Nanofabrication via DNA Origami
Transcript of Part 2: Nanofabrication via DNA Single Stranded Bricks
00:00:06.28 Welcome back - 00:00:08.04 this is the second part of the lecture 00:00:09.29 on structural DNA nanotechnology. 00:00:12.16 In the previous lecture, we discussed a method, 00:00:14.25 scaffolded DNA Origami, 00:00:16.13 that's proven powerful enough to self-assemble DNA strands 00:00:20.03 into objects that are about twice the mass of a ribosome, 00:00:23.00 about 5 megaDaltons in size, 00:00:25.02 involving a long single-stranded scaffold 00:00:26.28 that's folded by many short staple strands 00:00:29.12 into the desired object. 00:00:31.28 For the second segment, 00:00:33.08 I'm going to discuss a new method 00:00:35.17 that was just reported in the last year. 00:00:37.29 This is work that's primarily been led by 00:00:40.21 my colleague Peng Yin at Harvard 00:00:42.21 and the Wyss Institute 00:00:44.07 that he calls DNA Single-Stranded Bricks. 00:00:48.10 And it turns out that this method seems to be 00:00:50.24 roughly comparable in its power 00:00:53.06 of self-assembling structures 00:00:54.22 of this kind of complexity. 00:00:56.21 My group assisted in collaboration 00:00:58.22 at the endpoint of moving this into 3-dimensions. 00:01:01.15 I think it's a really interesting method, 00:01:03.01 that's why I'd like to discuss it in this 00:01:04.27 iBio seminar. 00:01:08.20 The scaffolded DNA Origami method 00:01:10.28 is analogous to some toys 00:01:12.24 that you might have played with. 00:01:14.10 So this is a DNA snake 00:01:16.00 that you can fold into 3-dimensional structures. 00:01:19.02 Here's another related idea 00:01:20.24 of a snake-like polymer 00:01:22.09 that we can fold into objects. 00:01:24.13 It's also familiar to biologists 00:01:26.17 who think about polypeptide chains 00:01:28.19 that are individual chains that fold up 00:01:30.16 into some kind of 3-dimensional configuration. 00:01:33.11 And in this way you can achieve 00:01:35.04 almost any shape by having a long polymer, 00:01:37.17 folding that up into that shape. 00:01:40.26 However, from a human point of view, 00:01:43.05 there might be a simpler way if that would actually work, 00:01:46.19 and that's using the example of Lego bricks. 00:01:49.08 So if you can imagine that you have a set of bricks 00:01:52.15 that have a stereotyped shape, 00:01:55.23 if we just have a lot of them 00:01:57.09 and we can connect them at different angles, 00:01:59.14 then now you could argue that gives us 00:02:01.08 even more design flexibility 00:02:03.07 in building these large 3-dimensional shapes. 00:02:05.10 You no longer have to worry about 00:02:06.21 the connectivity of the chain going through the object. 00:02:11.29 And what Peng Yin's group has demonstrated 00:02:14.03 is that, in fact, we can do this with DNA. 00:02:17.19 Now, when DNA Origami came out, 00:02:20.04 it was a shock to everybody that, 00:02:22.23 wow, we can build these very complex structures, 00:02:25.08 and then it was immediately assumed 00:02:27.02 that the key to success for the method 00:02:28.27 was the fact that you did have this very long strand 00:02:32.15 that keeps all the short strands into order. 00:02:35.04 It was a master template 00:02:36.25 and if you didn't have that 00:02:38.00 maybe everything would descend into chaos. 00:02:40.11 And what Peng Yin's group demonstrated is, 00:02:42.10 in fact, that's not so, 00:02:43.22 although we still think that long strand might be helping, 00:02:46.15 but now we know that it's certainly not a necessary component 00:02:49.07 for a successful strategy to build structures of this size. 00:02:53.21 So the first part of this story 00:02:54.24 was developed in Peng Yin's lab, 00:02:56.25 work led by Bryan Wei and Mingjie Dai in his lab. 00:03:02.17 And the idea is as follows: 00:03:04.05 so this is, if you remember from the first lecture, 00:03:06.12 we had that idea of a double-crossover tile 00:03:08.22 and we said, 00:03:09.25 "Oh, if we could only have double-crossover tiles 00:03:12.17 of many different sequences 00:03:14.03 and they would actually behave themselves, 00:03:15.22 then we could now make a complex tapestry 00:03:18.03 where every single one of the elements 00:03:19.14 has a unique sequence." 00:03:21.16 And what Bryan and his colleagues demonstrated 00:03:24.09 is that they could do that, 00:03:25.18 but with a slightly different motif. 00:03:27.25 The idea is as follows: 00:03:29.07 so you have each one of your bricks or tiles 00:03:31.12 has the same stereotyped architecture 00:03:33.06 where it has four different domains. 00:03:35.13 In each one of the domains 00:03:36.24 is one turn of a helix, 00:03:40.14 about let's say 10 bases long. 00:03:43.13 And it's a flexible polymer, 00:03:45.05 but in the final design tapestry 00:03:47.03 that it's supposed to self-assemble into, 00:03:49.06 each one of those tiles is supposed to adopt 00:03:51.16 a very fixed orientation 00:03:53.12 where it's like a horseshoe: 00:03:55.07 one half of the horseshoe is part of one double helix, 00:03:57.22 and then the other part of the horseshoe 00:03:59.09 is part of a second double helix. 00:04:01.23 And they assemble with each other 00:04:03.03 using the following rule: 00:04:04.23 that if you have, in your solution, 00:04:07.11 you might have Domain 1 of one of your bricks or tiles 00:04:11.04 is going to be compatible with Domain 3 of another tile, 00:04:14.09 and then Domain 2 of one of your tiles 00:04:16.07 is going to be compatible with Domain 4 00:04:18.21 of another one of the tiles. 00:04:20.24 Each one of these tiles in this assembly, in this tapestry, 00:04:23.18 has a unique sequence. 00:04:25.05 It has four unique nearest neighbors. 00:04:28.02 So if you can design all of these strands 00:04:30.14 with the same overall length and structure, 00:04:32.12 but each one with a different sequence, 00:04:34.04 and with the sequence complementarity rules that I just described, 00:04:37.00 it turns out you can make these objects 00:04:40.01 of the size and complexity of a DNA Origami. 00:04:42.20 No long strand required. 00:04:45.03 Here's another representation of that motif, 00:04:48.07 an abstract representation 00:04:50.01 that more closely resembles a Lego brick. 00:04:53.01 So what we have are, again, four domains. 00:04:56.07 We have Domain 1, 2, 3, and then 4. 00:05:01.11 And we can Domain 4 of one of these tiles 00:05:03.17 is now interacting with Domain 2 00:05:05.29 of another one of the tiles, 00:05:07.23 and we have the double helices 00:05:09.06 are running the lower left-hand corner 00:05:11.27 and now up to the upper right. 00:05:16.00 There's a stereospecificity that's indicated 00:05:19.15 by the shape of the key and the hole, 00:05:22.06 so we can hopefully understand 00:05:23.28 that the key and the hole can only interact in one orientation, 00:05:26.18 and that enforces this coplanarity of the tiles. 00:05:32.13 And the actual physical basis, of course, 00:05:34.18 is that each one of those interactions is 00:05:36.23 one complete turn of the double helix. 00:05:38.19 That forces it to be coplanar. 00:05:41.16 And once you understand those principals, 00:05:43.19 hopefully you can see that if you had a bunch of these tiles, 00:05:46.15 each with a different sequence, 00:05:48.02 each with those sequence complementarity 00:05:50.01 between plugs and holes that I described, 00:05:51.25 you can now self-assemble very large tapestries, 00:05:53.29 in principle, 00:05:55.09 where every location in the tapestry 00:05:57.00 is occupied by a unique tile. 00:06:00.13 And what Bryan and his colleagues demonstrated was, 00:06:02.15 remarkably, that this works 00:06:04.15 - no long strand required. 00:06:07.06 So here what they've done is 00:06:07.28 they've self-assembled a structure with something 00:06:10.11 on the order of a few hundred unique tiles, 00:06:13.00 each one with a stereotyped design position 00:06:15.16 within the tapestry, 00:06:17.03 and they found that they could make these 00:06:18.19 in fairly high yield. 00:06:20.13 So on the left we can see an agarose gel 00:06:22.22 where we can monitor roughly the formation of the object. 00:06:25.00 U means unpurified, 00:06:27.00 and we have the initial building blocks in the bottom. 00:06:29.15 You just cook these for a while, 00:06:31.29 again you do this annealing profile 00:06:33.02 where you heat to 65°C and you cool to room temperature 00:06:35.12 over the course of a day or so, 00:06:37.03 and then when you look on a gel after a day, 00:06:39.09 you can see a large fraction of these building blocks 00:06:41.17 have self-assembled into an object of discrete size. 00:06:45.06 Of course there's some mis-assemblies as well, 00:06:46.26 that's where the smears are coming from, 00:06:48.29 but then you can now cut out that band from the gel 00:06:51.28 and then you have a population of molecules 00:06:55.03 that are enriched for the one that you really want. 00:06:57.16 And in their case, they then looked at these objects 00:06:59.18 using atomic force microscopy, 00:07:02.07 and they say that they were making rectangles 00:07:04.07 of the desired shape and size. 00:07:08.01 First of all it's just amazing that, 00:07:10.12 to a lot of us, that this works. 00:07:12.12 You just throw all these sequences together, 00:07:14.00 there's no sequence design, 00:07:15.19 all of the plugs and holes were designed 00:07:17.23 using a random sequence generator, 00:07:19.20 and the method just works. 00:07:21.26 One of the remarkable aspects of this method 00:07:23.27 is that you can now generate new structures 00:07:26.19 simply by repipetting the strand sets 00:07:28.11 and leaving out strands. 00:07:30.09 So for example, if we imagine pipetting 00:07:32.23 that rectangle but we just leave out the strands corresponding 00:07:36.00 to eyes and the mouth, 00:07:38.03 then now we could generate something 00:07:40.00 like this smiley face. 00:07:44.29 And one could imagine, again, just having... 00:07:47.25 repipetting these strands 00:07:49.22 with different subsets 00:07:51.10 and you can now generate different shapes 00:07:53.02 in this way. 00:07:54.26 You can either pipet manually, 00:07:56.05 which becomes tedious if you try to build 00:07:58.20 something like the hundred objects that Peng Yin's group demonstrated. 00:08:02.00 What will be more efficient, 00:08:03.22 which they eventually implemented, 00:08:05.10 is if you have some pipetting robot 00:08:07.07 that actually does all the pipetting for you. 00:08:10.17 So maybe with the standard pipetting robot 00:08:13.01 to pipet the pools to build a hundred different objects 00:08:16.09 might take a couple of days, 00:08:18.02 but then that can be basically unsupervised. 00:08:23.29 And then comes currently a lot of hard work of the imaging, 00:08:27.04 so far Peng's group and my group 00:08:29.20 -- I'm not aware of any group that has 00:08:31.03 an automated imaging platform for these objects -- 00:08:34.05 but after a lot of labor on the atomic force microscope, 00:08:37.18 one can see that something over 00:08:40.07 95% of the designed objects 00:08:42.02 actually were able to fold as predicted. 00:08:45.24 So we can see different letter, 00:08:47.00 we can see numbers, 00:08:48.17 Chinese characters, 00:08:49.22 emoticons, 00:08:51.27 we can see a journalist, Ed Jong, 00:08:53.24 was inspired so in Photoshop 00:08:55.27 he cut out some of these letters and made a message 00:08:58.01 that says "Wyss Institute for 00:08:59.22 Biologically Inspired Engineering at Harvard University". 00:09:03.29 So in the future we'd like to be able 00:09:05.15 to assemble the letters into this kind of arrangement 00:09:08.11 on their own, without the use of Photoshop, 00:09:11.03 but for now we think it's already an advance 00:09:13.04 that we can at least make the letters. 00:09:16.03 So here's a movie prepared 00:09:17.15 by Gael McGill that illustrates 00:09:19.29 how we imagine the self-assembly might occur. 00:09:23.06 Again, each one of these tiles has four nearest neighbors, 00:09:26.09 and at some point it's going to have to nucleate, 00:09:29.01 and then once you form a nucleus, 00:09:30.21 we believe that that will then grow to the larger structure. 00:09:34.24 Actually we think that the key to the success 00:09:36.24 of this method 00:09:38.12 is that we designed 00:09:41.27 it in a way that the nucleation is very slow 00:09:44.22 and the growth is very fast. 00:09:46.24 And in that way it's kind of like population control. 00:09:49.20 That any time you form a seed, 00:09:52.03 then it's going to have an abundant supply 00:09:54.07 of food or building blocks 00:09:56.03 in order to grow to its full size. 00:09:58.08 I mean imagine a situation 00:09:59.19 where nucleation was fast and growth was fast. 00:10:02.21 Then you'd basically get nuclei and seeds forming 00:10:05.21 everywhere, 00:10:07.06 and very quickly you'd deplete the pool 00:10:09.16 of building blocks 00:10:12.15 and at that point you'd be in trouble 00:10:13.25 because a lot of the seeds would have grown into 00:10:15.28 partial structures. 00:10:17.26 In order to complete their growth, 00:10:19.14 because there's no more building blocks, 00:10:20.22 they would have to start cannibalizing each other. 00:10:23.08 So we think that a robust design principle 00:10:26.01 for programmable self-assembly 00:10:29.00 is to try to build your system so that 00:10:31.28 nucleation is slow or controlled. 00:10:34.24 So we can see with DNA origami, 00:10:36.08 we can now envision those long scaffolds as controlled seeds, 00:10:40.03 that if we're adding in an excess of the staple strands, 00:10:43.06 then we know the number of seeds 00:10:45.01 is basically the number of those long strands 00:10:47.11 that we're adding. 00:10:48.20 And in that way you never run out of the building blocks. 00:10:51.25 In this case with the single-stranded tiles, 00:10:53.24 it's because that nucleation event is slow 00:10:56.27 and the growth is fast. 00:11:02.03 Alright, so I was just in the peanut gallery 00:11:04.27 watching this amazing work going on in 00:11:07.02 my colleague Peng Yin's lab. 00:11:10.00 Yonggang Ke is a postdoctoral fellow in my group. 00:11:12.08 Luvena Ong is a graduate student in Peng Yin's group. 00:11:15.28 And Yonggang and Luvena decided 00:11:17.09 they wanted to collaborate with Peng 00:11:20.11 and extend this into 3 dimensions. 00:11:23.10 So that's the work I'm going to tell you about next. 00:11:25.13 So just like we were able to extend 2-dimensional DNA Origami 00:11:28.15 into 3-dimensional solid structures, 00:11:30.17 Yonggang and Luvena were able to do this 00:11:32.26 using single-stranded bricks. 00:11:37.19 It turns out the principle for 00:11:39.24 converting from 2 dimensions to 3 dimensions 00:11:42.09 is extremely simple 00:11:44.29 - in principle, if it works. 00:11:46.14 So on the upper left-hand corner 00:11:47.29 we have the diagram that I showed you previously 00:11:50.09 - the 2-dimensional single-stranded tiles, 00:11:53.13 where since each one of these plugs and holes 00:11:56.08 is exactly one turn of the double helix, 00:11:58.16 that enforces a stereospecific geometry 00:12:01.21 between the tiles such that they're coplanar. 00:12:06.06 But if you think about it, 00:12:08.00 you could get something that's not coplanar 00:12:09.24 just by changing the length of those plugs and holes, 00:12:12.29 so that they're no longer integral numbers 00:12:14.17 of turns of the double helix. 00:12:15.21 So for example, here what Yonggang did 00:12:18.25 was he designed these plugs and holes 00:12:20.13 to be only 8 basepairs instead of 10. 00:12:24.01 And so now 8 basepairs 00:12:25.23 is roughly three quarters of a turn, 00:12:28.23 and because it's three quarters of a turn 00:12:30.16 then the stereospecific interaction between these bricks 00:12:33.14 is now going to form a dihedral angle of 90 degrees. 00:12:37.02 And we illustrate that 00:12:38.25 with the following arrangement of plugs and holes 00:12:41.15 so that you can see, again, 00:12:43.04 the key and the keyhole are only going to fit together 00:12:45.20 making that dihedral angle of 90 degrees, 00:12:48.11 and that's in physical reality enforced by the fact 00:12:51.01 that it's only three quarters of a turn, 00:12:53.14 8 basepairs interacting. 00:12:56.00 So now let's go through a thought experiment 00:12:57.25 that further elaborates this idea 00:13:00.04 that this 90 degree dihedral angle 00:13:01.27 allows the self-assembly 00:13:03.16 of a 3-dimensional solid cuboid structure. 00:13:06.27 Imagine that we have in our CAD program 00:13:09.01 a bunch of these single-stranded bricks, 00:13:11.28 and then the first thing that we do is 00:13:12.24 we lump together a bunch of these bricks 00:13:15.11 into these planar groupings. 00:13:17.11 And in this representation, 00:13:18.28 the bricks are not actually interacting with each other 00:13:21.06 with any base pairing, 00:13:22.24 we're just grouping them together in our CAD program 00:13:24.13 for explanatory purposes. 00:13:27.02 The next step is we generate another planar grouping of these bricks, 00:13:30.26 where we've now rotated the orientation of the bricks 00:13:33.02 by 90 degrees counterclockwise. 00:13:34.28 So hopefully by looking at the 00:13:37.25 orientation of these keyholes, 00:13:39.19 you can see that we've rotated the orientation 00:13:41.04 of the bricks by 90 degrees counterclockwise. 00:13:44.19 We can now repeat the process, 00:13:46.11 another 90 degrees counterclockwise, 00:13:48.06 another 90 degrees counterclockwise, 00:13:50.21 and then another 90 degrees counterclockwise. 00:13:54.25 Now the next step is we program those plugs and holes 00:13:57.16 to have unique sequence complementarity. 00:13:59.29 So for example, this plug here is going to be complementary 00:14:02.26 with this hole here, etc, etc. 00:14:05.18 Again, each one of these single-stranded bricks 00:14:07.13 has a unique sequence, 00:14:09.08 has four unique nearest neighbors, 00:14:11.17 and has the desired base complementarity 00:14:15.07 between those nearest neighbor domains. 00:14:18.07 And if you do that hopefully you can see 00:14:19.25 how you could self-assemble these different planes 00:14:22.17 into this cuboid structure, 00:14:24.26 in fact you'd just be throwing all those single-stranded bricks 00:14:27.08 together into a pool 00:14:28.25 and having them self-assemble just like before, 00:14:30.25 but now in 3 dimensions. 00:14:34.28 Furthermore, we can abstract this 00:14:36.17 in terms of the design process, 00:14:38.23 in terms of a 3-dimensional canvas, 00:14:41.23 a 3-dimensional cuboid canvas, 00:14:44.02 where each one of these volume elements, or voxels, 00:14:46.23 is 2.5 nm x 2.5 nm x 2.5 nm. 00:14:50.18 So in this case, 00:14:51.19 the double helices again are running 00:14:52.25 from the lower left-hand corner 00:14:54.17 to the upper right-hand corner. 00:14:58.13 And each one of these, again, it's 8 basepairs, 00:15:00.24 that represents one domain 00:15:03.09 from each of those bricks interacting with each other. 00:15:06.05 So in design space what we do is we 00:15:07.11 start from this 3-dimensional cuboid canvas, 00:15:10.09 we start removing voxels 00:15:12.10 until we end up with a 3-dimensional object that we want. 00:15:15.19 Then we have a computer program 00:15:17.10 that will compile this abstract voxel element representation 00:15:22.11 into the brick representation, 00:15:24.27 so the program will ask, 00:15:26.07 "OK, what series of bricks do I need to remove 00:15:29.00 in order to allow me to remove 00:15:31.12 individual volume elements." 00:15:34.22 Then whatever series of bricks 00:15:36.21 that are remaining for us to pipet, 00:15:38.20 that's now translated into instructions to the pipetting robot, 00:15:41.20 which will then go and pipet subsets of strands 00:15:44.04 corresponding to whatever kind of object 00:15:46.06 that we want to build. 00:15:48.24 So again, Peng loves the number 100, 00:15:52.08 so Yonggang and Luvena 00:15:55.01 strove to build over 100 different objects, 00:15:57.26 just like before but now in 3 dimensions. 00:15:59.27 This represents the different designs that can be created, 00:16:02.23 now we have letters that are in 3-dimensional relief, 00:16:06.03 we have Chinese characters 00:16:07.25 that are inscribed into 3-dimensional bricks/blocks, 00:16:11.03 same thing with numbers. 00:16:13.01 In this row here, it's an interesting representation 00:16:15.18 where now the solid is supposed to represent 00:16:19.13 bricks that we left out of the assembly, 00:16:22.22 and the translucent represents bricks that we left in. 00:16:26.10 So what this means is that this is 00:16:27.06 supposed to self-assemble into a solid object 00:16:30.09 with a completely enclosed cavity 00:16:32.15 that has a toroidal-type arrangement. 00:16:37.09 And then some pipetting was done by a pipetting robot, 00:16:40.10 so just feed the instructions to the robot, 00:16:41.26 come back in two days, and again, 00:16:43.21 we don't yet have an automated imaging platform, 00:16:45.21 so then there was a lot of work involved 00:16:49.16 in generating this figure 00:16:51.14 where we have electron micrographs now, 00:16:53.18 of these different objects. 00:16:54.21 These are projection images, 00:16:57.27 for example here we can see a little spaceship 00:17:00.24 that we were trying to design. 00:17:04.09 Here's an animation from Gael McGill 00:17:05.27 at Harvard Medical School 00:17:07.15 that is illustrating what we think 00:17:09.28 the dynamics of the structure might be. 00:17:15.28 So now I'm going to go through a series of 00:17:17.24 examples of different kinds of structures, 00:17:19.17 just to give you, again, a feeling of the generality. 00:17:22.03 Again, you start from this 3-dimensional canvas, 00:17:24.02 you start removing volume elements, 00:17:26.08 whittle it down until you get the object you want. 00:17:31.23 So here's an object with that cavity on the inside 00:17:35.08 I just mentioned. 00:17:36.28 So now when you image this in transmission electron microscopy, 00:17:40.01 you're going to get projection images 00:17:42.00 -- they're kind of like X-rays -- 00:17:43.27 so if you look at the particles in different orientations, 00:17:46.13 then you'd expect to see different images. 00:17:49.27 So for example, 00:17:51.06 you'd expect to see the "O" 00:17:52.25 if you're looking from the top down, 00:17:54.21 but if you're looking from the side, 00:17:56.04 then you'd actually expect to see 00:17:57.26 something more like this. 00:18:03.03 Here's an object, it's a 3-dimensional smiley face. 00:18:06.13 Again, each one of these volume elements 00:18:08.11 is 2.5 nm x 2.5 nm x 2.5 nm, 8 base pairs. 00:18:13.28 And looking down from the top 00:18:15.11 we can see the smiley face, 00:18:16.22 looking from the side 00:18:17.23 then we see a different kind of image. 00:18:23.25 Here's an object that's designed to form 00:18:27.05 kind of like a 6-sided die 00:18:29.29 except it's a cheating die 00:18:31.26 and it only has 3 numbers, 00:18:33.16 so it has different crisscrossing channels 00:18:36.07 through the object and, again, 00:18:37.24 depending on which face lands on the grid, 00:18:39.15 you expect to see different images. 00:18:42.14 So, 1, 2, 3. 00:18:44.22 All the same object, 00:18:45.29 just landing in different orientations on the grid. 00:18:51.13 Here's an object that when you look from the top 00:18:53.17 is supposed to look like the letter "B" 00:18:55.15 and when you look from the side 00:18:56.21 is supposed to look like the letter "A". 00:18:58.21 And again, that's something that we can see. 00:19:03.28 Here's another object, 00:19:05.02 looks like "C" from the top, 00:19:06.15 and "D" from the side. 00:19:13.09 Here's an object with basically a channel in the top, 00:19:18.03 and if we look from the top 00:19:19.28 then we can see this characteristic channel pattern, 00:19:23.16 again if we look from the side, 00:19:25.10 then we can see we only removed strands 00:19:27.07 for part of the top of the object, 00:19:29.16 the bottom of the object remains solid. 00:19:34.12 For the 2-dimensional structures, 00:19:36.02 Peng's group developed some software 00:19:38.05 that allows them to quickly design 00:19:39.24 any shape they want. 00:19:41.15 So you can start from some kind of image 00:19:43.12 that you upload into the software, 00:19:45.14 the software will do edge detection 00:19:47.28 and then figure out where the boundaries 00:19:48.23 of the object are, 00:19:50.20 and then based on that algorithm 00:19:53.25 the program can automatically determine 00:19:55.24 which strands to include in the self-assembly, 00:19:57.29 which ones to leave out. 00:20:03.23 For the 3-dimensional structures, 00:20:05.27 this is something that's still in process, 00:20:07.11 but what Yonggang did was he took 00:20:10.01 his favorite 3-dimensional rendering program, 00:20:12.24 told it to render this series of volume elements, 00:20:15.22 and then just what he's doing now in real time 00:20:17.18 is he's carving channels into this cuboid structure, 00:20:22.20 so he's just removing channels. 00:20:25.23 In real time we can see him create two crisscrossing channels 00:20:29.00 that are orthogonal. 00:20:32.24 And this gives you a feeling that, 00:20:34.15 within just minutes, 00:20:36.06 you can now design any structure you want, 00:20:38.12 very much analogous to what a sculptor is doing. 00:20:41.04 But then it's going to take some time for the pipetting robot 00:20:43.08 to pipet all the strands, 00:20:44.23 for the folding to occur, 00:20:46.09 and then for the imaging, 00:20:48.02 it's going to be somewhat time-consuming 00:20:50.08 until we have an automated platform for that. 00:21:01.01 So just to recap, 00:21:02.12 we have a design phase 00:21:04.04 where we start from our canvas 00:21:05.24 -- 2-dimensional/3-dimensional canvas -- 00:21:08.01 we figure out which of the bricks 00:21:11.05 we want to include/exclude. 00:21:13.06 That gets converted by software into pipetting instructions to the robot. 00:21:16.19 Robot does it's thing. 00:21:18.16 And then we heat up and cool down the strands 00:21:20.28 over the course of a day, 00:21:22.09 or longer for the more complicated objects, 00:21:25.09 and then we look at them using atomic force microscopy 00:21:28.07 or transmission electron microscopy. 00:21:33.06 It's always nice to have some more movies 00:21:34.19 so we can see the pipetting robot in action. 00:21:42.01 And we can envision hopefully 00:21:44.21 a day not too far from now where everything is automated, 00:21:47.21 so we can just design the objects 00:21:49.28 and then everything else will be handled automatically, 00:21:52.20 including the imaging. 00:21:53.29 It might make a wonderful resource for students that, 00:21:58.04 if they can go online, 00:21:59.18 submit their designs online 00:22:01.01 and then maybe there's a chance 00:22:02.28 that a lab will actually build the object in the laboratory 00:22:06.00 and then the student can see their object, 00:22:08.23 an electron micrograph 00:22:10.15 or an atomic force micrograph 00:22:11.24 of the object they designed. 00:22:14.02 To summarize up to this point, 00:22:15.22 now we have a second method that allows us to 00:22:18.14 generate objects that are 00:22:20.20 roughly twice the mass of a ribosome or larger, 00:22:23.20 that was just published in the last year 00:22:25.02 from Peng Yin's lab, 00:22:27.29 DNA tiles and bricks now, 00:22:29.29 that doesn't require a long single strand. 00:22:33.06 And for some applications you can imagine 00:22:35.02 with this overlapping capability, 00:22:37.05 you could arbitrarily choose which one you want to select. 00:22:40.22 However, when we look closer 00:22:42.04 we could imagine that the independent methods 00:22:44.13 might have different advantages depending on the application. 00:22:48.18 So for example, with DNA Origami, 00:22:50.18 we've noticed so far that the assemblies 00:22:52.15 seem to be faster. 00:22:53.27 So although that long strand doesn't seem to be absolutely necessary, 00:22:57.07 we could imagine it does help to speed things up 00:22:59.24 by grabbing the individual strands 00:23:01.08 and bringing them together more quickly. 00:23:04.08 A second advantage is that 00:23:05.22 we believe that the DNA Origami, 00:23:07.24 at least how it's currently constituted, 00:23:09.12 could be thermodynamically more stable 00:23:11.11 to have this long strand running through the entire object. 00:23:14.06 We could imagine the thought experiment of 00:23:15.11 what if we started from the DNA tiles 00:23:17.19 and then just started ligating some of those tiles or bricks together 00:23:20.15 to make a long strand. 00:23:21.28 Then it should be more stable, 00:23:24.05 so in that way we imagine DNA Origami 00:23:26.05 has more linkages between the strands, 00:23:27.29 longer strand, 00:23:29.08 then it should be more stable, 00:23:30.29 at least currently. 00:23:32.15 And finally we can imagine that DNA Origami 00:23:34.20 probably can offer greater mechanical strength, 00:23:37.20 that if you have that long scaffold strand 00:23:39.22 is crisscrossing throughout the entire structure, 00:23:42.17 now you have to break covalent bonds, probably, 00:23:44.26 in order to really disrupt the object. 00:23:46.25 Whereas with the DNA tile object, 00:23:48.22 now if you could imagine creating a facet, a breakage 00:23:52.22 without having to sever any covalent bonds. 00:23:56.05 So what are the potential advantages of 00:23:57.25 DNA tiles or bricks over Origami? 00:24:00.04 Well, one is that the design is more modular, 00:24:02.08 it corresponds more to our intuition of how Lego bricks 00:24:06.17 can be designed. 00:24:08.23 It's conceptually simpler 00:24:10.13 and that's usually something that is desirable. 00:24:13.12 Often times when the design process is simpler 00:24:15.12 then it's going to be more versatile 00:24:17.24 and more powerful. 00:24:19.11 It'll be better for teaching to students how this works. 00:24:23.06 And then finally the DNA tiles offer the 00:24:26.05 advantage of synthetic diversity, 00:24:28.05 because all of these elements are short strands 00:24:31.10 and they're accessible through synthetic chemistry, 00:24:34.05 which means we can put any kind of nucleoside analogue 00:24:36.11 that we want in there, 00:24:38.06 assuming it still base pairs, 00:24:39.27 whereas with the DNA Origami, 00:24:41.07 because it's relying on this long single strand, 00:24:44.10 currently our only way to generate these very long single strands 00:24:46.17 is enzymatically, 00:24:48.19 and therefore we're limited to those nucleoside triphosphates 00:24:51.06 that are recognized by DNA polymerases. 00:24:54.25 So where could this potentially be advantageous? 00:24:56.24 So let's say that you're trying to 00:24:58.06 self-assemble a drug delivery vehicle. 00:25:01.16 Maybe if you built it with DNA Origami, 00:25:03.11 you'd start to worry about, well, 00:25:05.14 maybe nucleases are going to digest my long strand. 00:25:08.14 Maybe my long strand is going to 00:25:09.28 trigger an innate immune response. 00:25:12.21 But if that's my concern, 00:25:14.11 then maybe I should think about designing the same kind of structure, 00:25:17.13 but with DNA bricks, 00:25:18.25 where I can use let's say mirror-image building blocks 00:25:21.15 that are nuclease resistant 00:25:23.18 and that are not recognized by the innate immune response. 00:25:27.14 What we found is that, again, 00:25:28.21 for these discrete objects, 00:25:30.13 maybe the performance of the two methods is similar, 00:25:32.26 but where the DNA brick method really seems to shine 00:25:36.00 is in building periodic structures. 00:25:38.13 So what we've done here is... 00:25:40.02 what Yonggang has done is 00:25:41.19 he's programmed the right-hand side 00:25:43.15 of this lightly shaded unit cell 00:25:45.20 to have complementary sticky ends 00:25:47.24 to the left hand side, 00:25:49.17 or complementary plugs and holes I should say, 00:25:51.25 and complementary plugs and holes 00:25:53.02 from the front end and the back end. 00:25:55.21 And so now what will happen is 00:25:57.09 that that unit cell won't stop with a discrete object, 00:25:59.19 it'll actually polymerize 00:26:01.23 into a 2-dimensional lattice. 00:26:03.20 Furthermore, it's not... 00:26:05.12 we don't think that it's forming hierarchically 00:26:07.02 - it's not that you form a bunch of unit cells 00:26:08.22 and those unit cells assemble. 00:26:10.13 Rather, we believe that the assembly 00:26:12.03 is growing piece by piece. 00:26:14.01 So each individual brick 00:26:15.06 is adding on one by one. 00:26:17.24 And in that way, looking at this periodic assembly, 00:26:20.04 actually, if you think about it 00:26:22.02 -- a thought experiment -- 00:26:23.27 the definition of the unit cell now 00:26:25.25 is a little bit arbitrary, 00:26:27.13 because we could just as easily draw 00:26:28.29 a unit cell connecting these four corners. 00:26:31.06 It's equivalent with these periodic structures. 00:26:34.28 Anyway, the important thing is that 00:26:36.09 this single-stranded brick method 00:26:37.29 seems to give us better performance 00:26:39.18 in the test tube 00:26:40.23 in making these periodic structures. 00:26:43.08 So this is a quite remarkable design 00:26:45.14 that was developed by Yonggang, 00:26:47.19 where the helices are pointing up 00:26:49.28 out of the plane of the DNA crystal 00:26:52.11 and the unit cell has dimensions of 00:26:54.21 6 helices x 6 helices, 00:26:56.12 so about 15 nm x 15 nm. 00:27:01.16 And in this particular example, 00:27:03.01 he designed a cavity within the unit cell 00:27:05.19 of a 2x2 helix, helices that are missing. 00:27:09.10 And then what this is going to do is now 00:27:10.18 self-assemble into a crystal that, 00:27:13.03 where again the unit cell has dimensions of about 15 nm, 00:27:15.20 the holes dimensions of about 5 nm, 00:27:17.27 and the entire crystal can grow to 00:27:19.29 multiple microns in dimension. 00:27:22.24 We believe that these kinds of structures 00:27:24.15 could have application as template 00:27:26.29 for perhaps growing inorganic materials 00:27:29.16 to make molecular wires 00:27:31.06 and plasmonic devices. 00:27:32.29 We think that it might also have application in biology 00:27:35.16 for something like the host-guest crystallography 00:27:38.04 that Ned Seeman envisioned. 00:27:40.11 In this example it would be two dimensional, 00:27:42.24 so what if we could get membrane proteins 00:27:45.03 to assemble into stereotyped orientations 00:27:47.26 and locations 00:27:49.05 within these cavities 00:27:50.27 and use the DNA crystal 00:27:52.14 in order to impose that crystalline order 00:27:53.18 on those proteins. 00:27:55.10 That could be a way to accelerate structural biology research. 00:28:00.00 So this is just some more examples of 00:28:01.29 periodic 2-dimensional crystals. 00:28:03.21 In this case, what Yonggang is doing is 00:28:05.27 he's polymerizing in the direction of the helices, 00:28:09.10 so again, every cylinder is a double helix, 00:28:14.24 and we can see these precise channels. 00:28:16.11 It's the same story as with the discrete objects, 00:28:18.15 he just starts from a solid cuboid unit cell 00:28:21.15 and then removes strands in order to create 00:28:23.22 the cavity features, 00:28:25.14 and in this way can create 00:28:27.01 an extremely diverse set of crystals 00:28:29.04 with intricate features. 00:28:31.06 That is basically not accessible 00:28:33.28 using any other known method. 00:28:35.27 So this is an interesting example where what he did was 00:28:37.23 he made a very think crystal that was only 00:28:40.19 I believe 32 basepairs in height, 00:28:43.07 and now it turned out with his design, 00:28:46.11 the structure no longer wanted to be planar, 00:28:48.21 but instead had a tendency 00:28:50.16 to wrap around to make a tube. 00:28:53.19 And we can see these nanotubes 00:28:55.07 that have an appearance that's somewhat reminiscent 00:28:57.27 of biological assemblies such as... 00:28:59.25 this is a Tobacco mosaic virus. 00:29:02.29 Of course, this object is made entirely out of DNA 00:29:04.29 - it's not infective. 00:29:10.07 Yonggang and Wei Sun in Peng Yin's lab 00:29:12.16 have gone on to use these crystals 00:29:15.00 to template the self-assembly 00:29:16.25 of gold nanoparticles onto them. 00:29:18.16 Again, potentially for electronics 00:29:20.05 or photonics-type applications. 00:29:22.16 And so what they've done here is they've decorated 00:29:23.24 5 nm gold particles with single-stranded glue, 00:29:30.08 and then they have the complementary glue 00:29:31.24 that's lining the inside of these channels, 00:29:34.15 and in that way they're able to get high densities 00:29:36.12 of these gold particles into those channels. 00:29:39.10 Here, what they've done is they've just 00:29:41.09 coated the entire surface with a high density 00:29:44.04 of these 5 nm gold nanoparticles. 00:29:52.03 I should mention that although DNA Origami 00:29:54.08 is not as good as DNA bricks 00:29:56.08 for making these 2-dimensional structures, 00:29:58.01 it does have some ability to do that. 00:30:00.07 So this is work from Yonggang 00:30:01.23 that we didn't publish 00:30:03.08 where he built these honeycomb building blocks, 00:30:08.00 hexagonal building blocks that self-assemble 00:30:10.08 into a hexagonal crystal 00:30:11.24 that has similar dimensions as what I showed you before 00:30:14.01 - a couple microns x a couple microns. 00:30:16.24 And Ned Seeman's group published a very nice work 00:30:19.21 in which they designed a building block 00:30:21.17 that looks kind of like a two layer 00:30:24.13 Rodeman-style Origami 00:30:26.29 and we able to self-assemble this 00:30:29.11 into a rectangular array, 00:30:30.23 again a couple microns x a couple microns. 00:30:33.07 But I'd like to emphasize that with DNA Origami 00:30:35.22 it's just a couple of idiosyncratic cases 00:30:39.00 where we've been able to succeed 00:30:40.22 to build these very large crystals, 00:30:42.24 but with the single-stranded bricks, 00:30:44.12 it seems like most of the things we try work, 00:30:46.29 and it's just much, much easier to design. 00:30:48.24 You just leave out some strands 00:30:49.29 and then now you have a new crystal. 00:30:52.28 Thus far what we've observed 00:30:54.17 is that the DNA brick crystals seem to be more robust 00:30:57.29 than the scaffolded DNA Origami crystals. 00:31:00.16 With the Origami crystals, 00:31:02.01 we just have a couple of cases 00:31:03.06 where it seems to have worked. 00:31:04.17 With the DNA brick crystals, 00:31:05.22 it's very simple for us to just 00:31:07.00 leave out some of the strands 00:31:08.14 and make a new crystal, 00:31:10.05 and something that's more rigid, higher quality. 00:31:13.01 So hopefully in the future 00:31:15.00 we can develop methods for improving DNA Origami crystals, 00:31:18.03 but in the meantime we can speculate about why, currently, 00:31:22.09 the DNA brick crystals are forming better. 00:31:24.24 So we can do the thought experiment that maybe, 00:31:27.04 for the DNA Origami crystal, 00:31:28.24 you could imagine either pre-forming the unit cells... 00:31:32.25 you could imagine pre-forming the unit cells 00:31:34.17 and then now you mix them together, 00:31:36.04 and the problem is that because the unit cells are so large, 00:31:39.07 it can be very difficult to get reversible assemblies. 00:31:41.29 So you make so many contacts with the growing lattice 00:31:44.03 that it's hard to dislodge yourself. 00:31:45.24 And note that this the same kind of difficulty 00:31:47.21 that plagues macromolecular crystallography, 00:31:50.20 that it becomes very difficult to crystallize large complexes 00:31:53.15 for this reason, among others. 00:31:57.18 Let's contrast that with the DNA brick crystal growth, 00:32:00.05 where the growth is occurring 00:32:02.29 through these very short elements 00:32:04.23 that are only 32 bases long. 00:32:06.20 And because they're so short, 00:32:07.27 it's very easy for them to come in, come out. 00:32:10.02 If there's an error, it has a chance to leave. 00:32:12.27 But because there's many different kinds of bricks, 00:32:15.22 you can still achieve a very complicated unit cell. 00:32:19.03 So it's almost like you can have your cake and eat it too. 00:32:22.04 You can have a very complex unit cell, 00:32:24.04 but it's assembling one subcomponent at a time, 00:32:27.06 so you still get that reversible self-assembly 00:32:30.01 that seems to be critical for robust growth of a crystal. 00:32:38.19 So in conclusion, 00:32:39.29 what we've seen from the lab of Peng Yin 00:32:42.03 over the last year or two, 00:32:43.29 we also collaborated to help them on this, 00:32:46.12 is a fantastic new method for self-assembling structures 00:32:50.10 that are the size of a ribosome or maybe even larger. 00:32:53.17 We can build them in 2-dimensions, 00:32:54.27 we can build them in 3-dimensions. 00:32:57.03 Right now what it looks like is there's 00:32:58.21 a special advantage with these single-stranded bricks 00:33:01.07 in growing periodic structures, 00:33:03.13 and we believe this could have important applications 00:33:06.01 ranging from molecular electronics and photonics 00:33:08.20 to structural biology.