Method Article

Understanding the Changes in Mitochondrial Morphology through Dynamic and Three-dimensional Fluorescence Micrographs

DOI:

10.3791/68478

⸱

August 15th, 2025

 ,  ,  , 
1Department of Physiology, Stellenbosch University, 2Department of Biochemistry, University of Toronto, 3Department of Electrical and Electronic Engineering, Stellenbosch University

In This Article

Summary

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Here we describe the mitochondrial event localizer (MEL), an ImageJ plugin useful in the quantification of the 3-dimensional changes in mitochondrial fission and fusion activity over time. We also describe an image processing pipeline useful for the cleanup of micrographs prior to analysis in ImageJ.

Abstract

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Mitochondria are highly dynamic organelles that are vital to the survival of any animal, undergoing regular fission and fusion events in response to the needs or stresses of the host, leading to the constant remodeling of the mitochondrial network. Because of this, being able to evaluate the mitochondrial network in three dimensions, as well as over time, offers a benefit in understanding how the system responds to factors such as stress or pharmaceutical intervention. Fluorescence imaging of the mitochondrial networks of cells enables the ability to visualize and monitor these changes. However, the mitochondrial network is often described as a two-dimensional and static structure that is defined by unstandardized metrics. Therefore, we set out to describe a pipeline that enables the user to prepare their images for the mitochondrial event localizer (MEL), an ImageJ plugin tool that detects fission and fusion events in the mitochondrial network over time and in a 3-dimensional manner, thus, offering insight into the dynamic changes that this network undergoes. Additionally, we describe the benefits of understanding fission and fusion in light of the changes in the mitochondrial count and morphological changes.

Introduction

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Mitochondria are highly dynamic organelles present in all eukaryotic cells, providing them with energy and regulate their metabolism. Thus, mitochondria are at the crossroads of cellular death and survival. Mitochondria have been shown to be essential for a variety of processes, ranging from lysosomal acidification and molecular motor action to muscle contraction and synapse firing1,2.

Mitochondria undergo regular fission and fusion events to maintain a mitochondrial network that efficiently produces ATP in response to the cell's metabolic demand and stress. Indeed, mitochondria have been shown to undergo fission to facilitate mitophagy, the selective removal of mitochondrial fragments. Hence, only actively respiring and not depolarized mitochondria are left in the cellular system3,4. Fusion, however, occurs as a means of increasing the ATP output of the network should there be an increased need5,6. Additionally, both fission and fusion have also been shown to play an important role in the partitioning and protection of mitochondrial DNA7,8. It should be noted that the extent of fission and fusion requires careful homeostatic control to ensure a healthy mitochondrial network, as too much or too little of either process has been shown to be detrimental.

Excessive fission has been shown to lead to a fragmented mitochondrial network with subsequent decreased ATP levels in Alzheimer's disease, Parkinson's disease, and tauopathies9,10,11, and low levels of fission may lead to an accumulation of depolarized mitochondria, leading to Parkinson's disease-like symptoms12. Hyperfusion of the network has been known to occur during times of stress to increase ATP output. However, existing in this state for prolonged periods of time has been shown to increase ROS levels and autophagy activity, resulting in cell death onset9,12.

It becomes clear, therefore, that understanding the state of the mitochondrial network offers key insights into understanding the state of the cell, and therefore the organism. The clear importance of understanding the mitochondrial network in the context of health and disease, its ability to undergo fission and fusion events, and their impact on cellular health is what has motivated the development of this protocol and the associated analysis tools. Specifically, tools that enable the characterization of mitochondrial dynamics are largely limited and poorly described in the literature.

Mitochondrial morphology is typically determined using confocal microscopy followed by computational analysis, which requires raw micrographs to undergo some degree of processing to enhance their quality for evaluation, as this best describes the mitochondrial organization. In this way, users can determine many morphometric outcomes of the mitochondrial network, such as count, volume, length, and aspect ratio13,14,15. Users can make use of either 2D or 3D micrographs for morphological assessments, although 3D analysis does offer greater accuracy and insight since the mitochondrial network consists of 3D structures. For the purpose of analyzing fission and fusion, micrographs with a z-axis are recommended for use as this best compensates for the 3-dimensionality of the mitochondrial network16.

Many studies involve the categorization of mitochondria into fragmented, filamentous or intermediate states as a means of describing the network16,17. 3D analysis is particularly beneficial due to the different shapes that mitochondria take in the cell. Adding 3-dimensionality to one's study lends confidence, especially to mitochondrial counts, as mitochondria are likely to move either up or down along a z-axis. MEL is an ImageJ plugin that is dependent upon 3D captured images18. Here, we made use of GT1-7 mouse hippocampal neuronal cells stained with TMRE and Hoechst to visualize the mitochondrial network as well as the nucleus of the cell. Cells were then placed through a preprocessing pipeline to enhance the quality of the micrographs in preparation for image analysis.

Many techniques have been made available that allow for the determination of mitochondrial morphology based on static metrics. Few include fission and fusion activities and enable the capturing of the dynamic behavior of mitochondria quantitatively13,19,20,21. Here we will describe a protocol for image enhancement prior to the determination of network characteristics, with a focus on mitochondrial fission and fusion activity. We will demonstrate how this technique can complement previously published methods of determining mitochondrial morphology.

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Protocol

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1. Cell treatment and microscopy acquisition

  1. Culture GT1-7 cells in 8 chamber dishes in DMEM supplemented with 10% FBS and 1% Penstrep (complete media). Allow cells to attach overnight and then treat with 2.5 mM metformin hydrochloride made in DMEM with 10% FBS for 72 h, being sure to replace the media every 24 h. Co-treat the cells with 10 µM CCCP 6 h before imaging and 400 nM of Bafilomycin A1 (Baf) 4 h before imaging.
    NOTE: This can be done with any eukaryotic cell line of choice.
  2. Prior to imaging, prepare a cocktail of prewarmed complete media containing 5 nM Hoechst and 100 nM TMRE.
  3. Replace the cell treatment media with the imaging cocktail and allow 10 min incubation time before imaging.
    NOTE: Due to the dynamic activity of the mitochondria, cells should be imaged using a microscope with an incubation chamber set to 37 °C and 5% CO2.

2. Imaging

  1. Image cells using 100x magnification with 1.4 NA.
  2. Adjust the laser power such that the power is low enough to avoid photobleaching, ~2%. Ensure the scanning speed is high. Capture images at 512 x 512 resolution.
  3. Set the Z-slice intervals between 0.25 µm increments. To follow this protocol, image the cells such that they consist of 10 Z-stacks. Acquire five time frames without any interval between the acquisition of a Z-stack.
    NOTE: Once optimized, this protocol should not be adjusted between treatment groups or experiment groups, as the macros listed here require the imaging protocol to be standardized across all cells.

3. Computational assessment

NOTE: All subsequent processing was done using ImageJ v1.53t. The MEL plugin, as well as supporting modules, can be found at https://github.com/rensutheart/MEL-Fiji-Plugin, whilst all macros used can be found at https://github.com/rensutheart/FMPP/tree/master/Sections.

  1. Image preparation
    1. Open the raw file in ImageJ.
    2. To crop multiple cells from a single micrograph, start by duplicating the image up to the number of single cells that are to be analyzed.
    3. Use the synchronize windows tool, the freehand drawing tool, and color adjustment to draw a region of interest around a cell of interest where there are multiple cells in a field of view (Supplemental Figure S1).
    4. Click on Edit | Clear Outside.
    5. Split the red and blue channels from each other and save the mitochondrial channel as a .Tiff file.
  2. Point spread function generation and deconvolution
    1. To generate a point spread function (PSF), use the PSF generator plugin using embedded micrograph information. Go to Plugins | PSF Generator to open the plugin. Additionally, go to Image | Show info… or press I to open the image information, and scroll to the bottom. Using voxel size and depth, from the show information box, change Pixelsize XY to 166.1 nm and Z-step to 200 nm. Change Wavelength to 568 nm, Size XYZ to match an image resolution of 512 x 512, and a Z-stack of 10 Z-slices (Supplemental Figure S2).
    2. Go to Plugins | Macros | Edit | Deconvolution_time_lapse_mine.ijm macros.
    3. Edit the input and output lines and press run (Supplemental Figure S3).
  3. Image contrast enhancement and blurring
    1. Go to Plugins | Macros | Edit | Preprocessing.ijm.
    2. Within the Preprocessing.ijm macros, use a background subtraction with a rolling ball radius equal to 6. Set Sigma Filter Plus such that the radius is equal to 1, the pixels used are equal to 2, and the minimum pixel fraction is equal to 0.2, with the plugin being outlier-aware. Adjust the CLAHE settings such that blocksize is 64; set the histogram bins to 256, the maximum slope to 2.5, and Gamma to 0.8.
      NOTE: All these settings have been optimized towards our datasets, and values should be optimized for alternative datasets before applying. Sigma filtering is applied to smooth the image and effectively blend nearby pixels to ensure consistent structures. Local contrast is applied to increase the contrast between light and dark pixels and coupled with a change in the gamma, enhances the presence of the mitochondrial structures whilst minimizing background pixels.
    3. Change the input line to the folder containing micrographs that have undergone deconvolution (Supplemental Figure S4).
    4. Click Run.
  4. Image thresholding
    NOTE: Although users can make use of any thresholding tool they choose, we recommend the adaptive thresholding plugin by Qingzong Tseng (https://sites.google.com/site/qingzongtseng/adaptivethreshold).
    1. Open a file of interest that has been modified by the Preprocessed.ijm macros in ImageJ.
    2. Go to Plugins | adaptiveThr.
    3. Set the local threshold to Weighted Mean and pixel block size according to the user's preference.
      NOTE: Pixel block size should be kept consistent between cells, as this value impacts mitochondrial dimensional assessments such as volume or aspect ratio.
    4. To optimize for time, click on preview and adjust the block size such that as many mitochondria are clearly included as possible. Also adjust the subtract value for each cell to eliminate unnecessary background. Save micrograph files in folders associated with the subtract value (Supplemental Figure S5).
      NOTE: The Threshold.ijm macros include a size filter to eliminate smaller particles.
    5. Select Plugins | Macros | Edit | Threshold.ijm.
    6. Edit the input_path and output_path lines, as well as blockSize, and subtract lines in the macros script (Supplemental Figure S6).
    7. Click Run.
  5. Detection of fission and fusion events by the mitochondrial event localizer (MEL) plugin
    NOTE: MEL is designed to process a time-lapse sequence of z-stacks consisting of a single channel, saved as a single Tiff at a time. Although this can be done by changing the input line to the thresholded Tiff file of interest, we will also demonstrate a method of processing multiple Tiff files at once using the concatenate function in ImageJ.
    1. Open up to 10 thresholded micrographs that belong to the same treatment conditions.
      NOTE: The micrographs must have the same number of z-slices for this to work.
    2. Go to Image | Stacks |Tools | Concatenate and press Ok.
    3. To remove the remaining small puncta left behind by thresholding, go to Plugins | Integral Image Filers | Remove outliers. Use preview to set the X and Y sizes to remove necessary fragments.
    4. Save the concatenated file as a Tiff.
    5. Go to Plugins | Macros | Edit | Quicktest_new.ijm and edit the input and output paths as necessary (Supplemental Figure S7).
      NOTE: Once completed, a folder called "MEL_results" will be found in the output folder with all MEL results. These are shown as fission and fusion events detected at each time point, and the last time point should always be removed due to the manner in which MEL works18.

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Results

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Selecting appropriate cells
Users should be aware that the mitochondrial network changes depending on the mitotic state of the cell. Should the nucleus appear dumbbell- or U-shaped, or if there is a space near the nucleus with a lack of fluorescence signal, then this may indicate that the cell is nearing mitosis. In this state, mitochondria are likely undergoing fission due to cell division and not because of the treatment intervention and its effect on the network (Supplemental Figure S8

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Discussion

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Although a growing number of approaches exist to describe mitochondrial morphology, limited techniques are available to adequately capture mitochondrial dynamics in a quantitative manner. Additionally, it should be noted that mitochondrial network morphology as well as the mechanisms governing this morphology are varied in nature. This results in networks that are linked to the needs of the cell, ranging from a branched formation for enhanced energy output to temporally distinct regions of fission to facilitate mitophagy...

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Disclosures

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The authors have no conflicts of interest to declare.

Acknowledgements

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This research was funded by Stellenbosch University, South Africa, the South African Medical Research Council (SAMRC), and the National Research Foundation (NRF) of South Africa, as well as the Canadian Institutes of Health Research (CIHR) and the Natural Sciences and Engineering Research Council of Canada (NSERC).

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Materials

List of materials used in this article
NameCompanyCatalog NumberComments
8 chamber dishesThermoFisher#Z734853
Adjusted thresholdinghttps://sites.google.com/site/qingzongtseng/adaptivethreshold
Bafilomycin A1LKT labs#B0026
Carbonyl cyanide chlorophenylhydrazone (CCCP)Merck#C2759
Confocal microscopeCarl Zeiss AGLSM780 ELYRA PS.1 super-resolution platform
Dulbecco's Modified Eagle Medium (DMEM)ThermoFisher#341956062
Fetal Bovine Serum (FBS)Sigma-Aldrich#F0679
Github linkhttps://github.com/rensutheart/MEL-Fiji-Plugin
GraphPad Prism v7.06
GT1-7 cellsATCCSCC116
HoecshtSigma-AldrichH6024
ImageJ v1.53tFiji
Macroshttps://github.com/rensutheart/FMPP/tree/master/Sections
MetforminEuropean PharmacopoeiaM06050000
Penicillin/streptomycin (PenStrep)Sigma-Aldrich#P4333
T25sBio-Smart Scientific#70025
TMREThermoFisher#T669
TrypsinSigma-Aldrich#T4049

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Tags

Mitochondrial DynamicsMitochondrial FissionMitochondrial FusionThree Dimensional ImagingImageJ AnalysisMitochondrial NetworkDeconvolution MicroscopyMitochondrial Event Localizer

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