Shih describes how DNA can be used as a building material to construct nanoscale objects. A long strand of DNA can be made to fold into a flat “sheet” by introducing short oligonucleotides that base pair with the long DNA strand and form crossovers to hold the structure together. Engineering complementary sticky ends onto some of the “sheets” allows the DNA to self-assemble into 2-dimensional shapes. Ultimately, these flat sheets of DNA can be designed so that they will self assemble into 3-D nanostructures or “DNA origami”, even larger than a ribosome.
In Part 2, Shih describes a complementary DNA building technique that uses DNA “bricks” rather than “sheets” to build 3-dimensional nanostructures. In the last section of his talk, Shih describes some of the practical uses of DNA-nanostructures. For example, DNA nanotube liquid crystals can be used to help align proteins in NMR experiments leading to improved atomic-resolution structures. DNA nanostructures may also prove useful as a mechanism to deliver therapeutics to cells and Shih and his colleagues have investigated the ability of nanostructures of different sizes and shapes to be taken up by cells.
All Course Materials for this Session (Educators only) – Created by Karen Cheng
00:00:07.15 My name is William Shih.
00:00:08.23 I'm an associate professor of Biological Chemistry
00:00:11.15 and Molecular Pharmacology
00:00:12.23 at Harvard Medical School,
00:00:14.11 Dana-Farber Cancer Institutes,
00:00:16.06 and the Wyss Institute for Biologically Inspired Engineering.
00:00:19.22 It's my pleasure to share with you today
00:00:21.25 some recent technical advances
00:00:23.13 in the field of structural DNA nanotechnology,
00:00:26.29 from my laboratory and those of my colleagues.
00:00:30.04 We're all familiar with the biological role of DNA
00:00:33.02 as an information repository,
00:00:35.22 principally for coding for protein sequence
00:00:38.06 and for regulation of protein expression.
00:00:41.08 And I'm not going to speak at all about that today.
00:00:44.08 Instead, I'll be talking about using DNA itself
00:00:47.12 as a building material
00:00:49.02 and harnessing that in order to us
00:00:51.04 to construct nanoscale objects.
00:00:53.17 For example, shown here is an electron micrograph
00:00:55.22 of actin filaments
00:00:57.19 that are about 7 nanometers (nm) in diameter.
00:01:01.04 And below, we can see this peculiar
00:01:03.10 Pac-Man-shaped object.
00:01:05.06 This is a structure that was built entirely from DNA
00:01:08.24 and we designed it for the purpose
00:01:10.27 of taking bite-size chunks
00:01:12.15 out of the actin filament.
00:01:14.10 Well, this project hasn't yet been successfully,
00:01:17.06 however hopefully this image drives home
00:01:19.05 the complementarity between the dimensions
00:01:21.16 of our designed DNA nanostructures
00:01:24.08 and biological macromolecular complexes.
00:01:28.01 So I've been working in this field
00:01:29.11 for over a decade now,
00:01:31.10 and I'm continually surprised
00:01:33.05 by advances in the field.
00:01:34.25 I have these preconceived notions of the limitations of DNA,
00:01:37.21 and they're always shattered
00:01:38.25 by the latest new discovery.
00:01:40.19 And today I'm going to be sharing with you,
00:01:42.10 in the first two sections, respectively,
00:01:44.25 two recent developments
00:01:46.16 that enable us to design and assemble
00:01:48.20 DNA nanostructures of the size and complexity
00:01:51.09 of this object shown here,
00:01:52.18 about 30 nm in diameter.
00:01:55.14 One of the most important goals for DNA nanotechnology
00:01:58.18 is to self-assemble objects
00:02:02.06 of ever-increasing complexity over time.
00:02:05.06 For example, is it possible that,
00:02:07.29 within the next decade or two,
00:02:09.14 that we can self-assemble objects
00:02:11.12 that are let's say a thousand times as complicated,
00:02:14.12 that have a thousand times as many unique components,
00:02:17.05 as the object shown here?
00:02:20.02 A second question is:
00:02:21.12 what are these things good for?
00:02:23.02 And in the third part of this lecture,
00:02:24.09 I'll be discussing some applications in my laboratory
00:02:27.11 as tools for molecular biophysics
00:02:29.22 and tools for future therapeutics
00:02:32.02 where we think these objects might prove useful.
00:02:35.00 We're inspired from natural systems,
00:02:37.02 we know that they can carry out many amazing behaviors:
00:02:40.18 they can build, they can adapt,
00:02:42.15 they can heal, they can reproduce.
00:02:45.05 And these are capabilities that human technology
00:02:47.04 struggles to reproduce on any kind of length scale,
00:02:50.00 but what's especially remarkable
00:02:52.00 is the ability of life to carry these out
00:02:54.08 on the molecular scale.
00:02:55.23 So, for example, here on the left
00:02:57.16 is a picture of a ribosome,
00:03:00.03 and this is of course a machine that's about 25 nm in diameter
00:03:04.05 that takes information encoded in messenger RNAs
00:03:07.10 and then translates that into
00:03:08.21 a specific sequence of amino acids,
00:03:10.26 to produce a polypeptide.
00:03:13.18 Quite amazing machine.
00:03:16.06 On the right, we have the T4 bacteriophage,
00:03:19.06 that's a bit larger in length scale.
00:03:21.02 We can see that the viral capsid itself -- head capsid --
00:03:23.26 is about 100 nm in diameter,
00:03:25.17 so it looks like a little nanoscale hypodermic syringe
00:03:27.19 that docks onto the surface of its bacterial hosts
00:03:30.26 and then injects its DNA cargo into the cell
00:03:33.25 against an osmotic pressure gradient.
00:03:37.05 Of course what makes this all possible
00:03:39.00 is that living systems have invented molecular manufacturing
00:03:43.08 and they've come up with a very robust and clever way of doing this.
00:03:46.08 They, of course, synthesize these biopolymers
00:03:49.07 -- they can be polypeptide chains, polynucleotide chains --
00:03:52.11 that then self-assemble into the desired structure.
00:03:56.25 Now, if somebody asked you about ten years ago,
00:04:00.08 "Would it be possible to generate an object
00:04:03.02 of this complexity using some kind of human-based technology?"
00:04:06.00 most people would have been skeptical.
00:04:08.17 And yet I'm going to show you a new technology,
00:04:10.27 DNA nanotechnology, developed in the last...
00:04:14.04 especially with advances from the last ten years,
00:04:16.24 that now make it possible for us to self-assemble,
00:04:19.12 in a programmable way,
00:04:21.12 structures of the same kind of complexity
00:04:23.12 as you see here.
00:04:24.26 Not yet of the functional complexity,
00:04:27.01 but nevertheless we think this is an encouraging first step
00:04:30.02 because along the pathway to this kind of functionality,
00:04:33.16 we first need to master structural complexity.
00:04:39.21 We're going to be using DNA as our building material,
00:04:43.04 and we know that DNA,
00:04:45.22 very similar to proteins and other macromolecules from life,
00:04:48.26 are very complicated molecules.
00:04:51.07 There's many different atoms,
00:04:52.24 but it turns out the key point for DNA nanotechnology
00:04:55.26 is that the robust base-pairing properties of DNA
00:04:58.20 allow us to abstract away those chemical details,
00:05:01.26 which is going to make the act of
00:05:04.00 designing the nanostructures much simpler.
00:05:06.23 And in fact there's only three characteristics of the DNA
00:05:09.19 that we need to remember
00:05:10.28 for the purpose of DNA nanoconstruction.
00:05:13.13 One, is that it's a ladder with antiparallel strands.
00:05:17.24 Secondly, there's a right-handed twist for B-DNA,
00:05:21.04 and we need to know that the twist
00:05:22.23 is around 10.5 basepairs per turn.
00:05:25.15 It turns out we can switch that around a little bit.
00:05:28.29 And finally we need to know that
00:05:31.11 A pairs with T,
00:05:32.16 C pairs with G,
00:05:34.08 and anytime you deviate from that pairing,
00:05:36.20 you're going to destabilize the structure.
00:05:39.25 And it's because the propensity of DNA
00:05:42.17 to form this very regular structure,
00:05:44.21 enforced very strictly according
00:05:46.08 to this Watson-Crick base pairing,
00:05:48.03 that gives us its power in being able to
00:05:50.19 generate these large structures with very little design work.
00:05:54.10 The father of the field of DNA nanotechnology
00:05:56.23 is Ned Seeman at NYU.
00:05:58.28 He invented this field about thirty years ago.
00:06:02.10 His training is as a crystallographer,
00:06:04.07 and the way he came up with the idea is as follows:
00:06:06.16 he was sitting in the campus pub one day,
00:06:08.20 just drinking his beer, and suddenly
00:06:10.24 what popped into his head was this woodcut
00:06:12.29 from M.C. Escher.
00:06:15.00 He had been collaborating with some of his friends
00:06:17.14 on DNA Holliday junctions,
00:06:19.28 and he had this eureka moment:
00:06:21.11 why not replace the flying fish
00:06:23.23 with DNA Holliday junctions?
00:06:25.24 The notion was that if you could rationally design
00:06:28.18 a porous crystal out of DNA,
00:06:30.24 and then he could take the target protein he was interested in,
00:06:33.02 and then dock that into each unit cell
00:06:35.02 into a stereotyped orientation,
00:06:37.13 then he would be able to impose that crystalline order
00:06:40.01 on the target protein,
00:06:41.19 and therefore make the X-ray crystallography problem easier
00:06:45.02 for these large macromolecules
00:06:46.20 that are otherwise difficult to crystallize.
00:06:50.20 He's been working on this problem for over thirty years,
00:06:53.00 it's an important goal,
00:06:54.01 and he's made some interesting progress,
00:06:55.25 I'll have more to say about that in the third segment.
00:06:59.26 But in the meantime, he's had some interesting
00:07:01.25 landmark successes.
00:07:03.25 So the first really noteworthy advance that he reported
00:07:07.13 came out in 1992 or so in Nature,
00:07:10.14 so this is a DNA cube
00:07:12.20 where each edge of the wireframe cube
00:07:14.20 is two turns of a double helix.
00:07:16.27 Each face is a circular strand of DNA
00:07:20.12 and the entire object has dimensions
00:07:22.09 of about 10 nm.
00:07:25.02 So the first time I think people saw this they thought,
00:07:27.15 "Wow, this is really cool,
00:07:29.21 but that doesn't look like biology to me."
00:07:32.22 And what I hope convince you today
00:07:34.17 is that this is in fact an extremely powerful technology.
00:07:38.12 Yes, it's fun,
00:07:39.18 but it's actually potentially very useful as well,
00:07:42.01 for many different applications.
00:07:48.06 Of course if we're building from DNA strands
00:07:50.19 and we're just making double helices
00:07:52.03 then that's boring.
00:07:53.15 The power of DNA nanotechnology
00:07:55.08 is that we can build with branched junctions.
00:07:57.27 With the previous example, the cube,
00:07:59.13 each one of those vertices
00:08:00.25 is a three-branch junction.
00:08:02.23 But it turns out the most powerful motif
00:08:04.21 so far in structural DNA nanotechnology
00:08:07.13 has been a four-branch junction;
00:08:09.09 a Holliday junction.
00:08:11.03 So on the upper left here we have a schematic
00:08:14.07 using simple letter notation of the strands,
00:08:17.22 so you can see the cyan strand starts
00:08:19.22 5'-CCGG, goes to it's 3' and,
00:08:23.05 and if you look closely at this you can see that
00:08:24.23 there's four different sequences
00:08:27.06 and they have the proper sequence complementarity
00:08:29.17 in order to generate a Holliday junction
00:08:31.20 that actually is immobile.
00:08:33.14 It can't branch migrate due to its sequence.
00:08:36.23 And we know from structural studies
00:08:38.15 that this object likes to stack into two double helices
00:08:42.12 that are connected at a joint,
00:08:44.18 and it turns out this is really the building block
00:08:46.10 that's been the most fruitful for DNA nanotechnology.
00:08:49.29 So the idea is as follows:
00:08:51.23 if you only have one Holliday junction,
00:08:53.28 now you have two helices that can wobble around.
00:08:56.13 In order to fix those two helices
00:08:58.01 to make a rigid building block,
00:08:59.22 what we do is we simply introduce
00:09:01.15 a second Holliday junction downstream,
00:09:04.00 and now when we fix those two double helices
00:09:06.04 with two Holliday junctions
00:09:07.17 we have that rigid building block that we want,
00:09:09.23 now with four sticky ends.
00:09:12.10 The next step is to build two versions of this building block.
00:09:15.24 In this example, we have a red one
00:09:17.24 and we have a blue one.
00:09:21.02 And we designed the sticky ends with the following
00:09:22.06 complementarity in this example.
00:09:24.17 So let's say we make the sticky ends
00:09:26.05 on the upper right-hand side of the red block,
00:09:29.02 and we make that compatible with
00:09:31.19 the lower left-hand side of the blue block.
00:09:35.00 And so on and so forth,
00:09:36.10 in order to create this kind of checkerboard fashion,
00:09:38.24 hopefully you can see that we would be able to self-assemble
00:09:41.15 these two bricks into an infinite two-dimensional lattice,
00:09:44.12 as shown below.
00:09:46.25 I don't have the experimental images for this,
00:09:48.29 but suffice it to say that this method actually worked.
00:09:52.24 Quite amazing - you can design a two-dimensional crystal.
00:09:56.29 The step after this would be to say,
00:09:58.21 "Well, instead of two bricks, two tiles,
00:10:02.02 what if I had ten tiles,
00:10:03.23 or what if I had 100 tiles?
00:10:05.05 Can I now make non-periodic structures
00:10:07.11 that are highly complex,
00:10:09.07 just with self-assembling tiles?"
00:10:10.26 And unfortunately, nobody has really
00:10:11.25 demonstrated this method,
00:10:14.12 extending this particular method to 100's of tiles,
00:10:17.09 although what I'll show you shortly is one method,
00:10:20.14 DNA Origami, that can achieve this kind of complexity,
00:10:23.03 and in the second segment,
00:10:25.06 something called single-stranded bricks,
00:10:26.17 that can do something very similar
00:10:27.29 to what I just described.
00:10:30.23 The method of DNA Origami
00:10:33.03 is a particular flavor of structural DNA nanotechnology.
00:10:36.05 It was developed by Paul Rothemund at Caltech,
00:10:38.18 he published this in 2006,
00:10:40.28 and the basic idea is as follows:
00:10:43.22 so imagine you have a long single strand of DNA,
00:10:46.16 the 7000 base genome of the M13 bacteriophage,
00:10:50.05 that's the gray strand in this animation.
00:10:52.21 We know what the sequence is,
00:10:54.01 and based on that known sequence,
00:10:56.01 we chemically synthesize 100's of short oligonucleotides
00:10:59.11 that are 20-60 bases long
00:11:01.17 that are programmed by Watson-Crick complementarity
00:11:04.16 to pinch that long strand
00:11:06.06 into a parallel array of helices,
00:11:08.17 after heating everything up to about 65°C
00:11:11.24 and then cooling down to room temperature
00:11:13.28 over the course of an hour.
00:11:16.11 At the end of the assembly,
00:11:18.05 you end up with this parallel array
00:11:20.09 of double helices
00:11:22.06 where adjacent double helices are held together
00:11:24.01 by these Holliday junction crossovers
00:11:25.28 that I described to you a couple slides ago.
00:11:27.27 So this is a half-crossover
00:11:29.11 and then here we have a full DNA crossover.
00:11:34.10 Importantly, what you just saw was an animation,
00:11:36.18 not a simulation.
00:11:37.24 In fact, we have a very poor understand
00:11:39.15 of the order of events of folding these objects,
00:11:42.19 we just know that if we program them
00:11:44.18 in a way where all of the scaffold ends up basepaired
00:11:47.24 to staple strands,
00:11:49.12 then we have an extremely high probability
00:11:50.27 of forming the desired structure.
00:11:53.04 So it's a very active area of research for us to
00:11:55.01 try to understand better the mechanism of folding,
00:11:58.03 and we hope that will actually help us to design more
00:12:01.01 complex structures in the future.
00:12:03.16 So Paul Rothemund used this method in 2006
00:12:05.29 to make structures such as this
00:12:07.24 'disc with three holes'
00:12:09.16 that has dimensions of about 100 nm x 100 nm x 2 nm,
00:12:13.17 this is an atomic force micrograph.
00:12:15.17 The example in the upper left-hand corner represents, in size,
00:12:18.14 just part of the upper lip of the object.
00:12:21.04 So this is quite large by macromolecular standards.
00:12:23.29 It's like we have two ribosomes worth of molecular Silly Putty
00:12:27.07 that we can mash into any desired
00:12:29.16 two-dimensional cookie cutter shape.
00:12:32.02 One of the very interesting things that he pioneered
00:12:34.12 was that he developed a way to
00:12:36.22 make this DNA origami
00:12:38.11 where he made each one of the staple strands
00:12:40.17 in two different flavors.
00:12:42.11 So one flavor
00:12:44.01 just made the structure as you saw.
00:12:46.17 The second flavor had the identical sequence,
00:12:49.01 but had a surface feature,
00:12:50.19 a dumbbell that's sticking out of one of its ends.
00:12:53.24 And so what that means is any time he used the original flavor
00:12:57.02 and he added it to the folding mix,
00:12:58.27 then you'd get a plain vanilla DNA Origami surface
00:13:02.05 at that location.
00:13:04.13 But then if he replaced that sequence
00:13:05.27 with the longer sequence, the one with the feature,
00:13:08.07 now you get that same shape,
00:13:09.25 but a bump over that feature.
00:13:12.22 And in that way,
00:13:13.27 he conceived that this rectangular DNA Origami
00:13:16.21 could be treated as a molecular breadboard,
00:13:18.28 where let's say it has 200 different positions,
00:13:21.17 we can decide at each position
00:13:23.06 whether we want to create a bump or have no bump.
00:13:26.18 In effect we have something that's like a bitmap,
00:13:28.28 and we can create new patterns
00:13:30.13 simply by repipetting different patterns
00:13:32.18 of the no-bump and plus-bump strands
00:13:35.01 for each one of the locations.
00:13:36.16 So for example, here we can see that he's designing
00:13:39.03 something that will say 'DNA'
00:13:41.01 and have a little picture of DNA.
00:13:42.20 These structures actually become very sticky at the ends
00:13:44.09 because they have lots of blunt ends,
00:13:46.08 and then they'll make a continuous ribbon
00:13:48.14 that says 'DNA'.
00:13:50.19 You can see that he made a map of the Americas.
00:13:53.14 He's a very humble guy,
00:13:55.01 so he apologized to the rest of the world
00:13:56.13 for stopping at the Americas,
00:13:57.25 but DNA is a little bit expensive,
00:14:00.14 so he stopped at the moment.
00:14:02.26 Maybe by now he's made the rest of the world.
00:14:06.20 And you can program them to link up in specific ways,
00:14:10.29 and in that way you can self-assemble
00:14:12.25 two-dimensional crystalline objects.
00:14:16.02 So what about getting to three dimensions, as I alluded?
00:14:18.28 Well, we can get our initial inspiration
00:14:21.07 from macroscale paper Origami,
00:14:23.14 where we're quite familiar
00:14:25.17 that if we fold flat paper in many ways
00:14:28.11 we can get quite intricate three-dimensional shapes.
00:14:30.29 So this is the famous crane.
00:14:34.00 And if you're really diabolical, like Robert Lang,
00:14:38.11 you might note that if you can fold these papers
00:14:42.02 in especially intricate ways,
00:14:44.13 then you can make incredibly complicated objects,
00:14:48.18 that we can see some examples of here.
00:14:51.18 Now, nothing I'm going to show you with DNA
00:14:53.15 is as complicated as this,
00:14:55.05 but again, as I mentioned,
00:14:56.13 one of our goals is to scale to ever-increasing complexity,
00:14:59.08 so we hope that someday we actually can
00:15:01.20 self-assemble DNA into objects of this kind of complexity.
00:15:07.00 So that group in Denmark that I just mentioned,
00:15:09.10 of Andersen, Jørgen Kjems, Kurt Gothelf,
00:15:12.23 they were able to design that M13
00:15:15.08 to fold into six different sheets,
00:15:18.13 and then they programmed those six sheets to fold up
00:15:20.18 into a three-dimensional box
00:15:22.00 with a hollow inside.
00:15:23.24 They designed a lid that can open in response
00:15:25.20 to some kind of molecular key.
00:15:27.26 So this was the first example of
00:15:30.02 a three-dimensional hollow DNA Origami.
00:15:35.18 So where my group wanted to contribute
00:15:37.14 was to make solid three-dimensional Origami structures,
00:15:41.02 and the idea is as follows:
00:15:44.00 so first of all, we know that we can
00:15:46.10 curl up DNA due to the helicity of the DNA helices,
00:15:49.16 and I'm going to go through a little thought experiment
00:15:51.17 just to give you a flavor of what this is about.
00:15:53.25 So here we have, on the far left,
00:15:55.25 three double helices that are arranged
00:15:57.13 into a little DNA Origami.
00:15:59.06 You can see, if you look closely,
00:16:00.25 they're connected by those Holliday junction crossovers
00:16:03.00 to keep the helices parallel.
00:16:06.14 And in this arrangement it's making a flat sheet of three helices.
00:16:10.12 So now imagine what would happen
00:16:12.11 if we moved these crossovers on the top
00:16:15.21 two base pairs to the left.
00:16:18.16 Then that's going to move that double helix
00:16:20.12 behind the plane of the page.
00:16:22.15 And likewise, if we move those two crossovers
00:16:24.14 two base pairs to the right,
00:16:26.09 that's going to move that double helix
00:16:27.29 in front of the plane of the page.
00:16:30.00 And the take-home message here is that
00:16:31.12 simply by shifting around the position of those crossovers
00:16:34.23 with respect to each other,
00:16:36.18 we can achieve curvature of these DNA Origami sheets
00:16:40.05 along the axis of the double helices.
00:16:42.21 So that's the first key.
00:16:45.00 So now let's extend that and build an actual
00:16:46.24 solid 3-dimensional structure.
00:16:49.00 So here we have another representation of a DNA Origami
00:16:51.03 where each one of these cylinders
00:16:52.19 represents one of those double helices,
00:16:54.15 so it's similar to the example in the upper left,
00:16:56.16 but now just rotated into this orientation.
00:17:00.00 So this would represent the pattern of the scaffold
00:17:02.12 running through those helices,
00:17:04.05 but for the purpose of this explanation,
00:17:06.09 I'm going to leave that invisible.
00:17:08.15 It's there, but I'm just not going to talk about it,
00:17:10.18 that or the staple strands.
00:17:12.15 And so what we're going to do is we're going to
00:17:14.08 shift around the position of those crossovers
00:17:16.24 so now these helices no longer prefer to be planar,
00:17:19.25 but instead prefer to curl up
00:17:22.25 into some kind of specific geometry.
00:17:25.14 And in this example what we're doing is
00:17:27.08 we're trying to curl up the structure into a corrugated S shape.
00:17:32.04 Furthermore, anywhere where we have the orange
00:17:34.29 that touches the white sheet that touches the blue sheet,
00:17:37.13 we're routing those staple strands through those interfaces.
00:17:40.15 So for example, we might have a staple strand
00:17:42.09 that starts 7 base pairs on this helix,
00:17:44.22 and then goes 7 base pairs here,
00:17:46.12 7 base pairs, 7 base pairs, 7, 7 base pairs.
00:17:50.04 And in that way, if the structure forms the way we intend it to,
00:17:53.03 it should be highly crosslinked
00:17:55.17 by these staple strands that are traversing the different helices.
00:17:59.26 So it looks good on paper, okay,
00:18:03.03 what happens in the test tube when we tried it?
00:18:05.19 And perhaps we can say,
00:18:07.18 "Of course, when we threw all the strands together
00:18:09.16 and tried to fold the object, then it didn't work."
00:18:12.24 We got a pile of molecular spaghetti
00:18:14.24 that we could see under the electron micrograph.
00:18:18.12 But we didn't want to give up,
00:18:20.04 and eventually Hendrik Dietz in the group
00:18:22.08 came up with a key insight,
00:18:24.01 which is it's not that these 3-dimesional objects
00:18:26.13 now are unstable thermodynamically,
00:18:28.28 simply they're more difficult to achieve kinetically.
00:18:32.23 And so what we found is that we could only get appreciable yields
00:18:34.28 of these objects
00:18:36.27 if we folded them instead of for an hour,
00:18:39.04 from 65°C to room temperature,
00:18:40.27 if we folded them for more like a week,
00:18:43.15 then we could start to get appreciable yields
00:18:45.16 of the objects ranging from 10-50% yield.
00:18:50.13 So we can see here one of the objects
00:18:52.16 that was built by Shawn Douglas.
00:18:54.10 Instead of 3 layers, it was with 10 layers.
00:18:57.26 And then we have the electron micrographs below.
00:19:00.29 We can see that we get a close resemblance
00:19:03.02 between what we observe in the electron micrograph
00:19:05.07 and the projection orientations
00:19:07.28 of our designed structure.
00:19:12.05 This is work that we published back in 2009,
00:19:14.22 in the meantime, our group and others have been hard at work
00:19:17.11 trying to improve the method.
00:19:18.26 So the important thing here was that
00:19:20.06 we could get something to fold at all,
00:19:22.08 and now we're trying to get better yields,
00:19:24.11 improve the folding times.
00:19:26.01 So there have been a couple of important discoveries since then.
00:19:28.21 One has come from Hendrik Dietz's lab in Munich,
00:19:31.20 where they've discovered that these structures
00:19:33.16 tend to have a favored temperature
00:19:35.20 at which they fold faster than the other temperatures.
00:19:37.27 So instead of spending the same amount of time
00:19:39.28 at 65°C down to room temperature,
00:19:43.10 for example this structure maybe folds faster at 50°C.
00:19:48.11 So what they found is if they do most of their folding at 50°C
00:19:51.13 then they can get it fold
00:19:53.01 maybe an order of magnitude faster,
00:19:55.25 which makes a lot of improvements
00:19:58.28 for our lives as scientists designing them,
00:20:00.19 they also suffer less thermal damage with a slower folding ramp.
00:20:04.28 We've also learned some details about
00:20:06.26 how to design the strands, the crossovers,
00:20:09.12 the breakpoints,
00:20:11.01 that I don't have time to go into in this presentation,
00:20:13.24 but I encourage you to look at some of our publications
00:20:16.00 if you want to see the latest discoveries
00:20:18.01 in how to make this process work better.
00:20:22.23 So now I'm going to go through a panel,
00:20:24.21 a gallery of different objects built using this method
00:20:27.17 by our laboratory to give you a flavor of the generality of the method. S
00:20:31.20 o the example on the top is what I just showed you,
00:20:33.28 we call it the "Monolith", it was built by Shawn Douglas.
00:20:37.08 You might say that it looks a little bit like a nanoscale crystal,
00:20:40.23 honeycomb array crystal,
00:20:43.06 but it's important to keep in mind that every element
00:20:45.23 of the object is associated with a unique sequence
00:20:48.25 and therefore is independently addressable.
00:20:51.24 This is quite different from most nanoparticles
00:20:53.22 that we see in synthetic nanotechnology today.
00:20:58.27 The example on the bottom was built by Franziska Graf,
00:21:01.15 we call it the "Genie Bottle".
00:21:02.29 We called it that because one version,
00:21:04.29 not shown here,
00:21:06.15 we only folded part of the M13 scaffold
00:21:08.15 and the rest of it was coming out of the lip
00:21:10.00 of the object kind of like wisps of smoke.
00:21:13.13 These are all 20 nm scale bars.
00:21:18.10 So here again, 20 nm scale bars,
00:21:20.20 on the left is an object built by Shawn Douglas,
00:21:22.26 we call it the "Square Nut".
00:21:24.28 It has a 7 nm hole in the middle,
00:21:27.23 it has a front end and a back end,
00:21:30.16 and if we make the sticky ends on the front end compatible
00:21:32.14 with the sticky ends on the back end,
00:21:34.07 then we can self-assemble filaments
00:21:36.24 that are somewhat reminiscent of
00:21:39.05 actin filaments and microtubules,
00:21:40.29 although in this case they don't yet demonstrate any dynamics.
00:21:43.23 They're just equilibrium formation of these long polymers.
00:21:48.07 On the right is an object built by Tim Liedl,
00:21:50.12 we call it the "Railed Bridge".
00:21:52.22 Again, every cylinder is one double helix,
00:21:54.22 and we can see as we go through cross-sections of the object
00:21:57.08 we have a different arrangement of double helices,
00:21:59.22 and we can understand from this example
00:22:02.20 that it is kind of analogous to sculpture.
00:22:06.02 That you could imagine the sculptor begins
00:22:08.01 with a solid block of marble,
00:22:10.02 in our case these parallel arrays of double helices,
00:22:12.28 and in design space we're chipping away
00:22:14.20 at that solid block to achieve whatever 3-dimensional structure
00:22:17.25 we actually want in relief.
00:22:20.15 Once we have our final design,
00:22:22.01 then what we're doing is we're compiling that 3-dimensional structure
00:22:26.07 into a series of DNA strands
00:22:28.12 that are going to self-assemble with the M13 scaffold
00:22:30.27 into that object.
00:22:36.00 Here's an object built by Björn Högberg
00:22:38.05 when he was in the group,
00:22:39.11 we called it the "Slotted Cross",
00:22:41.03 I'll have more to say about this object in the next slide.
00:22:43.20 This is another crossed object we called
00:22:45.24 the "Stacked Cross", built by Hendrik Dietz.
00:22:48.24 Again, these are all 20 nm scale bars.
00:22:50.19 This one looks a little bit like stacked molecular celery.
00:22:54.20 We even designed a little molecular cavity on the top
00:22:57.17 where we initially imagined we could host protein guests
00:23:00.00 on the inside of that cavity.
00:23:02.21 So let's take a closer look at that "Slotted Cross"
00:23:05.08 from Björn Högberg.
00:23:07.05 So here what he's done is he's generated an animation
00:23:10.25 where he's stylized the routing of the scaffold strand
00:23:13.25 through the structure.
00:23:15.18 It's designed as an "H-domain" and an "O-domain",
00:23:20.14 and the middle of the H-domain is designed
00:23:22.13 to pass through the middle of the O-domain
00:23:24.14 and it's all folding from just one long M13 scaffold.
00:23:28.24 I was quite amazed that this folded at all,
00:23:32.01 but the yields are not so great at the moment,
00:23:34.09 just a few percent.
00:23:38.25 So now what I'm doing is I'm zooming in
00:23:40.28 on what we call the strand diagram
00:23:42.22 that describes the blueprint of the object.
00:23:45.04 It's like we take all the helices
00:23:46.22 and then we splay them out
00:23:48.05 onto a 2-dimensional surface.
00:23:50.13 And in this case the blue represents that M13 scaffold strand
00:23:54.19 and those colored strands represent the staple strands.
00:23:57.17 And this part of the object is the upper left-hand corner
00:24:01.04 of the H-domain.
00:24:03.03 And so if you look closely you can see that the staple strands,
00:24:06.00 what they're doing is they're binding to part of the scaffold strand
00:24:08.12 and then they're crossing over to a different part of the scaffold strand
00:24:11.15 to pull those components together
00:24:13.23 to make the 3-dimesional shape.
00:24:16.19 We can zoom out,
00:24:18.07 and then here you can get an appreciation
00:24:20.26 that it is kind of like a blueprint.
00:24:22.19 You can make out which part is the H-domain
00:24:25.03 , which part is the O-domain,
00:24:27.02 and if you look closely you can actually see
00:24:29.15 where the H-domain and O-domain are being connected
00:24:31.16 by that long scaffold strand.
00:24:38.08 All of the examples that I've shown you so far
00:24:40.07 have been built using this honeycomb lattice paradigm
00:24:45.10 where we're using these corrugated sheets.
00:24:47.29 It turns out that it more naturally fits
00:24:50.07 the preferred twist of DNA
00:24:53.01 at 10.5 basepairs per turn,
00:24:55.13 but it turns out we can also self-assemble these
00:24:57.26 DNA sheets in a square lattice format.
00:25:03.10 The only proviso is that now we have to
00:25:05.29 underwind the DNA to 10.67 basepairs per turn,
00:25:10.05 which is slightly disfavored.
00:25:12.06 And, quite interesting,
00:25:13.22 what happens is the structure will still form,
00:25:15.27 but it then compensates
00:25:17.22 by having a global supertwist
00:25:20.11 in the right-handed direction.
00:25:21.26 So it's quite analogous to how plasmid DNA, for example,
00:25:25.15 will have a right-handed supertwist
00:25:27.11 when it's underwound, as we find in most cells.
00:25:34.13 One very important development in the field
00:25:37.24 is software with a graphical user interface
00:25:39.26 to make it accessible to people who are outside the field,
00:25:42.13 but also just to make the process faster,
00:25:45.09 more robust and convenient,
00:25:47.01 for experienced practitioners.
00:25:49.12 So for this really powerful software suite
00:25:52.06 called "cadnano",
00:25:54.05 we owe our thanks to Shawn Douglas,
00:25:55.22 he developed this software when he was a graduate student in my group,
00:25:58.18 now he's an assistant professor at UCSF,
00:26:01.24 at the time of this filming.
00:26:03.14 So I encourage you to check out the software,
00:26:06.01 he's continually improving it, at cadnano.org.
00:26:09.10 And what we can do, now again with the graphical user interface,
00:26:12.21 within an hour or so,
00:26:14.03 we can design different shapes
00:26:15.21 and then compile that into the sequence of DNA strands
00:26:18.20 that can self-assemble into that object.
00:26:25.07 What if you wanted to build larger structures?
00:26:28.08 Well, the most obvious idea is to
00:26:30.13 just get more parts.
00:26:32.00 So you can remember as a kid,
00:26:34.15 the first time you got a Lego set it was enthralling,
00:26:37.19 but then about two hours later,
00:26:39.07 you now were hungry for additional Lego pieces.
00:26:42.10 So that's the big drive for our field:
00:26:44.07 can we get more Lego pieces into the structure?
00:26:46.21 But in the meantime we can do other things
00:26:48.19 that will allow us to get a little bit bigger.
00:26:50.16 So one example is just to build with wireframes,
00:26:52.21 that have high strength-to-weight.
00:26:54.24 So in this example what we've done
00:26:56.10 is we've added staple strands that fold that M13 scaffold
00:26:59.18 into this wireframe structure.
00:27:03.09 Each one of these struts in this example
00:27:04.28 is a 6-helix DNA nanotube,
00:27:07.12 and then we designed sticky ends such that they're compatible,
00:27:10.21 and we can get this structure,
00:27:12.27 this Z-shaped structure,
00:27:14.17 to fold into a double triangle,
00:27:17.09 with now 10 of these 6-helix bundle termini,
00:27:20.17 each with a unique set of sticky ends.
00:27:23.15 In this example, what we did is we programmed
00:27:25.13 3 separate double triangles to form
00:27:27.11 in 3 separate test tubes,
00:27:29.15 and we programmed it to form this network on the bottom.
00:27:32.22 This is a Schlegel diagram,
00:27:34.08 and for those of you who might recognize this,
00:27:36.18 you might see that this is actually a Schlegel diagram
00:27:38.23 for a wireframe icosahedron.
00:27:41.25 This object has an overall diameter of about 100 nm,
00:27:45.18 each one of the struts has a length of about 45 nm.
00:27:49.25 And here on the lower left-hand corner
00:27:51.19 we can see an animation, macroscale animation, reenactment,
00:27:55.05 of the self-assembly of these double triangles
00:27:57.13 into a wireframe icosahedron.
00:28:07.24 What we find is that this process works in the test tube as well -
00:28:09.27 no hands required.
00:28:12.01 So again, what we do is we fold each of the double triangles
00:28:14.12 in three separate test tubes,
00:28:16.02 we then mix them together to form the desired wireframe object.
00:28:21.01 So let's take a look using electron microscopy.
00:28:23.18 So here we see with a 1 micron (um) scale bar,
00:28:26.01 we see a bunch of objects
00:28:28.06 that seem to have about the right size, about 100 nm in diameter.
00:28:31.07 There's aggregates as well,
00:28:32.19 so the self-assembly's not perfect,
00:28:35.14 but we're glass-half-full kind of folk,
00:28:38.03 we're encouraged by something that works
00:28:40.05 even partially.
00:28:42.04 So now we've zoomed in, you have a 500 nm scale bar,
00:28:45.00 and we can tell that there's some kind of wireframe action going on.
00:28:48.11 Zoom in some more,
00:28:49.23 now we have a 200 nm scale bar,
00:28:51.28 and it's starting to look like the wireframe structure
00:28:55.00 that we imagined.
00:28:56.27 Of course you have some mis-assemblies as well.
00:28:59.26 And then now if we go to the highest effective magnification
00:29:01.23 for this negative stain method,
00:29:03.17 100 nm scale bar,
00:29:05.11 we can see the objects, in fact,
00:29:06.29 they look like they have lots of these triangular faces,
00:29:09.18 they look like they have 5-fold vertices.
00:29:14.14 And we're able to make an object that now is
00:29:17.09 something like five times the mass of a ribosome,
00:29:20.17 it has overall dimensions the size of a medium size virus.
00:29:24.16 And this is all just powered
00:29:25.18 by Watson-Crick base pairing:
00:29:27.05 A pairs with T,
00:29:28.12 C pairs with G.
00:29:30.12 It's remarkable that we can push it this far,
00:29:32.25 ~but we're greedy and we dream about
00:29:34.13 being able to extend this to objects
00:29:36.17 that are a thousand times more complex
00:29:39.09 or even more than that some day.
00:29:43.26 Another kind of wireframe structure
00:29:45.25 from macroscale engineering
00:29:47.15 that inspired us are these floating compression sculptures
00:29:50.06 from the artist Ken Snelson.
00:29:53.10 And the idea here for these sculptures
00:29:55.11 is that you have these beams,
00:29:57.09 that are bearing compression,
00:29:58.14 that aren't touching each other directly,
00:30:00.09 but instead they're connected by cables
00:30:02.08 that are bearing tension.
00:30:03.20 And if you wire this up in the correct way,
00:30:06.15 then it's a balance between the
00:30:08.00 tension of the cables
00:30:09.20 and the compression of the beams,
00:30:11.00 and you end up with an object that has
00:30:12.03 high strength-to-weight
00:30:13.17 and has other interesting features.
00:30:15.09 For example, if those cables have some elasticity,
00:30:17.21 then if you put a global force on the object,
00:30:19.20 then it will deform,
00:30:21.17 and every individual strut will rearrange in 3-dimensional space.
00:30:26.05 When you now relieve that stress,
00:30:27.26 then it'll bounce back to the original shape.
00:30:30.05 So we wanted to see if we could implement this
00:30:32.07 using DNA Origami.
00:30:34.24 This is work that was led by Tim Liedl and Björn Högberg
00:30:37.22 when they were in the group,
00:30:38.16 in collaboration with Don Ingber.
00:30:40.26 So what they did was
00:30:42.09 to design the staple strands to fold this M13 scaffold
00:30:46.17 into 3 different struts,
00:30:49.11 each of the struts in this case is 13 helices.
00:30:52.05 It's actually grabbing 3 separate segments
00:30:54.11 of that scaffold in order to make each one
00:30:56.21 of those 13-helix struts.
00:30:59.28 So we again add everything together,
00:31:01.06 heat it up, cool it down,
00:31:03.00 and remarkably enough you can form structures
00:31:05.16 like this in the test tube.
00:31:07.27 In fact, we started to play games about
00:31:09.21 looking at how much stress we could put the objects under
00:31:13.05 and have them still fold.
00:31:14.19 So what happens is that you have these single-stranded
00:31:16.16 DNA elements that are acting like entropic coils
00:31:19.08 - they're exerting tension.
00:31:21.06 And if we simply design those cables to be shorter,
00:31:25.02 have fewer number of bases,
00:31:26.28 then it's going to exert a larger force
00:31:30.01 over the same design distance between the two compressed elements.
00:31:34.18 And what we found by continually shortening these cables
00:31:38.15 is that we could self-assemble the structures
00:31:40.18 up to about 14 piconewtons of force,
00:31:43.09 that was the calculated force for the shortest cable
00:31:45.20 that were able to self-assemble the objects.
00:31:48.25 In other words, we're able to self-assemble these DNA objects
00:31:52.02 against twice the force that can be generated by
00:31:55.14 powerful cytoskeletal motors such as kinesin or myosin.
00:31:59.09 This is all powered by just DNA base pairing.
00:32:03.17 We believe that these kind of structures may prove
00:32:07.26 useful for applications in tissue engineering
00:32:10.01 and regenerative medicine.
00:32:11.23 So of course cell biologists have noted for a while now
00:32:15.11 that cells, especially going through development,
00:32:17.29 can communicate with their outside environment,
00:32:20.28 with each other, using mechanics.
00:32:22.26 So they might pull on the extracellular matrix
00:32:25.00 and that extracellular matrix may pull back,
00:32:28.10 and you might have,
00:32:29.29 by introducing deformations into the extracellular matrix
00:32:33.03 or within the cytoskeleton of the cell,
00:32:35.25 you can trigger biochemical events.
00:32:37.29 So we envision a day where we can
00:32:39.11 use these kind of DNA nanostructures
00:32:41.15 that can deform in response to some kind of mechanical stress
00:32:45.10 and then translate into a biochemical event,
00:32:47.05 it could be release of a growth factor,
00:32:49.27 or maybe it could involve catalysis of some kind of chemical reaction.
00:32:54.14 So we believe that this could be useful for regenerative medicine.
00:33:00.02 So the last thing that I'd like to show you for this section
00:33:03.04 is work from Hendrik Dietz,
00:33:05.13 Shawn Douglas assisted on this work.
00:33:07.18 Everything that I've shown you thus far has involved double helices
00:33:10.18 that are straight.
00:33:12.14 And Hendrik wanted to ask the question,
00:33:14.07 "Could you build structures, curved structures,
00:33:16.24 where the helices now are following an arc,
00:33:18.28 instead of going straight?"
00:33:21.02 And the basic strategy for implementing this is as follows:
00:33:24.04 so here we have, again every cylinder represents a double helix,
00:33:27.21 these planes that are slicing through the double helices
00:33:31.06 represent the positions at which those crossovers are occurring.
00:33:34.14 So it turns out in this example
00:33:36.01 they're only occurring every 7 basepairs.
00:33:40.21 And he asked the question, well,
00:33:42.09 what would happen if he replaced
00:33:44.09 the double helical segments on the top, so the orange segments,
00:33:48.21 with shorter double helices
00:33:50.14 that only have 6 base pairs between planes.
00:33:53.23 And what if he replaced the helices on the bottom,
00:33:56.08 the blue ones,
00:33:57.25 instead of 7 basepair segments,
00:33:59.21 he had 8 basepair segments.
00:34:01.25 So mechanically, now on the top,
00:34:04.06 those elements are going to be under tension
00:34:07.09 because you have less material in the same amount of space,
00:34:09.15 they're going to be stretched out.
00:34:12.08 The helices are the bottom are going to be under compression,
00:34:14.29 because we now just stuffed more material
00:34:17.09 into the same amount of space.
00:34:19.24 And the system is under stress
00:34:21.07 and so it's going to relax, of course, by bending.
00:34:24.26 So this is the way to relieve that tension on the top
00:34:27.10 and compression on the bottom.
00:34:29.08 Does this actually work
00:34:30.06 when we attempt this in the test tube?
00:34:32.08 And the answer is yes.
00:34:34.08 So Hendrik implemented this,
00:34:36.02 using an 18 helix DNA bundle
00:34:39.13 that's illustrated on the upper left-hand side.
00:34:43.20 And so what he did was he had a stereotyped straight region,
00:34:46.18 these white regions,
00:34:48.09 and then he had an experimental region
00:34:50.19 that's indicated here in red.
00:34:52.24 So that's where he's going to be introducing
00:34:54.13 those longer and shorter elements
00:34:56.10 to induce the bending of the structure.
00:34:59.12 You can see for the control you get this nice rigid straight object.
00:35:04.18 So what happens when he introduces
00:35:06.17 some small number of shorter strands
00:35:08.21 in the double helices on top,
00:35:10.12 and then longer ones on the bottom.
00:35:11.27 He could get a reliably predicted
00:35:13.21 30 degree arc at that position.
00:35:16.27 If he has roughly twice the number of perturbations,
00:35:19.20 then you can get to a 60 degree angle.
00:35:24.14 Kept on going, you get 90 degree,
00:35:26.00 you can get a 120 degree angle,
00:35:27.19 that's quite remarkable.
00:35:30.03 This is now getting down to a 10 nm radius of curvature.
00:35:32.29 But then he kept on going,
00:35:35.07 and he found he could go all the way
00:35:36.21 to 180 degrees in this example.
00:35:39.29 So this is something that has a 6 nm radius of curvature
00:35:42.20 , it's comparable to the tightness of wrapping of
00:35:45.15 DNA double helices around histones in a nucleosome.
00:35:49.12 So in that case that's powered by protein-DNA interactions,
00:35:51.27 in our case this is powered by
00:35:54.04 DNA base pairing interactions.
00:35:56.22 So here what we have is an animation prepared by Shawn
00:35:59.28 that explains the bending principle.
00:36:02.20 So again, what we're doing is
00:36:03.28 we're introducing more basepairs,
00:36:08.00 or long double helices on the left,
00:36:10.05 and then shorter ones on the right.
00:36:14.05 And you can see a little graph on the lower left-hand side
00:36:16.15 that tells us how many basepairs per turn
00:36:18.13 that we have
00:36:20.05 for each of these different elements.
00:36:21.24 And at the most extreme example we're actually
00:36:23.17 getting 15 basepairs per turn on the left,
00:36:26.21 which is severely underwound,
00:36:29.02 and only six basepairs per turn on the right,
00:36:31.11 which is severely overwound.
00:36:33.19 And I was quite flabbergasted
00:36:35.27 that it should be possible for us to
00:36:37.26 torture DNA to this extent.
00:36:40.10 Now in fact once you get to those extremes,
00:36:41.27 our folding yields do start to go down,
00:36:44.05 so we can see that we're at the edge
00:36:46.02 of what we can do to DNA,
00:36:47.29 but still it's quite remarkable that DNA is so robust,
00:36:52.14 that the 10.5 basepairs per turn
00:36:54.26 is simply what it prefers to do,
00:36:56.11 but if you put enough stress on it,
00:36:58.01 you can make it do things that deviate
00:36:59.29 from that ideal by quite a bit.
00:37:06.20 So Hendrik and Shawn
00:37:08.14 now used the method to make a variety of different structures.
00:37:11.09 So on the upper left-hand corner
00:37:13.10 we have a 6-helix DNA bundle
00:37:15.03 that's folded into a series of
00:37:18.18 180 degree arcs of increasing radii of curvature,
00:37:21.27 so you make a spiral.
00:37:24.22 On the lower left-hand corner we have an object
00:37:27.02 that's programmed to self-assemble
00:37:29.07 into a beach ball, out of 6-helix bundles.
00:37:34.01 You can see objects that are making concave triangles,
00:37:37.19 this is designed by Shawn Douglas.
00:37:39.22 And then here we have those
00:37:41.19 120 degree arcs that are repurposed,
00:37:44.13 so we made sticky ends on the two ends
00:37:46.15 of this little boomerang to be complementary,
00:37:49.11 so that you can have three identical versions of them
00:37:51.27 will come together to make a larger triangle.
00:37:56.07 So in conclusion,
00:37:57.10 hopefully I've persuaded you that DNA Origami
00:38:00.09 is a highly versatile method
00:38:01.28 for building both 2-dimensional
00:38:03.19 and 3-dimensional structures of
00:38:06.06 quite remarkable complexity,
00:38:07.25 about twice the mass of a ribosome.
00:38:10.22 Where we're moving to next is to try to build structures
00:38:13.13 that are more complicated.
00:38:14.26 You might wonder what's preventing us from building
00:38:16.20 something 1000 times larger already, today.
00:38:19.12 And the main problem is that
00:38:20.21 we have errors in the self-assembly.
00:38:24.00 For example, for one of these objects,
00:38:26.09 we might have a yield, in the best cases,
00:38:29.06 75% of so,
00:38:30.22 which might sound pretty good.
00:38:32.11 But now if you wanted to build an object that's 1000 times bigger,
00:38:36.00 then you might argue that the probability,
00:38:38.02 if you just mixed these things together,
00:38:39.21 1000 of them together,
00:38:40.27 the probability that none of the 1000
00:38:43.13 would have any defect,
00:38:44.28 would be 0.75^1000,
00:38:47.08 which if you do the math, that's basically zero.
00:38:50.00 So there's a lot of activity in the field
00:38:52.12 trying to improve the fidelity of this self-assembly,
00:38:55.26 other methods like hierarchical self-assembly,
00:38:57.28 error correction,
00:38:59.08 that'll allow us to scale amount complexity
00:39:01.16 and build really very complicated objects of the future.
- What are advantages and disadvantages of DNA origami vs. DNA tiles?
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,
00:08:51.27 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
00:10:07.06 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.
- Why does nucleation of DNA tile formation potentially dictate the success of complex structure formation?
- What are reasons that DNA brick crystals form more easily than scaffolded DNA origami crystals?
00:00:07.17 Welcome to the third part of this lecture
00:00:09.13 on structural DNA nanotechnology.
00:00:12.22 My colleague George Church said,
00:00:15.08 "The problem with your field is that it looks like
00:00:17.16 you're having too much fun."
00:00:20.00 And the reality is that learning how to build
00:00:23.13 with these structures is fun,
00:00:24.28 but we also firmly believe that the power behind it
00:00:28.07 also has to do with what kinds of applications
00:00:31.17 might be possible.
00:00:33.05 And in particular, we're very interested in applications
00:00:35.29 in molecular biophysics
00:00:37.26 and future therapeutics,
00:00:39.16 so I'm going to share with some of the research directions
00:00:42.04 in my laboratory trying to find useful applications
00:00:45.03 for these DNA nanostructures.
00:00:49.23 So the first application is...
00:00:52.01 we were able to create a tool that allows
00:00:55.04 the NMR structure determination
00:00:57.12 of an alpha helical membrane protein.
00:00:59.09 This is work that was done in collaboration
00:01:02.07 primarily with James Chou's group at Harvard Medical School.
00:01:07.11 So the story starts, again, with Ned Seeman,
00:01:09.18 the person who started the field
00:01:11.22 of structural DNA nanotechnology.
00:01:13.21 You might recall from the first part
00:01:15.18 that we had this vision of flying fish,
00:01:18.01 of the host-guest crystal,
00:01:20.04 and that maybe this would make structural biology
00:01:22.14 much easier if we had access to these host-guest crystals.
00:01:26.10 Ned Seeman's group made an important landmark discovery
00:01:29.20 along the road to this eventual goal.
00:01:32.11 In 2009, they reported this nice paper
00:01:34.17 where they rationally designed a unit cell
00:01:37.19 -- they called it a tensegrity triangle --
00:01:40.05 that basically has three crisscrossing double helices
00:01:43.17 that define three different axes in 3-dimensional space.
00:01:46.11 And if they program with sticky ends to self-assemble,
00:01:49.06 then they could actually make a macroscale crystal
00:01:52.08 that has dimensions of about 0.2 mm per side
00:01:56.06 and diffracted X-rays down to 4 Ångstrom resolution.
00:02:00.08 So this is an important first step.
00:02:02.01 A next step will be to improve the resolution of the crystal,
00:02:05.08 and then another difficult step on top of that
00:02:07.22 will be to get the target proteins to dock into very ordered,
00:02:11.06 stereotyped positioned
00:02:12.23 within each unit cell.
00:02:14.23 So I think this is a fantastic goal for the field,
00:02:18.05 maybe because it's very hard.
00:02:20.06 I think it's important to have hard goals to reach for,
00:02:23.00 but it may still take a while before we have a workable object
00:02:26.11 that will actually help us with 3-dimensional protein crystallization.
00:02:31.28 In the meantime what James Chou's group and my group have been able to do
00:02:35.28 is to do something that's technologically much more modest,
00:02:39.29 and yet it achieves the same end goal of
00:02:42.05 enabling atomic resolution protein structure determination.
00:02:45.24 And this is a method that's know as weak ordering.
00:02:49.04 It's something that I'll explain in a little bit,
00:02:51.03 something that's been known about for a number of decades now,
00:02:54.13 but hasn't really been well applicable
00:02:56.28 to membrane proteins until recently,
00:02:59.09 we believe because of our tool.
00:03:02.05 So host-guest crystallization,
00:03:04.24 you can think of that as a very strong sort of ordering,
00:03:07.09 that you want to force the protein
00:03:09.07 into a very stereotyped translational position,
00:03:12.01 very stereotyped rotational orientation.
00:03:15.06 And if that becomes too messed up,
00:03:17.24 that's going to destroy the process of structure determination.
00:03:24.09 In contrast, with weak ordering what we're doing
00:03:26.13 is we're just barely trying to change the population
00:03:29.21 of solution tumbling molecules away from isotropic.
00:03:32.15 So what does that mean?
00:03:34.08 So in a nutshell, we have this animation to explain the concept.
00:03:37.24 So imagine that the little red dots floating around
00:03:39.27 represent our target protein of interest
00:03:42.06 that we're trying to solve the structure of.
00:03:44.27 And what we want to do is to introduce weak order
00:03:47.26 into those proteins by mixing it into a dilute liquid crystal
00:03:51.27 of these long molecular rods.
00:03:55.11 So we can zoom in here - the notion is that
00:03:57.12 -- this is supposed to represent a membrane,
00:03:59.17 this is the detergent micelle solubilizing the membrane protein --
00:04:02.27 and the idea is that most proteins are not perfectly spherical in their shape,
00:04:07.12 and they'll have a higher tendency to bump into these molecular trees
00:04:11.25 if their long axis is perpendicular
00:04:13.23 to the long axis of the molecular trees -
00:04:17.16 is perpendicular.
00:04:19.18 And so, therefore, if you could
00:04:21.12 now mix your protein with an aligned sample of molecular trees,
00:04:24.19 that should now provide a slight orientational bias
00:04:28.18 so that the proteins tend to spend a little bit longer
00:04:31.25 with their long axis pointing parallel to the aligning material,
00:04:36.01 than perpendicular.
00:04:38.05 So in this case, usually you want these long trees
00:04:41.14 to be about 1-2% weight:volume,
00:04:44.11 and furthermore for the NMR experiment,
00:04:47.07 you want them to have a magnetic susceptibility
00:04:49.16 so that if we put them in a magnetic field,
00:04:51.22 then they'll basically globally align.
00:04:55.03 And it turns out if you can now get your proteins to be partially aligned,
00:04:59.12 you can now extract out information
00:05:01.09 that otherwise would be invisible,
00:05:03.12 and that information can be very precise
00:05:05.03 and allow you to calculate the atomic resolution structure of the protein.
00:05:09.26 So I mentioned that this weak alignment method
00:05:11.19 has been around for a while,
00:05:13.15 unfortunately it's only been accessible to people
00:05:15.29 studying soluble proteins,
00:05:18.07 because the most popular alignment media
00:05:21.02 turns out to be a natural bacteriophage
00:05:23.06 related to the M13 bacteriophage.
00:05:26.11 And these work quite well for soluble proteins,
00:05:29.05 we can make large amounts of them in bacteria,
00:05:32.01 they have magnetic susceptibility,
00:05:34.04 they'll line up with each other, works great.
00:05:36.19 But the problem is that these bacteriophage
00:05:40.19 will denature in the detergents that you need to
00:05:42.16 solubilize membrane proteins,
00:05:44.21 and therefore we haven't been able to use the bacteriophage
00:05:47.15 to weakly align membrane proteins,
00:05:49.19 because they just fall apart.
00:05:51.21 That's where we came in.
00:05:53.10 We decided we wanted to build DNA nanostructures
00:05:57.03 that would be a shape mimetic of these filamentous bacteriophage,
00:06:02.16 but because they're self-assembled from DNA,
00:06:04.14 they should be impervious to denaturation by the detergents,
00:06:07.27 and therefore we should now make this method of weak alignment
00:06:10.28 available to detergent-solubilized membrane proteins.
00:06:18.28 On the upper left-hand panel what we have are
00:06:21.10 electron micrographs of our DNA nanotubes.
00:06:24.01 In this case, we designed M13s
00:06:26.20 to fold into a 6-helix DNA nanotube
00:06:28.29 that's about 400 nm long and about 7 nm in diameter.
00:06:32.16 We actually programmed two of them to come together
00:06:34.15 to make a structure that is almost a micron in length. I
00:06:38.15 t turns out if you can make the structures longer
00:06:41.11 then they'll do a better job as these molecular trees.
00:06:45.07 They'll actually line up more easily.
00:06:47.21 And what we find is that when we concentrate them
00:06:50.13 to about 2% weight:volume,
00:06:52.18 then in fact they do start lining up.
00:06:54.26 So this is an entropic phenomenon
00:06:57.01 that's behind liquid crystal formation.
00:06:59.17 And one signature of liquid crystal formation is birefringence,
00:07:03.13 so if we look at our DNA nanotubes under crossed polars,
00:07:07.11 then we see this beautiful birefringence pattern
00:07:09.18 which is indicating that we're forming the liquid crystal.
00:07:12.05 So when the sample is much more dilute you don't see anything,
00:07:14.24 but then now when the sample is concentrated
00:07:16.16 it forms a liquid crystalline phase
00:07:18.11 and then you can see this nice pattern.
00:07:20.25 But most importantly, now if we take a test protein of known structure,
00:07:24.23 so this is a transmembrane domain from the
00:07:27.16 -- zeta transmembrane domain from the T-cell receptor --
00:07:30.17 we know what the structure is and based on that known structure
00:07:33.04 what we can do is we can calculate what
00:07:36.02 the kind of magnetic response will be
00:07:38.05 for a partially aligned structure,
00:07:40.20 so these are called dipole-dipole couplings.
00:07:43.17 And then we can compare those predicted couplings
00:07:46.07 against the ones that we experimentally measure
00:07:49.28 when we now mix the protein with the dilute liquid crystal
00:07:53.27 in the magnetic field.
00:07:55.28 And then on the lower left-hand side
00:07:57.25 what we're doing is we're comparing
00:08:00.19 on the y-axis the predicted couplings,
00:08:03.21 and on the x-axis the observed couplings,
00:08:06.08 and what we get is a very nice correlation between what we predict
00:08:09.10 and what we observe,
00:08:10.17 with high signal-to-noise.
00:08:13.00 We published this result in 2007,
00:08:15.11 we were very excited about it because we knew that this meant that
00:08:18.12 we had a tool that really works.
00:08:19.28 That we could use this to solve the structure
00:08:22.08 of membrane proteins.
00:08:24.28 However, there's of course many different hurdles
00:08:28.21 that still have to be overcome in order to solve the structure,
00:08:30.28 but just to review,
00:08:32.10 what we want to do is have these molecular trees,
00:08:34.14 our DNA nanotubes,
00:08:36.06 that when you concentrate them to 2% weight:volume
00:08:37.19 they start lining up.
00:08:39.17 We put them in an external magnetic field,
00:08:41.06 so we get global lining up.
00:08:43.07 We mix that with our protein of interest,
00:08:44.26 the protein bounces off the rods,
00:08:46.19 and we get that weak alignment.
00:08:48.10 So we get that bias in the orientation of the population of molecules.
00:08:52.28 Introducing that bias allows us to measure these
00:08:55.00 dipole-dipole couplings that encode precise
00:08:57.29 structural information about the protein,
00:09:00.18 which we can then throw into a computer program that,
00:09:03.14 if it has enough data, it can calculate the atomic resolution structure,
00:09:07.13 at least of the backbone chain.
00:09:09.18 Currently, we are not able to use this method
00:09:12.07 to experimentally measure the configuration of the sidechains.
00:09:16.01 But even if you can just generate the atomic resolution backbone,
00:09:19.25 then there are several very nice algorithms
00:09:22.13 that will allow you to predict
00:09:23.20 how the sidechains are going to pack onto that backbone.
00:09:29.12 So Marcelo Berardi in James Chou's lab
00:09:33.13 was able to use our DNA nanotubes
00:09:35.26 in order to solve the structure of a protein from the UCP family.
00:09:40.22 So this is the uncoupler proteins
00:09:42.28 that exist in the inner mitochondrial membrane,
00:09:45.12 and they're known to have an activity of translocating protons
00:09:49.23 back into the mitochondrion.
00:09:53.25 UCP1 i the most famous member of this family,
00:09:57.13 it's present in brown fat, and what it's doing is it's
00:09:59.04 just leaking protons across that membrane
00:10:01.19 and that generates heat.
00:10:03.10 So it's a mechanism to generate heat in brown fat.
00:10:07.04 Marcelo solved the structure of UCP2,
00:10:09.21 it's a related family member that's thought to be involved
00:10:11.27 in energy source selection,
00:10:15.01 so whether fatty acids vs. amino acids vs. pyruvate
00:10:19.26 should be metabolized for energy, as a source of energy.
00:10:23.29 So he wanted to solve the structure,
00:10:25.16 he tried for a long time using crystallography,
00:10:27.18 using conventional NMR,
00:10:29.15 but wasn't having a lot of luck.
00:10:31.14 Of course, for any kind of structural biology problem,
00:10:33.07 you first need to solve the problem of
00:10:35.21 being able to overexpress your protein,
00:10:38.00 being able to fold it to high homogeneity.
00:10:40.25 That's always going to be difficult
00:10:42.12 and it took many years to do that,
00:10:44.06 but to make a long story short,
00:10:45.21 once he was able to generate a large amount of the protein
00:10:48.17 in a homogeneous state,
00:10:50.15 then he was able to mix it with our DNA nanotubes,
00:10:53.18 weakly align the protein,
00:10:55.13 use that in order to measure the dipole-dipole couplings,
00:10:58.25 and then use that to calculate the atomic resolution
00:11:01.11 backbone structure of the protein.
00:11:03.10 And so this is here the crystal structure of this protein,
00:11:06.04 it's a 6-transmembrane helix protein.
00:11:08.22 Looking at the structure doesn't immediately tell you
00:11:11.18 the mechanism of proton transport,
00:11:14.09 but now James Chou's group is very interested in
00:11:18.09 using this structure as a foundation
00:11:20.15 for further structure/function exploration of
00:11:23.09 what the mechanism might be.
00:11:24.26 So their current hypothesis is that
00:11:26.22 you actually have proton transported by these fatty acids
00:11:30.05 that are flipping through the membrane, so when it's neutral...
00:11:32.18 when it's protonated it can flip through the membrane,
00:11:35.00 but now when it's deprotonated on the other side it can't flip back,
00:11:37.16 so you're not actually going to get net proton transfer.
00:11:40.09 So the idea is that the ionized fatty acid
00:11:42.28 can now diffuse into the inside
00:11:45.20 of this 6-helix barrel
00:11:47.14 and then you have a hydrophilic environment on the inside of that
00:11:49.14 where the fatty acid can flip back,
00:11:51.11 and therefore this might provide a mechanism
00:11:53.17 for multiple turnover of transfer of protons
00:11:57.07 across the membrane by fatty acids.
00:12:02.09 So that was a very satisfying experiment
00:12:04.14 because we were able to demonstrate utility
00:12:06.21 to a very urgent need in structural biology,
00:12:10.18 and we're looking forward to further developments
00:12:13.16 in the technology to make it more general,
00:12:16.24 both for NMR experiments, also for cryo-EM,
00:12:20.07 maybe for X-ray crystallography as well.
00:12:22.18 So the next area of applications that I'd like to describe
00:12:26.17 are involving single molecule biophysics.
00:12:29.12 So this is work making rigid handles for optical tweezing experiments
00:12:33.25 that was done in the lab of Hendrik Dietz.
00:12:36.24 He actually initiated this experiment when he was
00:12:39.21 doing his postdoctoral training with my group at Harvard,
00:12:42.29 and now finally was able to carry the project to fruition.
00:12:47.11 It's going to be a great tool and he was kind enough
00:12:48.20 to include me on the author list.
00:12:51.29 So the basic idea is let's say that you want to
00:12:54.27 use your optical traps in order to monitor single molecule
00:12:58.27 conformational changes of your molecule of interest.
00:13:02.17 So let's say that it's a DNA hairpin
00:13:04.27 that is opening and closing, and you want to be able to observe this.
00:13:08.06 So ordinarily this could be kind of hard to watch,
00:13:11.11 but you also want to be able to watch this
00:13:13.14 as a function of the force that you're applying
00:13:15.13 on the ends of the hairpin.
00:13:17.08 So you want to be able to dial in greater and greater amounts of force
00:13:19.23 on my elbows to peel it away
00:13:21.26 and watch what the effect is of that force
00:13:24.27 on the binding and peeling and unpeeling kinetics
00:13:28.09 of this DNA hairpin.
00:13:30.28 So the way that you actually observe this
00:13:32.24 is you now want to place your molecule of interest
00:13:36.21 in between two large microspheres,
00:13:41.06 and it turns out for technical reason you can't have these two microspheres
00:13:44.16 too close together,
00:13:46.02 so you need to give you some space,
00:13:48.20 and in order to create that space,
00:13:50.08 people like to use these double-stranded tethers
00:13:52.29 that are on the order of 300 nm long.
00:13:56.03 And so what you do is you have your beads, your tethers,
00:13:59.03 and then your molecule of interest,
00:14:00.18 and then you start to pull the beads apart,
00:14:02.26 that generates a force on the molecule in the middle.
00:14:05.25 And so if we now pull the beads further and further apart
00:14:07.21 that generates a greater and greater force
00:14:09.05 on the molecule in the middle,
00:14:10.27 and in principle we can watch the opening and closing
00:14:13.12 of that molecule in the middle
00:14:15.26 by watching how the distance between the beads changes.
00:14:18.24 So for example, we can imagine that
00:14:21.20 if you have the molecule now opens up,
00:14:24.28 now the beads should move further apart.
00:14:27.15 If the molecule in the middle now snaps shut,
00:14:29.17 then those two large beads should move closer together.
00:14:32.13 And so by measuring the distance between the beads,
00:14:34.28 in principle we can infer the conformational state
00:14:37.14 of the molecule in the middle.
00:14:39.15 Alright, sounds good, so where's the problem?
00:14:42.14 The problem is that you have Brownian motion.
00:14:44.29 So these large microspheres are actually
00:14:47.09 undergoing a lot of motion,
00:14:49.04 and it can be difficult to tell what is
00:14:51.06 just random motion and what is reporting on
00:14:53.15 something that's actually opening up in the middle.
00:14:56.03 And the problem becomes especially bad
00:14:58.11 when you have a very low force,
00:15:00.01 because at low force these double-stranded tethers
00:15:03.06 are going to be very floppy
00:15:04.23 and you're going to get lots and lots of Brownian motion.
00:15:07.24 So for example, on the lower right-hand trace,
00:15:12.05 what we see is a time trace of the distance between the beads
00:15:16.18 for the example on the top in B,
00:15:19.17 where we're looking at a DNA hairpin
00:15:21.18 that presumably is opening and closing at some specific force.
00:15:26.20 And I'm not going to explain
00:15:28.24 what's going on in the lower left-hand corner,
00:15:30.24 I encourage you to check out the publication,
00:15:32.28 but suffice it to say,
00:15:34.23 just looking at this bottom trace in E,
00:15:36.25 you can't really tell when that hairpin is opening or closing.
00:15:40.14 You can just see a lot of noise.
00:15:44.17 In contrast, if we now replace those long tethers
00:15:47.25 with a DNA Origami bundle of helices,
00:15:51.00 it's going to be much, much more rigid,
00:15:53.02 so even at those lower forces,
00:15:55.01 the Brownian noise is going to be suppressed.
00:15:57.16 And so if we look at the same force,
00:15:59.12 at the opening and closing of this object in the middle,
00:16:02.07 so D represents with these DNA Origami handles,
00:16:05.28 now you can hopefully tell that the noise is greatly suppressed,
00:16:09.09 and we have some more confidence
00:16:10.27 about assigning when the hairpin
00:16:13.13 is opening and closing.
00:16:16.06 So we think that this is a very nice application
00:16:18.15 of these rigid DNA nanorod elements
00:16:22.17 that will be useful for force spectroscopy
00:16:25.13 looking at single molecule dynamics
00:16:27.13 and energetics of biomolecules.
00:16:32.01 So the next area of application
00:16:34.01 I like to call Breadboard Biochemistry,
00:16:36.08 the notion that we can constrain the position of
00:16:38.29 many different protein actors in a molecular play
00:16:42.13 in order to understand and tease out
00:16:44.07 their individual roles in the process
00:16:46.00 - how are they interacting with each other?
00:16:47.26 What are the stoichiometric requirements?
00:16:49.21 What are the geometric requirements of this process?
00:16:53.12 So the first example of this Breadboard Biochemistry
00:16:56.00 I'd like to discuss is work that was
00:16:58.04 led in the lab of Sam Reck-Peterson,
00:17:00.13 my colleague at Harvard Medical School.
00:17:02.10 It was done primarily by two students:
00:17:06.15 Nate Derr and Brian Goodman.
00:17:08.11 Nate is a student that was co-advised by myself and by Sam.
00:17:12.15 And here we were interested in studying
00:17:15.06 how ensembles of cytoskeletal or microtubule motors
00:17:18.18 could antagonize each other
00:17:20.16 in terms of determining direction of motion
00:17:23.11 on a microtubule.
00:17:25.04 And the motivation is that it's been observed
00:17:27.07 that vesicles often times
00:17:28.16 bear both kinesins and dyneins,
00:17:31.08 which of course move in opposite directions along a microtubule,
00:17:34.05 and yet within a cell one can often observe that
00:17:36.24 the vesicles will choose one direction to go and then,
00:17:39.23 remarkably, will often times switch directions,
00:17:41.25 but what they don't typically do is just to stall.
00:17:44.22 So how can we try to study this in a reduced system,
00:17:47.06 an in vitro system?
00:17:49.05 And as a first step, what we did was we self-assembled
00:17:51.20 a chassis that is a 12-helix DNA nanotube
00:17:55.02 that's about 200 nm in dimensions.
00:17:58.16 And what we did was we decorated this chassis
00:18:01.00 with single-stranded DNA handles
00:18:03.00 that would come at regular intervals,
00:18:04.26 at 7 different positions.
00:18:07.07 And we can control and have any sequence we want
00:18:08.28 come out at any one of these positions.
00:18:11.19 In this particular case, what we did was
00:18:13.07 we had two different sequences:
00:18:15.01 one sequence for capturing dynein,
00:18:17.04 and another sequence for capturing kinesins.
00:18:19.15 In this example, 2 dyneins and 5 kinesins.
00:18:22.03 And the way that we capture
00:18:23.18 is that we express the protein,
00:18:26.00 let's say dynein, with a SNAP-tag
00:18:28.02 and then the SNAP-tag is used
00:18:30.00 to capture an oligonucleotide with a SNAP-tag ligand,
00:18:33.12 and in that way we're able to generate a protein-DNA conjugate.
00:18:38.05 And we purposely choose the sequence of that conjugate
00:18:40.23 so that it will be complementary
00:18:42.18 with the single-stranded DNA sequence
00:18:44.13 that comes out of the DNA Origami.
00:18:47.05 So in this example, we're creating
00:18:48.21 a chain gang of 2 dyneins and 5 kinesins,
00:18:51.13 and we wanted to see what happens
00:18:53.15 when we put this on a microtubules
00:18:55.03 - which way is it going to go?
00:18:57.05 And what Nate and Brian found is that
00:18:59.21 they basically stalled:
00:19:01.14 there was an irreversible tug-of-war,
00:19:03.14 at least in this first study,
00:19:06.02 and furthermore they were able to demonstrate
00:19:08.00 if they introduced photocleavable elements
00:19:10.14 either to release the dynein dynamically,
00:19:13.07 or else to release the kinesins dynamically,
00:19:15.27 then they could resolve this tug-of-war.
00:19:18.05 So for example, in one experiment,
00:19:20.09 if they released the dyneins,
00:19:22.09 then now that stall would be relieved
00:19:24.09 and the chassis would move towards the
00:19:25.19 plus end of the microtubules.
00:19:27.24 Likewise, if they cause the kinesins to be cleaved off,
00:19:31.17 then now the complex would no longer stall,
00:19:33.23 and move towards the negative end of the microtubule.
00:19:37.17 So we think this is a promising experimental platform
00:19:41.06 for now further trying to find out:
00:19:43.21 what else do we need to add in order to recapitulate
00:19:46.08 the very interesting behavior we see inside of a cell,
00:19:48.23 where the vesicles, they don't just stall,
00:19:50.17 but in fact they can move in one direction or the other,
00:19:52.22 and even more interestingly, change in direction.
00:19:56.02 So here's another application of Breadboard Biochemistry
00:19:59.05 where we're trying to study SNARE-dependent membrane fusion.
00:20:02.10 So in this process,
00:20:04.19 we have cells that are trying to fuse the vesicles
00:20:07.24 with let's say the plasma membrane,
00:20:09.27 and it's known that there are these transmembrane proteins
00:20:12.04 that are mediating this called SNARE proteins.
00:20:14.16 They have one domain that's...
00:20:18.05 let's say this is the vesicle membrane,
00:20:19.24 they have a transmembrane and they have a cytoplasmic domain
00:20:22.10 that's a coiled-coil,
00:20:24.07 and then in the target membrane you have something analogous,
00:20:26.09 you have a transmembrane domain and then a complementary
00:20:28.15 coiled-coil domain.
00:20:30.02 There's two other helices that are involved as well,
00:20:33.02 and so you can actually zipper up to form a 4-helix bundle,
00:20:36.25 and that's thought to provide the energy
00:20:39.01 for driving a vesicle in close proximity
00:20:41.29 to the plasma membrane,
00:20:43.13 because otherwise that's an energetically unfavorable process.
00:20:47.16 And then once the two vesicle membranes
00:20:49.00 are brought close together,
00:20:50.19 then some other process happens
00:20:52.25 -- that's not very well understood --
00:20:54.14 causing the membranes to fuse.
00:20:57.08 And there's some outstanding biophysical questions about this process
00:21:01.10 that can be very simply articulated
00:21:02.29 but difficult to nail down very precisely.
00:21:06.04 For example, how many different SNARE proteins
00:21:08.21 does it take to trigger the fusion event?
00:21:11.13 And then, more subtly,
00:21:13.10 how does the geometry of these proteins
00:21:15.06 affect the kinetics of this process?
00:21:18.07 And there have been some different measurements
00:21:20.21 of the number of SNAREs required,
00:21:23.06 and depending on the context it seems
00:21:25.07 to vary between 1 and 10.
00:21:27.08 In our case, what we're interested in
00:21:29.00 is generating a more robust method
00:21:30.29 for measuring these stoichiometric requirements,
00:21:33.02 and therefore we think we can use our system
00:21:35.04 to study this problem at higher resolution.
00:21:39.20 So here's the idea:
00:21:40.26 imagine you have a supported bilayer with your t-SNAREs down there,
00:21:45.19 and you're trying to now down a vesicle on there,
00:21:50.14 but you want to only have a controlled number of t-SNAREs
00:21:53.15 that could possibly participate in the reaction.
00:21:56.29 So what if we were to create a molecular corral
00:21:59.17 where we had 3 and only 3 of the t-SNAREs
00:22:02.01 in the corral.
00:22:03.26 And now what we could ask,
00:22:05.11 "Well, is 3 SNAREs enough for membrane fusion?"
00:22:07.25 So the idea here is that we chemically synthesize,
00:22:11.18 we chemically link to the N-terminus of this SNARE protein,
00:22:14.23 this white oligonucleotide in one test tube,
00:22:17.23 and then in another test tube we self-assemble this
00:22:20.09 red DNA nanostructure.
00:22:22.14 And the red DNA nanostructure again is decorated with these
00:22:24.14 single-stranded DNA handles
00:22:26.18 that are complementary to the white anti-handles.
00:22:29.06 Now when we mix the two together,
00:22:30.26 we should get a controlled stoichiometry
00:22:33.09 of the greens and whites onto the red,
00:22:35.09 in this case three.
00:22:36.29 And now we can say,
00:22:38.09 "Well, what happens when a vesicle tries to dock into the molecular corral?
00:22:42.01 What happens when we have 3 SNAREs in the corral?
00:22:44.06 What happens when we have 2?
00:22:45.25 What happens when we have 1?"
00:22:47.12 So certainly if we have no SNAREs in the corral,
00:22:49.12 then you wouldn't expect anything about background,
00:22:51.05 and then as we start to increase the number of SNAREs in the corral,
00:22:54.08 hopefully the proteins now will be able to cooperate
00:22:56.18 to trigger the membrane fusion above background.
00:23:00.24 So I should mention that this is work that's still in progress.
00:23:04.12 It's a collaboration between our lab
00:23:06.15 and the lab of Jim Rotheman.
00:23:09.00 Chenxiang Lin was a postdoctoral fellow in the group,
00:23:11.11 he initiated the project
00:23:13.07 along with Weiming Xu in Jim's lab.
00:23:15.15 Now Chenxiang is an assistant professor at Yale,
00:23:17.21 and in our lab the project has been taken over
00:23:20.07 by Bhavik Nathwani at the time of this taping.
00:23:30.27 So again, what's thought to happen is that
00:23:32.20 you have engagement of the t-SNAREs and the v-SNAREs
00:23:36.15 and then that brings the membranes close together,
00:23:39.00 some magic happens,
00:23:40.16 membranes fuse.
00:23:42.12 And the details of that biophysical process are very interesting,
00:23:45.06 but we're starting off by asking a simpler questions
00:23:47.16 of just, how many SNAREs are involved and how does the geometry affect that?
00:23:51.28 So just to prove our ability to
00:23:53.20 decorate our DNA rings with different guests,
00:23:56.01 we started with gold nanoparticles
00:23:57.22 instead of SNARE proteins,
00:23:59.20 they're just easier to see in the electron microscope.
00:24:02.09 And we can demonstrate that we can now
00:24:04.18 decorate our DNA rings with 3 gold particles on the side,
00:24:07.18 3 on the outside,
00:24:09.07 6 on the inside,
00:24:11.06 4 on the inside,
00:24:12.28 8 on the inside.
00:24:15.06 And what we've observed so far is that we can get
00:24:17.20 something on the order of 90% occupancy of our sites.
00:24:21.13 We'd like to get higher than that,
00:24:22.29 something more like 99.99%,
00:24:25.12 it's something we're working on,
00:24:27.21 but currently we think even with a 90% occupancy rate,
00:24:30.14 this is still useful because it provides an upper bound
00:24:33.11 on the number of SNARE proteins, or whatever guest,
00:24:36.00 that could be bound in our assembly.
00:24:40.02 So here what we're doing is we're actually
00:24:41.06 binding SNARE proteins now, detergent-solubilized SNARE proteins
00:24:44.02 instead of gold nanoparticles.
00:24:46.14 And again, what we're doing is
00:24:48.29 we have these green SNARE proteins
00:24:51.00 conjugated to a white oligonucleotide
00:24:53.10 that are now self-assembling with this complementary red oligonucleotide
00:24:56.21 being displayed on the outside of this DNA nanostructure.
00:25:00.06 And in the electron microscope,
00:25:01.26 we can see enough contrast from the proteins
00:25:03.21 to count the number of guest molecules
00:25:05.22 on the DNA nanostructure.
00:25:08.18 And then what we do is we,
00:25:10.11 so in this case what we're trying to do is create liposomes
00:25:13.09 with controlled numbers of SNARE proteins
00:25:15.04 and see how they behave in a fusion assay.
00:25:18.13 And so the next step here is that we mix our DNA rings
00:25:22.00 with protein guest
00:25:23.28 with giant liposomes
00:25:25.23 in the presence of slight amounts of detergent,
00:25:28.14 and through some process we don't quite understand,
00:25:30.24 these DNA nanostructures with hydrophobic groups
00:25:34.20 somehow take a bite out of the giant liposomes
00:25:37.11 and end up with smaller liposomes
00:25:39.19 basically filling the interior of the ring.
00:25:43.18 So in this way, through a process we don't quite understand,
00:25:45.24 we're able to capture liposomes on the inside of our DNA nanorings,
00:25:49.25 with controlled numbers of SNARE proteins.
00:25:53.01 Then here's a fusion assay that we use,
00:25:55.18 it was developed by Erdem Karatekin at Yale.
00:25:58.21 It's a microfluidic assay where we're
00:26:01.11 fluorescently labeling these vesicles that we've captured
00:26:04.14 in the DNA ring
00:26:06.18 and then when we get fusion,
00:26:08.18 what will hopefully happen is that those dyes
00:26:10.25 will then diffuse out into the supported bilayer.
00:26:13.28 So initially what happens is the dyes
00:26:15.22 are quenching each other somewhat,
00:26:17.28 the first thing is actually the spot should grow brighter
00:26:20.16 as the dyes de-quench,
00:26:22.13 but then as the dyes diffuse away from each other,
00:26:24.11 then you should now get rapid
00:26:27.12 dilution of the fluorescence response.
00:26:30.03 And so we have here a microfluidic setup
00:26:32.04 where we have an Eppendorf tube
00:26:34.05 with our labeled vesicles
00:26:36.18 that are being pulled through this microfluidic chamber.
00:26:39.13 We're observing it using TIRF microscopy,
00:26:41.20 and then trying to see whether or not those vesicles
00:26:45.03 can fuse to the supported bilayer,
00:26:46.27 as a function of number the number of SNARE proteins.
00:26:49.15 So this is just an example of the assay in action.
00:26:53.18 Your eye might be drawn to this giant blob on the bottom,
00:26:56.16 but I'd like you to try to ignore that.
00:26:58.06 Instead, focus on this red circle here, where we can see
00:27:01.19 -- it's on a loop --
00:27:03.01 so we can see a vesicle docking and shortly after docking
00:27:05.24 then we get fusion.
00:27:07.12 So that's the kind of event that we're trying to score,
00:27:10.06 so far we're still in the process of trying to see
00:27:12.25 whether or not our scaffolded DNA rings
00:27:15.02 actually can increase the rate of liposome docking and fusion.
00:27:19.05 Hopefully we'll be able to make some progress on that
00:27:21.05 for the next lecture.
00:27:25.15 So the other class of applications
00:27:27.15 I'd like to discuss with you involve
00:27:29.20 potentially using DNA nanostructures
00:27:32.07 as therapeutic delivery devices.
00:27:34.19 And as a first step towards that,
00:27:36.25 Franziska Graf, who was a student co-advised
00:27:39.13 by myself and Don Ingber,
00:27:41.07 we set out to do some pilot studies
00:27:43.06 to look at how the shape of DNA nanostructures
00:27:45.18 might affect their uptake into a model cell line.
00:27:48.23 In this case, these are umbilical vein endothelial cells.
00:27:52.29 So how the shape of DNA Origami
00:27:54.16 affect their tastiness to these cells?
00:27:56.27 And if you think about it, you could come up with
00:27:58.05 a long list of potential descriptors
00:28:00.27 that might make a difference,
00:28:03.06 and it would require very a systematic study
00:28:07.01 to look through all of these,
00:28:08.16 but we just started out with a pilot study
00:28:10.04 looking at aspect ratio.
00:28:12.17 So in this case, we wanted to ask the question:
00:28:15.07 if we have bunch of particles of the same mass,
00:28:17.22 but then we made them in different shapes,
00:28:20.08 then what would be the relatively uptake?
00:28:22.13 So let's first look at these 3 blue shapes
00:28:24.18 that are highlighted in yellow.
00:28:26.13 So they're the same mass,
00:28:27.22 they're about 5 megaDaltons,
00:28:29.11 but they're made into a very long spindly rod that's a 6-helix bundle,
00:28:32.23 and then we have this 24-helix bundle,
00:28:35.22 and this is a 48-helix bundle,
00:28:37.14 that are successively more compact.
00:28:39.24 So if we predict that structures
00:28:41.17 with a longer aspect ratio
00:28:43.13 should get into cells better,
00:28:44.29 then we'd predict that this longer one
00:28:47.15 should do the best in terms of getting into the cell,
00:28:49.19 being taken up.
00:28:51.11 In contrast, if we hypothesize
00:28:53.11 that the more compact structure should get in better,
00:28:55.16 then we expect to observe the opposite behavior:
00:28:58.07 that this more compact structure
00:29:00.00 should get in more quickly.
00:29:01.22 And we also made some other shapes.
00:29:03.08 So here we made a wireframe octahedron,
00:29:06.13 we also made three different orange shapes that are analogues,
00:29:10.06 but now just 40% the mass.
00:29:12.25 And so we can say,
00:29:14.01 "Well, with this starting panel of eight structures,
00:29:16.06 what's the relative rate of uptake into these HUVEC cells?"
00:29:22.04 And what we observed
00:29:24.13 -- so I have a very busy slide here
00:29:27.05 but I'll try to give you the overview --
00:29:30.04 so first thing is if you kind of squint your eyes
00:29:33.22 you might notice that the bars,
00:29:35.21 which represent some kind of uptake,
00:29:37.19 tend to be taller on the right-hand side of the slide
00:29:41.09 than one the left-hand side of the slide.
00:29:44.15 And also, if you look at it for a little bit longer,
00:29:47.14 you might notice that the structures
00:29:49.15 that are on the right-hand side
00:29:51.11 tend to be more compact
00:29:53.03 than the structures on the left-hand side.
00:29:54.24 So just from a simple eyeballing of the figure,
00:29:57.10 we can get the feeling that,
00:29:59.06 at least for this class of particles,
00:30:01.01 when the structures are more compact,
00:30:03.10 then they get in more easily into cells.
00:30:06.25 So the second order piece of information that we learned is -
00:30:10.05 so you might notice here that there's hollow bars and solid bars,
00:30:12.03 so what does that mean?
00:30:13.27 So the hollow bar represents the amount of
00:30:16.22 fluorescently labeled nanostructures
00:30:18.21 that were taken up by the cell
00:30:20.25 after some incubation time, 16 hours,
00:30:24.04 and then the solid bar represents the same experiment,
00:30:27.19 but what we did was we treated the cells with DNase I
00:30:31.21 before we did the fluorescence analysis.
00:30:33.27 So what that's going to do is to remove
00:30:35.22 any membrane-bound DNA nanostructures
00:30:38.18 and now the assay will only report on those structures
00:30:41.05 that actually have been internalized,
00:30:43.07 that are now protected from DNase digestion.
00:30:46.04 And so what we observe is something quite interesting.
00:30:48.11 That for these compact structures,
00:30:50.01 the height of the bars is very similar,
00:30:52.03 so that says that most of the particles
00:30:53.27 that stick to the cells immediately go in,
00:30:58.03 or are getting in quite efficiently.
00:30:59.25 But for these extended structures,
00:31:02.00 you might notice that the hollow bar
00:31:03.26 is actually much taller than the solid bar,
00:31:06.13 and what that suggests is that if you're really extended,
00:31:09.02 now maybe you can resist getting internalized,
00:31:11.28 which we can rationalize by saying for this class of cells,
00:31:14.27 they're going to have a hard...
00:31:16.08 they don't really have good mechanisms
00:31:17.23 for engulfing structures that are 400 nm long.
00:31:22.00 So this might present an interesting opportunity therapeutically,
00:31:24.16 that we could design structures
00:31:26.19 that have the tendency to persist on the outside of the cell,
00:31:29.29 that can resist internalization.
00:31:32.08 It might be useful for creating
00:31:34.18 a sentinel or a beacon for other nanoparticles
00:31:37.01 to then deliver their contents to that particular cell,
00:31:39.18 because if you just get swallowed,
00:31:41.04 then you're not going to be able to act as that sentinel.
00:31:46.07 Here what Franziska has done is she's taken one of the structures,
00:31:49.06 this nanocylinder,
00:31:50.27 and she's decorated the outside of the nanocylinder
00:31:53.23 with a bunch of ligands, in this case
00:31:55.20 cyclic RGD ligands that are known to bind the
00:31:58.17 alpha V beta 3 integrin receptors
00:32:00.03 that are overexpressed on this cell line.
00:32:02.18 So the prediction is that if we decorate our nanoparticles
00:32:06.04 with a high density of these ligands,
00:32:08.08 then we're going to increase the rate of internalization.
00:32:12.06 And then we can also do a control with cyclic RAD peptides
00:32:15.23 that should not interact with those receptors.
00:32:18.25 And in fact that's basically what we observe,
00:32:22.01 that when we have the cyclic RGD-labeled structures
00:32:26.11 we get an order of magnitude greater uptake of these particles
00:32:31.07 compared to controls that had no peptide
00:32:33.29 or controls that have the mock peptide sequence cyclic RAD.
00:32:39.14 And so what this says is that we have the ability
00:32:42.05 to make these particles of different shapes
00:32:44.02 to either help their uptake
00:32:46.07 or help prevent their uptake.
00:32:47.28 We can decorate these particles with a high density, controlled density,
00:32:51.28 of ligands to help further modulate that process,
00:32:54.21 either get faster uptake or not.
00:33:01.12 So far I've shown you naked DNA particles.
00:33:06.20 Again, we might be worried about things
00:33:08.18 like nuclease digestion of these particles,
00:33:11.10 we might have a desire to encapsulate
00:33:13.21 soluble factors in these structures.
00:33:15.24 So if we try to make a wireframe cage
00:33:18.18 and then we put soluble factors on the inside of the cage,
00:33:20.27 then those soluble factors
00:33:23.11 might just diffuse out through those windows.
00:33:25.08 So how do we keep those factors on the inside?
00:33:27.19 So Steve Perrault in the group
00:33:29.08 has pioneered a method for encapsulating
00:33:31.19 these DNA nanostructures within liposomes
00:33:34.11 to create something that's structurally analogous
00:33:36.15 to an enveloped virus.
00:33:38.14 So the way that he's done this is
00:33:40.06 he again self-assembles a DNA octahedron
00:33:42.19 with single-stranded DNA handles coming,
00:33:45.14 and then he hybridizes on a complementary oligonucleotide anti-handle
00:33:49.26 that has a lipid conjugate covalently linked to it,
00:33:52.14 solubilized by detergent.
00:33:54.18 And so through base pairing interaction,
00:33:56.07 he basically gets these detergent solubilized lipids
00:33:59.28 to cover his DNA octahedron,
00:34:02.18 he has something like on the order of 50 copies of these lipids
00:34:05.09 covering his octahedron,
00:34:07.18 solubilized by detergent.
00:34:09.27 The next step is that he mixes this...
00:34:11.19 he dilutes this into a solution of giant liposomes
00:34:14.04 and then dialyzes out the remaining detergent.
00:34:17.07 Through a process that we still don't quite understand,
00:34:19.23 we get shrink-wrapping of the liposomes
00:34:21.28 around our DNA nanostructures,
00:34:24.03 creating something, again, that resembles
00:34:25.20 under the transmission electron microscope,
00:34:28.05 envelope viruses.
00:34:29.18 So here we can see on the left the naked DNA octahedra,
00:34:33.09 they're about 50 nm in diameter,
00:34:36.11 and then on the right
00:34:38.07 we can see the liposome-encapsulated DNA octahedra.
00:34:42.25 Again, very reminiscent of an envelope virus.
00:34:46.04 So we think this is an important step
00:34:48.09 towards the versatile use of DNA nanostructures
00:34:51.19 for delivery, for example, of soluble factors,
00:34:54.18 or if we wanted to simply protect the DNA from nucleases
00:34:58.16 or factors that would try to digest it.
00:35:00.21 We of course still want to be able to
00:35:03.02 decorate the surface of the structures
00:35:05.02 with functionalities,
00:35:06.27 so now we're working on the ability
00:35:08.13 to present transmembrane features,
00:35:10.16 starting from the inside,
00:35:12.25 going through the membrane,
00:35:14.13 and then basically controlling the spatial orientation of ligands
00:35:17.04 through the puppet master on the inside.
00:35:21.27 And then the final thing that I'd like to show you
00:35:23.24 is actually not from my laboratory,
00:35:26.03 it's from Shawn Douglas,
00:35:28.09 when he was a postdoctoral fellow in George Church's group,
00:35:31.05 and he did this fascinating...
00:35:33.04 along with Ido Bachelet,
00:35:34.19 they did this fascinating pilot study
00:35:36.16 where they generated something they called
00:35:37.25 a DNA Origami nanorobot,
00:35:40.19 that was designed, at least in this test tube example,
00:35:44.08 to specifically recognize cancerous lymphocytes and kill those off
00:35:50.00 -- program them to commit suicide --
00:35:52.10 while leaving the healthy cells alone.
00:35:54.12 So how is this supposed to work?
00:35:55.28 The idea is that Shawn designed this DNA barrel
00:35:58.25 that's about 60 nm in diameter,
00:36:01.28 and he placed on the inside
00:36:04.05 antibodies that are known to bind to receptors
00:36:07.24 and cause receptor clustering
00:36:09.13 that then leads to apoptosis.
00:36:13.04 So kind of like what a natural killer cell might do.
00:36:15.21 But in this case, because the antibodies are sequestered
00:36:17.22 on the inside of the barrel,
00:36:19.06 they're not actually accessible to the cells.
00:36:21.26 The cells have fat fingers, if you will,
00:36:23.21 so they can't actually reach in
00:36:26.02 and touch those antibodies.
00:36:28.08 And the notion is that Shawn and Ido
00:36:29.27 wanted to program this robot
00:36:32.00 to open up when it encountered the cancerous cell
00:36:34.27 but not when it encountered the healthy cell.
00:36:37.23 And if the robot were to open up,
00:36:39.22 then now the antibodies that trigger apoptosis
00:36:42.17 could now be accessible to the cell surface.
00:36:45.12 So how do they get this robot to open up
00:36:47.00 only in the presence of the cancerous cell
00:36:49.01 but not in the healthy cell?
00:36:50.22 So what they did was they designed
00:36:52.08 this lock-and-key mechanism
00:36:54.03 where they had single-stranded DNA
00:36:56.13 on the two ends that would hybridize together,
00:36:59.20 and they designed this sequence
00:37:01.05 with something called a structure-specific aptamer.
00:37:05.15 And what happens is this aptamer
00:37:08.18 recognizes some protein ligand,
00:37:12.02 and so basically that protein ligand
00:37:13.22 is competing for interaction between the partner strand,
00:37:19.11 so in other words these two strands will interact with each other,
00:37:23.04 but that can be competed off by some specific protein ligand.
00:37:26.20 And they used sequence on one of the locks
00:37:29.04 that was derived from the literature,
00:37:30.20 a sequence that somebody else had isolated
00:37:33.06 through a SELEX experiment,
00:37:35.04 that sequence was known to bind to EGFR,
00:37:37.23 which is overexpressed on their sample cancer cell line.
00:37:41.06 And then they found in the literature another sequence
00:37:44.14 that was found to recognize something that's enriched
00:37:47.14 on their cancer cell line
00:37:48.28 but not on the healthy cell line.
00:37:50.28 So they're able to do a logical AND statement here.
00:37:53.27 Only if the target cell can open both locks
00:37:57.02 would the shell open up
00:37:59.03 and then reveal the antibodies to the cell surface.
00:38:03.00 So in this way you could argue that
00:38:04.21 it's an intelligent robot
00:38:06.19 in that it can do this more complicated logical argument.
00:38:10.29 And they were able to show, at least in the test tube,
00:38:13.01 that in fact this nanorobot
00:38:14.27 seems to trigger the apoptosis
00:38:16.29 a couple of orders of magnitude more easily
00:38:19.06 than in the healthy cell.
00:38:21.16 Of course, moving this into an actual therapeutic environment
00:38:24.27 requires several hurdles,
00:38:26.29 because if you wanted these things circulating in your blood,
00:38:29.22 you'd want them to avoid nuclease digestion,
00:38:32.08 you'd want them to avoid clearance by the immune system.
00:38:36.07 So, of course, there are going to be many hurdles to overcome
00:38:39.00 in order to translate this to the clinic,
00:38:41.11 but we think this is a fascinating first step in that direction.
00:38:48.04 So to conclude, DNA nanotechnology
00:38:51.28 is more than just smiley faces, we think.
00:38:54.26 In particular, we're very interested in two classes of applications.
00:38:59.13 One is as tools for molecular biophysics,
00:39:01.29 whether it be in tools for structural biology
00:39:04.10 or especially single molecule biophysics.
00:39:06.29 And secondly, we are exploring the notion that
00:39:10.00 these DNA nanostructures might be useful
00:39:13.07 as therapeutic delivery devices,
00:39:15.14 and of course there's many different platforms that
00:39:18.07 scientists are trying to explore for delivery,
00:39:20.24 but we're motivated by the insight
00:39:23.16 that our immune systems, in fact,
00:39:25.26 can be thought of as very complicated nanotechnologies.
00:39:29.17 They can process lots of information,
00:39:31.25 they can actuate all kinds of things,
00:39:33.13 they can punch holes into cells,
00:39:35.12 they can program each other to expand,
00:39:37.18 they can squeeze through tight spaces,
00:39:39.14 and the only way to achieve this really diverse,
00:39:42.19 sophisticated behavior
00:39:44.09 is by creating complex nanoscale objects.
00:39:47.03 At the moment, we believe that
00:39:49.12 the DNA nanotechnology platform
00:39:51.17 provides a very powerful method in that direction.
00:39:55.14 So I'd like to thank the following sources for support,
00:39:59.02 especially NIH and the Office of Naval Research.
00:40:02.10 We also got some support from the Wyss Institute
00:40:03.26 for Biologically Inspired Engineering.
00:40:06.07 I've tried to acknowledge
00:40:07.26 the different folks who have been doing the work
00:40:10.00 on the slides describing the work.
00:40:12.02 Thanks a lot.
- How are “DNA bundles” used in optical tweezer experiments to look at changes in conformation of a molecule? Why does this reduce experimental noise?
- How could the observation that long, thin structures resist uptake into the cell be useful for therapeutic considerations?
- Thought question: In addition to the applications that Shih describes in the third lecture, what are some potential novel applications of DNA nanotechnology in biology and technology?
Paper for this Session’s Discussion
Derr, N.D., Goodman, B.S., Jungmann, R., Leschziner, A.E., Shih, W.M., and Reck-Peterson, S.L. (2012). Tug-of-War in Motor Protein Ensembles Revealed with a Programmable DNA Origami Scaffold. Science 338, 662–665.
Discussion Questions for the Paper
- Describe how Derr et al. used DNA origami to design a synthetic cargo that could be attached selectively to kinesin or dynein motor proteins.
- How do the motile properties of dynein and kinesin cargo transport differ according to the single molecule chassis-motor assays (see Figure 2)?
- How does the mixed-motor photocleavable chassis experiments support a tug-of-war model for opposite-polarity motor proteins (see Figure 4)?
- Are you convinced that this engineered chassis-motor protein complex accurately recapitulates motor-protein directed microtubule transport?
William Shih is an Associate Professor in the Department of Biological Chemistry and Molecular Pharmacology at Harvard Medical School and the Department of Cancer Biology at the Dana-Farber Cancer Institute and a member of the Wyss Institute for biologically inspired engineering. He was a graduate student in the Department of Biochemistry at Stanford University, and… Continue Reading