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.