In this post for our Online Science Exhibition, PhD student Yash Bogawat from The Simmel lab at TUM exhibits the process of Developing Rollerskates for DNA Nanorobots.
Hi everyone! If you have been following our posts you might know that we are working along the lines of functional DNA Nanotechnology to make DNA nanorobots. All us ESRs have a defined role, and my role here is to make our DNA robots move! So, today I will be telling you a little about it.
Jumping right to it, we use microparticles and convert them to micromotors. We do so by asymmetrically modifying one side of the particle by metals or biomolecules, this modification leads to the creation of a Janus particle where the particle has two different faces much like the Greco-Roman god Janus who also has two faces.
Now, of the two surfaces in our context we use one surface as an active part and the other surface as a functional part. The active part is responsible for catalysing a reaction with fuel that generates motion, it can be a metal catalyst like platinum or an enzyme like urease. Whereas the functional part can be our DNA nanorobots, drug molecules, antibodies or aptamers depending on the desired application of the micromotors. The microparticles we use are pre-functionalized with chemical groups like amines or carboxylic acids and using linkers we can conjugate enzymes, DNA, antibodies etc. on the Janus particles. The particles are characterised using scanning electron microscopy.
One of my most favourite days at work is tracking day, where we track the micromotors after placing them in suitable fuels and study their velocities and trajectories. Inverted microscopes are preferred for tracking. Micromotor tracking is often done in bright field however if the motors are fluorescent they can be tracked using fluorescent channels. Darkfield tracking is also an interesting available option. One critical aspect of tracking is selecting the right frame rate. In the case of active particles, it is very interesting to observe the transition of a particle from Brownian motion to active Brownian motion when fuel is added and for visualising these transitions we use frame rates of 10-15 fps.
The movies we record are immensely useful as they allow us to extrapolate data about the nature of movement exhibited by the micromotors and how they behave in varying fuel concentrations to assess this we use an image analysis software- FIJI and then a Matlab script to wrap up the experiment.
The software allows us to understand the changes that the micromotors go through, put numbers on those changes and present data in an appreciable way.
The next step is to mount out DNA nanorobots onto these micromotors and study how they move.
Interesting ain’t it?