High-resolution Imaging and Analysis of Individual Astral Microtubule Dynamics in Budding Yeast

Summary

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.

Abstract

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.

Introduction

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 microtubules^4,5. 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 population^9,10. 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.

Protocol

1. Preparing -LEU Dropout Plates

1. Add 800 mL of sterile, deionized water (DiH₂O) to a 2 L flask. Add 26.71 g of dropout base (DOB) powder, 0.69 g of CSM-Leu, and 20 g of agar. Mix with a magnetic stir bar. Bring to 1 L with DiH₂O.
2. Autoclave at 121 °C and 19 psi for 30 min.
3. Remove the flask from the autoclave and allow it to cool to ~65 °C with gentle stirring.
4. Pour 30 mL/plate into 100 mm diameter plates. Let these sit at room temperature for 36-48 h before storing at 4 °C.

2. Integrating GFP-Tub1 for the Constitutive Expression of GFP-labeled Tubulin

1. Boil a stock concentration of 10 mg/mL single-stranded DNA (ssDNA) for 3 min.
2. Combine 2 µL of boiled ssDNA with 400 ng of purified GFP-Tub1 plasmid DNA¹¹.
   NOTE: There are a variety of plasmids that can be used for the stable, fluorescent labeling of microtubules in yeast that utilize alternative fluorophores and selectable markers. Some of these alternatives are described in the Discussion section.
3. Add the DNA mixture to a 50 µL aliquot of competent yeast cells.
   NOTE: Prepare competent yeast cells with sorbitol solution (SORB; 100 mM LiOAc, 10 mM Tris-HCl pH 8, 1 mM EDTA pH 8, and 1 M sorbitol, adjusted with dilute acetic acid to pH 8). For a detailed description of making competent yeast cells, see reference¹².
4. Vortex thoroughly.
5. Add 6x the total volume of polyethylene glycol (PEG) solution (100 mM LiOAc, 10 mM Tris-HCl pH 8, 1 mM EDTA/NaOH pH 8, and 40% PEG3350) to the DNA-cell mixture.
6. Vortex thoroughly.
7. Incubate for at least 1 h at 30 °C with gentle agitation.
8. Add dimethyl sulfoxide (DMSO) to 10% of the total volume and vortex.
9. Incubate at 42 °C for 15 min.
10. Centrifuge at 2,500 x g for 5 min.
11. Remove the supernatant and suspend the cells in 100 µL of DiH₂O.
12. Plate cells on an -LEU dropout plate.
    NOTE: The GFP-Tub1 construct contains a leucine auxotrophic marker, while other constructs could use an alternative marker. The dropout medium should be specific to the auxotrophic marker of the construct.
13. Allow the transformed cells to grow for 3-5 days at 30 °C.
14. Re-streak single transformation colonies on a fresh -LEU dropout plate by transferring colonies using an autoclaved toothpick. Incubate the plate overnight at 30 °C.
15. Visually screen each transformant for a GFP signal by suspending approximately 1 µL of cells from a single colony from the plate in step 2.14 in 2 µL of water on a clean slide. Cover the suspended cells with a coverslip and observe using fluorescence microscopy.
    1. Excite the transformants with 488 nm light and look for the presence of a GFP signal.
       NOTE: This step can use a spinning disk confocal microscope, but a widefield microscope equipped with a 100X objective and appropriate optics for imaging GFP will suffice. Cells with no visible GFP-labeled microtubules, or those with abnormally high fluorescence, should be rejected. After this, the yeast strains are ready to be cultured for imaging.

3. Growing Liquid Yeast Culture

1. Inoculate ~1 µL of yeast cells from a single colony on a plate in 3 mL of synthetic complete medium. Allow the cells to grow overnight at 30 °C with shaking at 250 rpm.
2. Dilute the saturated culture the following day to an OD₆₀₀ of <0.1 in the medium. Return the diluted culture to the 30 °C shaker for 3-4 h or until the OD₆₀₀ is between 0.4 and 0.8, when the cells are ready to be loaded into chambers for imaging.

4. Preparing Flow Chamber Slides

1. Cut a sheet of paraffin film into a piece 4 in wide and 7 in long.
2. Place a standard microscope slide lengthwise on the 4 inch width of paraffin film and wrap the film around the slide 3-4 times such that the slide is covered in film that is 3-4 layers thick. Press the layers together to ensure there is a reasonable seal; this will make it easier to cut. Avoid pressing too hard or causing the film to wrinkle.
3. Cut the paraffin film along the outside edges of the slide using a clean box cutter razor. Use another slide to provide a straight edge for cutting.
4. Peel the excess film off the working slide and discard. The remaining stacks of film will cover one face of the slide and will be used to make the walls between imaging chambers.
5. Using the second slide as a straight edge, cut the stacked film layers along the long axis of the slide into approximately ten strips, each 2-4 mm in width.
6. Cut the stacked paraffin film layers perpendicularly to the cuts made in step 4.5, along the short axis of the slide, to create strips 2-4 mm in width, 23-24 mm in length, and 3-4 layers thick.
7. Peel the cut strips using a razor at a 45° angle. Using forceps, evenly distribute the cut film strips on a clean microscope slide to create the desired numbers of chambers. Up to 3 chambers (made with 4 paraffin film strips) can be created on one slide.
8. Place a single coverslip on top of the strips of film and move it into position with forceps.
9. Place the prepared slide, coverslip up, on a hotplate set at ~95 °C (this is approximately the low-medium setting on most hotplates). Heat the slide until the film strips are translucent (approximately 3 min) and then seal the slide and coverslip together by gently pressing on the coverslip with the tweezers.
   NOTE: It is important to only press on the regions of the coverslip that are directly above the paraffin film strips, as scratching the regions above the chambers can diminish image quality. Take care not to overheat the slide; vaporized film will coat the inside of the chamber with a hydrophobic film that prevents the cells from adhering.
10. Use forceps to remove the slide from the hotplate (the slide will be hot) and set it aside to cool with the coverslip side facing up; as the slide cools, the film will return to a white, opaque coloration.
    NOTE: At this point, the slides are ready to be loaded with cultured yeast cells. Extra slides can be made to this point and stored in a clean and dry location for an indefinite amount of time.

5. Loading Yeast Cells into Prepared Flow Chamber Slides

1. Thaw a frozen, 200 µL aliquot of Concanavalin A (2 mg/mL in sterile DiH₂O).
2. Add approximately 100 µL of thawed Concanavalin A to each chamber of the prepared slide by pipetting into one of the open ends. Add enough Concanavalin A to fill the chamber. For this step, the Concanavalin A will be pulled through the chamber via capillary action.
3. Hang the slide, coverslip-down, between two microfuge tube racks or pens for 5 min to allow the Concanavalin A to adhere to the coverslip (i.e., the chamber).
4. Return the slide to a coverslip-up position. Wash the chamber with 2x the chamber volume (determined in step 5.2, above; approximately 200 µL) of synthetic complete medium to remove the Concanavalin A.
   1. Flow the medium through the chamber, using either the corner of a paper towel or a filter paper folded in half to draw fluid out of one end of the chamber while simultaneously pipetting fresh fluid into the other. Alternatively, use a vacuum aspirator to carefully draw fluid out of one end while pipetting fresh fluid into the other.
5. Load cells by flowing 2x the chamber volume (approximately 200 µL) of cell suspension (from step 3.4) using the directed flow method described in step 5.4.1.
   NOTE: The goal is to image a single layer of cells at the coverslip. Therefore, it is important to load a concentration of cells small enough that they do not pile on top of one another, but large enough that sufficient cells are present in the field to collect the maximum amount of data per image acquisition. The optimal concentration of cells within the chamber can vary and should be empirically determined. To increase the concentration of cells in the flow chamber, gently pellet the cell culture at 1,000 x g for 2 min and remove some of the supernatant. To decrease the concentration of cells, add fresh synthetic complete medium to the cell suspension. The concentration of cells can be evaluated after step 5.7 (below) by inspecting the chamber on a phase contrast microscope. At this point, additional cells can be added to the chamber by flowing in more cell suspension. However, reducing cell density at this point is not feasible and is best achieved by loading a new chamber with a diluted cell suspension.
6. Hang the slide coverslip-down, as described in step 5.3, for 10 min to allow the cells to adhere to the coverslip.
7. Flush out any unadhered cells by flowing through 4x the chamber volume (approximately 400 µL) of synthetic complete medium. This will eliminate free-floating cells in the chamber, which can contaminate image acquisition.
8. Carefully dry each end of the chamber with a clean corner of a paper towel, bringing the meniscus to the edge of the coverslip.
9. Seal each end of the chamber with warm (75 °C) sealant (e.g., VALAP; equal parts petroleum jelly, wool wax, and paraffin wax).
   NOTE: At this point, the slide is ready to image. The chambers can be imaged for up to 9 h, depending on the density of cells in each chamber. The cells will produce carbon dioxide (CO₂) over time, which will gradually create bubbles that displace the cells.

6. Image Acquisition

1. Bring the slide to an inverted microscope equipped with a 1.45 NA 100X CFI Plan Apo objective, a piezoelectric stage, a spinning disk confocal scanner unit, an illumination laser, and an EMCCD camera. Maintain the temperature of the stage at room temperature (25 °C) during acquisition.
   NOTE: The high numerical aperture allows for a greater image resolution, and the piezoelectric stage enables high-speed z-stack acquisition. The minimum microscope requirements are the 100X objective, the illumination laser, and the EMCCD camera.
2. Bring the cells into focus. Ensure that the acquisition field has at least 5-6 cells clustered together by searching the slide for cells grouped together.
   NOTE: Although it is not necessary to image more than one cell at a time, imaging multiple cells in the same field improves the efficiency of data acquisition without impairing the quality of data. However, it is critical that the cells are on the same focal plane and not piled on top of one another.
3. Program a multi-dimensional acquisition to collect multiple z-series over time.
   1. Adjust the settings within the active acquisition settings to the following: total Z-depth = 6 µm, to encompass the entire yeast cell; Z-step size = 300 nm, to maximize light collection without oversampling; total time duration = 10 min; time intervals = Z-series every 4 s; and exposure time = 90 ms for each z-plane.
      NOTE: The optimal exposure time will vary depending on the camera, the excitation light source, and other factors. In general, the optimal exposure time will be the minimum time necessary to achieve a 3:1 ratio of signal from GFP-labeled astral microtubules to signal from the cytoplasm based on pixel values measured by the EMCCD.
4. Begin the acquisition by clicking 'Run'. Allow it to run for the full 10 min duration. Save the data as an image series (.tiff or .nd2).
5. Select a new field to image within the chamber and repeat steps 6.2-6.4.
   NOTE: A single chamber should yield between 15 and 20 fields of cells, depending on the cell density within the chamber. Higher cell densities will yield lower total fields, as the CO₂ in the chamber will build up faster.

7. Image Analysis

1. Open the image file in ImageJ (https://imagej.nih.gov) as a hyperstack by using the bio-formats importer plugin.
   1. Open ImageJ and drag the acquired image stack from section 6 directly onto the control panel. The bio-formats importer will start automatically. Select "Ok" to load the image stack as a hyperstack.
      NOTE: The "Bio-formats Import Options" prompt will open and ensure that under the "Stack Viewing" section, "View stack with:" is set to "Hyperstack" and "Stack Order:" is set to "XYCZT."
2. Collapse each Z-series into a maximum-intensity, 2-dimensional projection using the Z-project function.
   1. Select the "Image" menu, the "Stack" submenu, and the "Z-project" feature. Select "Maximum projection" from the drop-down list and click "Ok."
3. Apply a median filter to the image stack to reduce speckling noise by selecting the "Process" menu, the "Filters" submenu, and the "Median" feature. Enter 2.0 pixels into the radius box and click "Ok."
4. Identify pre-anaphase cells in the field.
   NOTE: These are characterized by medium-sized buds and a short mitotic spindle, 1-1.5 µm in length (Figure 1; the arrowhead points to a short mitotic spindle). Cells at this stage are ideal for analyzing microtubule dynamics because they typically exhibit only a few astral microtubules emanating the spindle poles⁷. Astral microtubules can be identified as linear filaments that exhibit a uniform GFP signal intensity from the minus end, at the SPB, to the plus end, in the cytoplasm.
5. Starting at the first timepoint of the image series, use the segmented line tool to draw a line along a single astral microtubule, from the minus end, located at the junction between the astral microtubule and the more intensely labeled spindle microtubules, to the plus end, which is in the cytoplasm (see the red lines in Figure 1). Double-click on the end of the microtubule to complete the segmented line.
6. Record the measurement in the results table using the "measure" feature by pressing the "M" button on the keyboard.
   NOTE: Additional features, such as gray intensity, can be recorded by setting the measurements in the "Analyze" menu and "Set Measurements" submenu.
7. Advance to the next timepoint by either clicking the right arrow in the Z-slider bar or pressing the "./>" key on the keyboard. Repeat steps 7.5 and 7.6 for all timepoints in the image sequence.
8. Copy the data from the results table and paste them into a spreadsheet. Save the data by selecting "Save As."
   NOTE: These measurements will include several values, but the most important values are the slice, which indicates the timepoint in the series, and the length, which indicates the length of the line (i.e., the astral microtubule). Columns containing other measurements (e.g., the area, mean pixel value, angle, etc.) can either be kept or discarded.
9. Create a new column in the spreadsheet by right-clicking and using the "Insert" function. Input the time (s) of each timepoint.
10. Create an XY scatter plot of microtubule length (µm) as a function of time (s) using the "Insert Line Chart" feature; select the length measurements as "Y" and the time column as "X."
    1. Identify regions of polymerization, depolymerization, and pause by looking for positive and negative changes in length over time on the scatter plot from step 7.10.
       NOTE: Polymerization events increase microtubule length by at least 0.5 µm across a minimum of 3 timepoints and fit a linear regression with an R² ≥ 0.9 and an R-value > 0 (Figure 1B and D, green points). Depolymerization events decrease microtubule length by at least 0.5 µm across a minimum of 3 timepoints and fit a linear regression with an R² ≥ 0.9 and an R-value < 0 (Figure 1B and D, pink points). Pause events are separate from polymerization and depolymerization. Pauses are defined as net changes in microtubule length less than 0.5 µm across a minimum of 3 timepoints; fit a linear regression with an R-value between -0.4 and 0.4; and have a slope that is not statistically different from zero, as determined by an F-test.
    2. Using the scatter plot as a guide, identify the sections of time when the length change is positive and highlight them in a green color. Highlight regions of time when the length change is negative in a pink color.
    3. Calculate the linear regression of each highlighted section and record the R- and R²-value for each section in the columns proximal to the regions. Classify each colored region as either a polymerization event or a depolymerization event.
11. Calculate the polymerization rates by dividing the net change in length by the change in time for each growth event. Record these results in the column adjacent to the linear regression values from step 7.10.3.
12. Calculate the depolymerization rates by dividing the net change in length by the change in time for each shrinking event. Record these results in the column adjacent to the polymerization rate values from step 7.11.
13. Calculate the catastrophe frequency by dividing the number of polymerization-to-depolymerization transitions by the total time of all growth events¹³. Record these results in the column adjacent to the depolymerization rate values from step 7.12.
14. Calculate the rescue frequency by dividing the number of depolymerization-to-polymerization transitions by the total time of all shrinkage events¹³. Record these results in the column adjacent to the catastrophe frequency values from step 7.13.
15. Calculate the dynamicity by dividing the sum of the absolute value of all length changes by the total duration of the image acquisition, calculated in step 7.10.1, and multiplying by 27.0833 to obtain tubulin subunits per second¹⁴. Record these results in the column adjacent to the rescue frequency values from step 7.14 and save the results table.
    NOTE: It is possible to automate the analysis process described in steps 7.10-7.15 using a custom-made program (Estrem, et al., manuscript submitted). The program is designed to identify periods of polymerization and depolymerization in the length measurements by following the parameters detailed above.

Results

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-tubul...

Discussion

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 ter...

Materials

List of materials used in this article

Name	Company	Catalog Number	Comments
DOB (dropout bases)	Sunrise science	1650
CSM-Leu	Sunrise science	1005
Agar	Ameresco	N833
100 mm 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)