WEBVTT 00:00:01.000 --> 00:00:02.000 Complexity. 00:00:02.000 --> 00:00:05.000 Nothing quite embodies the word like the human brain. 00:00:05.000 --> 00:00:10.000 So for centuries we've studied the complexity of the human brain 00:00:10.000 --> 00:00:13.000 using the tools and technology of the day. 00:00:13.000 --> 00:00:16.000 If that's pen and paper from the age of da Vinci 00:00:16.000 --> 00:00:18.000 through advents in microscopy 00:00:18.000 --> 00:00:21.000 to be able to look more deeply into the brain 00:00:21.000 --> 00:00:25.000 to a lot of the new technologies that you've heard about today 00:00:25.000 --> 00:00:28.000 through imaging, magnetic resonance imaging, 00:00:28.000 --> 00:00:31.000 able to look a the details of the brain. 00:00:31.000 --> 00:00:35.000 Now one of the first things you notice when you look 00:00:35.000 --> 00:00:38.000 at a fresh human brain is the amount of vasculatur 00:00:38.000 --> 00:00:40.000 that's completely covering this. 00:00:40.000 --> 00:00:44.000 The brain is this metabolically voracious organ. 00:00:44.000 --> 00:00:47.000 Approximately a quarter of the oxygen in your blood, 00:00:47.000 --> 00:00:51.000 approximately a fifth of the glucose in your blood 00:00:51.000 --> 00:00:53.000 is being used by this organ. 00:00:53.000 --> 00:00:57.000 It's so metabolically active there's a waste stream which comes out 00:00:57.000 --> 00:01:02.000 into your cervical spinal fluid. You generate 0.5 liter of CSF every day. 00:01:02.000 --> 00:01:06.000 So, as you know, researchers have taken advantage 00:01:06.000 --> 00:01:10.000 of this massive amount of blood flow and metabolic activity 00:01:10.000 --> 00:01:15.000 to begin to map regions of the brain, to functionally annotate the brain 00:01:15.000 --> 00:01:16.000 in very meaningful ways. 00:01:16.000 --> 00:01:20.000 You'll hear a lot more about those kinds of studies, 00:01:20.000 --> 00:01:25.000 but basically taking advantage of the fact that there's active metabolism 00:01:25.000 --> 00:01:27.000 with certain tasks going on. 00:01:27.000 --> 00:01:31.000 You can put a living human in a machine and you can see various areas 00:01:31.000 --> 00:01:33.000 that are lighting up. 00:01:33.000 --> 00:01:37.000 For example, going around right now is the temporal cortex auditory 00:01:37.000 --> 00:01:41.000 processing going on there, you're listening to my words, 00:01:41.000 --> 00:01:44.000 you're processing what I'm saying. 00:01:44.000 --> 00:01:48.000 Moving to the front of this brain is your prefontal cortex, 00:01:48.000 --> 00:01:50.000 your executive decision-making, 00:01:50.000 --> 00:01:53.000 your higher-thinking areas of the brain. 00:01:53.000 --> 00:01:56.000 And so the thing that we're very much interested in 00:01:56.000 --> 00:01:59.000 from the perspective of the Allen Institute 00:01:59.000 --> 00:02:03.000 is to go deeper, to get down to the cellular level. 00:02:03.000 --> 00:02:08.000 So when you look at this slice, it doesn't really look like gray matter, does it? 00:02:08.000 --> 00:02:11.000 It's more tan matter, or beige matter. 00:02:11.000 --> 00:02:14.000 And scientists about, I guess, around the late 1800's 00:02:14.000 --> 00:02:18.000 discovered that they could stain tissue in various ways, 00:02:18.000 --> 00:02:23.000 and this sort of came along with various microscopy techniques. 00:02:23.000 --> 00:02:27.000 And so this is a stain, it's called Nissl, and it stains cell bodies, 00:02:27.000 --> 00:02:30.000 it stains the cell bodies purple. 00:02:30.000 --> 00:02:33.000 And so you can see a lot more structure and texture 00:02:33.000 --> 00:02:36.000 when you look at something like this. 00:02:36.000 --> 00:02:40.000 You can see the outer layers of the brain and the neocortex, 00:02:40.000 --> 00:02:45.000 there's a six-layer structure, arguably what makes us most uniquely human. 00:02:45.000 --> 00:02:49.000 As you've heard before about there's on average in a human 00:02:49.000 --> 00:02:53.000 there's about 86 billion neurons and those 86 billion neurons 00:02:53.000 --> 00:02:59.000 you can see are not evenly distributed, they're very focused and specific structures. 00:02:59.000 --> 00:03:02.000 And each of them has their own specific function 00:03:02.000 --> 00:03:06.000 both on an anatomic level and at a cellular level. 00:03:06.000 --> 00:03:10.000 So if we zoom in on these cells, what you can see is large cells 00:03:10.000 --> 00:03:14.000 and small support cells that are glias and astrocytes 00:03:14.000 --> 00:03:19.000 and these cells are as we know connected in a variety of different ways. 00:03:22.000 --> 00:03:26.000 And we like to think about although there's 86 billion cells, 00:03:26.000 --> 00:03:31.000 each cell might be considered a snowflake, they're actually able to be binned 00:03:31.000 --> 00:03:34.000 into a large number of cell types or classes. 00:03:34.000 --> 00:03:38.000 What flavor of activity that particular cell class has 00:03:38.000 --> 00:03:42.000 is driven by the underlying genes that are turned on in that cell, 00:03:42.000 --> 00:03:47.000 those drive protein expression which guide the function of those cells, 00:03:47.000 --> 00:03:51.000 who they're connected to, what their morphology is 00:03:51.000 --> 00:03:55.000 and we're very much interested in understanding these cell classes. 00:03:55.000 --> 00:03:57.000 So how do we do that? 00:03:57.000 --> 00:04:00.000 Well, we look inside the cell at the nucleus, 00:04:00.000 --> 00:04:02.000 and it will get to the nucleus, 00:04:02.000 --> 00:04:05.000 and so inside we've got 23 pairs of chromosomes, 00:04:05.000 --> 00:04:08.000 we've got a pair from mom, a pair from dad, 00:04:11.000 --> 00:04:17.000 on those chromosomes about 25000 genes and we're very much again interested in 00:04:17.000 --> 00:04:19.000 understanding which of these 25000 genes 00:04:19.000 --> 00:04:23.000 are turned on and at what levels they're turned on. 00:04:23.000 --> 00:04:28.000 Those are going of course to drive the underlying biochemistry of the cells 00:04:28.000 --> 00:04:33.000 they're turned on in and again every cell in our bodies more or less has these 00:04:33.000 --> 00:04:35.000 and we want to understand better 00:04:35.000 --> 00:04:39.000 what the driving biochemistry driven by our genome is. 00:04:39.000 --> 00:04:41.000 So how do we do that? 00:04:41.000 --> 00:04:44.000 We're going to deconstruct a brain in several easy steps. 00:04:44.000 --> 00:04:47.000 So we start at a medical examiner's office. 00:04:47.000 --> 00:04:51.000 This is a place where the dead are brought in 00:04:51.000 --> 00:04:52.000 and obviously it's useful, 00:04:52.000 --> 00:04:56.000 for the kind of work we do is not non-invasive, 00:04:56.000 --> 00:04:59.000 we actually need to obtain fresh brain tissue 00:04:59.000 --> 00:05:04.000 and we need to obtain it within 24 hours because the tissues start to degrade. 00:05:04.000 --> 00:05:06.000 We also wanted for our projects304306.1 00:05:06.000 --> 00:05:10.000 to have normal tissue as much normal as we could possibly get. 00:05:10.000 --> 00:05:15.000 So over the course of a two- or three-year collection time window 00:05:15.000 --> 00:05:20.000 we collected 6 very high-quality brains, 5 of them were male, one was female, 00:05:20.000 --> 00:05:24.000 That's only because males tend to die untimely deaths 00:05:24.000 --> 00:05:28.000 more frequently than females and then to add to that females 00:05:28.000 --> 00:05:33.000 are much more likely to give consent for us to take the brain than vice versa. 00:05:33.000 --> 00:05:35.000 We have to figure that one out. 00:05:35.000 --> 00:05:39.000 We've heard people say, “He wasn't using it anyway!” 00:05:39.000 --> 00:05:43.000 So, once the brain comes in we have to move very, very quickly. 00:05:43.000 --> 00:05:47.000 So first we capture a magnetic resonance image. 00:05:47.000 --> 00:05:50.000 This, of course, will look very familiar to you, 00:05:50.000 --> 00:05:55.000 but this is going to be the structure in which we hang all of this information, 00:05:55.000 --> 00:06:00.000 it's also a common coordinate framework by which the many, many researchers 00:06:00.000 --> 00:06:02.000 who do imaging studies can map 00:06:02.000 --> 00:06:06.000 into our ultimate database, an Atlas framework. 00:06:06.000 --> 00:06:08.000 We also collect diffusion tensor images 00:06:08.000 --> 00:06:12.000 so we get some of the wiring from these brains 00:06:12.000 --> 00:06:16.000 and then the brain is removed from the skull. It's slabbed and frozen, 00:06:16.000 --> 00:06:20.000 frozen solid, and then it's shipped to Seattle 00:06:20.000 --> 00:06:23.000 where we have the Allen Institute for Brain Science. 00:06:23.000 --> 00:06:26.000 We have great technicians who've worked out 00:06:26.000 --> 00:06:29.000 a lot of great techniques for further processing. 00:06:29.000 --> 00:06:34.000 So first, we take a very thin section, this is 25µm thin section, 00:06:34.000 --> 00:06:36.000 which is about a baby's hair width. 00:06:36.000 --> 00:06:41.000 That's transferred to a microscope slide and then that is stained 00:06:41.000 --> 00:06:45.000 with one of those histological stains that I talked about before. 00:06:45.000 --> 00:06:50.000 And this is going to give us more contrast as our team of anatomists 00:06:50.000 --> 00:06:52.000 start to make assignments of anatomy. 00:06:52.000 --> 00:06:54.000 So we digitize these images, 00:06:54.000 --> 00:06:58.000 everything goes from being wet lab to being dry lab. 00:06:58.000 --> 00:07:04.000 And then combined with anatomy that we get from the MR we further fragment the brain. 00:07:04.000 --> 00:07:08.000 This is to get it into a smaller framework for which we can do this. 00:07:08.000 --> 00:07:12.000 So here's a technician who's doing additional cutting. 00:07:12.000 --> 00:07:14.000 This is again a 25µm thin section. 00:07:14.000 --> 00:07:20.000 You'll see da Vinci's tools, the paintbrush, being use here to smooth this out. 00:07:20.000 --> 00:07:22.000 This is fresh frozen brain tissue. 00:07:22.000 --> 00:07:26.000 And it can be very carefully melted to a microscope slide. 00:07:26.000 --> 00:07:29.000 You'll note that there's a barcode on the slide. 00:07:29.000 --> 00:07:32.000 We process 1000's and 1000's of samples, 00:07:32.000 --> 00:07:36.000 we track all of it in a backend information management system. 00:07:36.000 --> 00:07:38.000 Those are stained. 00:07:38.000 --> 00:07:41.000 And then we get more detailed anatomic information. 00:07:41.000 --> 00:07:46.000 That information is, playing here, this is a laser capture microscope, 00:07:46.000 --> 00:07:50.000 the lab technician is actually describing an area on that slide. 00:07:50.000 --> 00:07:54.000 And a laser, you see the blue light cutting around there, 00:07:54.000 --> 00:07:56.000 very James Bond like. 00:07:56.000 --> 00:07:59.000 Cutting out part of that, and underneath there, 00:07:59.000 --> 00:08:04.000 you can see the blue light again, from the microscope in real-time. 00:08:04.000 --> 00:08:07.000 It's collecting in a microscope tube that tissue. 00:08:07.000 --> 00:08:08.000 We extract RNA, 00:08:08.000 --> 00:08:12.000 RNA is the product of the genes that are being turned on, 00:08:12.000 --> 00:08:15.000 and we label it, we put a fluorescent tag on it. 00:08:15.000 --> 00:08:21.000 Now what you are looking at here is a constellation of the entire human genome 00:08:21.000 --> 00:08:23.000 spread out over a glass slide. 00:08:23.000 --> 00:08:26.000 Those little bits are representing the 25000 genes. 00:08:26.000 --> 00:08:31.000 There's about 60000 of these spots and that fluorescently labeled RNA 00:08:31.000 --> 00:08:36.000 is put onto this microscope slide and then we read out quantitatively 00:08:36.000 --> 00:08:38.000 what genes are turned on at what levels. 00:08:38.000 --> 00:08:44.000 So we do this over and over and over again for brains that we've collected. 00:08:44.000 --> 00:08:47.000 As I mentioned we've collected 6 brains in total. 00:08:47.000 --> 00:08:51.000 We collect samples from about 1000 structures in every brain 00:08:51.000 --> 00:08:55.000 that we've looked at, so it's a massive amount of data. 00:08:55.000 --> 00:08:59.000 And we pull all of this together, back into a common framework, 00:08:59.000 --> 00:09:04.000 that is a free and open resource for scientists around the world to use. 00:09:04.000 --> 00:09:09.000 So at the Allen Institute for Brain Science, we've been generating 00:09:09.000 --> 00:09:12.000 these kinds of data resources for almost a decade. 00:09:12.000 --> 00:09:16.000 They're free to use for anybody, they're online tools, 00:09:16.000 --> 00:09:21.000 just for example today a given workday, there'll be about 1000 unique visitors 00:09:21.000 --> 00:09:26.000 that come in from labs around the world to come use our resources and data. 00:09:26.000 --> 00:09:30.000 They get access to tools like this, which allows them 00:09:30.000 --> 00:09:34.000 to see all that anatomy and the structure that we created before 00:09:34.000 --> 00:09:40.000 and to start mapping in then the things that they're particularly interested in. 00:09:40.000 --> 00:09:43.000 So in this case you're looking at the structure 00:09:43.000 --> 00:09:46.000 and they're going to look at these color balls 00:09:46.000 --> 00:09:49.000 are representing a particular gene 00:09:49.000 --> 00:09:53.000 they're interested in that's either being turned up or down 00:09:53.000 --> 00:09:58.000 in those various areas depending on the heat color that's specified there. 00:09:58.000 --> 00:10:02.000 So what are people doing when they come in and using these resources? 00:10:02.000 --> 00:10:06.000 Well, one of the things that you might hear lots about 00:10:06.000 --> 00:10:08.000 is human genetic studies. 00:10:08.000 --> 00:10:12.000 Obviously if you're very interested in understanding disease 00:10:12.000 --> 00:10:15.000 there's a genetic underpinning to many of them. 00:10:15.000 --> 00:10:17.000 So you'd like more information, 00:10:17.000 --> 00:10:21.000 you do a large-scale study and you get out of those studies 00:10:21.000 --> 00:10:25.000 collections of genes and one of the first things 00:10:25.000 --> 00:10:28.000 you're going to want to know is more information. 00:10:28.000 --> 00:10:33.000 Is there something I can learn about the location of these genes 00:10:33.000 --> 00:10:36.000 that gives me additional clues as to their function, 00:10:36.000 --> 00:10:40.000 ways in which I might intervene in the disease process. 00:10:40.000 --> 00:10:45.000 They're also very interested in understanding human genetic diversity. 00:10:45.000 --> 00:10:50.000 Now we've already looked at 6 brains but as we know, every human is very unique. 00:10:50.000 --> 00:10:52.000 We celebrate our differences; 00:10:52.000 --> 00:10:58.000 this is a snapshot of the great workforce at the Allen Institute for Brain Science 00:10:58.000 --> 00:11:02.000 who does all the great work that I'm talking about today. 00:11:02.000 --> 00:11:06.000 But remarkably when we look at this level at the underlying data 00:11:06.000 --> 00:11:11.000 and this is a lot of data from 2 completely unrelated individuals 00:11:11.000 --> 00:11:15.000 there's a very high degree of correlation, correspondence. 00:11:15.000 --> 00:11:20.000 So this is looking at 1000's of different measurements of gene expressions 00:11:20.000 --> 00:11:23.000 across many, many different areas of the brain. 00:11:23.000 --> 00:11:26.000 And there's a very high degree of correspondence. 00:11:26.000 --> 00:11:28.000 This was very reassuring to us. 00:11:28.000 --> 00:11:32.000 First because when you generate data on this scale 00:11:32.000 --> 00:11:38.000 you want to make sure that it's high quality, so reproducibility is obviously important, 00:11:38.000 --> 00:11:42.000 but it was also important because we feel that it's given us 00:11:42.000 --> 00:11:45.000 a great snapshot into the human brain. 00:11:45.000 --> 00:11:49.000 And the people using the data, even with our low n have confidence 00:11:49.000 --> 00:11:52.000 that what they're seeing has some relevance. 00:11:52.000 --> 00:11:57.000 Now not everything is correlated here, you can see some outliers, 00:11:57.000 --> 00:12:00.000 and of course those outliers are going to be interesting 00:12:00.000 --> 00:12:02.000 related to human differences. 00:12:02.000 --> 00:12:05.000 We did study a couple of years ago 00:12:05.000 --> 00:12:10.000 in which we tried to understand a little better about those differences 00:12:10.000 --> 00:12:14.000 and looked at multiple individuals and different gene products 00:12:14.000 --> 00:12:19.000 and what we find is that a tendency and as a rule is that those differences 00:12:19.000 --> 00:12:23.000 tend to be in very specific cell populations or cell types, 00:12:23.000 --> 00:12:25.000 cell classes as I mentioned before. 00:12:25.000 --> 00:12:30.000 So, this is an example of 2 different genes that are turned on 00:12:30.000 --> 00:12:34.000 in a very specific layers of the neocortex only in one individual 00:12:34.000 --> 00:12:36.000 nd not found in another. 00:12:36.000 --> 00:12:40.000 Now we have no idea if that's due to environmental changes, 00:12:40.000 --> 00:12:43.000 environmental influences or if it's just genetics. 00:12:43.000 --> 00:12:48.000 But we did do a study in which we looked at the mouse several years ago 00:12:48.000 --> 00:12:53.000 and we were looking at genes that encode for, in this case a DRD2, 00:12:53.000 --> 00:12:56.000 the gene listed on the top is a dopamine receptor. 00:12:56.000 --> 00:13:01.000 Tyrosine hydroxylase (TH) is a gene involved in dopamine biosynthesis 00:13:01.000 --> 00:13:05.000 and those 2 gene products are very different in the cell types 00:13:05.000 --> 00:13:08.000 in these individual mouse brains. 00:13:08.000 --> 00:13:13.000 So, over on the left is “C57 black 6” which is a commonly used mouse strain, 00:13:13.000 --> 00:13:17.000 and then spread at the other end is a wild type strain. 00:13:17.000 --> 00:13:21.000 And so the further you go the more genetically unrelated you are. 00:13:21.000 --> 00:13:26.000 And when we looked in total across, sort of evolution if you will, 00:13:26.000 --> 00:13:30.000 across genetic relatedness, the further you were genetically unrelated, 00:13:30.000 --> 00:13:33.000 the more of these very specific cell types, 00:13:33.000 --> 00:13:36.000 specific changes, you could see. 00:13:36.000 --> 00:13:39.000 So at the Allen Institute for the next decade 00:13:39.000 --> 00:13:42.000 we're embarking on a pretty ambitious program 00:13:42.000 --> 00:13:47.000 to start to understand the cell types, understand the cell differences 00:13:47.000 --> 00:13:52.000 and how they ultimately relate to the functional properties of the brain. 00:13:52.000 --> 00:13:56.000 This is, I think, critical information for the entire field, 00:13:56.000 --> 00:14:00.000 to start linking up all of these fundamental parts which are cells, 00:14:00.000 --> 00:14:02.000 to how they're connected, 00:14:02.000 --> 00:14:06.000 the underlying molecules that drive those connections, 00:14:06.000 --> 00:14:10.000 the underlying molecules that drive the physiological properties, 00:14:10.000 --> 00:14:14.000 the electric chemical properties and then ultimately 00:14:14.000 --> 00:14:17.000 the functional properties of those cells. 00:14:17.000 --> 00:14:20.000 So we're doing this in 3 different areas of research. 00:14:20.000 --> 00:14:24.000 First we're focusing on the mouse, the mouse visual system, 00:14:24.000 --> 00:14:27.000 to look at, in real-time, in the living animal 00:14:27.000 --> 00:14:31.000 the functions of a variety of different cells. 00:14:31.000 --> 00:14:35.000 We're linking these in this concept in the middle of cell types, 00:14:35.000 --> 00:14:38.000 trying really understand the underlying molecules 00:14:38.000 --> 00:14:42.000 in all the properties as they relate to those functions 00:14:42.000 --> 00:14:45.000 and then we're looking at the human. 00:14:45.000 --> 00:14:49.000 In the human we're doing this both in the middle and cell types 00:14:49.000 --> 00:14:53.000 using the tissue driven work that I talked about before 00:14:53.000 --> 00:14:57.000 but also we're doing it in vitro using stem cell technology. 00:14:57.000 --> 00:15:01.000 We're learning how to make very specific cell types 00:15:01.000 --> 00:15:03.000 within the dish and then being able to test 00:15:03.000 --> 00:15:07.000 those functional properties and go back and forth 00:15:07.000 --> 00:15:10.000 between what we learn in the mouse to the human. 00:15:10.000 --> 00:15:15.000 So, with that I will finish and just say that it's an exciting time 00:15:15.000 --> 00:15:16.000 to be in biology 00:15:16.000 --> 00:15:19.000 and an exciting time to be in neuroscience. 00:15:19.000 --> 00:15:24.000 I think the technology of the day has come well beyond the pen and paper 00:15:24.000 --> 00:15:29.000 and it's really time for a renaissance in our understanding of this complex organ. 00:15:29.000 --> 00:15:30.000 Thanks.