Vitória José J M, de Paula Vinícius, da Cruz E Silva Odete A B, Trigo Diogo
Neuroscience and Signalling Laboratory, Institute of Biomedicine (iBiMED), Department of Medical Sciences, University of Aveiro.
Neuroscience and Signalling Laboratory, Institute of Biomedicine (iBiMED), Department of Medical Sciences, University of Aveiro;
J Vis Exp. 2023 Jul 28(197). doi: 10.3791/65452.
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.
复杂的线粒体网络使得对活细胞进行分割、追踪和分析极具挑战性。MATLAB工具可用于分析延时拍摄文件中的线粒体,极大地简化并加速了图像处理过程。尽管如此,现有工具产生的输出量很大,需要人工逐一处理,而且基本的实验设置会产生数千个文件,每个文件都需要大量且耗时的处理。为了解决这些问题,开发了一种常规优化方法,以MATLAB代码和实时脚本两种形式呈现,可实现快速的文件分析,并显著减少文档读取和数据处理。该优化方法速度为每分钟100个文件,可实现整体快速分析。通过对各个线粒体在整个时间帧内的特定帧数据进行平均来输出结果,以一种既定的方式分析数据,与现有工具的输出结果一致。使用四甲基罗丹明甲酯染料进行实时共聚焦成像,并通过用视黄酸受体(RAR)激动剂处理神经元细胞来验证常规优化方法,其对神经元线粒体的影响已在文献中得到证实。结果与文献一致,并允许进一步表征线粒体网络对异构体特异性RAR调节的反应。这种新方法能够快速且经过验证地表征全神经元线粒体网络,同时还能区分轴突和细胞体线粒体,这是在神经科学领域应用的一个基本特征。此外,该方案可应用于使用快速起效处理的实验,允许在处理前后对同一细胞进行成像,超越了神经科学领域。