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Classification of Microglial Morphological Phenotypes Using Machine Learning

Microglia are the brain’s immunocompetent macrophages with a unique feature that allows surveillance of the surrounding microenvironment and subsequent reactions to tissue damage, infection, or homeostatic perturbations. Thereby, microglia’s striking morphological plasticity is one of their prominen...

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Autores principales: Leyh, Judith, Paeschke, Sabine, Mages, Bianca, Michalski, Dominik, Nowicki, Marcin, Bechmann, Ingo, Winter, Karsten
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8276040/
https://www.ncbi.nlm.nih.gov/pubmed/34267628
http://dx.doi.org/10.3389/fncel.2021.701673
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author Leyh, Judith
Paeschke, Sabine
Mages, Bianca
Michalski, Dominik
Nowicki, Marcin
Bechmann, Ingo
Winter, Karsten
author_facet Leyh, Judith
Paeschke, Sabine
Mages, Bianca
Michalski, Dominik
Nowicki, Marcin
Bechmann, Ingo
Winter, Karsten
author_sort Leyh, Judith
collection PubMed
description Microglia are the brain’s immunocompetent macrophages with a unique feature that allows surveillance of the surrounding microenvironment and subsequent reactions to tissue damage, infection, or homeostatic perturbations. Thereby, microglia’s striking morphological plasticity is one of their prominent characteristics and the categorization of microglial cell function based on morphology is well established. Frequently, automated classification of microglial morphological phenotypes is performed by using quantitative parameters. As this process is typically limited to a few and especially manually chosen criteria, a relevant selection bias may compromise the resulting classifications. In our study, we describe a novel microglial classification method by morphological evaluation using a convolutional neuronal network on the basis of manually selected cells in addition to classical morphological parameters. We focused on four microglial morphologies, ramified, rod-like, activated and amoeboid microglia within the murine hippocampus and cortex. The developed method for the classification was confirmed in a mouse model of ischemic stroke which is already known to result in microglial activation within affected brain regions. In conclusion, our classification of microglial morphological phenotypes using machine learning can serve as a time-saving and objective method for post-mortem characterization of microglial changes in healthy and disease mouse models, and might also represent a useful tool for human brain autopsy samples.
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spelling pubmed-82760402021-07-14 Classification of Microglial Morphological Phenotypes Using Machine Learning Leyh, Judith Paeschke, Sabine Mages, Bianca Michalski, Dominik Nowicki, Marcin Bechmann, Ingo Winter, Karsten Front Cell Neurosci Cellular Neuroscience Microglia are the brain’s immunocompetent macrophages with a unique feature that allows surveillance of the surrounding microenvironment and subsequent reactions to tissue damage, infection, or homeostatic perturbations. Thereby, microglia’s striking morphological plasticity is one of their prominent characteristics and the categorization of microglial cell function based on morphology is well established. Frequently, automated classification of microglial morphological phenotypes is performed by using quantitative parameters. As this process is typically limited to a few and especially manually chosen criteria, a relevant selection bias may compromise the resulting classifications. In our study, we describe a novel microglial classification method by morphological evaluation using a convolutional neuronal network on the basis of manually selected cells in addition to classical morphological parameters. We focused on four microglial morphologies, ramified, rod-like, activated and amoeboid microglia within the murine hippocampus and cortex. The developed method for the classification was confirmed in a mouse model of ischemic stroke which is already known to result in microglial activation within affected brain regions. In conclusion, our classification of microglial morphological phenotypes using machine learning can serve as a time-saving and objective method for post-mortem characterization of microglial changes in healthy and disease mouse models, and might also represent a useful tool for human brain autopsy samples. Frontiers Media S.A. 2021-06-29 /pmc/articles/PMC8276040/ /pubmed/34267628 http://dx.doi.org/10.3389/fncel.2021.701673 Text en Copyright © 2021 Leyh, Paeschke, Mages, Michalski, Nowicki, Bechmann and Winter. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Cellular Neuroscience
Leyh, Judith
Paeschke, Sabine
Mages, Bianca
Michalski, Dominik
Nowicki, Marcin
Bechmann, Ingo
Winter, Karsten
Classification of Microglial Morphological Phenotypes Using Machine Learning
title Classification of Microglial Morphological Phenotypes Using Machine Learning
title_full Classification of Microglial Morphological Phenotypes Using Machine Learning
title_fullStr Classification of Microglial Morphological Phenotypes Using Machine Learning
title_full_unstemmed Classification of Microglial Morphological Phenotypes Using Machine Learning
title_short Classification of Microglial Morphological Phenotypes Using Machine Learning
title_sort classification of microglial morphological phenotypes using machine learning
topic Cellular Neuroscience
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8276040/
https://www.ncbi.nlm.nih.gov/pubmed/34267628
http://dx.doi.org/10.3389/fncel.2021.701673
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