1 00:00:00,000 --> 00:00:05,340 In the last decade, the ability to have genetic targeting of cell types in mice and to be 2 00:00:05,340 --> 00:00:11,080 able to manipulate specific cell types, work out their connectivity, has really provided 3 00:00:11,080 --> 00:00:14,280 huge insights into the way the brain works. 4 00:00:14,280 --> 00:00:19,420 And there's going to come a day, it might be 30 years or 50 years from now, where there 5 00:00:19,420 --> 00:00:25,020 will be gene therapy in the human brain targeting cell types and manipulating circuits that 6 00:00:25,020 --> 00:00:30,320 are defective in brain disorders. 7 00:00:30,320 --> 00:00:34,800 The human brain is the most complex structure in the known universe and we are in the middle 8 00:00:34,800 --> 00:00:38,320 of a scientific revolution to understand its inner workings. 9 00:00:38,320 --> 00:00:43,040 Join us for a conversation with world-renowned neuroscientists as they visit Rochester. 10 00:00:43,040 --> 00:00:47,480 I am Dr. John Foxe, Director of the Del Monte Institute for Neuroscience at the University 11 00:00:47,480 --> 00:00:53,760 of Rochester, and you are listening to Neuroscience Perspectives. 12 00:00:53,760 --> 00:00:58,640 So Ed Calvi, we never met in person before, that's why it's really, I mean, having read 13 00:00:58,640 --> 00:01:02,240 your papers over the years, it's really great to have you here in Rochester. 14 00:01:02,240 --> 00:01:07,000 And wanted to introduce you to our community and ask you a few questions about your science 15 00:01:07,000 --> 00:01:11,840 and how you ended up where you're at in the world. 16 00:01:11,840 --> 00:01:16,800 So you're at the Salk Institute and have you spent your entire career on the west coast? 17 00:01:16,800 --> 00:01:20,440 Well my first faculty position for three years I was in Colorado at the Health Sciences Center 18 00:01:20,440 --> 00:01:24,120 in Denver and then moved to, in 1995, to Salk Institute. 19 00:01:24,120 --> 00:01:26,400 So it's been 24 years there now. 20 00:01:26,400 --> 00:01:27,400 Excellent, excellent. 21 00:01:27,400 --> 00:01:28,960 Well let's dive into your science. 22 00:01:28,960 --> 00:01:33,240 I actually, you know, of course I was reading beforehand trying to get ready to meet with 23 00:01:33,240 --> 00:01:38,000 you and I got very struck by something that was on one of the sets of materials and it 24 00:01:38,000 --> 00:01:43,400 was describing the brain as a veritable bowl of spaghetti. 25 00:01:43,400 --> 00:01:49,000 And so a lot of your work has been about untangling the mysteries of the circuitry of the brain 26 00:01:49,000 --> 00:01:50,360 and that. 27 00:01:50,360 --> 00:01:54,600 And would you like to tell us a little bit about how you think about brain circuitry 28 00:01:54,600 --> 00:01:57,400 and this massive neurons and interconnectivity? 29 00:01:57,400 --> 00:02:01,080 Well there are two levels of different ways you can entangle that. 30 00:02:01,080 --> 00:02:05,600 And the one that I think is the most tractable right now and where the field is really moving 31 00:02:05,600 --> 00:02:07,560 is cell types, right? 32 00:02:07,560 --> 00:02:12,760 Because even though it's a big tangle, if we can, you know, label one of those pieces 33 00:02:12,760 --> 00:02:17,840 of spaghetti and follow it, or it's maybe not just a big piece of spaghetti, it's got 34 00:02:17,840 --> 00:02:23,120 some penne pasta and rigatoni and different kind of things in it, but, you know, without 35 00:02:23,120 --> 00:02:28,240 having the ability to target and see that that's all in there and each of those is different. 36 00:02:28,240 --> 00:02:30,880 It's pretty clear to us now that cell types matter. 37 00:02:30,880 --> 00:02:34,600 Each cell type is connected in a different way, mediates brain function in different 38 00:02:34,600 --> 00:02:35,760 ways. 39 00:02:35,760 --> 00:02:42,200 And in the last decade, the ability to have genetic targeting of cell types in mice and 40 00:02:42,200 --> 00:02:48,120 to be able to manipulate specific cell types, work out their connectivity has really provided 41 00:02:48,120 --> 00:02:51,320 huge insights into the way the brain works. 42 00:02:51,320 --> 00:02:57,120 And there's going to come a day, it might be 30 years or 50 years from now, where there 43 00:02:57,120 --> 00:03:02,720 will be gene therapy in the human brain targeting cell types and manipulating circuits that 44 00:03:02,720 --> 00:03:05,800 are defective in brain disorders. 45 00:03:05,800 --> 00:03:12,160 Is it the idea then that there may be disorders that are allied to specific deficits in specific 46 00:03:12,160 --> 00:03:13,160 cell types? 47 00:03:13,160 --> 00:03:14,160 That's why we need to know that. 48 00:03:14,160 --> 00:03:16,440 Yeah, and there's evidence for that. 49 00:03:16,440 --> 00:03:22,160 So for example, post-mortem tissue from schizophrenic patients, there's a particular cell type called 50 00:03:22,160 --> 00:03:23,160 chandelier cells. 51 00:03:23,160 --> 00:03:28,600 They make a particular kind of synapse on pyramidal cell axon terminals, beautiful chandelier-like 52 00:03:28,600 --> 00:03:29,880 axons. 53 00:03:29,880 --> 00:03:34,880 That's how they got their name from the old days when you've, Golgi labeling of neurons. 54 00:03:34,880 --> 00:03:39,400 It turns out in schizophrenic patients, they have fewer than normal number of these axon 55 00:03:39,400 --> 00:03:41,940 terminals on the pyramidal neurons. 56 00:03:41,940 --> 00:03:44,920 So that's just a clue that there might be something wrong there. 57 00:03:44,920 --> 00:03:49,280 But there now exist mouse lines that you can target gene expression to just the chandelier 58 00:03:49,280 --> 00:03:50,280 cells. 59 00:03:50,280 --> 00:03:53,760 And you can inactivate and activate them and see what their role is in the function of 60 00:03:53,760 --> 00:03:55,120 a mouse brain. 61 00:03:55,120 --> 00:03:59,200 And within a couple of years, there will be ways to target chandelier cells in a monkey 62 00:03:59,200 --> 00:04:03,400 brain, which is much closer to a human brain in its function and where you can study higher 63 00:04:03,400 --> 00:04:09,280 level cognitive functions and manipulate them and look at them in that context of something. 64 00:04:09,280 --> 00:04:13,680 So you're talking about looking at these cells and of course, people... 65 00:04:13,680 --> 00:04:16,280 Yeah, I'm sort of looking in a figurative sense. 66 00:04:16,280 --> 00:04:17,280 Yeah, I know, right? 67 00:04:17,280 --> 00:04:18,280 So we do look at them. 68 00:04:18,280 --> 00:04:19,280 Exactly. 69 00:04:19,280 --> 00:04:23,280 And in some ways, that's where your lab has really become famous around that. 70 00:04:23,280 --> 00:04:29,160 So there are ways to stain these neurons, but you brought new technologies using a virus, 71 00:04:29,160 --> 00:04:30,160 right? 72 00:04:30,160 --> 00:04:35,520 A very famous virus to bear in terms of our ability to really paint these pictures. 73 00:04:35,520 --> 00:04:36,520 Would you want to talk about that? 74 00:04:36,520 --> 00:04:37,520 Yeah, sure. 75 00:04:37,520 --> 00:04:42,840 So for many years, we wanted to have a way of...if all these cell types are mixed together 76 00:04:42,840 --> 00:04:47,360 and you want to entangle the connectivity, you'd like to know where are all the cells 77 00:04:47,360 --> 00:04:51,960 in the brain that connect to a particular cell type in a particular place that starts 78 00:04:51,960 --> 00:04:54,400 in a particular place in the brain. 79 00:04:54,400 --> 00:05:00,600 And back, I guess it's been about 2007 now, about 12 years ago, we first developed this 80 00:05:00,600 --> 00:05:06,500 method working very closely with Ian Wickersham, who was a graduate student in the lab. 81 00:05:06,500 --> 00:05:12,580 We modified rabies virus, which has natural abilities to spread very selectively only 82 00:05:12,580 --> 00:05:15,640 across connected neurons. 83 00:05:15,640 --> 00:05:20,320 But it doesn't have a property where you'd inject it in the brain, it would only infect 84 00:05:20,320 --> 00:05:21,500 the cell type. 85 00:05:21,500 --> 00:05:25,520 And it doesn't have the property where it would spread one step and stop, so you could 86 00:05:25,520 --> 00:05:27,520 unambiguously say, those are the cells connected. 87 00:05:27,520 --> 00:05:29,420 It would just keep spreading. 88 00:05:29,420 --> 00:05:33,280 And in fact, in animals that are infected, like bats, they'll eventually change their 89 00:05:33,280 --> 00:05:37,720 behavior and they'll spread to other bats and they die. 90 00:05:37,720 --> 00:05:42,200 But we modified the virus in ways that allowed us to target its infection to specific cell 91 00:05:42,200 --> 00:05:46,640 types, and so that it would only spread one synaptic step and label the direct inputs 92 00:05:46,640 --> 00:05:47,640 to those cells. 93 00:05:47,640 --> 00:05:50,120 Then you can look across the entire brain and look at those. 94 00:05:50,120 --> 00:05:51,640 And you can even do functional studies. 95 00:05:51,640 --> 00:05:56,360 You have people here in Rochester that are taking advantage of this virus to express 96 00:05:56,360 --> 00:06:00,800 optogenetic constructs, which allow you to turn those cells that are connected in specific 97 00:06:00,800 --> 00:06:05,880 ways on and off to look at their roles in function in the intact brain. 98 00:06:05,880 --> 00:06:10,160 And 15 years ago, if somebody had said to you, it would be possible to switch on and 99 00:06:10,160 --> 00:06:14,680 off cells with light that are transfected with the rabies virus. 100 00:06:14,680 --> 00:06:17,280 Would you have laughed at them or did you see it coming? 101 00:06:17,280 --> 00:06:23,480 I mean, optogenetics has been around, probably is it getting close to 15 years? 102 00:06:23,480 --> 00:06:24,480 I'm not sure. 103 00:06:24,480 --> 00:06:30,400 And we worked on other methods to inactivate that were chemical, because we did want to 104 00:06:30,400 --> 00:06:35,240 be able to target cell types in brains of animals like monkeys. 105 00:06:35,240 --> 00:06:40,200 We're only just now getting the ability to target cell types in monkeys very selectively. 106 00:06:40,200 --> 00:06:43,280 And it's something we imagined 15 years ago. 107 00:06:43,280 --> 00:06:48,000 I think the first experiments that got us thinking about tracing connections in cell 108 00:06:48,000 --> 00:06:53,800 type specific ways were ones that used a different virus called PRV. 109 00:06:53,800 --> 00:07:00,440 It's in the herpes family of viruses, like chickenpox viruses and things like that. 110 00:07:00,440 --> 00:07:07,640 And Jeff Friedman and Lynn Enquist made a version of that that could label the inputs 111 00:07:07,640 --> 00:07:11,400 to a specific cell type, but it kept spreading multiple synaptic contacts. 112 00:07:11,400 --> 00:07:16,520 And we actually worked with that for a couple of years trying to get a way to make it stop. 113 00:07:16,520 --> 00:07:20,360 But all those things didn't work, and it was those failures that led us to the properties 114 00:07:20,360 --> 00:07:24,240 of rabies virus as one that we would be able to engineer in a way that would work. 115 00:07:24,240 --> 00:07:25,920 That was more than 15 years ago. 116 00:07:25,920 --> 00:07:30,080 They published that paper in 2001 showing that tracing. 117 00:07:30,080 --> 00:07:35,880 So all this work, it led to quite an honor for you just this past year, right? 118 00:07:35,880 --> 00:07:40,520 I believe you were inducted into the National Academy of Sciences, which is our most prestigious 119 00:07:40,520 --> 00:07:42,040 scientific society. 120 00:07:42,040 --> 00:07:43,040 How did you find out? 121 00:07:43,040 --> 00:07:44,040 Did you get a phone call? 122 00:07:44,040 --> 00:07:45,040 Did they send you a letter? 123 00:07:45,040 --> 00:07:49,120 Well, that's how it usually works, apparently, is they have this thing of...especially if 124 00:07:49,120 --> 00:07:53,920 you're on the West Coast and it's early in the morning, it must have...well, to me early, 125 00:07:53,920 --> 00:07:55,800 I sleep late, eight in the morning. 126 00:07:55,800 --> 00:08:01,600 But I actually found out from my daughter, strangely, because I had my...I go silence 127 00:08:01,600 --> 00:08:06,360 my cell phone when I go to bed and I had gotten up and was in the shower and I didn't notice 128 00:08:06,360 --> 00:08:08,680 that people had called and left messages. 129 00:08:08,680 --> 00:08:12,660 And then I opened my computer to check traffic in the morning, to have a look at my emails 130 00:08:12,660 --> 00:08:16,260 and see all these sort of congratulations messages. 131 00:08:16,260 --> 00:08:19,500 And I was like, oh, what's that for, I wonder? 132 00:08:19,500 --> 00:08:23,560 And then next thing I know, my phone's ringing and it's my daughter telling me. 133 00:08:23,560 --> 00:08:24,560 So yeah. 134 00:08:24,560 --> 00:08:27,440 And then, of course, I called and talked to the... 135 00:08:27,440 --> 00:08:28,440 Yeah. 136 00:08:28,440 --> 00:08:29,440 Tell us, how did it feel? 137 00:08:29,440 --> 00:08:30,440 Were you surprised or was it... 138 00:08:30,440 --> 00:08:34,960 Well, yeah, because I mean, it's not something I was really on my radar thinking about. 139 00:08:34,960 --> 00:08:36,800 They do have a meeting every year at a certain time. 140 00:08:36,800 --> 00:08:40,860 I didn't know this was the time when the meeting was happening and they would be doing this 141 00:08:40,860 --> 00:08:41,860 voting. 142 00:08:41,860 --> 00:08:46,240 And of course, you have a sense that you're being nominated because the people nominating 143 00:08:46,240 --> 00:08:48,880 you have to ask for materials. 144 00:08:48,880 --> 00:08:53,800 They can't tell you they're nominating you, but you can tell from the format of it. 145 00:08:53,800 --> 00:09:00,840 It's a nice honor and really mostly a reflection of all the stuff that people in my lab over 146 00:09:00,840 --> 00:09:01,840 the years have done. 147 00:09:01,840 --> 00:09:03,960 So here you are, a National Academy member. 148 00:09:03,960 --> 00:09:09,320 But let's go back in time and track...how do you end up as a National Academy member? 149 00:09:09,320 --> 00:09:12,840 Tell us about the academic trajectory. 150 00:09:12,840 --> 00:09:15,160 When did you figure out you wanted to be a neuroscientist? 151 00:09:15,160 --> 00:09:18,840 Was this something when you were 10 years of age or was that later? 152 00:09:18,840 --> 00:09:23,000 Well, I mean, I've always...even since when I was a kid, fascinated with how things work 153 00:09:23,000 --> 00:09:28,880 and sort of would take apart mechanical things like to work on bikes and cars and things 154 00:09:28,880 --> 00:09:29,880 like that. 155 00:09:29,880 --> 00:09:33,440 And like to build things, which helps a lot with neuroscience because you have to, especially 156 00:09:33,440 --> 00:09:35,840 longer ago, build all of your own things. 157 00:09:35,840 --> 00:09:39,360 But just really a fascination with how things work. 158 00:09:39,360 --> 00:09:43,280 But it wasn't until I was in college, I knew I was interested in biology and was dabbling 159 00:09:43,280 --> 00:09:45,920 with the idea of medical school. 160 00:09:45,920 --> 00:09:52,120 But during my sophomore year in college is when I first started the first biology lectures, 161 00:09:52,120 --> 00:09:58,240 first years you take chemistry, physics, math, and learned about neurons and that they have 162 00:09:58,240 --> 00:10:01,120 this electrical activity and action potentials. 163 00:10:01,120 --> 00:10:07,000 These beautiful lectures that Corey Goodman gave, he was assistant professor at Stanford 164 00:10:07,000 --> 00:10:09,480 at the time, that's like 30 years ago. 165 00:10:09,480 --> 00:10:13,320 And that was when it just hit me, this is what makes me who I am. 166 00:10:13,320 --> 00:10:18,000 Everything about me and how I work and think and feel is because of these neurons in the 167 00:10:18,000 --> 00:10:20,240 brain and their activity together. 168 00:10:20,240 --> 00:10:21,240 How does that work? 169 00:10:21,240 --> 00:10:24,020 And what would give me...could be more fascinating. 170 00:10:24,020 --> 00:10:30,120 So I just started at that time signing up for every neuroscience class I could find 171 00:10:30,120 --> 00:10:35,400 and found a lab to work in with Jack McMahon and studied neuromuscular development and got 172 00:10:35,400 --> 00:10:37,880 interested in nervous system development. 173 00:10:37,880 --> 00:10:40,640 Now, I happen to know you and I have something in common. 174 00:10:40,640 --> 00:10:45,560 I don't think you know this because I think we both started out life as dumb jocks. 175 00:10:45,560 --> 00:10:47,960 Did you end up as university... 176 00:10:47,960 --> 00:10:52,680 I hope I wasn't dumb. 177 00:10:52,680 --> 00:10:57,760 Stanford has some academic standards, but I do think they are a little relaxed if you're 178 00:10:57,760 --> 00:11:03,200 a fast runner because I might not have gotten in if I wasn't a fast runner, but that led 179 00:11:03,200 --> 00:11:06,120 to all kinds of opportunities. 180 00:11:06,120 --> 00:11:08,240 It kind of goes with being a scientist. 181 00:11:08,240 --> 00:11:15,040 Distance runners are a bit obsessive and you have to just run a long time for what might 182 00:11:15,040 --> 00:11:19,040 be a reward that may or may not happen later to win. 183 00:11:19,040 --> 00:11:25,760 But I was very competitive and so it's a sport where hard work pays off. 184 00:11:25,760 --> 00:11:30,240 And I think that's true of science too and I think I learned a lot of lessons through 185 00:11:30,240 --> 00:11:34,560 that and also being an athlete in college, you have to be very efficient with how you 186 00:11:34,560 --> 00:11:40,040 use your time because you're taking a full course load and you've got to work out every 187 00:11:40,040 --> 00:11:45,080 day and all your friends are out laying on the lawn in the sun or playing volleyball 188 00:11:45,080 --> 00:11:49,620 or something for fun and you're out working hard running and come back tired and still 189 00:11:49,620 --> 00:11:52,160 have to go to the library and study. 190 00:11:52,160 --> 00:11:54,960 So I think it taught a lot of self-discipline. 191 00:11:54,960 --> 00:11:59,800 And I love just the camaraderie of that and that's something that the lab also does for 192 00:11:59,800 --> 00:12:00,800 you, right? 193 00:12:00,800 --> 00:12:02,840 That you have this group of people that you work with every day. 194 00:12:02,840 --> 00:12:08,440 That are almost family because of the closeness and intensity of it, the engagement. 195 00:12:08,440 --> 00:12:12,960 And the graduate students in post-docs, they're putting their career in your hands and you 196 00:12:12,960 --> 00:12:17,560 got to look out for them. 197 00:12:17,560 --> 00:12:20,840 So I didn't mention that before but I came to the United States on the track and field 198 00:12:20,840 --> 00:12:21,840 scholarship. 199 00:12:21,840 --> 00:12:22,840 Oh really? 200 00:12:22,840 --> 00:12:28,880 And I also believe that there's this sort of obsessional aspect to folks in the sciences 201 00:12:28,880 --> 00:12:33,960 and you find out when you dig a little deeply that they've run track and field at a high 202 00:12:33,960 --> 00:12:37,680 level or they've played a musical instrument at a high level or they're a very fine artist 203 00:12:37,680 --> 00:12:39,840 as well and there's a little bit of the manage... 204 00:12:39,840 --> 00:12:44,600 Musicians are great to work in the lab because they do the same thing over and over and practice 205 00:12:44,600 --> 00:12:45,600 and practice. 206 00:12:45,600 --> 00:12:46,600 Exactly. 207 00:12:46,600 --> 00:12:50,320 And the delayed gratification, the part where you need to stay applied to something for 208 00:12:50,320 --> 00:12:55,000 hours and hours and hours with very little return but the long-term goal. 209 00:12:55,000 --> 00:12:56,360 Very good. 210 00:12:56,360 --> 00:13:03,200 You have three children and I was fascinated by this some more from a human interest perspective 211 00:13:03,200 --> 00:13:07,920 because they've grown up in a household where their dad is a very, very prominent scientist. 212 00:13:07,920 --> 00:13:11,560 Are any of them following in your footsteps or they steering care of it? 213 00:13:11,560 --> 00:13:18,680 One of them is, I mean mostly they have remained...it's good to be kind of oblivious to exactly everything 214 00:13:18,680 --> 00:13:19,680 we're doing. 215 00:13:19,680 --> 00:13:20,680 I mean you're just their dad. 216 00:13:20,680 --> 00:13:22,400 What's the difference as a scientist? 217 00:13:22,400 --> 00:13:30,080 So I have my three children and the oldest is a computer science engineering guy and 218 00:13:30,080 --> 00:13:33,440 of course works in the Bay Area and does very well in that. 219 00:13:33,440 --> 00:13:38,120 My older daughter is in grad school at Berkeley in immunology, virology and she was the one 220 00:13:38,120 --> 00:13:42,200 who paid the most attention especially during the early rabies days and I gave talks at 221 00:13:42,200 --> 00:13:48,400 her school about rabies virus stuff and maybe that influenced her interest in virology and 222 00:13:48,400 --> 00:13:49,400 diseases. 223 00:13:49,400 --> 00:13:55,440 She's childhood malaria in a lab at UC San Francisco and through a graduate program at 224 00:13:55,440 --> 00:14:00,720 Berkeley and then my youngest daughter is applying to law school this year and working 225 00:14:00,720 --> 00:14:03,160 in the public defender's office in Oakland. 226 00:14:03,160 --> 00:14:05,760 So they're all doing great things. 227 00:14:05,760 --> 00:14:07,380 Very proud of all of them. 228 00:14:07,380 --> 00:14:12,320 So what about work-life balance and all through the years when you're raising children, I 229 00:14:12,320 --> 00:14:18,000 think people are often think about scientists and the quantity of hours that have to be 230 00:14:18,000 --> 00:14:21,560 put in the lab and I think it's a reality of the world. 231 00:14:21,560 --> 00:14:25,960 And especially in the earlier years I spent a lot more time in the lab and I'm very fortunate 232 00:14:25,960 --> 00:14:33,080 with my wife who's very supportive of that and I also travel more than I do now at times 233 00:14:33,080 --> 00:14:41,440 and that's really helpful to have a partner who helps you to manage all that and tolerates 234 00:14:41,440 --> 00:14:47,000 it to a certain degree too because there were times when we did all night experiments and 235 00:14:47,000 --> 00:14:48,000 things. 236 00:14:48,000 --> 00:14:53,440 I do think you have to be able to turn it off at times too and when you're done at the 237 00:14:53,440 --> 00:14:58,360 end of the day to be able to come home and that was when the kids were younger, one of 238 00:14:58,360 --> 00:15:04,880 my favorite things was it was time to go home, give everybody a bath and read and I would 239 00:15:04,880 --> 00:15:06,240 read them books. 240 00:15:06,240 --> 00:15:11,840 And what about some advice, so young graduate student thinking about life as a scientist 241 00:15:11,840 --> 00:15:17,000 today, 2019 going forward, the challenges of it, the young graduate student thinking 242 00:15:17,000 --> 00:15:23,040 about having a family in this field and in this business, do you have any pearls of wisdom? 243 00:15:23,040 --> 00:15:27,340 Yeah, I mean it's nothing that's not common sense, you have to make the choice that works 244 00:15:27,340 --> 00:15:28,340 for you. 245 00:15:28,340 --> 00:15:34,040 I mean when I was in grad school I was very focused, I probably was in the lab 12 to 16 246 00:15:34,040 --> 00:15:41,560 hours a day and didn't really have a lot of balance but didn't have any kids yet either. 247 00:15:41,560 --> 00:15:49,360 And so, and I consciously, my wife and I discussed that we should wait until I'm a postdoc to 248 00:15:49,360 --> 00:15:51,040 have our first kid. 249 00:15:51,040 --> 00:15:59,680 But then our first kid, our son was born while I was a postdoc and that worked out just fine. 250 00:15:59,680 --> 00:16:03,440 Everything's hard, no matter where you do it there's no easy time. 251 00:16:03,440 --> 00:16:12,880 But if you believe in what you're doing and you like it, it's difficult but it's not hard 252 00:16:12,880 --> 00:16:18,320 in the sense that you're enjoying what you're doing. 253 00:16:18,320 --> 00:16:24,280 Is there a difference for youngsters these days, when maybe you and I were coming up 254 00:16:24,280 --> 00:16:30,680 through the system, you became an expert in a very specific domain and today you really 255 00:16:30,680 --> 00:16:33,600 have to start to work across disciplines? 256 00:16:33,600 --> 00:16:37,360 Yeah, it's harder for sure. 257 00:16:37,360 --> 00:16:43,360 And in neuroscience now in particular, because what you could possibly do with the range 258 00:16:43,360 --> 00:16:48,980 of tools that has emerged, the expectation for a high profile paper is that it's going 259 00:16:48,980 --> 00:16:52,080 to have a little bit of everything and how could you do all that? 260 00:16:52,080 --> 00:16:58,200 So I think it becomes more important to work as a team and to have different people in 261 00:16:58,200 --> 00:17:04,840 the group that you see more co-first author or even three co-all gave the same amount 262 00:17:04,840 --> 00:17:08,200 of effort to the paper going on. 263 00:17:08,200 --> 00:17:11,960 But it's also good to learn from the other people that you're working with because eventually 264 00:17:11,960 --> 00:17:17,640 you do need to go start your own lab and it's good to have learned multiple skills and bring 265 00:17:17,640 --> 00:17:19,880 that and to be able to train the people that are coming in. 266 00:17:19,880 --> 00:17:20,880 Well, can I lean on that? 267 00:17:20,880 --> 00:17:24,000 Eventually you have to go start your own lab. 268 00:17:24,000 --> 00:17:25,760 Are our models correct today? 269 00:17:25,760 --> 00:17:26,760 You don't have to. 270 00:17:26,760 --> 00:17:31,440 You get the opportunity. 271 00:17:31,440 --> 00:17:36,040 So would it be fair, you can push back on this, would it be fair to say that the way 272 00:17:36,040 --> 00:17:42,800 we do business today with a new investigator gets a job in a lab at a university, opens 273 00:17:42,800 --> 00:17:48,120 up their lab and starts, it hasn't changed much over the last 40 or 50 years. 274 00:17:48,120 --> 00:17:51,080 We still operate under the same basic model. 275 00:17:51,080 --> 00:17:53,000 Is that the right way to do science these days? 276 00:17:53,000 --> 00:17:57,040 If you were shaking it up, would you approach it in a different way? 277 00:17:57,040 --> 00:17:58,040 Perhaps. 278 00:17:58,040 --> 00:18:01,680 I think it probably depends on the kinds of institution and other, I mean a lot of institutions 279 00:18:01,680 --> 00:18:08,360 have a really important emphasis on teaching as well and other things that need to be done. 280 00:18:08,360 --> 00:18:13,320 A really good model of doing it a different way is the Allen Brain Institute, right? 281 00:18:13,320 --> 00:18:18,420 And they have a very different structure where they bring in people, they actually pay people 282 00:18:18,420 --> 00:18:21,140 at postdoc level a lot more than our postdocs get paid. 283 00:18:21,140 --> 00:18:24,080 We should pay our postdocs a lot more. 284 00:18:24,080 --> 00:18:32,240 But I mean that's dictated largely by government levels that are set. 285 00:18:32,240 --> 00:18:33,800 So what's the model there for our audience? 286 00:18:33,800 --> 00:18:36,040 Much more a team kind of science. 287 00:18:36,040 --> 00:18:42,660 And so they do have people who are more specialized and have careers that are doing science. 288 00:18:42,660 --> 00:18:48,500 And some of those people move through the ranks and become the people who are more interested 289 00:18:48,500 --> 00:18:50,960 in asking big questions and directing programs. 290 00:18:50,960 --> 00:18:55,560 Other people would rather stay in what they're doing, but they can get paid a good wage. 291 00:18:55,560 --> 00:19:00,920 I mean often better than what some starting assistant professors do and have a career 292 00:19:00,920 --> 00:19:01,920 in that program. 293 00:19:01,920 --> 00:19:04,080 As a career scientist as part of a... 294 00:19:04,080 --> 00:19:10,840 Working as somebody who does bioinformatics or somebody who does slice physiology or does 295 00:19:10,840 --> 00:19:17,560 calcium imaging in vivo or does electron microscopy and all these different things. 296 00:19:17,560 --> 00:19:22,280 Because that's one of the real inefficiencies in the way that things work now is that I 297 00:19:22,280 --> 00:19:24,480 have somebody who comes as a postdoc. 298 00:19:24,480 --> 00:19:28,040 And just as they get really good at what they're doing, they leave and I have to replace them 299 00:19:28,040 --> 00:19:31,400 with somebody else that's going to come in and start over. 300 00:19:31,400 --> 00:19:38,260 We do often have career research assistants who stay in the lab, but the system is much 301 00:19:38,260 --> 00:19:43,880 more designed to have funding for postdocs and grad students through fellowships and 302 00:19:43,880 --> 00:19:44,980 different kind of things. 303 00:19:44,980 --> 00:19:48,760 So we rely on that labor I think more than we should. 304 00:19:48,760 --> 00:19:53,080 There are other models in other countries where they give you a budget and say, hey, 305 00:19:53,080 --> 00:19:58,320 this is for technicians and then here's the budget for these people, but here it works 306 00:19:58,320 --> 00:19:59,320 differently. 307 00:19:59,320 --> 00:20:07,640 So I think that it's kind of like how things are in the US. 308 00:20:07,640 --> 00:20:12,780 It's not the perfect system, but it works well and we do well with it. 309 00:20:12,780 --> 00:20:18,040 You could design a better system, but then if you try to impose things, then you have 310 00:20:18,040 --> 00:20:21,680 unintended consequences at times too. 311 00:20:21,680 --> 00:20:23,120 So look into the future. 312 00:20:23,120 --> 00:20:28,920 Now you mentioned specific cell type targeting and genetic manipulation of specific cell 313 00:20:28,920 --> 00:20:31,240 types as ways to get after disease. 314 00:20:31,240 --> 00:20:35,360 But looking at 10 years, 15 years from now, what's on the horizon? 315 00:20:35,360 --> 00:20:39,960 What do you see as the really exciting areas that we should be? 316 00:20:39,960 --> 00:20:45,040 In terms of targeting cell types, I think that the field is just now in the last, it's 317 00:20:45,040 --> 00:20:49,880 probably been in the last year, but what I'm seeing in publications and there's more and 318 00:20:49,880 --> 00:20:53,040 more of a trend of people to publish preprints on bio archive. 319 00:20:53,040 --> 00:20:59,080 And just in the last six months, there have been what people call enhancers, cell type 320 00:20:59,080 --> 00:21:05,160 specific regulatory elements in the genome that are responsible for the normal mechanisms 321 00:21:05,160 --> 00:21:07,960 that give rise to the differences between cell types. 322 00:21:07,960 --> 00:21:11,880 Cell types are different not because they have different genes in them. 323 00:21:11,880 --> 00:21:15,760 They all have the same genetic material, but it's which genes get turned on and which genes 324 00:21:15,760 --> 00:21:20,360 turned off during development and the specification of a cell to a type. 325 00:21:20,360 --> 00:21:27,360 And there are marks of that in the genome and there are analyses that can be done called 326 00:21:27,360 --> 00:21:32,960 epigenetic analysis that can look at those marks in the genome and find out where are 327 00:21:32,960 --> 00:21:37,680 these elements that might be enhancer elements in the genome that might be responsible for 328 00:21:37,680 --> 00:21:41,280 the difference between this cell and this cell and this cell. 329 00:21:41,280 --> 00:21:45,960 And there are some of these publications now that are coming out showing that you can use 330 00:21:45,960 --> 00:21:48,600 that analysis to predict enhancers. 331 00:21:48,600 --> 00:21:52,520 You can put them in a viral vector, the same viruses that are used now for gene therapy 332 00:21:52,520 --> 00:21:53,940 in humans. 333 00:21:53,940 --> 00:22:00,600 And you can then, in fact, even human tissue in one case from the Allen Institute where 334 00:22:00,600 --> 00:22:07,400 they have tissue from people that are having tumor surgery or epilepsy surgeries and those 335 00:22:07,400 --> 00:22:12,720 patients donate some of their brain that can then be cultured and you can test those viruses. 336 00:22:12,720 --> 00:22:18,440 And they've shown that you can get the targeting to the cell type that was intended based on 337 00:22:18,440 --> 00:22:21,760 their epigenetic analysis of these marks. 338 00:22:21,760 --> 00:22:26,000 And I think that in the next just couple years there's going to be a huge explosion in our 339 00:22:26,000 --> 00:22:31,600 ability to target gene expression to cell types, not just in mice where we have to genetically 340 00:22:31,600 --> 00:22:36,720 engineer the genome of the mouse, but where we can go into any species including humans 341 00:22:36,720 --> 00:22:41,560 and target a cell type for therapeutic purposes. 342 00:22:41,560 --> 00:22:43,880 A great future and it's on the horizon here. 343 00:22:43,880 --> 00:22:46,880 It's not a million miles away at all. 344 00:22:46,880 --> 00:22:49,280 And I don't think it's overhyped at all. 345 00:22:49,280 --> 00:22:53,440 Now, how long is it going to be until you actually do gene therapy in a human? 346 00:22:53,440 --> 00:22:57,340 You're going to have to have...that would require a cell type targeting. 347 00:22:57,340 --> 00:23:03,900 There will probably be types of diseases that are more tractable and the cost benefit kind 348 00:23:03,900 --> 00:23:09,520 of equations where, you know, if maybe somebody who has epilepsy, the treatment now is to 349 00:23:09,520 --> 00:23:11,280 remove that part of the brain. 350 00:23:11,280 --> 00:23:14,760 So there is not a huge cost to saying, well, we have a potential gene therapy. 351 00:23:14,760 --> 00:23:16,560 We can try this first before we take it out. 352 00:23:16,560 --> 00:23:17,560 Yeah. 353 00:23:17,560 --> 00:23:23,200 We can do something where we would express a gene, a transgene only in a cell type that 354 00:23:23,200 --> 00:23:27,440 would then if you activated that cell type would suppress the activity. 355 00:23:27,440 --> 00:23:31,120 And then if you detected a seizure coming, you could maybe do that. 356 00:23:31,120 --> 00:23:38,080 Or if you had a drug, the transgene is affected by a drug that the person takes that's targeted 357 00:23:38,080 --> 00:23:42,480 only to those cells rather than the drug that would have all kinds of side effects for the 358 00:23:42,480 --> 00:23:44,920 epilepsy drug somebody might be taking now. 359 00:23:44,920 --> 00:23:48,800 You've now targeted to just that part of the brain where they have the epileptic focus 360 00:23:48,800 --> 00:23:54,680 and you can just regulate with the dose of this drug, which doesn't have any side effects 361 00:23:54,680 --> 00:23:57,180 because there are no receptors for it in the brain. 362 00:23:57,180 --> 00:24:02,400 So that's something that you could imagine coming along. 363 00:24:02,400 --> 00:24:07,000 And you start small with something that's cost benefit is big. 364 00:24:07,000 --> 00:24:09,600 Because if it doesn't work or something goes wrong, you're just going to cut that tissue 365 00:24:09,600 --> 00:24:11,660 out anyway. 366 00:24:11,660 --> 00:24:17,040 But then building to things that are more sophisticated over the years. 367 00:24:17,040 --> 00:24:21,760 I think the basic research is necessary to identify how the normal circuit mechanisms 368 00:24:21,760 --> 00:24:26,640 work and then to be able to say there is a target, there is a treatment that you might 369 00:24:26,640 --> 00:24:27,640 try. 370 00:24:27,640 --> 00:24:31,360 And there's going to have to be a translation across species. 371 00:24:31,360 --> 00:24:34,580 You're not going to go straight from mice to humans. 372 00:24:34,580 --> 00:24:40,000 It's very clear now that you can find homologous cell types, 100 cell types in the cerebral 373 00:24:40,000 --> 00:24:41,840 cortex of people and mice. 374 00:24:41,840 --> 00:24:44,880 And you can find nearly all of them are homologous. 375 00:24:44,880 --> 00:24:52,640 But the genes that are expressed in the primate cell types in monkeys and chimps and humans, 376 00:24:52,640 --> 00:24:55,320 that's highly conserved, but it's different in mice. 377 00:24:55,320 --> 00:25:01,320 So the way we're going to target those cell types and the genes and how they relate to 378 00:25:01,320 --> 00:25:05,320 genetic disorders and genetic prevalence for disorders is going to be different between 379 00:25:05,320 --> 00:25:06,320 mice and monkeys. 380 00:25:06,320 --> 00:25:10,920 You need a transitional species to work with to do that. 381 00:25:10,920 --> 00:25:12,520 So we're getting close on our time. 382 00:25:12,520 --> 00:25:16,960 I wanted to ask you, at the end of the day, I assume these days you're not running cross 383 00:25:16,960 --> 00:25:19,520 country to wind down. 384 00:25:19,520 --> 00:25:20,720 How do you switch off? 385 00:25:20,720 --> 00:25:22,480 What's your thing? 386 00:25:22,480 --> 00:25:25,560 Well the last few months haven't been as diligent. 387 00:25:25,560 --> 00:25:30,960 But I do still try to run a few days a week, go for a five or six mile run. 388 00:25:30,960 --> 00:25:32,960 Like going hiking, I like going fishing. 389 00:25:32,960 --> 00:25:37,740 Do you find, like I do, when you're running that there's a clarity, it's a really good 390 00:25:37,740 --> 00:25:41,520 time to think about science actually? 391 00:25:41,520 --> 00:25:44,320 Usually when you're not thinking about it, yeah. 392 00:25:44,320 --> 00:25:45,320 That happens sometimes. 393 00:25:45,320 --> 00:25:48,480 You just have a chance to go back and clear your head. 394 00:25:48,480 --> 00:25:53,160 That neurotransmitter milieu that allows for that kind of clarity when you're on a night. 395 00:25:53,160 --> 00:25:54,160 Now they're not all nights. 396 00:25:54,160 --> 00:25:55,520 Sometimes it's just a slog. 397 00:25:55,520 --> 00:25:59,800 But when the mind clears and we're on and all the rest of the world falls away, sometimes 398 00:25:59,800 --> 00:26:02,240 I have some of my best thoughts about experiments. 399 00:26:02,240 --> 00:26:05,280 It's such a pleasure to have you here in Rochester. 400 00:26:05,280 --> 00:26:06,280 Thank you. 401 00:26:06,280 --> 00:26:07,280 I really appreciate you coming in. 402 00:26:07,280 --> 00:26:08,280 Excellent. 403 00:26:08,280 --> 00:26:19,000 Thank you.