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哺乳动物线粒体形态成像方法:关于线粒体图像分析软件(MitoGraph)的展望

Methods for imaging mammalian mitochondrial morphology: A prospective on MitoGraph.

作者信息

Harwig Megan C, Viana Matheus P, Egner John M, Harwig Jason J, Widlansky Michael E, Rafelski Susanne M, Hill R Blake

机构信息

Department of Biochemistry, Medical College of Wisconsin, Milwaukee, WI, 53226, United States.

Visual Analytics and Comprehension Group, IBM Research, Brazil; Department of Developmental and Cell Biology and Center for Complex Biological Systems, University of California Irvine, Irvine, CA, 92697, United States.

出版信息

Anal Biochem. 2018 Jul 1;552:81-99. doi: 10.1016/j.ab.2018.02.022. Epub 2018 Mar 2.

DOI:10.1016/j.ab.2018.02.022
PMID:29505779
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC6322684/
Abstract

Mitochondria are found in a variety of shapes, from small round punctate structures to a highly interconnected web. This morphological diversity is important for function, but complicates quantification. Consequently, early quantification efforts relied on various qualitative descriptors that understandably reduce the complexity of the network leading to challenges in consistency across the field. Recent application of state-of-the-art computational tools have resulted in more quantitative approaches. This prospective highlights the implementation of MitoGraph, an open-source image analysis platform for measuring mitochondrial morphology initially optimized for use with Saccharomyces cerevisiae. Here Mitograph was assessed on five different mammalian cells types, all of which were accurately segmented by MitoGraph analysis. MitoGraph also successfully differentiated between distinct mitochondrial morphologies that ranged from entirely fragmented to hyper-elongated. General recommendations are also provided for confocal imaging of labeled mitochondria (using mito-YFP, MitoTracker dyes and immunostaining parameters). Widespread adoption of MitoGraph will help achieve a long-sought goal of consistent and reproducible quantification of mitochondrial morphology.

摘要

线粒体有多种形态,从小的圆形点状结构到高度相互连接的网络状。这种形态多样性对其功能很重要,但使定量分析变得复杂。因此,早期的定量分析工作依赖于各种定性描述符,这些描述符可以理解地降低了网络的复杂性,导致该领域在一致性方面面临挑战。最近,先进计算工具的应用带来了更定量的方法。本前瞻性研究重点介绍了MitoGraph的应用,MitoGraph是一个开源图像分析平台,用于测量线粒体形态,最初是为酿酒酵母优化的。在此,对五种不同的哺乳动物细胞类型进行了MitoGraph评估,所有这些细胞类型均通过MitoGraph分析准确分割。MitoGraph还成功区分了从完全碎片化到超伸长的不同线粒体形态。还提供了关于标记线粒体共聚焦成像(使用线粒体黄色荧光蛋白、MitoTracker染料和免疫染色参数)的一般建议。广泛采用MitoGraph将有助于实现长期以来一直追求的对线粒体形态进行一致且可重复定量的目标。

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