49.384
We have to figure that one out.

0:05:49.384,0:05:52.520
We've heard people say,[br]"He wasn't using it anyway!"

0:05:52.520,0:05:55.489
(Laughter)

0:05:56.900,0:06:00.558
So, once the brain comes in[br]we have to move very, very quickly.

0:06:00.558,0:06:06.316
So first we capture[br]a magnetic resonance image.

0:06:06.316,0:06:08.502
This, of course,[br]will look very familiar to you,

0:06:08.502,0:06:12.004
but this is going to be the structure[br]in which we hang all this information,

0:06:12.004,0:06:15.548
it's also a common coordinate framework[br]by which the many, many researchers

0:06:15.548,0:06:17.274
who do imaging studies can map

0:06:17.274,0:06:20.391
into our ultimate database,[br]an Atlas framework.

0:06:20.391,0:06:22.964
We also collect diffusion tensor images,

0:06:22.964,0:06:25.412
so we get some of the wiring[br]from these brains.

0:06:25.412,0:06:27.891
And then the brain[br]is removed from the skull.

0:06:27.891,0:06:32.884
It's slabbed and frozen solid,[br]and then it's shipped to Seattle

0:06:32.884,0:06:35.225
where we have[br]the Allen Institute for Brain Science.

0:06:35.225,0:06:37.200
We have great technicians[br]who've worked out

0:06:37.200,0:06:39.449
a lot of great techniques[br]for further processing.

0:06:39.749,0:06:45.746
So first, we take a very thin section,[br]this is a 25 micron thin section,

0:06:45.746,0:06:48.148
which is about a baby's hair width.

0:06:48.148,0:06:52.302
That's transferred to a microscope slide[br]and then that is stained

0:06:52.302,0:06:55.509
with one of those histological stains[br]that I talked about before.

0:06:55.509,0:06:58.913
And this is going to give us more contrast[br]as our team of anatomists

0:06:58.913,0:07:02.719
start to make assignments of anatomy.

0:07:04.800,0:07:07.238
So we digitize these images,

0:07:07.238,0:07:11.168
everything goes from being[br]wet lab to being dry lab.

0:07:11.168,0:07:15.967
And then combined with anatomy[br]that we get from the MR,

0:07:15.967,0:07:17.963
we further fragment the brain.

0:07:17.963,0:07:24.476
This is to get it into a smaller framework[br]for which we can do this.

0:07:24.476,0:07:27.284
So here's a technician[br]who's doing additional cutting.

0:07:27.284,0:07:30.400
This is again a 25 micron thin section.

0:07:30.400,0:07:32.614
You'll see da Vinci's tools,[br]the paintbrush,

0:07:32.614,0:07:35.205
being used here to smooth this out.

0:07:35.205,0:07:38.775
This is fresh frozen brain tissue.

0:07:39.236,0:07:42.942
And it can be very carefully[br]melted to a microscope slide.

0:07:42.942,0:07:45.107
You'll note that there's a barcode[br]on the slide.

0:07:45.107,0:07:47.008
We process 1000's and 1000's of samples,

0:07:47.008,0:07:50.654
we track all of it in a backend[br]information management system.

0:07:50.654,0:07:53.536
Those are stained.

0:07:53.536,0:07:57.143
And then we get[br]more detailed anatomic information.

0:07:57.143,0:07:58.453
That information...

0:08:01.924,0:08:08.178
This is a laser capture microscope.

0:08:08.178,0:08:12.638
The lab technician is actually describing[br]an area on that slide.

0:08:12.638,0:08:15.244
And a laser, you see the blue light[br]cutting around there,

0:08:15.244,0:08:19.411
very James Bond-like,[br]cutting out part of that.

0:08:19.411,0:08:21.973
And underneath there,[br]you can see the blue light again,

0:08:21.973,0:08:23.538
from the microscope in real-time,

0:08:23.538,0:08:28.800
it's collecting,[br]in a microscope tube, that tissue.

0:08:28.800,0:08:30.619
We extract RNA,

0:08:30.619,0:08:35.207
RNA is the product of the genes[br]that are being turned on,

0:08:35.207,0:08:38.056
and we label it,[br]we put a fluorescent tag on it.

0:08:38.056,0:08:39.775
Now what you are looking at here

0:08:39.775,0:08:43.126
is a constellation[br]of the entire human genome

0:08:43.126,0:08:45.466
spread out over a glass slide.

0:08:45.466,0:08:48.579
Those little bits are representing[br]the 25,000 genes.

0:08:48.579,0:08:52.543
There's about 60,000 of these spots[br]and that fluorescently labeled RNA

0:08:52.543,0:08:56.915
is put onto this microscope slide[br]and then we read out quantitatively

0:08:56.915,0:09:00.942
what genes are turned on at what levels.

0:09:00.942,0:09:04.641
So we do this over and over and over again[br]for brains that we've collected;

0:09:04.641,0:09:07.195
as I mentioned we've collected[br]6 brains in total.

0:09:07.195,0:09:10.813
We collect samples[br]from about 1000 structures in every brain

0:09:10.813,0:09:14.501
that we've looked at,[br]so it's a massive amount of data.

0:09:14.501,0:09:18.959
And we pull all of this together,[br]back into a common framework,

0:09:18.959,0:09:22.765
that is a free and open resource[br]for scientists around the world to use.

0:09:22.765,0:09:24.759
So at the Allen Institute[br]for Brain Science,

0:09:24.759,0:09:28.674
we've been generating these kinds[br]of data resources for almost a decade.

0:09:28.674,0:09:32.439
They're free to use for anybody,[br]they're online tools,

0:09:32.439,0:09:38.350
just for example today a given workday,[br]there'll be about 1000 unique visitors

0:09:38.350,0:09:43.629
that come in from labs around the world,[br]to come use our resources and data.

0:09:43.629,0:09:47.625
They get access to tools like this,[br]which allows them to see

0:09:47.625,0:09:50.961
all of that anatomy and the structure[br]that we created before

0:09:50.961,0:09:55.733
and to start mapping in then the things[br]that they're particularly interested in.

0:09:55.733,0:09:57.916
So in this case you're looking[br]at the structure

0:09:57.916,0:09:59.998
and they're going to look[br]at these color balls

0:09:59.998,0:10:02.666
are representing a particular gene[br]they're interested in

0:10:02.666,0:10:05.394
that's either being turned up or down

0:10:05.394,0:10:11.669
in those various areas depending upon[br]the heat color that's specified there.

0:10:11.669,0:10:14.532
So what are people doing[br]when they start using these resources?

0:10:14.532,0:10:17.481
Well, one of the things[br]that you might hear lots about

0:10:17.481,0:10:19.740
is human genetic studies.

0:10:19.740,0:10:23.311
Obviously, if you're very interested[br]in understanding disease

0:10:23.311,0:10:25.584
there's a genetic underpinning[br]to many of them.

0:10:25.584,0:10:28.223
So you'd like more information,[br]you do a large-scale study

0:10:28.223,0:10:31.275
and you get out of those studies[br]collections of genes

0:10:31.275,0:10:34.789
and one of the first things you're going[br]to want to know is more information.

0:10:34.789,0:10:41.040
Is there something I can learn[br]about the location of these genes

0:10:41.040,0:10:44.175
that gives me additional clues[br]as to their function,

0:10:44.175,0:10:49.189
ways in which I might intervene[br]in the disease process.

0:10:49.189,0:10:52.421
They're also very interested[br]in understanding human genetic diversity.

0:10:52.421,0:10:55.425
We've only looked at 6 brains,

0:10:55.425,0:10:58.983
but, as we know,[br]every human is very unique.

0:10:58.983,0:11:00.624
We celebrate our differences;

0:11:00.624,0:11:05.223
this is a snapshot of the great workforce[br]at the Allen Institute for Brain Science

0:11:05.223,0:11:09.167
who does all the great work[br]that I'm talking about today.

0:11:09.167,0:11:15.365
But remarkably when we look at this level[br]at the underlying data,

0:11:15.365,0:11:20.058
and this is a lot of data from[br]2 completely unrelated individuals,

0:11:20.058,0:11:24.313
there's a very high degree[br]of correlation, correspondence.

0:11:24.313,0:11:27.010
So this is looking at thousands[br]of different measurements

0:11:27.010,0:11:30.124
of gene expression across[br]many, many different areas of the brain;

0:11:30.124,0:11:32.420
and there's a very high degree[br]of correspondence.

0:11:32.420,0:11:33.922
This was very reassuring to us.

0:11:33.922,0:11:36.927
First, because when you generate[br]data on this scale,

0:11:36.927,0:11:38.800
you want to make sure it's high quality,

0:11:38.800,0:11:41.057
so reproducibility is obviously important,

0:11:41.057,0:11:43.930
but it was also important[br]because we feel that it's given us

0:11:43.930,0:11:46.904
a great snapshot into the human brain.

0:11:46.904,0:11:50.873
And the people using the data,[br]even with our low N, have confidence

0:11:50.873,0:11:53.939
that what they're seeing[br]has some relevance.

0:11:53.939,0:11:58.014
Now, not everything is correlated here,[br]you can see some outliers,

0:11:58.014,0:12:00.717
and, of course, those outliers[br]are going to be interesting

0:12:00.717,0:12:03.043
related to human differences.

0:12:03.043,0:12:04.865
We did a study a couple of years ago,

0:12:04.865,0:12:09.238
in which we tried to understand[br]a little better about those differences,

0:12:09.238,0:12:12.500
and looked at multiple individuals[br]and different gene products,

0:12:12.500,0:12:15.993
and what we find, as a tendency[br]and as a rule,

0:12:15.993,0:12:19.572
is that those differences tend to be[br]in very specific cell populations

0:12:19.572,0:12:23.797
or cell types, cell classes,[br]as I mentioned before.

0:12:23.797,0:12:27.407
So, this is an example[br]of 2 different genes that are turned on

0:12:27.407,0:12:29.929
in very specific layers of the neocortex

0:12:29.929,0:12:32.767
only in one individual[br]and not found in another.

0:12:32.767,0:12:36.395
Now we have no idea[br]if that's due to environmental changes,

0:12:36.395,0:12:39.269
environmental influences[br]or if it's just genetics,

0:12:39.269,0:12:43.197
but we did do a study in which we looked[br]at the mouse several years ago

0:12:43.197,0:12:48.124
and we were looking at genes[br]that encode for, in this case a DRD2,

0:12:48.124,0:12:52.249
the gene listed on the top[br]is a dopamine receptor.

0:12:52.249,0:12:58.585
Tyrosine hydroxylase, TH, is a gene[br]involved in dopamine biosynthesis

0:12:58.585,0:13:03.386
and those 2 gene products[br]are very different in the cell types

0:13:03.386,0:13:06.038
in these individual mouse brains.

0:13:06.038,0:13:11.643
So, over on the left is "C57 Black 6"[br]which is a commonly used mouse strain,

0:13:11.643,0:13:15.461
and then spread at the other end[br]is a wild type strain.

0:13:15.461,0:13:19.704
And so the further you go[br]the more genetically unrelated you are.

0:13:19.704,0:13:23.798
And when we looked in total across,[br]sort of evolution if you will,

0:13:23.798,0:13:25.523
across genetic relatedness,

0:13:25.523,0:13:28.199
the further you were[br]genetically unrelated,

0:13:28.199,0:13:30.328
the more of these[br]very specific cell types,

0:13:30.328,0:13:32.934
specific changes, you could see.

0:13:33.809,0:13:36.498
So at the Allen Institute[br]for the next decade

0:13:36.498,0:13:38.830
we're embarking[br]on a pretty ambitious program

0:13:38.830,0:13:43.445
to start to understand the cell types,[br]understand the cell differences

0:13:43.445,0:13:46.956
and how they ultimately relate[br]to the functional properties of the brain.

0:13:46.956,0:13:50.783
This is, I think, critical information[br]for the entire field,

0:13:50.783,0:13:54.093
to start linking up all[br]of these fundamental parts

0:13:54.093,0:13:57.327
which are the cells,[br]to how they're connected,

0:13:57.327,0:14:00.619
the underlying molecules[br]that drive those connections,

0:14:00.619,0:14:04.422
the underlying molecules driving[br]the electrophysiological properties,

0:14:04.422,0:14:06.549
the electrochemical properties

0:14:06.549,0:14:09.993
and then ultimately[br]the functional properties of those cells.

0:14:09.993,0:14:14.207
So we're doing this[br]in 3 different areas of research.

0:14:14.207,0:14:17.273
First, we're focusing on the mouse,[br]the mouse visual system,

0:14:17.273,0:14:21.198
to look at, in real-time,[br]in the living animal,

0:14:21.198,0:14:25.791
the functions of a variety[br]of different cells.

0:14:25.791,0:14:28.900
We're linking these in this concept[br]in the middle of cell types,

0:14:28.900,0:14:33.653
trying to really understand[br]the underlying molecules

0:14:33.653,0:14:37.256
in all the properties[br]as they relate to those functions

0:14:37.256,0:14:40.113
and then we're looking at the human.

0:14:40.113,0:14:44.013
In the human we're doing this both[br]in the middle and cell types

0:14:44.013,0:14:46.735
using the tissue driven work[br]that I talked about before,

0:14:46.735,0:14:51.795
but also we're doing it in vitro[br]using stem cell technology.

0:14:51.795,0:14:55.359
We're learning how to make[br]very specific cell types within the dish

0:14:55.359,0:14:58.358
and then being able to test[br]those functional properties

0:14:58.358,0:15:04.652
and go back and forth between[br]what we learn in the mouse to the human.

0:15:04.652,0:15:08.620
So, with that I will finish[br]and just say that it's an exciting time

0:15:08.620,0:15:11.487
to be in biology and an exciting time[br]to be in neuroscience.

0:15:11.487,0:15:15.493
I think the technology of the day[br]has come well beyond the pen and paper

0:15:15.493,0:15:20.584
and it's really time for a renaissance in[br]our understanding of this complex organ.

0:15:20.584,0:15:21.990
Thanks.

0:15:21.990,0:15:24.183
(Applause)