Budding yeast is an advantageous model for studying microtubule dynamics in vivo due to its powerful genetics and the simplicity of its microtubule cytoskeleton. The following protocol describes how to transform and culture yeast cells, acquire confocal microscopy images, and quantitatively analyze microtubule dynamics in living yeast cells.
Dynamic microtubules are fundamental to many cellular processes, and accurate measurements of microtubule dynamics can provide insight into how cells regulate these processes and how genetic mutations impact regulation. The quantification of microtubule dynamics in metazoan models has a number of associated challenges, including a high microtubule density and limitations on genetic manipulations. In contrast, the budding yeast model offers advantages that overcome these challenges. This protocol describes a method to measure the dynamics of single microtubules in living yeast cells. Cells expressing fluorescently tagged tubulin are adhered to assembled slide chambers, allowing for stable time-lapse image acquisition. A detailed guide for high-speed, four-dimensional image acquisition is also provided, as well as a protocol for quantifying the properties of dynamic microtubules in confocal image stacks. This method, combined with conventional yeast genetics, provides an approach that is uniquely suited for quantitatively assessing the effects of microtubule regulators or mutations that alter the activity of tubulin subunits.
Microtubules are cytoskeletal polymers made of αβ-tubulin protein subunits and are used in a wide variety of cellular contexts, including intracellular transport, cell division, morphogenesis, and motility. To build microtubule networks for these diverse roles, cells must carefully regulate where and when microtubules form. This regulation is accomplished by controlling the reactions that either assemble αβ-tubulin subunits into microtubule polymers or disassemble microtubules into free subunits; this is known as microtubule dynamics.
A major goal of the microtubule field is to elucidate the molecular mechanisms that regulate microtubule dynamics, including studies of the αβ-tubulin subunits and extrinsic regulators that bind to tubulin and/or to microtubules. A well-established experimental approach is to reconstitute this system in vitro using purified αβ-tubulin protein, often in combination with purified extrinsic regulators. Although this is a useful approach, it is clear that microtubule dynamics in reconstituted systems differs strongly from that observed in living cells. For example, microtubules grow faster and shrink slower in vivo than in vitro. These differences may be attributed to the availability of known extrinsic regulators1, as well as to yet-undefined factors in cells. Therefore, it is critical to determine the activities of microtubule regulators and mutants that disrupt dynamics in a native cellular context.
Although metazoan models have proven to be the prevailing systems for investigating microtubule function and higher-order organization, several practical concerns severely limit the utility of these models for the precise measurement of microtubule dynamics. First, the high number of microtubules, ranging from dozens to thousands per cell, makes it difficult to confidently track individual microtubules over time. Many studies address this challenge by imaging proteins that selectively localize to microtubule ends, such as proteins in the end-binding (EB) family. However, these proteins are known to only localize to the ends of growing microtubules in metazoans2. Therefore, the utility of these proteins is limited to directly measuring growth rates, while only indirectly measuring other aspects of dynamics, such as frequency of catastrophe. Second, despite advances in genome editing technology, creating cells that stably express fluorescently labeled tubulin or introducing mutations to selectively manipulate tubulin or microtubule regulators remains a significant challenge. Moreover, the presence of many tubulin isotypes in metazoans confounds the study of how mutations impact individual tubulin genes.
The budding yeast system provides several important advantages for measuring in vivo microtubule dynamics. Yeast has a simplified microtubule network that permits the visualization of individual microtubules. In yeast, microtubules emanate from organizing centers known as spindle pole bodies (SPBs), which are embedded in the nuclear envelope3. The SPBs serve as scaffolds for γ-tubulin small complexes that nucleate microtubules4,5. SPBs nucleate two classes of microtubules, spindle microtubules and astral microtubules. Spindle microtubules project into the nucleoplasm and are important for attaching to chromosomes via kinetochore microtubules and for stabilizing the spindle via overlapping interpolar microtubules6. In contrast, astral microtubules project outwards into the cytosol and are relatively rare compared to the dense network of spindle microtubules. During mitosis, pre-anaphase cells have only 1-2 astral microtubules emanating from either SPB; these exist as individual microtubules rather than as bundles7. The role of astral microtubules during mitosis is to move the nucleus and spindle into the junction between the mother and bud compartments, known as the bud neck. This movement involves well-defined pathways that generate force on astral microtubules, pulling the nucleus and spindle toward and eventually into the bud neck8.
Another advantage of the yeast system is the utility of its genetics, which can be used to investigate microtubule regulators and tubulin subunits with unparalleled precision. Yeast also possess a simplified repertoire of tubulin isotypes: a single β-tubulin gene (TUB2) and two α-tubulin genes (TUB1 and TUB3). Mutations can be readily introduced into these genes and thereby studied in a homogenous tubulin population9,10. There are a number of widely available constructs for labeling microtubules, and these can be targeted for integration at chromosomal loci for stable expression (see the Discussion).
The overall goal of this method is to image single microtubules in living yeast cells in four dimensions (X, Y, Z, and T) for high-resolution measurements of microtubule dynamics. Methods for integrating constructs for the constitutive, low-level expression of fluorescently labeled tubulin in yeast cells are described. Prior to imaging, living cells are mounted into slide chambers coated with the lectin Concanavalin A to stabilize the cells for long-term imaging. The optimal parameters for image acquisition, as well as a workflow for data analysis, are also described.
1. Preparing -LEU Dropout Plates
2. Integrating GFP-Tub1 for the Constitutive Expression of GFP-labeled Tubulin
3. Growing Liquid Yeast Culture
4. Preparing Flow Chamber Slides
5. Loading Yeast Cells into Prepared Flow Chamber Slides
6. Image Acquisition
7. Image Analysis
Measuring microtubule dynamics in living yeast cells provides a compelling tool to assess how mutations in genes encoding microtubule regulators or tubulin subunits impact polymerization and depolymerization rates, as well as the frequency of transition between these states. Figure 1 displays a time series of astral microtubule dynamics in a wild-type cell and a mutant cell with a mutation in β-tubulin (tub2-430Δ). Microtubules are labeled with GFP-tagged α-tubulin to visualize microtubule length. The red arrows trace the length of the astral microtubule in each image (Figure1A and 1C). Microtubule lengths were measured at each timepoint for 8 min, and these compiled lengths are depicted in life plots (Figure 1B and 1D). These data were used to determine the polymerization rate, depolymerization rate, polymerization duration, depolymerization duration, rescue frequency, catastrophe frequency, and dynamicity.
Figure 1. Time-lapse Imaging of GFP-labeled Microtubules and Measurements of Astral Microtubule Dynamics. (A and C) The first five sequential images of time-lapse imaging for the wild-type and tub2-430Δ mutant cells. Microtubules are labeled with GFP-Tub1 and imaged in pre-anaphase of the cell cycle. Each image is a maximum-intensity projection from a confocal Z-series. Scale bars: 1 µm. Timestamps shows the total time at each frame acquisition. The red arrows follow along the total length of an astral microtubule, with the arrowheads denoting the plus ends. (B and D) Microtubule life plots display the lengths of single astral microtubules over time. The green points denote polymerization and the pink points denote depolymerization. The arrowheads point to catastrophe events. Please click here to view a larger version of this figure.
Figure 1 demonstrates altered microtubule dynamics between a wild-type strain and a mutant strain lacking the 27 amino acids of β-tubulin on the C-terminal, a mutant that was previously shown to have more stable microtubules15. As displayed in the microtubule life plots, the microtubule in the tub2-430Δ mutant is longer and exhibits fewer catastrophes than the wild-type microtubule (Figure 1B and D arrowheads; Table 1). In addition, the polymerization and depolymerization rates, determined from the slopes of the ascending and descending microtubule lengths, are decreased in the mutant compared to the wild-type (Figure 1B and 1D; Table 1). Finally, the microtubule in the tub2-430Δ mutant cell spends more time in states of polymerization and depolymerization and has decreased dynamicity compared to the wild-type (Table 1).
Strain | Dynamicity (subunits/s) | Polymerization rate (µm/min) | % Time in Polymerization | Depolym rate (µm/min) | % Time in Depoly- merization |
% Time in Pause | Rescue frequency (events/min) | Catastrophe frequency (events/min) |
Wild-type n=1 | 54 | 1.8 ± 0.5 | 48 | 3.0 ± 0.3 | 29 | 3 | 1.8 | 1.4 |
tub2-430∆ n=1 | 40 | 1.4 ± 0.1 | 41 | 1.5 ± 0.2 | 32 | 0 | 0.8 | 0.7 |
Abbreviations are as follows: µm, micron; min, minute; s, second; n, single microtubule analyzed from microtubule life plot (Fig1B,D). Values are mean ± standard error of the mean. |
Table 1: Microtubule Dynamics Measurements for Cells in Figure 1. The values are the mean ± standard error of the mean from measurements of the single microtubules shown in Figure 1.
Data such as this can be acquired from individual life plots of multiple microtubules (on average, at least 50 microtubules are analyzed) and used in statistical analysis to determine significant changes in microtubule dynamics across a population.
The budding yeast model offers major advantages for gathering high-resolution measurements of microtubule dynamics in an in vivo setting, including the ability to image single microtubules over time and the ability to manipulate tubulins and microtubule regulators using the tools of yeast genetics.
The Concanavalin A-coated chambers provide a number of advantages over previously described apparatuses, including molten agar pads. Slides with chambers can be pre-made and stored long term or can be used immediately. Additionally, the Concanavalin A-coated chambers are capable of culturing yeast cells for >6 h in the same medium in which the cells were originally cultured. It is important to note that the number of cells within the chamber will dictate how long the chamber can be imaged. The coated chambers ensure that only a single layer of yeast cells adheres to the coverslip. Finally, creating multiple chambers allows for several different yeast strains to be imaged on the same slide.
There are major challenges to imaging microtubule dynamics in budding yeast, including relatively low signal levels and the highly dynamic nature of the process. Widefield microscopes are adequate for capturing single-timepoint images of labeled microtubules. However, widefield microscopy causes faster photobleaching due to longer exposure times and the bombardment of the sample with out-of-focus light. Likewise, laser scanning confocal microscopes are inadequate due to faster photobleaching. Spinning disk confocal microscopy is uniquely suited for imaging microtubule dynamics in budding yeast. The spinning disk confocal microscope system used here was designed with these requirements in mind.
The microscope system must be designed for high speed and high sensitivity. Although a variety of microscope systems may prove suitable, several components are essential. A 100X objective with a high numerical aperture (1.45NA) is necessary to resolve individual microtubules and length changes on the order of 100 nm. A spinning disk confocal scanner is necessary to minimize photobleaching and toxicity, by blocking out-of-focus light, and to maximize resolution and sensitivity, by gathering only in-focus light. The EMCCD camera must be fast and sensitive to collect enough GFP-Tub1 signal during each 90-ms of the time-lapse acquisition. Currently, EMCCD cameras are the best choice for this application. Current CMOS sensor cameras lack the sensitivity of high-end EMCCDs; however, this technology is improving significantly and may soon be suitable. Finally, a piezoelectric stage is needed for fast and precise movement in Z, which can be the rate-limiting step in four-dimensional image acquisition.
The specific microscope system described in this protocol may present a barrier if similar equipment is not readily available. Alterations can be made to accommodate different imaging systems, but these modifications may come at the expense of temporal and/or spatial resolution. A spindle-driven stage motor can be used in place of the piezoelectric stage. However, these motors are slower, less accurate, and prone to drift. Post-acquisition image-stabilizing plug-ins can be used to minimize the drift in the XY dimensions associated with these motor-driven stages. In addition, alternate cameras with slower readout speeds can be used. To accommodate a slower acquisition rate, Z-series could be collected less often (i.e., more time between Z-series). However, increasing the time between Z-series risks missing transitions that may occur between timepoints. Alternatively, the distance between Z-planes can be increased as a way of decreasing the number of images collected per Z-series. However, increasing this spacing risks losing image information between planes, which will impair the experimenter's ability to accurately identify microtubule ends. Clearly, alterations can be made to accommodate alternative imaging systems, but these alterations must be weighed against the potential loss of image information.
Another limitation of this protocol is the loss of spatial information when three-dimensional image stacks are converted to two-dimensional image projections. An individual astral microtubule in a pre-anaphase yeast cell is typically between 0.5 and 2.0 µm in length and projects across the three-dimensional space of the cytoplasm. It is therefore critical to acquire images from a series of Z-positions to collect all the spatial information of an individual microtubule. In a time-lapse acquisition, this can produce a complex data set, with 20 Z-slices at each of the 150 timepoints, for a total of 3,000 images. To address this challenge and to simplify the image analysis, the spatial information of the Z-stacks is compressed during the analysis. While this benefits analysis, it comes at the cost of spatial information from the Z-dimension. How much information is lost when the Z-dimension is compressed? To estimate this loss, experiments were conducted to measure the distance between SPBs in pre-anaphase cells, which are relatively easy to measure because Spc110-GFP-labeled SPBs exhibit spherical foci with high signal-to-noise ratios. Although the spindle may exhibit different displacement in Z than astral microtubules, the average length is similar (~1.5 µm) and therefore provides a useful proxy. Spindle lengths are, on average, 15% longer when measured in full three-dimensional image stacks than when the same spindles are measured in two-dimensional projections (J.M., unpublished results). Thus, the benefit of the simplified data set and measurements should be weighed against the cost of discarded Z information.
The yeast cell biology community has developed a versatile toolkit for imaging microtubule networks. This includes a variety of fluorescent proteins fused to α-tubulin/Tub1 and marked with various auxotrophic markers. These constructs include LEU2::GFP-TUB1 (pSK1050)11, which integrates at the chromosomal LEU2 locus, and several fusions to GFP and other fluorophores, which integrate at the URA3 locus, including URA3::GFP-TUB1 (pAFS125)16, URA3::CFP-TUB1 (pAFS125-CFP), and URA3::pHIS3-mCherry-TUB1 (pAK011)17. In addition, a set of Tub1 fusions to diverse fluorescent proteins has recently been created that target integration to either the chromosomal URA3 locus or to a locus adjacent to the native TUB1 locus18. This extensive palette of fluorophores and markers is very useful for co-localization experiments with differentially tagged proteins. The GFP-Tub1 fusion remains the best tool for the time-lapse imaging of microtubule dynamics due to its brightness and photostability. The GFP-Tub1 fusion created by Song & Lee (pSK1050)11 contains a fusion of GFP to the amino-terminus of α-tubulin/Tub1 and integrates at the LEU2 locus to rescue leucine auxotrophy. Thus, the fusion protein is expressed ectopically in addition to native α-tubulin and comprises ~20% of the total α-tubulin in the cell9. Importantly, the GFP-Tub1 fusion does not rescue the function of native α-tubulin/Tub1 (J.M., unpublished results). This is also true of other TUB1 fusion constructs18.
With the available tools to visualize microtubules and their binding proteins, budding yeast is an ideal system to study mutations in tubulins and other microtubule regulators. Additionally, the limited number of tubulin genes allows for the direct study of a homogenous pool of mutant tubulin. In addition to mutations in tubulin genes, budding yeast has a number of microtubule-associated proteins (MAPs) that are homologous to metazoan counterparts. These MAPs can be studied directly, and the effects that mutations have on single microtubule filaments can also be examined. The protocol detailed here allows for the investigation of both the intrinsic and extrinsic processes that influence microtubule dynamics.
In summary, microtubules must be dynamic to function properly during many cellular processes. It is necessary to understand the factors intrinsic to tubulin and the extrinsic associated proteins that regulate microtubule dynamics. Budding yeast is an ideal system to study the effect of these factors by directly quantifying the dynamics of a single microtubule. The protocol detailed above describes how to stably integrate a fluorescent fusion protein into the yeast genome and how to acquire and analyze confocal image stacks to quantify microtubule dynamics in vivo. Finally, this versatile protocol can be adapted to study the dynamics of any protein in budding yeast, so long as the protein of interest is fused to a suitable fluorescent protein.
The authors have nothing to disclose.
We thank Kerry Bloom (University of North Carolina), Kyung Lee (NCI), Steven Markus (Colorado State University), and Elmar Schiebel (Universität Heidelberg) for sharing various FP-TUB1 plasmids. We are grateful to Melissa Gardner (University of Minnesota) for training us in the slide chamber preparation method. This work was supported by the National Institutes of Health (NIH) grant R01GM112893-01A1 (to J.K.M.) and T32GM008730 (to C.E.).
DOB (dropout bases) | Sunrise science | 1650 | |
CSM-Leu | Sunrise science | 1005 | |
Agar | Ameresco | N833 | |
100mm polystyrene plates | Fisher Scientific | FB0875713 | |
ssDNA (Samon Sperm) in sterile DiH2O | Sigma-Aldrich | D7656 | resuspend at 10 mg/mL in DiH2O. Store aliquots at -20 ºC |
Synthetic Complete Media | Sunrise science | 1459-100 | |
Concanavalin A | Sigma-Aldrich | L7647 | resuspend at 2 mg/mL in DiH2O. Store aliquots at -20 ºC |
Microscope slides | Fisher Scientific | 12-544-1 | |
Microscope Coverslips | Fisher Scientific | 12-541-B | |
Parafilm | Fisher Scientific | 13-374-12 | paraffin film |
VALAP (Equal parts of Vaseline, lanolin and paraffin) | melt at 75 ºC before use | ||
forceps | Fisher Scientific | 16-100-106 | |
Poyethylene glycol (PEG) 3350 | Sigma-Aldrich | 202444 | |
Name | Company | Catalog Number | Comments |
Microscope | |||
Ti E inverted Perfect Focus microscope | Nikon Instruments | MEA53100 | |
1.45 NA 100x CFI Plan Apo objective | Nikon Instruments | MRD01905 | |
Piezo electric stage | Physik Instrumente | P-736 | |
Spinning disk scanner | Yokogawa | CSU10 | |
Laser combiner module | Agilent Technologies | MCL400B | |
EMCCD camera | Andor Technology | iXon Ultra 897 | |
Name | Company | Catalog Number | Comments |
Software | |||
NIS Elements software | Nikon Instruments | MQS31100 | |
Microsoft Excel software | Microsoft | ||
MATLAB software | MathWorks, Inc | ||
ImageJ64 | NIH | Rasband, W.S., ImageJ, U. S. National Institutes of Health, Bethesda, Maryland, USA, http://imagej.nih.gov/ij/, 1997-2016. | |
Bio-Formats Importer plug-in | Open Microscopy Environment | ||
Name | Company | Catalog Number | Comments |
Plasmids | |||
pUC19-LEU2::GFP-TUB1 | pSK1050 | reference: Song, S. and Lee, K. S. A novel function of Saccharomyces cerevisiae CDC5 in cytokinesis. J Cell Biol. 152 (3), 451-469 (2001) |