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Compartments of the endomembrane system are in constant flux, and their full characterization requires live cell imaging. Described here is a protocol that employs 4D (time-lapse 3D) confocal microscopy to visualize fluorescently labeled compartments in budding yeasts. The method was developed to track the dynamics of secretory compartments in Pichia pastoris and Saccharomyces cerevisiae1,2,3. This protocol focuses on S. cerevisiae, which has a nonstacked Golgi in which the individual cisternae are optically resolvable4. The unusual Golgi organization in S. cerevisiae enabled the demonstration by 4D microscopy that a Golgi cisterna initially labels with resident early Golgi proteins, and then loses those proteins while acquiring resident late Golgi proteins3,5. This transition can be visualized by creating a strain in which the early Golgi protein Vrg4 is labeled with GFP while the late Golgi protein Sec7 is labeled with a monomeric red fluorescent protein. When individual cisternae are tracked, maturation is observed as a green-to-red conversion3. This type of analysis can provide valuable information about protein localization and compartment identity. For example, two proteins with slightly offset arrival and departure times might sometimes appear to label different compartments in static images, but can be seen in 4D movies to label the same compartment at different time points6,7. Thus, 4D microscopy reveals phenomena that would not otherwise be evident.
Informative 4D microscopy of yeast compartments can be achieved with appropriate procedures and equipment2. Whenever possible, fluorescent protein tagging is performed by gene replacement8 to avoid overexpression artifacts. Because intracellular structures are often very dynamic, 4D imaging is needed to ensure that a structure is tracked reliably over time. The protocol described here employs a laser scanning confocal microscope equipped with high sensitivity detectors. With this device, the entire cell volume of S. cerevisiae can be imaged by confocal microscopy approximately every 1-3 s, with 2 s intervals being typical. Data can be collected for up to 5-15 min depending on the labeling densities of the fluorophores and their photophysical properties. The main hurdle is to minimize photobleaching. For this purpose, the laser intensities are kept as low as possible, the confocal scan speed is maximized, and the optical parameters are configured to image at the Nyquist limit in order to capture the relevant information while avoiding excessive light exposure. These settings are also expected to alleviate phototoxicity, a factor that is often overlooked during live cell imaging9,10,11. The resulting noisy data are processed with bleach correction and deconvolution algorithms to facilitate quantification of fluorescence intensities.
Even with high quality 4D movies, the analysis is tricky because yeast compartments tend to be numerous, heterogeneous, and mobile. Due to the intrinsic limitations of confocal microscopy and the non-optimal settings required for prolonged 4D imaging, fluorescent structures that are near each other are hard to resolve. This problem can be circumvented by focusing on the small number of fluorescent structures that remain optically resolvable for the duration of the labeling period, with the assumption that those structures are representative of the whole population of labeled compartments. Fluorescent compartments that can be reliably tracked are identified by viewing movies of projected Z-stacks and by creating a series of montages in which the optical sections for each time point are arrayed on a computer screen. This analysis employs custom ImageJ12 plugins, which allow an individual structure to be tracked in isolation.
Recent methods papers covered the use of fluorescent proteins in yeast13 as well as the theory and practice of 4D confocal imaging of yeast cells2. This protocol focuses on the key practical aspects of a 4D imaging experiment. It includes some enhancements to previously described procedures, as well as updated versions of the ImageJ plugin code and documentation. The example shown focuses on Golgi dynamics, but this protocol is equally suitable for imaging other yeast compartments.