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

用于分析神经科学中线粒体网络的 MATLAB 优化

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.

作者聚焦：研究神经元线粒体中视黄酸受体的优化自动化方法

通过亚型特异性视黄酸受体对活神经元线粒体稳态调节进行优化的自动分析

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 Imaging Mitochondria to Understand its Behavior in Response to Isoform-Specific RAR Modulation at JoVE.com

对线粒体进行成像，以了解其响应亚型特异性 RAR 调节的行为

首先将 SHSY 5Y 电池以 15 x 10 射线的密度接种在玻璃底电池培养皿中，每毫升可为 4 个电池供电。在含有 1%FBS 的培养基中用全反式视黄酸处理细胞 5 天，然后用脑源性神经营养因子处理 2 天。然后，用无菌PBS洗涤细胞。使用等量的 MEM 和 F12 培养基结合 1% FBS，用 10 至 7 摩尔 RA 或亚型激动剂处理细胞 72 小时。用含有 1% FBS 的新鲜培养基与 20 纳摩尔 TMRM 混合 45 分钟替换培养基。将细胞置于连接到设置为37摄氏度的激光扫描共聚焦显微镜的培养箱中。使用 63 倍油浸复消色差物镜拍摄图像。将图像大小设置为 512 x 512 像素，针孔孔径为一个面积单位。从每个单元板中的五个不同视野和八个等距 Z 平面的 Z 堆栈收集 15 帧的时间序列。生成的 LSM 文件应包含时间、位置和 Z 堆栈数据。

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

69 次观看.  University of Aveiro. 首先将 SHSY 5Y 电池以 15 x 10 射线的密度接种在玻璃底电池培养皿中，每毫升可为 4 个电池供电。在含有 1%FBS 的培养基中用全反式视黄酸处理细胞 5 天，然后用脑源性神经营养因子处理 2 天。然后，用无菌PBS洗涤细胞。使用等量的 MEM 和 F12 培养基结合 1% FBS，用 10 至 7 摩尔 RA 或亚型激动剂处理细胞 72 小时。用含有 1% FBS 的新鲜培养基与 20 纳摩尔 TMRM 混合 45 分钟替换培养...

视频: 对线粒体进行成像，以了解其响应亚型特异性 RAR 调节的行为

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 ...

科研

神经科学

Begin by plating SHSY 5Y cells in glass bottom cell dishes at a density of 15 by 10 rays to the power four cells per milliliter. Treat the cells with all-trans retinoic acid in 1%FBS containing culture medium for five days, followed by treatment with brain-derived neurotrophic factor for two days. Then, wash the cells with sterile PBS.Treat the cells for 72 hours with 10 to 7 molar RA or isoform agonists using equal parts of MEM and F12 medium combined with 1%FBS. Replace the medium with fresh 1%FBS containing culture medium mixed with 20 nano molar TMRM for 45 minutes. Place the cells in an incubator connected to a laser scanning confocal microscope set at 37 degrees Celsius.Capture the images using a 63x oil immersion apochromatic objective. Set the image size to 512 by 512 pixels with a pinhole aperture of one area unit. Collect a time series of 15 frames from five different visual fields in each cell plate and a Z stack of eight equidistant Z-planes.The resulting LSM file should contain time, position, and Z-stack data.

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.