Name: 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
Uploaded: 2023-07-28T13:00:00
Description: Watch this Scientific Journal Video about Imaging Mitochondria to Understand its Behavior in Response to Isoform-Specific RAR Modulation at JoVE.com

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。

Research

JoVE Journal

Neuroscience

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

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 ...