Ouellet Mathieu, Guillebaud Gérald, Gervais Valerie, Lupien St-Pierre David, Germain Marc
Département de Chimie, Biochimie et Physique, Université du Québec à Trois-Rivières, Trois-Rivières, Quebec, Canada.
Groupe de Recherche en Signalisation Cellulaire, Université du Québec à Trois-Rivières, Trois-Rivières, Quebec, Canada.
PLoS Comput Biol. 2017 Jun 22;13(6):e1005612. doi: 10.1371/journal.pcbi.1005612. eCollection 2017 Jun.
Mitochondria exist as a highly interconnected network that is exquisitely sensitive to variations in nutrient availability, as well as a large array of cellular stresses. Changes in length and connectivity of this network, as well as alterations in the mitochondrial inner membrane (cristae), regulate cell fate by controlling metabolism, proliferation, differentiation, and cell death. Given the key roles of mitochondrial dynamics, the process by which mitochondria constantly fuse and fragment, the measure of mitochondrial length and connectivity provides crucial information on the health and activity of various cell populations. However, despite the importance of accurately measuring mitochondrial networks, the tools required to rapidly and accurately provide this information are lacking. Here, we developed a novel probabilistic approach to automatically measure mitochondrial length distribution and connectivity from confocal images. This method accurately identified mitochondrial changes caused by starvation or the inhibition of mitochondrial function. In addition, we successfully used the algorithm to measure changes in mitochondrial inner membrane/matrix occurring in response to Complex III inhibitors. As cristae rearrangements play a critical role in metabolic regulation and cell survival, this provides a rapid method to screen for proteins or compounds affecting this process. The algorithm will thus provide a robust tool to dissect the molecular mechanisms underlying the key roles of mitochondria in the regulation of cell fate.
线粒体以高度相互连接的网络形式存在，对营养物质可用性的变化以及大量细胞应激极为敏感。该网络的长度和连接性变化，以及线粒体内膜（嵴）的改变，通过控制代谢、增殖、分化和细胞死亡来调节细胞命运。鉴于线粒体动力学（线粒体不断融合和分裂的过程）的关键作用，线粒体长度和连接性的测量为各种细胞群体的健康和活性提供了关键信息。然而，尽管准确测量线粒体网络很重要，但缺乏能够快速准确提供此类信息的工具。在此，我们开发了一种新颖的概率方法，可从共聚焦图像中自动测量线粒体长度分布和连接性。该方法准确识别了由饥饿或线粒体功能抑制引起的线粒体变化。此外，我们成功地使用该算法测量了响应于复合物III抑制剂而发生的线粒体内膜/基质的变化。由于嵴的重排在代谢调节和细胞存活中起关键作用，这提供了一种快速筛选影响该过程的蛋白质或化合物的方法。因此，该算法将提供一个强大的工具来剖析线粒体在调节细胞命运中的关键作用背后的分子机制。
