Team:Glasgow/Project/Measurements

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Floating Cells Measurement System

In order to characterise the floatation behavior of gas vesicle-filled E.coli, and confirm and/or revise the existing model, we would have to make some measurements. The preparation for this began before the gas vesicles were produced in the lab, so that when they were made, the characterisation process would be more efficient.

We decided to utilise the optical properties of the gas vesicles – they are known to scatter light. We devised an experimental set up that would light up and image the cells in suspension. When the cells float, their distribution in the fluid will change, thus changing the proportion of light that gets through at a given height. By tracking these changes over time, we would gain information on the speed of floatation, and perhaps how the cells distribute themselves – we should be able to see any clumping or filament formation.

Experimental Set-up

insert labelled picture of experimental set up

Experiment 1: Red Silicone Beads

For the first experiment, we would be tracking the sedimentation of red silicone beads through water. With a very similar size (1.1um) and density (1100kg/m^3) to E.coli, we felt these would be an acceptable substitute for the cells. Please see the Protocols page for a full experimental method.

Initially, the plan was to take an image every 10 minutes, but it very quickly became clear that there would be no changes in this time frame – at least no changes big enough to register. We changed the time between images to be 1 hour. In the end, this was also far too short, and even after 3 – 4 days there had been little change in the image brightness (again, nothing measurable). A quick calculation had put a preliminary estimate of 150 hours to travel the 3cm, but we had assumed changes would be visible in the time before this.
One reason for this could be the shear number of beads we had used: our engineering adviser agreed that the concentration we'd used was far too high and we'd be unlikely to see anything.

In order to be able to optimise the experiment, we would need a set-up which ran far quicker - otherwise, it could take days to get even the camera settings right! With this in mind, we procured some different beads for experiment 2.

Experiment 2: Yellow Glass Beads

For ease of repeatability, we needed to find a quicker experiment to run. Using glass beads 5um in diameter, and of density 200kg/m^3, the system could be tested multiple times per day. Preliminary calculations supported out hypothesis.
Again, please see relevant Protocols entry for full experimental method.

This set up was much better, in a number of ways. Though the solution was less opaque than the previous, changes were still visible thanks to further changes in the camera setting. We had noticed in the previous experiment that the overall brightness had varied between images, making comparison tricky. This was due to the gain settings on the camera, which were still active. Turned off, we were able to see the true brightness each time. Also, the beads did sink very quickly – the majority of them had sunk in ~10 minutes, meaning the experiment could be run multiple times.

The need for further testing
Initially, the glass beads were only to be used to optimise the system for its final use in tracking bacteria. However, as it became apparent that the issues with the gas vesicles (see the relevant Project page here) were likely to extend beyond the 10 weeks allocated time, it was decided that we would more thoroughly test the capabilties of the system, by attempting to measure and compare the beads' sinking velocities in liquids of different densities.

Experiment 3: Sinking Bead Velocities in Multiple NaCl Molarities

The above yellow bead protocol was repeated using NaCl solutions of 1M, 3M and 5M. 3 runs were made at each molarity.

Once the images had been obtained, there came the process of extracting useful information from them. This was the procedure followed:

1. All the images from a single run were imported as an 18 image sequence into ImageJ, a free and powerful image processing program.
2. Images were rotated, so that the cuvette was lying on its side.
3. A bounding box was drawn to encompass the area we were interested in: namely, the water column. A wider box was used to reduce the effects of blotches from the paper (see later). This was kept the same width between image sequences/runs.
4. The Profile of this box was plotted for each image in turn, giving us pixels (height) and their corresponding brightness levels (0-255). All this data was saved as a .txt file for import to MATLAB.
5. Data was opened in MATLAB, and the background removed from each of the images. The resulting profiles were plotted, and displayed the return to zero (or to the background levels) of the cuvette. A simple moving average filter was applied to the curves, to smooth them out for easier interpretation.

Results
To gain the speed data, a “brightness point” was tracked. As we knew the time at each which profile was created, and the height of the cuvette each point is at (the horizontal axis), we were able to obtain a speed by:

1. Finding the point (I.e, the height) at which each profile intersected the brightness level of interest using a simple MATLAB script.
2. Taking two times (generally 1min and 10 mins) and finding the distance travelled between them, then using a simple v=d/t equation to find v.
3. This was repeated for ~5 brightness levels from the y-axis, on average, to gain a total average for the run.