To measure the viscosity of a biological membrane, we introduce a fluorescent molecule in this membrane, and measure how fast it moves. For this project, we used a fluorescent marker called Nile Red, well known to specialists. Because it moves randomly in any direction instead of going from a point A to a point B, we call its motion diffusion. The faster Nile Red diffuses, the lower the viscosity.
To measure the diffusion speed of a membrane marker, we used a technique called fluorescence correlation spectroscopy. This technique consists in observing a cell (bacillus subtilis here) with fluorescent molecules (Nile Red in its membrane) under a microscope. When fluorescent molecules diffuse, they induce intensity fluctuations. These fluctuations contain information about the diffusion speed of the fluorescent molecule: to simplify, we can say that faster diffusion cause faster intensity fluctuations. Concretely, to achieve this, we had to:
◦ Find the correct membrane marker.
◦ Calibrate the instrument: how much excitation laser power (excitation laser is used to activate the fluorescence of the membrane marker), how much membrane marker to add, how long to do the acquisition? This was a delicate tradeoff between quality of signal and maintaining the cell integrity. For instance, increasing the excitation laser power increases the quality of the signal but also risks damaging the cell. This was done by a long trial and error process, in which we compared experimental results done multiple times in different conditions, and in which we checked whether cells were still intact after imaging
◦ Develop the analysis software. We monitor intensity fluctuations, so there is a lot of processing to do to measure a diffusion speed.
◦ Verify how much the small size of bacteria changes the result. Indeed, the size of bacteria is close to the resolution limit of even the best microscopes, and we wanted to make sure that our results were not blurred by this. For this, we used computer simulations and artificial membranes which we created with known sizes.
To verify that our system was working well, we used it to study a partially-known system: the response in temperature of the membrane of the bacterium Bacillus subtilis. We submitted this bacterium to a cold shock (a sudden drop in temperature, from 37°C to 20°C) and measured its membrane viscosity. Previous research showed that the viscosity was expected to first drop due to the cold, then progressively increase as the bacteria “fights back” and adapts to recover its fluidity. We observed exactly that, which convinced us that our technique was working well.
