Machine learning-based classification of mitochondrial morphology in primary neurons and brain

Abstract 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 mo...

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Autores principales:	Garrett M. Fogo, Anthony R. Anzell, Kathleen J. Maheras, Sarita Raghunayakula, Joseph M. Wider, Katlynn J. Emaus, Timothy D. Bryson, Melissa J. Bukowski, Robert W. Neumar, Karin Przyklenk, Thomas H. Sanderson
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/047536fedba94d2d94407b4fbb6affde
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spelling	oai:doaj.org-article:047536fedba94d2d94407b4fbb6affde2021-12-02T13:34:57ZMachine learning-based classification of mitochondrial morphology in primary neurons and brain10.1038/s41598-021-84528-82045-2322https://doaj.org/article/047536fedba94d2d94407b4fbb6affde2021-03-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-84528-8https://doaj.org/toc/2045-2322Abstract 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.Garrett M. FogoAnthony R. AnzellKathleen J. MaherasSarita RaghunayakulaJoseph M. WiderKatlynn J. EmausTimothy D. BrysonMelissa J. BukowskiRobert W. NeumarKarin PrzyklenkThomas H. SandersonNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-12 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Garrett M. Fogo Anthony R. Anzell Kathleen J. Maheras Sarita Raghunayakula Joseph M. Wider Katlynn J. Emaus Timothy D. Bryson Melissa J. Bukowski Robert W. Neumar Karin Przyklenk Thomas H. Sanderson Machine learning-based classification of mitochondrial morphology in primary neurons and brain
description	Abstract 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.
format	article
author	Garrett M. Fogo Anthony R. Anzell Kathleen J. Maheras Sarita Raghunayakula Joseph M. Wider Katlynn J. Emaus Timothy D. Bryson Melissa J. Bukowski Robert W. Neumar Karin Przyklenk Thomas H. Sanderson
author_facet	Garrett M. Fogo Anthony R. Anzell Kathleen J. Maheras Sarita Raghunayakula Joseph M. Wider Katlynn J. Emaus Timothy D. Bryson Melissa J. Bukowski Robert W. Neumar Karin Przyklenk Thomas H. Sanderson
author_sort	Garrett M. Fogo
title	Machine learning-based classification of mitochondrial morphology in primary neurons and brain
title_short	Machine learning-based classification of mitochondrial morphology in primary neurons and brain
title_full	Machine learning-based classification of mitochondrial morphology in primary neurons and brain
title_fullStr	Machine learning-based classification of mitochondrial morphology in primary neurons and brain
title_full_unstemmed	Machine learning-based classification of mitochondrial morphology in primary neurons and brain
title_sort	machine learning-based classification of mitochondrial morphology in primary neurons and brain
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/047536fedba94d2d94407b4fbb6affde
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Machine learning-based classification of mitochondrial morphology in primary neurons and brain

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