imaging mitochondria to understand its behavior in response to isoform-specific rar modulation

Optimización de MATLAB para el análisis de redes mitocondriales en neurociencia

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.

Autor destacado: Un método automatizado optimizado para investigar los receptores de ácido retinoico en las mitocondrias neuronales

Análisis automatizado optimizado de la modulación de la homeostasis de las mitocondrias neuronales vivas mediante receptores de ácido retinoico específicos de isoformas

Watch this Scientific Journal Video about Imaging Mitochondria to Understand its Behavior in Response to Isoform-Specific RAR Modulation at JoVE.com

Obtención de imágenes de mitocondrias para comprender su comportamiento en respuesta a la modulación RAR específica de isoformas

Comience colocando las células SHSY 5Y en placas de células con fondo de vidrio a una densidad de 15 por 10 rayos para alimentar cuatro células por mililitro. Tratar las células con ácido transretinoico en medio de cultivo al 1% que contenga FBS durante cinco días, seguido de un tratamiento con factor neurotrófico derivado del cerebro durante dos días. Luego, lave las células con PBS estéril.Tratar las células durante 72 horas con 10 a 7 agonistas molares de AR o isoformas utilizando partes iguales de MEM y medio F12 combinado con FBS al 1%. Reemplace el medio con medio de cultivo fresco que contenga 1% de FBS mezclado con TMRM de 20 nanomolares durante 45 minutos. Coloque las células en una incubadora conectada a un microscopio confocal de barrido láser a 37 grados centígrados.Capture las imágenes con un objetivo apocromático de inmersión en aceite de 63x. Establezca el tamaño de la imagen en 512 x 512 píxeles con una apertura estenopeica de una unidad de área. Recopila una serie temporal de 15 fotogramas de cinco campos visuales diferentes en cada placa de celda y una pila Z de ocho planos Z equidistantes.El archivo LSM resultante debe contener datos de tiempo, posición y pila Z.

imaging-mitochondria-to-understand-its-behavior-response-to-isoform

Research

JoVE Journal

Neuroscience

Imaging Mitochondria to Understand its Behavior in Response to Isoform-Specific RAR Modulation

71 vistas. Comience colocando las células SHSY 5Y en placas de células con fondo de vidrio a una densidad de 15 por 10 rayos para alimentar cuatro células por mililitro. Tratar las células con ácido transretinoico en medio de cultivo al 1% que contenga FBS durante cinco días, seguido de un tratamiento con factor neurotrófico derivado del cerebro durante dos días. Luego, lave las células con PBS estéril.Tratar las células durante 72 horas con 10 a 7 agonistas molares de AR o isoformas u...

Video: Obtención de imágenes de mitocondrias para comprender su comportamiento en respuesta a la modulación RAR específica de isoformas

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 timelapse files, considerably simplifying and speeding up the process of image processing. Nonetheless, existing tools produce a large output volume, requiring individual manual attention, and basic experimental setups have an output of thousands of files, each requiring extensive and time-consuming handling. 
To address these issues, a routine optimization was developed, in both MATLAB code and live-script forms, allowing for swift file analysis and significantly reducing document reading and data processing. With a speed of 100 files/min, the optimization allows an overall rapid analysis. The optimization achieves the results output by averaging frame-specific data for individual mitochondria throughout time frames, analyzing data in a defined manner, consistent with those output from existing tools. Live confocal imaging was performed using the dye tetramethylrhodamine methyl ester, and the routine optimization was validated by treating neuronal cells with retinoic acid receptor (RAR) agonists, whose effects on neuronal mitochondria are established in the literature. The results were consistent with the literature and allowed further characterization of mitochondrial network behavior in response to isoform-specific RAR modulation. 
This new methodology allowed rapid and validated characterization of whole-neuron mitochondria network, but it also allows for differentiation between axon and cell body mitochondria, an essential feature to apply in the neuroscience field. Moreover, this protocol can be applied to experiments using fast-acting treatments, allowing the imaging of the same cells before and after treatments, transcending the field of neuroscience.

The mitochondrial network is extremely complex, making it very challenging to analyze. A novel MATLAB tool analyzes live confocal imaged mitochondria in timelapse images but results in a large output volume requiring individual manual attention. To address this issue, a routine optimization was developed, allowing for speedy file analysis.

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