Name: Bildeanalyse for å karakterisere hele neuron mitokondrier nettverk
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-optimalisering for analyse av mitokondrielle nettverk i nevrovitenskap

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 optimalisert automatisert metode for å undersøke retinsyrereseptorer i nevronale mitokondrier

Optimalisert automatisert analyse av levende neuronale mitokondrier Homeostasemodulasjon av isoform-spesifikke retinsyrereseptorer

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

Bildeanalyse for å karakterisere hele neuron mitokondrier nettverk

Begynn med å åpne time-lapse mitokondrielle bilder i bilde J2.1.0. Skill posisjonsstabler basert på det visuelle feltet ved å navigere til menylinjen og klikke på Bilde. Gå deretter til Dupliser, Skriv inn stykke eller Rammenummer, og klikk OK. Deretter tegner du et interesseområde rundt cellen ved hjelp av Free Selection Tool.For å kjøre makroen, åpne Image J, naviger til menylinjen og klikk på Plugins, Macros og Run. Velg deretter Tøm bakgrunnen og Lagre bildets MAKRO. Hvis du vil bestemme pikselstørrelse og vokseldybde, åpner du Bilde J og velger Bilde.Gå til menylinjen, velg Bilde og klikk på Egenskaper. Etter å ha bestemt pikselstørrelsen og voxeldybden, tegner du et interesseområde som omfatter hele cellen ved hjelp av Free Selection Tool for å inkludere hele cellen gjennom alle 15 rammene. Naviger til menylinjen, klikk på Plugins, Makroer, og velg Fjern bakgrunnen, og lagre bildets MAKRO.Velg ønsket lagringsmappe, og klikk Lagre for å lagre filen i TIFF-format. Deretter oppretter du tre hovedmapper med tittelen Alle spor, Perinukleære spor og Telenukleære spor. Opprett en nummerert undermappe for hvert bilde som skal behandles i hver hovedmappe, fra én.Legg til en kopi av de to tilleggsfilene for rutineoptimalisering i hver bildemappe. For å identifisere eller endre standard mattefilversjon, naviger til Hjem-fanen i Miljø-delen og klikk på Innstillinger. Velg deretter MATLAB.Åpne MATLAB-programvaren på datamaskinen, naviger til menylinjen, velg apper og åpne Mitometer. Velg Start 3D og angi pikselstørrelsen til 0,1395089 mikrometer, tid mellom bilder til 15 sekunder, antall z-plan til 8 og aksial avstand mellom z-plan til 0,418809 mikrometer. Velg bildene du vil legge inn.Gå nå til Mitometer sidemeny, velg Bilde, og klikk Velg uthevet. Naviger til Mitometer-menylinjen, velg Velg spor etterfulgt av sporvisninger. Velg deretter mellom Alle spor, Telenukleære eller Perinukleære alternativer.Lagre resultatene i en tekstfil ved å velge Lagre til txt, og pakk ut resultatfilene til de opprettede mappene. Forbered randomization xlsx-filen, som inneholder nøkkelen for bildekoding. Sett inn en sekvens med påfølgende heltall, fra ett, i kolonne A, fyll kolonne B med alfanumeriske tegn, som utgjør de analytiske variablene.Dobbeltklikk på den kjørbare mlx, skriv inn antall mapper, klikk Spesifiser mappens katalog, og velg Lagre katalog. Velg regnearknavnet i utdatafilen og klikk Kjør. Etter dette, utfør den nødvendige statistiske analysen.Representative bilder av mitokondrier etter behandling, respektive overflateplott og automatisert segmentering presenteres. En signifikant reduksjon i gjennomsnittlig mitokondriell lengde ble observert hos celler behandlet med AM580. Mitokondrieoverflaten ble signifikant redusert i celler behandlet med AM580 og BMS493.TMRM-intensitet normalisert for mitokondrievolum viste en signifikant reduksjon 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 ...