Analysis of the Mitochondrial Density and Longitudinal Distribution in Rat Live-Skeletal Muscle Fibers by Confocal Microscopy

All animal experimentation procedures were evaluated and approved by the Animal Use and Care Committee (CICUAL) of Tecnologico de Monterrey (Protocol 2019-007). Male Zucker (+/+ and fa/fa) rats aged 12 to 13 weeks were used for this study. Animals were kept in standard husbandry conditions (12 h/12 h light/dark cycle, 40-60% humidity) and had access to food (standard rat chow) and water ad libitum.

1. Solution composition

1. Prepare fresh Relax solution by mixing the components shown in Table 1. Adjust pH to 7.3 using sodium hydroxide (NaOH).
2. Calculate the free calcium concentration in the Relax solution using a simulation program for determining the free metal concentration.
3. Prepare a 5 mM tetramethylrhodamine methyl ester (TMRE) stock in dimethyl sulfoxide (DMSO) and a subsequent dilution of 0.1 mM in DMSO.

2. Dissection of the gastrocnemius muscle fiber bundles

1. Place the animal inside the induction chamber and close the lid. Turn the oxygen source on, set the gas flow rate at 0.5 L/min, and set the vaporizer at 4% sevoflurane to induce anesthesia.
2. Once the rat is asleep, place it outside the chamber in a supine position while maintaining anesthesia. Pinch the toe or tail to verify the lack of reflexes.
3. With scissors, cut through the abdomen's skin and muscles. Then, cut the thoracic cage to gain access to the heart.
4. To perform a cardiectomy, grasp the heart from the topmost veins and arteries with forceps and cut the blood vessels with scissors. Remove the heart.
5. Immediately after euthanasia, disinfect the hindlimb with ethanol and shave it using a shaving machine.
6. Hold the hindfoot and make an incision with scissors through the skin at the level of the Achilles tendon.
7. Cut the hindlimb with scissors at the level of the proximal tibia. Transfer it to a 60 mm Petri dish containing 3 mL of ice-cold Relax solution in a dorsal position. Cover it with sufficient solution to prevent the muscles from drying out.
8. Identify the Achilles tendon, carefully raise it with forceps, and dissect the muscles away from the bone with iris scissors. Use a stereomicroscope from this step onward of the dissection.
9. Identify and separate the whole gastrocnemius muscle, the main bulk at the back of the hindlimb.
10. Dissect and discard the connective and fat tissue surrounding the muscle with fine-tip forceps. Transfer the lateral head of the muscle to a new Petri dish with ice-cold Relax solution (see Figure 2A).
11. Gently hold the muscle with forceps from one end and carefully separate it into bundles with microscissors. Always manipulate bundles by gently holding them at one end with forceps.
    NOTE: Bundles are approximately 10 mm long and 2 mm wide.
12. Transfer the fiber bundles to a new Petri dish with 2 mL of ice-cold Relax solution. Only select the ones that have a smooth appearance, are complete from one end to the other, and are not shortened (Figure 2B).

3. Live-cell imaging acquisition of mitochondria in skeletal muscle by confocal microscopy

1. Incubate the fibers in 2.5 × 10^-4 mM TMRE in Relax solution for 20 min at room temperature.
   NOTE: With the recommended working concentrations of TMRE, it is expected to be in a non-quenching mode. Protect from light exposure from this point onwards.
2. During the incubation time:
   1. Open the confocal microscope standard software, select the configuration framework, and in the hardware configuration dialog box select laser and check HeNe 543 option.
   2. In the acquire framework, select the acquisition dialog box, and in the acquisition mode, select XYZ panel.
   3. In the XY dialog box, select the 512 x 512 format, 400 Hz speed, and check pinhole panel. In the displayed pinhole dialog box, select AU for unit and add a value of 3 Airy for the pinhole.
      NOTE: Consider reducing the pinhole size closest to the 1 Airy optimal criterion, if an adequate fluorescence signal is conserved.
   4. In the Beam Path Settings dialog box, select a water immersion objective of 20x and numerical aperture (NA) of 0.7 (20x/0.7 IMM), and select the emission wavelength window of 576-700 nm. Select DD488/543 and 15% of laser power for the 543 laser.
      NOTE: A water immersion objective lens for live imaging scanning is strongly recommended (if available). Since a similar refractive index of the immersion medium and the incubation medium is achieved.
3. After 20 min of incubation, exchange the incubation medium twice. Ensure that the confocal images are acquired within 20-30 min after incubation.
4. Prepare the recording chamber with a 0.15 mm coverslip of borosilicate glass.
   NOTE: Select the thickness of the coverslip according to the available recording chamber, considering that thickness usually ranges from 0.15 to 0.22 mm for optimal performance.
5. Add 200 µL of Relax solution to the recording chamber and transfer the fiber bundles.
6. In the confocal microscope, use the brightfield mode to identify viable fibers for the fluorescent recording of mitochondria. Viable fibers are complete, not contracted, and have an intact striation pattern.
7. Separate the fiber bundles from each other and align them with forceps. Select the ones that are closest to the coverslip.
8. Scan the fluorescence using the live button to adjust gain and offset in the Control Panel console considering the following:
   1. Select low fluorescent intensity values for a background of nearly 0 Arbitrary Units (A.U.).
   2. Select a gain level between 100 and 200 A.U. to avoid saturation of the recording system. Hence, do not record the highest fluorescence levels.
   3. Select a pixel size of 150-190 nm to acquire the full width of the fiber, adjusting the zoom factor in the XY dialog box.
      NOTE: Consider using the pixel size closer to the Nyquist criteria (90 nm), which allows scanning of the full width of the fiber.
9. In the Z stack dialog box, adjust the Z distance to acquire the fluorescence signal starting at a fiber depth of 15 µm (begin button) up to 22 µm (end button). Select 3 µm as the Z-step size.
10. Click the Start button to acquire the confocal images. Obtain a Z-stack composed of three confocal images acquired at 15, 18, and 21 µm of depth.

4. Analysis of mitochondrial density

1. In the open-source platform for biological image analysis¹⁸ open the Z-stack file and rotate the images to place the fiber horizontally, as shown in Figure 3B.
2. Randomly select a rectangular region of interest (ROI) that includes the area occupied by the mitochondria (ROI_mito). Select ROI_mito sizes ranging from 65 to 90 µm for X. For Y, the size will depend on the fiber width, avoiding its periphery.
3. Duplicate the Z-stack with the selected ROI_mito and save it as a new Z-stack (Mito-stack) in TIFF format. Save the ROI_mito position selected on the original stack with the ROI Manager tool.
4. Calculate the threshold for background subtraction as follows:
   1. Use the shortcut command+shift+t to open the Threshold dialog box.
   2. Select the threshold algorithm Otsu |B&W | Dark background option. Observe that the image is now binarized.
   3. In the Threshold dialog box, observe the histogram of the fluorescence intensity distribution and the threshold value that is displayed.
      NOTE: Mitochondria-positive pixels have fluorescence intensity values greater than the threshold and appear as white pixels in the binary image.
   4. Apply the threshold to the binary image stack by selecting Apply. In the displayed Convert Stack to Binary dialog box, select the options Calculate threshold for each image, Black background, Create new stack, and click OK.
5. Observe that a stack with the three binary images is generated (BinDMito-stack). Save it in TIFF format.
6. Calculate mitochondrial density in the BinDMito-stack as follows:
   1. Select the Analyze menu. Next, click Histogram. In the displayed histogram dialog box, click Yes to include all the images of the stack for analysis.
   2. In the Histogram of Stack dialog box, click List to obtain the histogram data. Transfer the histogram data to a spreadsheet.
   3. In a spreadsheet, identify Mito-pixels that are those with the 255 value. Calculate Mitochondrial density using equation (1):
      Mitochondrial density =  × 100     (1)
      Total pixels are identified as N in the Histogram dialog box or calculated by summing in the spreadsheet the pixels count of the histogram.
   4. To calculate the area occupied by mitochondria (µm²), multiply the Mito-pixels of the BinDMito-stack by the pixel size.

5. Analysis of mitochondrial distribution by Fast Fourier Transform

1. In the open-source platform for biological image analysis, open the BinDMito-stack and draw a rectangular ROI of 256 pixels width and 5 µm height. Locate an ROI in a central position and another in a lateral position, as shown in Figure 4A,B.
2. Use the ROI Manager tool to configure the ROIs for FFT analysis and save it.
   NOTE: According to the necessities of the analysis, other ROIs can be selected in different positions, and their sizes can be modified. Nevertheless, the width must equal 2ⁿ pixels to perform FFT (e.g., 128, 256, 512 pixels).
3. Obtain the plot profile of the ROIs using the shortcut command + k and transfer the data to a spreadsheet.
4. In the spreadsheet, fill B and C columns with the data obtained from the plot profile. The B column contains the distance in µm, and the C column the corresponding gray value of the fluorescence intensity (A.U.).
5. The D column corresponds to the FFT frequency (event/µm), which will be filled as follows:
   1. Calculate the sampling frequency (Fs): Fs = 1/Δd, where Δd is the distance step (the second value of B).
   2. Calculate the ΔFs: ΔFs = Fs/N, where N is the number of data points of B (256).
   3. Calculate the stop value of FFT frequency (S): S = (N/2) ×ΔFs.
   4. Fill D column as follows:
      1. The first value of D column equals 0.
      2. Select the second cell videodan D column, go to the home menu, select fill, and click series. Select columns. For the step value, use the calculated Δ Fs. For the stop value, use the calculated S.
   5. Next, fill the E column with the FFT complex values as follows:
      1. Insert 0 in the first cell of the C column, because the distance 0 must coincide with a signal 0 for the calculation of the FFT.
      2. Then, go to the Data menu, click Data Analysis, and select Fourier Analysis.
      3. In the new window, select the range of data points of the fluorescent signal described in column C for the input range.
         NOTE: The number of data points must be 2ⁿ (e.g., 256 or 128 data points of fluorescent signal).
      4. Select the corresponding range of the E for the output range.
      5. Select Ok and let the FFT complex values be filled automatically.
6. Fill F column with the FFT magnitude as follows:
   1. Use the IMABS function to return the absolute value in F from the complex number in E and multiply by 2/N to normalize (equation (2)):
      FFT magnitude = (IM.ABS (E₁…E_n) × (2/N)     (2)
7. Plot the FFT spectrum using the FFT magnitude in F, as a function of FFT frequency in D, until S. Find the point of the maximum peak and its corresponding FFT frequency.
8. Convert the FFT frequency of the maximum peak to distance with equation (3). This calculated distance represents the longitudinal distance of the mitochondrial distribution.
   Distance (µm) = 1/FFT frequency     (3)
   NOTE: Due to FFT symmetry, do not plot the FFT frequency beyond the S value, which corresponds to half of the data points, avoiding duplication of the FFT spectrum.

6. Optional preprocessing steps to reduce the image noise before image analysis

1. Apply a median filter or a 2D deconvolution as follows:
   1. For median filter:
      1. Select Process | Filters and click on the Median option.
      2. In the displayed Median dialog box, select 2.0 for Radius and click OK. Choose Yes to apply the process to all images in the Mito-stack.
      3. Observe the reduction of noise in the images.
   2. For 2D deconvolution:
      1. Generate a theoretical Point Spread Function (PSF).
      2. Download the plugin PSF_Generator.jar and place the file into the "Plugins" folder.
      3. In the PSF generator dialog box, click the option Born & Wolf 3D optical model, enter the Refractive index immersion value of 1.33 used during confocal scanning, and select Best for the Accuracy computation option.
      4. Capture 576 nm for emission wavelength, the NA of the objective lens used of 0.7, and the Z-step of 3,000 nm.
      5. Capture the pixel size XY of the confocal image to be deconvoluted, as well as the size XYZ indicating the number of pixels of X and Y and the number of Z steps (3 for this protocol).
      6. In the Display menu of the PSF generator, click the option linear, select 8 bits resolution, and then select the grays option for the lookup table (LUT).
      7. Run the PSF generator and save the theoretical PSF created in TIFF format.
      8. Make a sum of slices of the PSF created by opening the Z Project tool of the option Stacks that is located in the Image menu.
      9. In the displayed ZProjection dialog box, select Sum slices for the Projection type, and click OK. Save the new PSF in TIFF format.
         NOTE: A PSF obtained experimentally by confocal scanning fluorescent microbeads with a known diameter can be used instead of the theoretical PSF.
      10. Download the plugin "DeconvolutionLab_2.jar"¹⁹ and place it into the plugin folder.
      11. Click the menu Plugins. Next, click DeconvolutionLab2.
      12. Separate the confocal images from the Mito-stack using the tool Images to stack from the Stacks option located in the Image menu and save it in TIFF format.
      13. In the DeconvolutionLab2 dialog box, select one of the images obtained from the Mito-stack. Select the theoretical PSF created, and select the algorithm Richardson-Lucy with 15 iterations.
      14. Run DeconvolutionLab2, verify signal improvement and noise reduction in the image, and save it in TIFF format.
2. Continue with step 4.4 for image analysis.
3. For the thresholding of the deconvoluted images, convert them to an 8-bit mask during the thresholding process. Create a stack with the binary and deconvoluted images by selecting Images to Stack in the Stacks tool of the Image menu.
4. For the FFT analysis of deconvoluted images, the plot profile contains distance in pixel units. Transform the pixel units to µm as follows:
   1. In the spreadsheet, after completing step 5.4 transfer the data from B için A. Next, fill B with the distance videodan µm by multiplying each pixel number from A by the pixel size.