Strawberry DNA extraction and viewing with a cheap USB Microscope

I had recently bought a cheap USB microscope (10/60/200x magnification) and thought a fun use for it was to try and see DNA!

DNA is really small though, so we can't expect to see the components of it with a cheap microscope, however it is very long, and it turns out strawberry DNA is the easiest to see and extract.

Microscope with DNA on a slide
I followed this DNA extraction lab by Steve Spangler, which was very easy and fun to understand. I took an album of videos and photos during the process. We can clearly see strands 

10x Magnification - DNA strands clearly visible within slide
As we zoom in further, we can see the clumps of DNA strands start to show up.
60x Magnification - we can see some foreign debris in the slide, the strands look like spiderwebs.
One thing to note, the microscope puts out color images and default camera settings, which with a backlight makes it pretty hard to see anything. I added a light source to the side as well as pushed the contrast waaay up on the images, the color drops away and we get the images we see here.
200x Magnification - DNA clumps visible
Very cool, fun little test.

What next?

So we could do this with an optical microscope, this isn't that interesting by itself. The real magic comes from the fact that these are images a computer can see, and not only that but we can take several photos in bursts or timelapse. From this we could do tricks to increase the resolution (superresolution) of the images, and or apply basic computer vision to identify and track components within the microscope.

One possible mini-experiment would be to dissolve some salt crystals and take a series of timelapse photos, tracking the rate of dissolution.


Popular posts from this blog

Building a PID hover controller for Kerbal Space Program with kOS and IPython Notebook

Learning TensorFlow #1 - Using Computer Vision to turn a Chessboard image into chess tiles

Learning TensorFlow #2 - Predicting chess pieces from images using a single-layer classifier