Name: Billedanalyse til karakterisering af helneuronmitokondrienetværk
Uploaded: 2023-07-28T13:00:00
Description: 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

MATLAB-optimering til analyse af mitokondrienetværk i neurovidenskab

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.

Forfatter Spotlight: En optimeret automatiseret metode til undersøgelse af retinsyrereceptorer i neuronale mitokondrier

Optimeret automatiseret analyse af levende neuronal mitokondriehomeostasemodulation ved isoformspecifikke retinsyrereceptorer

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

Billedanalyse til karakterisering af helneuronmitokondrienetværk

Begynd med at åbne mitokondriebillederne med time-lapse i billede J2.1.0. Adskil positionsstakke baseret på synsfeltet ved at navigere til menulinjen og klikke på Billede. Gå derefter til Dupliker, Indtast Udsnit eller Rammenummer, og klik på OK. Tegn derefter et område af interesse omkring cellen ved hjælp af værktøjet til fri markering.For at køre makroen skal du åbne Image J, navigere til menulinjen og klikke på Plugins, makroer og Kør. Vælg derefter Ryd baggrunden og Gem billedets MAKRO. For at bestemme pixelstørrelse og voxeldybde skal du åbne Billede J og vælge Billede.Åbn menulinjen, vælg Billede, og klik på Egenskaber. Når du har bestemt pixelstørrelsen og voxeldybden, skal du tegne et interesseområde, der omfatter hele cellen, ved hjælp af værktøjet til frit valg for at inkludere hele cellen i alle 15 billeder. Naviger til menulinjen, klik på plugins, makroer, og vælg Ryd baggrunden, og gem billedets MACRO.Vælg den ønskede lagringsmappe, og klik på Gem for at gemme filen i TIFF-format. Opret derefter tre hovedmapper med titlen Alle spor, perinukleare spor og telenukleare spor. Opret en nummereret undermappe for hvert billede, der skal behandles i hver hovedmappe, startende fra en.Tilføj en kopi af de to supplerende rutineoptimeringsfiler i hver billedmappe. For at identificere eller ændre standardmåttefilversionen skal du navigere til fanen Hjem i afsnittet Miljø og klikke på Indstillinger. Vælg derefter MATLAB.Åbn MATLAB-software på computeren, naviger til menulinjen, vælg apps, og åbn Mitometer. Vælg Start 3D, og indstil pixelstørrelsen til 0,1395089 mikrometer, tiden mellem billeder til 15 sekunder, antallet af z-planer til 8 og den aksiale afstand mellem z-planer til 0,418809 mikrometer. Vælg billeder, der skal indtastes.Gå nu til Mitometer-sidemenuen, vælg Billede, og klik på Vælg fremhævet. Naviger til Mitometer-menulinjen, vælg Vælg spor efterfulgt af Sporvisninger. Vælg derefter mellem Alle spor, Telenukleare eller Perinuclear Options.Gem resultaterne i en tekstfil ved at vælge Gem til txt, og udpak resultatfilerne til de oprettede mapper. Forbered randomiseringsxlsx-filen, som indeholder nøglen til billedkodning. Indsæt en sekvens af fortløbende heltal, startende fra et, i kolonne A, udfyld kolonne B med alfanumeriske tegn, som udgør de analytiske variabler.Dobbeltklik på den eksekverbare mlx, indtast antallet af mapper, klik på Angiv mappens mappe, og vælg Gem mappe. Vælg regnearkets navn i outputfilen, og klik på Kør. Udfør derefter den nødvendige statistiske analyse.Repræsentative billeder af mitokondrier efter behandling, respektive overfladeplots og automatiseret segmentering præsenteres. Der blev observeret et signifikant fald i den gennemsnitlige mitokondrielængde hos celler behandlet med AM580. Mitokondrieoverfladearealet faldt signifikant i celler behandlet med AM580 og BMS493.TMRM-intensitet normaliseret for mitokondrievolumen viste et signifikant fald i celler behandlet med retinsyre og CH55.

Research

JoVE Journal

Neuroscience

Image Analysis for Characterizing Whole-Neuron Mitochondria Network

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

The complex mitochondrial network makes it very challenging to segment, follow, and analyze live cells. MATLAB tools allow the analysis of mitochondria in ...