Monday, 5 September 2016

The website for one of my new major research projects is now live!

TrypTag is a project to tag every gene in the trypanosome genome with a fluorescent marker to see where it goes in the cell.

Do you have no idea what I'm talking about? Read on to see what all that jargon means!

Trypanosomes are one of the parasites my research involves. They are single cell parasites that live in the blood, and they cause the diseases sleeping sickness in humans and nagana in livestock across Africa. All in all, not very nice.

Like all cells, trypanosomes are made up of protein machinery. Each protein is encoded by a gene in genome. A first step in finding a protein's function is finding where it goes in the cell. If you can map it to a particular structure then you have a good idea it's going to function there too.

To find where a protein goes in a cell we genetically modify the cell, sticking a fluorescent marker to the protein so we can see where it goes using a microscope. This is the process of tagging.

We are going to tag every protein gene in the genome, around 8000 genes, and build a complete map of the protein composition of the cell.

Tuesday, 16 February 2016

Looking at the structure inside cells

How complex and structured is the inside of a cell? It's hard to imagine, but the internal organisation of cells is typically precisely controlled by molecular skeletons and scaffolds, giving cells the shape they need to function.

We can discover the 3D organisation of the inside of cells using electron tomography; a process where you capture a series of images with an electron microscope, with the sample tilted at a slightly different angle for each image. This can then be used to calculate the 3D shape of the sample, using the same maths as for an X-ray CT scan.

Leishmania parasites are exquisitely structured. While they are only 2 micrometres wide (100 would fit across a human hair) they have a precise internal organisation which they faithfully replicate each time they divide. One of the distinctive parts of this organisation is the flagellar pocket, where the cell membrane folds in on itself at the base of the whip-like flagellum that the cell uses to swim.

In my latest paper, "Flagellar pocket restructuring through the Leishmania life cycle involves a discrete flagellum attachment zone", I used electron tomography to reconstruct the three-dimensional organisation of the Leishmania flagellar pocket. The structure in this area of the cell is incredible, and the journal picked a rendering of it for the cover image.

Volume covered in this 3D reconstruction is only 3 by 2 by 1 micrometres, about the size of a typical bacterial cell, but has enormous complexity. I have shown the microtubules (which make up most of the cytoskeleton) in red and membranes in blue. Each microtubule is only about 5 molecules wide, and is about 10,000 times narrower than a human hair! Some other specialised parts of the cytoskeleton are in green.

You can download the paper for free here to take a look at the structures in this area of the cell in more detail.

Software used:
IMod: Electron tomography structure
Blender: Tidying and rendering of the 3D structure

Sunday, 8 November 2015

Ergodic Analysis

My review paper about ergodic analysis came out on Thursday. Does ergodic analysis sound terrifying? It's actually quite a simple concept and it is a powerful method for extracting information about the dynamics of a cell division cycle from a single snapshot of cells at random stages of the cell cycle.

Ergodic analysis is particularly useful if a time-lapse video is impossible, for example if the cells swim or you want to do an analysis that kills the cells.

Does this sound interesting for your research? Drop me a message: @Zephyris.

Software used:
Autodesk Sketchbook Pro: Drawing the cells.
Inkscape: Page layout.

Monday, 1 June 2015

Pebbling in colour

The Pebble Time is finally out! This fantastically simple, yet massively functional, little smartwatch is now shipping to the Kickstarter backers who pledged their renewed support to the company that produced the original Pebble.

I've been lucky enough to be beta testing a developer preview model of the Pebble Time, and have had it on my wrist for the last few weeks. I used this time to put together some animated watchfaces which make the most of the colour screen, and learn some C programming along the way!

An elegant animated watchface, with each digit built from curving paths. Animated minute transitions, and tap-triggered animation to improve readability under low light. Animations, line widths and colours can be customised.

Inspired by the watchface shown on the red Pebble Time Steel advertising images:

A fun, animated, easy to read watchface. Every minute the bubbles in the background pop, and a set of new ones appear (by default) in a new colour. Alternatively you can customsise the colour of the bubbles. Tapping or shaking the watch also triggers the animation.

Inspired by the watchface shown on the red Pebble Time advertising images:

A colourful interpretation of the classic arc watchface design, with a Pebble Time-style loading animation and dynamic colour schemes. Colour schemes and whether or not to show the second hand can be customised.

A colourful interpretation of the classic pixel array digital watchface design, with loading animations, animated minute transitions and dynamic colour schemes. Colour schemes, pixel styles and animations can be customised.

Software used:
CloudPebble: Watchface programming. CloudPebble is an online IDE for Pebble watchfaces and apps.
Notepad++: Server side HTML/Javascript for the watchface settings.

Friday, 17 April 2015

Light-Years of DNA

Light-year, and DNA. Not two scientific terms you expect to see on the same page, but over your lifetime your body will produce around one light-year of DNA! That is about one trillion kilometres. Don't believe me? Let's do some maths:

Every cell in your body has two copies of your genome, held in 23 pairs of chromosomes. The human genome is approximately three billion (3×109) base pairs of DNA.

The famous double helix of DNA has about 10 base pairs per twist, and each twist is 3.4 nanometers long (3.4×10-9 metres, the same as roughly 20 carbon-carbon bonds).

This means that the total length of DNA contained in every cell of your body is approximately 2 meters (3×109 base pairs multiplied by 0.34×10-9 metres per base pair, doubled because of the two copies).

Your body has about ten trillion (1×1013) cells (excluding red blood cells), and this remains roughly constant through your life. There is a huge turnover of these cells though, as your body replaces cells to maintain itself.

Every time a cell is replaced its 2 metres of DNA must be produced. In most tissues the cells are replaced in a couple of months, and in many they are replaced in just a couple of days. Even cells in bones are replaced every few years.

The average lifetime of a cell is probably one or two months, so if you live to 80 then your cells are replaced about 500 times throughout the course of your life.

This means that the total length of DNA your body produces in your lifetime is approximately 1×1016 metres (2 metres multiplied by 1×1013 cells, multiplied by 500 replacements). 1×1016 metres (ten thousand trillion metres) is about one light-year (0.946×1016 metres)! Most amazingly it would not be a light-year of random DNA sequence, but ten thousand trillion identical copies of your DNA, faithfully replicated by your cells.

An estimation of the number of cells in the human body
How quickly do different cells in the body replace themselves?
Thanks to Rob Phillips for making me think about this!

Wednesday, 28 January 2015

Smooth Videos - AKA Correcting NASA

What makes a video look smooth? Your eye is extremely sensitive to problems with videos, and for any video to look smooth it has to have:

  • A high frame rate
  • A steady camera
  • Roughly even brightness each frame

Normally these are easy to get. Any modern camera will give a decent frame rate, and the exposure time for each shot will be accurate, giving an even brightness of images each frame. Camera steadiness is more difficult, but a basic tripod will solve that.

This is a lot harder in space! For a NASA space probe floating through deep space, keeping a steady orientation is a challenge. Spacecraft can do this well quite well, using thrusters and reaction wheels. They still make some small mistakes though. Getting an even exposure time for each frame of a video is also harder in deep space, especially as it might take minutes or hours for radio commands to reach the space probe so you have to trust its autoexposure. Luckily, given ok starting material, correcting camera shake and frame brightness problems by image processing is quite easy.

NASA's Dawn space probe is currently approaching Ceres, getting sharper pictures of this dwarf planet than ever before. A series of these pictures even shows this tiny world rotating. Unfortunately, they didn't correct the shake or brightness problems in the video released to the press:

A quick fix in ImageJ to remove the shake and even out the frame brightness makes a (dwarf) world of difference:

As the probe gets closer and closer to Ceres its shots are getting more and more spectacular, but the videos still need shake and brightness correction.

Interested in improving some NASA videos? I did the corrections using the free scientific image editing software ImageJ, and these are two handy macro scripts for video corrections in ImageJ:

Image stabilisation
//Stabilise based on signal intensity centroid (centre of gravity)
//Stabilises using translation only, using frame 1 as the reference location
//This method is suitable for stabilising videos of bright objects on a dark background
for (z=0; z<nSlices(); z++) {
 //For each slice
 //Do a weighted sum of signal for centroid determination
 for (x=0; x<getWidth(); x++) {
  for (y=0; y<getHeight(); y++) {
   v=getPixel(x, y);
 //Calculate the centroid location
 if (z==0) {
  //If the first slice, record as the reference location
  print(rcx, rcy);
 } else {
  //Otherwise calculate the image shift and correct
  print(dx, dy);
  makeRectangle(0, 0, getWidth(), getHeight());
  makeRectangle(-dx, -dy, getWidth(), getHeight());
Brightness normalisation
//Normalise image brightness to reduce video flicker
//Scales intensity based on the mean and standard deviation, using frame 1 as the reference frame
//This method is suitable for reducing flicker in most videos
for (z=0; z<nSlices(); z++) {
 //For each slice
 //Find the signal mean and standard deviation
 run("Select All");
 getRawStatistics(area, mean, min, max, stdev);
 if (z==0) {
  //If the first slice, record as the reference signal mean and stdev
  print(rmean, rstdev);
 } else {
  //Otherwise calculate the brightness and scaling correction
  run("Macro...", "code=v="+rmean+"+"+rstdev+"*(v-"+mean+")/"+stdev);
  print(mean, stdev);

Software used:
ImageJ: Image corrections
GIMP: Animated gif file size optimisation