Visualization of cortex organization and dynamics in microorganisms, using total internal reflection fluorescence microscopy.

TIRF microscopy has emerged as a powerful imaging technology to study spatio-temporal dynamics of fluorescent molecules in vitro and in living cells. The optical phenomenon of total internal reflection occurs when light passes from a medium with high refractive index into a medium with low refractive index at an angle larger than a characteristic critical angle (i.e. closer to being parallel with the boundary). Although all light is reflected back under such conditions, an evanescent wave is created that propagates across and along the boundary, which decays exponentially with distance, and only penetrates sample areas that are 100-200 nm near the interface. In addition to providing superior axial resolution, the reduced excitation of out of focus fluorophores creates a very high signal to noise ratios and minimizes damaging effects of photobleaching. Being a widefield technique, TIRF also allows faster image acquisition than most scanning based confocal setups. At first glance, the low penetration depth of TIRF seems to be incompatible with imaging of bacterial and fungal cells, which are often surrounded by thick cell walls. On the contrary, we have found that the cell walls of yeast and bacterial cells actually improve the usability of TIRF and increase the range of observable structures. Many cellular processes can therefore be directly accessed by TIRF in small, single-cell microorganisms, which often offer powerful genetic manipulation techniques. This allows us to perform in vivo biochemistry experiments, where kinetics of protein interactions and activities can be directly assessed in living cells. We describe here the individual steps required to obtain high quality TIRF images for Saccharomyces cerevisiae or Bacillus subtilis cells. We point out various problems that can affect TIRF visualization of fluorescent probes in cells and illustrate the procedure with several application examples. Finally, we demonstrate how TIRF images can be further improved using established image restoration techniques.