Mitochondria act like fuel stations for the cell, supplying the energy needed to keep it functioning and healthy. During certain activities, they can produce bursts of superoxide, known as ‘mitoflashes.’ These short, intense events occur in individual mitochondria and can be observed in cardiomyocytes, skeletal muscle, hippocampal neurons, chondrocytes, isolated mitochondria, and among other types of eukaryotic cells using confocal microscopy. Commonly detected with a mitochondria-targeted circularly permuted fluorescent protein (mt-cpYFP), mitoflashes provide real-time insights into mitochondrial respiration function in situ and act as a biosensor for superoxide levels, reflecting the activity of the mitochondrial electron transport chain.

Mitoflashes are triggered by elevated reactive oxygen species (ROS) levels, and various physiological and stress-related signals including fluctuations in mitochondrial membrane potential, transient openings of the mitochondrial permeability transition pore (mPTP), calcium level changes, and bioenergetic stress (like nutrient deprivation or high metabolic demand, Wang et al. 2022). Additionally, they can be induced during specific cellular processes, like differentiation and certain developmental stages, where mitochondrial function is tightly regulated. This burst event activates the mPTP, causing a depolarization of the mitochondrial membrane potential (ΔΨm) and a mild alkalization of the matrix (Feng et al. 2019). While mitoflashes signal mitochondrial activity and ROS changes, they do not precisely quantify ROS levels or other cellular signatures. These events, however, are able to distinguish active from static mitochondria, making them valuable biomarkers for tracking mitochondrial health and function.

Importantly, the frequency and kinetics of mitoflashes hold physiological and pathophysiological implications. They correlate with key processes such as muscle contraction, cell differentiation, neuron development, wound healing, and lifespan prediction. The frequency and characteristics of mitoflashes vary by cell type; for example, adult cardiomyocytes display approximately 3.8 ± 0.5 mitoflashes, while primary cultured hippocampal neurons show around 31 ± 4 per cell (Li et al. 2020).

In this tutorial, you will learn to track mitochondria in live-cell imaging data and detect mitoflashes using Galaxy tools for image analysis. You will work with time-lapse microscopy data, often stored as TIFF files with image stacks, to observe and analyze mitochondrial events across multiple time points. By identifying these events and quantifying their frequency through intensity measurements fitted to a curve, you’ll gain insights into mitochondrial behavior and activity over time.

This tutorial is based on a workflow originally described in Wang et al. 2022.

Agenda

In this tutorial, we will cover:

1. Understanding the concept and biological relevance of mitoflashes.
2. Processing live-cell imaging data for mitochondrial tracking.
3. Detecting and analyzing mitoflashes with Galaxy tools.
4. Analysis and Interpretation of mitoflash events.

Preparing Your Data

First, we need to upload the data we’ll work with. Ensure that you have the necessary files prepared from Zenodo or a shared data library within Galaxy.

Data Upload

Hands-on: Data Upload

1. Create a new history for this tutorial in Galaxy.

   Tip: Creating a new history

   To create a new history simply click the new-history icon at the top of the history panel:

   

2. Import the mitoflash imaging data from Zenodo or from the shared data library:
   • Important: Choose the correct data type if prompted (tiff).

   https://zenodo.org/records/14071552/files/mitoflashes_8bit.tiff
   

   Tip: Importing via links

   • Copy the link location

   • Click galaxy-upload Upload Data at the top of the tool panel

   • Select galaxy-wf-edit Paste/Fetch Data

   • Paste the link(s) into the text field

   • Press Start

   • Close the window

   Tip: Importing data from a data library

   As an alternative to uploading the data from a URL or your computer, the files may also have been made available from a shared data library:

   1. Go into Data (top panel) then Data libraries
   2. Navigate to the correct folder as indicated by your instructor.
      • On most Galaxies tutorial data will be provided in a folder named GTN - Material –> Topic Name -> Tutorial Name.
   3. Select the desired files
   4. Click on Add to History galaxy-dropdown near the top and select as Datasets from the dropdown menu

   5. In the pop-up window, choose

      • “Select history”: the history you want to import the data to (or create a new one)
   6. Click on Import

3. Confirm that the datatypes are correct for each file:
   • mitoflashes_8bit.tiff should be an image in tiff file format.

   Tip: Changing the datatype

   • Click on the galaxy-pencil pencil icon for the dataset to edit its attributes
   • In the central panel, click galaxy-chart-select-data Datatypes tab on the top
   • In the galaxy-chart-select-data Assign Datatype, select datatypes from “New type” dropdown
     • Tip: you can start typing the datatype into the field to filter the dropdown menu
   • Click the Save button

4. Tag each dataset with a label like “mitoflash_1” for easy identification later if you are working with multiple images.

Analyzing Mitochondrial Movement

In this section, we will focus on the detection of the mitochondria in a time-lapse image sequence based on local intensity maxima. This will provide insights into mitochondrial energy production and cellular health dynamics in the subsequent analysis.

Step 1: Mitochondrion Detection

Hands-on: Mitochondrion Detection

1. Spot Detection ( Galaxy version 0.0.1) with the following recommended parameters:
   • param-file “Image input”: mitoflashes_8bit.tiff
   • Starting time point: 1 (first frame in the stack)
   • Ending time point: 0 (use 0 to process until the last frame)
   • Intensity measurement method: Set to Smoothed for a more averaged intensity or Robust for resilience to outliers.
   • Threshold: Start at 10%; lower to 5-8% to catch weaker signals, or raise to 15-20% to focus on the strongest signals.
   • Gaussian filter sigma: Use a value around 1 to suppress noise; increase to 1.5 if noise is high.
   • Boundary pixels: Set to 10 to ignore spots within the image edges, adjusting as needed for image size.

   This tool detects mitochondria based on intensity maxima. Adjusting the threshold parameter can improve the accuracy of spot detection, allowing to reduce the number of missed or spuriously detected mitochondria (as a rule of thumb, the latter is better tolerable for our analysis).

Step 2: Tracking Mitochondrial Movement

Mitochondria are dynamic organelles, constantly moving, and changing position within the cell. Without tracking, intensity measurements could be inconsistent or misleading, as they might reflect overlapping signals from different mitochondria rather than continuous changes in a single organelle.

By linking each mitochondrion’s position across frames, we ensure that intensity fluctuations represent true changes within the same mitochondrial structure, enabling precise analysis of events like mitoflashes.

Hands-on: Tracking Mitochondrial Movement

1. Perform linking in time series (nearest neighbors) ( Galaxy version 0.0.3-2) with the following recommended parameters:
   • param-file “Coordinates (and intensities) of input points”: Output from the Spot Detection tool.
   • Neighborhood size: Set to 6 pixels as a starting value. Adjust within 1-10 pixels based on the density of points and proximity between spots across frames.
   • Intensity threshold: A recommended starting value is 25% of the global maximum intensity. Increase to 30-40% for stricter filtering if spots have low intensity.
   • Minimum track length: Use 50% of the sequence length as a baseline. Adjust based on the expected duration of mitoflash events.

   This tool links detected spots across frames to track their movement. The Neighborhood size parameter controls the maximum allowed distance for associating points between frames (in pixels). Increase the Intensity threshold parameter to filter out more weak signals (e.g., to remove spurious tracks).

Detecting Mitoflashes

Mitoflashes are identified based on sudden changes in fluorescence intensity in mitochondria, signifying superoxide bursts. We will use a curve-fitting tool to capture these fluctuations and measure their characteristics.

Step 3: Curve Fitting for Mitoflash Detection

Hands-on: Mitoflash Detection

1. Perform curve fitting ( Galaxy version 0.0.3-2) with the following parameters:
   • param-file “File name of input data points (xlsx)”: Output from the Perform linking in time series (nearest neighbors) tool.
   • Degree of the polynomial function: Set to 2nd degree.
   • Penalty: Choose Least absolute deviations (LAD) for robust fitting to intensity fluctuations.
   • Alpha: Set a significance level, such as 0.01, to generate assistive curves if needed.

   This step uses polynomial curve fitting to model the basic underlying intensity patterns over time. The chosen Penalty ensures that the curve remains robust against brief, high-intensity deviations (e.g., mitoflashes or image noise), preserving the basic intensity trends. Mitoflash events are then identified as intensity values that significantly exceed the fitted curve by a specified threshold. To aid in this identification, an assistive curve is generated to represent the significance threshold, allowing mitoflash events to be easily recognized as those values exceeding the assistive curve.

Analysis and Interpretation of Results

Congratulations! You’ve reached the final step of your analysis and now have a fully prepared results table, ready for interpretation and further exploration! The output is an Excel file containing the columns FRAME, POS_X, POS_Y, INTENSITY, CURVE, and CURVE_A. This information is available for all detected spots in your image stack and can be used to calculate metrics for interpreting and plotting your mitoflash events results.

Therefore, we can now analyze the frequency, duration, and intensity of the mitoflashes. This data will allow us to understand the physiological significance of mitoflashes in processes like muscle contraction, neuron development, and wound healing. In the upcoming section, we will expand on how to interpret and plot these mitoflash events to give them biological meaning.

Here is an example of what the final output from the curve fitting tool in this tutorial looks like:

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Question: Example Analysis

Consider the following example data table.

FRAME	POS_X	POS_Y	INTENSITY	CURVE	CURVE_A
1	57	474	114.84	116.10	165.38
2	57	474	115.91	116.11	165.39
3	57	474	166.00	166.05	165.40
4	57	474	116.24	116.13	165.42
5	57	474	116.40	116.15	165.43
6	57	474	164.50	165.00	165.44
7	57	474	165.10	165.20	165.45
8	57	474	115.69	116.33	165.62

1. Which frames show INTENSITY values that correspond to mitoflash events?
2. What does a case like Frame 7 (where both INTENSITY and CURVE are close to CURVE_A) indicate in terms of biological significance or computational interpretation?

Solution: Solutions

1. Frame 3 demonstrates a brief but significant increase of the intensity value (INTENSITY > CURVE_A), that corresponds to a mitoflash event. Frame 6 shows CURVE > CURVE_A, while INTENSITY remains below CURVE_A.
2. Frame 7 represents a borderline case where both INTENSITY and CURVE are near CURVE_A. This could reflect computational noise, weak mitoflash activity, or background fluctuations that do not meet the threshold for a significant event.

Key Parameters for Analysis and Plotting

1. FRAME: Represents time points in the data, with each row corresponding to a frame in the time-lapse sequence. Use FRAME as the x-axis for time-based plotting.

2. POS_X and POS_Y: These columns provide the coordinates of each detected mitoflash, allowing visualization of mitochondrial movement and spatial distribution within the cell.

3. INTENSITY: Shows the raw intensity values for each mitochondrion at each time point. Plotting INTENSITY against FRAME reveals fluctuations over time, with peaks indicating mitoflash events.

4. CURVE and CURVE_A: Contain fitted curve data that smooths out noise to highlight true intensity peaks. CURVE is the main fit, while CURVE_A may show adjusted fits if available.

   • Amplitude (F/F0): The peak value in CURVE or CURVE_A, representing the magnitude of each mitoflash. Higher amplitudes indicate stronger bursts, which can have biological significance.
   • Tpk (Time to Peak): The frame where CURVE or CURVE_A reaches maximum intensity, indicating the speed of each mitoflash.
   • T50 (Duration): The frames where the curve values are at least half of the peak value, representing the mitoflash duration. Longer durations may reflect sustained mitochondrial activity.

Suggested Plots

• Mitoflash Intensity Over Time: Plot FRAME (x-axis) against INTENSITY or CURVE (y-axis) to observe intensity fluctuations over time, with peaks marking mitoflash events.

• Amplitude and Duration Analysis: Create a histogram or scatter plot of Amplitude (from CURVE peaks) and T50 to explore the distribution of mitoflash magnitudes and durations.

• Spatial Mapping of Mitoflashes: Use POS_X and POS_Y for a 2D plot, with each point representing a mitoflash location. Color-code points by Amplitude to show intensity distribution across the cell.

This format seamlessly integrates the example data and a question for the user to reflect on and interact with the tutorial, helping them understand how to interpret differences between INTENSITY, CURVE, and CURVE_A.

These analyses and visualizations provide an understanding of mitochondrial activity and responses to cellular conditions, enabling insights into mitochondrial health and function in various states.

Open image in new tab

Conclusion

In this tutorial, we have covered key techniques for tracking mitochondria and detecting mitoflashes. By processing and analyzing live-cell imaging data, you can gain valuable insights into mitochondrial behavior and health in cells. Tracking mitoflashes can contribute to studies in metabolism, aging, and diseases linked to mitochondrial dysfunction in verious cell types and eukaryotic organisms.

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Key points

• Mitoflashes are superoxide events in mitochondria that signal functional states and stress responses.

• Bioinformatics tools can facilitate the visualization, tracking, and quantitative analysis of these events.

• Proper tracking and analysis of mitoflashes contribute to understanding mitochondrial behavior and health in cells.

Frequently Asked Questions

Useful literature

Further information, including links to documentation and original publications, regarding the tools, analysis techniques and the interpretation of results described in this tutorial can be found here.

References

1. Feng, G., B. Liu, J. Li, T. Cheng, Z. Huang et al., 2019 Mitoflash biogenesis and its role in the autoregulation of mitochondrial proton electrochemical potential. The Journal of General Physiology 151: 727–737. 10.1085/jgp.201812176
2. Li, A., Y. Qin, M. Gao, W. Jiang, B. Liu et al., 2020 Protocol for Imaging of Mitoflashes in Live Cardiomyocytes. STAR Protocols 1: 100101. 10.1016/j.xpro.2020.100101
3. Wang, D., Q. Gao, I. Schaefer, H. Moerz, U. Hoheisel et al., 2022 TRPM3-mediated dynamic mitochondrial activity in nerve growth factor-induced latent sensitization of chronic low back pain. Pain 163: e1115–e1128. 10.1097/j.pain.0000000000002642

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Citing this Tutorial

1. Diana Chiang Jurado, Leonid Kostrykin, Tracking of mitochondria and capturing mitoflashes (Galaxy Training Materials). https://training.galaxyproject.org/training-material/topics/imaging/tutorials/detection-of-mitoflashes/tutorial.html Online; accessed TODAY
2. Hiltemann, Saskia, Rasche, Helena et al., 2023 Galaxy Training: A Powerful Framework for Teaching! PLOS Computational Biology 10.1371/journal.pcbi.1010752
3. Batut et al., 2018 Community-Driven Data Analysis Training for Biology Cell Systems 10.1016/j.cels.2018.05.012

BibTeX

@misc{imaging-detection-of-mitoflashes,
author = "Diana Chiang Jurado and Leonid Kostrykin",
	title = "Tracking of mitochondria and capturing mitoflashes (Galaxy Training Materials)",
	year = "",
	month = "",
	day = "",
	url = "\url{https://training.galaxyproject.org/training-material/topics/imaging/tutorials/detection-of-mitoflashes/tutorial.html}",
	note = "[Online; accessed TODAY]"
}
@article{Hiltemann_2023,
	doi = {10.1371/journal.pcbi.1010752},
	url = {https://doi.org/10.1371%2Fjournal.pcbi.1010752},
	year = 2023,
	month = {jan},
	publisher = {Public Library of Science ({PLoS})},
	volume = {19},
	number = {1},
	pages = {e1010752},
	author = {Saskia Hiltemann and Helena Rasche and Simon Gladman and Hans-Rudolf Hotz and Delphine Larivi{\`{e}}re and Daniel Blankenberg and Pratik D. Jagtap and Thomas Wollmann and Anthony Bretaudeau and Nadia Gou{\'{e}} and Timothy J. Griffin and Coline Royaux and Yvan Le Bras and Subina Mehta and Anna Syme and Frederik Coppens and Bert Droesbeke and Nicola Soranzo and Wendi Bacon and Fotis Psomopoulos and Crist{\'{o}}bal Gallardo-Alba and John Davis and Melanie Christine Föll and Matthias Fahrner and Maria A. Doyle and Beatriz Serrano-Solano and Anne Claire Fouilloux and Peter van Heusden and Wolfgang Maier and Dave Clements and Florian Heyl and Björn Grüning and B{\'{e}}r{\'{e}}nice Batut and},
	editor = {Francis Ouellette},
	title = {Galaxy Training: A powerful framework for teaching!},
	journal = {PLoS Comput Biol}
}

                   

Galaxy Administrators: Install the missing tools

You can use Ephemeris's shed-tools install command to install the tools used in this tutorial.

shed-tools install [-g GALAXY] [-a API_KEY] -t <(curl https://training.galaxyproject.org/training-material/api/topics/imaging/tutorials/detection-of-mitoflashes/tutorial.json | jq .admin_install_yaml -r)

Alternatively you can copy and paste the following YAML

---
install_tool_dependencies: true
install_repository_dependencies: true
install_resolver_dependencies: true
tools:
- name: curve_fitting
  owner: imgteam
  revisions: 2dc244356765
  tool_panel_section_label: Imaging
  tool_shed_url: https://toolshed.g2.bx.psu.edu/
- name: points_association_nn
  owner: imgteam
  revisions: f3fad67af22c
  tool_panel_section_label: Imaging
  tool_shed_url: https://toolshed.g2.bx.psu.edu/
- name: spot_detection_2d
  owner: imgteam
  revisions: d78372040976
  tool_panel_section_label: Imaging
  tool_shed_url: https://toolshed.g2.bx.psu.edu/

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