TEDGlobal 2011
Allan Jones: A map of the brain
Filmed:
Readability: 4.4
1,269,611 views
How can we begin to understand the way the brain works? The same way we begin to understand a city: by making a map. In this visually stunning talk, Allan Jones shows how his team is mapping which genes are turned on in each tiny region, and how it all connects up.
Allan Jones - Brain scientist
As CEO of the Allen Institute for Brain Science, Allan Jones leads an ambitious project to build an open, online, interactive atlas of the human brain. Full bio
As CEO of the Allen Institute for Brain Science, Allan Jones leads an ambitious project to build an open, online, interactive atlas of the human brain. Full bio
Double-click the English transcript below to play the video.
00:15
Humans have long held a fascination
0
0
2000
00:17
for the human brain.
1
2000
2000
00:19
We chart it, we've described it,
2
4000
3000
00:22
we've drawn it,
3
7000
2000
00:24
we've mapped it.
4
9000
3000
00:27
Now just like the physical maps of our world
5
12000
3000
00:30
that have been highly influenced by technology --
6
15000
3000
00:33
think Google Maps,
7
18000
2000
00:35
think GPS --
8
20000
2000
00:37
the same thing is happening for brain mapping
9
22000
2000
00:39
through transformation.
10
24000
2000
00:41
So let's take a look at the brain.
11
26000
2000
00:43
Most people, when they first look at a fresh human brain,
12
28000
3000
00:46
they say, "It doesn't look what you're typically looking at
13
31000
3000
00:49
when someone shows you a brain."
14
34000
2000
00:51
Typically, what you're looking at is a fixed brain. It's gray.
15
36000
3000
00:54
And this outer layer, this is the vasculature,
16
39000
2000
00:56
which is incredible, around a human brain.
17
41000
2000
00:58
This is the blood vessels.
18
43000
2000
01:00
20 percent of the oxygen
19
45000
3000
01:03
coming from your lungs,
20
48000
2000
01:05
20 percent of the blood pumped from your heart,
21
50000
2000
01:07
is servicing this one organ.
22
52000
2000
01:09
That's basically, if you hold two fists together,
23
54000
2000
01:11
it's just slightly larger than the two fists.
24
56000
2000
01:13
Scientists, sort of at the end of the 20th century,
25
58000
3000
01:16
learned that they could track blood flow
26
61000
2000
01:18
to map non-invasively
27
63000
3000
01:21
where activity was going on in the human brain.
28
66000
3000
01:24
So for example, they can see in the back part of the brain,
29
69000
3000
01:27
which is just turning around there.
30
72000
2000
01:29
There's the cerebellum; that's keeping you upright right now.
31
74000
2000
01:31
It's keeping me standing. It's involved in coordinated movement.
32
76000
3000
01:34
On the side here, this is temporal cortex.
33
79000
3000
01:37
This is the area where primary auditory processing --
34
82000
3000
01:40
so you're hearing my words,
35
85000
2000
01:42
you're sending it up into higher language processing centers.
36
87000
2000
01:44
Towards the front of the brain
37
89000
2000
01:46
is the place in which all of the more complex thought, decision making --
38
91000
3000
01:49
it's the last to mature in late adulthood.
39
94000
4000
01:53
This is where all your decision-making processes are going on.
40
98000
3000
01:56
It's the place where you're deciding right now
41
101000
2000
01:58
you probably aren't going to order the steak for dinner.
42
103000
3000
02:01
So if you take a deeper look at the brain,
43
106000
2000
02:03
one of the things, if you look at it in cross-section,
44
108000
2000
02:05
what you can see
45
110000
2000
02:07
is that you can't really see a whole lot of structure there.
46
112000
3000
02:10
But there's actually a lot of structure there.
47
115000
2000
02:12
It's cells and it's wires all wired together.
48
117000
2000
02:14
So about a hundred years ago,
49
119000
2000
02:16
some scientists invented a stain that would stain cells.
50
121000
2000
02:18
And that's shown here in the the very light blue.
51
123000
3000
02:21
You can see areas
52
126000
2000
02:23
where neuronal cell bodies are being stained.
53
128000
2000
02:25
And what you can see is it's very non-uniform. You see a lot more structure there.
54
130000
3000
02:28
So the outer part of that brain
55
133000
2000
02:30
is the neocortex.
56
135000
2000
02:32
It's one continuous processing unit, if you will.
57
137000
3000
02:35
But you can also see things underneath there as well.
58
140000
2000
02:37
And all of these blank areas
59
142000
2000
02:39
are the areas in which the wires are running through.
60
144000
2000
02:41
They're probably less cell dense.
61
146000
2000
02:43
So there's about 86 billion neurons in our brain.
62
148000
4000
02:47
And as you can see, they're very non-uniformly distributed.
63
152000
3000
02:50
And how they're distributed really contributes
64
155000
2000
02:52
to their underlying function.
65
157000
2000
02:54
And of course, as I mentioned before,
66
159000
2000
02:56
since we can now start to map brain function,
67
161000
3000
02:59
we can start to tie these into the individual cells.
68
164000
3000
03:02
So let's take a deeper look.
69
167000
2000
03:04
Let's look at neurons.
70
169000
2000
03:06
So as I mentioned, there are 86 billion neurons.
71
171000
2000
03:08
There are also these smaller cells as you'll see.
72
173000
2000
03:10
These are support cells -- astrocytes glia.
73
175000
2000
03:12
And the nerves themselves
74
177000
3000
03:15
are the ones who are receiving input.
75
180000
2000
03:17
They're storing it, they're processing it.
76
182000
2000
03:19
Each neuron is connected via synapses
77
184000
4000
03:23
to up to 10,000 other neurons in your brain.
78
188000
3000
03:26
And each neuron itself
79
191000
2000
03:28
is largely unique.
80
193000
2000
03:30
The unique character of both individual neurons
81
195000
2000
03:32
and neurons within a collection of the brain
82
197000
2000
03:34
are driven by fundamental properties
83
199000
3000
03:37
of their underlying biochemistry.
84
202000
2000
03:39
These are proteins.
85
204000
2000
03:41
They're proteins that are controlling things like ion channel movement.
86
206000
3000
03:44
They're controlling who nervous system cells partner up with.
87
209000
4000
03:48
And they're controlling
88
213000
2000
03:50
basically everything that the nervous system has to do.
89
215000
2000
03:52
So if we zoom in to an even deeper level,
90
217000
3000
03:55
all of those proteins
91
220000
2000
03:57
are encoded by our genomes.
92
222000
2000
03:59
We each have 23 pairs of chromosomes.
93
224000
3000
04:02
We get one from mom, one from dad.
94
227000
2000
04:04
And on these chromosomes
95
229000
2000
04:06
are roughly 25,000 genes.
96
231000
2000
04:08
They're encoded in the DNA.
97
233000
2000
04:10
And the nature of a given cell
98
235000
3000
04:13
driving its underlying biochemistry
99
238000
2000
04:15
is dictated by which of these 25,000 genes
100
240000
3000
04:18
are turned on
101
243000
2000
04:20
and at what level they're turned on.
102
245000
2000
04:22
And so our project
103
247000
2000
04:24
is seeking to look at this readout,
104
249000
3000
04:27
understanding which of these 25,000 genes is turned on.
105
252000
3000
04:30
So in order to undertake such a project,
106
255000
3000
04:33
we obviously need brains.
107
258000
3000
04:36
So we sent our lab technician out.
108
261000
3000
04:39
We were seeking normal human brains.
109
264000
2000
04:41
What we actually start with
110
266000
2000
04:43
is a medical examiner's office.
111
268000
2000
04:45
This a place where the dead are brought in.
112
270000
2000
04:47
We are seeking normal human brains.
113
272000
2000
04:49
There's a lot of criteria by which we're selecting these brains.
114
274000
3000
04:52
We want to make sure
115
277000
2000
04:54
that we have normal humans between the ages of 20 to 60,
116
279000
3000
04:57
they died a somewhat natural death
117
282000
2000
04:59
with no injury to the brain,
118
284000
2000
05:01
no history of psychiatric disease,
119
286000
2000
05:03
no drugs on board --
120
288000
2000
05:05
we do a toxicology workup.
121
290000
2000
05:07
And we're very careful
122
292000
2000
05:09
about the brains that we do take.
123
294000
2000
05:11
We're also selecting for brains
124
296000
2000
05:13
in which we can get the tissue,
125
298000
2000
05:15
we can get consent to take the tissue
126
300000
2000
05:17
within 24 hours of time of death.
127
302000
2000
05:19
Because what we're trying to measure, the RNA --
128
304000
3000
05:22
which is the readout from our genes --
129
307000
2000
05:24
is very labile,
130
309000
2000
05:26
and so we have to move very quickly.
131
311000
2000
05:28
One side note on the collection of brains:
132
313000
3000
05:31
because of the way that we collect,
133
316000
2000
05:33
and because we require consent,
134
318000
2000
05:35
we actually have a lot more male brains than female brains.
135
320000
3000
05:38
Males are much more likely to die an accidental death in the prime of their life.
136
323000
3000
05:41
And men are much more likely
137
326000
2000
05:43
to have their significant other, spouse, give consent
138
328000
3000
05:46
than the other way around.
139
331000
2000
05:48
(Laughter)
140
333000
4000
05:52
So the first thing that we do at the site of collection
141
337000
2000
05:54
is we collect what's called an MR.
142
339000
2000
05:56
This is magnetic resonance imaging -- MRI.
143
341000
2000
05:58
It's a standard template by which we're going to hang the rest of this data.
144
343000
3000
06:01
So we collect this MR.
145
346000
2000
06:03
And you can think of this as our satellite view for our map.
146
348000
2000
06:05
The next thing we do
147
350000
2000
06:07
is we collect what's called a diffusion tensor imaging.
148
352000
3000
06:10
This maps the large cabling in the brain.
149
355000
2000
06:12
And again, you can think of this
150
357000
2000
06:14
as almost mapping our interstate highways, if you will.
151
359000
2000
06:16
The brain is removed from the skull,
152
361000
2000
06:18
and then it's sliced into one-centimeter slices.
153
363000
3000
06:21
And those are frozen solid,
154
366000
2000
06:23
and they're shipped to Seattle.
155
368000
2000
06:25
And in Seattle, we take these --
156
370000
2000
06:27
this is a whole human hemisphere --
157
372000
2000
06:29
and we put them into what's basically a glorified meat slicer.
158
374000
2000
06:31
There's a blade here that's going to cut across
159
376000
2000
06:33
a section of the tissue
160
378000
2000
06:35
and transfer it to a microscope slide.
161
380000
2000
06:37
We're going to then apply one of those stains to it,
162
382000
2000
06:39
and we scan it.
163
384000
2000
06:41
And then what we get is our first mapping.
164
386000
3000
06:44
So this is where experts come in
165
389000
2000
06:46
and they make basic anatomic assignments.
166
391000
2000
06:48
You could consider this state boundaries, if you will,
167
393000
3000
06:51
those pretty broad outlines.
168
396000
2000
06:53
From this, we're able to then fragment that brain into further pieces,
169
398000
4000
06:57
which then we can put on a smaller cryostat.
170
402000
2000
06:59
And this is just showing this here --
171
404000
2000
07:01
this frozen tissue, and it's being cut.
172
406000
2000
07:03
This is 20 microns thin, so this is about a baby hair's width.
173
408000
3000
07:06
And remember, it's frozen.
174
411000
2000
07:08
And so you can see here,
175
413000
2000
07:10
old-fashioned technology of the paintbrush being applied.
176
415000
2000
07:12
We take a microscope slide.
177
417000
2000
07:14
Then we very carefully melt onto the slide.
178
419000
3000
07:17
This will then go onto a robot
179
422000
2000
07:19
that's going to apply one of those stains to it.
180
424000
3000
07:26
And our anatomists are going to go in and take a deeper look at this.
181
431000
3000
07:29
So again this is what they can see under the microscope.
182
434000
2000
07:31
You can see collections and configurations
183
436000
2000
07:33
of large and small cells
184
438000
2000
07:35
in clusters and various places.
185
440000
2000
07:37
And from there it's routine. They understand where to make these assignments.
186
442000
2000
07:39
And they can make basically what's a reference atlas.
187
444000
3000
07:42
This is a more detailed map.
188
447000
2000
07:44
Our scientists then use this
189
449000
2000
07:46
to go back to another piece of that tissue
190
451000
3000
07:49
and do what's called laser scanning microdissection.
191
454000
2000
07:51
So the technician takes the instructions.
192
456000
3000
07:54
They scribe along a place there.
193
459000
2000
07:56
And then the laser actually cuts.
194
461000
2000
07:58
You can see that blue dot there cutting. And that tissue falls off.
195
463000
3000
08:01
You can see on the microscope slide here,
196
466000
2000
08:03
that's what's happening in real time.
197
468000
2000
08:05
There's a container underneath that's collecting that tissue.
198
470000
3000
08:08
We take that tissue,
199
473000
2000
08:10
we purify the RNA out of it
200
475000
2000
08:12
using some basic technology,
201
477000
2000
08:14
and then we put a florescent tag on it.
202
479000
2000
08:16
We take that tagged material
203
481000
2000
08:18
and we put it on to something called a microarray.
204
483000
3000
08:21
Now this may look like a bunch of dots to you,
205
486000
2000
08:23
but each one of these individual dots
206
488000
2000
08:25
is actually a unique piece of the human genome
207
490000
2000
08:27
that we spotted down on glass.
208
492000
2000
08:29
This has roughly 60,000 elements on it,
209
494000
3000
08:32
so we repeatedly measure various genes
210
497000
3000
08:35
of the 25,000 genes in the genome.
211
500000
2000
08:37
And when we take a sample and we hybridize it to it,
212
502000
3000
08:40
we get a unique fingerprint, if you will,
213
505000
2000
08:42
quantitatively of what genes are turned on in that sample.
214
507000
3000
08:45
Now we do this over and over again,
215
510000
2000
08:47
this process for any given brain.
216
512000
3000
08:50
We're taking over a thousand samples for each brain.
217
515000
3000
08:53
This area shown here is an area called the hippocampus.
218
518000
3000
08:56
It's involved in learning and memory.
219
521000
2000
08:58
And it contributes to about 70 samples
220
523000
3000
09:01
of those thousand samples.
221
526000
2000
09:03
So each sample gets us about 50,000 data points
222
528000
4000
09:07
with repeat measurements, a thousand samples.
223
532000
3000
09:10
So roughly, we have 50 million data points
224
535000
2000
09:12
for a given human brain.
225
537000
2000
09:14
We've done right now
226
539000
2000
09:16
two human brains-worth of data.
227
541000
2000
09:18
We've put all of that together
228
543000
2000
09:20
into one thing,
229
545000
2000
09:22
and I'll show you what that synthesis looks like.
230
547000
2000
09:24
It's basically a large data set of information
231
549000
3000
09:27
that's all freely available to any scientist around the world.
232
552000
3000
09:30
They don't even have to log in to come use this tool,
233
555000
3000
09:33
mine this data, find interesting things out with this.
234
558000
4000
09:37
So here's the modalities that we put together.
235
562000
3000
09:40
You'll start to recognize these things from what we've collected before.
236
565000
3000
09:43
Here's the MR. It provides the framework.
237
568000
2000
09:45
There's an operator side on the right that allows you to turn,
238
570000
3000
09:48
it allows you to zoom in,
239
573000
2000
09:50
it allows you to highlight individual structures.
240
575000
3000
09:53
But most importantly,
241
578000
2000
09:55
we're now mapping into this anatomic framework,
242
580000
3000
09:58
which is a common framework for people to understand where genes are turned on.
243
583000
3000
10:01
So the red levels
244
586000
2000
10:03
are where a gene is turned on to a great degree.
245
588000
2000
10:05
Green is the sort of cool areas where it's not turned on.
246
590000
3000
10:08
And each gene gives us a fingerprint.
247
593000
2000
10:10
And remember that we've assayed all the 25,000 genes in the genome
248
595000
5000
10:15
and have all of that data available.
249
600000
4000
10:19
So what can scientists learn about this data?
250
604000
2000
10:21
We're just starting to look at this data ourselves.
251
606000
3000
10:24
There's some basic things that you would want to understand.
252
609000
3000
10:27
Two great examples are drugs,
253
612000
2000
10:29
Prozac and Wellbutrin.
254
614000
2000
10:31
These are commonly prescribed antidepressants.
255
616000
3000
10:34
Now remember, we're assaying genes.
256
619000
2000
10:36
Genes send the instructions to make proteins.
257
621000
3000
10:39
Proteins are targets for drugs.
258
624000
2000
10:41
So drugs bind to proteins
259
626000
2000
10:43
and either turn them off, etc.
260
628000
2000
10:45
So if you want to understand the action of drugs,
261
630000
2000
10:47
you want to understand how they're acting in the ways you want them to,
262
632000
3000
10:50
and also in the ways you don't want them to.
263
635000
2000
10:52
In the side effect profile, etc.,
264
637000
2000
10:54
you want to see where those genes are turned on.
265
639000
2000
10:56
And for the first time, we can actually do that.
266
641000
2000
10:58
We can do that in multiple individuals that we've assayed too.
267
643000
3000
11:01
So now we can look throughout the brain.
268
646000
3000
11:04
We can see this unique fingerprint.
269
649000
2000
11:06
And we get confirmation.
270
651000
2000
11:08
We get confirmation that, indeed, the gene is turned on --
271
653000
3000
11:11
for something like Prozac,
272
656000
2000
11:13
in serotonergic structures, things that are already known be affected --
273
658000
3000
11:16
but we also get to see the whole thing.
274
661000
2000
11:18
We also get to see areas that no one has ever looked at before,
275
663000
2000
11:20
and we see these genes turned on there.
276
665000
2000
11:22
It's as interesting a side effect as it could be.
277
667000
3000
11:25
One other thing you can do with such a thing
278
670000
2000
11:27
is you can, because it's a pattern matching exercise,
279
672000
3000
11:30
because there's unique fingerprint,
280
675000
2000
11:32
we can actually scan through the entire genome
281
677000
2000
11:34
and find other proteins
282
679000
2000
11:36
that show a similar fingerprint.
283
681000
2000
11:38
So if you're in drug discovery, for example,
284
683000
3000
11:41
you can go through
285
686000
2000
11:43
an entire listing of what the genome has on offer
286
688000
2000
11:45
to find perhaps better drug targets and optimize.
287
690000
4000
11:49
Most of you are probably familiar
288
694000
2000
11:51
with genome-wide association studies
289
696000
2000
11:53
in the form of people covering in the news
290
698000
3000
11:56
saying, "Scientists have recently discovered the gene or genes
291
701000
3000
11:59
which affect X."
292
704000
2000
12:01
And so these kinds of studies
293
706000
2000
12:03
are routinely published by scientists
294
708000
2000
12:05
and they're great. They analyze large populations.
295
710000
2000
12:07
They look at their entire genomes,
296
712000
2000
12:09
and they try to find hot spots of activity
297
714000
2000
12:11
that are linked causally to genes.
298
716000
3000
12:14
But what you get out of such an exercise
299
719000
2000
12:16
is simply a list of genes.
300
721000
2000
12:18
It tells you the what, but it doesn't tell you the where.
301
723000
3000
12:21
And so it's very important for those researchers
302
726000
3000
12:24
that we've created this resource.
303
729000
2000
12:26
Now they can come in
304
731000
2000
12:28
and they can start to get clues about activity.
305
733000
2000
12:30
They can start to look at common pathways --
306
735000
2000
12:32
other things that they simply haven't been able to do before.
307
737000
3000
12:36
So I think this audience in particular
308
741000
3000
12:39
can understand the importance of individuality.
309
744000
3000
12:42
And I think every human,
310
747000
2000
12:44
we all have different genetic backgrounds,
311
749000
4000
12:48
we all have lived separate lives.
312
753000
2000
12:50
But the fact is
313
755000
2000
12:52
our genomes are greater than 99 percent similar.
314
757000
3000
12:55
We're similar at the genetic level.
315
760000
3000
12:58
And what we're finding
316
763000
2000
13:00
is actually, even at the brain biochemical level,
317
765000
2000
13:02
we are quite similar.
318
767000
2000
13:04
And so this shows it's not 99 percent,
319
769000
2000
13:06
but it's roughly 90 percent correspondence
320
771000
2000
13:08
at a reasonable cutoff,
321
773000
3000
13:11
so everything in the cloud is roughly correlated.
322
776000
2000
13:13
And then we find some outliers,
323
778000
2000
13:15
some things that lie beyond the cloud.
324
780000
3000
13:18
And those genes are interesting,
325
783000
2000
13:20
but they're very subtle.
326
785000
2000
13:22
So I think it's an important message
327
787000
3000
13:25
to take home today
328
790000
2000
13:27
that even though we celebrate all of our differences,
329
792000
3000
13:30
we are quite similar
330
795000
2000
13:32
even at the brain level.
331
797000
2000
13:34
Now what do those differences look like?
332
799000
2000
13:36
This is an example of a study that we did
333
801000
2000
13:38
to follow up and see what exactly those differences were --
334
803000
2000
13:40
and they're quite subtle.
335
805000
2000
13:42
These are things where genes are turned on in an individual cell type.
336
807000
4000
13:46
These are two genes that we found as good examples.
337
811000
3000
13:49
One is called RELN -- it's involved in early developmental cues.
338
814000
3000
13:52
DISC1 is a gene
339
817000
2000
13:54
that's deleted in schizophrenia.
340
819000
2000
13:56
These aren't schizophrenic individuals,
341
821000
2000
13:58
but they do show some population variation.
342
823000
3000
14:01
And so what you're looking at here
343
826000
2000
14:03
in donor one and donor four,
344
828000
2000
14:05
which are the exceptions to the other two,
345
830000
2000
14:07
that genes are being turned on
346
832000
2000
14:09
in a very specific subset of cells.
347
834000
2000
14:11
It's this dark purple precipitate within the cell
348
836000
3000
14:14
that's telling us a gene is turned on there.
349
839000
3000
14:17
Whether or not that's due
350
842000
2000
14:19
to an individual's genetic background or their experiences,
351
844000
2000
14:21
we don't know.
352
846000
2000
14:23
Those kinds of studies require much larger populations.
353
848000
3000
14:28
So I'm going to leave you with a final note
354
853000
2000
14:30
about the complexity of the brain
355
855000
3000
14:33
and how much more we have to go.
356
858000
2000
14:35
I think these resources are incredibly valuable.
357
860000
2000
14:37
They give researchers a handle
358
862000
2000
14:39
on where to go.
359
864000
2000
14:41
But we only looked at a handful of individuals at this point.
360
866000
3000
14:44
We're certainly going to be looking at more.
361
869000
2000
14:46
I'll just close by saying
362
871000
2000
14:48
that the tools are there,
363
873000
2000
14:50
and this is truly an unexplored, undiscovered continent.
364
875000
4000
14:54
This is the new frontier, if you will.
365
879000
4000
14:58
And so for those who are undaunted,
366
883000
2000
15:00
but humbled by the complexity of the brain,
367
885000
2000
15:02
the future awaits.
368
887000
2000
15:04
Thanks.
369
889000
2000
15:06
(Applause)
370
891000
9000
ABOUT THE SPEAKER
Allan Jones - Brain scientistAs CEO of the Allen Institute for Brain Science, Allan Jones leads an ambitious project to build an open, online, interactive atlas of the human brain.
Why you should listen
The Allen Institute for Brain Science -- based in Seattle, kickstarted by Microsoft co-founder Paul Allen -- has a mission to fuel discoveries about the human brain by building tools the entire scientific community can use. As CEO, one of Allan Jones' first projects was to lead the drive to create a comprehensive atlas of the brain of a mouse. Flash forward to April 2011, when the Allen Institute announced the first milestone in its online interactive atlas of the human brain, showing the activity of the more than 20,000 human genes it contains. It's based on a composite of 15 brains, since every human brain is unique.
Think of the Allen Human Brain Atlas as a high-tech bridge between brain anatomy and genetics. Using this atlas, scientists will be able to determine where in the brain genes that encode specific proteins are active, including proteins that are affected by medication. Or researchers could zoom in on brain structures thought to be altered in mental disorders such as schizophrenia to find their molecular footprint. The atlas may provide clues to memory, attention, motor coordination, hunger, and perhaps emotions such as happiness or anxiety.
He says: "Understanding how our genes are used in our brains will help scientists and the medical community better understand and discover new treatments for the full spectrum of brain diseases and disorders."
Watch Dr. Jones' latest TEDx talk on the map of the brain, from TEDxCaltech 2013 >>
Allan Jones | Speaker | TED.com