image analysis for characterizing whole-neuron mitochondria network

MATLAB Optimization for Analyzing Mitochondrial Networks in Neuroscience

Cellular mitochondria sit at the center of all physiological states, and a thorough understanding of their homeostasis (mitostasis) and behavior is paramount to assist in identifying pharmacological treatment for a wide range of illnesses, including cancer and Alzheimer&#39;s disease1,2.
Mitochondria play crucial cellular roles in energy homeostasis, ATP generation, calcium buffering, and ROS regulation, and mitostasis is essential for maintaining protein homeostasis as molecular chaperones are energy-dependent3. These require a constant and dynamic network modulation and adaptation to efficiently meet cellular needs, and mitochondria transport is regulated by different signaling pathways; previous work has described one such pathway, that of retinoic acid receptors (RARs)4,5. Retinoic acid (RA) promotes axonal and neurite outgrowth via RAR activation. In mouse primary cortical neurons, activation of RAR-&#946; encourages mitochondrial growth, speed, and mobility in the neurite6.
Considering the mitochondrial network adaptability and dynamics, the possibility of assessing mitostasis in &#34;real time&#34; is essential not only for investigating energy homeostasis, but also for proteostasis, cellular health, proliferation, or signaling. A commonly used method for evaluating mitostasis relies on confocal microscopy after highlighting mitochondria using a fluorescent dye or marker, as well as a specific microscopy setup allowing temperature and/or CO2 regulation7. This type of experimental setup entails that one experimental replicate be performed at a time. In addition to experimental repetition of different treatments, it should be considered that most experiments should have their technical replicates (where more than one position is imaged per plate), with a series of focal planes (z-stacks) being recorded in a series of time points. Thus, an experimental design with three repetitions of one control and two treatments, with five imaging positions per plate, and 15 time points, results in 225 stacks to be processed. Classically, videos of live mitochondria were analyzed by plotting kymographs, which would be individually analyzed8, in a time-consuming process requiring extensive manual input, even when relying on computer tools.
An algorithm was recently described9 that allows automated segmentation and tracking of mitochondria in live-cell 2-D and 3-D time-lapse files. Other quantification techniques are available, and all have their limitations10. Mitometer, an automated open-source application, is particularly adequate for time lapse and mitochondria dynamics analysis, requiring low user input. This application has a series of advantages over other existing MATLAB-based tools, namely allowing the automatic processing of individual TIF stacks, using up to 13 different parameters, particularly interesting for neurosciences, as it differentiates between peri- and tele-nuclear mitochondria.
However, for an experiment such as the above described, these 13 parameters applied to 225 stacks result in 2,925 individual output files. These require four individual computer inputs, which sum up to over 10,000 manual inputs being required to download all output files. For large experimental designs, this results in a needlessly extremely time-consuming analysis of each file and data integration. Herein we present a routine optimization that allows swift file analysis, greatly reducing document reading and data processing, analyzing data in a defined manner, consistent with the output from existing tools.

Author Spotlight: An Optimized Automated Method for Investigating Retinoic Acid Receptors in Neuronal Mitochondria

Optimized Automated Analysis of Live Neuronal Mitochondria Homeostasis Modulation by Isoform-Specific Retinoic Acid Receptors

My research has focused on the area of neuroscience, and in particular, we want to understand the molecular basis of disease. This has led us to look at cells, their structure, and the organelles that carry out the different cellular functions. Increasingly, mitochondria, they have received more and more attention.Anomalies at the mitochondrial level are becoming absolutely fundamental to understand disease processes, and they may even provide interesting targets for novel drugs or therapeutic strategies. Here at the University of Aveiro, we have been able to develop tools that are of interest to study mitochondria. Not only microscopy and the more traditional approaches, but also we have been able to optimize automated analysis tools coupled with bioinformatic strategies to be able to quantify and provide more detailed analysis when addressing mitochondrial function.The most recent advances include the machine learning algorithms for image segmentation and quantification of mitochondrial parameters. And also the high throughput imaging platforms and deep learning algorithms for improving accuracy and efficiency of mitochondrial characterization. Technologies currently used to advance the field of mitochondrial analysis include high resolution microscopy, live imaging systems, computational analysis tools coupled with machine learning protocols, and CRISPR-Cas9 genome editing for mitochondrial morphology and biology analysis.Current experimental challenges in the field of mitochondria include developing reliable and accurate measurements of mitochondrial parameters, considering the heterogeneity of mitochondrial populations, analyzing the interplay between mitochondrial and cellular biology, and developing non-invasive tools for studying mitochondria in vivo. Our tool enhances efficiency and reliability, allowing for a robust automated analysis of mitochondria. Furthermore, we can explore the potential of hyaluronic acid receptor modulation in personalized medicine for mitochondria.

Watch this Scientific Journal Video about Image Analysis for Characterizing Whole-Neuron Mitochondria Network at JoVE.com

Image Analysis for Characterizing Whole-Neuron Mitochondria Network

Begin by opening the time-lapse mitochondrial images in Image J2.1.0. Separate position stacks based on the visual field by navigating to the Menu bar and clicking on Image. Then, go to Duplicate, Enter Slice, or Frame Number, and click OK.Next, draw a region of interest around the cell using the Free Selection Tool.To run the Macro, open Image J, navigate to Menu bar and click on Plugins, Macros, and Run. Then, select Clear the Background and Save the image's MACRO. To determine pixel size and voxel depth, open Image J and select Image.Access the Menu bar, select Image, and click on Properties. After determining the pixel size and voxel depth, draw a region of interest encompassing the entire cell using the Free Selection Tool to include the whole cell throughout all 15 frames. Navigate to the Menu bar, click on Plugins, Macros, and select Clear the Background, and Save the image's MACRO.Choose the desired saving folder, and click Save to store the file in TIFF Format. Then, create three main folders titled All Tracks, Perinuclear Tracks, and Telenuclear Tracks. Create a numbered sub folder for each image to process within each main folder, starting from one.Add a copy of the two routine optimization supplementary files into each image folder. To identify or alter the default mat file version, navigate to the Home tab in the Environment section and click on Preferences. Then, select MATLAB.Open MATLAB software on the computer, navigate to the Menu bar, select apps, and open Mitometer. Select Start 3D and set the pixel size to 0.1395089 micrometers, time between frames to 15 seconds, number of z-planes to 8, and axial distance between z-planes to 0.418809 micrometers. Select images to input.Now, go to the Mitometer side menu, select Image, and click Select Highlighted. Navigate to the Mitometer Menu bar, choose Select Tracks followed by Track Views. Then, choose from All Tracks, Telenuclear, or Perinuclear Options.Save the results to a text file by selecting Save to txt, and extract the result files to the created folders. Prepare the randomization xlsx file, which contains the key for image encoding. Insert a sequence of consecutive integers, starting from one, in column A, populate column B with alphanumeric characters, which constitute the analytic variables.Double click on the executable mlx, input the number of folders, click Specify the folder's directory, and select Save Directory. Select the spreadsheet name in the output file and click Run. Following this, perform the necessary statistical analysis.Representative images of mitochondria after treatment, respective surface plots, and automated segmentation are presented. A significant decrease in average mitochondrial length was observed in cells treated with AM580. The mitochondrial surface area significantly decreased in cells treated with AM580 and BMS493.TMRM intensity normalized for mitochondrial volume showed a significant decrease in cells treated with retinoic acid and CH55.

image-analysis-for-characterizing-whole-neuron-mitochondria-network

Research

JoVE Journal

Neuroscience

85 Views.  University of Aveiro. Begin by opening the time-lapse mitochondrial images in Image J2.1.0. Separate position stacks based on the visual field by navigating to the Menu bar and clicking on Image. Then, go to Duplicate, Enter Slice, or Frame Number, and click OK.Next, draw a region of interest around the cell using the Free Selection Tool.To run the Macro, open Image J, navigate to Menu bar and click on Plugins, Macros, and Run. Then, select Clear the Background and Save the image's MACRO. To determine pixel...