Machine learning-based classification of mitochondrial morphology in primary neurons and brain.
Machine learning-based classification of mitochondrial morphology in primary neurons and brain.
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DOI：
10.1038/s41598-021-84528-8
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发表时间：
2021-03-04
影响因子：
4.6
通讯作者：
Sanderson TH
中科院分区：
文献类型：
--
作者：
Fogo GM;Anzell AR;Maheras KJ;Raghunayakula S;Wider JM;Emaus KJ;Bryson TD;Bukowski MJ;Neumar RW;Przyklenk K;Sanderson TH
线粒体网络不断经历分裂和融合的事件。在生理条件下，该网络处于平衡状态，其特征在于存在细长和点状线粒体。然而，这种平衡的，稳态的线粒体档案可以改变形态分布，以应对各种压力。因此，开发一种以高精度稳健地测量线粒体形态的方法势在必行。在这里，我们开发了一个半自动化的图像分析管道，用于体外和体内应用的线粒体形态的定量。利用转基因小鼠的原代皮层神经元的图像生成并验证图像分析管道，从而允许线粒体动力学的关键组分的遗传消融。该分析管道进一步扩展，通过脑切片的免疫标记以及连续块面扫描电子显微镜来评估体内线粒体形态。这些数据证明了一种高度特异性和灵敏度的方法，可以准确地对不同的生理和病理线粒体形态进行分类。此外，该工作流程采用了易于获得的免费开源软件，该软件专为高通量图像处理、分割和分析而设计，可针对各种生物模型进行定制。
The mitochondrial network continually undergoes events of fission and fusion. Under physiologic conditions, the network is in equilibrium and is characterized by the presence of both elongated and punctate mitochondria. However, this balanced, homeostatic mitochondrial profile can change morphologic distribution in response to various stressors. Therefore, it is imperative to develop a method that robustly measures mitochondrial morphology with high accuracy. Here, we developed a semi-automated image analysis pipeline for the quantitation of mitochondrial morphology for both in vitro and in vivo applications. The image analysis pipeline was generated and validated utilizing images of primary cortical neurons from transgenic mice, allowing genetic ablation of key components of mitochondrial dynamics. This analysis pipeline was further extended to evaluate mitochondrial morphology in vivo through immunolabeling of brain sections as well as serial block-face scanning electron microscopy. These data demonstrate a highly specific and sensitive method that accurately classifies distinct physiological and pathological mitochondrial morphologies. Furthermore, this workflow employs the use of readily available, free open-source software designed for high throughput image processing, segmentation, and analysis that is customizable to various biological models.
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影响因子：
4.3
作者：
Halestrap, AP;Connern, CP;Kerr, PM
通讯作者：
Kerr, PM
影响因子：
5.8
作者：
Arganda-Carreras, Ignacio;Kaynig, Verena;Seung, H. Sebastian
通讯作者：
Seung, H. Sebastian
影响因子：
3.7
作者：
Li J;Ma X;Yu W;Lou Z;Mu D;Wang Y;Shen B;Qi S
通讯作者：
Qi S
影响因子：
5.1
作者：
Anzell AR;Maizy R;Przyklenk K;Sanderson TH
通讯作者：
Sanderson TH
DOI：
10.1016/j.bbamcr.2014.11.002
发表时间：
2015-02
影响因子：
5.1
作者：
Leonard, Anthony P.;Cameron, Robert B.;Speiser, Jaime L.;Wolf, Bethany J.;Peterson, Yuri K.;Schnellmann, Rick G.;Beeson, Craig C.;Rohrer, Baerbel
通讯作者：
Rohrer, Baerbel