Nikolaisen Julie, Nilsson Linn I H, Pettersen Ina K N, Willems Peter H G M, Lorens James B, Koopman Werner J H, Tronstad Karl J
Department of Biomedicine, University of Bergen, Bergen, Norway.
Department of Biochemistry (286), Nijmegen Centre for Molecular Life Sciences, Radboud University Medical Centre, Nijmegen, The Netherlands.
PLoS One. 2014 Jul 2;9(7):e101365. doi: 10.1371/journal.pone.0101365. eCollection 2014.
Mitochondrial morphology and function are coupled in healthy cells, during pathological conditions and (adaptation to) endogenous and exogenous stress. In this sense mitochondrial shape can range from small globular compartments to complex filamentous networks, even within the same cell. Understanding how mitochondrial morphological changes (i.e. "mitochondrial dynamics") are linked to cellular (patho) physiology is currently the subject of intense study and requires detailed quantitative information. During the last decade, various computational approaches have been developed for automated 2-dimensional (2D) analysis of mitochondrial morphology and number in microscopy images. Although these strategies are well suited for analysis of adhering cells with a flat morphology they are not applicable for thicker cells, which require a three-dimensional (3D) image acquisition and analysis procedure. Here we developed and validated an automated image analysis algorithm allowing simultaneous 3D quantification of mitochondrial morphology and network properties in human endothelial cells (HUVECs). Cells expressing a mitochondria-targeted green fluorescence protein (mitoGFP) were visualized by 3D confocal microscopy and mitochondrial morphology was quantified using both the established 2D method and the new 3D strategy. We demonstrate that both analyses can be used to characterize and discriminate between various mitochondrial morphologies and network properties. However, the results from 2D and 3D analysis were not equivalent when filamentous mitochondria in normal HUVECs were compared with circular/spherical mitochondria in metabolically stressed HUVECs treated with rotenone (ROT). 2D quantification suggested that metabolic stress induced mitochondrial fragmentation and loss of biomass. In contrast, 3D analysis revealed that the mitochondrial network structure was dissolved without affecting the amount and size of the organelles. Thus, our results demonstrate that 3D imaging and quantification are crucial for proper understanding of mitochondrial shape and topology in non-flat cells. In summary, we here present an integrative method for unbiased 3D quantification of mitochondrial shape and network properties in mammalian cells.
在健康细胞、病理状态以及（对内源和外源应激的）适应过程中，线粒体形态与功能相互关联。从这个意义上讲，即使在同一细胞内，线粒体形态也可以从小球状区室到复杂的丝状网络不等。了解线粒体形态变化（即“线粒体动力学”）如何与细胞（病理）生理学相关联，是当前深入研究的课题，需要详细的定量信息。在过去十年中，已经开发了各种计算方法，用于对显微镜图像中线粒体形态和数量进行自动二维（2D）分析。尽管这些策略非常适合分析形态扁平的贴壁细胞，但不适用于较厚的细胞，后者需要三维（3D）图像采集和分析程序。在这里，我们开发并验证了一种自动图像分析算法，可同时对人内皮细胞（HUVECs）中线粒体形态和网络特性进行3D定量分析。通过3D共聚焦显微镜观察表达线粒体靶向绿色荧光蛋白（mitoGFP）的细胞，并使用既定的2D方法和新的3D策略对线粒体形态进行定量分析。我们证明，这两种分析方法均可用于表征和区分各种线粒体形态和网络特性。然而，当将正常HUVECs中的丝状线粒体与用鱼藤酮（ROT）处理的代谢应激HUVECs中的圆形/球形线粒体进行比较时，2D和3D分析的结果并不相同。2D定量分析表明，代谢应激导致线粒体碎片化和生物量损失。相比之下，3D分析显示线粒体网络结构被溶解，但不影响细胞器的数量和大小。因此，我们的结果表明，3D成像和定量分析对于正确理解非扁平细胞中线粒体的形状和拓扑结构至关重要。总之，我们在此提出了一种用于无偏3D定量分析哺乳动物细胞中线粒体形状和网络特性的综合方法。
