Mitochondria are organelles within cells that facilitate energy mobilization, central metabolism, production of vital macromolecules, calcium storage, and signal transduction. As a result, they play a crucial role in cellular function, fitness, and lifespan control. Our research focuses on investigating the impact of mitochondria on aging, using the budding yeast Saccharomyces cerevisiae as a model organism.
Live cell imaging is our best tool to observe variation in mitochondrial function. Here we describe a method that utilizes a genetically-encoded, ratiometric, spectral-shifting biosensor called mitochondrial HyPer 7. MtHyPer 7 detects hydrogen peroxide, a reactive oxygen species that can damage cellular constituents, but also functions as a signaling molecule in mitochondria of live cells.
MtHyPer 7 has a high affinity for hydrogen peroxide and low sensitivity to pH. It is integrated into the yeast genome, which results in stable and consistent expression in yeast cell populations. Finally, it is quantitatively targeted to mitochondria, and has no detectable effect on cellular or mitochondria function or fitness.
Biosensors like mtHyPer 7 are critical for studying mitochondrial function at subcellular resolution, for finding mechanisms underlying mitochondrial quality control, and for examining how that process affects cell fitness, fate, and lifespan. The mtHyPer 7 then provides consistent labeling in contrast to organic dyes utilized for reactive oxygen species detection. Compared to FRET-based sensors, mtHyPer 7 is more compact and avoids issues such as cross excitation and bleed through, as well as the precise positioning and the orientation requirements for FRET.
To begin, take one milliliter of a mid-log-phase budding yeast cell culture expressing mitochondria-targeted HyPer 7. Centrifuge the cells at 6, 000 G for 30 seconds and remove the supernatant, leaving 10 to 20 microliters in the tube. Re-suspend the cell pellet by gently mixing it with residual media using a micro pipette.
Next, use an air blower or lint-free tissue to clean a glass microscope slide, and add 1.8 microliters of the cell suspension to the slide. Finally, cover the cells with number 1.5 glass cover slip by lowering it slowly at an angle to avoid introducing bubbles. Then place the slide on the microscope stage.
To image the cells, set up the acquisition conditions to ensure a detectable signal and acceptable resolution in each fluorescence channel. For example, on a spinning disc confocal microscope with an sCMOS camera, use two by two binning, 20%to 40%laser power and 200 to 600 milliseconds exposure. Next, examine the image histogram.
In a 12-bit image, ensure the total dynamic range of the pixel values is at least several hundred gray levels without saturation. Additionally, ensure the range is one order of magnitude higher than the noise level. To calculate the noise level, measure the standard deviation of pixel values in a cell-free area of an image.
Then collect time-lapse images with no delay between acquisitions to evaluate the effects of the imaging conditions on signal stability and oxidative stress in mitochondria. Using the microscope acquisition software, or ImageJ, measure the average pixel value in each fluorescence channel to ensure signal stability. If the experiment does not involve time-lapse imaging, ensure that the fluorescence is stable over two or three repeated Z-stacks.
Acquire images with a Z-interval of 0.5 micrometers and a total stack depth of six micrometers for budding yeast. If collecting Z-stacks, ensure that the Z-interval is the same for all images in the dataset and include the entire cell. Also, acquire transmitted light images to document cell boundaries.
To begin, open a multichannel Z-stack image in Fiji and open the desired biosensor analysis script file. Click Run in the script editor window to run the script. Then enter the requested information in the dialogue window that appears.
Select the channel numbers of the numerator and denominator for ratio calculation. For mitochondria-targeted HyPer 7, the numerator is the channel excited at 488 nanometers and the denominator is the channel excited at 405 nanometers. Select the channel number of the transmitted light image, if present, or zero if there is none.
Then choose the desired background subtraction method. If the background is uniform, click Select an Image Area to measure the background level in the image. Afterward, choose the desired noise subtraction method to reduce random variation in the detector readout.
Click Select an Image Area. Select the fixed value option to enter a previously-measured noise level, which usually works well if the imaging conditions are kept constant. For accurate and consistent detection of mitochondria, select a thresholding algorithm, such as O2 or maximum entropy.
Use the same algorithm for all images in an experiment, but ensure that mitochondria are accurately recognized. Then select the number of regions of interest per cell. For example, if measuring mother bud differences, select two.
Select the output folder where measurements and ratio images will be saved. For background or noise correction, choose Select an Image Area, follow the prompts to draw a background area using the rectangle ROI tool, and click Okay. While analyzing individual cells or subcellular regions, draw regions of interest based on the bright field image.
The ROI does not need to precisely match the cell outline, as only thresholded mitochondria within the ROI will be measured. After creating each ROI, press T to add the selected ROI to the ROI Manager. Check Show All in the ROI manager to document the marked cells.
Each added region will appear as a numbered item in the ROI manager list. If analyzing more than one ROI per cell, mark the ROIs in the same order for each analyzed cell. After all the desired ROIs have been added to the ROI manager, click Okay in the Mark Cells dialogue window.
Next, select the measurement table format. In the multi-measure dialogue window, check Measure All Slices and One Row Per Slice options to produce a table with the desired format. Use the ProcessMultiROITables.
R script to process the tables created with the One Row Per Slice option. Do not check the Append Results option. Save the output files to the selected folder using the script.
Now open the ratio Z-Stack image in Fiji to generate a colorized ratio image. Then open the colorize_ratio_image. ijm script file and click Run in the Script Editor window.
A dialogue window prompting to enter the requested information will appear. In the Unmodulated option, all mitochondrial pixels appear at the same brightness. Some images may appear noisy, as dim and bright pixels contribute to the ratio image.
Use the Intensity Modulated option to reduce the noise. In the minimum and maximum displayed values method, choose values near the average minimum and maximum values observed in an experiment. To ensure consistency, acquire all images using the same imaging conditions and display all images using the same minimum and maximum values.
Then select the projection mode to project the Z-stack and show the entire mitochondrial population before colorization. For a maximum intensity projection, select Maximum. To create an average intensity projection, select Average.
Finally, select the folder to save the colorized images. If the Unmodulated option is selected, choose a color scheme in the dialogue window that appears. Utilize the Fire or Rainbow RGB lookup tables built into Fiji, or any desired lookup table, in ImageJ'S LUT format.
Use the script to save the output files to the selected folder. The oxidized reduced ratio of mitochondria-tagged HyPer 7 showed a dose-dependent response to hydrogen peroxide concentration, which reached a plateau at one to two millimolar externally-added hydrogen peroxide. Differences in mitochondrial hydrogen peroxide were observed within the yeast cells.
A statistically significant decrease in hydrogen peroxide biosensor readout was detected in mitochondria in the bud, compared to the mother cell.
